2. We propose a novel software package to automate the annotation of tandem MS data. This software consists of two major components. The first, is a free, semi-automated MSn data interpreter called the Glycomic Elucidation and Annotation Tool (GELATO). This tool extends and automates the functionality of existing open source projects, namely, GlycoWorkbench (GWB) and GlycomeDB. The second is a machine learning model called Smart Anotation Enhancement Graph (SAGE), which learns the behavior of glycoanalysts to select annotations generated by GELATO that emulate human interpretation of the spectra.
Decision making is often based on Bayesian networks. The building blocks for Bayesian networks are its conditional probability tables (CPTs). These tables are obtained by parameter estimation methods, or they are elicited from subject matter experts (SME). Some of these knowledge representations are insufficient approximations. Using knowledge fusion of cause and effect observations lead to better predictive decisions. We propose three new methods to generate CPTs, which even work when only soft evidence is provided. The first two are novel ways of mapping conditional expectations to the probability space. The third is a column extraction method, which obtains CPTs from nonlinear functions such as the multinomial logistic regression. Case studies on military effects and burnt forest desertification have demonstrated that so derived CPTs have highly reliable predictive power, including superiority over the CPTs obtained from SMEs. In this context, new quality measures for determining the goodness of a CPT and for comparing CPTs with each other have been introduced. The predictive power and enhanced reliability of decision making based on the novel CPT generation methods presented in this paper have been confirmed and validated within the context of the case studies.
Model-free reinforcement learning algorithms, such as Q-learning, perform poorly in the early stages of learning in noisy environments, because much effort is spent unlearning biased estimates of the state-action value function. The bias results from selecting, among several noisy estimates, the apparent optimum, which may actually be suboptimal. We propose G-learning, a new off-policy learning algorithm that regularizes the value estimates by penalizing deterministic policies in the beginning of the learning process. We show that this method reduces the bias of the value-function estimation, leading to faster convergence to the optimal value and the optimal policy. Moreover, G-learning enables the natural incorporation of prior domain knowledge, when available. The stochastic nature of G-learning also makes it avoid some exploration costs, a property usually attributed only to on-policy algorithms. We illustrate these ideas in several examples, where G-learning results in significant improvements of the convergence rate and the cost of the learning process.
Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered methods such as deep neural networks for natural language inference (NLI). In this paper, we propose a special long short-term memory (LSTM) architecture for NLI. Our model builds on top of a recently proposed neural attention model for NLI but is based on a significantly different idea. Instead of deriving sentence embeddings for the premise and the hypothesis to be used for classification, our solution uses a match-LSTM to perform word-by-word matching of the hypothesis with the premise. This LSTM is able to place more emphasis on important word-level matching results. In particular, we observe that this LSTM remembers important mismatches that are critical for predicting the contradiction or the neutral relationship label. On the SNLI corpus, our model achieves an accuracy of 86.1%, outperforming the state of the art.
Many recent algorithms for approximate model counting are based on a reduction to combinatorial searches over random subsets of the space defined by parity or XOR constraints. Long parity constraints (involving many variables) provide strong theoretical guarantees but are computationally difficult. Short parity constraints are easier to solve but have weaker statistical properties. It is currently not known how long these parity constraints need to be. We close the gap by providing matching necessary and sufficient conditions on the required asymptotic length of the parity constraints. Further, we provide a new family of lower bounds and the first non-trivial upper bounds on the model count that are valid for arbitrarily short XORs. We empirically demonstrate the effectiveness of these bounds on model counting benchmarks and in a Satisfiability Modulo Theory (SMT) application motivated by the analysis of contingency tables in statistics.
A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors. The model builds a novel Markov chain that sends the latent gamma random variables at time $(t-1)$ as the shape parameters of those at time $t$, which are linked to observed or latent counts under the Poisson likelihood. The significant challenge of inferring the gamma shape parameters is fully addressed, using unique data augmentation and marginalization techniques for the negative binomial distribution. The same nonparametric Bayesian model also applies to the factorization of a dynamic binary matrix, via a Bernoulli-Poisson link that connects a binary observation to a latent count, with closed-form conditional posteriors for the latent counts and efficient computation for sparse observations. We apply the model to text and music analysis, with state-of-the-art results.
We consider effort allocation in crowdsourcing, where we wish to assign labeling tasks to imperfect homogeneous crowd workers to maximize overall accuracy in a continuous-time Bayesian setting, subject to budget and time constraints. The Bayes-optimal policy for this problem is the solution to a partially observable Markov decision process, but the curse of dimensionality renders the computation infeasible. Based on the Lagrangian Relaxation technique in Adelman & Mersereau (2008), we provide a computationally tractable instance-specific upper bound on the value of this Bayes-optimal policy, which can in turn be used to bound the optimality gap of any other sub-optimal policy. In an approach similar in spirit to the Whittle index for restless multiarmed bandits, we provide an index policy for effort allocation in crowdsourcing and demonstrate numerically that it outperforms other stateof- arts and performs close to optimal solution.
We investigate the 3-architecture Connected Facility Location Problem arising in the design of urban telecommunication access networks. We propose an original optimization model for the problem that includes additional variables and constraints to take into account wireless signal coverage. Since the problem can prove challenging even for modern state-of-the art optimization solvers, we propose to solve it by an original primal heuristic which combines a probabilistic fixing procedure, guided by peculiar Linear Programming relaxations, with an exact MIP heuristic, based on a very large neighborhood search. Computational experiments on a set of realistic instances show that our heuristic can find solutions associated with much lower optimality gaps than a state-of-the-art solver.
We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers. The goal is to select a classifier that will give near-optimal accuracy when trained on all data, while also minimizing the cost of misallocated samples. This is motivated by large modern datasets and ML toolkits with many combinations of learning algorithms and hyper-parameters. Inspired by the principle of "optimism under uncertainty," we propose an innovative strategy, Data Allocation using Upper Bounds (DAUB), which robustly achieves these objectives across a variety of real-world datasets. We further develop substantial theoretical support for DAUB in an idealized setting where the expected accuracy of a classifier trained on $n$ samples can be known exactly. Under these conditions we establish a rigorous sub-linear bound on the regret of the approach (in terms of misallocated data), as well as a rigorous bound on suboptimality of the selected classifier. Our accuracy estimates using real-world datasets only entail mild violations of the theoretical scenario, suggesting that the practical behavior of DAUB is likely to approach the idealized behavior.
The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
We present a new representation of harmonic sounds that linearizes the dynamics of pitch and spectral envelope, while remaining stable to deformations in the time-frequency plane. It is an instance of the scattering transform, a generic operator which cascades wavelet convolutions and modulus nonlinearities. It is derived from the pitch spiral, in that convolutions are successively performed in time, log-frequency, and octave index. We give a closed-form approximation of spiral scattering coefficients for a nonstationary generalization of the harmonic source-filter model.
We present a unified approach for learning the parameters of Sum-Product networks (SPNs). We prove that any complete and decomposable SPN is equivalent to a mixture of trees where each tree corresponds to a product of univariate distributions. Based on the mixture model perspective, we characterize the objective function when learning SPNs based on the maximum likelihood estimation (MLE) principle and show that the optimization problem can be formulated as a signomial program. We construct two parameter learning algorithms for SPNs by using sequential monomial approximations (SMA) and the concave-convex procedure (CCCP), respectively. The two proposed methods naturally admit multiplicative updates, hence effectively avoiding the projection operation. With the help of the unified framework, we also show that, in the case of SPNs, CCCP leads to the same algorithm as Expectation Maximization (EM) despite the fact that they are different in general.
The BusPlus project aims at improving the off-peak hours public transit service in Canberra, Australia. To address the difficulty of covering a large geographic area, BusPlus proposes a hub and shuttle model consisting of a combination of a few high-frequency bus routes between key hubs and a large number of shuttles that bring passengers from their origin to the closest hub and take them from their last bus stop to their destination. This paper focuses on the design of bus network and proposes an efficient solving method to this multimodal network design problem based on the Benders decomposition method. Starting from a MIP formulation of the problem, the paper presents a Benders decomposition approach using dedicated solution techniques for solving independent sub-problems, Pareto optimal cuts, cut bundling, and core point update. Computational results on real-world data from Canberra's public transit system justify the design choices and show that the approach outperforms the MIP formulation by two orders of magnitude. Moreover, the results show that the hub and shuttle model may decrease transit time by a factor of 2, while staying within the costs of the existing transit system.
Sequence-to-sequence neural translation models learn semantic and syntactic relations between sentence pairs by optimizing the likelihood of the target given the source, i.e., $p(y|x)$, an objective that ignores other potentially useful sources of information. We introduce an alternative objective function for neural MT that maximizes the mutual information between the source and target sentences, modeling the bi-directional dependency of sources and targets. We implement the model with a simple re-ranking method, and also introduce a decoding algorithm that increases diversity in the N-best list produced by the first pass. Applied to the WMT German/English and French/English tasks, the proposed models offers a consistent performance boost on both standard LSTM and attention-based neural MT architectures.
Information hierarchies are organizational structures that often used to organize and present large and complex information as well as provide a mechanism for effective human navigation. Fortunately, many statistical and computational models exist that automatically generate hierarchies; however, the existing approaches do not consider linkages in information {\em networks} that are increasingly common in real-world scenarios. Current approaches also tend to present topics as an abstract probably distribution over words, etc rather than as tangible nodes from the original network. Furthermore, the statistical techniques present in many previous works are not yet capable of processing data at Web-scale. In this paper we present the Hierarchical Document Topic Model (HDTM), which uses a distributed vertex-programming process to calculate a nonparametric Bayesian generative model. Experiments on three medium size data sets and the entire Wikipedia dataset show that HDTM can infer accurate hierarchies even over large information networks.
An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image. This is particularly challenging due to the partial observability inherent in projecting a 3D object onto the image space, and the ill-posedness of inferring object shape and pose. However, we can train a neural network to address the problem if we restrict our attention to specific object categories (in our case faces and chairs) for which we can gather ample training data. In this paper, we propose a novel recurrent convolutional encoder-decoder network that is trained end-to-end on the task of rendering rotated objects starting from a single image. The recurrent structure allows our model to capture long-term dependencies along a sequence of transformations. We demonstrate the quality of its predictions for human faces on the Multi-PIE dataset and for a dataset of 3D chair models, and also show its ability to disentangle latent factors of variation (e.g., identity and pose) without using full supervision.
This article discusses open scientific challenges for understanding development and evolution of speech forms, as a commentary to Moulin-Frier et al. (Moulin-Frier et al., 2015). Based on the analysis of mathematical models of the origins of speech forms, with a focus on their assumptions , we study the fundamental question of how speech can be formed out of non--speech, at both developmental and evolutionary scales. In particular, we emphasize the importance of embodied self-organization , as well as the role of mechanisms of motivation and active curiosity-driven exploration in speech formation. Finally , we discuss an evolutionary-developmental perspective of the origins of speech.
Semantic parsing methods are used for capturing and representing semantic meaning of text. Meaning representation capturing all the concepts in the text may not always be available or may not be sufficiently complete. Ontologies provide a structured and reasoning-capable way to model the content of a collection of texts. In this work, we present a novel approach to joint learning of ontology and semantic parser from text. The method is based on semi-automatic induction of a context-free grammar from semantically annotated text. The grammar parses the text into semantic trees. Both, the grammar and the semantic trees are used to learn the ontology on several levels -- classes, instances, taxonomic and non-taxonomic relations. The approach was evaluated on the first sentences of Wikipedia pages describing people.
We present a new concept - Wikiometrics - the derivation of metrics and indicators from Wikipedia. Wikipedia provides an accurate representation of the real world due to its size, structure, editing policy and popularity. We demonstrate an innovative mining methodology, where different elements of Wikipedia - content, structure, editorial actions and reader reviews - are used to rank items in a manner which is by no means inferior to rankings produced by experts or other methods. We test our proposed method by applying it to two real-world ranking problems: top world universities and academic journals. Our proposed ranking methods were compared to leading and widely accepted benchmarks, and were found to be extremely correlative but with the advantage of the data being publically available.
In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).
In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. To validate the efficiency of the machine learning algorithms used in this study, we systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset for this study. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotion than negative one in beta and gamma bands; the neural patterns of neutral emotion have higher alpha responses at parietal and occipital sites; and for negative emotion, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition system shows that the neural patterns are relatively stable within and between sessions.
Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated content provide diverse sources for recommendation beyond ratings, which present opportunities as well as challenges for traditional RSs. Although social matrix factorization (Social MF) can integrate ratings with social relations and topic matrix factorization can integrate ratings with item reviews, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the two approaches, in two steps. First, we extend Social MF to exploit the graph structure of neighbors. Second, we propose a novel framework MR3 to jointly model these three types of information effectively for rating prediction by aligning latent factors and hidden topics. We achieve more accurate rating prediction on two real-life datasets. Furthermore, we measure the contribution of each data source to the proposed framework.
Collaborative vocabulary development in the context of data integration is the process of finding consensus between the experts of the different systems and domains. The complexity of this process is increased with the number of involved people, the variety of the systems to be integrated and the dynamics of their domain. In this paper we advocate that the realization of a powerful version control system is the heart of the problem. Driven by this idea and the success of Git in the context of software development, we investigate the applicability of Git for collaborative vocabulary development. Even though vocabulary development and software development have much more similarities than differences there are still important differences. These need to be considered within the development of a successful versioning and collaboration system for vocabulary development. Therefore, this paper starts by presenting the challenges we were faced with during the creation of vocabularies collaboratively and discusses its distinction to software development. Based on these insights we propose Git4Voc which comprises guidelines how Git can be adopted to vocabulary development. Finally, we demonstrate how Git hooks can be implemented to go beyond the plain functionality of Git by realizing vocabulary-specific features like syntactic validation and semantic diffs.
In this paper we present the initial development of a general theory for mapping inference in predicate logic to computation over Tensor Product Representations (TPRs; Smolensky (1990), Smolensky & Legendre (2006)). After an initial brief synopsis of TPRs (Section 0), we begin with particular examples of inference with TPRs in the 'bAbI' question-answering task of Weston et al. (2015) (Section 1). We then present a simplification of the general analysis that suffices for the bAbI task (Section 2). Finally, we lay out the general treatment of inference over TPRs (Section 3). We also show the simplification in Section 2 derives the inference methods described in Lee et al. (2016); this shows how the simple methods of Lee et al. (2016) can be formally extended to more general reasoning tasks.
Finding inclusion-minimal "hitting sets" for a given collection of sets is a fundamental combinatorial problem with applications in domains as diverse as Boolean algebra, computational biology, and data mining. Much of the algorithmic literature focuses on the problem of *recognizing* the collection of minimal hitting sets; however, in many of the applications, it is more important to *generate* these hitting sets. We survey twenty algorithms from across a variety of domains, considering their history, classification, useful features, and computational performance on a variety of synthetic and real-world inputs. We also provide a suite of implementations of these algorithms with a ready-to-use, platform-agnostic interface based on Docker containers and the AlgoRun framework, so that interested computational scientists can easily perform similar tests with inputs from their own research areas on their own computers or through a convenient Web interface.
We consider the problem of maximizing a monotone submodular function under noise. There has been a great deal of work on optimization of submodular functions under various constraints, resulting in algorithms that provide desirable approximation guarantees. In many applications, however, we do not have access to the submodular function we aim to optimize, but rather to some erroneous or noisy version of it. This raises the question of whether provable guarantees are obtainable in presence of error and noise. We provide initial answers, by focusing on the question of maximizing a monotone submodular function under a cardinality constraint when given access to a noisy oracle of the function. We show that: - For a cardinality constraint $k \geq 2$, there is an approximation algorithm whose approximation ratio is arbitrarily close to $1-1/e$; - For $k=1$ there is an algorithm whose approximation ratio is arbitrarily close to $1/2$. No randomized algorithm can obtain an approximation ratio better than $1/2+o(1)$; -If the noise is adversarial, no non-trivial approximation guarantee can be obtained.
Model checking has been successfully used in many computer science fields, including artificial intelligence, theoretical computer science, and databases. Most of the proposed solutions make use of classical, point-based temporal logics, while little work has been done in the interval temporal logic setting. Recently, a non-elementary model checking algorithm for Halpern and Shoham's modal logic of time intervals HS over finite Kripke structures (under the homogeneity assumption) and an EXPSPACE model checking procedure for two meaningful fragments of it have been proposed. In this paper, we show that more efficient model checking procedures can be developed for some expressive enough fragments of HS.
Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task. It differs from a traditional link prediction task in that the links in a knowledge graph are categorized into different predicates and the link prediction performance of different predicates in a knowledge graph generally varies widely. In this work, we propose a latent feature embedding based link prediction model which considers the prediction task for each predicate disjointly. To learn the model parameters it utilizes a Bayesian personalized ranking based optimization technique. Experimental results on large-scale knowledge bases such as YAGO2 show that our link prediction approach achieves substantially higher performance than several state-of-art approaches. We also show that for a given predicate the topological properties of the knowledge graph induced by the given predicate edges are key indicators of the link prediction performance of that predicate in the knowledge graph.
In this paper we propose a special type of aggregation function which generalizes the notion of Ordered Weighted Averaging Function - OWA. The resulting functions are called Dynamic Ordered Weighted Averaging Functions --- DYOWAs. This generalization will be developed in such way that the weight vectors are variables depending on the input vector. Particularly, this operators generalize the aggregation functions: Minimum, Maximum, Arithmetic Mean, Median, etc, which are extensively used in image processing. In this field of research two problems are considered: The determination of methods to reduce images and the construction of techniques which provide noise reduction. The operators described here are able to be used in both cases. In terms of image reduction we apply the methodology provided by Patermain et al. We use the noise reduction operators obtained here to treat the images obtained in the first part of the paper, thus obtaining images with better quality.
Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the existing approaches based on how they conceptualize this problem, viz., models that cast description as either generation problem or as a retrieval problem over a visual or multimodal representational space. We provide a detailed review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image datasets and the evaluation measures that have been developed to assess the quality of machine-generated image descriptions. Finally we extrapolate future directions in the area of automatic image description generation.
We introduce the problem of Task Assignment and Sequencing (TAS), which adds the timeline perspective to expert crowdsourcing optimization. Expert crowdsourcing involves macrotasks, like document writing, product design, or web development, which take more time than typical binary microtasks, require expert skills, assume varying degrees of knowledge over a topic, and require crowd workers to build on each other's contributions. Current works usually assume offline optimization models, which consider worker and task arrivals known and do not take into account the element of time. Realistically however, time is critical: tasks have deadlines, expert workers are available only at specific time slots, and worker/task arrivals are not known a-priori. Our work is the first to address the problem of optimal task sequencing for online, heterogeneous, time-constrained macrotasks. We propose tas-online, an online algorithm that aims to complete as many tasks as possible within budget, required quality and a given timeline, without future input information regarding job release dates or worker availabilities. Results, comparing tas-online to four typical benchmarks, show that it achieves more completed jobs, lower flow times and higher job quality. This work has practical implications for improving the Quality of Service of current crowdsourcing platforms, allowing them to offer cost, quality and time improvements for expert tasks.
In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. We further design the One-Step Sparse Inference (1-SI) module, as an efficient and light-weighted feed-forward approximation of sparse coding. Extensive experiments verify the superiority of the proposed $D^3$ model over several state-of-the-art methods. Specifically, our best model is capable of outperforming the latest deep model for around 1 dB in PSNR, and is 30 times faster.
Visual recognition research often assumes a sufficient resolution of the region of interest (ROI). That is usually violated in practice, inspiring us to explore the Very Low Resolution Recognition (VLRR) problem. Typically, the ROI in a VLRR problem can be smaller than $16 \times 16$ pixels, and is challenging to be recognized even by human experts. We attempt to solve the VLRR problem using deep learning methods. Taking advantage of techniques primarily in super resolution, domain adaptation and robust regression, we formulate a dedicated deep learning method and demonstrate how these techniques are incorporated step by step. Any extra complexity, when introduced, is fully justified by both analysis and simulation results. The resulting \textit{Robust Partially Coupled Networks} achieves feature enhancement and recognition simultaneously. It allows for both the flexibility to combat the LR-HR domain mismatch, and the robustness to outliers. Finally, the effectiveness of the proposed models is evaluated on three different VLRR tasks, including face identification, digit recognition and font recognition, all of which obtain very impressive performances.
The current scenario in the field of computing is largely affected by the speed at which data can be accessed and recalled. In this paper, we present the word existence algorithm which is used to check if the word given as an input is part of a particular database or not. We have taken the English language as an example here. This algorithm tries to solve the problem of lookup by using a uniformly distributed hash function. We have also addressed the problem of clustering and collision. A further contribution is that we follow a direct hashed model where each hash value is linked to another table if the continuity for the function holds true. The core of the algorithm lies in the data model being used during preordering. Our focus lies on the formation of a continuity series and validating the words that exists in the database. This algorithm can be used in applications where we there is a requirement to search for just the existence of a word, example Artificial Intelligence responding to input ,look up for neural networks and dictionary lookups and more. We have observed that this algorithm provides a faster search time
We study the semantic foundation of expressive probabilistic programming languages, that support higher-order functions, continuous distributions, and soft constraints (such as Anglican, Church, and Venture). We define a metalanguage (an idealised version of Anglican) for probabilistic computation with the above features, develop both operational and denotational semantics, and prove soundness, adequacy, and termination. They involve measure theory, stochastic labelled transition systems, and functor categories, but admit intuitive computational readings, one of which views sampled random variables as dynamically allocated read-only variables. We apply our semantics to validate nontrivial equations underlying the correctness of certain compiler optimisations and inference algorithms such as sequential Monte Carlo simulation. The language enables defining probability distributions on higher-order functions, and we study their properties.
Vector space representations of words capture many aspects of word similarity, but such methods tend to make vector spaces in which antonyms (as well as synonyms) are close to each other. We present a new signed spectral normalized graph cut algorithm, signed clustering, that overlays existing thesauri upon distributionally derived vector representations of words, so that antonym relationships between word pairs are represented by negative weights. Our signed clustering algorithm produces clusters of words which simultaneously capture distributional and synonym relations. We evaluate these clusters against the SimLex-999 dataset (Hill et al.,2014) of human judgments of word pair similarities, and also show the benefit of using our clusters to predict the sentiment of a given text.
Rubik's Revenge, a 4x4x4 variant of the Rubik's puzzles, remains to date as an unsolved puzzle. That is to say, we do not have a method or successful categorization to optimally solve every one of its approximately $7.401 \times 10^{45}$ possible configurations. Rubik's Cube, Rubik's Revenge's predecessor (3x3x3), with its approximately $4.33 \times 10^{19}$ possible configurations, has only recently been completely solved by Rokicki et. al, further finding that any configuration requires no more than 20 moves. With the sheer dimension of Rubik's Revenge and its total configuration space, a brute-force method of finding all optimal solutions would be in vain. Similar to the methods used by Rokicki et. al on Rubik's Cube, in this paper we develop a method for solving arbitrary configurations of Rubik's Revenge in phases, using a combination of a powerful algorithm known as IDA* and a useful definition of distance in the cube space. While time-series results were not successfully gathered, it will be shown that this method far outweighs current human-solving methods and can be used to determine loose upper bounds for the cube space. Discussion will suggest that this method can also be applied to other puzzles with the proper transformations.
We present a system for online monitoring of maritime activity over streaming positions from numerous vessels sailing at sea. It employs an online tracking module for detecting important changes in the evolving trajectory of each vessel across time, and thus can incrementally retain concise, yet reliable summaries of its recent movement. In addition, thanks to its complex event recognition module, this system can also offer instant notification to marine authorities regarding emergency situations, such as risk of collisions, suspicious moves in protected zones, or package picking at open sea. Not only did our extensive tests validate the performance, efficiency, and robustness of the system against scalable volumes of real-world and synthetically enlarged datasets, but its deployment against online feeds from vessels has also confirmed its capabilities for effective, real-time maritime surveillance.
Use of knowledge-based planning tools can help alleviate the challenges of planning a complex operation by a coalition of diverse parties in an adversarial environment. We explore these challenges and potential contributions of knowledge-based tools using as an example the CADET system, a knowledge-based tool capable of producing automatically (or with human guidance) battle plans with realistic degree of detail and complexity. In ongoing experiments, it compared favorably with human planners. Interleaved planning, scheduling, routing, attrition and consumption processes comprise the computational approach of this tool. From the coalition operations perspective, such tools offer an important aid in rapid synchronization of assets and actions of heterogeneous assets belonging to multiple organizations, potentially with distinct doctrine and rules of engagement. In this paper, we discuss the functionality of the tool, provide a brief overview of the technical approach and experimental results, and outline the potential value of such tools.
Based on the assumption that there exists a neural network that efficiently represents a set of Boolean functions between all binary inputs and outputs, we propose a process for developing and deploying neural networks whose weight parameters, bias terms, input, and intermediate hidden layer output signals, are all binary-valued, and require only basic bit logic for the feedforward pass. The proposed Bitwise Neural Network (BNN) is especially suitable for resource-constrained environments, since it replaces either floating or fixed-point arithmetic with significantly more efficient bitwise operations. Hence, the BNN requires for less spatial complexity, less memory bandwidth, and less power consumption in hardware. In order to design such networks, we propose to add a few training schemes, such as weight compression and noisy backpropagation, which result in a bitwise network that performs almost as well as its corresponding real-valued network. We test the proposed network on the MNIST dataset, represented using binary features, and show that BNNs result in competitive performance while offering dramatic computational savings.
We consider a setting for Inverse Reinforcement Learning (IRL) where the learner is extended with the ability to actively select multiple environments, observing an agent's behavior on each environment. We first demonstrate that if the learner can experiment with any transition dynamics on some fixed set of states and actions, then there exists an algorithm that reconstructs the agent's reward function to the fullest extent theoretically possible, and that requires only a small (logarithmic) number of experiments. We contrast this result to what is known about IRL in single fixed environments, namely that the true reward function is fundamentally unidentifiable. We then extend this setting to the more realistic case where the learner may not select any transition dynamic, but rather is restricted to some fixed set of environments that it may try. We connect the problem of maximizing the information derived from experiments to submodular function maximization and demonstrate that a greedy algorithm is near optimal (up to logarithmic factors). Finally, we empirically validate our algorithm on an environment inspired by behavioral psychology.
We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (EXPoSE), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with EXPoSE can be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, EXPoSE can make predictions in constant time, while it requires only constant memory. In addition, we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being an order of magnitude faster than most other approaches.
Theories of natural language and concepts have been unable to model the flexibility, creativity, context-dependence, and emergence, exhibited by words, concepts and their combinations. The mathematical formalism of quantum theory has instead been successful in capturing these phenomena such as graded membership, situational meaning, composition of categories, and also more complex decision making situations, which cannot be modeled in traditional probabilistic approaches. We show how a formal quantum approach to concepts and their combinations can provide a powerful extension of prototype theory. We explain how prototypes can interfere in conceptual combinations as a consequence of their contextual interactions, and provide an illustration of this using an intuitive wave-like diagram. This quantum-conceptual approach gives new life to original prototype theory, without however making it a privileged concept theory, as we explain at the end of our paper.
The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person, obtaining a rich representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on `CASIA' dataset (parts B and C), `TUM GAID' dataset, `CMU MoBo' dataset and the recent `AVA Multiview Gait' dataset. The results show that this new approach achieves state-of-the-art results in the problem of gait recognition, allowing to recognize walking people from diverse viewpoints on single and multiple camera setups, wearing different clothes, carrying bags, walking at diverse speeds and not limited to straight walking paths.
Identifying the type of font (e.g., Roman, Blackletter) used in historical documents can help optical character recognition (OCR) systems produce more accurate text transcriptions. Towards this end, we present an active-learning strategy that can significantly reduce the number of labeled samples needed to train a font classifier. Our approach extracts image-based features that exploit geometric differences between fonts at the word level, and combines them into a bag-of-word representation for each page in a document. We evaluate six sampling strategies based on uncertainty, dissimilarity and diversity criteria, and test them on a database containing over 3,000 historical documents with Blackletter, Roman and Mixed fonts. Our results show that a combination of uncertainty and diversity achieves the highest predictive accuracy (89% of test cases correctly classified) while requiring only a small fraction of the data (17%) to be labeled. We discuss the implications of this result for mass digitization projects of historical documents.
We consider a general class of models, where a reinforcement learning (RL) agent learns from cyclic interactions with an external environment via classical signals. Perceptual inputs are encoded as quantum states, which are subsequently transformed by a quantum channel representing the agent's memory, while the outcomes of measurements performed at the channel's output determine the agent's actions. The learning takes place via stepwise modifications of the channel properties. They are described by an update rule that is inspired by the projective simulation (PS) model and equipped with a glow mechanism that allows for a backpropagation of policy changes, analogous to the eligibility traces in RL and edge glow in PS. In this way, the model combines features of PS with the ability for generalization, offered by its physical embodiment as a quantum system. We apply the agent to various setups of an invasion game and a grid world, which serve as elementary model tasks allowing a direct comparison with a basic classical PS agent.
In recent years, the planning community has observed that techniques for learning heuristic functions have yielded improvements in performance. One approach is to use offline learning to learn predictive models from existing heuristics in a domain dependent manner. These learned models are deployed as new heuristic functions. The learned models can in turn be tuned online using a domain independent error correction approach to further enhance their informativeness. The online tuning approach is domain independent but instance specific, and contributes to improved performance for individual instances as planning proceeds. Consequently it is more effective in larger problems. In this paper, we mention two approaches applicable in Partial Order Causal Link (POCL) Planning that is also known as Plan Space Planning. First, we endeavor to enhance the performance of a POCL planner by giving an algorithm for supervised learning. Second, we then discuss an online error minimization approach in POCL framework to minimize the step-error associated with the offline learned models thus enhancing their informativeness. Our evaluation shows that the learning approaches scale up the performance of the planner over standard benchmarks, specially for larger problems.
Local search algorithms and iterated local search algorithms are a basic technique. Local search can be a stand along search methods, but it can also be hybridized with evolutionary algorithms. Recently, it has been shown that it is possible to identify improving moves in Hamming neighborhoods for k-bounded pseudo-Boolean optimization problems in constant time. This means that local search does not need to enumerate neighborhoods to find improving moves. It also means that evolutionary algorithms do not need to use random mutation as a operator, except perhaps as a way to escape local optima. In this paper, we show how improving moves can be identified in constant time for multiobjective problems that are expressed as k-bounded pseudo-Boolean functions. In particular, multiobjective forms of NK Landscapes and Mk Landscapes are considered.
This paper describes about information extraction system, which is an extension of the system developed by team Hitachi for "Disease/Disorder Template filling" task organized by ShARe/CLEF eHealth Evolution Lab 2014. In this extension module we focus on extraction of numerical attributes and values from discharge summary records and associating correct relation between attributes and values. We solve the problem in two steps. First step is extraction of numerical attributes and values, which is developed as a Named Entity Recognition (NER) model using Stanford NLP libraries. Second step is correctly associating the attributes to values, which is developed as a relation extraction module in Apache cTAKES framework. We integrated Stanford NER model as cTAKES pipeline component and used in relation extraction module. Conditional Random Field (CRF) algorithm is used for NER and Support Vector Machines (SVM) for relation extraction. For attribute value relation extraction, we observe 95% accuracy using NER alone and combined accuracy of 87% with NER and SVM.
Most Software Defined Networks (SDN) traffic engineering applications use excessive and frequent global monitoring in order to find the optimal Quality-of-Service (QoS) paths for the current state of the network. In this work, we present the motivations, architecture and initial evaluation of a SDN application called Cognitive Routing Engine (CRE) which is able to find near-optimal paths for a user-specified QoS while using a very small monitoring overhead compared to global monitoring which is required to guarantee that optimal paths are found. Smaller monitoring overheads bring the advantage of smaller response time for the SDN controllers and switches. The initial evaluation of CRE on a SDN representation of the GEANT academic network shows that it is possible to find near-optimal paths with a small optimality gap of 1.65% while using 9.5 times less monitoring.
People are producing more written material then anytime in the history. The increase is so high that professionals from the various fields are no more able to cope with this amount of publications. Text mining tools can offer tools to help them and one of the tools that can aid information retrieval and information extraction is semantic text annotation. In this report we present Marvin, a text annotator written in Java, which can be used as a command line tool and as a Java library. Marvin is able to annotate text using multiple sources, including WordNet, MetaMap, DBPedia and thesauri represented as SKOS.
Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons.
The understanding of the buildings operation has become a challenging task due to the large amount of data recorded in energy efficient buildings. Still, today the experts use visual tools for analyzing the data. In order to make the task realistic, a method has been proposed in this paper to automatically detect the different patterns in buildings. The K Means clustering is used to automatically identify the ON (operational) cycles of the chiller. In the next step the ON cycles are transformed to symbolic representation by using Symbolic Aggregate Approximation (SAX) method. Then the SAX symbols are converted to bag of words representation for hierarchical clustering. Moreover, the proposed technique is applied to real life data of adsorption chiller. Additionally, the results from the proposed method and dynamic time warping (DTW) approach are also discussed and compared.
Statistical language models are central to many applications that use semantics. Recurrent Neural Networks (RNN) are known to produce state of the art results for language modelling, outperforming their traditional n-gram counterparts in many cases. To generate a probability distribution across a vocabulary, these models require a softmax output layer that linearly increases in size with the size of the vocabulary. Large vocabularies need a commensurately large softmax layer and training them on typical laptops/PCs requires significant time and machine resources. In this paper we present a new technique for implementing RNN based large vocabulary language models that substantially speeds up computation while optimally using the limited memory resources. Our technique, while building on the notion of factorizing the output layer by having multiple output layers, improves on the earlier work by substantially optimizing on the individual output layer size and also eliminating the need for a multistep prediction process.
Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time. The subtle semantics and non-linear dynamics of fashion evolution raise unique challenges especially considering the sparsity and large scale of the underlying datasets. In this paper we build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback. To uncover the complex and evolving visual factors that people consider when evaluating products, our method combines high-level visual features extracted from a deep convolutional neural network, users' past feedback, as well as evolving trends within the community. Experimentally we evaluate our method on two large real-world datasets from Amazon.com, where we show it to outperform state-of-the-art personalized ranking measures, and also use it to visualize the high-level fashion trends across the 11-year span of our dataset.
Categorical compositional distributional semantics is a model of natural language; it combines the statistical vector space models of words with the compositional models of grammar. We formalise in this model the generalised quantifier theory of natural language, due to Barwise and Cooper. The underlying setting is a compact closed category with bialgebras. We start from a generative grammar formalisation and develop an abstract categorical compositional semantics for it, then instantiate the abstract setting to sets and relations and to finite dimensional vector spaces and linear maps. We prove the equivalence of the relational instantiation to the truth theoretic semantics of generalised quantifiers. The vector space instantiation formalises the statistical usages of words and enables us to, for the first time, reason about quantified phrases and sentences compositionally in distributional semantics.
The coordination of multiple autonomous vehicles into convoys or platoons is expected on our highways in the near future. However, before such platoons can be deployed, the new autonomous behaviors of the vehicles in these platoons must be certified. An appropriate representation for vehicle platooning is as a multi-agent system in which each agent captures the "autonomous decisions" carried out by each vehicle. In order to ensure that these autonomous decision-making agents in vehicle platoons never violate safety requirements, we use formal verification. However, as the formal verification technique used to verify the agent code does not scale to the full system and as the global verification technique does not capture the essential verification of autonomous behavior, we use a combination of the two approaches. This mixed strategy allows us to verify safety requirements not only of a model of the system, but of the actual agent code used to program the autonomous vehicles.
The model of cognition developed in (Smolensky and Legendre, 2006) seeks to unify two levels of description of the cognitive process: the connectionist and the symbolic. The theory developed brings together these two levels into the Integrated Connectionist/Symbolic Cognitive architecture (ICS). Clark and Pulman (2007) draw a parallel with semantics where meaning may be modelled on both distributional and symbolic levels, developed by Coecke et al, 2010 into the Distributional Compositional (DisCo) model of meaning. In the current work, we revisit Smolensky and Legendre (S&L)'s model. We describe the DisCo framework, summarise the key ideas in S&L's architecture, and describe how their description of harmony as a graded measure of grammaticality may be applied in the DisCo model.
We can program a Real-Time (RT) music improvisation system in C++ without a formal semantic or we can model it with process calculi such as the Non-deterministic Timed Concurrent Constraint (ntcc) calculus. "A Concurrent Constraints Factor Oracle (FO) model for Music Improvisation" (Ccfomi) is an improvisation model specified on ntcc. Since Ccfomi improvises non-deterministically, there is no control on choices and therefore little control over the sequence variation during the improvisation. To avoid this, we extended Ccfomi using the Probabilistic Non-deterministic Timed Concurrent Constraint calculus. Our extension to Ccfomi does not change the time and space complexity of building the FO, thus making our extension compatible with RT. However, there was not a ntcc interpreter capable of RT to execute Ccfomi. We developed Ntccrt --a RT capable interpreter for ntcc-- and we executed Ccfomi on Ntccrt. In the future, we plan to extend Ntccrt to execute our extension to Ccfomi.
Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called "matching dependencies" (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language "LogiQL" -an extended form of Datalog supported by the "LogicBlox" platform- for all activities related to data processing, and the specification and enforcement of MDs.
We study an ancient problem that in a static or dynamical system, sought an optimal path, which the context always means within an extremal condition. In fact, through those discussions about this theme, we established a universal essential calculated model to serve for these complex systems. Meanwhile we utilize the sample space to character the system. These contents in this paper would involve in several major areas including the geometry, probability, graph algorithms and some prior approaches, which stands the ultimately subtle linear algorithm to solve this class problem. Along with our progress, our discussion would demonstrate more general meaning and robust character, which provides clear ideas or notion to support our concrete applications, who work in a more popular complex system.
In recent years there is a growing interest in using deep representations for reinforcement learning. In this paper, we present a methodology and tools to analyze Deep Q-networks (DQNs) in a non-blind matter. Moreover, we propose a new model, the Semi Aggregated Markov Decision Process (SAMDP), and an algorithm that learns it automatically. The SAMDP model allows us to identify spatio-temporal abstractions directly from features and may be used as a sub-goal detector in future work. Using our tools we reveal that the features learned by DQNs aggregate the state space in a hierarchical fashion, explaining its success. Moreover, we are able to understand and describe the policies learned by DQNs for three different Atari2600 games and suggest ways to interpret, debug and optimize deep neural networks in reinforcement learning.
We propose deep distributed recurrent Q-networks (DDRQN), which enable teams of agents to learn to solve communication-based coordination tasks. In these tasks, the agents are not given any pre-designed communication protocol. Therefore, in order to successfully communicate, they must first automatically develop and agree upon their own communication protocol. We present empirical results on two multi-agent learning problems based on well-known riddles, demonstrating that DDRQN can successfully solve such tasks and discover elegant communication protocols to do so. To our knowledge, this is the first time deep reinforcement learning has succeeded in learning communication protocols. In addition, we present ablation experiments that confirm that each of the main components of the DDRQN architecture are critical to its success.
We adress the problem of dueling bandits defined on partially ordered sets, or posets. In this setting, arms may not be comparable, and there may be several (incomparable) optimal arms. We propose an algorithm, UnchainedBandits, that efficiently finds the set of optimal arms of any poset even when pairs of comparable arms cannot be distinguished from pairs of incomparable arms, with a set of minimal assumptions. This algorithm relies on the concept of decoys, which stems from social psychology. For the easier case where the incomparability information may be accessible, we propose a second algorithm, SlicingBandits, which takes advantage of this information and achieves a very significant gain of performance compared to UnchainedBandits. We provide theoretical guarantees and experimental evaluation for both algorithms.
We study the strategic aspects of social influence in a society of agents linked by a trust network, introducing a new class of games called games of influence. A game of influence is an infinite repeated game with incomplete information in which, at each stage of interaction, an agent can make her opinions visible (public) or invisible (private) in order to influence other agents' opinions. The influence process is mediated by a trust network, as we assume that the opinion of a given agent is only affected by the opinions of those agents that she considers trustworthy (i.e., the agents in the trust network that are directly linked to her). Each agent is endowed with a goal, expressed in a suitable temporal language inspired from linear temporal logic (LTL). We show that games of influence provide a simple abstraction to explore the effects of the trust network structure on the agents' behaviour, by considering solution concepts from game-theory such as Nash equilibrium, weak dominance and winning strategies.
We introduce a problem set-up we call the Iterated Matching Pennies (IMP) game and show that it is a powerful framework for the study of three problems: adversarial learnability, conventional (i.e., non-adversarial) learnability and approximability. Using it, we are able to derive the following theorems. (1) It is possible to learn by example all of $\Sigma^0_1 \cup \Pi^0_1$ as well as some supersets; (2) in adversarial learning (which we describe as a pursuit-evasion game), the pursuer has a winning strategy (in other words, $\Sigma^0_1$ can be learned adversarially, but $\Pi^0_1$ not); (3) some languages in $\Pi^0_1$ cannot be approximated by any language in $\Sigma^0_1$. We show corresponding results also for $\Sigma^0_i$ and $\Pi^0_i$ for arbitrary $i$.
We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.
The problem of scheduling under resource constraints is widely applicable. One prominent example is power management, in which we have a limited continuous supply of power but must schedule a number of power-consuming tasks. Such problems feature tightly coupled continuous resource constraints and continuous temporal constraints. We address such problems by introducing the Time Resource Network (TRN), an encoding for resource-constrained scheduling problems. The definition allows temporal specifications using a general family of representations derived from the Simple Temporal network, including the Simple Temporal Network with Uncertainty, and the probabilistic Simple Temporal Network (Fang et al. (2014)). We propose two algorithms for determining the consistency of a TRN: one based on Mixed Integer Programing and the other one based on Constraint Programming, which we evaluate on scheduling problems with Simple Temporal Constraints and Probabilistic Temporal Constraints.
Existing research in crowdsourcing has investigated how to recommend tasks to workers based on which task the workers have already completed, referred to as {\em implicit feedback}. We, on the other hand, investigate the task recommendation problem, where we leverage both implicit feedback and explicit features of the task. We assume that we are given a set of workers, a set of tasks, interactions (such as the number of times a worker has completed a particular task), and the presence of explicit features of each task (such as, task location). We intend to recommend tasks to the workers by exploiting the implicit interactions, and the presence or absence of explicit features in the tasks. We formalize the problem as an optimization problem, propose two alternative problem formulations and respective solutions that exploit implicit feedback, explicit features, as well as similarity between the tasks. We compare the efficacy of our proposed solutions against multiple state-of-the-art techniques using two large scale real world datasets.
For complex, high-dimensional Markov Decision Processes (MDPs), it may be necessary to represent the policy with function approximation. A problem is misspecified whenever, the representation cannot express any policy with acceptable performance. We introduce IHOMP : an approach for solving misspecified problems. IHOMP iteratively learns a set of context specialized options and combines these options to solve an otherwise misspecified problem. Our main contribution is proving that IHOMP enjoys theoretical convergence guarantees. In addition, we extend IHOMP to exploit Option Interruption (OI) enabling it to decide where the learned options can be reused. Our experiments demonstrate that IHOMP can find near-optimal solutions to otherwise misspecified problems and that OI can further improve the solutions.
We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that (1) learns skills (i.e., temporally extended actions or options) as well as (2) where to apply them. We believe that both (1) and (2) are necessary for a truly general skill learning framework, which is a key building block needed to scale up to lifelong learning agents. The ASAP framework can also solve related new tasks simply by adapting where it applies its existing learned skills. We prove that ASAP converges to a local optimum under natural conditions. Finally, our experimental results, which include a RoboCup domain, demonstrate the ability of ASAP to learn where to reuse skills as well as solve multiple tasks with considerably less experience than solving each task from scratch.
Collaborative human activities are grounded in social and moral norms, which humans consciously and subconsciously use to guide and constrain their decision-making and behavior, thereby strengthening their interactions and preventing emotional and physical harm. This type of norm-based processing is also critical for robots in many human-robot interaction scenarios (e.g., when helping elderly and disabled persons in assisted living facilities, or assisting humans in assembly tasks in factories or even the space station). In this position paper, we will briefly describe how several components in an integrated cognitive architecture can be used to implement processes that are required for normative human-robot interactions, especially in collaborative tasks where actions and situations could potentially be perceived as threatening and thus need a change in course of action to mitigate the perceived threats.
An important problem in the field of bioinformatics is to identify interactive effects among profiled variables for outcome prediction. In this paper, a logistic regression model with pairwise interactions among a set of binary covariates is considered. Modeling the structure of the interactions by a graph, our goal is to recover the interaction graph from independently identically distributed (i.i.d.) samples of the covariates and the outcome. When viewed as a feature selection problem, a simple quantity called influence is proposed as a measure of the marginal effects of the interaction terms on the outcome. For the case when the underlying interaction graph is known to be acyclic, it is shown that a simple algorithm that is based on a maximum-weight spanning tree with respect to the plug-in estimates of the influences not only has strong theoretical performance guarantees, but can also outperform generic feature selection algorithms for recovering the interaction graph from i.i.d. samples of the covariates and the outcome. Our results can also be extended to the model that includes both individual effects and pairwise interactions via the help of an auxiliary covariate.
Hospital readmissions are expensive and reflect the inadequacies in healthcare system. In the United States alone, treatment of readmitted diabetic patients exceeds 250 million dollars per year. Early identification of patients facing a high risk of readmission can enable healthcare providers to to conduct additional investigations and possibly prevent future readmissions. This not only improves the quality of care but also reduces the medical expenses on readmission. Machine learning methods have been leveraged on public health data to build a system for identifying diabetic patients facing a high risk of future readmission. Number of inpatient visits, discharge disposition and admission type were identified as strong predictors of readmission. Further, it was found that the number of laboratory tests and discharge disposition together predict whether the patient will be readmitted shortly after being discharged from the hospital (i.e. <30 days) or after a longer period of time (i.e. >30 days). These insights can help healthcare providers to improve inpatient diabetic care. Finally, the cost analysis suggests that \$252.76 million can be saved across 98,053 diabetic patient encounters by incorporating the proposed cost sensitive analysis model.
Sum-Product Networks (SPNs) are a class of expressive yet tractable hierarchical graphical models. LearnSPN is a structure learning algorithm for SPNs that uses hierarchical co-clustering to simultaneously identifying similar entities and similar features. The original LearnSPN algorithm assumes that all the variables are discrete and there is no missing data. We introduce a practical, simplified version of LearnSPN, MiniSPN, that runs faster and can handle missing data and heterogeneous features common in real applications. We demonstrate the performance of MiniSPN on standard benchmark datasets and on two datasets from Google's Knowledge Graph exhibiting high missingness rates and a mix of discrete and continuous features.
Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.
Change management for evolving collaborative business process development is crucial when the business logic, transections and workflow change due to changes in business strategies or organizational and technical environment. During the change implementation, business processes are analyzed and improved ensuring that they capture the proposed change and they do not contain any undesired functionalities or change side-effects. This paper presents Business Process Change Management approach for the efficient and effective implementation of change in the business process. The key technology behind our approach is our proposed Business Process Change Management Ontology (BPCMont) which is the main contribution of this paper. BPCMont, as a formalized change specification, helps to revert BP into a consistent state in case of system crash, intermediate conflicting stage or unauthorized change done, aid in change traceability in the new and old versions of business processes, change effects can be seen and estimated effectively, ease for Stakeholders to validate and verify change implementation, etc.
Concept drift has potential in smart grid analysis because the socio-economic behaviour of consumers is not governed by the laws of physics. Likewise there are also applications in wind power forecasting. In this paper we present decision tree ensemble classification method based on the Random Forest algorithm for concept drift. The weighted majority voting ensemble aggregation rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method. Base learner weight in our case is computed for each sample evaluation using base learners accuracy and intrinsic proximity measure of Random Forest. Our algorithm exploits both temporal weighting of samples and ensemble pruning as a forgetting strategy. We present results of empirical comparison of our method with original random forest with incorporated "replace-the-looser" forgetting andother state-of-the-art concept-drfit classifiers like AWE2.
We analyze dropout in deep networks with rectified linear units and the quadratic loss. Our results expose surprising differences between the behavior of dropout and more traditional regularizers like weight decay. For example, on some simple data sets dropout training produces negative weights even though the output is the sum of the inputs. This provides a counterpoint to the suggestion that dropout discourages co-adaptation of weights. We also show that the dropout penalty can grow exponentially in the depth of the network while the weight-decay penalty remains essentially linear, and that dropout is insensitive to various re-scalings of the input features, outputs, and network weights. This last insensitivity implies that there are no isolated local minima of the dropout training criterion. Our work uncovers new properties of dropout, extends our understanding of why dropout succeeds, and lays the foundation for further progress.
Consequence-based calculi are a family of reasoning algorithms for description logics (DLs), and they combine hypertableau and resolution in a way that often achieves excellent performance in practice. Up to now, however, they were proposed for either Horn DLs (which do not support disjunction), or for DLs without counting quantifiers. In this paper we present a novel consequence-based calculus for SRIQ---a rich DL that supports both features. This extension is non-trivial since the intermediate consequences that need to be derived during reasoning cannot be captured using DLs themselves. The results of our preliminary performance evaluation suggest the feasibility of our approach in practice.
Data Warehouses are structures with large amount of data collected from heterogeneous sources to be used in a decision support system. Data Warehouses analysis identifies hidden patterns initially unexpected which analysis requires great memory and computation cost. Data reduction methods were proposed to make this analysis easier. In this paper, we present a hybrid approach based on Genetic Algorithms (GA) as Evolutionary Algorithms and the Multiple Correspondence Analysis (MCA) as Analysis Factor Methods to conduct this reduction. Our approach identifies reduced subset of dimensions from the initial subset p where p'1$ provide superior performance. Our approach easily integrates with stochastic or incremental optimization algorithms to scale to millions of examples. Experiments training mixture models of image patches and topic models for news articles show that our approach produces better-quality models in far less time than baseline methods.
Recent neural network sequence models with softmax classifiers have achieved their best language modeling performance only with very large hidden states and large vocabularies. Even then they struggle to predict rare or unseen words even if the context makes the prediction unambiguous. We introduce the pointer sentinel mixture architecture for neural sequence models which has the ability to either reproduce a word from the recent context or produce a word from a standard softmax classifier. Our pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank (70.9 perplexity) while using far fewer parameters than a standard softmax LSTM. In order to evaluate how well language models can exploit longer contexts and deal with more realistic vocabularies and larger corpora we also introduce the freely available WikiText corpus.
[Background]: Systematic Literature Review (SLR) has become an important software engineering research method but costs tremendous efforts. [Aim]: This paper proposes an approach to leverage on empirically evolved ontology to support automating key SLR activities. [Method]: First, we propose an ontology, SLRONT, built on SLR experiences and best practices as a groundwork to capture common terminologies and their relationships during SLR processes; second, we present an extended version of SLRONT, the COSONT and instantiate it with the knowledge and concepts extracted from structured abstracts. Case studies illustrate the details of applying it for supporting SLR steps. [Results]: Results show that through using COSONT, we acquire the same conclusion compared with sheer manual works, but the efforts involved is significantly reduced. [Conclusions]: The approach of using ontology could effectively and efficiently support the conducting of systematic literature review.
We introduce an online neural sequence to sequence model that learns to alternate between encoding and decoding segments of the input as it is read. By independently tracking the encoding and decoding representations our algorithm permits exact polynomial marginalization of the latent segmentation during training, and during decoding beam search is employed to find the best alignment path together with the predicted output sequence. Our model tackles the bottleneck of vanilla encoder-decoders that have to read and memorize the entire input sequence in their fixed-length hidden states before producing any output. It is different from previous attentive models in that, instead of treating the attention weights as output of a deterministic function, our model assigns attention weights to a sequential latent variable which can be marginalized out and permits online generation. Experiments on abstractive sentence summarization and morphological inflection show significant performance gains over the baseline encoder-decoders.
Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well-known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.
Decision making is an important component in a speaker verification system. For the conventional GMM-UBM architecture, the decision is usually conducted based on the log likelihood ratio of the test utterance against the GMM of the claimed speaker and the UBM. This single-score decision is simple but tends to be sensitive to the complex variations in speech signals (e.g. text content, channel, speaking style, etc.). In this paper, we propose a decision making approach based on multiple scores derived from a set of cohort GMMs (cohort scores). Importantly, these cohort scores are not simply averaged as in conventional cohort methods; instead, we employ a powerful discriminative model as the decision maker. Experimental results show that the proposed method delivers substantial performance improvement over the baseline system, especially when a deep neural network (DNN) is used as the decision maker, and the DNN input involves some statistical features derived from the cohort scores.
Robotic code needs to be verified to ensure its safety and functional correctness, especially when the robot is interacting with people. Testing real code in simulation is a viable option. However, generating tests that cover rare scenarios, as well as exercising most of the code, is a challenge amplified by the complexity of the interactions between the environment and the software. Model-based test generation methods can automate otherwise manual processes and facilitate reaching rare scenarios during testing. In this paper, we compare using Belief-Desire-Intention (BDI) agents as models for test generation with more conventional automata-based techniques that exploit model checking, in terms of practicality, performance, transferability to different scenarios, and exploration (`coverage'), through two case studies: a cooperative manufacturing task, and a home care scenario. The results highlight the advantages of using BDI agents for test generation. BDI agents naturally emulate the agency present in Human-Robot Interactions (HRIs), and are thus more expressive than automata. The performance of the BDI-based test generation is at least as high, and the achieved coverage is higher or equivalent, compared to test generation based on model checking automata.
PLDA is a popular normalization approach for the i-vector model, and it has delivered state-of-the-art performance in speaker verification. However, PLDA training requires a large amount of labelled development data, which is highly expensive in most cases. We present a cheap PLDA training approach, which assumes that speakers in the same session can be easily separated, and speakers in different sessions are simply different. This results in `weak labels' which are not fully accurate but cheap, leading to a weak PLDA training. Our experimental results on real-life large-scale telephony customer service achieves demonstrated that the weak training can offer good performance when human-labelled data are limited. More interestingly, the weak training can be employed as a discriminative adaptation approach, which is more efficient than the prevailing unsupervised method when human-labelled data are insufficient.
While the possibility of time travel in physics is still debated, the explosive growth of virtual-reality simulations opens up new possibilities to rigorously explore such time travel and its consequences in the digital domain. Here we provide a computational model of time travel and a computer program that allows exploring digital time travel. In order to explain our method we formalize a simplified version of the famous grandfather paradox, show how the system can allow the participant to go back in time, try to kill their ancestors before they were born, and experience the consequences. The system has even come up with scenarios that can be considered consistent "solutions" of the grandfather paradox. We discuss the conditions for digital time travel, which indicate that it has a large number of practical applications.
In this paper, we present UbuntuWorld 1.0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system. Specifically, we propose to use the Bash terminal as a simulator of the Ubuntu environment for a learning-based agent and demonstrate the usefulness of adopting reinforcement learning (RL) techniques for basic problem solving and troubleshooting in this environment. We provide a plug-and-play interface to the simulator as a python package where different types of agents can be plugged in and evaluated, and provide pathways for integrating data from online support forums like AskUbuntu into an automated agent's learning process. Finally, we show that the use of this data significantly improves the agent's learning efficiency. We believe that this platform can be adopted as a real-world test bed for research on automated technical support.
Table (database) / Relational database Classification for big/smart/fast data machine learning is one of the most important tasks of predictive analytics and extracting valuable information from data. It is core applied technique for what now understood under data science and/or artificial intelligence. Widely used Decision Tree (Random Forest) and rare used rule based PRISM , VFST, etc classifiers are empirical substitutions of theoretically correct to use Boolean functions minimization. Developing Minimization of Boolean functions algorithms is started long time ago by Edward Veitch's 1952. Since it, big efforts by wide scientific/industrial community was done to find feasible solution of Boolean functions minimization. In this paper we propose consider table data classification from mathematical point of view, as minimization of Boolean functions. It is shown that data representation may be transformed to Boolean functions form and how to use known algorithms. For simplicity, binary output function is used for development, what opens doors for multivalued outputs developments.
Academic researchers often need to face with a large collection of research papers in the literature. This problem may be even worse for postgraduate students who are new to a field and may not know where to start. To address this problem, we have developed an online catalog of research papers where the papers have been automatically categorized by a topic model. The catalog contains 7719 papers from the proceedings of two artificial intelligence conferences from 2000 to 2015. Rather than the commonly used Latent Dirichlet Allocation, we use a recently proposed method called hierarchical latent tree analysis for topic modeling. The resulting topic model contains a hierarchy of topics so that users can browse the topics from the top level to the bottom level. The topic model contains a manageable number of general topics at the top level and allows thousands of fine-grained topics at the bottom level. It also can detect topics that have emerged recently.
This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments. Unlike traditional approaches to tracking, this method is learned end-to-end, and is able to directly predict a full unoccluded occupancy grid map from raw laser input data. Inspired by the recently presented DeepTracking approach [Ondruska, 2016], we employ a recurrent neural network (RNN) to capture the temporal evolution of the state of the environment, and propose to use Spatial Transformer modules to exploit estimates of the egomotion of the vehicle. Our results demonstrate the ability to track a range of objects, including cars, buses, pedestrians, and cyclists through occlusion, from both moving and stationary platforms, using a single learned model. Experimental results demonstrate that the model can also predict the future states of objects from current inputs, with greater accuracy than previous work.
We compare the effectiveness of four different syntactic CCG parsers for a semantic slot-filling task to explore how much syntactic supervision is required for downstream semantic analysis. This extrinsic, task-based evaluation provides a unique window to explore the strengths and weaknesses of semantics captured by unsupervised grammar induction systems. We release a new Freebase semantic parsing dataset called SPADES (Semantic PArsing of DEclarative Sentences) containing 93K cloze-style questions paired with answers. We evaluate all our models on this dataset. Our code and data are available at https://github.com/sivareddyg/graph-parser.
This paper presents a study of improvement in stability in a single machine connected to infinite bus (SMIB) power system by using static compensator (STATCOM). The gains of Proportional-Integral-Derivative (PID) controller in STATCOM are being optimized by heuristic technique based on Particle swarm optimization (PSO). Further, Bacterial Foraging Optimization (BFO) as an alternative heuristic method is also applied to select optimal gains of PID controller. The performance of STATCOM with the above soft-computing techniques are studied and compared with the conventional PID controller under various scenarios. The simulation results are accompanied with performance indices based quantitative analysis. The analysis clearly signifies the robustness of the new scheme in terms of stability and voltage regulation when compared with conventional PID.
Detecting a small number of outliers from a set of data observations is always challenging. This problem is more difficult in the setting of multiple network samples, where computing the anomalous degree of a network sample is generally not sufficient. In fact, explaining why the network is exceptional, expressed in the form of subnetwork, is also equally important. In this paper, we develop a novel algorithm to address these two key problems. We treat each network sample as a potential outlier and identify subnetworks that mostly discriminate it from nearby regular samples. The algorithm is developed in the framework of network regression combined with the constraints on both network topology and L1-norm shrinkage to perform subnetwork discovery. Our method thus goes beyond subspace/subgraph discovery and we show that it converges to a global optimum. Evaluation on various real-world network datasets demonstrates that our algorithm not only outperforms baselines in both network and high dimensional setting, but also discovers highly relevant and interpretable local subnetworks, further enhancing our understanding of anomalous networks.
Web Service is one of the most significant current discussions in information sharing technologies and one of the examples of service oriented processing. To ensure accurate execution of web services operations, it must be adaptable with policies of the social networks in which it signs up. This adaptation implements using controls called 'Commitment'. This paper describes commitments structure and existing research about commitments and social web services, then suggests an algorithm for consistency of commitments in social web services. As regards the commitments may be executed concurrently, a key challenge in web services execution based on commitment structure is consistency ensuring in execution time. The purpose of this research is providing an algorithm for consistency ensuring between web services operations based on commitments structure.
Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the brain optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, the neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network can learn to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful representations---the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the dendritic morphology of neocortical pyramidal neurons.
A number of attempts have been made to improve accuracy and/or scalability of the PC (Peter and Clark) algorithm, some well known (Buhlmann, et al., 2010; Kalisch and Buhlmann, 2007; 2008; Zhang, 2012, to give some examples). We add here one more tool to the toolbox: the simple observation that if one is forced to choose between a variety of possible conditioning sets for a pair of variables, one should choose the one with the highest p-value. One can use the CPC (Conservative PC, Ramsey et al., 2012) algorithm as a guide to possible sepsets for a pair of variables. However, whereas CPC uses a voting rule to classify colliders versus noncolliders, our proposed algorithm, PC-Max, picks the conditioning set with the highest p-value, so that there are no ambiguities. We combine this with two other optimizations: (a) avoiding bidirected edges in the orientation of colliders, and (b) parallelization. For (b) we borrow ideas from the PC-Stable algorithm (Colombo and Maathuis, 2014). The result is an algorithm that scales quite well both in terms of accuracy and time, with no risk of bidirected edges.
Many real-world problems involving constraints can be regarded as instances of the Max-SAT problem, which is the optimization variant of the classic satisfiability problem. In this paper, we propose a novel probabilistic approach for Max-SAT called ProMS. Our algorithm relies on a stochastic local search strategy using a novel probability distribution function with two strategies for picking variables, one based on available information and another purely random one. Moreover, while most previous algorithms based on WalkSAT choose unsatisfied clauses randomly, we introduce a novel clause selection strategy to improve our algorithm. Experimental results illustrate that ProMS outperforms many state-of-the-art stochastic local search solvers on hard unweighted random Max-SAT benchmarks.
Perturb and Combine (P&C) group of methods generate multiple versions of the predictor by perturbing the training set or construction and then combining them into a single predictor (Breiman, 1996b). The motive is to improve the accuracy in unstable classification and regression methods. One of the most well known method in this group is Bagging. Arcing or Adaptive Resampling and Combining methods like AdaBoost are smarter variants of P&C methods. In this extended abstract, we lay the groundwork for a new family of methods under the P&C umbrella, known as Evolutionary Sampling (ES). We employ Evolutionary algorithms to suggest smarter sampling in both the feature space (sub-spaces) as well as training samples. We discuss multiple fitness functions to assess ensembles and empirically compare our performance against randomized sampling of training data and feature sub-spaces.
We consider the problem of efficient "on the fly" tuning of existing, or {\it legacy}, Artificial Intelligence (AI) systems. The legacy AI systems are allowed to be of arbitrary class, albeit the data they are using for computing interim or final decision responses should posses an underlying structure of a high-dimensional topological real vector space. The tuning method that we propose enables dealing with errors without the need to re-train the system. Instead of re-training a simple cascade of perceptron nodes is added to the legacy system. The added cascade modulates the AI legacy system's decisions. If applied repeatedly, the process results in a network of modulating rules "dressing up" and improving performance of existing AI systems. Mathematical rationale behind the method is based on the fundamental property of measure concentration in high dimensional spaces. The method is illustrated with an example of fine-tuning a deep convolutional network that has been pre-trained to detect pedestrians in images.
In principle, reinforcement learning and policy search methods can enable robots to learn highly complex and general skills that may allow them to function amid the complexity and diversity of the real world. However, training a policy that generalizes well across a wide range of real-world conditions requires far greater quantity and diversity of experience than is practical to collect with a single robot. Fortunately, it is possible for multiple robots to share their experience with one another, and thereby, learn a policy collectively. In this work, we explore distributed and asynchronous policy learning as a means to achieve generalization and improved training times on challenging, real-world manipulation tasks. We propose a distributed and asynchronous version of Guided Policy Search and use it to demonstrate collective policy learning on a vision-based door opening task using four robots. We show that it achieves better generalization, utilization, and training times than the single robot alternative.
A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation -- pushing objects -- and can handle novel objects not seen during training.
There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article is making a case for the community to move to larger data and as a step in that direction it is proposing the BookTest, a new dataset similar to the popular Children's Book Test (CBT), however more than 60 times larger. We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture. On one version of the dataset our ensemble even exceeds the human baseline provided by Facebook. We then show in our own human study that there is still space for further improvement.
Over the years, several meta-heuristic algorithms were proposed and are now emerging as common methods for constrained optimization problems. Among them, genetic algorithms (GA's) shine as popular evolutionary algorithms (EA's) in engineering optimization. Most engineering design problems are difficult to resolve with conventional optimization algorithms because they are highly nonlinear and contain constraints. In order to handle these constraints, the most common technique is to apply penalty functions. The major drawback is that they require tuning of parameters, which can be very challenging. In this paper, we present a constraint-handling technique for GA's solely using the violation factor, called VCH (Violation Constraint-Handling) method. Several benchmark problems from the literature are examined. The VCH technique was able to provide a consistent performance and match results from other GA-based techniques.
Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged. In this tutorial, we build a neural-based approach to answer questions about images. We base our tutorial on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the models that we present here can achieve a competitive performance on both datasets, in fact, they are among the best methods that use a combination of LSTM with a global, full frame CNN representation of an image. We hope that after reading this tutorial, the reader will be able to use Deep Learning frameworks, such as Keras and introduced Kraino, to build various architectures that will lead to a further performance improvement on this challenging task.
Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (average decision cost). With the advent of crowd-sensing systems, there is a need to redesign binary hypothesis testing rules for behavioral agents, whose cognitive behavior is not captured by traditional utility functions such as Bayes risk. In this paper, we adopt prospect theory based models for decision makers. We consider special agent models namely optimists and pessimists in this paper, and derive optimal detection rules under different scenarios. Using an illustrative example, we also show how the decision rule of a human agent deviates from the Bayesian decision rule under various behavioral models, considered in this paper.
We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the large-scale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.
Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks, especially when the policies are represented using rich function approximators like deep neural networks. Model-based methods where the real-world target domain is approximated using a simulated source domain provide an avenue to tackle the above challenges by augmenting real data with simulated data. However, discrepancies between the simulated source domain and the target domain pose a challenge for simulated training. We introduce the EPOpt algorithm, which uses an ensemble of simulated source domains and a form of adversarial training to learn policies that are robust and generalize to a broad range of possible target domains, including unmodeled effects. Further, the probability distribution over source domains in the ensemble can be adapted using data from target domain and approximate Bayesian methods, to progressively make it a better approximation. Thus, learning on a model ensemble, along with source domain adaptation, provides the benefit of both robustness and learning/adaptation.
Face recognition (FR) is the most preferred mode for biometric-based surveillance, due to its passive nature of detecting subjects, amongst all different types of biometric traits. FR under surveillance scenario does not give satisfactory performance due to low contrast, noise and poor illumination conditions on probes, as compared to the training samples. A state-of-the-art technology, Deep Learning, even fails to perform well in these scenarios. We propose a novel soft-margin based learning method for multiple feature-kernel combinations, followed by feature transformed using Domain Adaptation, which outperforms many recent state-of-the-art techniques, when tested using three real-world surveillance face datasets.
With a large proportion of people carrying location-aware smartphones, we have an unprecedented platform from which to understand individuals and predict their future actions. This work builds upon the Context Tree data structure that summarises the historical contexts of individuals from augmented geospatial trajectories, and constructs a predictive model for their likely future contexts. The Predictive Context Tree (PCT) is constructed as a hierarchical classifier, capable of predicting both the future locations that a user will visit and the contexts that a user will be immersed within. The PCT is evaluated over real-world geospatial trajectories, and compared against existing location extraction and prediction techniques, as well as a proposed hybrid approach that uses identified land usage elements in combination with machine learning to predict future interactions. Our results demonstrate that higher predictive accuracies can be achieved using this hybrid approach over traditional extracted location datasets, and the PCT itself matches the performance of the hybrid approach at predicting future interactions, while adding utility in the form of context predictions. Such a prediction system is capable of understanding not only where a user will visit, but also their context, in terms of what they are likely to be doing.
Visual Question Answering (VQA) is a recent problem in computer vision and natural language processing that has garnered a large amount of interest from the deep learning, computer vision, and natural language processing communities. In VQA, an algorithm needs to answer text-based questions about images. Since the release of the first VQA dataset in 2014, additional datasets have been released and many algorithms have been proposed. In this review, we critically examine the current state of VQA in terms of problem formulation, existing datasets, evaluation metrics, and algorithms. In particular, we discuss the limitations of current datasets with regard to their ability to properly train and assess VQA algorithms. We then exhaustively review existing algorithms for VQA. Finally, we discuss possible future directions for VQA and image understanding research.
In this paper, we study the Temporal Difference (TD) learning with linear value function approximation. It is well known that most TD learning algorithms are unstable with linear function approximation and off-policy learning. Recent development of Gradient TD (GTD) algorithms has addressed this problem successfully. However, the success of GTD algorithms requires a set of well chosen features, which are not always available. When the number of features is huge, the GTD algorithms might face the problem of overfitting and being computationally expensive. To cope with this difficulty, regularization techniques, in particular $\ell_1$ regularization, have attracted significant attentions in developing TD learning algorithms. The present work combines the GTD algorithms with $\ell_1$ regularization. We propose a family of $\ell_1$ regularized GTD algorithms, which employ the well known soft thresholding operator. We investigate convergence properties of the proposed algorithms, and depict their performance with several numerical experiments.
Deep Neural Networks (DNNs) have become very popular for prediction in many areas. Their strength is in representation with a high number of parameters that are commonly learned via gradient descent or similar optimization methods. However, the representation is non-standardized, and the gradient calculation methods are often performed using component-based approaches that break parameters down into scalar units, instead of considering the parameters as whole entities. In this work, these problems are addressed. Standard notation is used to represent DNNs in a compact framework. Gradients of DNN loss functions are calculated directly over the inner product space on which the parameters are defined. This framework is general and is applied to two common network types: the Multilayer Perceptron and the Deep Autoencoder.
Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predictions made by a formalization of bounded rationality based on ideas from statistical mechanics and information-theory. We systematically tested human subjects in their ability to solve combinatorial puzzles under different time limitations. We found that our bounded-rational model accounts well for the data. The decomposition of the fitted model parameter into the subjects' expected utility function and resource parameter provide interesting insight into the subjects' information capacity limits. Our results confirm that humans gradually fall back on their learned prior choice patterns when confronted with increasing resource limitations.
Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting), and are believed to have higher complexity than both of them. We propose XOR_MMAP, a novel approach to solve the Marginal MAP Problem, which represents the intractable counting subproblem with queries to NP oracles, subject to additional parity constraints. XOR_MMAP provides a constant factor approximation to the Marginal MAP Problem, by encoding it as a single optimization in polynomial size of the original problem. We evaluate our approach in several machine learning and decision making applications, and show that our approach outperforms several state-of-the-art Marginal MAP solvers.
We present an interpretable neural network approach to predicting and understanding politeness in natural language requests. Our models are based on simple convolutional neural networks directly on raw text, avoiding any manual identification of complex sentiment or syntactic features, while performing better than such feature-based models from previous work. More importantly, we use the challenging task of politeness prediction as a testbed to next present a much-needed understanding of what these successful networks are actually learning. For this, we present several network visualizations based on activation clusters, first derivative saliency, and embedding space transformations, helping us automatically identify several subtle linguistics markers of politeness theories. Further, this analysis reveals multiple novel, high-scoring politeness strategies which, when added back as new features, reduce the accuracy gap between the original featurized system and the neural model, thus providing a clear quantitative interpretation of the success of these neural networks.
The crux of the problem in KDD Cup 2016 involves developing data mining techniques to rank research institutions based on publications. Rank importance of research institutions are derived from predictions on the number of full research papers that would potentially get accepted in upcoming top-tier conferences, utilizing public information on the web. This paper describes our solution to KDD Cup 2016. We used a two step approach in which we first identify full research papers corresponding to each conference of interest and then train two variants of exponential smoothing models to make predictions. Our solution achieves an overall score of 0.7508, while the winning submission scored 0.7656 in the overall results.
Hierarchical Reinforcement Learning has been previously shown to speed up the convergence rate of RL planning algorithms as well as mitigate feature-based model misspecification (Mankowitz et. al. 2016a,b, Bacon 2015). To do so, it utilizes hierarchical abstractions, also known as skills -- a type of temporally extended action (Sutton et. al. 1999) to plan at a higher level, abstracting away from the lower-level details. We incorporate risk sensitivity, also referred to as Situational Awareness (SA), into hierarchical RL for the first time by defining and learning risk aware skills in a Probabilistic Goal Semi-Markov Decision Process (PG-SMDP). This is achieved using our novel Situational Awareness by Risk-Conscious Skills (SARiCoS) algorithm which comes with a theoretical convergence guarantee. We show in a RoboCup soccer domain that the learned risk aware skills exhibit complex human behaviors such as `time-wasting' in a soccer game. In addition, the learned risk aware skills are able to mitigate reward-based model misspecification.
An interesting research problem in our age of Big Data is that of determining provenance. Granular evaluation of provenance of physical goods--e.g. tracking ingredients of a pharmaceutical or demonstrating authenticity of luxury goods--has often not been possible with today's items that are produced and transported in complex, inter-organizational, often internationally-spanning supply chains. Recent adoption of Internet of Things and Blockchain technologies give promise at better supply chain provenance. We are particularly interested in the blockchain as many favoured use cases of blockchain are for provenance tracking. We are also interested in applying ontologies as there has been some work done on knowledge provenance, traceability, and food provenance using ontologies. In this paper, we make a case for why ontologies can contribute to blockchain design. To support this case, we analyze a traceability ontology and translate some of its representations to smart contracts that execute a provenance trace and enforce traceability constraints on the Ethereum blockchain platform.
In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mechanism connecting inputs and outputs. In the natural sciences, however, finding an interpretable function for a phenomenon is the prime goal as it allows to understand and generalize results. This paper proposes a novel type of function learning network, called equation learner (EQL), that can learn analytical expressions and is able to extrapolate to unseen domains. It is implemented as an end-to-end differentiable feed-forward network and allows for efficient gradient based training. Due to sparsity regularization concise interpretable expressions can be obtained. Often the true underlying source expression is identified.
Modern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural language provides a flexible and efficient medium through which robots can exchange information with their human partners. Significant advancements have been made in developing robots capable of interpreting free-form instructions, but less attention has been devoted to endowing robots with the ability to generate natural language. We propose a navigational guide model that enables robots to generate natural language instructions that allow humans to navigate a priori unknown environments. We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations via inverse reinforcement learning. We then "translate" this information into a natural language instruction using a neural sequence-to-sequence model that learns to generate free-form instructions from natural language corpora. We evaluate our method on a benchmark route instruction dataset and achieve a BLEU score of 72.18% when compared to human-generated reference instructions. We additionally conduct navigation experiments with human participants that demonstrate that our method generates instructions that people follow as accurately and easily as those produced by humans.
We describe a general method of detecting valid chains or links of pieces on a two-dimensional grid. Specifically, using the example of the chess variant known as Switch-Side Chain-Chess (SSCC). Presently, no foolproof method of detecting such chains in any given chess position is known and existing graph theory, to our knowledge, is unable to fully address this problem either. We therefore propose a solution implemented and tested using the C++ programming language. We have been unable to find an incorrect result and therefore offer it as the most viable solution thus far to the chain-detection problem in this chess variant. The algorithm is also scalable, in principle, to areas beyond two-dimensional grids such as 3D analysis and molecular chemistry.
In the context of contemporary monophonic music, expression can be seen as the difference between a musical performance and its symbolic representation, i.e. a musical score. In this paper, we show how Maximum Entropy (MaxEnt) models can be used to generate musical expression in order to mimic a human performance. As a training corpus, we had a professional pianist play about 150 melodies of jazz, pop, and latin jazz. The results show a good predictive power, validating the choice of our model. Additionally, we set up a listening test whose results reveal that on average, people significantly prefer the melodies generated by the MaxEnt model than the ones without any expression, or with fully random expression. Furthermore, in some cases, MaxEnt melodies are almost as popular as the human performed ones.
An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than two-fold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100-1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of >0.9 achieved for apples and mangoes.
A fall is an abnormal activity that occurs rarely, so it is hard to collect real data for falls. It is, therefore, difficult to use supervised learning methods to automatically detect falls. Another challenge in using machine learning methods to automatically detect falls is the choice of engineered features. In this paper, we propose to use an ensemble of autoencoders to extract features from different channels of wearable sensor data trained only on normal activities. We show that the traditional approach of choosing a threshold as the maximum of the reconstruction error on the training normal data is not the right way to identify unseen falls. We propose two methods for automatic tightening of reconstruction error from only the normal activities for better identification of unseen falls. We present our results on two activity recognition datasets and show the efficacy of our proposed method against traditional autoencoder models and two standard one-class classification methods.
A methodology for the development of a fuzzy expert system (FES) with application to earthquake prediction is presented. The idea is to reproduce the performance of a human expert in earthquake prediction. To do this, at the first step, rules provided by the human expert are used to generate a fuzzy rule base. These rules are then fed into an inference engine to produce a fuzzy inference system (FIS) and to infer the results. In this paper, we have used a Sugeno type fuzzy inference system to build the FES. At the next step, the adaptive network-based fuzzy inference system (ANFIS) is used to refine the FES parameters and improve its performance. The proposed framework is then employed to attain the performance of a human expert used to predict earthquakes in the Zagros area based on the idea of coupled earthquakes. While the prediction results are promising in parts of the testing set, the general performance indicates that prediction methodology based on coupled earthquakes needs more investigation and more complicated reasoning procedure to yield satisfactory predictions.
With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory. Among many techniques, feature selection has been growing in interest as an important tool to identify relevant features on huge datasets --both in number of instances and features--. The purpose of this work is to demonstrate that standard feature selection methods can be parallelized in Big Data platforms like Apache Spark, boosting both performance and accuracy. We thus propose a distributed implementation of a generic feature selection framework which includes a wide group of well-known Information Theoretic methods. Experimental results on a wide set of real-world datasets show that our distributed framework is capable of dealing with ultra-high dimensional datasets as well as those with a huge number of samples in a short period of time, outperforming the sequential version in all the cases studied.
Bilinear models provide rich representations compared with linear models. They have been applied in various visual tasks, such as object recognition, segmentation, and visual question-answering, to get state-of-the-art performances taking advantage of the expanded representations. However, bilinear representations tend to be high-dimensional, limiting the applicability to computationally complex tasks. We propose low-rank bilinear pooling using Hadamard product for an efficient attention mechanism of multimodal learning. We show that our model outperforms compact bilinear pooling in visual question-answering tasks with the state-of-the-art results on the VQA dataset, having a better parsimonious property.
According to the distributional inclusion hypothesis, entailment between words can be measured via the feature inclusions of their distributional vectors. In recent work, we showed how this hypothesis can be extended from words to phrases and sentences in the setting of compositional distributional semantics. This paper focuses on inclusion properties of tensors; its main contribution is a theoretical and experimental analysis of how feature inclusion works in different concrete models of verb tensors. We present results for relational, Frobenius, projective, and holistic methods and compare them to the simple vector addition, multiplication, min, and max models. The degrees of entailment thus obtained are evaluated via a variety of existing word-based measures, such as Weed's and Clarke's, KL-divergence, APinc, balAPinc, and two of our previously proposed metrics at the phrase/sentence level. We perform experiments on three entailment datasets, investigating which version of tensor-based composition achieves the highest performance when combined with the sentence-level measures.
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
Real life data often includes information from different channels. For example, in computer vision, we can describe an image using different image features, such as pixel intensity, color, HOG, GIST feature, SIFT features, etc.. These different aspects of the same objects are often called multi-view (or multi-modal) data. Low-rank regression model has been proved to be an effective learning mechanism by exploring the low-rank structure of real life data. But previous low-rank regression model only works on single view data. In this paper, we propose a multi-view low-rank regression model by imposing low-rank constraints on multi-view regression model. Most importantly, we provide a closed-form solution to the multi-view low-rank regression model. Extensive experiments on 4 multi-view datasets show that the multi-view low-rank regression model outperforms single-view regression model and reveals that multi-view low-rank structure is very helpful.
Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item. The question is how to build a seed set that can give enough preference information for making good recommendations. One of the most successful approaches, called Representative Based Matrix Factorization, is based on Maxvol algorithm. Unfortunately, this approach has one important limitation --- a seed set of a particular size requires a rating matrix factorization of fixed rank that should coincide with that size. This is not necessarily optimal in the general case. In the current paper, we introduce a fast algorithm for an analytical generalization of this approach that we call Rectangular Maxvol. It allows the rank of factorization to be lower than the required size of the seed set. Moreover, the paper includes the theoretical analysis of the method's error, the complexity analysis of the existing methods and the comparison to the state-of-the-art approaches.
The weekly maintenance schedule specifies when maintenance activities should be performed on the equipment, taking into account the availability of workers and maintenance bays, and other operational constraints. The current approach to generating this schedule is labour intensive and requires coordination between the maintenance schedulers and operations staff to minimise its impact on the operation of the mine. This paper presents methods for automatically generating this schedule from the list of maintenance tasks to be performed, the availability roster of the maintenance staff, and time windows in which each piece of equipment is available for maintenance. Both Mixed-Integer Linear Programming (MILP) and genetic algorithms are evaluated, with the genetic algorithm shown to significantly outperform the MILP. Two fitness functions for the genetic algorithm are also examined, with a linear fitness function outperforming an inverse fitness function by up to 5% for the same calculation time. The genetic algorithm approach is computationally fast, allowing the schedule to be rapidly recalculated in response to unexpected delays and breakdowns.
We study a novel architecture and training procedure for locomotion tasks. A high-frequency, low-level "spinal" network with access to proprioceptive sensors learns sensorimotor primitives by training on simple tasks. This pre-trained module is fixed and connected to a low-frequency, high-level "cortical" network, with access to all sensors, which drives behavior by modulating the inputs to the spinal network. Where a monolithic end-to-end architecture fails completely, learning with a pre-trained spinal module succeeds at multiple high-level tasks, and enables the effective exploration required to learn from sparse rewards. We test our proposed architecture on three simulated bodies: a 16-dimensional swimming snake, a 20-dimensional quadruped, and a 54-dimensional humanoid. Our results are illustrated in the accompanying video at https://youtu.be/sboPYvhpraQ
We consider a set of learning agents in a collaborative peer-to-peer network, where each agent learns a personalized model according to its own learning objective. The question addressed in this paper is: how can agents improve upon their locally trained model by communicating with other agents that have similar objectives? We introduce and analyze two asynchronous gossip algorithms running in a fully decentralized manner. Our first approach, inspired from label propagation, aims to smooth pre-trained local models over the network while accounting for the confidence that each agent has in its initial model. In our second approach, agents jointly learn and propagate their model by making iterative updates based on both their local dataset and the behavior of their neighbors. To optimize this challenging objective, our decentralized algorithm is based on ADMM.
The number of optimization techniques in the combinatorial domain is large and diversified. Nevertheless, there is still a lack of real benchmarks to validate optimization algorithms. In this work we introduce VRPBench, a tool to create instances and visualize solutions to the Vehicle Routing Problem (VRP) in a planar graph embedded in the Euclidean 2D space. We use VRPBench to model a real-world mail delivery case of the city of Artur Nogueira. Such scenarios were characterized as a multi-objective optimization of the VRP. We extracted a weighted graph from a digital map of the city to create a challenging benchmark for the VRP. Each instance models one generic day of mail delivery with hundreds to thousands of delivery points, thus allowing both the comparison and validation of optimization algorithms for routing problems.
The problem of makespan optimal solving of cooperative path finding (CPF) is addressed in this paper. The task in CPF is to relocate a group of agents in a non-colliding way so that each agent eventually reaches its goal location from the given initial location. The abstraction adopted in this work assumes that agents are discrete items moving in an undirected graph by traversing edges. Makespan optimal solving of CPF means to generate solutions that are as short as possi-ble in terms of the total number of time steps required for the execution of the solution. We show that reducing CPF to propositional satisfiability (SAT) represents a viable option for obtaining makespan optimal solutions. Several encodings of CPF into propositional formulae are suggested and experimentally evaluated. The evaluation indicates that SAT based CPF solving outperforms other makespan optimal methods significantly in highly constrained situations (environments that are densely occupied by agents).
Recent work on weighted model counting has been very successfully applied to the problem of probabilistic inference in Bayesian networks. The probability distribution is encoded into a Boolean normal form and compiled to a target language, in order to represent local structure expressed among conditional probabilities more efficiently. We show that further improvements are possible, by exploiting the knowledge that is lost during the encoding phase and incorporating it into a compiler inspired by Satisfiability Modulo Theories. Constraints among variables are used as a background theory, which allows us to optimize the Shannon decomposition. We propose a new language, called Weighted Positive Binary Decision Diagrams, that reduces the cost of probabilistic inference by using this decomposition variant to induce an arithmetic circuit of reduced size.
In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound and complete algorithm for identification of Dynamic Causal Net- works, namely, for computing the effect of an intervention or experiment, based on passive observations only, whenever possible. We note the existence of two types of confounder variables that affect in substantially different ways the iden- tification procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs. We further propose a procedure for the transportability of causal effects in Dynamic Causal Network settings, where the re- sult of causal experiments in a source domain may be used for the identification of causal effects in a target domain.
The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian estimation, but as shown in this paper, the results can be sensitive to the prior choice for the global shrinkage hyperparameter. We argue that the previous default choices are dubious due to their tendency to favor solutions with more unshrunk coefficients than we typically expect a priori. This can lead to bad results if this parameter is not strongly identified by data. We derive the relationship between the global parameter and the effective number of nonzeros in the coefficient vector, and show an easy and intuitive way of setting up the prior for the global parameter based on our prior beliefs about the number of nonzero coefficients in the model. The results on real world data show that one can benefit greatly -- in terms of improved parameter estimates, prediction accuracy, and reduced computation time -- from transforming even a crude guess for the number of nonzero coefficients into the prior for the global parameter using our framework.
In this paper, we define a novel census signal temporal logic (CensusSTL) that focuses on the number of agents in different subsets of a group that complete a certain task specified by the signal temporal logic (STL). CensusSTL consists of an "inner logic" STL formula and an "outer logic" STL formula. We present a new inference algorithm to infer CensusSTL formulae from the trajectory data of a group of agents. We first identify the "inner logic" STL formula and then infer the subgroups based on whether the agents' behaviors satisfy the "inner logic" formula at each time point. We use two different approaches to infer the subgroups based on similarity and complementarity, respectively. The "outer logic" CensusSTL formula is inferred from the census trajectories of different subgroups. We apply the algorithm in analyzing data from a soccer match by inferring the CensusSTL formula for different subgroups of a soccer team.
Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training. Subword classes in speech recognition systems correspond to context-dependent tied states or senones. The present work addresses some limitations of GMM-HMM senone alignments for DNN training. We hypothesize that the senone probabilities obtained from a DNN trained with binary labels can provide more accurate targets to learn better acoustic models. However, DNN outputs bear inaccuracies which are exhibited as high dimensional unstructured noise, whereas the informative components are structured and low-dimensional. We exploit principle component analysis (PCA) and sparse coding to characterize the senone subspaces. Enhanced probabilities obtained from low-rank and sparse reconstructions are used as soft-targets for DNN acoustic modeling, that also enables training with untranscribed data. Experiments conducted on AMI corpus shows 4.6% relative reduction in word error rate.
Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference is often intractable, and existing PPLs mostly rely on expensive, approximate sampling-based methods. To alleviate this problem, one could try to learn from past inferences, so that future inferences run faster. This strategy is known as amortized inference; it has recently been applied to Bayesian networks and deep generative models. This paper proposes a system for amortized inference in PPLs. In our system, amortization comes in the form of a parameterized guide program. Guide programs have similar structure to the original program, but can have richer data flow, including neural network components. These networks can be optimized so that the guide approximately samples from the posterior distribution defined by the original program. We present a flexible interface for defining guide programs and a stochastic gradient-based scheme for optimizing guide parameters, as well as some preliminary results on automatically deriving guide programs. We explore in detail the common machine learning pattern in which a 'local' model is specified by 'global' random values and used to generate independent observed data points; this gives rise to amortized local inference supporting global model learning.
Classical stochastic gradient methods for optimization rely on noisy gradient approximations that become progressively less accurate as iterates approach a solution. The large noise and small signal in the resulting gradients makes it difficult to use them for adaptive stepsize selection and automatic stopping. We propose alternative "big batch" SGD schemes that adaptively grow the batch size over time to maintain a nearly constant signal-to-noise ratio in the gradient approximation. The resulting methods have similar convergence rates to classical SGD, and do not require convexity of the objective. The high fidelity gradients enable automated learning rate selection and do not require stepsize decay. Big batch methods are thus easily automated and can run with little or no oversight.
Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular it has recently been demonstrated, using the artificial intelligence algorithm known as reservoir computing, that photonic implementations of such systems solve complex tasks such as speech recognition. Here we show how the backpropagation algorithm can be physically implemented on the same electro-optical delay-coupled architecture used for computation with only minor changes to the original design. We find that, compared when the backpropagation algorithm is not used, the error rate of the resulting computing device, evaluated on three benchmark tasks, decreases considerably. This demonstrates that electro-optical analog computers can embody a large part of their own training process, allowing them to be applied to new, more difficult tasks.
This paper examines use of dynamic probabilistic networks (DPN) for human action recognition. The actions of lifting objects and walking in the room, sitting in the room and neutral standing pose were used for testing the classification. The research used the dynamic interrelation between various different regions of interest (ROI) on the human body (face, body, arms, legs) and the time series based events related to the these ROIs. This dynamic links are then used to recognize the human behavioral aspects in the scene. First a model is developed to identify the human activities in an indoor scene and this model is dependent on the key features and interlinks between the various dynamic events using DPNs. The sub ROI are classified with DPN to associate the combined interlink with a specific human activity. The recognition accuracy performance between indoor (controlled lighting conditions) is compared with the outdoor lighting conditions. The accuracy in outdoor scenes was lower than the controlled environment.
The long-term memory of most connectionist systems lies entirely in the weights of the system. Since the number of weights is typically fixed, this bounds the total amount of knowledge that can be learned and stored. Though this is not normally a problem for a neural network designed for a specific task, such a bound is undesirable for a system that continually learns over an open range of domains. To address this, we describe a lifelong learning system that leverages a fast, though non-differentiable, content-addressable memory which can be exploited to encode both a long history of sequential episodic knowledge and semantic knowledge over many episodes for an unbounded number of domains. This opens the door for investigation into transfer learning, and leveraging prior knowledge that has been learned over a lifetime of experiences to new domains.
Hypothesis testing is an important cognitive process that supports human reasoning. In this paper, we introduce a computational hypothesis testing approach based on memory augmented neural networks. Our approach involves a hypothesis testing loop that reconsiders and progressively refines a previously formed hypothesis in order to generate new hypotheses to test. We apply the proposed approach to language comprehension task by using Neural Semantic Encoders (NSE). Our NSE models achieve the state-of-the-art results showing an absolute improvement of 1.2% to 2.6% accuracy over previous results obtained by single and ensemble systems on standard machine comprehension benchmarks such as the Children's Book Test (CBT) and Who-Did-What (WDW) news article datasets.
Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently. Estimating accuracy of such automatically constructed KGs is a challenging problem due to their size and diversity. This important problem has largely been ignored in prior research we fill this gap and propose KGEval. KGEval binds facts of a KG using coupling constraints and crowdsources the facts that infer correctness of large parts of the KG. We demonstrate that the objective optimized by KGEval is submodular and NP-hard, allowing guarantees for our approximation algorithm. Through extensive experiments on real-world datasets, we demonstrate that KGEval is able to estimate KG accuracy more accurately compared to other competitive baselines, while requiring significantly lesser number of human evaluations.
Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bails to defendants on a daily basis. Such decisions typically involve weighting the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task of learning \emph{cost-effective, interpretable and actionable treatment regimes}. We formulate this as a problem of learning a decision list -- a sequence of if-then-else rules -- which maps characteristics of subjects (eg., diagnostic test results of patients) to treatments. We propose a novel objective to construct a decision list which maximizes outcomes for the population, and minimizes overall costs. We model the problem of learning such a list as a Markov Decision Process (MDP) and employ a variant of the Upper Confidence Bound for Trees (UCT) strategy which leverages customized checks for pruning the search space effectively. Experimental results on real world observational data capturing judicial bail decisions and treatment recommendations for asthma patients demonstrate the effectiveness of our approach.
In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre- or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition.
We propose a novel method of regularization for recurrent neural networks called suprisal-driven zoneout. In this method, states zoneout (maintain their previous value rather than updating), when the suprisal (discrepancy between the last state's prediction and target) is small. Thus regularization is adaptive and input-driven on a per-neuron basis. We demonstrate the effectiveness of this idea by achieving state-of-the-art bits per character of 1.31 on the Hutter Prize Wikipedia dataset, significantly reducing the gap to the best known highly-engineered compression methods.
We extend the Frank-Wolfe (FW) optimization algorithm to solve constrained smooth convex-concave saddle point (SP) problems. Remarkably, the method only requires access to linear minimization oracles. Leveraging recent advances in FW optimization, we provide the first proof of convergence of a FW-type saddle point solver over polytopes, thereby partially answering a 30 year-old conjecture. We also survey other convergence results and highlight gaps in the theoretical underpinnings of FW-style algorithms. Motivating applications without known efficient alternatives are explored through structured prediction with combinatorial penalties as well as games over matching polytopes involving an exponential number of constraints.
This work presents a parametrized family of divergences, namely Alpha-Beta Log- Determinant (Log-Det) divergences, between positive definite unitized trace class operators on a Hilbert space. This is a generalization of the Alpha-Beta Log-Determinant divergences between symmetric, positive definite matrices to the infinite-dimensional setting. The family of Alpha-Beta Log-Det divergences is highly general and contains many divergences as special cases, including the recently formulated infinite dimensional affine-invariant Riemannian distance and the infinite-dimensional Alpha Log-Det divergences between positive definite unitized trace class operators. In particular, it includes a parametrized family of metrics between positive definite trace class operators, with the affine-invariant Riemannian distance and the square root of the symmetric Stein divergence being special cases. For the Alpha-Beta Log-Det divergences between covariance operators on a Reproducing Kernel Hilbert Space (RKHS), we obtain closed form formulas via the corresponding Gram matrices.
Ant colony system (ACS) is a promising approach which has been widely used in problems such as Travelling Salesman Problems (TSP), Job shop scheduling problems (JSP) and Quadratic Assignment problems (QAP). In its original implementation, parameters of the algorithm were selected by trial and error approach. Over the last few years, novel approaches have been proposed on adapting the parameters of ACS in improving its performance. The aim of this paper is to use a framework introduced for self-tuning optimization algorithms combined with the firefly algorithm (FA) to tune the parameters of the ACS solving symmetric TSP problems. The FA optimizes the problem specific parameters of ACS while the parameters of the FA are tuned by the selected framework itself. With this approach, the user neither has to work with the parameters of ACS nor the parameters of FA. Using common symmetric TSP problems we demonstrate that the framework fits well for the ACS. A detailed statistical analysis further verifies the goodness of the new ACS over the existing ACS and also of the other techniques used to tune the parameters of ACS.
Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible to the human eye. We further empirically analyze these universal perturbations and show, in particular, that they generalize very well across neural networks. The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images.
Statistical relational models provide compact encodings of probabilistic dependencies in relational domains, but result in highly intractable graphical models. The goal of lifted inference is to carry out probabilistic inference without needing to reason about each individual separately, by instead treating exchangeable, undistinguished objects as a whole. In this paper, we study the domain recursion inference rule, which, despite its central role in early theoretical results on domain-lifted inference, has later been believed redundant. We show that this rule is more powerful than expected, and in fact significantly extends the range of models for which lifted inference runs in time polynomial in the number of individuals in the domain. This includes an open problem called S4, the symmetric transitivity model, and a first-order logic encoding of the birthday paradox. We further identify new classes S2FO2 and S2RU of domain-liftable theories, which respectively subsume FO2 and recursively unary theories, the largest classes of domain-liftable theories known so far, and show that using domain recursion can achieve exponential speedup even in theories that cannot fully be lifted with the existing set of inference rules.
Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended to help model larger spans of text, e.g., documents, is intriguing, and further investigation would still be desirable. This paper aims to enhance neural network models for such a purpose. A typical problem of document-level modeling is automatic summarization, which aims to model documents in order to generate summaries. In this paper, we propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse between different content of a document so as to better grasp the overall meaning for summarization. Without engineering any features, we train the models on two large datasets. The models achieve the state-of-the-art performance, and they significantly benefit from the distraction modeling, particularly when input documents are long.
We formalize synthesis of shared control protocols with correctness guarantees for temporal logic specifications. More specifically, we introduce a modeling formalism in which both a human and an autonomy protocol can issue commands to a robot towards performing a certain task. These commands are blended into a joint input to the robot. The autonomy protocol is synthesized using an abstraction of possible human commands accounting for randomness in decisions caused by factors such as fatigue or incomprehensibility of the problem at hand. The synthesis is designed to ensure that the resulting robot behavior satisfies given safety and performance specifications, e.g., in temporal logic. Our solution is based on nonlinear programming and we address the inherent scalability issue by presenting alternative methods. We assess the feasibility and the scalability of the approach by an experimental evaluation.
Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian processes. The resulting model, GP-LSTM, fully encapsulates the inductive biases of long short-term memory (LSTM) recurrent networks, while retaining the non-parametric probabilistic advantages of Gaussian processes. We learn the properties of the proposed kernels by optimizing the Gaussian process marginal likelihood using a new provably convergent semi-stochastic gradient procedure and exploit the structure of these kernels for scalable training and prediction. This approach provides a practical representation for Bayesian LSTMs. We demonstrate state-of-the-art performance on several benchmarks, and thoroughly investigate a consequential autonomous driving application, where the predictive uncertainties provided by GP-LSTM are uniquely valuable.
We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model. We call the distribution to which the inference model maps observed samples, the learned latent distribution, which may not be consistent with the prior. We formulate a Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively decoding and encoding, which allows us to sample from the learned latent distribution. Since, the generative model learns to map from the learned latent distribution, rather than the prior, we may use MCMC to improve the quality of samples drawn from the generative model, especially when the learned latent distribution is far from the prior. Using MCMC sampling, we are able to reveal previously unseen differences between generative autoencoders trained either with or without a denoising criterion.
This paper proposes to use probabilistic model checking to synthesize optimal robot policies in multi-tasking autonomous systems that are subject to human-robot interaction. Given the convincing empirical evidence that human behavior can be related to reinforcement models, we take as input a well-studied Q-table model of the human behavior for flexible scenarios. We first describe an automated procedure to distill a Markov decision process (MDP) for the human in an arbitrary but fixed scenario. The distinctive issue is that -- in contrast to existing models -- under-specification of the human behavior is included. Probabilistic model checking is used to predict the human's behavior. Finally, the MDP model is extended with a robot model. Optimal robot policies are synthesized by analyzing the resulting two-player stochastic game. Experimental results with a prototypical implementation using PRISM show promising results.
In the literature, two series of models have been proposed to address prediction problems including classification and regression. Simple models, such as generalized linear models, have ordinary performance but strong interpretability on a set of simple features. The other series, including tree-based models, organize numerical, categorical and high dimensional features into a comprehensive structure with rich interpretable information in the data. In this paper, we propose a novel Discriminative Pattern-based Prediction framework (DPPred) to accomplish the prediction tasks by taking their advantages of both effectiveness and interpretability. Specifically, DPPred adopts the concise discriminative patterns that are on the prefix paths from the root to leaf nodes in the tree-based models. DPPred selects a limited number of the useful discriminative patterns by searching for the most effective pattern combination to fit generalized linear models. Extensive experiments show that in many scenarios, DPPred provides competitive accuracy with the state-of-the-art as well as the valuable interpretability for developers and experts. In particular, taking a clinical application dataset as a case study, our DPPred outperforms the baselines by using only 40 concise discriminative patterns out of a potentially exponentially large set of patterns.
Probabilistic modeling is a powerful approach for analyzing empirical information. We describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative process pioneered by George Box: build a model of a phenomenon, make inferences about the model given data, and criticize the model's fit to the data. Edward supports a broad class of probabilistic models, efficient algorithms for inference, and many techniques for model criticism. The library builds on top of TensorFlow to support distributed training and hardware such as GPUs. Edward enables the development of complex probabilistic models and their algorithms at a massive scale.
Cognitive engineering is a multi-disciplinary field and hence it is difficult to find a review article consolidating the leading developments in the field. The in-credible pace at which technology is advancing pushes the boundaries of what is achievable in cognitive engineering. There are also differing approaches to cognitive engineering brought about from the multi-disciplinary nature of the field and the vastness of possible applications. Thus research communities require more frequent reviews to keep up to date with the latest trends. In this paper we shall dis-cuss some of the approaches to cognitive engineering holistically to clarify the reasoning behind the different approaches and to highlight their strengths and weaknesses. We shall then show how developments from seemingly disjointed views could be integrated to achieve the same goal of creating cognitive machines. By reviewing the major contributions in the different fields and showing the potential for a combined approach, this work intends to assist the research community in devising more unified methods and techniques for developing cognitive machines.
Chinese poetry generation is a very challenging task in natural language processing. In this paper, we propose a novel two-stage poetry generating method which first plans the sub-topics of the poem according to the user's writing intent, and then generates each line of the poem sequentially, using a modified recurrent neural network encoder-decoder framework. The proposed planning-based method can ensure that the generated poem is coherent and semantically consistent with the user's intent. A comprehensive evaluation with human judgments demonstrates that our proposed approach outperforms the state-of-the-art poetry generating methods and the poem quality is somehow comparable to human poets.
This work proposes a novel framework for the development of new products and services in transportation through an open innovation approach based on automatic content analysis of social media data. The framework is able to extract users comments from Online Social Networks (OSN), to process and analyze text through information extraction and sentiment analysis techniques to obtain relevant information about product reception on the market. A use case was developed using the mobile application Uber, which is today one of the fastest growing technology companies in the world. We measured how a controversial, highly diffused event influences the volume of tweets about Uber and the perception of its users. While there is no change in the image of Uber, a large increase in the number of tweets mentioning the company is observed, which meant a free and important diffusion of its product.
We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do "compilation of inference" because our method transforms a denotational specification of an inference problem in the form of a probabilistic program written in a universal programming language into a trained neural network denoted in a neural network specification language. When at test time this neural network is fed observational data and executed, it performs approximate inference in the original model specified by the probabilistic program. Our training objective and learning procedure are designed to allow the trained neural network to be used as a proposal distribution in a sequential importance sampling inference engine. We illustrate our method on mixture models and Captcha solving and show significant speedups in the efficiency of inference.
Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural "programmer", i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic "computer", i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.
In this paper, we study the problem of using a planning algorithm to automatically create and update a Behavior Tree (BT), controlling a robot in a dynamic environment. Exploiting the characteristic of BTs, in terms of modularity and reactivity, the robot continually acts and plans to achieve a given goal using a set of abstract actions and conditions. The construction of the BT is based on an extension of the Hybrid Backward-Forward algorithm (HBF) that allows us to refine the acting process by mapping the descriptive models onto operational models of actions, thus integrating the ability of planning in infinite state space of HBF with the continuous modular reactive action execution of BTs. We believe that this might be a first step to address the recently raised open challenge in automated planning: the need of a hierarchical structure and a continuous online planning and acting framework. We prove the convergence of the proposed approach as well as the absence of deadlocks and livelocks, and we illustrate our approach in two different robotics scenarios.
The term "affordance" denotes the behavioral meaning of objects. We propose a cognitive architecture for the detection of affordances in the visual modality. This model is based on the internal simulation of movement sequences. For each movement step, the resulting sensory state is predicted by a forward model, which in turn triggers the generation of a new (simulated) motor command by an inverse model. Thus, a series of mental images in the sensory and in the motor domain is evoked. Starting from a real sensory state, a large number of such sequences is simulated in parallel. Final affordance detection is based on the generated motor commands. We apply this model to a real-world mobile robot which is faced with obstacle arrangements some of which are passable (corridor) and some of which are not (dead ends). The robot's task is to detect the right affordance ("pass-through-able" or "non-pass-through-able"). The required internal models are acquired in a hierarchical training process. Afterwards, the robotic agent is able to distinguish reliably between corridors and dead ends. This real-world result enhances the validity of the proposed mental simulation approach. In addition, we compare several key factors in the simulation process regarding performance and efficiency.
Whether officials can be trusted to protect national security information has become a matter of great public controversy, reigniting a long-standing debate about the scope and nature of official secrecy. The declassification of millions of electronic records has made it possible to analyze these issues with greater rigor and precision. Using machine-learning methods, we examined nearly a million State Department cables from the 1970s to identify features of records that are more likely to be classified, such as international negotiations, military operations, and high-level communications. Even with incomplete data, algorithms can use such features to identify 90% of classified cables with <11% false positives. But our results also show that there are longstanding problems in the identification of sensitive information. Error analysis reveals many examples of both overclassification and underclassification. This indicates both the need for research on inter-coder reliability among officials as to what constitutes classified material and the opportunity to develop recommender systems to better manage both classification and declassification.
The use of bots as virtual confederates in online field experiments holds extreme promise as a new methodological tool in computational social science. However, this potential tool comes with inherent ethical challenges. Informed consent can be difficult to obtain in many cases, and the use of confederates necessarily implies the use of deception. In this work we outline a design space for bots as virtual confederates, and we propose a set of guidelines for meeting the status quo for ethical experimentation. We draw upon examples from prior work in the CSCW community and the broader social science literature for illustration. While a handful of prior researchers have used bots in online experimentation, our work is meant to inspire future work in this area and raise awareness of the associated ethical issues.
Pairwise comparison is an important tool in multi-attribute decision making. Pairwise comparison matrices (PCM) have been applied for ranking criteria and for scoring alternatives according to a given criterion. Our paper presents a special application of incomplete PCMs: ranking of professional tennis players based on their results against each other. The selected 25 players have been on the top of the ATP rankings for a shorter or longer period in the last 40 years. Some of them have never met on the court. One of the aims of the paper is to provide ranking of the selected players, however, the analysis of incomplete pairwise comparison matrices is also in the focus. The eigenvector method and the logarithmic least squares method were used to calculate weights from incomplete PCMs. In our results the top three players of four decades were Nadal, Federer and Sampras. Some questions have been raised on the properties of incomplete PCMs and remains open for further investigation.
Link prediction, the problem of identifying missing links among a set of inter-related data entities, is a popular field of research due to its application to graph-like domains. Producing consistent evaluations of the performance of the many link prediction algorithms being proposed can be challenging due to variable graph properties, such as size and density. In this paper we first discuss traditional data mining solutions which are applicable to link prediction evaluation, arguing about their capacity for producing faithful and useful evaluations. We also introduce an innovative modification to a traditional evaluation methodology with the goal of adapting it to the problem of evaluating link prediction algorithms when applied to large graphs, by tackling the problem of class imbalance. We empirically evaluate the proposed methodology and, building on these findings, make a case for its importance on the evaluation of large-scale graph processing.
In this work, we are interested in structure learning for a set of spatially distributed dynamical systems, where individual subsystems are coupled via latent variables and observed through a filter. We represent this model as a directed acyclic graph (DAG) that characterises the unidirectional coupling between subsystems. Standard approaches to structure learning are not applicable in this framework due to the hidden variables, however we can exploit the properties of certain dynamical systems to formulate exact methods based on state space reconstruction. We approach the problem by using reconstruction theorems to analytically derive a tractable expression for the KL-divergence of a candidate DAG from the observed dataset. We show this measure can be decomposed as a function of two information-theoretic measures, transfer entropy and stochastic interaction. We then present two mathematically robust scoring functions based on transfer entropy and statistical independence tests. These results support the previously held conjecture that transfer entropy can be used to infer effective connectivity in complex networks.
Online recommender systems often deal with continuous, potentially fast and unbounded flows of data. Ensemble methods for recommender systems have been used in the past in batch algorithms, however they have never been studied with incremental algorithms that learn from data streams. We evaluate online bagging with an incremental matrix factorization algorithm for top-N recommendation with positive-only -- binary -- ratings. Our results show that online bagging is able to improve accuracy up to 35% over the baseline, with small computational overhead.
We consider the problem of consistently matching multiple sets of elements to each other, which is a common task in fields such as computer vision. To solve the underlying NP-hard objective, existing methods often relax or approximate it, but end up with unsatisfying empirical performance due to a misaligned objective. We propose a coordinate update algorithm that directly optimizes the target objective. By using pairwise alignment information to build an undirected graph and initializing the permutation matrices along the edges of its Maximum Spanning Tree, our algorithm successfully avoids bad local optima. Theoretically, with high probability our algorithm guarantees an optimal solution under reasonable noise assumptions. Empirically, our algorithm consistently and significantly outperforms existing methods on several benchmark tasks on real datasets.
Progressive filtering is a simple way to perform hierarchical classification, inspired by the behavior that most humans put into practice while attempting to categorize an item according to an underlying taxonomy. Each node of the taxonomy being associated with a different category, one may visualize the categorization process by looking at the item going downwards through all the nodes that accept it as belonging to the corresponding category. This paper is aimed at modeling the progressive filtering technique from a probabilistic perspective, in a hierarchical text categorization setting. As a result, the designer of a system based on progressive filtering should be facilitated in the task of devising, training, and testing it.
In this paper, we are going to find meaning of words based on distinct situations. Word Sense Disambiguation is used to find meaning of words based on live contexts using supervised and unsupervised approaches. Unsupervised approaches use online dictionary for learning, and supervised approaches use manual learning sets. Hand tagged data are populated which might not be effective and sufficient for learning procedure. This limitation of information is main flaw of the supervised approach. Our proposed approach focuses to overcome the limitation using learning set which is enriched in dynamic way maintaining new data. Trivial filtering method is utilized to achieve appropriate training data. We introduce a mixed methodology having Modified Lesk approach and Bag-of-Words having enriched bags using learning methods. Our approach establishes the superiority over individual Modified Lesk and Bag-of-Words approaches based on experimentation.
Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence networks, for the problem of learning continuous semantic representations of algebraic and logical expressions. These networks are trained to represent semantic equivalence, even of expressions that are syntactically very different. The challenge is that semantic representations must be computed in a syntax-directed manner, because semantics is compositional, but at the same time, small changes in syntax can lead to very large changes in semantics, which can be difficult for continuous neural architectures. We perform an exhaustive evaluation on the task of checking equivalence on a highly diverse class of symbolic algebraic and boolean expression types, showing that our model significantly outperforms existing architectures.
We propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject to an inherent baseline error. We assume that the causal system is acyclicity, but we do allow for hidden common causes. Our algorithm presupposes that the probability distributions $P(C)$ of a cause $C$ is independent from the probability distribution $P(E\mid C)$ of the cause-effect mechanism. While our classifier is trained with a Bayesian assumption of flat hyperpriors, we do not make this assumption about our test data. This work connects to recent developments on the identifiability of causal models over continuous variables under the assumption of "independent mechanisms". Carefully-commented Python notebooks that reproduce all our experiments are available online at http://vision.caltech.edu/~kchalupk/code.html.
We introduce a novel generalization of Counterexample-Guided Inductive Synthesis (CEGIS) and instantiate it to yield a novel, competitive algorithm for solving Quantified Boolean Formulas (QBF). Current QBF solvers based on counterexample-guided expansion use a recursive approach which scales poorly with the number of quantifier alternations. Our generalization of CEGIS removes the need for this recursive approach, and we instantiate it to yield a simple and efficient algorithm for QBF solving. Lastly, this research is supported by a competitive, though straightforward, implementation of the algorithm, making it possible to study the practical impact of our algorithm design decisions, along with various optimizations.
Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep's computation on the previous timestep's output limits parallelism and makes RNNs unwieldy for very long sequences. We introduce quasi-recurrent neural networks (QRNNs), an approach to neural sequence modeling that alternates convolutional layers, which apply in parallel across timesteps, and a minimalist recurrent pooling function that applies in parallel across channels. Despite lacking trainable recurrent layers, stacked QRNNs have better predictive accuracy than stacked LSTMs of the same hidden size. Due to their increased parallelism, they are up to 16 times faster at train and test time. Experiments on language modeling, sentiment classification, and character-level neural machine translation demonstrate these advantages and underline the viability of QRNNs as a basic building block for a variety of sequence tasks.
Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in a single model. We introduce a joint many-task model together with a strategy for successively growing its depth to solve increasingly complex tasks. Higher layers include shortcut connections to lower-level task predictions to reflect linguistic hierarchies. We use a simple regularization term to allow for optimizing all model weights to improve one task's loss without exhibiting catastrophic interference of the other tasks. Our single end-to-end model obtains state-of-the-art or competitive results on five different tasks from tagging, parsing, relatedness, and entailment tasks.
Several deep learning models have been proposed for question answering. However, due to their single-pass nature, they have no way to recover from local maxima corresponding to incorrect answers. To address this problem, we introduce the Dynamic Coattention Network (DCN) for question answering. The DCN first fuses co-dependent representations of the question and the document in order to focus on relevant parts of both. Then a dynamic pointing decoder iterates over potential answer spans. This iterative procedure enables the model to recover from initial local maxima corresponding to incorrect answers. On the Stanford question answering dataset, a single DCN model improves the previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains 80.4% F1.
Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advantage of off-policy data. In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experience from a replay buffer. This is motivated by making a connection between the fixed points of the regularized policy gradient algorithm and the Q-values. This connection allows us to estimate the Q-values from the action preferences of the policy, to which we apply Q-learning updates. We refer to the new technique as 'PGQL', for policy gradient and Q-learning. We also establish an equivalency between action-value fitting techniques and actor-critic algorithms, showing that regularized policy gradient techniques can be interpreted as advantage function learning algorithms. We conclude with some numerical examples that demonstrate improved data efficiency and stability of PGQL. In particular, we tested PGQL on the full suite of Atari games and achieved performance exceeding that of both asynchronous advantage actor-critic (A3C) and Q-learning.
One of the most important fields in robotics is the optimization of controllers. Currently, robots are treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. We propose an implementation of a modern physics engine, which has the ability to differentiate control parameters. This has been implemented on both CPU and GPU. We show how this speeds up the optimization process, even for small problems, and why it will scale to bigger problems. We explain why this is an alternative approach to deep Q-learning, for using deep learning in robotics. Lastly, we argue that this is a big step for deep learning in robotics, as it opens up new possibilities to optimize robots, both in hardware and software.
Causality has been recently introduced in databases, to model, characterize, and possibly compute causes for query answers. Connections between QA-causality and consistency-based diagnosis and database repairs (wrt. integrity constraint violations) have already been established. In this work we establish precise connections between QA-causality and both abductive diagnosis and the view-update problem in databases, allowing us to obtain new algorithmic and complexity results for QA-causality. We also obtain new results on the complexity of view-conditioned causality, and investigate the notion of QA-causality in the presence of integrity constraints, obtaining complexity results from a connection with view-conditioned causality. The abduction connection under integrity constraints allows us to obtain algorithmic tools for QA-causality.
We present an approach to sensorimotor control in immersive environments. Our approach utilizes a high-dimensional sensory stream and a lower-dimensional measurement stream. The cotemporal structure of these streams provides a rich supervisory signal, which enables training a sensorimotor control model by interacting with the environment. The model is trained using supervised learning techniques, but without extraneous supervision. It learns to act based on raw sensory input from a complex three-dimensional environment. The presented formulation enables learning without a fixed goal at training time, and pursuing dynamically changing goals at test time. We conduct extensive experiments in three-dimensional simulations based on the classical first-person game Doom. The results demonstrate that the presented approach outperforms sophisticated prior formulations, particularly on challenging tasks. The results also show that trained models successfully generalize across environments and goals. A model trained using the presented approach won the Full Deathmatch track of the Visual Doom AI Competition, which was held in previously unseen environments.
Question answering (QA) has been the subject of a resurgence over the past years. The said resurgence has led to a multitude of question answering (QA) systems being developed both by companies and research facilities. While a few components of QA systems get reused across implementations, most systems do not leverage the full potential of component reuse. Hence, the development of QA systems is currently still a tedious and time-consuming process. We address the challenge of accelerating the creation of novel or tailored QA systems by presenting a concept for a self-wiring approach to composing QA systems. Our approach will allow the reuse of existing, web-based QA systems or modules while developing new QA platforms. To this end, it will rely on QA modules being described using the Web Ontology Language. Based on these descriptions, our approach will be able to automatically compose QA systems using a data-driven approach automatically.
A framework is presented for unsupervised learning of representations based on infomax principle for large-scale neural populations. We use an asymptotic approximation to the Shannon's mutual information for a large neural population to demonstrate that a good initial approximation to the global information-theoretic optimum can be obtained by a hierarchical infomax method. Starting from the initial solution, an efficient algorithm based on gradient descent of the final objective function is proposed to learn representations from the input datasets, and the method works for complete, overcomplete, and undercomplete bases. As confirmed by numerical experiments, our method is robust and highly efficient for extracting salient features from input datasets. Compared with the main existing methods, our algorithm has a distinct advantage in both the training speed and the robustness of unsupervised representation learning. Furthermore, the proposed method is easily extended to the supervised or unsupervised model for training deep structure networks.
Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged-DQN is a simple extension to the DQN algorithm, based on averaging previously learned Q-values estimates, which leads to a more stable training procedure and improved performance by reducing approximation error variance in the target values. To understand the effect of the algorithm, we examine the source of value function estimation errors and provide an analytical comparison within a simplified model. We further present experiments on the Arcade Learning Environment benchmark that demonstrate significantly improved stability and performance due to the proposed extension.
Many algorithms for data analysis exist, especially for classification problems. To solve a data analysis problem, a proper algorithm should be chosen, and also its hyperparameters should be selected. In this paper, we present a new method for the simultaneous selection of an algorithm and its hyperparameters. In order to do so, we reduced this problem to the multi-armed bandit problem. We consider an algorithm as an arm and algorithm hyperparameters search during a fixed time as the corresponding arm play. We also suggest a problem-specific reward function. We performed the experiments on 10 real datasets and compare the suggested method with the existing one implemented in Auto-WEKA. The results show that our method is significantly better in most of the cases and never worse than the Auto-WEKA.
Mastering a video game requires skill, tactics and strategy. While these attributes may be acquired naturally by human players, teaching them to a computer program is a far more challenging task. In recent years, extensive research was carried out in the field of reinforcement learning and numerous algorithms were introduced, aiming to learn how to perform human tasks such as playing video games. As a result, the Arcade Learning Environment (ALE) (Bellemare et al., 2013) has become a commonly used benchmark environment allowing algorithms to train on various Atari 2600 games. In many games the state-of-the-art algorithms outperform humans. In this paper we introduce a new learning environment, the Retro Learning Environment --- RLE, that can run games on the Super Nintendo Entertainment System (SNES), Sega Genesis and several other gaming consoles. The environment is expandable, allowing for more video games and consoles to be easily added to the environment, while maintaining the same interface as ALE. Moreover, RLE is compatible with Python and Torch. SNES games pose a significant challenge to current algorithms due to their higher level of complexity and versatility.
We introduce the hierarchical compositional network (HCN), a directed generative model able to discover and disentangle, without supervision, the building blocks of a set of binary images. The building blocks are binary features defined hierarchically as a composition of some of the features in the layer immediately below, arranged in a particular manner. At a high level, HCN is similar to a sigmoid belief network with pooling. Inference and learning in HCN are very challenging and existing variational approximations do not work satisfactorily. A main contribution of this work is to show that both can be addressed using max-product message passing (MPMP) with a particular schedule (no EM required). Also, using MPMP as an inference engine for HCN makes new tasks simple: adding supervision information, classifying images, or performing inpainting all correspond to clamping some variables of the model to their known values and running MPMP on the rest. When used for classification, fast inference with HCN has exactly the same functional form as a convolutional neural network (CNN) with linear activations and binary weights. However, HCN's features are qualitatively very different.
We propose the Gaussian attention model for content-based neural memory access. With the proposed attention model, a neural network has the additional degree of freedom to control the focus of its attention from a laser sharp attention to a broad attention. It is applicable whenever we can assume that the distance in the latent space reflects some notion of semantics. We use the proposed attention model as a scoring function for the embedding of a knowledge base into a continuous vector space and then train a model that performs question answering about the entities in the knowledge base. The proposed attention model can handle both the propagation of uncertainty when following a series of relations and also the conjunction of conditions in a natural way. On a dataset of soccer players who participated in the FIFA World Cup 2014, we demonstrate that our model can handle both path queries and conjunctive queries well.
We consider the problem of density estimation on Riemannian manifolds. Density estimation on manifolds has many applications in fluid-mechanics, optics and plasma physics and it appears often when dealing with angular variables (such as used in protein folding, robot limbs, gene-expression) and in general directional statistics. In spite of the multitude of algorithms available for density estimation in the Euclidean spaces $\mathbf{R}^n$ that scale to large n (e.g. normalizing flows, kernel methods and variational approximations), most of these methods are not immediately suitable for density estimation in more general Riemannian manifolds. We revisit techniques related to homeomorphisms from differential geometry for projecting densities to sub-manifolds and use it to generalize the idea of normalizing flows to more general Riemannian manifolds. The resulting algorithm is scalable, simple to implement and suitable for use with automatic differentiation. We demonstrate concrete examples of this method on the n-sphere $\mathbf{S}^n$.
Every design choice will have different effects on different units. However traditional A/B tests are often underpowered to identify these heterogeneous effects. This is especially true when the set of unit-level attributes is high-dimensional and our priors are weak about which particular covariates are important. However, there are often observational data sets available that are orders of magnitude larger. We propose a method to combine these two data sources to estimate heterogeneous treatment effects. First, we use observational time series data to estimate a mapping from covariates to unit-level effects. These estimates are likely biased but under some conditions the bias preserves unit-level relative rank orderings. If these conditions hold, we only need sufficient experimental data to identify a monotonic, one-dimensional transformation from observationally predicted treatment effects to real treatment effects. This reduces power demands greatly and makes the detection of heterogeneous effects much easier. As an application, we show how our method can be used to improve Facebook page recommendations.
We analyze the data complexity of ontology-mediated querying where the ontologies are formulated in a description logic (DL) of the ALC family and queries are conjunctive queries, positive existential queries, or acyclic conjunctive queries. Our approach is non-uniform in the sense that we aim to understand the complexity of each single ontology instead of for all ontologies formulated in a certain language. While doing so, we quantify over the queries and are interested, for example, in the question whether all queries can be evaluated in polynomial time w.r.t. a given ontology. Our results include a PTime/coNP-dichotomy for ontologies of depth one in the description logic ALCFI, the same dichotomy for ALC- and ALCI-ontologies of unrestricted depth, and the non-existence of such a dichotomy for ALCF-ontologies. For the latter DL, we additionally show that it is undecidable whether a given ontology admits PTime query evaluation. We also consider the connection between PTime query evaluation and rewritability into (monadic) Datalog.
Humans can learn concepts or recognize items from just a handful of examples, while machines require many more samples to perform the same task. In this paper, we build a computational model to investigate the possibility of this kind of rapid learning. The proposed method aims to improve the learning task of input from sensory memory by leveraging the information retrieved from long-term memory. We present a simple and intuitive technique called cognitive discriminative mappings (CDM) to explore the cognitive problem. First, CDM separates and clusters the data instances retrieved from long-term memory into distinct classes with a discrimination method in working memory when a sensory input triggers the algorithm. CDM then maps each sensory data instance to be as close as possible to the median point of the data group with the same class. The experimental results demonstrate that the CDM approach is effective for learning the discriminative features of supervised classifications with few training sensory input instances.
We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control how much of the conditioning sequence the channel model needs to read in order to generate a subsequent symbol, we obtain a tractable and effective beam search decoder. Experimental results on abstractive sentence summarisation, morphological inflection, and machine translation show that noisy channel models outperform direct models, and that they significantly benefit from increased amounts of unpaired output data that direct models cannot easily use.
Modeling the structure of coherent texts is a key NLP problem. The task of coherently organizing a given set of sentences has been commonly used to build and evaluate models that understand such structure. We propose an end-to-end unsupervised deep learning approach based on the set-to-sequence framework to address this problem. Our model strongly outperforms prior methods in the order discrimination task and a novel task of ordering abstracts from scientific articles. Furthermore, our work shows that useful text representations can be obtained by learning to order sentences. Visualizing the learned sentence representations shows that the model captures high-level logical structure in paragraphs. Our representations perform comparably to state-of-the-art pre-training methods on sentence similarity and paraphrase detection tasks.
CP-nets and their variants constitute one of the main AI approaches for specifying and reasoning about preferences. CI-nets, in particular, are a CP-inspired formalism for representing ordinal preferences over sets of goods, which are typically required to be monotonic. Considering also that goods often come in multi-sets rather than sets, a natural question is whether CI-nets can be used more or less directly to encode preferences over multi-sets. We here provide some initial ideas on how to achieve this, in the sense that at least a restricted form of reasoning on our framework, which we call "confined reasoning", can be efficiently reduced to reasoning on CI-nets. Our framework nevertheless allows for encoding preferences over multi-sets with unbounded multiplicities. We also show the extent to which it can be used to represent preferences where multiplicites of the goods are not stated explicitly ("purely qualitative preferences") as well as a potential use of our generalization of CI-nets as a component of a recent system for evidence aggregation.
A new agent architecture called Limited Instruction Set Agent (LISA) is introduced for autonomous control. The new architecture is based on previous implementations of AgentSpeak and it is structurally simpler than its predecessors with the aim of facilitating design-time and run-time verification methods. The process of abstracting the LISA system to two different types of discrete probabilistic models (DTMC and MDP) is investigated and illustrated. The LISA system provides a tool for complete modelling of the agent and the environment for probabilistic verification. The agent program can be automatically compiled into a DTMC or a MDP model for verification with Prism. The automatically generated Prism model can be used for both design-time and run-time verification. The run-time verification is investigated and illustrated in the LISA system as an internal modelling mechanism for prediction of future outcomes.
We propose a major revision of the format XCSP 2.1, called XCSP3, to build integrated representations of combinatorial constrained problems. This new format is able to deal with mono/multi optimization, many types of variables, cost functions, reification, views, annotations, variable quantification, distributed, probabilistic and qualitative reasoning. The new format is made compact, highly readable, and rather easy to parse. Interestingly, it captures the structure of the problem models, through the possibilities of declaring arrays of variables, and identifying syntactic and semantic groups of constraints. The number of constraints is kept under control by introducing a limited set of basic constraint forms, and producing almost automatically some of their variations through lifting, restriction, sliding, logical combination and relaxation mechanisms. As a result, XCSP3 encompasses practically all constraints that can be found in major constraint solvers developed by the CP community. A website, which is developed conjointly with the format, contains many models and series of instances. The user can make sophisticated queries for selecting instances from very precise criteria. The objective of XCSP3 is to ease the effort required to test and compare different algorithms by providing a common test-bed of combinatorial constrained instances.
Importance sampling is often used in machine learning when training and testing data come from different distributions. In this paper we propose a new variant of importance sampling that can reduce the variance of importance sampling-based estimates by orders of magnitude when the supports of the training and testing distributions differ. After motivating and presenting our new importance sampling estimator, we provide a detailed theoretical analysis that characterizes both its bias and variance relative to the ordinary importance sampling estimator (in various settings, which include cases where ordinary importance sampling is biased, while our new estimator is not, and vice versa). We conclude with an example of how our new importance sampling estimator can be used to improve estimates of how well a new treatment policy for diabetes will work for an individual, using only data from when the individual used a previous treatment policy.
Inference in expressive probabilistic models is generally intractable, which makes them difficult to learn and limits their applicability. Sum-product networks are a class of deep models where, surprisingly, inference remains tractable even when an arbitrary number of hidden layers are present. In this paper, we generalize this result to a much broader set of learning problems: all those where inference consists of summing a function over a semiring. This includes satisfiability, constraint satisfaction, optimization, integration, and others. In any semiring, for summation to be tractable it suffices that the factors of every product have disjoint scopes. This unifies and extends many previous results in the literature. Enforcing this condition at learning time thus ensures that the learned models are tractable. We illustrate the power and generality of this approach by applying it to a new type of structured prediction problem: learning a nonconvex function that can be globally optimized in polynomial time. We show empirically that this greatly outperforms the standard approach of learning without regard to the cost of optimization.
This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests. We have built two systems for entity discovery and mention detection (MD): one uses the conditional RNNLM and the other one uses the attention-based encoder-decoder framework. The entity linking (EL) system consists of two modules: a rule based candidate generation and a neural networks probability ranking model. Moreover, some simple string matching rules are used for NIL clustering. At the end, our best system has achieved an F1 score of 0.624 in the end-to-end typed mention ceaf plus metric.
Most neural network models for document classification on social media focus on text infor-mation to the neglect of other information on these platforms. In this paper, we classify post stance on social media channels and develop UTCNN, a neural network model that incorporates user tastes, topic tastes, and user comments on posts. UTCNN not only works on social media texts, but also analyzes texts in forums and message boards. Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0.755 macro-average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld. This model design greatly mitigates the lack of data for the minor class without the use of oversampling. In addition, UTCNN yields a 0.842 accuracy on English online debate forum data, which also significantly outperforms results from previous work as well as other deep learning models, showing that UTCNN performs well regardless of language or platform.
Subjective questions such as `does neymar dive', or `is clinton lying', or `is trump a fascist', are popular queries to web search engines, as can be seen by autocompletion suggestions on Google, Yahoo and Bing. In the era of cognitive computing, beyond search, they could be handled as hypotheses issued for evaluation. Our vision is to leverage on unstructured data and metadata of the rich user-generated multimedia that is often shared as material evidence in favor or against hypotheses in social media platforms. In this paper we present two preliminary experiments along those lines and discuss challenges for a cognitive computing system that collects material evidence from user-generated multimedia towards aggregating it into some form of collective decision on the hypothesis.
Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents. In this work we formulate the navigation question as a reinforcement learning problem and show that data efficiency and task performance can be dramatically improved by relying on additional auxiliary tasks leveraging multimodal sensory inputs. In particular we consider jointly learning the goal-driven reinforcement learning problem with auxiliary depth prediction and loop closure classification tasks. This approach can learn to navigate from raw sensory input in complicated 3D mazes, approaching human-level performance even under conditions where the goal location changes frequently. We provide detailed analysis of the agent behaviour, its ability to localise, and its network activity dynamics, showing that the agent implicitly learns key navigation abilities.
Designing effective exploration-exploitation algorithms in Markov decision processes (MDPs) with large state-action spaces is the main challenge in reinforcement learning (RL). In fact, the learning performance degrades with the number of states and actions in the MDP. However, MDPs often exhibit a low-dimensional latent structure in practice, where a small hidden state is observable through a possibly large number of observations. In this paper, we study the setting of rich-observation Markov decision processes (\richmdp), where hidden states are mapped to observations through an injective mapping, so that an observation can be generated by only one hidden state. While this mapping is unknown a priori, we introduce a spectral decomposition method that consistently estimates how observations are clustered in the hidden states. The estimated clustering is then integrated into an optimistic algorithm for RL (UCRL), which operates on the smaller clustered space. The resulting algorithm proceeds through phases and we show that its per-step regret (i.e., the difference in cumulative reward between the algorithm and the optimal policy) decreases as more observations are clustered together and finally, matches the (ideal) performance of an RL algorithm running directly on the hidden MDP.
Causal discovery studies the problem of mining causal relationships between variables from data, which is of primary interest in science. During the past decades, significant amount of progresses have been made toward this fundamental data mining paradigm. Recent years, as the availability of abundant large-sized and complex observational data, the constrain-based approaches have gradually attracted a lot of interest and have been widely applied to many diverse real-world problems due to the fast running speed and easy generalizing to the problem of causal insufficiency. In this paper, we aim to review the constraint-based causal discovery algorithms. Firstly, we discuss the learning paradigm of the constraint-based approaches. Secondly and primarily, the state-of-the-art constraint-based casual inference algorithms are surveyed with the detailed analysis. Thirdly, several related open-source software packages and benchmark data repositories are briefly summarized. As a conclusion, some open problems in constraint-based causal discovery are outlined for future research.
We propose a scalable approach to learn video-based question answering (QA): answer a "free-form natural language question" about a video content. Our approach automatically harvests a large number of videos and descriptions freely available online. Then, a large number of candidate QA pairs are automatically generated from descriptions rather than manually annotated. Next, we use these candidate QA pairs to train a number of video-based QA methods extended fromMN (Sukhbaatar et al. 2015), VQA (Antol et al. 2015), SA (Yao et al. 2015), SS (Venugopalan et al. 2015). In order to handle non-perfect candidate QA pairs, we propose a self-paced learning procedure to iteratively identify them and mitigate their effects in training. Finally, we evaluate performance on manually generated video-based QA pairs. The results show that our self-paced learning procedure is effective, and the extended SS model outperforms various baselines.
The budgeted information gathering problem - where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world - appears in practice across a wide range of applications in autonomous exploration and inspection with mobile robots. Although there is an extensive amount of prior work investigating effective approximations of the problem, these methods do not address the fact that their performance is heavily dependent on distribution of objects in the world. In this paper, we attempt to address this issue by proposing a novel data-driven imitation learning framework. We present an efficient algorithm, EXPLORE, that trains a policy on the target distribution to imitate a clairvoyant oracle - an oracle that has full information about the world and computes non-myopic solutions to maximize information gathered. We validate the approach on a spectrum of results on a number of 2D and 3D exploration problems that demonstrates the ability of EXPLORE to adapt to different object distributions. Additionally, our analysis provides theoretical insight into the behavior of EXPLORE. Our approach paves the way forward for efficiently applying data-driven methods to the domain of information gathering.
Measuring research impact and ranking academic achievement are important and challenging problems. Having an objective picture of research institution is particularly valuable for students, parents and funding agencies, and also attracts attention from government and industry. KDD Cup 2016 proposes the paper acceptance rank prediction task, in which the participants are asked to rank the importance of institutions based on predicting how many of their papers will be accepted at the 8 top conferences in computer science. In our work, we adopt a three-step feature engineering method, including basic features definition, finding similar conferences to enhance the feature set, and dimension reduction using PCA. We propose three ranking models and the ensemble methods for combining such models. Our experiment verifies the effectiveness of our approach. In KDD Cup 2016, we achieved the overall rank of the 2nd place.
Sentiment analysis is crucial for extracting social signals from social media content. Due to the prevalence of images in social media, image sentiment analysis is receiving increasing attention in recent years. However, most existing systems are black-boxes that do not provide insight on how image content invokes sentiment and emotion in the viewers. Psychological studies have confirmed that salient objects in an image often invoke emotions. In this work, we investigate more fine-grained and more comprehensive interaction between visual saliency and visual sentiment. In particular, we partition images in several primary scene-type dimensions, including: open-closed, natural-manmade, indoor-outdoor, and face-noface. Using state of the art saliency detection algorithm and sentiment classification algorithm, we examine how the sentiment of the salient region(s) in an image relates to the overall sentiment of the image. The experiments on a representative image emotion dataset have shown interesting correlation between saliency and sentiment in different scene types and in turn shed light on the mechanism of visual sentiment evocation.
Recent studies on knowledge base completion, the task of recovering missing facts based on observed facts, demonstrate the importance of learning embeddings from multi-step relations. Due to the size of knowledge bases, previous works manually design relation paths of observed triplets in symbolic space (e.g. random walk) to learn multi-step relations during training. However, these approaches suffer some limitations as most paths are not informative, and it is prohibitively expensive to consider all possible paths. To address the limitations, we propose learning to traverse in vector space directly without the need of symbolic space guidance. To remember the connections between related observed triplets and be able to adaptively change relation paths in vector space, we propose Implicit ReasoNets (IRNs), that is composed of a global memory and a controller module to learn multi-step relation paths in vector space and infer missing facts jointly without any human-designed procedure. Without using any axillary information, our proposed model achieves state-of-the-art results on popular knowledge base completion benchmarks.
Network data mining has become an important area of study due to the large number of problems it can be applied to. This paper presents NOESIS, an open source framework for network data mining that provides a large collection of network analysis techniques, including the analysis of network structural properties, community detection methods, link scoring, and link prediction, as well as network visualization algorithms. It also features a complete stand-alone graphical user interface that facilitates the use of all these techniques. The NOESIS framework has been designed using solid object-oriented design principles and structured parallel programming. As a lightweight library with minimal external dependencies and a permissive software license, NOESIS can be incorporated into other software projects. Released under a BSD license, it is available from http://noesis.ikor.org.
Real-time parking occupancy information is critical for a parking management system to facilitate drivers to park more efficiently. Recent advances in connected and automated vehicle technologies enable sensor-equipped cars (probe cars) to detect and broadcast available parking spaces when driving through parking lots. In this paper, we evaluate the impact of market penetration of probe cars on the system performance, and investigate different parking guidance policies to improve the data acquisition process. We adopt a simulation-based approach to impose four policies on an off- street parking lot influencing the behavior of probe cars to park in assigned parking spaces. This in turn effects the scanning route and the parking space occupancy estimations. The last policy we propose is a near-optimal guidance strategy that maximizes the information gain of posteriors. The results suggest that an efficient information gathering policy can compensate for low penetration of connected and automated vehicles. We also highlight the policy trade-off that occur while attempting to maximize information gain through explorations and improve assignment accuracy through exploitations. Our results can assist urban policy makers in designing and managing smart parking systems.
Answer Set Programming (ASP) is an expressive knowledge representation and reasoning framework. Due to its rather simple syntax paired with high-performance solvers, ASP is interesting for industrial applications. However, to err is human and thus debugging is an important activity during the development process. Therefore, tools for debugging non-ground answer set programs are needed. In this paper, we present a new graphical debugging interface for non-ground answer set programs. The tool is based on the recently-introduced DWASP approach for debugging and it simplifies the interaction with the debugger. Furthermore, the debugging interface is integrated in ASPIDE, a rich IDE for answer set programs. With our extension ASPIDE turns into a full-fledged IDE by offering debugging support.
In this work, a study on Variable Neighborhood Search algorithms for multi-depot dial-a-ride problems is presented. In dial-a-ride problems patients need to be transported from pre-specified pickup locations to pre-specified delivery locations, under different considerations. The addressed problem presents several constraints and features, such as heterogeneous vehicles, distributed in different depots, and heterogeneous patients. The aim is of minimizing the total routing cost, while respecting time-window, ride-time, capacity and route duration constraints. The objective of the study is of determining the best algorithm configuration in terms of initial solution, neighborhood and local search procedures. At this aim, two different procedures for the computation of an initial solution, six different type of neighborhoods and five local search procedures, where only intra-route changes are made, have been considered and compared. We have also evaluated an "adjusting procedure" that aims to produce feasible solutions from infeasible solutions with small constraints violations. The different VNS algorithms have been tested on instances from literature as well as on random instances arising from a real-world healthcare application.
The CDCL algorithm is the leading solution adopted by state-of-the-art solvers for SAT, SMT, ASP, and others. Experiments show that the performance of CDCL solvers can be significantly boosted by embedding domain-specific heuristics, especially on large real-world problems. However, a proper integration of such criteria in off-the-shelf CDCL implementations is not obvious. In this paper, we distill the key ingredients that drive the search of CDCL solvers, and propose a general framework for designing and implementing new heuristics. We implemented our strategy in an ASP solver, and we experimented on two industrial domains. On hard problem instances, state-of-the-art implementations fail to find any solution in acceptable time, whereas our implementation is very successful and finds all solutions.
Just recently, the concept of augmented and virtual reality (AR/VR) over wireless has taken the entire 5G ecosystem by storm spurring an unprecedented interest from both academia, industry and others. Yet, the success of an immersive VR experience hinges on solving a plethora of grand challenges cutting across multiple disciplines. This article underscores the importance of VR technology as a disruptive use case of 5G (and beyond) harnessing the latest development of storage/memory, fog/edge computing, computer vision, artificial intelligence and others. In particular, the main requirements of wireless interconnected VR are described followed by a selection of key enablers, then, research avenues and their underlying grand challenges are presented. Furthermore, we examine three VR case studies and provide numerical results under various storage, computing and network configurations. Finally, this article exposes the limitations of current networks and makes the case for more theory, and innovations to spearhead VR for the masses.
With the large volume of new information created every day, determining the validity of information in a knowledge graph and filling in its missing parts are crucial tasks for many researchers and practitioners. To address this challenge, a number of knowledge graph completion methods have been developed using low-dimensional graph embeddings. Although researchers continue to improve these models using an increasingly complex feature space, we show that simple changes in the architecture of the underlying model can outperform state-of-the-art models without the need for complex feature engineering. In this work, we present a shared variable neural network model called ProjE that fills-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph's entities and edges, and through subtle, but important, changes to the standard loss function. In doing so, ProjE has a parameter size that is smaller than 11 out of 15 existing methods while performing $37\%$ better than the current-best method on standard datasets. We also show, via a new fact checking task, that ProjE is capable of accurately determining the veracity of many declarative statements.
Part of the appeal of Visual Question Answering (VQA) is its promise to answer new questions about previously unseen images. Most current methods demand training questions that illustrate every possible concept, and will therefore never achieve this capability, since the volume of required training data would be prohibitive. Answering general questions about images requires methods capable of Zero-Shot VQA, that is, methods able to answer questions beyond the scope of the training questions. We propose a new evaluation protocol for VQA methods which measures their ability to perform Zero-Shot VQA, and in doing so highlights significant practical deficiencies of current approaches, some of which are masked by the biases in current datasets. We propose and evaluate several strategies for achieving Zero-Shot VQA, including methods based on pretrained word embeddings, object classifiers with semantic embeddings, and test-time retrieval of example images. Our extensive experiments are intended to serve as baselines for Zero-Shot VQA, and they also achieve state-of-the-art performance in the standard VQA evaluation setting.
When a processing unit relies on data from external streams, we may face the problem that the stream data needs to be rearranged in a way that allows the unit to perform its task(s). On arrival of new data, we must decide whether there is sufficient information available to start processing or whether to wait for more data. Furthermore, we need to ensure that the data meets the input specification of the processing step. In the case of multiple input streams it is also necessary to coordinate which data from which incoming stream should form the input of the next process instantiation. In this work, we propose a declarative approach as an interface between multiple streams and a processing unit. The idea is to specify via answer-set programming how to arrange incoming data in packages that are suitable as input for subsequent processing. Our approach is intended for use in asynchronous multi-context systems (aMCSs), a recently proposed framework for loose coupling of knowledge representation formalisms that allows for online reasoning in a dynamic environment. Contexts in aMCSs process data streams from external sources and other contexts.
The current trend in object detection and localization is to learn predictions with high capacity deep neural networks trained on a very large amount of annotated data and using a high amount of processing power. In this work, we propose a new neural model which directly predicts bounding box coordinates. The particularity of our contribution lies in the local computations of predictions with a new form of local parameter sharing which keeps the overall amount of trainable parameters low. Key components of the model are spatial 2D-LSTM recurrent layers which convey contextual information between the regions of the image. We show that this model is more powerful than the state of the art in applications where training data is not as abundant as in the classical configuration of natural images and Imagenet/Pascal VOC tasks. We particularly target the detection of text in document images, but our method is not limited to this setting. The proposed model also facilitates the detection of many objects in a single image and can deal with inputs of variable sizes without resizing.
Feature subspace selection is an important part in speech emotion recognition. Most of the studies are devoted to finding a feature subspace for representing all emotions. However, some studies have indicated that the features associated with different emotions are not exactly the same. Hence, traditional methods may fail to distinguish some of the emotions with just one global feature subspace. In this work, we propose a new divide and conquer idea to solve the problem. First, the feature subspaces are constructed for all the combinations of every two different emotions (emotion-pair). Bi-classifiers are then trained on these feature subspaces respectively. The final emotion recognition result is derived by the voting and competition method. Experimental results demonstrate that the proposed method can get better results than the traditional multi-classification method.
We introduce two novel non-parametric statistical hypothesis tests. The first test, called the relative test of dependency, enables us to determine whether one source variable is significantly more dependent on a first target variable or a second. Dependence is measured via the Hilbert-Schmidt Independence Criterion (HSIC). The second test, called the relative test of similarity, is use to determine which of the two samples from arbitrary distributions is significantly closer to a reference sample of interest and the relative measure of similarity is based on the Maximum Mean Discrepancy (MMD). To construct these tests, we have used as our test statistics the difference of HSIC statistics and of MMD statistics, respectively. The resulting tests are consistent and unbiased, and have favorable convergence properties. The effectiveness of the relative dependency test is demonstrated on several real-world problems: we identify languages groups from a multilingual parallel corpus, and we show that tumor location is more dependent on gene expression than chromosome imbalance. We also demonstrate the performance of the relative test of similarity over a broad selection of model comparisons problems in deep generative models.
At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the model, or the effort required to make predictions about a model's behavior. In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear. We compare aLIME to linear LIME with simulated experiments, and demonstrate the flexibility of aLIME with qualitative examples from a variety of domains and tasks.
Training deep neural networks for solving machine learning problems is one great challenge in the field, mainly due to its associated optimisation problem being highly non-convex. Recent developments have suggested that many training algorithms do not suffer from undesired local minima under certain scenario, and consequently led to great efforts in pursuing mathematical explanations for such observations. This work provides an alternative mathematical understanding of the challenge from a smooth optimisation perspective. By assuming exact learning of finite samples, sufficient conditions are identified via a critical point analysis to ensure any local minimum to be globally minimal as well. Furthermore, a state of the art algorithm, known as the Generalised Gauss-Newton (GGN) algorithm, is rigorously revisited as an approximate Newton's algorithm, which shares the property of being locally quadratically convergent to a global minimum under the condition of exact learning.
We study the task of teaching a machine to classify objects using features and labels. We introduce the Error-Driven-Featuring design pattern for teaching using features and labels in which a teacher prefers to introduce features only if they are needed. We analyze the potential risks and benefits of this teaching pattern through the use of teaching protocols, illustrative examples, and by providing bounds on the effort required for an optimal machine teacher using a linear learning algorithm, the most commonly used type of learners in interactive machine learning systems. Our analysis provides a deeper understanding of potential trade-offs of using different learning algorithms and between the effort required for featuring (creating new features) and labeling (providing labels for objects).
In this paper we present an algorithm to build a road network map enriched with traffic rules such as one-way streets and forbidden turns, based on the interpretation of already detected and classified traffic signs. Such algorithm helps to automatize the elaboration of maps for commercial navigation systems. Our solution is based on simulating navigation along the road network, determining at each point of interest the visibility of the signs and their effect on the roads. We test our approach in a small urban network and discuss various ways to generalize it to support more complex environments.
The Team-maxmin equilibrium prescribes the optimal strategies for a team of rational players sharing the same goal and without the capability of correlating their strategies in strategic games against an adversary. This solution concept can capture situations in which an agent controls multiple resources-corresponding to the team members-that cannot communicate. It is known that such equilibrium always exists and it is unique (unless degeneracy) and these properties make it a credible solution concept to be used in real-world applications, especially in security scenarios. Nevertheless, to the best of our knowledge, the Team-maxmin equilibrium is almost completely unexplored in the literature. In this paper, we investigate bounds of (in)efficiency of the Team-maxmin equilibrium w.r.t. the Nash equilibria and w.r.t. the Maxmin equilibrium when the team members can play correlated strategies. Furthermore, we study a number of algorithms to find and/or approximate an equilibrium, discussing their theoretical guarantees and evaluating their performance by using a standard testbed of game instances.
Recurrent neural networks (RNNs) have been used extensively and with increasing success to model various types of sequential data. Much of this progress has been achieved through devising recurrent units and architectures with the flexibility to capture complex statistics in the data, such as long range dependency or localized attention phenomena. However, while many sequential data (such as video, speech or language) can have highly variable information flow, most recurrent models still consume input features at a constant rate and perform a constant number of computations per time step, which can be detrimental to both speed and model capacity. In this paper, we explore a modification to existing recurrent units which allows them to learn to vary the amount of computation they perform at each step, without prior knowledge of the sequence's time structure. We show experimentally that not only do our models require fewer operations, they also lead to better performance overall on evaluation tasks.
In this paper we introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process,data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far, relates to the decision which expert to deploy at test time. We introduce a set of gating autoencoders that learn a representation for the task at hand, and, at test time, automatically forward the test sample to the relevant expert. This also brings memory efficiency as only one expert network has to be loaded into memory at any given time. Further, the autoencoders inherently capture the relatedness of one task to another, based on which the most relevant prior model to be used for training a new expert, with finetuning or learning without-forgetting, can be selected. We evaluate our method on image classification and video prediction problems.
Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq) models have shown promising results for unstructured tasks, such as word-level dialogue response generation. The hope is that such models will be able to leverage massive amounts of data to learn meaningful natural language representations and response generation strategies, while requiring a minimum amount of domain knowledge and hand-crafting. An important challenge is to develop models that can effectively incorporate dialogue context and generate meaningful and diverse responses. In support of this goal, we review recently proposed models based on generative encoder-decoder neural network architectures, and show that these models have better ability to incorporate long-term dialogue history, to model uncertainty and ambiguity in dialogue, and to generate responses with high-level compositional structure.
New developments in HPC technology in terms of increasing computing power on multi/many core processors, high-bandwidth memory/IO subsystems and communication interconnects, pose a direct impact on software and runtime system development. These advancements have become useful in producing high-performance collective communication interfaces that integrate efficiently on a wide variety of platforms and environments. However, number of optimization options that shows up with each new technology or software framework has resulted in a \emph{combinatorial explosion} in feature space for tuning collective parameters such that finding the optimal set has become a nearly impossible task. Applicability of algorithmic choices available for optimizing collective communication depends largely on the scalability requirement for a particular usecase. This problem can be further exasperated by any requirement to run collective problems at very large scales such as in the case of exascale computing, at which impractical tuning by brute force may require many months of resources. Therefore application of statistical, data mining and artificial Intelligence or more general hybrid learning models seems essential in many collectives parameter optimization problems. We hope to explore current and the cutting edge of collective communication optimization and tuning methods and culminate with possible future directions towards this problem.
Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes. Additionally, we evaluate the design of cGANs. The combination of an encoder with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real images with deterministic complex modifications.
It is critical for advanced manufacturing machines to autonomously execute a task by following an end-user's natural language (NL) instructions. However, NL instructions are usually ambiguous and abstract so that the machines may misunderstand and incorrectly execute the task. To address this NL-based human-machine communication problem and enable the machines to appropriately execute tasks by following the end-user's NL instructions, we developed a Machine-Executable-Plan-Generation (exePlan) method. The exePlan method conducts task-centered semantic analysis to extract task-related information from ambiguous NL instructions. In addition, the method specifies machine execution parameters to generate a machine-executable plan by interpreting abstract NL instructions. To evaluate the exePlan method, an industrial robot Baxter was instructed by NL to perform three types of industrial tasks {'drill a hole', 'clean a spot', 'install a screw'}. The experiment results proved that the exePlan method was effective in generating machine-executable plans from the end-user's NL instructions. Such a method has the promise to endow a machine with the ability of NL-instructed task execution.
We propose a novel deep network architecture for grayscale and color image denoising that is based on a non-local image model. Our motivation for the overall design of the proposed network stems from variational methods that exploit the inherent non-local self-similarity property of natural images. We build on this concept and introduce deep networks that perform non-local processing and at the same time they significantly benefit from discriminative learning. Experiments on the Berkeley segmentation dataset, comparing several state-of-the-art methods, show that the proposed non-local models achieve the best reported denoising performance both for grayscale and color images for all the tested noise levels. It is also worth noting that this increase in performance comes at no extra cost on the capacity of the network compared to existing alternative deep network architectures. In addition, we highlight a direct link of the proposed non-local models to convolutional neural networks. This connection is of significant importance since it allows our models to take full advantage of the latest advances on GPU computing in deep learning and makes them amenable to efficient implementations through their inherent parallelism.
Many prediction problems can be phrased as inferences over local neighborhoods of graphs. The graph represents the interaction between entities, and the neighborhood of each entity contains information that allows the inferences or predictions. We present an approach for applying machine learning directly to such graph neighborhoods, yielding predicitons for graph nodes on the basis of the structure of their local neighborhood and the features of the nodes in it. Our approach allows predictions to be learned directly from examples, bypassing the step of creating and tuning an inference model or summarizing the neighborhoods via a fixed set of hand-crafted features. The approach is based on a multi-level architecture built from Long Short-Term Memory neural nets (LSTMs); the LSTMs learn how to summarize the neighborhood from data. We demonstrate the effectiveness of the proposed technique on a synthetic example and on real-world data related to crowdsourced grading, Bitcoin transactions, and Wikipedia edit reversions.
We propose a new method to study the internal memory used by reinforcement learning policies. We estimate the amount of relevant past information by estimating mutual information between behavior histories and the current action of an agent. We perform this estimation in the passive setting, that is, we do not intervene but merely observe the natural behavior of the agent. Moreover, we provide a theoretical justification for our approach by showing that it yields an implementation-independent lower bound on the minimal memory capacity of any agent that implement the observed policy. We demonstrate our approach by estimating the use of memory of DQN policies on concatenated Atari frames, demonstrating sharply different use of memory across 49 games. The study of memory as information that flows from the past to the current action opens avenues to understand and improve successful reinforcement learning algorithms.
Entity resolution (ER) is about identifying and merging records in a database that represent the same real-world entity. Matching dependencies (MDs) have been introduced and investigated as declarative rules that specify ER policies. An ER process induced by MDs over a dirty instance leads to multiple clean instances, in general. General "answer sets programs" have been proposed to specify the MD-based cleaning task and its results. In this work, we extend MDs to "relational MDs", which capture more application semantics, and identify classes of relational MDs for which the general ASP can be automatically rewritten into a stratified Datalog program, with the single clean instance as its standard model.
We propose a higher-level associative memory for learning adversarial networks. Generative adversarial network (GAN) framework has a discriminator and a generator network. The generator (G) maps white noise (z) to data samples while the discriminator (D) maps data samples to a single scalar. To do so, G learns how to map from high-level representation space to data space, and D learns to do the opposite. We argue that higher-level representation spaces need not necessarily follow a uniform probability distribution. In this work, we use Restricted Boltzmann Machines (RBMs) as a higher-level associative memory and learn the probability distribution for the high-level features generated by D. The associative memory samples its underlying probability distribution and G learns how to map these samples to data space. The proposed associative adversarial networks (AANs) are generative models in the higher-levels of the learning, and use adversarial non-stochastic models D and G for learning the mapping between data and higher-level representation spaces. Experiments show the potential of the proposed networks.
We model coherent conversation continuation via RNN-based dialogue models equipped with a dynamic attention mechanism. Our attention-RNN language model dynamically increases the scope of attention on the history as the conversation continues, as opposed to standard attention (or alignment) models with a fixed input scope in a sequence-to-sequence model. This allows each generated word to be associated with the most relevant words in its corresponding conversation history. We evaluate the model on two popular dialogue datasets, the open-domain MovieTriples dataset and the closed-domain Ubuntu Troubleshoot dataset, and achieve significant improvements over the state-of-the-art and baselines on several metrics, including complementary diversity-based metrics, human evaluation, and qualitative visualizations. We also show that a vanilla RNN with dynamic attention outperforms more complex memory models (e.g., LSTM and GRU) by allowing for flexible, long-distance memory. We promote further coherence via topic modeling-based reranking.
Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support. In order to leverage large amounts of patient data, efficient optimisation routines are paramount. We propose an efficient training algorithm for the kernel survival support vector machine (SSVM). We directly optimise the primal objective function and employ truncated Newton optimisation and order statistic trees to significantly lower computational costs compared to previous training algorithms, which require $O(n^4)$ space and $O(p n^6)$ time for datasets with $n$ samples and $p$ features. Our results demonstrate that our proposed optimisation scheme allows analysing data of a much larger scale with no loss in prediction performance. Experiments on synthetic and 5 real-world datasets show that our technique outperforms existing kernel SSVM formulations if the amount of right censoring is high ($\geq85\%$), and performs comparably otherwise.
Automaton models are often seen as interpretable models. Interpretability itself is not well defined: it remains unclear what interpretability means without first explicitly specifying objectives or desired attributes. In this paper, we identify the key properties used to interpret automata and propose a modification of a state-merging approach to learn variants of finite state automata. We apply the approach to problems beyond typical grammar inference tasks. Additionally, we cover several use-cases for prediction, classification, and clustering on sequential data in both supervised and unsupervised scenarios to show how the identified key properties are applicable in a wide range of contexts.
Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found their way into the areas of low-level computer vision and image processing to solve regression problems mostly with relatively shallow networks. We present a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an improvement of up to 0.36 dB over the best previous ConvNet result. We show that a network trained for a specific quality factor (QF) is resilient to the QF used to compress the input image - a single network trained for QF 60 provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.
Understanding why a model made a certain prediction is crucial in many data science fields. Interpretable predictions engender appropriate trust and provide insight into how the model may be improved. However, with large modern datasets the best accuracy is often achieved by complex models even experts struggle to interpret, which creates a tension between accuracy and interpretability. Recently, several methods have been proposed for interpreting predictions from complex models by estimating the importance of input features. Here, we present how a model-agnostic additive representation of the importance of input features unifies current methods. This representation is optimal, in the sense that it is the only set of additive values that satisfies important properties. We show how we can leverage these properties to create novel visual explanations of model predictions. The thread of unity that this representation weaves through the literature indicates that there are common principles to be learned about the interpretation of model predictions that apply in many scenarios.
In this paper we introduce a new unsupervised reinforcement learning method for discovering the set of intrinsic options available to an agent. This set is learned by maximizing the number of different states an agent can reliably reach, as measured by the mutual information between the set of options and option termination states. To this end, we instantiate two policy gradient based algorithms, one that creates an explicit embedding space of options and one that represents options implicitly. The algorithms also provide an explicit measure of empowerment in a given state that can be used by an empowerment maximizing agent. The algorithm scales well with function approximation and we demonstrate the applicability of the algorithm on a range of tasks.
Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However, high prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientific applications require some explanation of the learned prediction function. Unfortunately, most methods do not come with out of the box straight forward interpretation. Even linear prediction functions are not straight forward to explain if features exhibit complex correlation structure. In this paper, we propose the Measure of Feature Importance (MFI). MFI is general and can be applied to any arbitrary learning machine (including kernel machines and deep learning). MFI is intrinsically non-linear and can detect features that by itself are inconspicuous and only impact the prediction function through their interaction with other features. Lastly, MFI can be used for both --- model-based feature importance and instance-based feature importance (i.e, measuring the importance of a feature for a particular data point).
Recent work in model-agnostic explanations of black-box machine learning has demonstrated that interpretability of complex models does not have to come at the cost of accuracy or model flexibility. However, it is not clear what kind of explanations, such as linear models, decision trees, and rule lists, are the appropriate family to consider, and different tasks and models may benefit from different kinds of explanations. Instead of picking a single family of representations, in this work we propose to use "programs" as model-agnostic explanations. We show that small programs can be expressive yet intuitive as explanations, and generalize over a number of existing interpretable families. We propose a prototype program induction method based on simulated annealing that approximates the local behavior of black-box classifiers around a specific prediction using random perturbations. Finally, we present preliminary application on small datasets and show that the generated explanations are intuitive and accurate for a number of classifiers.
A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail.
Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probability distribution as a product of marginal probabilities, it may fail to properly account for important dependencies between variables. We therefore replace the fully factorized distribution of Mean Field by a weighted mixture of such distributions, that similarly minimizes the KL-Divergence to the true posterior. By introducing two new ideas, namely, conditioning on groups of variables instead of single ones and using a parameter of the conditional random field potentials, that we identify to the temperature in the sense of statistical physics to select such groups, we can perform this minimization efficiently. Our extension of the clamping method proposed in previous works allows us to both produce a more descriptive approximation of the true posterior and, inspired by the diverse MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that this positively impacts real-world algorithms that initially relied on mean fields.
We consider an orienteering problem (OP) where an agent needs to visit a series (possibly a subset) of depots, from which the maximal accumulated profits are desired within given limited time budget. Different from most existing works where the profits are assumed to be static, in this work we investigate a variant that has arbitrary time-dependent profits. Specifically, the profits to be collected change over time and they follow different (e.g., independent) time-varying functions. The problem is of inherent nonlinearity and difficult to solve by existing methods. To tackle the challenge, we present a simple and effective framework that incorporates time-variations into the fundamental planning process. Specifically, we propose a deterministic spatio-temporal representation where both spatial description and temporal logic are unified into one routing topology. By employing existing basic sorting and searching algorithms, the routing solutions can be computed in an extremely efficient way. The proposed method is easy to implement and extensive numerical results show that our approach is time efficient and generates near-optimal solutions.
This work presents a multiscale framework to solve an inverse reinforcement learning (IRL) problem for continuous-time/state stochastic systems. We take advantage of a diffusion wavelet representation of the associated Markov chain to abstract the state space. This not only allows for effectively handling the large (and geometrically complex) decision space but also provides more interpretable representations of the demonstrated state trajectories and also of the resulting policy of IRL. In the proposed framework, the problem is divided into the global and local IRL, where the global approximation of the optimal value functions are obtained using coarse features and the local details are quantified using fine local features. An illustrative numerical example on robot path control in a complex environment is presented to verify the proposed method.
We study the problem of troubleshooting machine learning systems that rely on analytical pipelines of distinct components. Understanding and fixing errors that arise in such integrative systems is difficult as failures can occur at multiple points in the execution workflow. Moreover, errors can propagate, become amplified or be suppressed, making blame assignment difficult. We propose a human-in-the-loop methodology which leverages human intellect for troubleshooting system failures. The approach simulates potential component fixes through human computation tasks and measures the expected improvements in the holistic behavior of the system. The method provides guidance to designers about how they can best improve the system. We demonstrate the effectiveness of the approach on an automated image captioning system that has been pressed into real-world use.
We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks.
This paper explores a novel way for analyzing the tournament structures to find a best suitable one for the tournament under consideration. It concerns about three aspects such as tournament conducting cost, competitiveness development and ranking precision. It then proposes a new method using progress tree to detect potential throwaway matches. The analysis performed using the proposed method reveals the strengths and weaknesses of tournament structures. As a conclusion, single elimination is best if we want to qualify one winner only, all matches conducted are exciting in term of competitiveness. Double elimination with proper seeding system is a better choice if we want to qualify more winners. A reasonable number of extra matches need to be conducted in exchange of being able to qualify top four winners. Round-robin gives reliable ranking precision for all participants. However, its conduction cost is very high, and it fails to maintain competitiveness development.
Neutrosophic theory and applications have been expanding in all directions at an astonishing rate especially after the introduction the journal entitled Neutrosophic Sets and Systems. New theories, techniques, algorithms have been rapidly developed. One of the most striking trends in the neutrosophic theory is the hybridization of neutrosophic set with other potential sets such as rough set, bipolar set, soft set, hesitant fuzzy set, etc. The different hybrid structure such as rough neutrosophic set, single valued neutrosophic rough set, bipolar neutrosophic set, single valued neutrosophic hesitant fuzzy set, etc. are proposed in the literature in a short period of time. Neutrosophic set has been a very important tool in all various areas of data mining, decision making, e-learning, engineering, medicine, social science, and some more. The book New Trends in Neutrosophic Theories and Applications focuses on theories, methods, algorithms for decision making and also applications involving neutrosophic information. Some topics deal with data mining, decision making, e-learning, graph theory, medical diagnosis, probability theory, topology, and some more.
Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regression-based approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories. In our work, we present a novel local detector -- Convolutional Experts Network (CEN) -- that brings together the advantages of neural architectures and mixtures of experts in an end-to-end framework. We further propose a Convolutional Experts Constrained Local Model (CE-CLM) algorithm that uses CEN as local detectors. We demonstrate that our proposed CE-CLM algorithm outperforms competitive state-of-the-art baselines for facial landmark detection by a large margin on four publicly-available datasets. Our approach is especially accurate and robust on challenging profile images.
Training robots to perceive, act and communicate using multiple modalities still represents a challenging problem, particularly if robots are expected to learn efficiently from small sets of example interactions. We describe a learning approach as a step in this direction, where we teach a humanoid robot how to play the game of noughts and crosses. Given that multiple multimodal skills can be trained to play this game, we focus our attention to training the robot to perceive the game, and to interact in this game. Our multimodal deep reinforcement learning agent perceives multimodal features and exhibits verbal and non-verbal actions while playing. Experimental results using simulations show that the robot can learn to win or draw up to 98% of the games. A pilot test of the proposed multimodal system for the targeted game---integrating speech, vision and gestures---reports that reasonable and fluent interactions can be achieved using the proposed approach.
Inventing targeted proof search strategies for specific problem sets is a difficult task. State-of-the-art automated theorem provers (ATPs) such as E allow a large number of user-specified proof search strategies described in a rich domain specific language. Several machine learning methods that invent strategies automatically for ATPs were proposed previously. One of them is the Blind Strategymaker (BliStr), a system for automated invention of ATP strategies. In this paper we introduce BliStrTune -- a hierarchical extension of BliStr. BliStrTune allows exploring much larger space of E strategies by interleaving search for high-level parameters with their fine-tuning. We use BliStrTune to invent new strategies based also on new clause weight functions targeted at problems from large ITP libraries. We show that the new strategies significantly improve E's performance in solving problems from the Mizar Mathematical Library.
Random embedding has been applied with empirical success to large-scale black-box optimization problems with low effective dimensions. This paper proposes the EmbeddedHunter algorithm, which incorporates the technique in a hierarchical stochastic bandit setting, following the optimism in the face of uncertainty principle and breaking away from the multiple-run framework in which random embedding has been conventionally applied similar to stochastic black-box optimization solvers. Our proposition is motivated by the bounded mean variation in the objective value for a low-dimensional point projected randomly into the decision space of Lipschitz-continuous problems. In essence, the EmbeddedHunter algorithm expands optimistically a partitioning tree over a low-dimensional---equal to the effective dimension of the problem---search space based on a bounded number of random embeddings of sampled points from the low-dimensional space. In contrast to the probabilistic theoretical guarantees of multiple-run random-embedding algorithms, the finite-time analysis of the proposed algorithm presents a theoretical upper bound on the regret as a function of the algorithm's number of iterations. Furthermore, numerical experiments were conducted to validate its performance. The results show a clear performance gain over recently proposed random embedding methods for large-scale problems, provided the intrinsic dimensionality is low.
Autonomous driving is one of the most recent topics of interest which is aimed at replicating human driving behavior keeping in mind the safety issues. We approach the problem of learning synthetic driving using generative neural networks. The main idea is to make a controller trainer network using images plus key press data to mimic human learning. We used the architecture of a stable GAN to make predictions between driving scenes using key presses. We train our model on one video game (Road Rash) and tested the accuracy and compared it by running the model on other maps in Road Rash to determine the extent of learning.
Boundary estimation in images and videos has been a very active topic of research, and organizing visual information into boundaries and segments is believed to be a corner stone of visual perception. While prior work has focused on estimating boundaries for observed frames, our work aims at predicting boundaries of future unobserved frames. This requires our model to learn about the fate of boundaries and corresponding motion patterns -- including a notion of "intuitive physics". We experiment on natural video sequences along with synthetic sequences with deterministic physics-based and agent-based motions. While not being our primary goal, we also show that fusion of RGB and boundary prediction leads to improved RGB predictions.
We introduce the BIN_COUNTS constraint, which deals with the problem of counting the number of decision variables in a set which are assigned values that lie in given bins. We illustrate a decomposition and a filtering algorithm that achieves generalised arc consistency. We contrast the filtering power of these two approaches and we discuss a number of applications. We show that BIN_COUNTS can be employed to develop a decomposition for the $\chi^2$ test constraint, a new statistical constraint that we introduce in this work. We also show how this new constraint can be employed in the context of the Balanced Academic Curriculum Problem and of the Balanced Nursing Workload Problem. For both these problems we carry out numerical studies involving our reformulations. Finally, we present a further application of the $\chi^2$ test constraint in the context of confidence interval analysis.
This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of entities within it. We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. Set prediction is demonstrated on the problem of multi-class image classification. Moreover, we show that the proposed cardinality loss can also trivially be applied to the tasks of object counting and pedestrian detection. Our approach outperforms existing methods in all three cases on standard datasets.
This paper presents a novel form of policy gradient for model-free reinforcement learning (RL) with improved exploration properties. Current policy-based methods use entropy regularization to encourage undirected exploration of the reward landscape, which is ineffective in high dimensional spaces with sparse rewards. We propose a more directed exploration strategy that promotes exploration of under-appreciated reward regions. An action sequence is considered under-appreciated if its log-probability under the current policy under-estimates its resulting reward. The proposed exploration strategy is easy to implement, requiring small modifications to an implementation of the REINFORCE algorithm. We evaluate the approach on a set of algorithmic tasks that have long challenged RL methods. Our approach reduces hyper-parameter sensitivity and demonstrates significant improvements over baseline methods. Our algorithm successfully solves a benchmark multi-digit addition task and generalizes to long sequences. This is, to our knowledge, the first time that a pure RL method has solved addition using only reward feedback.
We describe a neural attention model with a learnable retinal sampling lattice. The model is trained on a visual search task requiring the classification of an object embedded in a visual scene amidst background distractors using the smallest number of fixations. We explore the tiling properties that emerge in the model's retinal sampling lattice after training. Specifically, we show that this lattice resembles the eccentricity dependent sampling lattice of the primate retina, with a high resolution region in the fovea surrounded by a low resolution periphery. Furthermore, we find conditions where these emergent properties are amplified or eliminated providing clues to their function.
There exist many problem domains where the interpretability of neural network models is essential for deployment. Here we introduce a recurrent architecture composed of input-switched affine transformations - in other words an RNN without any explicit nonlinearities, but with input-dependent recurrent weights. This simple form allows the RNN to be analyzed via straightforward linear methods: we can exactly characterize the linear contribution of each input to the model predictions; we can use a change-of-basis to disentangle input, output, and computational hidden unit subspaces; we can fully reverse-engineer the architecture's solution to a simple task. Despite this ease of interpretation, the input switched affine network achieves reasonable performance on a text modeling tasks, and allows greater computational efficiency than networks with standard nonlinearities.
We consider the problem of maximizing a non-monotone DR-submodular function subject to a cardinality constraint. Diminishing returns (DR) submodularity is a generalization of the diminishing returns property for functions defined over the integer lattice. This generalization can be used to solve many machine learning or combinatorial optimization problems such as optimal budget allocation, revenue maximization, etc. In this work we propose the first polynomial-time approximation algorithms for non-monotone constrained maximization. We implement our algorithms for a revenue maximization problem with a real-world dataset to check their efficiency and performance.
Designing appropriate features for acoustic event recognition tasks is an active field of research. Expressive features should both improve the performance of the tasks and also be interpret-able. Currently, heuristically designed features based on the domain knowledge requires tremendous effort in hand-crafting, while features extracted through deep network are difficult for human to interpret. In this work, we explore the experience guided learning method for designing acoustic features. This is a novel hybrid approach combining both domain knowledge and purely data driven feature designing. Based on the procedure of log Mel-filter banks, we design a filter bank learning layer. We concatenate this layer with a convolutional neural network (CNN) model. After training the network, the weight of the filter bank learning layer is extracted to facilitate the design of acoustic features. We smooth the trained weight of the learning layer and re-initialize it in filter bank learning layer as audio feature extractor. For the environmental sound recognition task based on the Urban- sound8K dataset, the experience guided learning leads to a 2% accuracy improvement compared with the fixed feature extractors (the log Mel-filter bank). The shape of the new filter banks are visualized and explained to prove the effectiveness of the feature design process.
This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.
An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes. Most research has focused on learning from fixed training sets of labeled data rather than interacting with a dialogue partner in an online fashion. In this paper we explore this direction in a reinforcement learning setting where the bot improves its question-answering ability from feedback a teacher gives following its generated responses. We build a simulator that tests various aspects of such learning in a synthetic environment, and introduce models that work in this regime. Finally, real experiments with Mechanical Turk validate the approach.
We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning. A thorough analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (0.198 in F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available at https://datasets.maluuba.com/NewsQA.
Exploration in multi-task reinforcement learning is critical in training agents to deduce the underlying MDP. Many of the existing exploration frameworks such as $E^3$, $R_{max}$, Thompson sampling assume a single stationary MDP and are not suitable for system identification in the multi-task setting. We present a novel method to facilitate exploration in multi-task reinforcement learning using deep generative models. We supplement our method with a low dimensional energy model to learn the underlying MDP distribution and provide a resilient and adaptive exploration signal to the agent. We evaluate our method on a new set of environments and provide intuitive interpretation of our results.
This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent network that, given a set of city coordinates, predicts a distribution over different city permutations. Using negative tour length as the reward signal, we optimize the parameters of the recurrent network using a policy gradient method. We compare learning the network parameters on a set of training graphs against learning them on individual test graphs. Despite the computational expense, without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes. Applied to the KnapSack, another NP-hard problem, the same method obtains optimal solutions for instances with up to 200 items.
The relevance and importance of contextualizing data analytics is described. Qualitative characteristics might form the context of quantitative analysis. Topics that are at issue include: contrast, baselining, secondary data sources, supplementary data sources, dynamic and heterogeneous data. In geometric data analysis, especially with the Correspondence Analysis platform, various case studies are both experimented with, and are reviewed. In such aspects as paradigms followed, and technical implementation, implicitly and explicitly, an important point made is the major relevance of such work for both burgeoning analytical needs and for new analytical areas including Big Data analytics, and so on. For the general reader, it is aimed to display and describe, first of all, the analytical outcomes that are subject to analysis here, and then proceed to detail the more quantitative outcomes that fully support the analytics carried out.
We present an online deliberation system using mutual evaluation in order to collaboratively develop solutions. Participants submit their proposals and evaluate each other's proposals; some of them may then be invited by the system to rewrite 'problematic' proposals. Two cases are discussed: a proposal supported by many, but not by a given person, who is then invited to rewrite it for making yet more acceptable; and a poorly presented but presumably interesting proposal. The first of these cases has been successfully implemented. Proposals are evaluated along two axes-understandability (or clarity, or, more generally, quality), and agreement. The latter is used by the system to cluster proposals according to their ideas, while the former is used both to present the best proposals on top of their clusters, and to find poorly written proposals candidates for rewriting. These functionalities may be considered as important components of a large scale online deliberation system.
Emotion estimation in music listening is confronting challenges to capture the emotion variation of listeners. Recent years have witnessed attempts to exploit multimodality fusing information from musical contents and physiological signals captured from listeners to improve the performance of emotion recognition. In this paper, we present a study of fusion of signals of electroencephalogram (EEG), a tool to capture brainwaves at a high-temporal resolution, and musical features at decision level in recognizing the time-varying binary classes of arousal and valence. Our empirical results showed that the fusion could outperform the performance of emotion recognition using only EEG modality that was suffered from inter-subject variability, and this suggested the promise of multimodal fusion in improving the accuracy of music-emotion recognition.
Understanding how brain functions has been an intriguing topic for years. With the recent progress on collecting massive data and developing advanced technology, people have become interested in addressing the challenge of decoding brain wave data into meaningful mind states, with many machine learning models and algorithms being revisited and developed, especially the ones that handle time series data because of the nature of brain waves. However, many of these time series models, like HMM with hidden state in discrete space or State Space Model with hidden state in continuous space, only work with one source of data and cannot handle different sources of information simultaneously. In this paper, we propose an extension of State Space Model to work with different sources of information together with its learning and inference algorithms. We apply this model to decode the mind state of students during lectures based on their brain waves and reach a significant better results compared to traditional methods.
This paper presents a concept of a novel method for adjusting hyper-parameters in Deep Learning (DL) algorithms. An external agent-observer monitors a performance of a selected Deep Learning algorithm. The observer learns to model the DL algorithm using a series of random experiments. Consequently, it may be used for predicting a response of the DL algorithm in terms of a selected quality measurement to a set of hyper-parameters. This allows to construct an ensemble composed of a series of evaluators which constitute an observer-assisted architecture. The architecture may be used to gradually iterate towards to the best achievable quality score in tiny steps governed by a unit of progress. The algorithm is stopped when the maximum number of steps is reached or no further progress is made.
Sequence modeling with neural networks has lead to powerful models of symbolic music data. We address the problem of exploiting these models to reach creative musical goals, by combining with human input. To this end we generalise previous work, which sampled Markovian sequence models under the constraint that the sequence belong to the language of a given finite state machine provided by the human. We consider more expressive non-Markov models, thereby requiring approximate sampling which we provide in the form of an efficient sequential Monte Carlo method. In addition we provide and compare with a beam search strategy for conditional probability maximisation. Our algorithms are capable of convincingly re-harmonising famous musical works. To demonstrate this we provide visualisations, quantitative experiments, a human listening test and audio examples. We find both the sampling and optimisation procedures to be effective, yet complementary in character. For the case of highly permissive constraint sets, we find that sampling is to be preferred due to the overly regular nature of the optimisation based results. The generality of our algorithms permits countless other creative applications.
Stochastic network design is a general framework for optimizing network connectivity. It has several applications in computational sustainability including spatial conservation planning, pre-disaster network preparation, and river network optimization. A common assumption in previous work has been made that network parameters (e.g., probability of species colonization) are precisely known, which is unrealistic in real- world settings. We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers. We assume that fish passability probabilities are known only imprecisely, but are within some interval bounds. We then develop a planning approach that computes the policies with either high robust ratio or low regret. Empirically, our approach scales well to large river networks. We also provide insights into the solutions generated by our robust approach, which has significantly higher robust ratio than the baseline solution with mean parameter estimates.
Problems such as predicting a new shading field (Y) for an image (X) are ambiguous: many very distinct solutions are good. Representing this ambiguity requires building a conditional model P(Y|X) of the prediction, conditioned on the image. Such a model is difficult to train, because we do not usually have training data containing many different shadings for the same image. As a result, we need different training examples to share data to produce good models. This presents a danger we call "code space collapse" - the training procedure produces a model that has a very good loss score, but which represents the conditional distribution poorly. We demonstrate an improved method for building conditional models by exploiting a metric constraint on training data that prevents code space collapse. We demonstrate our model on two example tasks using real data: image saturation adjustment, image relighting. We describe quantitative metrics to evaluate ambiguous generation results. Our results quantitatively and qualitatively outperform different strong baselines.
The paper analyzes the interaction between humans and computers in terms of response time in solving the image-based CAPTCHA. In particular, the analysis focuses on the attitude of the different Internet users in easily solving four different types of image-based CAPTCHAs which include facial expressions like: animated character, old woman, surprised face, worried face. To pursue this goal, an experiment is realized involving 100 Internet users in solving the four types of CAPTCHAs, differentiated by age, Internet experience, and education level. The response times are collected for each user. Then, association rules are extracted from user data, for evaluating the dependence of the response time in solving the CAPTCHA from age, education level and experience in internet usage by statistical analysis. The results implicitly capture the users' psychological states showing in what states the users are more sensible. It reveals to be a novelty and a meaningful analysis in the state-of-the-art.
Systems for automatic extraction of semantic information about events from large textual resources are now available: these tools are capable to generate RDF datasets about text extracted events and this knowledge can be used to reason over the recognized events. On the other hand, text based tasks for event recognition, as for example event coreference (i.e. recognizing whether two textual descriptions refer to the same event), do not take into account ontological information of the extracted events in their process. In this paper, we propose a method to derive event coreference on text extracted event data using semantic based rule reasoning. We demonstrate our method considering a limited (yet representative) set of event types: we introduce a formal analysis on their ontological properties and, on the base of this, we define a set of coreference criteria. We then implement these criteria as RDF-based reasoning rules to be applied on text extracted event data. We evaluate the effectiveness of our approach over a standard coreference benchmark dataset.
Time-efficient link discovery is of central importance to implement the vision of the Semantic Web. Some of the most rapid Link Discovery approaches rely internally on planning to execute link specifications. In newer works, linear models have been used to estimate the runtime the fastest planners. However, no other category of models has been studied for this purpose so far. In this paper, we study non-linear runtime estimation functions for runtime estimation. In particular, we study exponential and mixed models for the estimation of the runtimes of planners. To this end, we evaluate three different models for runtime on six datasets using 400 link specifications. We show that exponential and mixed models achieve better fits when trained but are only to be preferred in some cases. Our evaluation also shows that the use of better runtime approximation models has a positive impact on the overall execution of link specifications.
We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations. We propose a factorization of a physical scene into composable object-based representations and a neural network architecture whose compositional structure factorizes object dynamics into pairwise interactions. Like a symbolic physics engine, the NPE is endowed with generic notions of objects and their interactions; realized as a neural network, it can be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds. We evaluate the efficacy of our approach on simple rigid body dynamics in two-dimensional worlds. By comparing to less structured architectures, we show that the NPE's compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass.
We present a method for inducing new dialogue systems from very small amounts of unannotated dialogue data, showing how word-level exploration using Reinforcement Learning (RL), combined with an incremental and semantic grammar - Dynamic Syntax (DS) - allows systems to discover, generate, and understand many new dialogue variants. The method avoids the use of expensive and time-consuming dialogue act annotations, and supports more natural (incremental) dialogues than turn-based systems. Here, language generation and dialogue management are treated as a joint decision/optimisation problem, and the MDP model for RL is constructed automatically. With an implemented system, we show that this method enables a wide range of dialogue variations to be automatically captured, even when the system is trained from only a single dialogue. The variants include question-answer pairs, over- and under-answering, self- and other-corrections, clarification interaction, split-utterances, and ellipsis. This generalisation property results from the structural knowledge and constraints present within the DS grammar, and highlights some limitations of recent systems built using machine learning techniques only.
The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news. Recent approaches for off-policy evaluation and learning in these settings appear promising. With this paper, we provide real-world data and a standardized test-bed to systematically investigate these algorithms using data from display advertising. In particular, we consider the problem of filling a banner ad with an aggregate of multiple products the user may want to purchase. This paper presents our test-bed, the sanity checks we ran to ensure its validity, and shows results comparing state-of-the-art off-policy learning methods like doubly robust optimization, POEM, and reductions to supervised learning using regression baselines. Our results show experimental evidence that recent off-policy learning methods can improve upon state-of-the-art supervised learning techniques on a large-scale real-world data set.
Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.
A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D environments, they typically suffer from challenges related to partial observability, combinatorial exploration spaces, path planning, and a scarcity of rewarding scenarios. Inspired from prior work in human cognition that indicates how humans employ a variety of semantic concepts and abstractions (object categories, localisation, etc.) to reason about the world, we build an agent-model that incorporates such abstractions into its policy-learning framework. We augment the raw image input to a Deep Q-Learning Network (DQN), by adding details of objects and structural elements encountered, along with the agent's localisation. The different components are automatically extracted and composed into a topological representation using on-the-fly object detection and 3D-scene reconstruction.We evaluate the efficacy of our approach in Doom, a 3D first-person combat game that exhibits a number of challenges discussed, and show that our augmented framework consistently learns better, more effective policies.
Due to physiological variation, patients diagnosed with the same condition may exhibit divergent, but related, responses to the same treatments. Hidden Parameter Markov Decision Processes (HiP-MDPs) tackle this transfer-learning problem by embedding these tasks into a low-dimensional space. However, the original formulation of HiP-MDP had a critical flaw: the embedding uncertainty was modeled independently of the agent's state uncertainty, requiring an unnatural training procedure in which all tasks visited every part of the state space---possible for robots that can be moved to a particular location, impossible for human patients. We update the HiP-MDP framework and extend it to more robustly develop personalized medicine strategies for HIV treatment.
An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (ABC) to condition model parameters to data and prior knowledge. As the case study we examine menu interaction, where we have click time data only to infer a cognitive model that implements a search behaviour with parameters such as fixation duration and recall probability. Our results demonstrate that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. ABC provides ample opportunities for theoretical HCI research by allowing principled inference of model parameter values and their uncertainty.
Automatic essay scoring (AES) refers to the process of scoring free text responses to given prompts, considering human grader scores as the gold standard. Writing such essays is an essential component of many language and aptitude exams. Hence, AES became an active and established area of research, and there are many proprietary systems used in real life applications today. However, not much is known about which specific linguistic features are useful for prediction and how much of this is consistent across datasets. This article addresses that by exploring the role of various linguistic features in automatic essay scoring using two publicly available datasets of non-native English essays written in test taking scenarios. The linguistic properties are modeled by encoding lexical, syntactic, discourse and error types of learner language in the feature set. Predictive models are then developed using these features on both datasets and the most predictive features are compared. While the results show that the feature set used results in good predictive models with both datasets, the question "what are the most predictive features?" has a different answer for each dataset.
A major challenge facing existing sequential Monte-Carlo methods for parameter estimation in physics stems from the inability of existing approaches to robustly deal with experiments that have different mechanisms that yield the results with equivalent probability. We address this problem here by proposing a form of particle filtering that clusters the particles that comprise the sequential Monte-Carlo approximation to the posterior before applying a resampler. Through a new graphical approach to thinking about such models, we are able to devise an artificial-intelligence based strategy that automatically learns the shape and number of the clusters in the support of the posterior. We demonstrate the power of our approach by applying it to randomized gap estimation and a form of low circuit-depth phase estimation where existing methods from the physics literature either exhibit much worse performance or even fail completely.
We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampling for problems where we can leverage (stochastic) gradients to define continuous dynamics which explore the target distribution. We outline a solution strategy for this setting based on stochastic gradient Hamiltonian Monte Carlo sampling (SGHMC) which we alter to include an elastic coupling term that ties together multiple MCMC instances. The proposed strategy turns inherently sequential HMC algorithms into asynchronous parallel versions. First experiments empirically show that the resulting parallel sampler significantly speeds up exploration of the target distribution, when compared to standard SGHMC, and is less prone to the harmful effects of stale gradients than a naive parallelization approach.
Semantic sparsity is a common challenge in structured visual classification problems; when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the training set. This paper studies semantic sparsity in situation recognition, the task of producing structured summaries of what is happening in images, including activities, objects and the roles objects play within the activity. For this problem, we find empirically that most object-role combinations are rare, and current state-of-the-art models significantly underperform in this sparse data regime. We avoid many such errors by (1) introducing a novel tensor composition function that learns to share examples across role-noun combinations and (2) semantically augmenting our training data with automatically gathered examples of rarely observed outputs using web data. When integrated within a complete CRF-based structured prediction model, the tensor-based approach outperforms existing state of the art by a relative improvement of 2.11% and 4.40% on top-5 verb and noun-role accuracy, respectively. Adding 5 million images with our semantic augmentation techniques gives further relative improvements of 6.23% and 9.57% on top-5 verb and noun-role accuracy.
We show that the Bellman operator underlying the options framework leads to a matrix splitting, an approach traditionally used to speed up convergence of iterative solvers for large linear systems of equations. Based on standard comparison theorems for matrix splittings, we then show how the asymptotic rate of convergence varies as a function of the inherent timescales of the options. This new perspective highlights a trade-off between asymptotic performance and the cost of computation associated with building a good set of options.
We present the Mim-Solution's approach to the RecSys Challenge 2016, which ranked 2nd. The goal of the competition was to prepare job recommendations for the users of the website Xing.com. Our two phase algorithm consists of candidate selection followed by the candidate ranking. We ranked the candidates by the predicted probability that the user will positively interact with the job offer. We have used Gradient Boosting Decision Trees as the regression tool.
This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces. We claim that, after being trained on the chorale harmonizations by Johann Sebastian Bach, our model is capable of generating highly convincing chorales in the style of Bach. DeepBach's strength comes from the use of pseudo-Gibbs sampling coupled with an adapted representation of musical data. This is in contrast with many automatic music composition approaches which tend to compose music sequentially. Our model is also steerable in the sense that a user can constrain the generation by imposing positional constraints such as notes, rhythms or cadences in the generated score. We also provide a plugin on top of the MuseScore music editor making the interaction with DeepBach easy to use.
Deep learning approaches have reached a celebrity status in artificial intelligence field, its success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By exploiting fundamental spatial properties of images and videos, the CNN always achieves dominant performance on visual tasks. And the Recurrent Networks (RNN) especially long short-term memory methods (LSTM) can successfully characterize the temporal correlation, thus exhibits superior capability for time series tasks. Traffic flow data have plentiful characteristics on both time and space domain. However, applications of CNN and LSTM approaches on traffic flow are limited. In this paper, we propose a novel deep architecture combined CNN and LSTM to forecast future traffic flow (CLTFP). An 1-dimension CNN is exploited to capture spatial features of traffic flow, and two LSTMs are utilized to mine the short-term variability and periodicities of traffic flow. Given those meaningful features, the feature-level fusion is performed to achieve short-term forecasting. The proposed CLTFP is compared with other popular forecasting methods on an open datasets. Experimental results indicate that the CLTFP has considerable advantages in traffic flow forecasting. in additional, the proposed CLTFP is analyzed from the view of Granger Causality, and several interesting properties of CLTFP are discovered and discussed .
Software estimation is a crucial task in software engineering. Software estimation encompasses cost, effort, schedule, and size. The importance of software estimation becomes critical in the early stages of the software life cycle when the details of software have not been revealed yet. Several commercial and non-commercial tools exist to estimate software in the early stages. Most software effort estimation methods require software size as one of the important metric inputs and consequently, software size estimation in the early stages becomes essential. One of the approaches that has been used for about two decades in the early size and effort estimation is called use case points. Use case points method relies on the use case diagram to estimate the size and effort of software projects. Although the use case points method has been widely used, it has some limitations that might adversely affect the accuracy of estimation. This paper presents some techniques using fuzzy logic and neural networks to improve the accuracy of the use case points method. Results showed that an improvement up to 22% can be obtained using the proposed approach.
We propose a scheme for training a computerized agent to perform complex human tasks such as highway steering. The scheme is designed to follow a natural learning process whereby a human instructor teaches a computerized trainee. The learning process consists of five elements: (i) unsupervised feature learning; (ii) supervised imitation learning; (iii) supervised reward induction; (iv) supervised safety module construction; and (v) reinforcement learning. We implemented the last four elements of the scheme using deep convolutional networks and applied it to successfully create a computerized agent capable of autonomous highway steering over the well-known racing game Assetto Corsa. We demonstrate that the use of the last four elements is essential to effectively carry out the steering task using vision alone, without access to a driving simulator internals, and operating in wall-clock time. This is made possible also through the introduction of a safety network, a novel way for preventing the agent from performing catastrophic mistakes during the reinforcement learning stage.
We examine the complexity of inference in Bayesian networks specified by logical languages. We consider representations that range from fragments of propositional logic to function-free first-order logic with equality; in doing so we cover a variety of plate models and of probabilistic relational models. We study the complexity of inferences when network, query and domain are the input (the inferential and the combined complexity), when the network is fixed and query and domain are the input (the query/data complexity), and when the network and query are fixed and the domain is the input (the domain complexity). We draw connections with probabilistic databases and liftability results, and obtain complexity classes that range from polynomial to exponential levels.
Extending the success of deep neural networks to natural language understanding and symbolic reasoning requires complex operations and external memory. Recent neural program induction approaches have attempted to address this problem, but are typically limited to differentiable memory, and consequently cannot scale beyond small synthetic tasks. In this work, we propose the Manager-Programmer-Computer framework, which integrates neural networks with non-differentiable memory to support abstract, scalable and precise operations through a friendly neural computer interface. Specifically, we introduce a Neural Symbolic Machine, which contains a sequence-to-sequence neural "programmer", and a non-differentiable "computer" that is a Lisp interpreter with code assist. To successfully apply REINFORCE for training, we augment it with approximate gold programs found by an iterative maximum likelihood training process. NSM is able to learn a semantic parser from weak supervision over a large knowledge base. It achieves new state-of-the-art performance on WebQuestionsSP, a challenging semantic parsing dataset, with weak supervision. Compared to previous approaches, NSM is end-to-end, therefore does not rely on feature engineering or domain specific knowledge.
In this paper we extend the principle of proportional representation to rankings. We consider the setting where alternatives need to be ranked based on approval preferences. In this setting, proportional representation requires that cohesive groups of voters are represented proportionally in each initial segment of the ranking. Proportional rankings are desirable in situations where initial segments of different lengths may be relevant, e.g., hiring decisions (if it is unclear how many positions are to be filled), the presentation of competing proposals on a liquid democracy platform (if it is unclear how many proposals participants are taking into consideration), or recommender systems (if a ranking has to accommodate different user types). We study the proportional representation provided by several ranking methods and prove theoretical guarantees. Furthermore, we experimentally evaluate these methods and present preliminary evidence as to which methods are most suitable for producing proportional rankings.
Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance techniques and this calls for more effective approaches for Android malware detection. Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. This approach enables automated feature discovery without relying on prior expert or domain knowledge for pre-determined features. Furthermore, by using a data segmentation technique for feature selection, our analysis is able to scale up to 10-gram opcodes. Our experiments on a dataset of 2520 samples showed an f-measure of 98% using the n-gram opcode based approach. We also provide empirical findings that illustrate factors that have probable impact on the overall n-gram opcodes performance trends.
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.
In the classic Vehicle Routing Problem (VRP) a fleet of of vehicles has to visit a set of customers while minimising the operations' costs. We study a rich variant of the VRP featuring split deliveries, an heterogeneous fleet, and vehicle-commodity incompatibility constraints. Our goal is twofold: define the cheapest routing and the most adequate fleet. To do so, we split the problem into two interdependent components: a fleet design component and a routing component. First, we define two Mixed Integer Programming (MIP) formulations for each component. Then we discuss several improvements in the form of valid cuts and symmetry breaking constraints. The main contribution of this paper is a comparison of the four resulting models for this Rich VRP. We highlight their strengths and weaknesses with extensive experiments. Finally, we explore a lightweight integration with Constraint Programming (CP). We use a fast CP model which gives good solutions and use the solution to warm-start our models.
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different "contexts". Bayesian optimization approaches to contextual policy search (CPS) offer data-efficient policy learning that generalize over a context space. We propose to improve data- efficiency by factoring typically considered contexts into two components: target- type contexts that correspond to a desired outcome of the learned behavior, e.g. target position for throwing a ball; and environment type contexts that correspond to some state of the environment, e.g. initial ball position or wind speed. Our key observation is that experience can be directly generalized over target-type contexts. Based on that we introduce Factored Contextual Policy Search with Bayesian Optimization for both passive and active learning settings. Preliminary results show faster policy generalization on a simulated toy problem.
Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as "the" and "of". Other words that may seem visual can often be predicted reliably just from the language model e.g., "sign" after "behind a red stop" or "phone" following "talking on a cell". In this paper, we propose a novel adaptive attention model with a visual sentinel. At each time step, our model decides whether to attend to the image (and if so, to which regions) or to the visual sentinel. The model decides whether to attend to the image and where, in order to extract meaningful information for sequential word generation. We test our method on the COCO image captioning 2015 challenge dataset and Flickr30K. Our approach sets the new state-of-the-art by a significant margin.
Ontologies in different natural languages often differ in quality in terms of richness of schema or richness of internal links. This difference is markedly visible when comparing a rich English language ontology with a non-English language counterpart. Discovering alignment between them is a useful endeavor as it serves as a starting point in bridging the disparity. In particular, our work is motivated by the absence of inter-language links for predicates in the localised versions of DBpedia. In this paper, we propose and demonstrate an ad-hoc system to find possible owl:equivalentProperty links between predicates in ontologies of different natural languages. We seek to achieve this mapping by using pre-existing inter-language links of the resources connected by the given predicate. Thus, our methodology stresses on semantic similarity rather than lexical. Moreover, through an evaluation, we show that our system is capable of outperforming a baseline system that is similar to the one used in recent OAEI campaigns.
Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced on-line iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly real-time by conducting much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.
This paper studies Value-at-Risk (VaR) problems in short- and long-horizon Markov decision processes (MDPs) with finite state space and two different reward functions. Firstly we examine the effects of two reward functions under two criteria in a short-horizon MDP. We show that under the VaR criterion, when the original reward function is on both current and next states, the reward simplification will change the VaR. Secondly, for long-horizon MDPs, we estimate the Pareto front of the total reward distribution set with the aid of spectral theory and the central limit theorem. Since the estimation is for a Markov process with the simplified reward function only, we present a transformation algorithm for the Markov process with the original reward function, in order to estimate the Pareto front with an intact total reward distribution.
Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.
A key limitation of sampling algorithms for approximate inference is that it is difficult to quantify their approximation error. Widely used sampling schemes, such as sequential importance sampling with resampling and Metropolis-Hastings, produce output samples drawn from a distribution that may be far from the target posterior distribution. This paper shows how to upper-bound the symmetric KL divergence between the output distribution of a broad class of sequential Monte Carlo (SMC) samplers and their target posterior distributions, subject to assumptions about the accuracy of a separate gold-standard sampler. The proposed method applies to samplers that combine multiple particles, multinomial resampling, and rejuvenation kernels. The experiments show the technique being used to estimate bounds on the divergence of SMC samplers for posterior inference in a Bayesian linear regression model and a Dirichlet process mixture model.
We study the online estimation of the optimal policy of a Markov decision process (MDP). We propose a class of Stochastic Primal-Dual (SPD) methods which exploit the inherent minimax duality of Bellman equations. The SPD methods update a few coordinates of the value and policy estimates as a new state transition is observed. These methods use small storage and has low computational complexity per iteration. The SPD methods find an absolute-$\epsilon$-optimal policy, with high probability, using $\mathcal{O}\left(\frac{|\mathcal{S}|^4 |\mathcal{A}|^2\sigma^2 }{(1-\gamma)^6\epsilon^2} \right)$ iterations/samples for the infinite-horizon discounted-reward MDP and $\mathcal{O}\left(\frac{|\mathcal{S}|^4 |\mathcal{A}|^2H^6\sigma^2 }{\epsilon^2} \right)$ for the finite-horizon MDP.
Compositional models were introduce by Jirousek and Shenoy in the general framework of valuation-based systems. They based their theory on an axiomatic system of valuations involving not only the operations of combination and marginalisation, but also of removal. They claimed that this systems covers besides the classical case of discrete probability distributions, also the cases of Gaussian densities and belief functions, and many other systems. Whereas their results on the compositional operator are correct, the axiomatic basis is not sufficient to cover the examples claimed above. We propose here a different axiomatic system of valuation algebras, which permits a rigorous mathematical theory of compositional operators in valuation-based systems and covers all the examples mentioned above. It extends the classical theory of inverses in semigroup theory and places thereby the present theory into its proper mathematical frame. Also this theory sheds light on the different structures of valuation-based systems, like regular algebras (represented by probability potentials), canncellative algebras (Gaussian potentials) and general separative algebras (density functions).
Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning. An executor for table querying typically requires multiple steps of execution because queries may have complicated structures. In previous studies, researchers have developed either fully distributed executors or symbolic executors for table querying. A distributed executor can be trained in an end-to-end fashion, but is weak in terms of execution efficiency and explicit interpretability. A symbolic executor is efficient in execution, but is very difficult to train especially at initial stages. In this paper, we propose to couple distributed and symbolic execution for natural language queries, where the symbolic executor is pretrained with the distributed executor's intermediate execution results in a step-by-step fashion. Experiments show that our approach significantly outperforms both distributed and symbolic executors, exhibiting high accuracy, high learning efficiency, high execution efficiency, and high interpretability.
Managing patients with multimorbidity often results in polypharmacy: the prescription of multiple drugs. However, the long-term effects of specific combinations of drugs and diseases are typically unknown. In particular, drugs prescribed for one condition may result in adverse effects for the other. To investigate which types of drugs may affect the further progression of multimorbidity, we query models of diseases and prescriptions that are learned from primary care data. State-of-the-art tractable Bayesian network representations, on which such complex queries can be computed efficiently, are employed for these large medical networks. Our results confirm that prescriptions may lead to unintended negative consequences in further development of multimorbidity in cardiovascular diseases. Moreover, a drug treatment for one disease group may affect diseases of another group.
Bayesian Optimization (BO) has become a core method for solving expensive black-box optimization problems. While much research focussed on the choice of the acquisition function, we focus on online length-scale adaption and the choice of kernel function. Instead of choosing hyperparameters in view of maximum likelihood on past data, we propose to use the acquisition function to decide on hyperparameter adaptation more robustly and in view of the future optimization progress. Further, we propose a particular kernel function that includes non-stationarity and local anisotropy and thereby implicitly integrates the efficiency of local convex optimization with global Bayesian optimization. Comparisons to state-of-the art BO methods underline the efficiency of these mechanisms on global optimization benchmarks.
Literature reviews can be time-consuming and tedious to complete. By cataloging and refactoring three state-of-the-art active learning techniques from evidence-based medicine and legal electronic discovery, this paper finds and implements FASTREAD, a faster technique for studying a large corpus of documents. This paper assesses FASTREAD using datasets generated from existing SE literature reviews (Hall, Wahono, Radjenovi\'c, Kitchenham et al.). Compared to manual methods, FASTREAD lets researchers find 95% relevant studies after reviewing an order of magnitude fewer papers. Compared to other state-of-the-art automatic methods, FASTREAD reviews 20-50% fewer studies while finding same number of relevant primary studies in a systematic literature review.
Reinforcement learning (RL) depends critically on the choice of reward functions used to capture the de- sired behavior and constraints of a robot. Usually, these are handcrafted by a expert designer and represent heuristics for relatively simple tasks. Real world applications typically involve more complex tasks with rich temporal and logical structure. In this paper we take advantage of the expressive power of temporal logic (TL) to specify complex rules the robot should follow, and incorporate domain knowledge into learning. We propose Truncated Linear Temporal Logic (TLTL) as specifications language, that is arguably well suited for the robotics applications, together with quantitative semantics, i.e., robustness degree. We propose a RL approach to learn tasks expressed as TLTL formulae that uses their associated robustness degree as reward functions, instead of the manually crafted heuristics trying to capture the same specifications. We show in simulated trials that learning is faster and policies obtained using the proposed approach outperform the ones learned using heuristic rewards in terms of the robustness degree, i.e., how well the tasks are satisfied. Furthermore, we demonstrate the proposed RL approach in a toast-placing task learned by a Baxter robot.
We provide a brief technical description of an online platform for disease monitoring, titled as the Flu Detector (fludetector.cs.ucl.ac.uk). Flu Detector, in its current version (v.0.5), uses either Twitter or Google search data in conjunction with statistical Natural Language Processing models to estimate the rate of influenza-like illness in the population of England. Its back-end is a live service that collects online data, utilises modern technologies for large-scale text processing, and finally applies statistical inference models that are trained offline. The front-end visualises the various disease rate estimates. Notably, the models based on Google data achieve a high level of accuracy with respect to the most recent four flu seasons in England (2012/13 to 2015/16). This highlighted Flu Detector as having a great potential of becoming a complementary source to the domestic traditional flu surveillance schemes.
Traditional sentiment analysis often uses sentiment dictionary to extract sentiment information in text and classify documents. However, emerging informal words and phrases in user generated content call for analysis aware to the context. Usually, they have special meanings in a particular context. Because of its great performance in representing inter-word relation, we use sentiment word vectors to identify the special words. Based on the distributed language model word2vec, in this paper we represent a novel method about sentiment representation of word under particular context, to be detailed, to identify the words with abnormal sentiment polarity in long answers. Result shows the improved model shows better performance in representing the words with special meaning, while keep doing well in representing special idiomatic pattern. Finally, we will discuss the meaning of vectors representing in the field of sentiment, which may be different from general object-based conditions.
Several methods exist for a computer to generate music based on data including Markov chains, recurrent neural networks, recombinancy, and grammars. We explore the use of unit selection and concatenation as a means of generating music using a procedure based on ranking, where, we consider a unit to be a variable length number of measures of music. We first examine whether a unit selection method, that is restricted to a finite size unit library, can be sufficient for encompassing a wide spectrum of music. We do this by developing a deep autoencoder that encodes a musical input and reconstructs the input by selecting from the library. We then describe a generative model that combines a deep structured semantic model (DSSM) with an LSTM to predict the next unit, where units consist of four, two, and one measures of music. We evaluate the generative model using objective metrics including mean rank and accuracy and with a subjective listening test in which expert musicians are asked to complete a forced-choiced ranking task. We compare our model to a note-level generative baseline that consists of a stacked LSTM trained to predict forward by one note.
Tensor decompositions have rich applications in statistics and machine learning, and developing efficient, accurate algorithms for the problem has received much attention recently. Here, we present a new method built on Kruskal's uniqueness theorem to decompose symmetric, nearly orthogonally decomposable tensors. Unlike the classical higher-order singular value decomposition which unfolds a tensor along a single mode, we consider unfoldings along two modes and use rank-1 constraints to characterize the underlying components. This tensor decomposition method provably handles a greater level of noise compared to previous methods and achieves a high estimation accuracy. Numerical results demonstrate that our algorithm is robust to various noise distributions and that it performs especially favorably as the order increases.
We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.
Record linkage is the process of identifying records that refer to the same entities from several databases. This process is challenging because commonly no unique entity identifiers are available. Linkage therefore has to rely on partially identifying attributes, such as names and addresses of people. Recent years have seen the development of novel techniques for linking data from diverse application areas, where a major focus has been on linking complex data that contain records about different types of entities. Advanced approaches that exploit both the similarities between record attributes as well as the relationships between entities to identify clusters of matching records have been developed. In this application paper we study the novel problem where rather than different types of entities we have databases where the same entity can have different roles, and where these roles change over time. We specifically develop novel techniques for linking historical birth, death, marriage and census records with the aim to reconstruct the population covered by these records over a period of several decades. Our experimental evaluation on real Scottish data shows that even with advanced linkage techniques that consider group, relationship, and temporal aspects it is challenging to achieve high quality linkage from such complex data.
Recent advances have shown the capability of Fully Convolutional Neural Networks (FCN) to model cost functions for motion planning in the context of learning driving preferences purely based on demonstration data from human drivers. While pure learning from demonstrations in the framework of Inverse Reinforcement Learning (IRL) is a promising approach, we can benefit from well informed human priors and incorporate them into the learning process. Our work achieves this by pretraining a model to regress to a manual cost function and refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When injecting prior knowledge as pretraining for the network, we achieve higher robustness, more visually distinct obstacle boundaries, and the ability to capture instances of obstacles that elude models that purely learn from demonstration data. Furthermore, by exploiting these human priors, the resulting model can more accurately handle corner cases that are scarcely seen in the demonstration data, such as stairs, slopes, and underpasses.
In this paper we present an agent-based model (ABM) of scientific inquiry aimed at investigating how different social networks impact the efficiency of scientists in acquiring knowledge. As such, the ABM is a computational tool for tackling issues in the domain of scientific methodology and science policy. In contrast to existing ABMs of science, our model aims to represent the argumentative dynamics that underlies scientific practice. To this end we employ abstract argumentation theory as the core design feature of the model. This helps to avoid a number of problematic idealizations which are present in other ABMs of science and which impede their relevance for actual scientific practice.
Whereas CNNs have demonstrated immense progress in many vision problems, they suffer from a dependence on monumental amounts of labeled training data. On the other hand, dictionary learning does not scale to the size of problems that CNNs can handle, despite being very effective at low-level vision tasks such as denoising and inpainting. Recently, interest has grown in adapting dictionary learning methods for supervised tasks such as classification and inverse problems. We propose two new network layers that are based on dictionary learning: a sparse factorization layer and a convolutional sparse factorization layer, analogous to fully-connected and convolutional layers, respectively. Using our derivations, these layers can be dropped in to existing CNNs, trained together in an end-to-end fashion with back-propagation, and leverage semisupervision in ways classical CNNs cannot. We experimentally compare networks with these two new layers against a baseline CNN. Our results demonstrate that networks with either of the sparse factorization layers are able to outperform classical CNNs when supervised data are few. They also show performance improvements in certain tasks when compared to the CNN with no sparse factorization layers with the same exact number of parameters.
Recurrent Neural Networks (RNN), particularly Long Short Term Memory (LSTM) RNNs, are a popular and very successful method for learning and generating sequences. However, current generative RNN techniques do not allow real-time interactive control of the sequence generation process, thus aren't well suited for live creative expression. We propose a method of real-time continuous control and 'steering' of sequence generation using an ensemble of RNNs and dynamically altering the mixture weights of the models. We demonstrate the method using character based LSTM networks and a gestural interface allowing users to 'conduct' the generation of text.
We introduce a method for imposing higher-level structure on generated, polyphonic music. A Convolutional Restricted Boltzmann Machine (C-RBM) as a generative model is combined with gradient descent constraint optimization to provide further control over the generation process. Among other things, this allows for the use of a "template" piece, from which some structural properties can be extracted, and transferred as constraints to newly generated material. The sampling process is guided with Simulated Annealing in order to avoid local optima, and find solutions that both satisfy the constraints, and are relatively stable with respect to the C-RBM. Results show that with this approach it is possible to control the higher level self-similarity structure, the meter, as well as tonal properties of the resulting musical piece while preserving its local musical coherence.
In many model-based diagnosis applications it is impossible to provide such a set of observations and/or measurements that allow to identify the real cause of a fault. Therefore, diagnosis systems often return many possible candidates, leaving the burden of selecting the correct diagnosis to a user. Sequential diagnosis techniques solve this problem by automatically generating a sequence of queries to some oracle. The answers to these queries provide additional information necessary to gradually restrict the search space by removing diagnosis candidates inconsistent with the answers. During query computation, existing sequential diagnosis methods often require the generation of many unnecessary query candidates and strongly rely on expensive logical reasoners. We tackle this issue by devising efficient heuristic query search methods. The proposed methods enable for the first time a completely reasoner-free query generation while at the same time guaranteeing optimality conditions, e.g. minimal cardinality or best understandability, of the returned query that existing methods cannot realize. Hence, the performance of this approach is independent of the (complexity of the) diagnosed system. Experiments conducted using real-world problems show that the new approach is highly scalable and outperforms existing methods by orders of magnitude.
We investigate a human-machine collaborative drawing environment in which an autonomous agent sketches images while optionally allowing a user to directly influence the agent's trajectory. We combine Monte Carlo Tree Search with image classifiers and test both shallow models (e.g. multinomial logistic regression) and deep Convolutional Neural Networks (e.g. LeNet, Inception v3). We found that using the shallow model, the agent produces a limited variety of images, which are noticably recogonisable by humans. However, using the deeper models, the agent produces a more diverse range of images, and while the agent remains very confident (99.99%) in having achieved its objective, to humans they mostly resemble unrecognisable 'random' noise. We relate this to recent research which also discovered that 'deep neural networks are easily fooled' \cite{Nguyen2015} and we discuss possible solutions and future directions for the research.
A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction. In this work, we explore this direction by designing a simulator and a set of synthetic tasks in the movie domain that allow such interactions between a learner and a teacher. We investigate how a learner can benefit from asking questions in both offline and online reinforcement learning settings, and demonstrate that the learner improves when asking questions. Finally, real experiments with Mechanical Turk validate the approach. Our work represents a first step in developing such end-to-end learned interactive dialogue agents.
Ontohub is a repository engine for managing distributed heterogeneous ontologies. The distributed nature enables communities to share and exchange their contributions easily. The heterogeneous nature makes it possible to integrate ontologies written in various ontology languages. Ontohub supports a wide range of formal logical and ontology languages, as well as various structuring and modularity constructs and inter-theory (concept) mappings, building on the OMG-standardized DOL language. Ontohub repositories are organised as Git repositories, thus inheriting all features of this popular version control system. Moreover, Ontohub is the first repository engine meeting a substantial amount of the requirements formulated in the context of the Open Ontology Repository (OOR) initiative, including an API for federation as well as support for logical inference and axiom selection.
Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are usually used with a constant batch size chosen by simple empirical inspection. The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance of the gradient estimates. This variance also changes over the optimization process; when using a constant batch size, stability and convergence is thus often enforced by means of a (manually tuned) decreasing learning rate schedule. We propose a practical method for dynamic batch size adaptation. It estimates the variance of the stochastic gradients and adapts the batch size to decrease the variance proportionally to the value of the objective function, removing the need for the aforementioned learning rate decrease. In contrast to recent related work, our algorithm couples the batch size to the learning rate, directly reflecting the known relationship between the two. On popular image classification benchmarks, our batch size adaptation yields faster optimization convergence, while simultaneously simplifying learning rate tuning. A TensorFlow implementation is available.
Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to large MAPF instances by searching for MAPF plans on 2 levels: The high-level search resolves collisions between agents, and the low-level search plans paths for single agents under the constraints imposed by the high-level search. We make the following contributions to solve the MAPF problem with imperfect plan execution with small average makespans: First, we formalize the MAPF Problem with Delay Probabilities (MAPF-DP), define valid MAPF-DP plans and propose the use of robust plan-execution policies for valid MAPF-DP plans to control how each agent proceeds along its path. Second, we discuss 2 classes of decentralized robust plan-execution policies (called Fully Synchronized Policies and Minimal Communication Policies) that prevent collisions during plan execution for valid MAPF-DP plans. Third, we present a 2-level MAPF-DP solver (called Approximate Minimization in Expectation) that generates valid MAPF-DP plans.
The National Basketball Association(NBA) has expanded their data gathering and have heavily invested in new technologies to gather advanced performance metrics on players. This expanded data set allows analysts to use unique performance metrics in models to estimate and classify player performance. Instead of grouping players together based on physical attributes and positions played, analysts can group together players that play similar to each other based on these tracked metrics. Existing methods for player classification have typically used offensive metrics for clustering [1]. There have been attempts to classify players using past defensive metrics, but the lack of quality metrics has not produced promising results. The classifications presented in the paper use newly introduced defensive metrics to find different defensive positions for each player. Without knowing the number of categories that players can be cast into, Gaussian Mixture Models (GMM) can be applied to find the optimal number of clusters. In the model presented, five different defensive player types can be identified.
In this paper we consider the problem of robot navigation in simple maze-like environments where the robot has to rely on its onboard sensors to perform the navigation task. In particular, we are interested in solutions to this problem that do not require localization, mapping or planning. Additionally, we require that our solution can quickly adapt to new situations (e.g., changing navigation goals and environments). To meet these criteria we frame this problem as a sequence of related reinforcement learning tasks. We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances. Our algorithm substantially decreases the required learning time after the first task instance has been solved, which makes it easily adaptable to changing environments. We validate our method in both simulated and real robot experiments with a Robotino and compare it to a set of baseline methods including classical planning-based navigation.
We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.
A softmax operator applied to a set of values acts somewhat like the maximization function and somewhat like an average. In sequential decision making, softmax is often used in settings where it is necessary to maximize utility but also to hedge against problems that arise from putting all of one's weight behind a single maximum utility decision. The Boltzmann softmax operator is the most commonly used softmax operator in this setting, but we show that this operator is prone to misbehavior. In this work, we study a differentiable softmax operator that, among other properties, is a non-expansion ensuring a convergent behavior in learning and planning. We introduce a variant of SARSA algorithm that, by utilizing the new operator, computes a Boltzmann policy with a state-dependent temperature parameter. We show that the algorithm is convergent and that it performs favorably in practice.
We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations show that DBMs train more effectively than restricted Boltzmann machines (RBM) with the same number of weights. Since sampling from Boltzmann distributions of a DBM is not classically feasible, this is evidence of advantage of a non-Turing sampling oracle. We then develop a framework for training the network as a quantum Boltzmann machine (QBM) in the presence of a significant transverse field for reinforcement learning. This further improves the reinforcement learning method using DBMs.
The increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambiguous positive feedback and ambiguous negative feedback. In this paper, we propose a principled kernel-based collaborative filtering method for top-N item recommendation with implicit feedback. We present an efficient implementation using the linear kernel, and we show how to generalize it to kernels of the dot product family preserving the efficiency. We also investigate on the elements which influence the sparsity of a standard cosine kernel. This analysis shows that the sparsity of the kernel strongly depends on the properties of the dataset, in particular on the long tail distribution. We compare our method with state-of-the-art algorithms achieving good results both in terms of efficiency and effectiveness.
Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model (HASMM): a versatile probabilistic model that is capable of capturing the modern electronic health record (EHR) data. Unlike exist- ing models, an HASMM accommodates irregularly sampled, temporally correlated, and informatively censored physiological data, and can describe non-stationary clinical state transitions. Learning an HASMM from the EHR data is achieved via a novel forward- filtering backward-sampling Monte-Carlo EM algorithm that exploits the knowledge of the end-point clinical outcomes (informative censoring) in the EHR data, and implements the E-step by sequentially sampling the patients' clinical states in the reverse-time direction while conditioning on the future states. Real-time inferences are drawn via a forward- filtering algorithm that operates on a virtually constructed discrete-time embedded Markov chain that mirrors the patient's continuous-time state trajectory. We demonstrate the di- agnostic and prognostic utility of the HASMM in a critical care prognosis setting using a real-world dataset for patients admitted to the Ronald Reagan UCLA Medical Center.
The mathematical formalism of quantum theory exhibits significant effectiveness when applied to cognitive phenomena that have resisted traditional (set theoretical) modeling. Relying on a decade of research on the operational foundations of micro-physical and conceptual entities, we present a theoretical framework for the representation of concepts and their conjunctions and disjunctions that uses the quantum formalism. This framework provides a unified solution to the 'conceptual combinations problem' of cognitive psychology, explaining the observed deviations from classical (Boolean, fuzzy set and Kolmogorovian) structures in terms of genuine quantum effects. In particular, natural concepts 'interfere' when they combine to form more complex conceptual entities, and they also exhibit a 'quantum-type context-dependence', which are responsible of the 'over- and under-extension' that are systematically observed in experiments on membership judgments.
Recently, the attention mechanism plays a key role to achieve high performance for Neural Machine Translation models. However, as it computes a score function for the encoder states in all positions at each decoding step, the attention model greatly increases the computational complexity. In this paper, we investigate the adequate vision span of attention models in the context of machine translation, by proposing a novel attention framework that is capable of reducing redundant score computation dynamically. The term "vision span" means a window of the encoder states considered by the attention model in one step. In our experiments, we found that the average window size of vision span can be reduced by over 50% with modest loss in accuracy on English-Japanese and German-English translation tasks.% This results indicate that the conventional attention mechanism performs a significant amount of redundant computation.
In this work we propose a novel representation learning model which computes semantic representations for tweets accurately. Our model systematically exploits the chronologically adjacent tweets ('context') from users' Twitter timelines for this task. Further, we make our model user-aware so that it can do well in modeling the target tweet by exploiting the rich knowledge about the user such as the way the user writes the post and also summarizing the topics on which the user writes. We empirically demonstrate that the proposed models outperform the state-of-the-art models in predicting the user profile attributes like spouse, education and job by 19.66%, 2.27% and 2.22% respectively.
The user equilibrium traffic assignment principle is very important in the traffic assignment problem. Mathematical programming models are designed to solve the user equilibrium problem in traditional algorithms. Recently, the Physarum shows the ability to address the user equilibrium and system optimization traffic assignment problems. However, the Physarum model are not efficient in real traffic networks with two-way traffic characteristics and multiple origin-destination pairs. In this article, a modified Physarum-inspired model for the user equilibrium problem is proposed. By decomposing traffic flux based on origin nodes, the traffic flux from different origin-destination pairs can be distinguished in the proposed model. The Physarum can obtain the equilibrium traffic flux when no shorter path can be discovered between each origin-destination pair. Finally, numerical examples use the Sioux Falls network to demonstrate the rationality and convergence properties of the proposed model.
Analyzing textual data is a very challenging task because of the huge volume of data generated daily. Fundamental issues in text analysis include the lack of structure in document datasets, the need for various preprocessing steps %(e.g., stem or lemma extraction, part-of-speech tagging, named entities recognition...), and performance and scaling issues. Existing text analysis architectures partly solve these issues, providing restrictive data schemas, addressing only one aspect of text preprocessing and focusing on one single task when dealing with performance optimization. %As a result, no definite solution is currently available. Thus, we propose in this paper a new generic text analysis architecture, where document structure is flexible, many preprocessing techniques are integrated and textual datasets are indexed for efficient access. We implement our conceptual architecture using both a relational and a document-oriented database. Our experiments demonstrate the feasibility of our approach and the superiority of the document-oriented logical and physical implementation.
This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as 'pseudo ground truth' to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed 'pretext' tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce.
We consider a committee voting setting in which each voter approves of a subset of candidates and based on the approvals, a target number of candidates are selected. Aziz et al. (2015) proposed two representation axioms called justified representation and extended justified representation. Whereas the former can be tested as well as achieved in polynomial time, the latter property is coNP-complete to test and no polynomial-time algorithm is known to achieve it. Interestingly, S{\'a}nchez-Fern{\'a}ndez et~al. (2016) proposed an intermediate property called proportional justified representation that admits a polynomial-time algorithm to achieve. The complexity of testing proportional justified representation has remained an open problem. In this paper, we settle the complexity by proving that testing proportional justified representation is coNP-complete. We complement the complexity result by showing that the problem admits efficient algorithms if any of the following parameters are bounded: (1) number of voters (2) number of candidates (3) maximum number of candidates approved by a voter (4) maximum number of voters approving a given candidate.
In pattern classification, polynomial classifiers are well-studied methods as they are capable of generating complex decision surfaces. Unfortunately, the use of multivariate polynomials is limited to kernels as in support vector machines, because polynomials quickly become impractical for high-dimensional problems. In this paper, we effectively overcome the curse of dimensionality by employing the tensor train format to represent a polynomial classifier. Based on the structure of tensor trains, two learning algorithms are proposed which involve solving different optimization problems of low computational complexity. Furthermore, we show how both regularization to prevent overfitting and parallelization, which enables the use of large training sets, are incorporated into these methods. Both the efficiency and efficacy of our tensor-based polynomial classifier are then demonstrated on the two popular datasets USPS and MNIST.
This paper analyzes customer product-choice behavior based on the recency and frequency of each customer's page views on e-commerce sites. Recently, we devised an optimization model for estimating product-choice probabilities that satisfy monotonicity, convexity, and concavity constraints with respect to recency and frequency. This shape-restricted model delivered high predictive performance even when there were few training samples. However, typical e-commerce sites deal in many different varieties of products, so the predictive performance of the model can be further improved by integration of such product heterogeneity. For this purpose, we develop a novel latent-class shape-restricted model for estimating product-choice probabilities for each latent class of products. We also give a tailored expectation-maximization algorithm for parameter estimation. Computational results demonstrate that higher predictive performance is achieved with our latent-class model than with the previous shape-restricted model and common latent-class logistic regression.
When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings. Existing benchmarks for visual question answering can help, but have strong biases that models can exploit to correctly answer questions without reasoning. They also conflate multiple sources of error, making it hard to pinpoint model weaknesses. We present a diagnostic dataset that tests a range of visual reasoning abilities. It contains minimal biases and has detailed annotations describing the kind of reasoning each question requires. We use this dataset to analyze a variety of modern visual reasoning systems, providing novel insights into their abilities and limitations.
Evaluating agent performance when outcomes are stochastic and agents use randomized strategies can be challenging when there is limited data available. The variance of sampled outcomes may make the simple approach of Monte Carlo sampling inadequate. This is the case for agents playing heads-up no-limit Texas hold'em poker, where man-machine competitions have involved multiple days of consistent play and still not resulted in statistically significant conclusions even when the winner's margin is substantial. In this paper, we introduce AIVAT, a low variance, provably unbiased value assessment tool that uses an arbitrary heuristic estimate of state value, as well as the explicit strategy of a subset of the agents. Unlike existing techniques which reduce the variance from chance events, or only consider game ending actions, AIVAT reduces the variance both from choices by nature and by players with a known strategy. The resulting estimator in no-limit poker can reduce the number of hands needed to draw statistical conclusions by more than a factor of 10.
In many personalized recommendation problems available data consists only of positive interactions (implicit feedback) between users and items. This problem is also known as One-Class Collaborative Filtering (OC-CF). Linear models usually achieve state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to improve the recommendations. Recent analysis show that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings. In this paper we propose a boolean kernel, called Disjunctive kernel, which is less expressive than the linear one but it is able to alleviate the sparsity issue in CF contexts. The embedding of this kernel is composed by all the combinations of a certain arity d of the input variables, and these combined features are semantically interpreted as disjunctions of the input variables. Experiments on several CF datasets show the effectiveness and the efficiency of the proposed kernel.
The raise of complexity of technical systems also raises knowledge required to set them up and to maintain them. The cost to evolve such systems can be prohibitive. In the field of Autonomic Computing, technical systems should therefore have various self-healing capabilities allowing system owners to provide only partial, potentially inconsistent updates of the system. The self-healing or self-integrating system shall find out the remaining changes to communications and functionalities in order to accommodate change and yet still restore function. This issue becomes even more interesting in context of Internet of Things and Industrial Internet where previously unexpected device combinations can be assembled in order to provide a surprising new function. In order to pursue higher levels of self-integration capabilities I propose to think of self-integration as sophisticated error correcting communications. Therefore, this paper discusses an extended scope of error correction with the purpose to emphasize error correction's role as an integrated element of bi-directional communication channels in self-integrating, autonomic communication scenarios.
We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles. We show that these models are suitable for representing causal models with additive error terms. We provide a set of sufficient graphical criteria for the identification of arbitrary causal effects when the new models contain directed and undirected edges but no bidirected edge. We also provide a necessary and sufficient graphical criterion for the identification of the causal effect of a single variable on the rest of the variables. Moreover, we develop an exact algorithm for learning the new models from observational and interventional data via answer set programming. Finally, we introduce gated models for causal effect identification, a new family of graphical models that exploits context specific independences to identify additional causal effects.
While the solution counting problem for propositional satisfiability (#SAT) has received renewed attention in recent years, this research trend has not affected other AI solving paradigms like answer set programming (ASP). Although ASP solvers are designed to enumerate all solutions, and counting can therefore be easily done, the involved materialization of all solutions is a clear bottleneck for the counting problem of ASP (#ASP). In this paper we propose dynamic programming-based #ASP algorithms that exploit the structure of the underlying (ground) ASP program. Experimental results for a prototype implementation show promise when compared to existing solvers.
Connections between relations in relation extraction, which we call class ties, are common. In distantly supervised scenario, one entity tuple may have multiple relation facts. Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction. However, previous models are not effective or ignore to model this property. In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network (CNN) with a general pairwise ranking framework, in which three novel ranking loss functions are introduced. Additionally, an effective method is presented to relieve the severe class imbalance problem from NR (not relation) for model training. Experiments on a widely used dataset show that leveraging class ties will enhance extraction and demonstrate the effectiveness of our model to learn class ties. Our model outperforms the baselines significantly, achieving state-of-the-art performance.
The past year saw the introduction of new architectures such as Highway networks and Residual networks which, for the first time, enabled the training of feedforward networks with dozens to hundreds of layers using simple gradient descent. While depth of representation has been posited as a primary reason for their success, there are indications that these architectures defy a popular view of deep learning as a hierarchical computation of increasingly abstract features at each layer. In this report, we argue that this view is incomplete and does not adequately explain several recent findings. We propose an alternative viewpoint based on unrolled iterative estimation -- a group of successive layers iteratively refine their estimates of the same features instead of computing an entirely new representation. We demonstrate that this viewpoint directly leads to the construction of Highway and Residual networks. Finally we provide preliminary experiments to discuss the similarities and differences between the two architectures.
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.
The Kaczmarz method is an iterative algorithm for solving systems of linear equalities and inequalities, that iteratively projects onto these constraints. Recently, Strohmer and Vershynin [J. Fourier Anal. Appl., 15(2):262-278, 2009] gave a non-asymptotic convergence rate analysis for this algorithm, spurring numerous extensions and generalizations of the Kaczmarz method. Rather than the randomized selection rule analyzed in that work, in this paper we instead discuss greedy and approximate greedy selection rules. We show that in some applications the computational costs of greedy and random selection are comparable, and that in many cases greedy selection rules give faster convergence rates than random selection rules. Further, we give the first multi-step analysis of Kaczmarz methods for a particular greedy rule, and propose a provably-faster randomized selection rule for matrices with many pairwise-orthogonal rows.
Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input. Traditionally, the performance of algorithms for this task is measured using pixel-wise reconstruction measures such as peak signal-to-noise ratio (PSNR) which have been shown to correlate poorly with the human perception of image quality. As a result, algorithms minimizing these metrics tend to produce over-smoothed images that lack high-frequency textures and do not look natural despite yielding high PSNR values. We propose a novel application of automated texture synthesis in combination with a perceptual loss focusing on creating realistic textures rather than optimizing for a pixel-accurate reproduction of ground truth images during training. By using feed-forward fully convolutional neural networks in an adversarial training setting, we achieve a significant boost in image quality at high magnification ratios. Extensive experiments on a number of datasets show the effectiveness of our approach, yielding state-of-the-art results in both quantitative and qualitative benchmarks.
Quantum inspired Evolutionary Algorithms were proposed more than a decade ago and have been employed for solving a wide range of difficult search and optimization problems. A number of changes have been proposed to improve performance of canonical QEA. However, canonical QEA is one of the few evolutionary algorithms, which uses a search operator with relatively large number of parameters. It is well known that performance of evolutionary algorithms is dependent on specific value of parameters for a given problem. The advantage of having large number of parameters in an operator is that the search process can be made more powerful even with a single operator without requiring a combination of other operators for exploration and exploitation. However, the tuning of operators with large number of parameters is complex and computationally expensive. This paper proposes a novel heuristic method for tuning parameters of canonical QEA. The tuned QEA outperforms canonical QEA on a class of discrete combinatorial optimization problems which, validates the design of the proposed parameter tuning framework. The proposed framework can be used for tuning other algorithms with both large and small number of tunable parameters.
Algorithms which sort lists of real numbers into ascending order have been studied for decades. They are typically based on a series of pairwise comparisons and run entirely on chip. However people routinely sort lists which depend on subjective or complex judgements that cannot be automated. Examples include marketing research; where surveys are used to learn about customer preferences for products, the recruiting process; where interviewers attempt to rank potential employees, and sporting tournaments; where we infer team rankings from a series of one on one matches. We develop a novel sorting algorithm, where each pairwise comparison reflects a subjective human judgement about which element is bigger or better. We introduce a finite and large error rate to each judgement, and we take the cost of each comparison to significantly exceed the cost of other computational steps. The algorithm must request the most informative sequence of comparisons from the user; in order to identify the correct sorted list with minimum human input. Our Discrete Adiabatic Monte Carlo approach exploits the gradual acquisition of information by tracking a set of plausible hypotheses which are updated after each additional comparison.
AUC (Area under the ROC curve) is an important performance measure for applications where the data is highly imbalanced. Learning to maximize AUC performance is thus an important research problem. Using a max-margin based surrogate loss function, AUC optimization problem can be approximated as a pairwise rankSVM learning problem. Batch learning methods for solving the kernelized version of this problem suffer from scalability and may not result in sparse classifiers. Recent years have witnessed an increased interest in the development of online or single-pass online learning algorithms that design a classifier by maximizing the AUC performance. The AUC performance of nonlinear classifiers, designed using online methods, is not comparable with that of nonlinear classifiers designed using batch learning algorithms on many real-world datasets. Motivated by these observations, we design a scalable algorithm for maximizing AUC performance by greedily adding the required number of basis functions into the classifier model. The resulting sparse classifiers perform faster inference. Our experimental results show that the level of sparsity achievable can be order of magnitude smaller than the Kernel RankSVM model without affecting the AUC performance much.
We propose to apply Simplicity Theory (ST) to model interest in creative situations. ST has been designed to describe and predict interest in communication. Here we use ST to derive a decision rule that we apply to a simplified version of a creative game, the Poietic Generator. The decision rule produces what can be regarded as an elementary form of creativity. This study is meant as a proof of principle. It suggests that some creative actions may be motivated by the search for unexpected simplicity.
Objects appear to scale differently in natural images. This fact requires methods dealing with object-centric tasks (e.g. object proposal) to have robust performance over variances in object scales. In the paper, we present a novel segment proposal framework, namely FastMask, which takes advantage of hierarchical features in deep convolutional neural networks to segment multi-scale objects in one shot. Innovatively, we adapt segment proposal network into three different functional components (body, neck and head). We further propose a weight-shared residual neck module as well as a scale-tolerant attentional head module for efficient one-shot inference. On MS COCO benchmark, the proposed FastMask outperforms all state-of-the-art segment proposal methods in average recall being 2~5 times faster. Moreover, with a slight trade-off in accuracy, FastMask can segment objects in near real time (~13 fps) with 800*600 resolution images, demonstrating its potential in practical applications. Our implementation is available on https://github.com/voidrank/FastMask.
Despite of the pain and limited accuracy of blood tests for early recognition of cardiovascular disease, they dominate risk screening and triage. On the other hand, heart rate variability is non-invasive and cheap, but not considered accurate enough for clinical practice. Here, we tackle heart beat interval based classification with deep learning. We introduce an end to end differentiable hybrid architecture, consisting of a layer of biological neuron models of cardiac dynamics (modified FitzHugh Nagumo neurons) and several layers of a standard feed-forward neural network. The proposed model is evaluated on ECGs from 474 stable at-risk (coronary artery disease) patients, and 1172 chest pain patients of an emergency department. We show that it can significantly outperform models based on traditional heart rate variability predictors, as well as approaching or in some cases outperforming clinical blood tests, based only on 60 seconds of inter-beat intervals.
We propose a new formalism for specifying and reasoning about problems that involve heterogeneous "pieces of information" -- large collections of data, decision procedures of any kind and complexity and connections between them. The essence of our proposal is to lift Codd's relational algebra from operations on relational tables to operations on classes of structures (with recursion), and to add a direction of information propagation. We observe the presence of information propagation in several formalisms for efficient reasoning and use it to express unary negation and operations used in graph databases. We carefully analyze several reasoning tasks and establish a precise connection between a generalized query evaluation and temporal logic model checking. Our development allows us to reveal a general correspondence between classical and modal logics and may shed a new light on the good computational properties of modal logics and related formalisms.
Temporal Difference learning or TD($\lambda$) is a fundamental algorithm in the field of reinforcement learning. However, setting TD's $\lambda$ parameter, which controls the timescale of TD updates, is generally left up to the practitioner. We formalize the $\lambda$ selection problem as a bias-variance trade-off where the solution is the value of $\lambda$ that leads to the smallest Mean Squared Value Error (MSVE). To solve this trade-off we suggest applying Leave-One-Trajectory-Out Cross-Validation (LOTO-CV) to search the space of $\lambda$ values. Unfortunately, this approach is too computationally expensive for most practical applications. For Least Squares TD (LSTD) we show that LOTO-CV can be implemented efficiently to automatically tune $\lambda$ and apply function optimization methods to efficiently search the space of $\lambda$ values. The resulting algorithm, ALLSTD, is parameter free and our experiments demonstrate that ALLSTD is significantly computationally faster than the na\"{i}ve LOTO-CV implementation while achieving similar performance.
Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a unified framework for the tasks of referring expression comprehension and generation. Our model is composed of three modules: speaker, listener, and reinforcer. The speaker generates referring expressions, the listener comprehends referring expressions, and the reinforcer introduces a reward function to guide sampling of more discriminative expressions. The listener-speaker modules are trained jointly in an end-to-end learning framework, allowing the modules to be aware of one another during learning while also benefiting from the discriminative reinforcer's feedback. We demonstrate that this unified framework and training achieves state-of-the-art results for both comprehension and generation on three referring expression datasets. Project and demo page: https://vision.cs.unc.edu/refer
In a Web Advertising Traffic Operation it's necessary to manage the day-to-day trafficking, pacing and optimization of digital and paid social campaigns. The data analyst on Traffic Operation can not only quickly provide answers but also speaks the language of the Process Manager and visually displays the discovered process problems. In order to solve a growing number of complaints in the customer service process, the weaknesses in the process itself must be identified and communicated to the department. With the help of Process Mining for the CRM data it is possible to identify unwanted loops and delays in the process. With this paper we propose a process discovery based on Machine Learning technique to automatically discover variations and detect at first glance what the problem is, and undertake corrective measures.
Non-negative matrix factorization (NMF) is a prob- lem with many applications, ranging from facial recognition to document clustering. However, due to the variety of algorithms that solve NMF, the randomness involved in these algorithms, and the somewhat subjective nature of the problem, there is no clear "correct answer" to any particular NMF problem, and as a result, it can be hard to test new algorithms. This paper suggests some test cases for NMF algorithms derived from matrices with enumerable exact non-negative factorizations and perturbations of these matrices. Three algorithms using widely divergent approaches to NMF all give similar solutions over these test cases, suggesting that these test cases could be used as test cases for implementations of these existing NMF algorithms as well as potentially new NMF algorithms. This paper also describes how the proposed test cases could be used in practice.
The Human Phenotype Ontology (HPO) is a structured repository of concepts (HPO Terms) that are associated to one or more diseases. The process of association is referred to as annotation. The relevance and the specificity of both HPO terms and annotations are evaluated by a measure defined as Information Content (IC). The analysis of annotated data is thus an important challenge for bioinformatics. There exist different approaches of analysis. From those, the use of Association Rules (AR) may provide useful knowledge, and it has been used in some applications, e.g. improving the quality of annotations. Nevertheless classical association rules algorithms do not take into account the source of annotation nor the importance yielding to the generation of candidate rules with low IC. This paper presents HPO-Miner (Human Phenotype Ontology-based Weighted Association Rules) a methodology for extracting Weighted Association Rules. HPO-Miner can extract relevant rules from a biological point of view. A case study on using of HPO-Miner on publicly available HPO annotation datasets is used to demonstrate the effectiveness of our methodology.
The Workshops on (Constraint) Logic Programming (WLP) are the annual meeting of the German Society of Logic Programming (Gesellschaft f\"ur Logische Programmierung e.V., GLP) and bring together researchers interested in logic programming, constraint programming, answer set programming, and related areas like databases and artificial intelligence (not only from Germany). The International Workshops on Functional and (Constraint) Logic Programming (WFLP) aim at bringing together researchers, students, and practitioners interested in functional programming, logic programming, and their integration. The workshops have a tradition of co-location to promote the cross-fertilizing exchange of ideas and experiences among and between the communities interested in the foundations, applications, and combinations of high-level, declarative programming languages and related areas.
Many robotic planning applications involve continuous actions with highly non-linear constraints, which cannot be modeled using modern planners that construct a propositional representation. We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints. The outputs of these generators interact with actions through possibly infinite streams of objects and static predicates. We provide two algorithms which both reduce STRIPStream problems to a sequence of finite-domain planning problems. The representation and algorithms are entirely domain independent. We demonstrate our framework on simple illustrative domains, and then on a high-dimensional, continuous robotic task and motion planning domain.
In the past, several models of consciousness have become popular and have led to the development of models for machine consciousness with varying degrees of success and challenges for simulation and implementations. Moreover, affective computing attributes that involve emotions, behavior and personality have not been the focus of models of consciousness as they lacked motivation for deployment in software applications and robots. The affective attributes are important factors for the future of machine consciousness with the rise of technologies that can assist humans. Personality and affection hence can give an additional flavor for the computational model of consciousness in humanoid robotics. Recent advances in areas of machine learning with a focus on deep learning can further help in developing aspects of machine consciousness in areas that can better replicate human sensory perceptions such as speech recognition and vision. With such advancements, one encounters further challenges in developing models that can synchronize different aspects of affective computing. In this paper, we review some existing models of consciousnesses and present an affective computational model that would enable the human touch and feel for robotic systems.
We address the problem of locating facilities on the $[0,1]$ interval based on reports from strategic agents. The cost of each agent is her distance to the closest facility, and the global objective is to minimize either the maximum cost of an agent or the social cost. As opposed to the extensive literature on facility location which considers the multiplicative error, we focus on minimizing the worst-case additive error. Minimizing the additive error incentivizes mechanisms to adapt to the size of the instance. I.e., mechanisms can sacrifice little efficiency in small instances (location profiles in which all agents are relatively close to one another), in order to gain more [absolute] efficiency in large instances. We argue that this measure is better suited for many manifestations of the facility location problem in various domains. We present tight bounds for mechanisms locating a single facility in both deterministic and randomized cases. We further provide several extensions for locating multiple facilities.
In this paper, a non-probabilistic method based on fuzzy logic is used to update finite element models (FEMs). Model updating techniques use the measured data to improve the accuracy of numerical models of structures. However, the measured data are contaminated with experimental noise and the models are inaccurate due to randomness in the parameters. This kind of aleatory uncertainty is irreducible, and may decrease the accuracy of the finite element model updating process. However, uncertainty quantification methods can be used to identify the uncertainty in the updating parameters. In this paper, the uncertainties associated with the modal parameters are defined as fuzzy membership functions, while the model updating procedure is defined as an optimization problem at each {\alpha}-cut level. To determine the membership functions of the updated parameters, an objective function is defined and minimized using two metaheuristic optimization algorithms: ant colony optimization (ACO) and particle swarm optimization (PSO). A structural example is used to investigate the accuracy of the fuzzy model updating strategy using the PSO and ACO algorithms. Furthermore, the results obtained by the fuzzy finite element model updating are compared with the Bayesian model updating results.
Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming for lifted stochastic planning (Boutilier et al, 2001) were introduced around the same time as algorithmic efforts to use abstraction in stochastic systems. Over the years, these ideas evolved into two distinct lines of research, each supported by a rich literature. Lifted probabilistic inference focused on efficient arithmetic operations on template-based graphical models under a finite domain assumption while symbolic dynamic programming focused on supporting sequential decision-making in rich quantified logical action models and on open domain reasoning. Given their common motivation but different focal points, both lines of research have yielded highly complementary innovations. In this chapter, we aim to help close the gap between these two research areas by providing an overview of lifted stochastic planning from the perspective of probabilistic inference, showing strong connections to other chapters in this book. This also allows us to define Generalized Lifted Inference as a paradigm that unifies these areas and elucidates open problems for future research that can benefit both lifted inference and stochastic planning.
This paper reviews the current status and challenges of Neural Networks (NNs) based machine learning approaches for modern power grid stability control including their design and implementation methodologies. NNs are widely accepted as Artificial Intelligence (AI) approaches offering an alternative way to control complex and ill-defined problems. In this paper various application of NNs for power system rotor angle stabilization and control problem is discussed. The main focus of this paper is on the use of Reinforcement Learning (RL) and Supervised Learning (SL) algorithms in power system wide-area control (WAC). Generally, these algorithms due to their capability in modeling nonlinearities and uncertainties are used for transient classification, neuro-control, wide-area monitoring and control, renewable energy management and control, and so on. The works of researchers in the field of conventional and renewable energy systems are reported and categorized. Paper concludes by presenting, comparing and evaluating various learning techniques and infrastructure configurations based on efficiency.
In this paper, we study learning generalized driving style representations from automobile GPS trip data. We propose a novel Autoencoder Regularized deep neural Network (ARNet) and a trip encoding framework trip2vec to learn drivers' driving styles directly from GPS records, by combining supervised and unsupervised feature learning in a unified architecture. Experiments on a challenging driver number estimation problem and the driver identification problem show that ARNet can learn a good generalized driving style representation: It significantly outperforms existing methods and alternative architectures by reaching the least estimation error on average (0.68, less than one driver) and the highest identification accuracy (by at least 3% improvement) compared with traditional supervised learning methods.
Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine's policy will prioritize each player's interests over time. Assuming the players have reached common knowledge of their situation, this paper derives a recursion that any Pareto optimal policy must satisfy. Two qualitative observations can be made from the recursion: the machine must (1) use each player's own beliefs in evaluating how well an action will serve that player's utility function, and (2) shift the relative priority it assigns to each player's expected utilities over time, by a factor proportional to how well that player's beliefs predict the machine's inputs. Observation (2) represents a substantial divergence from na\"{i}ve linear utility aggregation (as in Harsanyi's utilitarian theorem, and existing MORL algorithms), which is shown here to be inadequate for Pareto optimal sequential decision-making on behalf of players with different beliefs.
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active towards a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.
Two main techniques have been used so far to solve the #P-hard problem #SAT. The first one, used in practice, is based on an extension of DPLL for model counting called exhaustive DPLL. The second approach, more theoretical, exploits the structure of the input to compute the number of satisfying assignments by usually using a dynamic programming scheme on a decomposition of the formula. In this paper, we make a first step toward the separation of these two techniques by exhibiting a family of formulas that can be solved in polynomial time with the first technique but needs an exponential time with the second one. We show this by observing that both techniques implicitely construct a very specific boolean circuit equivalent to the input formula. We then show that every beta-acyclic formula can be represented by a polynomial size circuit corresponding to the first method and exhibit a family of beta-acyclic formulas which cannot be represented by polynomial size circuits corresponding to the second method. This result shed a new light on the complexity of #SAT and related problems on beta-acyclic formulas. As a byproduct, we give new handy tools to design algorithms on beta-acyclic hypergraphs.
Entities are essential elements of natural language. In this paper, we present methods for learning multi-level representations of entities on three complementary levels: character (character patterns in entity names extracted, e.g., by neural networks), word (embeddings of words in entity names) and entity (entity embeddings). We investigate state-of-the-art learning methods on each level and find large differences, e.g., for deep learning models, traditional ngram features and the subword model of fasttext (Bojanowski et al., 2016) on the character level; for word2vec (Mikolov et al., 2013) on the word level; and for the order-aware model wang2vec (Ling et al., 2015a) on the entity level. We confirm experimentally that each level of representation contributes complementary information and a joint representation of all three levels improves the existing embedding based baseline for fine-grained entity typing by a large margin. Additionally, we show that adding information from entity descriptions further improves multi-level representations of entities.
We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model. We derive a stochastic variational inference algorithm for the resulting model and, as examples of our framework, implement three different data-generating models---a mixture model, linear regression, and factor analysis---to robustly model non-random missing data in the context of clustering, prediction, and matrix factorization. In all three cases, we test our framework against models that ignore the missing-data mechanism on large scale studies with non-random missing data, and we show that explicitly modeling the missing-data mechanism substantially improves the quality of the results, as measured using data log likelihood on a held-out test set.
Despite enormous progress in object detection and classification, the problem of incorporating expected contextual relationships among object instances into modern recognition systems remains a key challenge. In this work we propose Information Pursuit, a Bayesian framework for scene parsing that combines prior models for the geometry of the scene and the spatial arrangement of objects instances with a data model for the output of high-level image classifiers trained to answer specific questions about the scene. In the proposed framework, the scene interpretation is progressively refined as evidence accumulates from the answers to a sequence of questions. At each step, we choose the question to maximize the mutual information between the new answer and the full interpretation given the current evidence obtained from previous inquiries. We also propose a method for learning the parameters of the model from synthesized, annotated scenes obtained by top-down sampling from an easy-to-learn generative scene model. Finally, we introduce a database of annotated indoor scenes of dining room tables, which we use to evaluate the proposed approach.
Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various duration variables quantifying product use: total playtime or session playtime are popular research targets, and active playtime is well-suited for subscription games. Such research often has the goal of increasing player retention or conversely decreasing player churn. Survival analysis is a framework of powerful tools well suited for retention type data. This paper contributes new methods to game analytics on how playtime can be analyzed using survival analysis without covariates. Survival and hazard estimates provide both a visual and an analytic interpretation of the playtime phenomena as a funnel type nonparametric estimate. Metrics based on the survival curve can be used to aggregate this playtime information into a single statistic. Comparison of survival curves between cohorts provides a scientific AB-test. All these methods work on censored data and enable computation of confidence intervals. This is especially important in time and sample limited data which occurs during game development. Throughout this paper, we illustrate the application of these methods to real world game development problems on the Hipster Sheep mobile game.
Deep Reinforcement Learning has enabled the learning of policies for complex tasks in partially observable environments, without explicitly learning the underlying model of the tasks. While such model-free methods achieve considerable performance, they often ignore the structure of task. We present a natural representation of to Reinforcement Learning (RL) problems using Recurrent Convolutional Neural Networks (RCNNs), to better exploit this inherent structure. We define 3 such RCNNs, whose forward passes execute an efficient Value Iteration, propagate beliefs of state in partially observable environments, and choose optimal actions respectively. Backpropagating gradients through these RCNNs allows the system to explicitly learn the Transition Model and Reward Function associated with the underlying MDP, serving as an elegant alternative to classical model-based RL. We evaluate the proposed algorithms in simulation, considering a robot planning problem. We demonstrate the capability of our framework to reduce the cost of replanning, learn accurate MDP models, and finally re-plan with learnt models to achieve near-optimal policies.
Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over shared representations. In this work, we apply MTL to audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn a mapping between audio-visual fused features and frame labels obtained from acoustic GMM/HMM model. This is combined with an auxiliary task which maps visual features to frame labels obtained from a separate visual GMM/HMM model. The MTL model is tested at various levels of babble noise and the results are compared with a base-line hybrid DNN-HMM AV-ASR model. Our results indicate that MTL is especially useful at higher level of noise. Compared to base-line, upto 7\% relative improvement in WER is reported at -3 SNR dB
Higher-order probabilistic programming languages allow programmers to write sophisticated models in machine learning and statistics in a succinct and structured way, but step outside the standard measure-theoretic formalization of probability theory. Programs may use both higher-order functions and continuous distributions, or even define a probability distribution on functions. But standard probability theory does not handle higher-order functions well: the category of measurable spaces is not cartesian closed. Here we introduce quasi-Borel spaces. We show that these spaces: form a new formalization of probability theory replacing measurable spaces; form a cartesian closed category and so support higher-order functions; form a well-pointed category and so support good proof principles for equational reasoning; and support continuous probability distributions. We demonstrate the use of quasi-Borel spaces for higher-order functions and probability by: showing that a well-known construction of probability theory involving random functions gains a cleaner expression; and generalizing de Finetti's theorem, that is a crucial theorem in probability theory, to quasi-Borel spaces.
We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and only using local inference. However, when automatically predicted part-of-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL-2009 dataset. We also consider Chinese, Czech and Spanish where our approach also achieves competitive results. Syntactic parsers are unreliable on out-of-domain data, so standard (i.e., syntactically-informed) SRL models are hindered when tested in this setting. Our syntax-agnostic model appears more robust, resulting in the best reported results on standard out-of-domain test sets.
We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We convert decoding - basically a discrete optimization problem - into a continuous optimization problem. The resulting constrained continuous optimisation problem is then tackled using gradient-based methods. Our powerful decoding framework enables decoding intractable models such as the intersection of left-to-right and right-to-left (bidirectional) as well as source-to-target and target-to-source (bilingual) NMT models. Our empirical results show that our decoding framework is effective, and leads to substantial improvements in translations generated from the intersected models where the typical greedy or beam search is not feasible. We also compare our framework against reranking, and analyse its advantages and disadvantages.
We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation). For example, given images and captions of "siamese cat" and "tiger cat", we generate language that describes the "siamese cat" in a way that distinguishes it from "tiger cat". Our key novelty is that we show how to do joint inference over a language model that is context-agnostic and a listener which distinguishes closely-related concepts. We first apply our technique to a justification task, namely to describe why an image contains a particular fine-grained category as opposed to another closely-related category of the CUB-200-2011 dataset. We then study discriminative image captioning to generate language that uniquely refers to one of two semantically-similar images in the COCO dataset. Evaluations with discriminative ground truth for justification and human studies for discriminative image captioning reveal that our approach outperforms baseline generative and speaker-listener approaches for discrimination.
In this paper, we try to predict the winning team of a match in the multiplayer eSports game Dota 2. To address the weaknesses of previous work, we consider more aspects of prior (pre-match) features from individual players' match history, as well as real-time (during-match) features at each minute as the match progresses. We use logistic regression, the proposed Attribute Sequence Model, and their combinations as the prediction models. In a dataset of 78362 matches where 20631 matches contain replay data, our experiments show that adding more aspects of prior features improves accuracy from 58.69% to 71.49%, and introducing real-time features achieves up to 93.73% accuracy when predicting at the 40th minute.
First-Order Logic (FOL) is widely regarded as one of the most important foundations for knowledge representation. Nevertheless, in this paper, we argue that FOL has several critical issues for this purpose. Instead, we propose an alternative called assertional logic, in which all syntactic objects are categorized as set theoretic constructs including individuals, concepts and operators, and all kinds of knowledge are formalized by equality assertions. We first present a primitive form of assertional logic that uses minimal assumed knowledge and constructs. Then, we show how to extend it by definitions, which are special kinds of knowledge, i.e., assertions. We argue that assertional logic, although simpler, is more expressive and extensible than FOL. As a case study, we show how assertional logic can be used to unify logic and probability, and more building blocks in AI.
Since Leonard Savage's epoch-making "Foundations of Statistics", Subjective Expected Utility Theory has been the presumptive model for decision-making. Savage provided an act-based axiomatization of standard expected utility theory. In this article, we provide a Savage-like axiomatization of nonstandard expected utility theory. It corresponds to a weakening of Savage's 6th axiom.
In this paper we investigate the links between instantiated argumentation systems and the axioms for non-monotonic reasoning described in [9] with the aim of characterising the nature of argument based reasoning. In doing so, we consider two possible interpretations of the consequence relation, and describe which axioms are met by ASPIC+ under each of these interpretations. We then consider the links between these axioms and the rationality postulates. Our results indicate that argument based reasoning as characterised by ASPIC+ is - according to the axioms of [9] - non-cumulative and non-monotonic, and therefore weaker than the weakest non-monotonic reasoning systems they considered possible. This weakness underpins ASPIC+'s success in modelling other reasoning systems, and we conclude by considering the relationship between ASPIC+ and other weak logical systems.
We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations---random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning. For flexibility, Edward makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation to variational inference to MCMC. In addition, Edward can reuse the modeling representation as part of inference, facilitating the design of rich variational models and generative adversarial networks. For efficiency, Edward is integrated into TensorFlow, providing significant speedups over existing probabilistic systems. For example, we show on a benchmark logistic regression task that Edward is at least 35x faster than Stan and 6x faster than PyMC3. Further, Edward incurs no runtime overhead: it is as fast as handwritten TensorFlow.
Task-oriented dialogue focuses on conversational agents that participate in user-initiated dialogues on domain-specific topics. In contrast to chatbots, which simply seek to sustain open-ended meaningful discourse, existing task-oriented agents usually explicitly model user intent and belief states. This paper examines bypassing such an explicit representation by depending on a latent neural embedding of state and learning selective attention to dialogue history together with copying to incorporate relevant prior context. We complement recent work by showing the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism. Our model outperforms more complex memory-augmented models by 7% in per-response generation and is on par with the current state-of-the-art on DSTC2.
Providing Reinforcement Learning agents with expert advice can dramatically improve various aspects of learning. Prior work has developed teaching protocols that enable agents to learn efficiently in complex environments; many of these methods tailor the teacher's guidance to agents with a particular representation or underlying learning scheme, offering effective but specialized teaching procedures. In this work, we explore protocol programs, an agent-agnostic schema for Human-in-the-Loop Reinforcement Learning. Our goal is to incorporate the beneficial properties of a human teacher into Reinforcement Learning without making strong assumptions about the inner workings of the agent. We show how to represent existing approaches such as action pruning, reward shaping, and training in simulation as special cases of our schema and conduct preliminary experiments on simple domains.
The combinatorial explosion that plagues planning and reinforcement learning (RL) algorithms can be moderated using state abstraction. Prohibitively large task representations can be condensed such that essential information is preserved, and consequently, solutions are tractably computable. However, exact abstractions, which treat only fully-identical situations as equivalent, fail to present opportunities for abstraction in environments where no two situations are exactly alike. In this work, we investigate approximate state abstractions, which treat nearly-identical situations as equivalent. We present theoretical guarantees of the quality of behaviors derived from four types of approximate abstractions. Additionally, we empirically demonstrate that approximate abstractions lead to reduction in task complexity and bounded loss of optimality of behavior in a variety of environments.
We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond to large enough areas in the image to capture information about large objects. We introduce the notion of an effective receptive field, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field. We analyze the effective receptive field in several architecture designs, and the effect of nonlinear activations, dropout, sub-sampling and skip connections on it. This leads to suggestions for ways to address its tendency to be too small.
In this paper, we improve the previously best known regret bound to achieve $\epsilon$-differential privacy in oblivious adversarial bandits from $\mathcal{O}{(T^{2/3}/\epsilon)}$ to $\mathcal{O}{(\sqrt{T} \ln T /\epsilon)}$. This is achieved by combining a Laplace Mechanism with EXP3. We show that though EXP3 is already differentially private, it leaks a linear amount of information in $T$. However, we can improve this privacy by relying on its intrinsic exponential mechanism for selecting actions. This allows us to reach $\mathcal{O}{(\sqrt{\ln T})}$-DP, with a regret of $\mathcal{O}{(T^{2/3})}$ that holds against an adaptive adversary, an improvement from the best known of $\mathcal{O}{(T^{3/4})}$. This is done by using an algorithm that run EXP3 in a mini-batch loop. Finally, we run experiments that clearly demonstrate the validity of our theoretical analysis.
We present a novel extension of Thompson Sampling for stochastic sequential decision problems with graph feedback, even when the graph structure itself is unknown and/or changing. We provide theoretical guarantees on the Bayesian regret of the algorithm, linking its performance to the underlying properties of the graph. Thompson Sampling has the advantage of being applicable without the need to construct complicated upper confidence bounds for different problems. We illustrate its performance through extensive experimental results on real and simulated networks with graph feedback. More specifically, we tested our algorithms on power law, planted partitions and Erdo's-Renyi graphs, as well as on graphs derived from Facebook and Flixster data. These all show that our algorithms clearly outperform related methods that employ upper confidence bounds, even if the latter use more information about the graph.
The problem where a tropical cyclone intensifies dramatically within a short period of time is known as rapid intensification. This has been one of the major challenges for tropical weather forecasting. Recurrent neural networks have been promising for time series problems which makes them appropriate for rapid intensification. In this paper, recurrent neural networks are used to predict rapid intensification cases of tropical cyclones from the South Pacific and South Indian Ocean regions. A class imbalanced problem is encountered which makes it very challenging to achieve promising performance. A simple strategy was proposed to include more positive cases for detection where the false positive rate was slightly improved. The limitations of building an efficient system remains due to the challenges of addressing the class imbalance problem encountered for rapid intensification prediction. This motivates further research in using innovative machine learning methods.
Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we for the first time formalize the concept of community profiling. With rich user information on the network, such as user published content and user diffusion links, we characterize a community in terms of both its internal content profile and external diffusion profile. The difficulty of community profiling is often underestimated. We novelly identify three unique challenges and propose a joint Community Profiling and Detection (CPD) model to address them accordingly. We also contribute a scalable inference algorithm, which scales linearly with the data size and it is easily parallelizable. We evaluate CPD on large-scale real-world data sets, and show that it is significantly better than the state-of-the-art baselines in various tasks.
Optimization is becoming a crucial element in industrial applications involving sustainable alternative energy systems. During the design of such systems, the engineer/decision maker would often encounter noise factors (e.g. solar insolation and ambient temperature fluctuations) when their system interacts with the environment. In this chapter, the sizing and design optimization of the solar powered irrigation system was considered. This problem is multivariate, noisy, nonlinear and multiobjective. This design problem was tackled by first using the Fuzzy Type II approach to model the noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the context of a weighted sum framework) was employed to solve this multiobjective fuzzy design problem. This method was then used to construct the approximate Pareto frontier as well as to identify the best solution option in a fuzzy setting. Comprehensive analyses and discussions were performed on the generated numerical results with respect to the implemented solution methods.
Crowdsourcing, a major economic issue, is the fact that the firm outsources internal task to the crowd. It is a form of digital subcontracting for the general public. The evaluation of the participants work quality is a major issue in crowdsourcing. Indeed, contributions must be controlled to ensure the effectiveness and relevance of the campaign. We are particularly interested in small, fast and not automatable tasks. Several methods have been proposed to solve this problem, but they are applicable when the "golden truth" is not always known. This work has the particularity to propose a method for calculating the degree of expertise in the presence of gold data in crowdsourcing. This method is based on the belief function theory and proposes a structuring of data using graphs. The proposed approach will be assessed and applied to the data.
Psychological traumas are thought to be present in a wide range of conditions, including post-traumatic stress disorder, disorganised attachment, personality disorders, dissociative identity disorder and psychosis. This work presents a new psychotherapy for psychological traumas, based on a functional model of the mind, built with elements borrowed from the fields of computer science, artificial intelligence and neural networks. The model revolves around the concept of hierarchical value and explains the emergence of dissociation and splitting in response to emotional pain. The key intuition is that traumas are caused by too strong negative emotions, which are in turn made possible by a low-value self, which is in turn determined by low-value self-associated ideas. The therapeutic method compiles a list of patient's traumas, identifies for each trauma a list of low-value self-associated ideas, and provides for each idea a list of counterexamples, to raise the self value and solve the trauma. Since the psychotherapy proposed has not been clinically tested, statements on its effectiveness are premature. However, since the conceptual basis is solid and traumas are hypothesised to be present in many psychological disorders, the potential gain may be substantial.
Humans are not only adept in recognizing what class an input instance belongs to (i.e., classification task), but perhaps more remarkably, they can imagine (i.e., generate) plausible instances of a desired class with ease, when prompted. Inspired by this, we propose a framework which allows transforming Cascade-Correlation Neural Networks (CCNNs) into probabilistic generative models, thereby enabling CCNNs to generate samples from a category of interest. CCNNs are a well-known class of deterministic, discriminative NNs, which autonomously construct their topology, and have been successful in giving accounts for a variety of psychological phenomena. Our proposed framework is based on a Markov Chain Monte Carlo (MCMC) method, called the Metropolis-adjusted Langevin algorithm, which capitalizes on the gradient information of the target distribution to direct its explorations towards regions of high probability, thereby achieving good mixing properties. Through extensive simulations, we demonstrate the efficacy of our proposed framework.
Internship assignment is a complicated process for universities since it is necessary to take into account a multiplicity of variables to establish a compromise between companies' requirements and student competencies acquired during the university training. These variables build up a complex relations map that requires the formulation of an exhaustive and rigorous conceptual scheme. In this research a domain ontological model is presented as support to the student's decision making for opportunities of University studies level of the University Lumiere Lyon 2 (ULL) education system. The ontology is designed and created using methodological approach offering the possibility of improving the progressive creation, capture and knowledge articulation. In this paper, we draw a balance taking the demands of the companies across the capabilities of the students. This will be done through the establishment of an ontological model of an educational learners' profile and the internship postings which are written in a free text and using uncontrolled vocabulary. Furthermore, we outline the process of semantic matching which improves the quality of query results.
This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for principled reasons not addressable with probabilistic means); and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: Logic Programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of Input/Output logic for dealing with uncertainty in dynamic normative contexts.
For agents and robots to become more useful, they must be able to quickly learn from non-technical users. This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has made the assumption that people provide feedback for decisions that is dependent on the behavior they are teaching and is independent from the learner's current policy. We present empirical results that show this assumption to be false---whether human trainers give a positive or negative feedback for a decision is influenced by the learner's current policy. We argue that policy-dependent feedback, in addition to being commonplace, enables useful training strategies from which agents should benefit. Based on this insight, we introduce Convergent Actor-Critic by Humans (COACH), an algorithm for learning from policy-dependent feedback that converges to a local optimum. Finally, we demonstrate that COACH can successfully learn multiple behaviors on a physical robot, even with noisy image features.
In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption. Homophily label propagation (i.e., two connected nodes share similar labels) in homogeneous graph (with same types of vertices and relations) has been extensively studied before. Unfortunately, real-life networks are heterogeneous, they contain different types of vertices (e.g., users, images, texts) and relations (e.g., friendships, co-tagging) and allow for each node to propagate both the same and opposite copy of labels to its neighbors. We propose a $\mathcal{K}$-partite label propagation model to handle the mystifying combination of heterogeneous nodes/relations and heterophily propagation. With this model, we develop a novel label inference algorithm framework with update rules in near-linear time complexity. Since real networks change over time, we devise an incremental approach, which supports fast updates for both new data and evidence (e.g., ground truth labels) with guaranteed efficiency. We further provide a utility function to automatically determine whether an incremental or a re-modeling approach is favored. Extensive experiments on real datasets have verified the effectiveness and efficiency of our approach, and its superiority over the state-of-the-art label propagation methods.
Many aspects of people's lives are proven to be deeply connected to their jobs. In this paper, we first investigate the distinct characteristics of major occupation categories based on tweets. From multiple social media platforms, we gather several types of user information. From users' LinkedIn webpages, we learn their proficiencies. To overcome the ambiguity of self-reported information, a soft clustering approach is applied to extract occupations from crowd-sourced data. Eight job categories are extracted, including Marketing, Administrator, Start-up, Editor, Software Engineer, Public Relation, Office Clerk, and Designer. Meanwhile, users' posts on Twitter provide cues for understanding their linguistic styles, interests, and personalities. Our results suggest that people of different jobs have unique tendencies in certain language styles and interests. Our results also clearly reveal distinctive levels in terms of Big Five Traits for different jobs. Finally, a classifier is built to predict job types based on the features extracted from tweets. A high accuracy indicates a strong discrimination power of language features for job prediction task.
The fifth Dialog State Tracking Challenge (DSTC5) introduces a new cross-language dialog state tracking scenario, where the participants are asked to build their trackers based on the English training corpus, while evaluating them with the unlabeled Chinese corpus. Although the computer-generated translations for both English and Chinese corpus are provided in the dataset, these translations contain errors and careless use of them can easily hurt the performance of the built trackers. To address this problem, we propose a multichannel Convolutional Neural Networks (CNN) architecture, in which we treat English and Chinese language as different input channels of one single CNN model. In the evaluation of DSTC5, we found that such multichannel architecture can effectively improve the robustness against translation errors. Additionally, our method for DSTC5 is purely machine learning based and requires no prior knowledge about the target language. We consider this a desirable property for building a tracker in the cross-language context, as not every developer will be familiar with both languages.
This paper focuses on modeling ride requests and their variations over location and time, based on analyzing extensive real-world data from a ride-sharing service. We introduce a graph model that captures the spatial and temporal variability of ride requests and the potentials for ride pooling. We discover these ride request graphs exhibit a well known property called densification power law often found in real graphs modelling human behaviors. We show the pattern of ride requests and the potential of ride pooling for a city can be characterized by the densification factor of the ride request graphs. Previous works have shown that it is possible to automatically generate synthetic versions of these graphs that exhibit a given densification factor. We present an algorithm for automatic generation of synthetic ride request graphs that match quite well the densification factor of ride request graphs from actual ride request data.
We develop a framework for rendering photographic images, taking into account display limitations, so as to optimize perceptual similarity between the rendered image and the original scene. We formulate this as a constrained optimization problem, in which we minimize a measure of perceptual dissimilarity, the Normalized Laplacian Pyramid Distance (NLPD), which mimics the early stage transformations of the human visual system. When rendering images acquired with higher dynamic range than that of the display, we find that the optimized solution boosts the contrast of low-contrast features without introducing significant artifacts, yielding results of comparable visual quality to current state-of-the art methods with no manual intervention or parameter settings. We also examine a variety of other display constraints, including limitations on minimum luminance (black point), mean luminance (as a proxy for energy consumption), and quantized luminance levels (halftoning). Finally, we show that the method may be used to enhance details and contrast of images degraded by optical scattering (e.g. fog).
Portable computing devices, which include tablets, smart phones and various types of wearable sensors, experienced a rapid development in recent years. One of the most critical limitations for these devices is the power consumption as they use batteries as the power supply. However, the bottleneck of the power saving schemes in both hardware design and software algorithm is the huge variability in power consumption. The variability is caused by a myriad of factors, including the manufacturing process, the ambient environment (temperature, humidity), the aging effects and etc. As the technology node scaled down to 28nm and even lower, the variability becomes more severe. As a result, a platform for variability characterization seems to be very necessary and helpful.
The $k$-Means clustering problem on $n$ points is NP-Hard for any dimension $d\ge 2$, however, for the 1D case there exist exact polynomial time algorithms. Previous literature reported an $O(kn^2)$ time dynamic programming algorithm that uses $O(kn)$ space. We present a new algorithm computing the optimal clustering in only $O(kn)$ time using linear space. For $k = \Omega(\lg n)$, we improve this even further to $n 2^{O(\sqrt{ \lg \lg n \lg k})}$ time. We generalize the new algorithm(s) to work for the absolute distance instead of squared distance and to work for any Bregman Divergence as well.
Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the average absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.
The popularity of image sharing on social media and the engagement it creates between users reflects the important role that visual context plays in everyday conversations. We present a novel task, Image-Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple-reference dataset of crowd-sourced, event-centric conversations on images. IGC falls on the continuum between chit-chat and goal-directed conversation models, where visual grounding constrains the topic of conversation to event-driven utterances. Experiments with models trained on social media data show that the combination of visual and textual context enhances the quality of generated conversational turns. In human evaluation, the gap between human performance and that of both neural and retrieval architectures suggests that multi-modal IGC presents an interesting challenge for dialogue research.
Natural-language-facilitated human-robot cooperation (NLC), in which natural language (NL) is used to share knowledge between a human and a robot for conducting intuitive human-robot cooperation (HRC), is continuously developing in the recent decade. Currently, NLC is used in several robotic domains such as manufacturing, daily assistance and health caregiving. It is necessary to summarize current NLC-based robotic systems and discuss the future developing trends, providing helpful information for future NLC research. In this review, we first analyzed the driving forces behind the NLC research. Regarding to a robot s cognition level during the cooperation, the NLC implementations then were categorized into four types {NL-based control, NL-based robot training, NL-based task execution, NL-based social companion} for comparison and discussion. Last based on our perspective and comprehensive paper review, the future research trends were discussed.
The study of mereology (parts and wholes) in the context of formal approaches to vagueness can be approached in a number of ways. In the context of rough sets, mereological concepts with a set-theoretic or valuation based ontology acquire complex and diverse behavior. In this research a general rough set framework called granular operator spaces is extended and the nature of parthood in it is explored from a minimally intrusive point of view. This is used to develop counting strategies that help in classifying the framework. The developed methodologies would be useful for drawing involved conclusions about the nature of data (and validity of assumptions about it) from antichains derived from context. The problem addressed is also about whether counting procedures help in confirming that the approximations involved in formation of data are indeed rough approximations?
Autonomous software agents operating in dynamic environments need to constantly reason about actions in pursuit of their goals, while taking into consideration norms which might be imposed on those actions. Normative practical reasoning supports agents making decisions about what is best for them to (not) do in a given situation. What makes practical reasoning challenging is the interplay between goals that agents are pursuing and the norms that the agents are trying to uphold. We offer a formalisation to allow agents to plan for multiple goals and norms in the presence of durative actions that can be executed concurrently. We compare plans based on decision-theoretic notions (i.e. utility) such that the utility gain of goals and utility loss of norm violations are the basis for this comparison. The set of optimal plans consists of plans that maximise the overall utility, each of which can be chosen by the agent to execute. We provide an implementation of our proposal in Answer Set Programming, thus allowing us to state the original problem in terms of a logic program that can be queried for solutions with specific properties. The implementation is proven to be sound and complete.
When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.
In a recent conference paper, we have reported a rhythm transcription method based on a merged-output hidden Markov model (HMM) that explicitly describes the multiple-voice structure of polyphonic music. This model solves a major problem of conventional methods that could not properly describe the nature of multiple voices as in polyrhythmic scores or in the phenomenon of loose synchrony between voices. In this paper we present a complete description of the proposed model and develop an inference technique, which is valid for any merged-output HMMs for which output probabilities depend on past events. We also examine the influence of the architecture and parameters of the method in terms of accuracies of rhythm transcription and voice separation and perform comparative evaluations with six other algorithms. Using MIDI recordings of classical piano pieces, we found that the proposed model outperformed other methods by more than 12 points in the accuracy for polyrhythmic performances and performed almost as good as the best one for non-polyrhythmic performances. This reveals the state-of-the-art methods of rhythm transcription for the first time in the literature. Publicly available source codes are also provided for future comparisons.
We introduce new diversification methods for zero-one optimization that significantly extend strategies previously introduced in the setting of metaheuristic search. Our methods incorporate easily implemented strategies for partitioning assignments of values to variables, accompanied by processes called augmentation and shifting which create greater flexibility and generality. We then show how the resulting collection of diversified solutions can be further diversified by means of permutation mappings, which equally can be used to generate diversified collections of permutations for applications such as scheduling and routing. These methods can be applied to non-binary vectors by the use of binarization procedures and by Diversification-Based Learning (DBL) procedures which also provide connections to applications in clustering and machine learning. Detailed pseudocode and numerical illustrations are provided to show the operation of our methods and the collections of solutions they create.
This research presents an innovative and unique way of solving the advertisement prediction problem which is considered as a learning problem over the past several years. Online advertising is a multi-billion-dollar industry and is growing every year with a rapid pace. The goal of this research is to enhance click through rate of the contextual advertisements using Linear Regression. In order to address this problem, a new technique propose in this paper to predict the CTR which will increase the overall revenue of the system by serving the advertisements more suitable to the viewers with the help of feature extraction and displaying the advertisements based on context of the publishers. The important steps include the data collection, feature extraction, CTR prediction and advertisement serving. The statistical results obtained from the dynamically used technique show an efficient outcome by fitting the data close to perfection for the LR technique using optimized feature selection.
This paper formalises the problem of online algorithm selection in the context of Reinforcement Learning. The setup is as follows: given an episodic task and a finite number of off-policy RL algorithms, a meta-algorithm has to decide which RL algorithm is in control during the next episode so as to maximize the expected return. The article presents a novel meta-algorithm, called Epochal Stochastic Bandit Algorithm Selection (ESBAS). Its principle is to freeze the policy updates at each epoch, and to leave a rebooted stochastic bandit in charge of the algorithm selection. Under some assumptions, a thorough theoretical analysis demonstrates its near-optimality considering the structural sampling budget limitations. ESBAS is first empirically evaluated on a dialogue task where it is shown to outperform each individual algorithm in most configurations. ESBAS is then adapted to a true online setting where algorithms update their policies after each transition, which we call SSBAS. SSBAS is evaluated on a fruit collection task where it is shown to adapt the stepsize parameter more efficiently than the classical hyperbolic decay, and on an Atari game, where it improves the performance by a wide margin.
This article shows how the recent breakthroughs in Reinforcement Learning (RL) that have enabled robots to learn to play arcade video games, walk or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems. We present the first use of RL for the control of systems modeled by discretized non-linear Partial Differential Equations (PDEs) and devise a novel algorithm to use non-parametric control techniques for large multi-agent systems. We show how neural network based RL enables the control of discretized PDEs whose parameters are unknown, random, and time-varying. We introduce an algorithm of Mutual Weight Regularization (MWR) which alleviates the curse of dimensionality of multi-agent control schemes by sharing experience between agents while giving each agent the opportunity to specialize its action policy so as to tailor it to the local parameters of the part of the system it is located in.
The focus of this work is to enumerate the various approaches and algorithms that center around application of reinforcement learning in robotic ma- ]]nipulation tasks. Earlier methods utilized specialized policy representations and human demonstrations to constrict the policy. Such methods worked well with continuous state and policy space of robots but failed to come up with generalized policies. Subsequently, high dimensional non-linear function approximators like neural networks have been used to learn policies from scratch. Several novel and recent approaches have also embedded control policy with efficient perceptual representation using deep learning. This has led to the emergence of a new branch of dynamic robot control system called deep r inforcement learning(DRL). This work embodies a survey of the most recent algorithms, architectures and their implementations in simulations and real world robotic platforms. The gamut of DRL architectures are partitioned into two different branches namely, discrete action space algorithms(DAS) and continuous action space algorithms(CAS). Further, the CAS algorithms are divided into stochastic continuous action space(SCAS) and deterministic continuous action space(DCAS) algorithms. Along with elucidating an organ- isation of the DRL algorithms this work also manifests some of the state of the art applications of these approaches in robotic manipulation tasks.
We examine the meaning and the complexity of probabilistic logic programs that consist of a set of rules and a set of independent probabilistic facts (that is, programs based on Sato's distribution semantics). We focus on two semantics, respectively based on stable and on well-founded models. We show that the semantics based on stable models (referred to as the "credal semantics") produces sets of probability models that dominate infinitely monotone Choquet capacities, we describe several useful consequences of this result. We then examine the complexity of inference with probabilistic logic programs. We distinguish between the complexity of inference when a probabilistic program and a query are given (the inferential complexity), and the complexity of inference when the probabilistic program is fixed and the query is given (the query complexity, akin to data complexity as used in database theory). We obtain results on the inferential and query complexity for acyclic, stratified, and cyclic propositional and relational programs, complexity reaches various levels of the counting hierarchy and even exponential levels.
We propose a novel rank aggregation method based on converting permutations into their corresponding Lehmer codes or other subdiagonal images. Lehmer codes, also known as inversion vectors, are vector representations of permutations in which each coordinate can take values not restricted by the values of other coordinates. This transformation allows for decoupling of the coordinates and for performing aggregation via simple scalar median or mode computations. We present simulation results illustrating the performance of this completely parallelizable approach and analytically prove that both the mode and median aggregation procedure recover the correct centroid aggregate with small sample complexity when the permutations are drawn according to the well-known Mallows models. The proposed Lehmer code approach may also be used on partial rankings, with similar performance guarantees.
Retrosynthesis is a technique to plan the chemical synthesis of organic molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a search tree is built by analysing molecules recursively and dissecting them into simpler molecular building blocks until one obtains a set of known building blocks. The search space is intractably large, and it is difficult to determine the value of retrosynthetic positions. Here, we propose to model retrosynthesis as a Markov Decision Process. In combination with a Deep Neural Network policy learned from essentially the complete published knowledge of chemistry, Monte Carlo Tree Search (MCTS) can be used to evaluate positions. In exploratory studies, we demonstrate that MCTS with neural network policies outperforms the traditionally used best-first search with hand-coded heuristics.
In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as pre-production events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this research, by considering the pre-production events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multi-objective evolutionary algorithm (MOEA) called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider pre-production events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling.
In this paper we present an approach to extract ordered timelines of events, their participants, locations and times from a set of multilingual and cross-lingual data sources. Based on the assumption that event-related information can be recovered from different documents written in different languages, we extend the Cross-document Event Ordering task presented at SemEval 2015 by specifying two new tasks for, respectively, Multilingual and Cross-lingual Timeline Extraction. We then develop three deterministic algorithms for timeline extraction based on two main ideas. First, we address implicit temporal relations at document level since explicit time-anchors are too scarce to build a wide coverage timeline extraction system. Second, we leverage several multilingual resources to obtain a single, inter-operable, semantic representation of events across documents and across languages. The result is a highly competitive system that strongly outperforms the current state-of-the-art. Nonetheless, further analysis of the results reveals that linking the event mentions with their target entities and time-anchors remains a difficult challenge. The systems, resources and scorers are freely available to facilitate its use and guarantee the reproducibility of results.
Safe interaction with human drivers is one of the primary challenges for autonomous vehicles. In order to plan driving maneuvers effectively, the vehicle's control system must infer and predict how humans will behave based on their latent internal state (e.g., intentions and aggressiveness). This research uses a simple model for human behavior with unknown parameters that make up the internal states of the traffic participants and presents a method for quantifying the value of estimating these states and planning with their uncertainty explicitly modeled. An upper performance bound is established by an omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of the internal states. A baseline lower bound is established by planning with MCTS assuming that all drivers have the same internal state. MCTS variants are then used to solve a partially observable Markov decision process (POMDP) that models the internal state uncertainty to determine whether inferring the internal state offers an advantage over the baseline. Applying this method to a freeway lane changing scenario reveals that there is a significant performance gap between the upper bound and baseline. POMDP planning techniques come close to closing this gap, especially when important hidden model parameters are correlated with measurable parameters.
The problem of quantizing the activations of a deep neural network is considered. An examination of the popular binary quantization approach shows that this consists of approximating a classical non-linearity, the hyperbolic tangent, by two functions: a piecewise constant sign function, which is used in feedforward network computations, and a piecewise linear hard tanh function, used in the backpropagation step during network learning. The problem of approximating the ReLU non-linearity, widely used in the recent deep learning literature, is then considered. An half-wave Gaussian quantizer (HWGQ) is proposed for forward approximation and shown to have efficient implementation, by exploiting the statistics of of network activations and batch normalization operations commonly used in the literature. To overcome the problem of gradient mismatch, due to the use of different forward and backward approximations, several piece-wise backward approximators are then investigated. The implementation of the resulting quantized network, denoted as HWGQ-Net, is shown to achieve much closer performance to full precision networks, such as AlexNet, ResNet, GoogLeNet and VGG-Net, than previously available low-precision networks, with 1-bit binary weights and 2-bit quantized activations.
Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.
Real-time optimization of traffic flow addresses important practical problems: reducing a driver's wasted time, improving city-wide efficiency, reducing gas emissions and improving air quality. Much of the current research in traffic-light optimization relies on extending the capabilities of traffic lights to either communicate with each other or communicate with vehicles. However, before such capabilities become ubiquitous, opportunities exist to improve traffic lights by being more responsive to current traffic situations within the current, already deployed, infrastructure. In this paper, we introduce a traffic light controller that employs bidding within micro-auctions to efficiently incorporate traffic sensor information; no other outside sources of information are assumed. We train and test traffic light controllers on large-scale data collected from opted-in Android cell-phone users over a period of several months in Mountain View, California and the River North neighborhood of Chicago, Illinois. The learned auction-based controllers surpass (in both the relevant metrics of road-capacity and mean travel time) the currently deployed lights, optimized static-program lights, and longer-term planning approaches, in both cities, measured using real user driving data.
Entity resolution (ER) is the task of identifying all records in a database that refer to the same underlying entity, and are therefore duplicates of each other. Due to inherent ambiguity of data representation and poor data quality, ER is a challenging task for any automated process. As a remedy, human-powered ER via crowdsourcing has become popular in recent years. Using crowd to answer queries is costly and time consuming. Furthermore, crowd-answers can often be faulty. Therefore, crowd-based ER methods aim to minimize human participation without sacrificing the quality and use a computer generated similarity matrix actively. While, some of these methods perform well in practice, no theoretical analysis exists for them, and further their worst case performances do not reflect the experimental findings. This creates a disparity in the understanding of the popular heuristics for this problem. In this paper, we make the first attempt to close this gap. We provide a thorough analysis of the prominent heuristic algorithms for crowd-based ER. We justify experimental observations with our analysis and information theoretic lower bounds.
We present a family of logics for reasoning about agents' positions and motion in the plane which have several potential applications in the area of multi-agent systems (MAS), such as multi-agent planning and robotics. The most general logic includes (i) atomic formulas for representing the truth of a given fact or the presence of a given agent at a certain position of the plane, (ii) atomic programs corresponding to the four basic orientations in the plane (up, down, left, right) as well as the four program constructs of propositional dynamic logic (sequential composition, nondeterministic composition, iteration and test). As this logic is not computably enumerable, we study some interesting decidable and axiomatizable fragments of it. We also present a decidable extension of the iteration-free fragment of the logic by special programs representing motion of agents in the plane.
This paper describes the details of Sighthound's fully automated vehicle make, model and color recognition system. The backbone of our system is a deep convolutional neural network that is not only computationally inexpensive, but also provides state-of-the-art results on several competitive benchmarks. Additionally, our deep network is trained on a large dataset of several million images which are labeled through a semi-automated process. Finally we test our system on several public datasets as well as our own internal test dataset. Our results show that we outperform other methods on all benchmarks by significant margins. Our model is available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud
We present a technique for automatically extracting mutual exclusion invariants from temporal planning instances. It first identifies a set of invariant templates by inspecting the lifted representation of the domain and then checks these templates against properties that assure invariance. Our technique builds on other approaches to invariant synthesis presented in the literature, but departs from their limited focus on instantaneous actions by addressing temporal domains. To deal with time, we formulate invariance conditions that account for the entire structure of the actions and the possible concurrent interactions between them. As a result, we construct a significantly more comprehensive technique than previous methods, which is able to find not only invariants for temporal domains, but also a broader set of invariants for non-temporal domains. The experimental results reported in this paper provide evidence that identifying a broader set of invariants results in the generation of fewer multi-valued state variables with larger domains. We show that, in turn, this reduction in the number of variables reflects positively on the performance of a number of temporal planners that use a variable/value representation by significantly reducing their running time.
We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to model the temporal nature of spoken speech, and show that it learns to extract both form and meaning-based linguistic knowledge from the input signal. We carry out an in-depth analysis of the representations used by different components of the trained model and show that encoding of semantic aspects tends to become richer as we go up the hierarchy of layers, whereas encoding of form-related aspects of the language input tends to initially increase and then plateau or decrease.
We propose a method to generate multiple diverse and valid human pose hypotheses in 3D all consistent with the 2D detection of joints in a monocular RGB image. We use a novel generative model uniform (unbiased) in the space of anatomically plausible 3D poses. Our model is compositional (produces a pose by combining parts) and since it is restricted only by anatomical constraints it can generalize to every plausible human 3D pose. Removing the model bias intrinsically helps to generate more diverse 3D pose hypotheses. We argue that generating multiple pose hypotheses is more reasonable than generating only a single 3D pose based on the 2D joint detection given the depth ambiguity and the uncertainty due to occlusion and imperfect 2D joint detection. We hope that the idea of generating multiple consistent pose hypotheses can give rise to a new line of future work that has not received much attention in the literature. We used the Human3.6M dataset for empirical evaluation.
The technique of kernelization consists in extracting, from an instance of a problem, an essentially equivalent instance whose size is bounded in a parameter k. Besides being the basis for efficient param-eterized algorithms, this method also provides a wealth of information to reason about in the context of constraint programming. We study the use of kernelization for designing propagators through the example of the Vertex Cover constraint. Since the classic kernelization rules often correspond to dominance rather than consistency, we introduce the notion of "loss-less" kernel. While our preliminary experimental results show the potential of the approach, they also show some of its limits. In particular, this method is more effective for vertex covers of large and sparse graphs, as they tend to have, relatively, smaller kernels.
We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two-view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style multiview representation learning technique that combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many independent sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn DGCCA representations on two distinct datasets for three downstream tasks: phonetic transcription from acoustic and articulatory measurements, and recommending hashtags and friends on a dataset of Twitter users. We find that DGCCA representations soundly beat existing methods at phonetic transcription and hashtag recommendation, and in general perform no worse than standard linear many-view techniques.
Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear. As a result, these models are generally treated as black boxes, yielding no insight of the underlying learned patterns. In this paper we consider Long Short Term Memory networks (LSTMs) and demonstrate a new approach for tracking the importance of a given input to the LSTM for a given output. By identifying consistently important patterns of words, we are able to distill state of the art LSTMs on sentiment analysis and question answering into a set of representative phrases. This representation is then quantitatively validated by using the extracted phrases to construct a simple, rule-based classifier which approximates the output of the LSTM.
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable. In pursuit of such models, we propose a causal regularizer to steer predictive models towards causally-interpretable solutions and theoretically study its properties. In a large-scale analysis of Electronic Health Records (EHR), our causally-regularized model outperforms its L1-regularized counterpart in causal accuracy and is competitive in predictive performance. We perform non-linear causality analysis by causally regularizing a special neural network architecture. We also show that the proposed causal regularizer can be used together with neural representation learning algorithms to yield up to 20% improvement over multilayer perceptron in detecting multivariate causation, a situation common in healthcare, where many causal factors should occur simultaneously to have an effect on the target variable.
In a smart city, real-time traffic sensors may be deployed for various applications, such as route planning. Unfortunately, sensors are prone to failures, which result in erroneous traffic data. Erroneous data can adversely affect applications such as route planning, and can cause increased travel time. To minimize the impact of sensor failures, we must detect them promptly and accurately. However, typical detection algorithms may lead to a large number of false positives (i.e., false alarms) and false negatives (i.e., missed detections), which can result in suboptimal route planning. In this paper, we devise an effective detector for identifying faulty traffic sensors using a prediction model based on Gaussian Processes. Further, we present an approach for computing the optimal parameters of the detector which minimize losses due to false-positive and false-negative errors. We also characterize critical sensors, whose failure can have high impact on the route planning application. Finally, we implement our method and evaluate it numerically using a real-world dataset and the route planning platform OpenTripPlanner.
Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples. Techniques such as iterative classification or relaxation labeling achieve this by propagating information between related samples during the inference process. When only a few samples are labeled and connections between samples are sparse, collective inference methods have shown large improvements over standard feature-based ML methods. However, in contrast to feature based ML, collective inference methods require complex inference procedures and often depend on the strong assumption of label consistency among related samples. In this paper, we introduce new relational features for standard ML methods by extracting information from direct and indirect relations. We show empirically on three standard benchmark datasets that our relational features yield results comparable to collective inference methods. Finally we show that our proposal outperforms these methods when additional information is available.
Parameterized algorithms are a way to solve hard problems more efficiently, given that a specific parameter of the input is small. In this paper, we apply this idea to the field of answer set programming (ASP). To this end, we propose two kinds of graph representations of programs to exploit their treewidth as a parameter. Treewidth roughly measures to which extent the internal structure of a program resembles a tree. Our main contribution is the design of parameterized dynamic programming algorithms, which run in linear time if the treewidth and weights of the given program are bounded. Compared to previous work, our algorithms handle the full syntax of ASP. Finally, we report on an empirical evaluation that shows good runtime behaviour for benchmark instances of low treewidth, especially for counting answer sets.
Matrix games like Prisoner's Dilemma have guided research on social dilemmas for decades. However, they necessarily treat the choice to cooperate or defect as an atomic action. In real-world social dilemmas these choices are temporally extended. Cooperativeness is a property that applies to policies, not elementary actions. We introduce sequential social dilemmas that share the mixed incentive structure of matrix game social dilemmas but also require agents to learn policies that implement their strategic intentions. We analyze the dynamics of policies learned by multiple self-interested independent learning agents, each using its own deep Q-network, on two Markov games we introduce here: 1. a fruit Gathering game and 2. a Wolfpack hunting game. We characterize how learned behavior in each domain changes as a function of environmental factors including resource abundance. Our experiments show how conflict can emerge from competition over shared resources and shed light on how the sequential nature of real world social dilemmas affects cooperation.
Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.
End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors. We introduce Hybrid Code Networks (HCNs), which combine an RNN with domain-specific knowledge encoded as software and system action templates. Compared to existing end-to-end approaches, HCNs considerably reduce the amount of training data required, while retaining the key benefit of inferring a latent representation of dialog state. In addition, HCNs can be optimized with supervised learning, reinforcement learning, or a mixture of both. HCNs attain state-of-the-art performance on the bAbI dialog dataset, and outperform two commercially deployed customer-facing dialog systems.
Synapse crossbar is an elementary structure in Neuromorphic Computing Systems (NCS). However, the limited size of crossbars and heavy routing congestion impedes the NCS implementations of big neural networks. In this paper, we propose a two-step framework (namely, group scissor) to scale NCS designs to big neural networks. The first step is rank clipping, which integrates low-rank approximation into the training to reduce total crossbar area. The second step is group connection deletion, which structurally prunes connections to reduce routing congestion between crossbars. Tested on convolutional neural networks of LeNet on MNIST database and ConvNet on CIFAR-10 database, our experiments show significant reduction of crossbar area and routing area in NCS designs. Without accuracy loss, rank clipping reduces total crossbar area to 13.62\% and 51.81\% in the NCS designs of LeNet and ConvNet, respectively. Following rank clipping, group connection deletion further reduces the routing area of LeNet and ConvNet to 8.1\% and 52.06\%, respectively.
We present Octopus, an AI agent to jointly balance three conflicting task objectives on a micro-crowdsourcing marketplace - the quality of work, total cost incurred, and time to completion. Previous control agents have mostly focused on cost-quality, or cost-time tradeoffs, but not on directly controlling all three in concert. A naive formulation of three-objective optimization is intractable; Octopus takes a hierarchical POMDP approach, with three different components responsible for setting the pay per task, selecting the next task, and controlling task-level quality. We demonstrate that Octopus significantly outperforms existing state-of-the-art approaches on real experiments. We also deploy Octopus on Amazon Mechanical Turk, showing its ability to manage tasks in a real-world dynamic setting.
The lack of diversity in a genetic algorithm's population may lead to a bad performance of the genetic operators since there is not an equilibrium between exploration and exploitation. In those cases, genetic algorithms present a fast and unsuitable convergence. In this paper we develop a novel hybrid genetic algorithm which attempts to obtain a balance between exploration and exploitation. It confronts the diversity problem using the named greedy diversification operator. Furthermore, the proposed algorithm applies a competition between parent and children so as to exploit the high quality visited solutions. These operators are complemented by a simple selection mechanism designed to preserve and take advantage of the population diversity. Additionally, we extend our proposal to the field of memetic algorithms, obtaining an improved model with outstanding results in practice. The experimental study shows the validity of the approach as well as how important is taking into account the exploration and exploitation concepts when designing an evolutionary algorithm.
This paper studies an auction design problem for a seller to sell a commodity in a social network, where each individual (the seller or a buyer) can only communicate with her neighbors. The challenge to the seller is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors so that more buyers will participate in the auction and hence, the seller will be able to make a higher revenue. We propose a novel auction mechanism, called information diffusion mechanism (IDM), which incentivizes the buyers to not only truthfully report their valuations on the commodity to the seller, but also further propagate the auction information to all their neighbors. In comparison, the direct extension of the well-known Vickrey-Clarke-Groves (VCG) mechanism in social networks can also incentivize the information diffusion, but it will decrease the seller's revenue or even lead to a deficit sometimes. The formalization of the problem has not yet been addressed in the literature of mechanism design and our solution is very significant in the presence of large-scale online social networks.
Agglutinative languages such as Turkish, Finnish and Hungarian require morphological disambiguation before further processing due to the complex morphology of words. A morphological disambiguator is used to select the correct morphological analysis of a word. Morphological disambiguation is important because it generally is one of the first steps of natural language processing and its performance affects subsequent analyses. In this paper, we propose a system that uses deep learning techniques for morphological disambiguation. Many of the state-of-the-art results in computer vision, speech recognition and natural language processing have been obtained through deep learning models. However, applying deep learning techniques to morphologically rich languages is not well studied. In this work, while we focus on Turkish morphological disambiguation we also present results for French and German in order to show that the proposed architecture achieves high accuracy with no language-specific feature engineering or additional resource. In the experiments, we achieve 84.12, 88.35 and 93.78 morphological disambiguation accuracy among the ambiguous words for Turkish, German and French respectively.
The Reservoir Computing (RC) paradigm utilizes a dynamical system, i.e., a reservoir, and a linear classifier, i.e., a read-out layer, to process data from sequential classification tasks. In this paper the usage of Cellular Automata (CA) as a reservoir is investigated. The use of CA in RC has been showing promising results. In this paper, selected state-of-the-art experiments are reproduced. It is shown that some CA-rules perform better than others, and the reservoir performance is improved by increasing the size of the CA reservoir itself. In addition, the usage of parallel loosely coupled CA-reservoirs, where each reservoir has a different CA-rule, is investigated. The experiments performed on quasi-uniform CA reservoir provide valuable insights in CA reservoir design. The results herein show that some rules do not work well together, while other combinations work remarkably well. This suggests that non-uniform CA could represent a powerful tool for novel CA reservoir implementations.
Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we propose a bilateral multi-perspective matching (BiMPM) model under the "matching-aggregation" framework. Given two sentences $P$ and $Q$, our model first encodes them with a BiLSTM encoder. Next, we match the two encoded sentences in two directions $P \rightarrow Q$ and $P \leftarrow Q$. In each matching direction, each time step of one sentence is matched against all time-steps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fix-length matching vector. Finally, based on the matching vector, the decision is made through a fully connected layer. We evaluate our model on three tasks: paraphrase identification, natural language inference and answer sentence selection. Experimental results on standard benchmark datasets show that our model achieves the state-of-the-art performance on all tasks.
Usually bilingual word vectors are trained "online". Mikolov et al. showed they can also be found "offline", whereby two pre-trained embeddings are aligned with a linear transformation, using dictionaries compiled from expert knowledge. In this work, we prove that the linear transformation between two spaces should be orthogonal. This transformation can be obtained using the singular value decomposition. We introduce a novel "inverted softmax" for identifying translation pairs, with which we improve the precision @1 of Mikolov's original mapping from 34% to 43%, when translating a test set composed of both common and rare English words into Italian. Orthogonal transformations are more robust to noise, enabling us to learn the transformation without expert bilingual signal by constructing a "pseudo-dictionary" from the identical character strings which appear in both languages, achieving 40% precision on the same test set. Finally, we extend our method to retrieve the true translations of English sentences from a corpus of 200k Italian sentences with a precision @1 of 68%.
We present our experiences using cloud computing to support data-intensive analytics on satellite imagery for commercial applications. Drawing from our background in high-performance computing, we draw parallels between the early days of clustered computing systems and the current state of cloud computing and its potential to disrupt the HPC market. Using our own virtual file system layer on top of cloud remote object storage, we demonstrate aggregate read bandwidth of 230 gigabytes per second using 512 Google Compute Engine (GCE) nodes accessing a USA multi-region standard storage bucket. This figure is comparable to the best HPC storage systems in existence. We also present several of our application results, including the identification of field boundaries in Ukraine, and the generation of a global cloud-free base layer from Landsat imagery.
The proposed algorithmic approach deals with finding the sense of a word in an electronic data. Now a day,in different communication mediums like internet, mobile services etc. people use few words, which are slang in nature. This approach detects those abusive words using supervised learning procedure. But in the real life scenario, the slang words are not used in complete word forms always. Most of the times, those words are used in different abbreviated forms like sounds alike forms, taboo morphemes etc. This proposed approach can detect those abbreviated forms also using semi supervised learning procedure. Using the synset and concept analysis of the text, the probability of a suspicious word to be a slang word is also evaluated.
Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to augment deep neural networks with a small "detector" subnetwork which is trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. Our method is orthogonal to prior work on addressing adversarial perturbations, which has mostly focused on making the classification network itself more robust. We show empirically that adversarial perturbations can be detected surprisingly well even though they are quasi-imperceptible to humans. Moreover, while the detectors have been trained to detect only a specific adversary, they generalize to similar and weaker adversaries. In addition, we propose an adversarial attack that fools both the classifier and the detector and a novel training procedure for the detector that counteracts this attack.
This paper describes the details of Sighthound's fully automated age, gender and emotion recognition system. The backbone of our system consists of several deep convolutional neural networks that are not only computationally inexpensive, but also provide state-of-the-art results on several competitive benchmarks. To power our novel deep networks, we collected large labeled datasets through a semi-supervised pipeline to reduce the annotation effort/time. We tested our system on several public benchmarks and report outstanding results. Our age, gender and emotion recognition models are available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud
This paper studies scenarios of cyclic dominance in a coevolutionary spatial model in which game strategies and links between agents adaptively evolve over time. The Optional Prisoner's Dilemma (OPD) game is employed. The OPD is an extended version of the traditional Prisoner's Dilemma where players have a third option to abstain from playing the game. We adopt an agent-based simulation approach and use Monte Carlo methods to perform the OPD with coevolutionary rules. The necessary conditions to break the scenarios of cyclic dominance are also investigated. This work highlights that cyclic dominance is essential in the sustenance of biodiversity. Moreover, we also discuss the importance of a spatial coevolutionary model in maintaining cyclic dominance in adverse conditions.
This paper investigates the validity of Kleinberg's axioms for clustering functions with respect to the quite popular clustering algorithm called $k$-means. While Kleinberg's axioms have been discussed heavily in the past, we concentrate here on the case predominantly relevant for $k$-means algorithm, that is behavior embedded in Euclidean space. We point at some contradictions and counter intuitiveness aspects of this axiomatic set within $\mathbb{R}^m$ that were evidently not discussed so far. Our results suggest that apparently without defining clearly what kind of clusters we expect we will not be able to construct a valid axiomatic system. In particular we look at the shape and the gaps between the clusters. Finally we demonstrate that there exist several ways to reconcile the formulation of the axioms with their intended meaning and that under this reformulation the axioms stop to be contradictory and the real-world $k$-means algorithm conforms to this axiomatic system.
The Minimum Weight Dominating Set (MWDS) problem is an important generalization of the Minimum Dominating Set (MDS) problem with extensive applications. This paper proposes a new local search algorithm for the MWDS problem, which is based on two new ideas. The first idea is a heuristic called two-level configuration checking (CC2), which is a new variant of a recent powerful configuration checking strategy (CC) for effectively avoiding the recent search paths. The second idea is a novel scoring function based on the frequency of being uncovered of vertices. Our algorithm is called CC2FS, according to the names of the two ideas. The experimental results show that, CC2FS performs much better than some state-of-the-art algorithms in terms of solution quality on a broad range of MWDS benchmarks.
This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that provide evidence for or against a certain class. It overcomes several shortcoming of previous methods and provides great additional insight into the decision making process of classifiers. Making neural network decisions interpretable through visualization is important both to improve models and to accelerate the adoption of black-box classifiers in application areas such as medicine. We illustrate the method in experiments on natural images (ImageNet data), as well as medical images (MRI brain scans).
Congestive Heart Failure, or CHF, is a serious medical condition that can result in fluid buildup in the body as a result of a weak heart. When the heart can't pump enough blood to efficiently deliver nutrients and oxygen to the body, kidney function may be impaired, resulting in fluid retention. CHF patients require a broad drug regimen to maintain the delicate system balance, particularly between their heart and kidneys. These drugs include ACE inhibitors and Beta Blockers to control blood pressure, anticoagulants to prevent blood clots, and diuretics to reduce fluid overload. Many of these drugs may interact, and potential effects of these interactions must be weighed against their benefits. For this project, we consider a set of 44 drugs identified as specifically relevant for treating CHF by pediatric cardiologists at Lucile Packard Children's Hospital. This list was generated as part of our current work at the LPCH Heart Center. The goal of this project is to identify and evaluate potentially harmful drug-drug interactions (DDIs) within pediatric patients with Congestive Heart Failure. This identification will be done autonomously, so that it may continuously update by evaluating newly published literature.
In modern machine learning, pattern recognition replaces realtime semantic reasoning. The mapping from input to output is learned with fixed semantics by training outcomes deliberately. This is an expensive and static approach which depends heavily on the availability of a very particular kind of prior raining data to make inferences in a single step. Conventional semantic network approaches, on the other hand, base multi-step reasoning on modal logics and handcrafted ontologies, which are ad hoc, expensive to construct, and fragile to inconsistency. Both approaches may be enhanced by a hybrid approach, which completely separates reasoning from pattern recognition. In this report, a quasi-linguistic approach to knowledge representation is discussed, motivated by spacetime structure. Tokenized patterns from diverse sources are integrated to build a lightly constrained and approximately scale-free network. This is then be parsed with very simple recursive algorithms to generate `brainstorming' sets of reasoned knowledge.
In this work we study the quantitative relation between the recursive teaching dimension (RTD) and the VC dimension (VCD) of concept classes of finite sizes. The RTD of a concept class $\mathcal C \subseteq \{0, 1\}^n$, introduced by Zilles et al. (2011), is a combinatorial complexity measure characterized by the worst-case number of examples necessary to identify a concept in $\mathcal C$ according to the recursive teaching model. For any finite concept class $\mathcal C \subseteq \{0,1\}^n$ with $\mathrm{VCD}(\mathcal C)=d$, Simon & Zilles (2015) posed an open problem $\mathrm{RTD}(\mathcal C) = O(d)$, i.e., is RTD linearly upper bounded by VCD? Previously, the best known result is an exponential upper bound $\mathrm{RTD}(\mathcal C) = O(d \cdot 2^d)$, due to Chen et al. (2016). In this paper, we show a quadratic upper bound: $\mathrm{RTD}(\mathcal C) = O(d^2)$, much closer to an answer to the open problem. We also discuss the challenges in fully solving the problem.
Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony. In contrast, the synchronous approach is often thought to be impractical due to idle time wasted on waiting for straggling workers. We revisit these conventional beliefs in this paper, and examine the weaknesses of both approaches. We demonstrate that a third approach, synchronous optimization with backup workers, can avoid asynchronous noise while mitigating for the worst stragglers. Our approach is empirically validated and shown to converge faster and to better test accuracies.
Distributed optimization algorithms are widely used in many industrial machine learning applications. However choosing the appropriate algorithm and cluster size is often difficult for users as the performance and convergence rate of optimization algorithms vary with the size of the cluster. In this paper we make the case for an ML-optimizer that can select the appropriate algorithm and cluster size to use for a given problem. To do this we propose building two models: one that captures the system level characteristics of how computation, communication change as we increase cluster sizes and another that captures how convergence rates change with cluster sizes. We present preliminary results from our prototype implementation called Hemingway and discuss some of the challenges involved in developing such a system.
Traditionally, multi-layer neural networks use dot product between the output vector of previous layer and the incoming weight vector as the input to activation function. The result of dot product is unbounded, thus increases the risk of large variance. Large variance of neuron makes the model sensitive to the change of input distribution, thus results in poor generalization, and aggravates the internal covariate shift which slows down the training. To bound dot product and decrease the variance, we propose to use cosine similarity or centered cosine similarity (Pearson Correlation Coefficient) instead of dot product in neural networks, which we call cosine normalization. We compare cosine normalization with batch, weight and layer normalization in fully-connected neural networks as well as convolutional networks on the data sets of MNIST, 20NEWS GROUP, CIFAR-10/100 and SVHN. Experiments show that cosine normalization achieves better performance than other normalization techniques.
Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it. Traditionally, such algorithms make decisions, i.e., select actions to execute, at every single time step of the agent-environment interactions. In this paper, we propose a novel framework, Fine Grained Action Repetition (FiGAR), which enables the agent to decide the action as well as the time scale of repeating it. FiGAR can be used for improving any Deep Reinforcement Learning algorithm which maintains an explicit policy estimate by enabling temporal abstractions in the action space. We empirically demonstrate the efficacy of our framework by showing performance improvements on top of three policy search algorithms in different domains: Asynchronous Advantage Actor Critic in the Atari 2600 domain, Trust Region Policy Optimization in Mujoco domain and Deep Deterministic Policy Gradients in the TORCS car racing domain.
Reason and inference require process as well as memory skills by humans. Neural networks are able to process tasks like image recognition (better than humans) but in memory aspects are still limited (by attention mechanism, size). Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve small memory contexts, but as context becomes larger than a threshold, it is difficult to use them. The Solution is to use large external memory. Still, it poses many challenges like, how to train neural networks for discrete memory representation, how to describe long term dependencies in sequential data etc. Most prominent neural architectures for such tasks are Memory networks: inference components combined with long term memory and Neural Turing Machines: neural networks using external memory resources. Also, additional techniques like attention mechanism, end to end gradient descent on discrete memory representation are needed to support these solutions. Preliminary results of above neural architectures on simple algorithms (sorting, copying) and Question Answering (based on story, dialogs) application are comparable with the state of the art. In this paper, I explain these architectures (in general), the additional techniques used and the results of their application.
Optimal stopping problems consider the question of deciding when to stop an observation-generating process in order to maximize a return. We examine the problem of simultaneously learning and planning in such domains, when data is collected directly from the environment. We propose GFSE, a simple and flexible model-free policy search method that reuses data for sample efficiency by leveraging problem structure. We bound the sample complexity of our approach to guarantee uniform convergence of policy value estimates, tightening existing PAC bounds to achieve logarithmic dependence on horizon length for our setting. We also examine the benefit of our method against prevalent model-based and model-free approaches on 3 domains taken from diverse fields.
We present a novel algorithm that synthesizes imperative programs for introductory programming courses. Given a set of input-output examples and a partial program, our algorithm generates a complete program that is consistent with every example. Our key idea is to combine enumerative program synthesis and static analysis, which aggressively prunes out a large search space while guaranteeing to find, if any, a correct solution. We have implemented our algorithm in a tool, called SIMPL, and evaluated it on 30 problems used in introductory programming courses. The results show that SIMPL is able to solve the benchmark problems in 6.6 seconds on average.
In order to obtain reliable accuracy estimates for automatic MOOC dropout predictors, it is important to train and test them in a manner consistent with how they will be used in practice. Yet most prior research on MOOC dropout prediction has measured test accuracy on the same course used for training the classifier, which can lead to overly optimistic accuracy estimates. In order to understand better how accuracy is affected by the training+testing regime, we compared the accuracy of a standard dropout prediction architecture (clickstream features + logistic regression) across 4 different training paradigms. Results suggest that (1) training and testing on the same course ("post-hoc") can overestimate accuracy by several percentage points; (2) dropout classifiers trained on proxy labels based on students' persistence are surprisingly competitive with post-hoc training (87.33% versus 90.20% AUC averaged over 8 weeks of 40 HarvardX MOOCs); and (3) classifier performance does not vary significantly with the academic discipline. Finally, we also research new dropout prediction architectures based on deep, fully-connected, feed-forward neural networks and find that (4) networks with as many as 5 hidden layers can statistically significantly increase test accuracy over that of logistic regression.
Colorization of grayscale images has been a hot topic in computer vision. Previous research mainly focuses on producing a colored image to match the original one. However, since many colors share the same gray value, an input grayscale image could be diversely colored while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test of 80 humans further indicates our generated color schemes are highly convincible.
Visual question answering (VQA) has witnessed great progress since May, 2015 as a classic problem unifying visual and textual data into a system. Many enlightening VQA works explore deep into the image and question encodings and fusing methods, of which attention is the most effective and infusive mechanism. Current attention based methods focus on adequate fusion of visual and textual features, but lack the attention to where people focus to ask questions about the image. Traditional attention based methods attach a single value to the feature at each spatial location, which losses many useful information. To remedy these problems, we propose a general method to perform saliency-like pre-selection on overlapped region features by the interrelation of bidirectional LSTM (BiLSTM), and use a novel element-wise multiplication based attention method to capture more competent correlation information between visual and textual features. We conduct experiments on the large-scale COCO-VQA dataset and analyze the effectiveness of our model demonstrated by strong empirical results.
Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in security-sensitive settings. It was observed that an adversary could easily generate adversarial samples by making a small perturbation on irrelevant feature dimensions that are unnecessary for the current classification task. To overcome this problem, we introduce a defensive mechanism called DeepCloak. By identifying and removing unnecessary features in a DNN model, DeepCloak limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs. Comparing with other defensive approaches, DeepCloak is easy to implement and computationally efficient. Experimental results show that DeepCloak can increase the performance of state-of-the-art DNN models against adversarial samples.
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity. Due to the halting problem, however, this notion is not computable. We hence propose to do causal inference by stochastic complexity. That is, we propose to approximate Kolmogorov complexity via the Minimum Description Length (MDL) principle, using a score that is mini-max optimal with regard to the model class under consideration. This means that even in an adversarial setting, such as when the true distribution is not in this class, we still obtain the optimal encoding for the data relative to the class. We instantiate this framework, which we call CISC, for pairs of univariate discrete variables, using the class of multinomial distributions. Experiments show that CISC is highly accurate on synthetic, benchmark, as well as real-world data, outperforming the state of the art by a margin, and scales extremely well with regard to sample and domain sizes.
The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent cooperation. Nevertheless, solving Distributed Constraint Optimization Problems (DCOPs) optimally is NP-hard. Therefore, in large-scale, complex applications, incomplete DCOP algorithms are necessary. Current incomplete DCOP algorithms suffer of one or more of the following limitations: they (a) find local minima without providing quality guarantees; (b) provide loose quality assessment; or (c) are unable to benefit from the structure of the problem, such as domain-dependent knowledge and hard constraints. Therefore, capitalizing on strategies from the centralized constraint solving community, we propose a Distributed Large Neighborhood Search (D-LNS) framework to solve DCOPs. The proposed framework (with its novel repair phase) provides guarantees on solution quality, refining upper and lower bounds during the iterative process, and can exploit domain-dependent structures. Our experimental results show that D-LNS outperforms other incomplete DCOP algorithms on both structured and unstructured problem instances.
The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques to solve Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field inception, the number of DCOP realistic applications and benchmark used to asses the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describe the problem of coordinating smart devices schedules across multiple homes as a multi-agent system, (ii) detail the physical models adopted to simulate smart sensors, smart actuators, and homes environments, and (iii) introduce a DCOP realistic benchmark for SHDS problems.
Devising an optimal strategy for navigation in a partially observable environment is one of the key objectives in AI. One of the problem in this context is the Canadian Traveler Problem (CTP). CTP is a navigation problem where an agent is tasked to travel from source to target in a partially observable weighted graph, whose edge might be blocked with a certain probability and observing such blockage occurs only when reaching upon one of the edges end points. The goal is to find a strategy that minimizes the expected travel cost. The problem is known to be P$\#$ hard. In this work we study the CTP theoretically and empirically. First, we study the Dep-CTP, a CTP variant we introduce which assumes dependencies between the edges status. We show that Dep-CTP is intractable, and further we analyze two of its subclasses on disjoint paths graph. Second, we develop a general algorithm Gen-PAO that optimally solve the CTP. Gen-PAO is capable of solving two other types of CTP called Sensing-CTP and Expensive-Edges CTP. Since the CTP is intractable, Gen-PAO use some pruning methods to reduce the space search for the optimal solution. We also define some variants of Gen-PAO, compare their performance and show some benefits of Gen-PAO over existing work.
Apprenticeship learning has recently attracted a wide attention due to its capability of allowing robots to learn physical tasks directly from demonstrations provided by human experts. Most previous techniques assumed that the state space is known a priori or employed simple state representations that usually suffer from perceptual aliasing. Different from previous research, we propose a novel approach named Sequence-based Multimodal Apprenticeship Learning (SMAL), which is capable to simultaneously fusing temporal information and multimodal data, and to integrate robot perception with decision making. To evaluate the SMAL approach, experiments are performed using both simulations and real-world robots in the challenging search and rescue scenarios. The empirical study has validated that our SMAL approach can effectively learn plans for robots to make decisions using sequence of multimodal observations. Experimental results have also showed that SMAL outperforms the baseline methods using individual images.
Representation learning of knowledge graphs encodes entities and relation types into a continuous low-dimensional vector space, learns embeddings of entities and relation types. Most existing methods only concentrate on knowledge triples, ignoring logic rules which contain rich background knowledge. Although there has been some work aiming at leveraging both knowledge triples and logic rules, they ignore the transitivity and antisymmetry of logic rules. In this paper, we propose a novel approach to learn knowledge representations with entities and ordered relations in knowledges and logic rules. The key idea is to integrate knowledge triples and logic rules, and approximately order the relation types in logic rules to utilize the transitivity and antisymmetry of logic rules. All entries of the embeddings of relation types are constrained to be non-negative. We translate the general constrained optimization problem into an unconstrained optimization problem to solve the non-negative matrix factorization. Experimental results show that our model significantly outperforms other baselines on knowledge graph completion task. It indicates that our model is capable of capturing the transitivity and antisymmetry information, which is significant when learning embeddings of knowledge graphs.
We introduce AI rationalization, an approach for generating explanations of autonomous system behavior as if a human had performed the behavior. We describe a rationalization technique that uses neural machine translation to translate internal state-action representations of an autonomous agent into natural language. We evaluate our technique in the Frogger game environment, training an autonomous game playing agent to rationalize its action choices using natural language. A natural language training corpus is collected from human players thinking out loud as they play the game. We motivate the use of rationalization as an approach to explanation generation and show the results of two experiments evaluating the effectiveness of rationalization. Results of these evaluations show that neural machine translation is able to accurately generate rationalizations that describe agent behavior, and that rationalizations are more satisfying to humans than other alternative methods of explanation.
Open forms of global constraints allow the addition of new variables to an argument during the execution of a constraint program. Such forms are needed for difficult constraint programming problems where problem construction and problem solving are interleaved, and fit naturally within constraint logic programming. However, in general, filtering that is sound for a global constraint can be unsound when the constraint is open. This paper provides a simple characterization, called contractibility, of the constraints where filtering remains sound when the constraint is open. With this characterization we can easily determine whether a constraint has this property or not. In the latter case, we can use it to derive a contractible approximation to the constraint. We demonstrate this work on both hard and soft constraints. In the process, we formulate two general classes of soft constraints.
Policy evaluation is a crucial step in many reinforcement-learning procedures, which estimates a value function that predicts states' long-term value under a given policy. In this paper, we focus on policy evaluation with linear function approximation over a fixed dataset. We first transform the empirical policy evaluation problem into a (quadratic) convex-concave saddle point problem, and then present a primal-dual batch gradient method, as well as two stochastic variance reduction methods for solving the problem. These algorithms scale linearly in both sample size and feature dimension. Moreover, they achieve linear convergence even when the saddle-point problem has only strong concavity in the dual variables but no strong convexity in the primal variables. Numerical experiments on benchmark problems demonstrate the effectiveness of our methods.
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted. The fundamental reason is the difficulty of back-propagation through discrete random variables combined with the inherent instability of the GAN training objective. To address these problems, we propose Maximum-Likelihood Augmented Discrete Generative Adversarial Networks. Instead of directly optimizing the GAN objective, we derive a novel and low-variance objective using the discriminator's output that follows corresponds to the log-likelihood. Compared with the original, the new objective is proved to be consistent in theory and beneficial in practice. The experimental results on various discrete datasets demonstrate the effectiveness of the proposed approach.
We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before. We apply our method to learning maximum entropy policies, resulting into a new algorithm, called soft Q-learning, that expresses the optimal policy via a Boltzmann distribution. We use the recently proposed amortized Stein variational gradient descent to learn a stochastic sampling network that approximates samples from this distribution. The benefits of the proposed algorithm include improved exploration and compositionality that allows transferring skills between tasks, which we confirm in simulated experiments with swimming and walking robots. We also draw a connection to actor-critic methods, which can be viewed performing approximate inference on the corresponding energy-based model.
Effective teams are crucial for organisations, especially in environments that require teams to be constantly created and dismantled, such as software development, scientific experiments, crowd-sourcing, or the classroom. Key factors influencing team performance are competences and personality of team members. Hence, we present a computational model to compose proficient and congenial teams based on individuals' personalities and their competences to perform tasks of different nature. With this purpose, we extend Wilde's post-Jungian method for team composition, which solely employs individuals' personalities. The aim of this study is to create a model to partition agents into teams that are balanced in competences, personality and gender. Finally, we present some preliminary empirical results that we obtained when analysing student performance. Results show the benefits of a more informed team composition that exploits individuals' competences besides information about their personalities.
Balancing fairness and efficiency in resource allocation is a classical economic and computational problem. The price of fairness measures the worst-case loss of economic efficiency when using an inefficient but fair allocation rule; for indivisible goods in many settings, this price is unacceptably high. One such setting is kidney exchange, where needy patients swap willing but incompatible kidney donors. In this work, we close an open problem regarding the theoretical price of fairness in modern kidney exchanges. We then propose a general hybrid fairness rule that balances a strict lexicographic preference ordering over classes of agents, and a utilitarian objective that maximizes economic efficiency. We develop a utility function for this rule that favors disadvantaged groups lexicographically; but if cost to overall efficiency becomes too high, it switches to a utilitarian objective. This rule has only one parameter which is proportional to a bound on the price of fairness, and can be adjusted by policymakers. We apply this rule to real data from a large kidney exchange and show that our hybrid rule produces more reliable outcomes than other fairness rules.
We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog, where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.
The runtime performance of modern SAT solvers on random $k$-CNF formulas is deeply connected with the 'phase-transition' phenomenon seen empirically in the satisfiability of random $k$-CNF formulas. Recent universal hashing-based approaches to sampling and counting crucially depend on the runtime performance of SAT solvers on formulas expressed as the conjunction of both $k$-CNF and XOR constraints (known as $k$-CNF-XOR formulas), but the behavior of random $k$-CNF-XOR formulas is unexplored in prior work. In this paper, we present the first study of the satisfiability of random $k$-CNF-XOR formulas. We show empirical evidence of a surprising phase-transition that follows a linear trade-off between $k$-CNF and XOR constraints. Furthermore, we prove that a phase-transition for $k$-CNF-XOR formulas exists for $k = 2$ and (when the number of $k$-CNF constraints is small) for $k > 2$.
We study the problem of causal structure learning over a set of random variables when the experimenter is allowed to perform at most $M$ experiments in a non-adaptive manner. We consider the optimal learning strategy in terms of minimizing the portions of the structure that remains unknown given the limited number of experiments in both Bayesian and minimax setting. We characterize the theoretical optimal solution and propose an algorithm, which designs the experiments efficiently in terms of time complexity. We show that for bounded degree graphs, in the minimax case and in the Bayesian case with uniform priors, our proposed algorithm is a $\rho$-approximation algorithm, where $\rho$ is independent of the order of the underlying graph. Simulations on both synthetic and real data show that the performance of our algorithm is very close to the optimal solution.
For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social interaction skills after 14 days of interacting with people in an uncontrolled real world. Each and every day during the 14 days, the system gathered robot interaction experiences with people through a hit-and-trial method and then trained the MDARQN on these experiences using end-to-end reinforcement learning approach. The results of interaction based learning indicate that the robot has learned to respond to complex human behaviors in a perceivable and socially acceptable manner.
This paper presents an approach for transforming data granularity in hierarchical databases for binary decision problems by applying regression to categorical attributes at the lower grain levels. Attributes from a lower hierarchy entity in the relational database have their information content optimized through regression on the categories histogram trained on a small exclusive labelled sample, instead of the usual mode category of the distribution. The paper validates the approach on a binary decision task for assessing the quality of secondary schools focusing on how logistic regression transforms the students and teachers attributes into school attributes. Experiments were carried out on Brazilian schools public datasets via 10-fold cross-validation comparison of the ranking score produced also by logistic regression. The proposed approach achieved higher performance than the usual distribution mode transformation and equal to the expert weighing approach measured by the maximum Kolmogorov-Smirnov distance and the area under the ROC curve at 0.01 significance level.
The optimal allocation of resources for maximizing influence, spread of information or coverage, has gained attention in the past years, in particular in machine learning and data mining. But in applications, the parameters of the problem are rarely known exactly, and using wrong parameters can lead to undesirable outcomes. We hence revisit a continuous version of the Budget Allocation or Bipartite Influence Maximization problem introduced by Alon et al. (2012) from a robust optimization perspective, where an adversary may choose the least favorable parameters within a confidence set. The resulting problem is a nonconvex-concave saddle point problem (or game). We show that this nonconvex problem can be solved exactly by leveraging connections to continuous submodular functions, and by solving a constrained submodular minimization problem. Although constrained submodular minimization is hard in general, here, we establish conditions under which such a problem can be solved to arbitrary precision $\epsilon$.
We consider elections where the voters come one at a time, in a streaming fashion, and devise space-efficient algorithms which identify an approximate winning committee with respect to common multiwinner proportional representation voting rules; specifically, we consider the Approval-based and the Borda-based variants of both the Chamberlin-- ourant rule and the Monroe rule. We complement our algorithms with lower bounds. Somewhat surprisingly, our results imply that, using space which does not depend on the number of voters it is possible to efficiently identify an approximate representative committee of fixed size over vote streams with huge number of voters.
We establish a new connection between value and policy based reinforcement learning (RL) based on a relationship between softmax temporal value consistency and policy optimality under entropy regularization. Specifically, we show that softmax consistent action values correspond to optimal entropy regularized policy probabilities along any action sequence, regardless of provenance. From this observation, we develop a new RL algorithm, Path Consistency Learning (PCL), that minimizes a notion of soft consistency error along multi-step action sequences extracted from both on- and off-policy traces. We examine the behavior of PCL in different scenarios and show that PCL can be interpreted as generalizing both actor-critic and Q-learning algorithms. We subsequently deepen the relationship by showing how a single model can be used to represent both a policy and the corresponding softmax state values, eliminating the need for a separate critic. The experimental evaluation demonstrates that PCL significantly outperforms strong actor-critic and Q-learning baselines across several benchmarks.
Task-oriented dialog systems have been applied in various tasks, such as automated personal assistants, customer service providers and tutors. These systems work well when users have clear and explicit intentions that are well-aligned to the systems' capabilities. However, they fail if users intentions are not explicit. To address this shortcoming, we propose a framework to interleave non-task content (i.e. everyday social conversation) into task conversations. When the task content fails, the system can still keep the user engaged with the non-task content. We trained a policy using reinforcement learning algorithms to promote long-turn conversation coherence and consistency, so that the system can have smooth transitions between task and non-task content. To test the effectiveness of the proposed framework, we developed a movie promotion dialog system. Experiments with human users indicate that a system that interleaves social and task content achieves a better task success rate and is also rated as more engaging compared to a pure task-oriented system.
One-sided matching mechanisms are fundamental for assigning a set of indivisible objects to a set of self-interested agents when monetary transfers are not allowed. Two widely-studied randomized mechanisms in multiagent settings are the Random Serial Dictatorship (RSD) and the Probabilistic Serial Rule (PS). Both mechanisms require only that agents specify ordinal preferences and have a number of desirable economic and computational properties. However, the induced outcomes of the mechanisms are often incomparable and thus there are challenges when it comes to deciding which mechanism to adopt in practice. In this paper, we first consider the space of general ordinal preferences and provide empirical results on the (in)comparability of RSD and PS. We analyze their respective economic properties under general and lexicographic preferences. We then instantiate utility functions with the goal of gaining insights on the manipulability, efficiency, and envyfreeness of the mechanisms under different risk-attitude models. Our results hold under various preference distribution models, which further confirm the broad use of RSD in most practical applications.
An increasing amount of information is generated from the rapidly increasing number of sensor networks and smart devices. A wide variety of sources generate and publish information in different formats, thus highlighting interoperability as one of the key prerequisites for the success of Internet of Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing and consumption of available datasets from a wide range of sources. In this work, we propose a semantic enrichment of the BT Hypercat Data Hub, using well-accepted Semantic Web standards and tools. We propose an ontology that captures the semantics of the imported data and present the BT SPARQL Endpoint by means of a mapping between SPARQL and SQL queries. Furthermore, federated SPARQL queries allow queries over multiple hub-based and external data sources. Finally, we provide two use cases in order to illustrate the advantages afforded by our semantic approach.
Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heuristics applied to intermediate goals. So far, machine learning has generally not been used to filter or generate these steps. In this paper, we introduce a new dataset based on Higher-Order Logic (HOL) proofs, for the purpose of developing new machine learning-based theorem-proving strategies. We make this dataset publicly available under the BSD license. We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving. We also benchmark a set of simple baseline machine learning models suited for the tasks (including logistic regression, convolutional neural networks and recurrent neural networks). The results of our baseline models show the promise of applying machine learning to HOL theorem proving.
Most exact methods for k-nearest neighbour search suffer from the curse of dimensionality; that is, their query times exhibit exponential dependence on either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing (DCI) offers a promising way of circumventing the curse and successfully reduces the dependence of query time on intrinsic dimensionality from exponential to sublinear. In this paper, we propose a variant of DCI, which we call Prioritized DCI, and show a remarkable improvement in the dependence of query time on intrinsic dimensionality. In particular, a linear increase in intrinsic dimensionality, or equivalently, an exponential increase in the number of points near a query, can be mostly counteracted with just a linear increase in space. We also demonstrate empirically that Prioritized DCI significantly outperforms prior methods. In particular, relative to Locality-Sensitive Hashing (LSH), Prioritized DCI reduces the number of distance evaluations by a factor of 14 to 116 and the memory consumption by a factor of 21.
Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimization problems present interesting challenges for existing reinforcement learning algorithms. We develop an extension that is suited to learning optimization algorithms in this setting and demonstrate that the learned optimization algorithm consistently outperforms other known optimization algorithms even on unseen tasks and is robust to changes in stochasticity of gradients and the neural net architecture. More specifically, we show that an optimization algorithm trained with the proposed method on the problem of training a neural net on MNIST generalizes to the problems of training neural nets on the Toronto Faces Dataset, CIFAR-10 and CIFAR-100.
This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks. These layers encode constraints and complex dependencies between the hidden states that traditional convolutional and fully-connected layers often cannot capture. In this paper, we explore the foundations for such an architecture: we show how techniques from sensitivity analysis, bilevel optimization, and implicit differentiation can be used to exactly differentiate through these layers and with respect to layer parameters; we develop a highly efficient solver for these layers that exploits fast GPU-based batch solves within a primal-dual interior point method, and which provides backpropagation gradients with virtually no additional cost on top of the solve; and we highlight the application of these approaches in several problems. In one notable example, we show that the method is capable of learning to play mini-Sudoku (4x4) given just input and output games, with no a priori information about the rules of the game; this highlights the ability of our architecture to learn hard constraints better than other neural architectures.
In this paper, we present a general framework for learning social affordance grammar as a spatiotemporal AND-OR graph (ST-AOG) from RGB-D videos of human interactions, and transfer the grammar to humanoids to enable a real-time motion inference for human-robot interaction (HRI). Based on Gibbs sampling, our weakly supervised grammar learning can automatically construct a hierarchical representation of an interaction with long-term joint sub-tasks of both agents and short term atomic actions of individual agents. Based on a new RGB-D video dataset with rich instances of human interactions, our experiments of Baxter simulation, human evaluation, and real Baxter test demonstrate that the model learned from limited training data successfully generates human-like behaviors in unseen scenarios and outperforms both baselines.
The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. This work is an important first step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.
The success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand. This paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods to topology, components, and hyperparameters, this method achieves results comparable to best human designs in standard benchmarks in object recognition and language modeling. It also supports building a real-world application of automated image captioning on a magazine website. Given the anticipated increases in available computing power, evolution of deep networks is promising approach to constructing deep learning applications in the future.
Online two-sided matching markets such as Q&A forums (e.g. StackOverflow, Quora) and online labour platforms (e.g. Upwork) critically rely on the ability to propose adequate matches based on imperfect knowledge of the two parties to be matched. This prompts the following question: Which matching recommendation algorithms can, in the presence of such uncertainty, lead to efficient platform operation? To answer this question, we develop a model of a task / server matching system. For this model, we give a necessary and sufficient condition for an incoming stream of tasks to be manageable by the system. We further identify a so-called back-pressure policy under which the throughput that the system can handle is optimized. We show that this policy achieves strictly larger throughput than a natural greedy policy. Finally, we validate our model and confirm our theoretical findings with experiments based on logs of Math.StackExchange, a StackOverflow forum dedicated to mathematics.
Machine-learning techniques have been recently used with spectacular results to generate artefacts such as music or text. However, these techniques are still unable to capture and generate artefacts that are convincingly structured. In this paper we present an approach to generate structured musical sequences. We introduce a mechanism for sampling efficiently variations of musical sequences. Given a input sequence and a statistical model, this mechanism samples a set of sequences whose distance to the input sequence is approximately within specified bounds. This mechanism is implemented as an extension of belief propagation, and uses local fields to bias the generation. We show experimentally that sampled sequences are indeed closely correlated to the standard musical similarity measure defined by Mongeau and Sankoff. We then show how this mechanism can used to implement composition strategies that enforce arbitrary structure on a musical lead sheet generation problem.
Plan Recognition algorithms require to recognize a complete hierarchy explaining the agent's actions and goals. While the output of such algorithms is informative to the recognizer, the cost of its calculation is high in run-time, space, and completeness. Moreover, performing plan recognition online requires the observing agent to reason about future actions that have not yet been seen and maintain a set of hypotheses to support all possible options. This paper presents a new and efficient algorithm for online plan recognition called SLIM (Semi-Lazy Inference Mechanism). It combines both a bottom-up and top-down parsing processes, which allow it to commit only to the minimum necessary actions in real-time, but still provide complete hypotheses post factum. We show both theoretically and empirically that although the computational cost of this process is still exponential, there is a significant improvement in run-time when compared to a state of the art of plan recognition algorithm.
Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets. Code and additional results are available in https://github.com/mingyuliutw/unit .
In this work, we open up the DAWT dataset - Densely Annotated Wikipedia Texts across multiple languages. The annotations include labeled text mentions mapping to entities (represented by their Freebase machine ids) as well as the type of the entity. The data set contains total of 13.6M articles, 5.0B tokens, 13.8M mention entity co-occurrences. DAWT contains 4.8 times more anchor text to entity links than originally present in the Wikipedia markup. Moreover, it spans several languages including English, Spanish, Italian, German, French and Arabic. We also present the methodology used to generate the dataset which enriches Wikipedia markup in order to increase number of links. In addition to the main dataset, we open up several derived datasets including mention entity co-occurrence counts and entity embeddings, as well as mappings between Freebase ids and Wikidata item ids. We also discuss two applications of these datasets and hope that opening them up would prove useful for the Natural Language Processing and Information Retrieval communities, as well as facilitate multi-lingual research.
Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders and holistic attribute discriminators for effective imposition of semantic structures. With differentiable approximation to discrete text samples, explicit constraints on independent attribute controls, and efficient collaborative learning of generator and discriminators, our model learns highly interpretable representations from even only word annotations, and produces realistic sentences with desired attributes. Quantitative evaluation validates the accuracy of sentence and attribute generation.
Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-value functions (PVFs) are a well-known approach for representation learning in MDPs. In this paper we address the option discovery problem by showing how PVFs implicitly define options. We do it by introducing eigenpurposes, intrinsic reward functions derived from the learned representations. The options discovered from eigenpurposes traverse the principal directions of the state space. They are useful for multiple tasks because they are discovered without taking the environment's rewards into consideration. Moreover, different options act at different time scales, making them helpful for exploration. We demonstrate features of eigenpurposes in traditional tabular domains as well as in Atari 2600 games.
Restricted Boltzmann machines~(RBMs) and conditional RBMs~(CRBMs) are popular models for a wide range of applications. In previous work, learning on such models has been dominated by contrastive divergence~(CD) and its variants. Belief propagation~(BP) algorithms are believed to be slow for structured prediction on conditional RBMs~(e.g., Mnih et al. [2011]), and not as good as CD when applied in learning~(e.g., Larochelle et al. [2012]). In this work, we present a matrix-based implementation of belief propagation algorithms on CRBMs, which is easily scalable to tens of thousands of visible and hidden units. We demonstrate that, in both maximum likelihood and max-margin learning, training conditional RBMs with BP as the inference routine can provide significantly better results than current state-of-the-art CD methods on structured prediction problems. We also include practical guidelines on training CRBMs with BP, and some insights on the interaction of learning and inference algorithms for CRBMs.
One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream modules are affected by earlier modules, and the performance of the entire system is not robust to the accumulated errors. This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues. Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks. The reinforcement learning based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket booking domain show that our end-to-end system not only outperforms modularized dialogue system baselines for both objective and subjective evaluation, but also is robust to noises as demonstrated by several systematic experiments with different error granularity and rates specific to the language understanding module.
We design a new approach that allows robot learning of new activities from unlabeled human example videos. Given videos of humans executing the same activity from a human's viewpoint (i.e., first-person videos), our objective is to make the robot learn the temporal structure of the activity as its future regression network, and learn to transfer such model for its own motor execution. We present a new deep learning model: We extend the state-of-the-art convolutional object detection network for the representation/estimation of human hands in training videos, and newly introduce the concept of using a fully convolutional network to regress (i.e., predict) the intermediate scene representation corresponding to the future frame (e.g., 1-2 seconds later). Combining these allows direct prediction of future locations of human hands and objects, which enables the robot to infer the motor control plan using our manipulation network. We experimentally confirm that our approach makes learning of robot activities from unlabeled human interaction videos possible, and demonstrate that our robot is able to execute the learned collaborative activities in real-time directly based on its camera input.
We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learning across multiple levels -- allowing it to utilise different resolutions of time. Our framework employs a Manager module and a Worker module. The Manager operates at a lower temporal resolution and sets abstract goals which are conveyed to and enacted by the Worker. The Worker generates primitive actions at every tick of the environment. The decoupled structure of FuN conveys several benefits -- in addition to facilitating very long timescale credit assignment it also encourages the emergence of sub-policies associated with different goals set by the Manager. These properties allow FuN to dramatically outperform a strong baseline agent on tasks that involve long-term credit assignment or memorisation. We demonstrate the performance of our proposed system on a range of tasks from the ATARI suite and also from a 3D DeepMind Lab environment.
Community-based question answering (CQA) services are facing key challenges to motivate domain experts to provide timely answers. Recently, CQA services are exploring new incentive models to engage experts and celebrities by allowing them to set a price on their answers. In this paper, we perform a data-driven analysis on two emerging payment-based CQA systems: Fenda (China) and Whale (US). By analyzing a large dataset of 220K questions (worth 1 million USD collectively), we examine how monetary incentives affect different players in the system. We find that, while monetary incentive enables quick answers from experts, it also drives certain users to aggressively game the system for profits. In addition, in this supplier-driven marketplace, users need to proactively adjust their price to make profits. Famous people are unwilling to lower their price, which in turn hurts their income and engagement over time. Finally, we discuss the key implications to future CQA design.
In recent years, work has been done to develop the theory of General Reinforcement Learning (GRL). However, there are few examples demonstrating these results in a concrete way. In particular, there are no examples demonstrating the known results regarding gener- alised discounting. We have added to the GRL simulation platform AIXIjs the functionality to assign an agent arbitrary discount functions, and an environment which can be used to determine the effect of discounting on an agent's policy. Using this, we investigate how geometric, hyperbolic and power discounting affect an informed agent in a simple MDP. We experimentally reproduce a number of theoretical results, and discuss some related subtleties. It was found that the agent's behaviour followed what is expected theoretically, assuming appropriate parameters were chosen for the Monte-Carlo Tree Search (MCTS) planning algorithm.
As statistical classifiers become integrated into real-world applications, it is important to consider not only their accuracy but also their robustness to changes in the data distribution. In this paper, we consider the case where there is an unobserved confounding variable $z$ that influences both the features $\mathbf{x}$ and the class variable $y$. When the influence of $z$ changes from training to testing data, we find that the classifier accuracy can degrade rapidly. In our approach, we assume that we can predict the value of $z$ at training time with some error. The prediction for $z$ is then fed to Pearl's back-door adjustment to build our model. Because of the attenuation bias caused by measurement error in $z$, standard approaches to controlling for $z$ are ineffective. In response, we propose a method to properly control for the influence of $z$ by first estimating its relationship with the class variable $y$, then updating predictions for $z$ to match that estimated relationship. By adjusting the influence of $z$, we show that we can build a model that exceeds competing baselines on accuracy as well as on robustness over a range of confounding relationships.
Plausible reasoning concerns situations whose inherent lack of precision is not quantified; that is, there are no degrees or levels of precision, and hence no use of numbers like probabilities. A hopefully comprehensive set of principles that clarifies what it means for a formal logic to do plausible reasoning is presented. A new propositional logic, called Propositional Plausible Logic (PPL), is defined and applied to some important examples. PPL is the only non-numeric non-monotonic logic we know of that satisfies all the principles and correctly reasons with all the examples. Some important results about PPL are proved.
To be able to interact better with humans, it is crucial for machines to understand sound - a primary modality of human perception. Previous works have used sound to learn embeddings for improved generic textual similarity assessment. In this work, we treat sound as a first-class citizen, studying downstream textual tasks which require aural grounding. To this end, we propose sound-word2vec - a new embedding scheme that learns specialized word embeddings grounded in sounds. For example, we learn that two seemingly (semantically) unrelated concepts, like leaves and paper are similar due to the similar rustling sounds they make. Our embeddings prove useful in textual tasks requiring aural reasoning like text-based sound retrieval and discovering foley sound effects (used in movies). Moreover, our embedding space captures interesting dependencies between words and onomatopoeia and outperforms prior work on aurally-relevant word relatedness datasets such as AMEN and ASLex.
Markov chain model is widely applied in many fields, especially the field of prediction. The classical Discrete-time Markov chain(DTMC) is a widely used method for prediction. However, the classical DTMC model has some limitation when the system is complex with uncertain information or state space is not discrete. To address it, a new belief Markov chain model is proposed by combining Dempster-Shafer evidence theory with Markov chain. In our model, the uncertain data is allowed to be handle in the form of interval number and the basic probability assignment(BPA) is generated based on the distance between interval numbers. The new belief Markov chain model overcomes the shortcomings of classical Markov chain and has an efficient ability in dealing with uncertain information. Moreover, an example of inventory prediction and the comparison between our model and classical DTMC model can show the effectiveness and rationality of our proposed model.
Supplier selection is a typical multi-criteria decision making (MCDM) problem and lots of uncertain information exist inevitably. To address this issue, a new method was proposed based on interval data fusion. Our method follows the original way to generate classical basic probability assignment(BPA) determined by the distance among the evidences. However, the weights of criteria are kept as interval numbers to generate interval BPAs and do the fusion of interval BPAs. Finally, the order is ranked and the decision is made according to the obtained interval BPAs. In this paper, a numerical example of supplier selection is applied to verify the feasibility and validity of our method. The new method is presented aiming at solving multiple-criteria decision-making problems in which the weights of criteria or experts are described in fuzzy data like linguistic terms or interval data.
We investigate the performance of the standard Greedy algorithm for cardinality constrained maximization of non-submodular nondecreasing set functions. While there are strong theoretical guarantees on the performance of Greedy for maximizing submodular functions, there are few guarantees for non-submodular ones. However, Greedy enjoys strong empirical performance for many important non-submodular functions, e.g., the Bayesian A-optimality objective in experimental design. We prove theoretical guarantees supporting the empirical performance. Our guarantees are characterized by a combination of the (generalized) curvature $\alpha$ and the submodularity ratio $\gamma$. In particular, we prove that Greedy enjoys a tight approximation guarantee of $\frac{1}{\alpha}(1- e^{-\gamma\alpha})$ for cardinality constrained maximization. In addition, we bound the submodularity ratio and curvature for several important real-world objectives, including the Bayesian A-optimality objective, the determinantal function of a square submatrix and certain linear programs with combinatorial constraints. We experimentally validate our theoretical findings for both synthetic and real-world applications.
Advances in neural network based classifiers have transformed automatic feature learning from a pipe dream of stronger AI to a routine and expected property of practical systems. Since the emergence of AlexNet every winning submission of the ImageNet challenge has employed end-to-end representation learning, and due to the utility of good representations for transfer learning, representation learning has become as an important and distinct task from supervised learning. At present, this distinction is inconsequential, as supervised methods are state-of-the-art in learning transferable representations. But recent work has shown that generative models can also be powerful agents of representation learning. Will the representations learned from these generative methods ever rival the quality of those from their supervised competitors? In this work, we argue in the affirmative, that from an information theoretic perspective, generative models have greater potential for representation learning. Based on several experimentally validated assumptions, we show that supervised learning is upper bounded in its capacity for representation learning in ways that certain generative models, such as Generative Adversarial Networks (GANs) are not. We hope that our analysis will provide a rigorous motivation for further exploration of generative representation learning.
Epistemic planning can be used for decision making in multi-agent situations with distributed knowledge and capabilities. Recently, Dynamic Epistemic Logic (DEL) has been shown to provide a very natural and expressive framework for epistemic planning. We extend the DEL-based epistemic planning framework to include perspective shifts, allowing us to define new notions of sequential and conditional planning with implicit coordination. With these, it is possible to solve planning tasks with joint goals in a decentralized manner without the agents having to negotiate about and commit to a joint policy at plan time. First we define the central planning notions and sketch the implementation of a planning system built on those notions. Afterwards we provide some case studies in order to evaluate the planner empirically and to show that the concept is useful for multi-agent systems in practice.
A Robust Markov Decision Process (RMDP) is a sequential decision making model that accounts for uncertainty in the parameters of dynamic systems. This uncertainty introduces difficulties in learning an optimal policy, especially for environments with large state spaces. We propose two algorithms, RTD-DQN and Deep-RoK, for solving large-scale RMDPs using nonlinear approximation schemes such as deep neural networks. The RTD-DQN algorithm incorporates the robust Bellman temporal difference error into a robust loss function, yielding robust policies for the agent. The Deep-RoK algorithm is a robust Bayesian method, based on the Extended Kalman Filter (EKF), that accounts for both the uncertainty in the weights of the approximated value function and the uncertainty in the transition probabilities, improving the robustness of the agent. We provide theoretical results for our approach and test the proposed algorithms on a continuous state domain.
The sure thing principle and the law of total probability are basic laws in classic probability theory. A disjunction fallacy leads to the violation of these two classical probability laws. In this paper, a new quantum dynamic belief decision making model based on quantum dynamic modelling and Dempster-Shafer (D-S) evidence theory is proposed to address this issue and model the real human decision-making process. Some mathematical techniques are borrowed from quantum mathematics. Generally, belief and action are two parts in a decision making process. The uncertainty in belief part is represented by a superposition of certain states. The uncertainty in actions is represented as an extra uncertainty state. The interference effect is produced due to the entanglement between beliefs and actions. Basic probability assignment (BPA) of decisions is generated by quantum dynamic modelling. Then BPA of the extra uncertain state and an entanglement degree defined by an entropy function named Deng entropy are used to measure the interference effect. Compared the existing model, the number of free parameters is less in our model. Finally, a classical categorization decision-making experiment is illustrated to show the effectiveness of our model.
A team of robots sharing a common goal can benefit from coordination of the activities of team members, helping the team to reach the goal more reliably or quickly. We address the problem of coordinating the actions of a team of robots with periodic communication capability executing an information gathering task. We cast the problem as a multi-agent optimal decision-making problem with an information theoretic objective function. We show that appropriate techniques for solving decentralized partially observable Markov decision processes (Dec-POMDPs) are applicable in such information gathering problems. We quantify the usefulness of coordinated information gathering through simulation studies, and demonstrate the feasibility of the method in a real-world target tracking domain.
We consider the problem of learning a causal graph over a set of variables with interventions. We study the cost-optimal causal graph learning problem: For a given skeleton (undirected version of the causal graph), design the set of interventions with minimum total cost, that can uniquely identify any causal graph with the given skeleton. We show that this problem is solvable in polynomial time. Later, we consider the case when the number of interventions is limited. For this case, we provide polynomial time algorithms when the skeleton is a tree or a clique tree. For a general chordal skeleton, we develop an efficient greedy algorithm, which can be improved when the causal graph skeleton is an interval graph.
This work shows that policies with simple linear and RBF parameterizations can be trained to solve a variety of continuous control tasks, including the OpenAI gym benchmarks. The performance of these trained policies are competitive with state of the art results, obtained with more elaborate parameterizations such as fully connected neural networks. Furthermore, existing training and testing scenarios are shown to be very limited and prone to over-fitting, thus giving rise to only trajectory-centric policies. Training with a diverse initial state distribution is shown to produce more global policies with better generalization. This allows for interactive control scenarios where the system recovers from large on-line perturbations; as shown in the supplementary video.
This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.
Cluster analysis plays an important role in decision making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approach based on big bang-big crunch algorithm is proposed for clustering problems. The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory based scheme as compared to its similar heuristic techniques. Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets. The experimental results show the significant superiority of the proposed method over the similar algorithms.
Categorization is necessary for many decision making tasks. However, the categorization process may interfere the decision making result and the law of total probability can be violated in some situations. To predict the interference effect of categorization, some model based on quantum probability has been proposed. In this paper, a new quantum dynamic belief (QDB) model is proposed. Considering the precise decision may not be made during the process, the concept of uncertainty is introduced in our model to simulate real human thinking process. Then the interference effect categorization can be predicted by handling the uncertain information. The proposed model is applied to a categorization decision-making experiment to explain the interference effect of categorization. Compared with other models, our model is relatively more succinct and the result shows the correctness and effectiveness of our model.
People can learn a wide range of tasks from their own experience, but can also learn from observing other creatures. This can accelerate acquisition of new skills even when the observed agent differs substantially from the learning agent in terms of morphology. In this paper, we examine how reinforcement learning algorithms can transfer knowledge between morphologically different agents (e.g., different robots). We introduce a problem formulation where two agents are tasked with learning multiple skills by sharing information. Our method uses the skills that were learned by both agents to train invariant feature spaces that can then be used to transfer other skills from one agent to another. The process of learning these invariant feature spaces can be viewed as a kind of "analogy making", or implicit learning of partial correspondences between two distinct domains. We evaluate our transfer learning algorithm in two simulated robotic manipulation skills, and illustrate that we can transfer knowledge between simulated robotic arms with different numbers of links, as well as simulated arms with different actuation mechanisms, where one robot is torque-driven while the other is tendon-driven.
Gene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data.
One of the critical issues when adopting Bayesian networks (BNs) to model dependencies among random variables is to "learn" their structure, given the huge search space of possible solutions, i.e., all the possible direct acyclic graphs. This is a well-known NP-hard problem, which is also complicated by known pitfalls such as the issue of I-equivalence among different structures. In this work we restrict the investigations on BN structure learning to a specific class of networks, i.e., those representing the dynamics of phenomena characterized by the monotonic accumulation of events. Such phenomena allow to set specific structural constraints based on Suppes' theory of probabilistic causation and, accordingly, to define constrained BNs, named Suppes-Bayes Causal Networks (SBCNs). We here investigate the structure learning of SBCNs via extensive simulations with various state-of-the-art search strategies, such as canonical local search techniques and Genetic Algorithms. Among the main results we show that Suppes' constraints deeply simplify the learning task, by reducing the solution search space and providing a temporal ordering on the variables.
The most recent financial upheavals have cast doubt on the adequacy of some of the conventional quantitative risk management strategies, such as VaR (Value at Risk), in many common situations. Consequently, there has been an increasing need for verisimilar financial stress testings, namely simulating and analyzing financial portfolios in extreme, albeit rare scenarios. Unlike conventional risk management which exploits statistical correlations among financial instruments, here we focus our analysis on the notion of probabilistic causation, which is embodied by Suppes-Bayes Causal Networks (SBCNs), SBCNs are probabilistic graphical models that have many attractive features in terms of more accurate causal analysis for generating financial stress scenarios. In this paper, we present a novel approach for conducting stress testing of financial portfolios based on SBCNs in combination with classical machine learning classification tools. The resulting method is shown to be capable of correctly discovering the causal relationships among financial factors that affect the portfolios and thus, simulating stress testing scenarios with a higher accuracy and lower computational complexity than conventional Monte Carlo Simulations.
Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have `long tails' and suffer from the problem of concept drift. In this paper, we propose a lightweight, feature-agnostic Information Extraction (IE) paradigm specifically designed for such domains. Our approach uses raw, unlabeled text from an initial corpus, and a few (12-120) seed annotations per domain-specific attribute, to learn robust IE models for unobserved pages and websites. Empirically, we demonstrate that our approach can outperform feature-centric Conditional Random Field baselines by over 18\% F-Measure on five annotated sets of real-world human trafficking datasets in both low-supervision and high-supervision settings. We also show that our approach is demonstrably robust to concept drift, and can be efficiently bootstrapped even in a serial computing environment.
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model. As a side effect, the embedding comes with an easy way of visualizing what specific parts of the sentence are encoded into the embedding. We evaluate our model on 3 different tasks: author profiling, sentiment classification, and textual entailment. Results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks.
This study proposes behavior-based navigation architecture, named BBFM, to deal with the problem of navigating the mobile robot in unknown environments in the presence of obstacles and local minimum regions. In the architecture, the complex navigation task is split into principal sub-tasks or behaviors. Each behavior is implemented by a fuzzy controller and executed independently to deal with a specific problem of navigation. The fuzzy controller is modified to contain only the fuzzification and inference procedures so that its output is a membership function representing the behavior's objective. The membership functions of all controllers are then used as the objective functions for a multi-objective optimization process to coordinate all behaviors. The result of this process is an overall control signal, which is Pareto-optimal, used to control the robot. A number of simulations, comparisons, and experiments were conducted. The results show that the proposed architecture outperforms some popular behavior-based architectures in term of accuracy, smoothness, traveled distance, and time response.
We propose a new linear algebraic approach to the computation of Tarskian semantics in logic. We embed a finite model M in first-order logic with N entities in N-dimensional Euclidean space R^N by mapping entities of M to N dimensional one-hot vectors and k-ary relations to order-k adjacency tensors (multi-way arrays). Second given a logical formula F in prenex normal form, we compile F into a set Sigma_F of algebraic formulas in multi-linear algebra with a nonlinear operation. In this compilation, existential quantifiers are compiled into a specific type of tensors, e.g., identity matrices in the case of quantifying two occurrences of a variable. It is shown that a systematic evaluation of Sigma_F in R^N gives the truth value, 1(true) or 0(false), of F in M. Based on this framework, we also propose an unprecedented way of computing the least models defined by Datalog programs in linear spaces via matrix equations and empirically show its effectiveness compared to state-of-the-art approaches.
In this paper we study selected argument forms involving counterfactuals and indicative conditionals under uncertainty. We selected argument forms to explore whether people with an Eastern cultural background reason differently about conditionals compared to Westerners, because of the differences in the location of negations. In a 2x2 between-participants design, 63 Japanese university students were allocated to four groups, crossing indicative conditionals and counterfactuals, and each presented in two random task orders. The data show close agreement between the responses of Easterners and Westerners. The modal responses provide strong support for the hypothesis that conditional probability is the best predictor for counterfactuals and indicative conditionals. Finally, the grand majority of the responses are probabilistically coherent, which endorses the psychological plausibility of choosing coherence-based probability logic as a rationality framework for psychological reasoning research.
We present a method for skin lesion segmentation for the ISIC 2017 Skin Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional Network architecture which is trained end to end, from scratch, on a limited dataset. Our semantic segmentation architecture utilizes several recent innovations in particularly in the combined use of (i) use of atrous convolutions to increase the effective field of view of the network's receptive field without increasing the number of parameters, (ii) the use of network-in-network $1\times1$ convolution layers to add capacity to the network and (iii) state-of-art super-resolution upsampling of predictions using subpixel CNN layers. We reported a mean IOU score of 0.642 on the validation set provided by the organisers.
Autonomous agents must often detect affordances: the set of behaviors enabled by a situation. Affordance detection is particularly helpful in domains with large action spaces, allowing the agent to prune its search space by avoiding futile behaviors. This paper presents a method for affordance extraction via word embeddings trained on a Wikipedia corpus. The resulting word vectors are treated as a common knowledge database which can be queried using linear algebra. We apply this method to a reinforcement learning agent in a text-only environment and show that affordance-based action selection improves performance most of the time. Our method increases the computational complexity of each learning step but significantly reduces the total number of steps needed. In addition, the agent's action selections begin to resemble those a human would choose.
Evaluating a policy by deploying it in the real world can be risky and costly. Off-policy policy evaluation (OPE) algorithms use historical data collected from running a previous policy to evaluate a new policy, which provides a means for evaluating a policy without requiring it to ever be deployed. Importance sampling is a popular OPE method because it is robust to partial observability and works with continuous states and actions. However, the amount of historical data required by importance sampling can scale exponentially with the horizon of the problem: the number of sequential decisions that are made. We propose using policies over temporally extended actions, called options, and show that combining these policies with importance sampling can significantly improve performance for long-horizon problems. In addition, we can take advantage of special cases that arise due to options-based policies to further improve the performance of importance sampling. We further generalize these special cases to a general covariance testing rule that can be used to decide which weights to drop in an IS estimate, and derive a new IS algorithm called Incremental Importance Sampling that can provide significantly more accurate estimates for a broad class of domains.
Training deep neural networks is a highly nontrivial task, involving carefully selecting appropriate training algorithms, scheduling step sizes and tuning other hyperparameters. Trying different combinations can be quite labor-intensive and time consuming. Recently, researchers have tried to use deep learning algorithms to exploit the landscape of the loss function of the training problem of interest, and learn how to optimize over it in an automatic way. In this paper, we propose a new learning-to-learn model and some useful and practical tricks. Our optimizer outperforms generic, hand-crafted optimization algorithms and state-of-the-art learning-to-learn optimizers by DeepMind in many tasks. We demonstrate the effectiveness of our algorithms on a number of tasks, including deep MLPs, CNNs, and simple LSTMs.
Our overall program objective is to provide more natural ways for soldiers to interact and communicate with robots, much like how soldiers communicate with other soldiers today. We describe how the Wizard-of-Oz (WOz) method can be applied to multimodal human-robot dialogue in a collaborative exploration task. While the WOz method can help design robot behaviors, traditional approaches place the burden of decisions on a single wizard. In this work, we consider two wizards to stand in for robot navigation and dialogue management software components. The scenario used to elicit data is one in which a human-robot team is tasked with exploring an unknown environment: a human gives verbal instructions from a remote location and the robot follows them, clarifying possible misunderstandings as needed via dialogue. We found the division of labor between wizards to be workable, which holds promise for future software development.
Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when conditions in training differ from those in test. Recent work on explanations for black-box models has produced tools (e.g. LIME) to show the implicit rules behind predictions, which can help us identify when models are right for the wrong reasons. However, these methods do not scale to explaining entire datasets and cannot correct the problems they reveal. We introduce a method for efficiently explaining and regularizing differentiable models by examining and selectively penalizing their input gradients, which provide a normal to the decision boundary. We apply these penalties both based on expert annotation and in an unsupervised fashion that encourages diverse models with qualitatively different decision boundaries for the same classification problem. On multiple datasets, we show our approach generates faithful explanations and models that generalize much better when conditions differ between training and test.
Brain-inspired learning models attempt to mimic the cortical architecture and computations performed in the neurons and synapses constituting the human brain to achieve its efficiency in cognitive tasks. In this work, we present convolutional spike timing dependent plasticity based feature learning with biologically plausible leaky-integrate-and-fire neurons in Spiking Neural Networks (SNNs). We use shared weight kernels that are trained to encode representative features underlying the input patterns thereby improving the sparsity as well as the robustness of the learning model. We demonstrate that the proposed unsupervised learning methodology learns several visual categories for object recognition with fewer number of examples and outperforms traditional fully-connected SNN architectures while yielding competitive accuracy. Additionally, we observe that the learning model performs out-of-set generalization further making the proposed biologically plausible framework a viable and efficient architecture for future neuromorphic applications.
We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation.
It is well-known that any admissible unidirectional heuristic search algorithm must expand all states whose $f$-value is smaller than the optimal solution cost when using a consistent heuristic. Such states are called "surely expanded" (s.e.). A recent study characterized s.e. pairs of states for bidirectional search with consistent heuristics: if a pair of states is s.e. then at least one of the two states must be expanded. This paper derives a lower bound, VC, on the minimum number of expansions required to cover all s.e. pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions. We further prove that no admissible front-to-end algorithm has a worst case better than 2VC. Experimental results show that NBS competes with or outperforms existing bidirectional search algorithms, and often outperforms A* as well.
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a new set of requirements, none of which are difficult to achieve in isolation, but the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources. We assert that a new distributed execution framework is needed for such ML applications and propose a candidate approach with a proof-of-concept architecture that achieves a 63x performance improvement over a state-of-the-art execution framework for a representative application.
Recently, reinforcement learning has been successfully applied to the logical game of Go, various Atari games, and even a 3D game, Labyrinth, though it continues to have problems in sparse reward settings. It is difficult to explore, but also difficult to exploit, a small number of successes when learning policy. To solve this issue, the subgoal and option framework have been proposed. However, discovering subgoals online is too expensive to be used to learn options in large state spaces. We propose Micro-objective learning (MOL) to solve this problem. The main idea is to estimate how important a state is while training and to give an additional reward proportional to its importance. We evaluated our algorithm in two Atari games: Montezuma's Revenge and Seaquest. With three experiments to each game, MOL significantly improved the baseline scores. Especially in Montezuma's Revenge, MOL achieved two times better results than the previous state-of-the-art model.
We introduce a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions. Unlike dynamics models that operate over individual discrete timesteps, we learn the distribution over future state trajectories conditioned on past state, past action, and planned future action trajectories, as well as a latent prior over action trajectories. Our approach is based on convolutional autoregressive models and variational autoencoders. It makes stable and accurate predictions over long horizons for complex, stochastic systems, effectively expressing uncertainty and modeling the effects of collisions, sensory noise, and action delays. The learned dynamics model and action prior can be used for end-to-end, fully differentiable trajectory optimization and model-based policy optimization, which we use to evaluate the performance and sample-efficiency of our method.
The problem of finding conflict-free trajectories for multiple agents of identical circular shape, operating in shared 2D workspace, is addressed in the paper and decoupled, e.g., prioritized, approach is used to solve this problem. Agents' workspace is tessellated into the square grid on which any-angle moves are allowed, e.g. each agent can move into an arbitrary direction as long as this move follows the straight line segment whose endpoints are tied to the distinct grid elements. A novel any-angle planner based on Safe Interval Path Planning (SIPP) algorithm is proposed to find trajectories for an agent moving amidst dynamic obstacles (other agents) on a grid. This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical, side we show that AA-SIPP(m) is complete under well-defined conditions. On the experimental side, in simulation tests with up to 200 agents involved, we show that our planner finds much better solutions in terms of cost (up to 20%) compared to the planners relying on cardinal moves only.
We approach structured output prediction by optimizing a deep value network (DVN) to precisely estimate the task loss on different output configurations for a given input. Once the model is trained, we perform inference by gradient descent on the continuous relaxations of the output variables to find outputs with promising scores from the value network. When applied to image segmentation, the value network takes an image and a segmentation mask as inputs and predicts a scalar estimating the intersection over union between the input and ground truth masks. For multi-label classification, the DVN's objective is to correctly predict the F1 score for any potential label configuration. The DVN framework achieves the state-of-the-art results on multi-label prediction and image segmentation benchmarks.
With the increasing popularity of machine learning techniques, it has become common to see prediction algorithms operating within some larger process. However, the criteria by which we train these algorithms often differ from the ultimate criteria on which we evaluate them. This paper proposes an end-to-end approach for learning probabilistic machine learning models in a manner that directly captures the ultimate task-based objective for which they will be used, within the context of stochastic programming. We present three experimental evaluations of the proposed approach: a classical inventory stock problem, a real-world electrical grid scheduling task, and a real-world energy storage arbitrage task. We show that the proposed approach can outperform both traditional modeling and purely black-box policy optimization approaches in these applications.
Use Case Points (UCP) is a well-known method to estimate the project size, based on Use Case diagram, at early phases of software development. Although the Use Case diagram is widely accepted as a de-facto model for analyzing object oriented software requirements over the world, UCP method did not take sufficient amount of attention because, as yet, there is no consensus on how to produce software effort from UCP. This paper aims to study the potential of using Fuzzy Model Tree to derive effort estimates based on UCP size measure using a dataset collected for that purpose. The proposed approach has been validated against Treeboost model, Multiple Linear Regression and classical effort estimation based on the UCP model. The obtained results are promising and show better performance than those obtained by classical UCP, Multiple Linear Regression and slightly better than those obtained by Tree boost model.
Case-Based Reasoning (CBR) has been widely used to generate good software effort estimates. The predictive performance of CBR is a dataset dependent and subject to extremely large space of configuration possibilities. Regardless of the type of adaptation technique, deciding on the optimal number of similar cases to be used before applying CBR is a key challenge. In this paper we propose a new technique based on Bisecting k-medoids clustering algorithm to better understanding the structure of a dataset and discovering the the optimal cases for each individual project by excluding irrelevant cases. Results obtained showed that understanding of the data characteristic prior prediction stage can help in automatically finding the best number of cases for each test project. Performance figures of the proposed estimation method are better than those of other regular K-based CBR methods.
In cooperative multiagent planning, it can often be beneficial for an agent to make commitments about aspects of its behavior to others, allowing them in turn to plan their own behaviors without taking the agent's detailed behavior into account. Extending previous work in the Bayesian setting, we consider instead a worst-case setting in which the agent has a set of possible environments (MDPs) it could be in, and develop a commitment semantics that allows for probabilistic guarantees on the agent's behavior in any of the environments it could end up facing. Crucially, an agent receives observations (of reward and state transitions) that allow it to potentially eliminate possible environments and thus obtain higher utility by adapting its policy to the history of observations. We develop algorithms and provide theory and some preliminary empirical results showing that they ensure an agent meets its commitments with history-dependent policies while minimizing maximum regret over the possible environments.
How can we explain the predictions of a black-box model? In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to influence functions can still provide valuable information. On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks.
Voting systems typically treat all voters equally. We argue that perhaps they should not: Voters who have supported good choices in the past should be given higher weight than voters who have supported bad ones. To develop a formal framework for desirable weighting schemes, we draw on no-regret learning. Specifically, given a voting rule, we wish to design a weighting scheme such that applying the voting rule, with voters weighted by the scheme, leads to choices that are almost as good as those endorsed by the best voter in hindsight. We derive possibility and impossibility results for the existence of such weighting schemes, depending on whether the voting rule and the weighting scheme are deterministic or randomized, as well as on the social choice axioms satisfied by the voting rule.
Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose a simple heuristic that guides the development of neural baseline systems for the extractive QA task. We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, a composition function that goes beyond simple bag-of-words modeling, such as recurrent neural networks. Our results show that FastQA, a system that meets these two requirements, can achieve very competitive performance compared with existing models. We argue that this surprising finding puts results of previous systems and the complexity of recent QA datasets into perspective.
Dempster-Shafer theory of evidence is widely applied to uncertainty modelling and knowledge reasoning because of its advantages in dealing with uncertain information. But some conditions or requirements, such as exclusiveness hypothesis and completeness constraint, limit the development and application of that theory to a large extend. To overcome the shortcomings and enhance its capability of representing the uncertainty, a novel model, called D numbers, has been proposed recently. However, many key issues, for example how to implement the combination of D numbers, remain unsolved. In the paper, we have explored the combination of D Numbers from a perspective of conflict redistribution, and proposed two combination rules being suitable for different situations for the fusion of two D numbers. The proposed combination rules can reduce to the classical Dempster's rule in Dempster-Shafer theory under a certain conditions. Numerical examples and discussion about the proposed rules are also given in the paper.
We introduce a criterion, resilience, which allows properties of a dataset (such as its mean or best low rank approximation) to be robustly computed, even in the presence of a large fraction of arbitrary additional data. Resilience is a weaker condition than most other properties considered so far in the literature, and yet enables robust estimation in a broader variety of settings. We provide new information-theoretic results on robust distribution learning, robust estimation of stochastic block models, and robust mean estimation under bounded $k$th moments. We also provide new algorithmic results on robust distribution learning, as well as robust mean estimation in $\ell_p$-norms. Among our proof techniques is a method for pruning a high-dimensional distribution with bounded $1$st moments to a stable "core" with bounded $2$nd moments, which may be of independent interest.
This paper presents a study of employing Ranking SVM and Convolutional Neural Network for two missions: legal information retrieval and question answering in the Competition on Legal Information Extraction/Entailment. For the first task, our proposed model used a triple of features (LSI, Manhattan, Jaccard), and is based on paragraph level instead of article level as in previous studies. In fact, each single-paragraph article corresponds to a particular paragraph in a huge multiple-paragraph article. For the legal question answering task, additional statistical features from information retrieval task integrated into Convolutional Neural Network contribute to higher accuracy.
Two-timescale Stochastic Approximation (SA) algorithms are widely used in Reinforcement Learning (RL). Their iterates have two parts that are updated using distinct stepsizes. In this work, we develop a novel recipe for their finite sample analysis. Using this, we provide a concentration bound, which is the first such result for a two-timescale SA. The type of bound we obtain is known as "lock-in probability". We also introduce a new projection scheme, in which the time between successive projections increases exponentially. This scheme allows one to elegantly transform a lock-in probability into a convergence rate result for projected two-timescale SA. From this latter result, we then extract key insights on stepsize selection. As an application, we finally obtain convergence rates for the projected two-timescale RL algorithms GTD(0), GTD2, and TDC.
Keyword spotting (KWS) constitutes a major component of human-technology interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprint size, latency and complexity are the goals for KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks (CRNNs). Inspired by large-scale state-of-the-art speech recognition systems, we combine the strengths of convolutional layers and recurrent layers to exploit local structure and long-range context. We analyze the effect of architecture parameters, and propose training strategies to improve performance. With only ~230k parameters, our CRNN model yields acceptably low latency, and achieves 97.71% accuracy at 0.5 FA/hour for 5 dB signal-to-noise ratio.
We consider the problem of provably optimal exploration in reinforcement learning for finite horizon MDPs. We show that an optimistic modification to value iteration achieves a regret bound of $\tilde{O}( \sqrt{HSAT} + H^2S^2A+H\sqrt{T})$ where $H$ is the time horizon, $S$ the number of states, $A$ the number of actions and $T$ the number of time-steps. This result improves over the best previous known bound $\tilde{O}(HS \sqrt{AT})$ achieved by the UCRL2 algorithm of Jaksch et al., 2010. The key significance of our new results is that when $T\geq H^3S^3A$ and $SA\geq H$, it leads to a regret of $\tilde{O}(\sqrt{HSAT})$ that matches the established lower bound of $\Omega(\sqrt{HSAT})$ up to a logarithmic factor. Our analysis contains two key insights. We use careful application of concentration inequalities to the optimal value function as a whole, rather than to the transitions probabilities (to improve scaling in $S$), and we define Bernstein-based "exploration bonuses" that use the empirical variance of the estimated values at the next states (to improve scaling in $H$).
The machining process is the most common method for metal cutting, and especially in the finishing of machined parts. In modern industry the goal of production is to manufacture products at a low cost, with high quality in the shortest time. In this research different biomaterials, machinability properties, surface characteristics, cutting tools, cutting fluids and machining conditions for biomaterials with machinability capability are reviewed. In the first step prosthetic acetabular (PA) hip is designed and printed by using selective laser melting (SLM) process then current limitations on fabrication are analyzed to optimize production process and obtain samples with higher quality. The feasibility of artificial intelligence (AI) in machining is determined and In order to calculate dimensional deviation the effect of tool path on tool deflection is modelled. The main focus of this research is determining the machining conditions on surface quality and osseointegration, work hardening and force analyzing of PA. Also the effect of heat treatment on machinability and mechanical properties of produced parts is determined.
The policy gradients of the expected return objective can react slowly to rare rewards. Yet, in some cases agents may wish to emphasize the low or high returns regardless of their probability. Borrowing from the economics and control literature, we review the risk-sensitive value function that arises from an exponential utility and illustrate its effects on an example. This risk-sensitive value function is not always applicable to reinforcement learning problems, so we introduce the particle value function defined by a particle filter over the distributions of an agent's experience, which bounds the risk-sensitive one. We illustrate the benefit of the policy gradients of this objective in Cliffworld.
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. We introduce Relational Graph Convolutional Networks (R-GCNs) and apply them to two standard knowledge base completion tasks: Link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes). R-GCNs are related to a recent class of neural networks operating on graphs, and are developed specifically to deal with the highly multi-relational data characteristic of realistic knowledge bases. We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification. We further show that factorization models for link prediction such as DistMult can be significantly improved by enriching them with an encoder model to accumulate evidence over multiple inference steps in the relational graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.
The principle of the common cause claims that if an improbable coincidence has occurred, there must exist a common cause. This is generally taken to mean that positive correlations between non-causally related events should disappear when conditioning on the action of some underlying common cause. The extended interpretation of the principle, by contrast, urges that common causes should be called for in order to explain positive deviations between the estimated correlation of two events and the expected value of their correlation. The aim of this paper is to provide the extended reading of the principle with a general probabilistic model, capturing the simultaneous action of a system of multiple common causes. To this end, two distinct models are elaborated, and the necessary and sufficient conditions for their existence are determined.
Many real-world tasks involve multiple agents with partial observability and limited communication. Learning is challenging in these settings due to local viewpoints of agents, which perceive the world as non-stationary due to concurrently-exploring teammates. Approaches that learn specialized policies for individual tasks face problems when applied to the real world: not only do agents have to learn and store distinct policies for each task, but in practice identities of tasks are often non-observable, making these approaches inapplicable. This paper formalizes and addresses the problem of multi-task multi-agent reinforcement learning under partial observability. We introduce a decentralized single-task learning approach that is robust to concurrent interactions of teammates, and present an approach for distilling single-task policies into a unified policy that performs well across multiple related tasks, without explicit provision of task identity.
Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic segmentation) thanks to a large repository of annotated image data. Large labeled datasets for other sensor modalities, e.g., multispectral imagery (MSI), are not available due to the large cost and manpower required. In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery. To overcome label scarcity for MSI data, we substitute real MSI for generated synthetic MSI in order to initialize a DCNN framework. We evaluate our network initialization scheme on the new RIT-18 dataset that we present in this paper. This dataset contains very-high resolution MSI collected by an unmanned aircraft system. The models initialized with synthetic imagery were less prone to over-fitting and provide a state-of-the-art baseline for future work.
Deliberating on large or continuous state spaces have been long standing challenges in reinforcement learning. Temporal Abstraction have somewhat made this possible, but efficiently planing using temporal abstraction still remains an issue. Moreover using spatial abstractions to learn policies for various situations at once while using temporal abstraction models is an open problem. We propose here an efficient algorithm which is convergent under linear function approximation while planning using temporally abstract actions. We show how this algorithm can be used along with randomly generated option models over multiple time scales to plan agents which need to act real time. Using these randomly generated option models over multiple time scales are shown to reduce number of decision epochs required to solve the given task, hence effectively reducing the time needed for deliberation.
The study of eye gaze fixations on photographic images is an active research area. In contrast, the image subcategory of freehand sketches has not received as much attention for such studies. In this paper, we analyze the results of a free-viewing gaze fixation study conducted on 3904 freehand sketches distributed across 160 object categories. Our analysis shows that fixation sequences exhibit marked consistency within a sketch, across sketches of a category and even across suitably grouped sets of categories. This multi-level consistency is remarkable given the variability in depiction and extreme image content sparsity that characterizes hand-drawn object sketches. In our paper, we show that the multi-level consistency in the fixation data can be exploited to (a) predict a test sketch's category given only its fixation sequence and (b) build a computational model which predicts part-labels underlying fixations on objects. We hope that our findings motivate the community to deem sketch-like representations worthy of gaze-based studies vis-a-vis photographic images.
Robust belief revision methods are crucial in streaming data situations for updating existing knowledge or beliefs with new incoming evidence. Bayes conditioning is the primary mechanism in use for belief revision in data fusion systems that use probabilistic inference. However, traditional conditioning methods face several challenges due to inherent data/source imperfections in big-data environments that harness soft (i.e., human or human-based) sources in addition to hard (i.e., physics-based) sensors. The objective of this paper is to investigate the most natural extension of Bayes conditioning that is suitable for evidence updating in the presence of such uncertainties. By viewing the evidence updating process as a thought experiment, an elegant strategy is derived for robust evidence updating in the presence of extreme uncertainties that are characteristic of big-data environments. In particular, utilizing the Fagin-Halpern conditional notions, a natural extension to Bayes conditioning is derived for evidence that takes the form of a general belief function. The presented work differs fundamentally from the Conditional Update Equation (CUE) and authors own extensions of it. An overview of this development is provided via illustrative examples. Furthermore, insights into parameter selection under various fusion contexts are also provided.
This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-based planning. It is a recurrent policy network, but it represents a policy for a parameterized set of tasks by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure of planning in a network learning architecture. The QMDP-net is fully differentiable and allows for end-to-end training. We train a QMDP-net on different tasks so that it can generalize to new ones in the parameterized task set and "transfer" to other similar tasks beyond the set. In preliminary experiments, QMDP-net showed strong performance on several robotic tasks in simulation. Interestingly, while QMDP-net encodes the QMDP algorithm, it sometimes outperforms the QMDP algorithm in the experiments, as a result of end-to-end learning.
Privacy has traditionally been a major motivation for distributed problem solving. Distributed Constraint Satisfaction Problem (DisCSP) as well as Distributed Constraint Optimization Problem (DCOP) are fundamental models used to solve various families of distributed problems. Even though several approaches have been proposed to quantify and preserve privacy in such problems, none of them is exempt from limitations. Here we approach the problem by assuming that computation is performed among utilitarian agents. We introduce a utilitarian approach where the utility of each state is estimated as the difference between the reward for reaching an agreement on assignments of shared variables and the cost of privacy loss. We investigate extensions to solvers where agents integrate the utility function to guide their search and decide which action to perform, defining thereby their policy. We show that these extended solvers succeed in significantly reducing privacy loss without significant degradation of the solution quality.
Language understanding is a key component in a spoken dialogue system. In this paper, we investigate how the language understanding module influences the dialogue system performance by conducting a series of systematic experiments on a task-oriented neural dialogue system in a reinforcement learning based setting. The empirical study shows that among different types of language understanding errors, slot-level errors can have more impact on the overall performance of a dialogue system compared to intent-level errors. In addition, our experiments demonstrate that the reinforcement learning based dialogue system is able to learn when and what to confirm in order to achieve better performance and greater robustness.
Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process models that describe highly frequent behavior, but these models do not always provide useful answers for questions posed by process analysts aiming at business process improvement. We propose a framework for goal-driven LPM discovery, based on utility functions and constraints. We describe four scopes on which these utility functions and constrains can be defined, and show that utility functions and constraints on different scopes can be combined to form composite utility functions/constraints. Finally, we demonstrate the applicability of our approach by presenting several actionable business insights discovered with LPM discovery on two real life data sets.
In all but the most trivial optimization problems, the structure of the solutions exhibit complex interdependencies between the input parameters. Decades of research with stochastic search techniques has shown the benefit of explicitly modeling the interactions between sets of parameters and the overall quality of the solutions discovered. We demonstrate a novel method, based on learning deep networks, to model the global landscapes of optimization problems. To represent the search space concisely and accurately, the deep networks must encode information about the underlying parameter interactions and their contributions to the quality of the solution. Once the networks are trained, the networks are probed to reveal parameter combinations with high expected performance with respect to the optimization task. These estimates are used to initialize fast, randomized, local search algorithms, which in turn expose more information about the search space that is subsequently used to refine the models. We demonstrate the technique on multiple optimization problems that have arisen in a variety of real-world domains, including: packing, graphics, job scheduling, layout and compression. The problems include combinatoric search spaces, discontinuous and highly non-linear spaces, and span binary, higher-cardinality discrete, as well as continuous parameters. Strengths, limitations, and extensions of the approach are extensively discussed and demonstrated.
Statistical performance bounds for reinforcement learning (RL) algorithms can be critical for high-stakes applications like healthcare. This paper introduces a new framework for theoretically measuring the performance of such algorithms called Uniform-PAC, which is a strengthening of the classical Probably Approximately Correct (PAC) framework. In contrast to the PAC framework, the uniform version may be used to derive high probability regret guarantees and so forms a bridge between the two setups that has been missing in the literature. We demonstrate the benefits of the new framework for finite-state episodic MDPs with a new algorithm that is Uniform-PAC and simultaneously achieves optimal regret and PAC guarantees except for a factor of the horizon.
In economics and psychology, delay discounting is often used to characterize how individuals choose between a smaller immediate reward and a larger delayed reward. People with higher delay discounting rate (DDR) often choose smaller but more immediate rewards (a "today person"). In contrast, people with a lower discounting rate often choose a larger future rewards (a "tomorrow person"). Since the ability to modulate the desire of immediate gratification for long term rewards plays an important role in our decision-making, the lower discounting rate often predicts better social, academic and health outcomes. In contrast, the higher discounting rate is often associated with problematic behaviors such as alcohol/drug abuse, pathological gambling and credit card default. Thus, research on understanding and moderating delay discounting has the potential to produce substantial societal benefits.
We propose a supervised algorithm for generating type embeddings in the same semantic vector space as a given set of entity embeddings. The algorithm is agnostic to the derivation of the underlying entity embeddings. It does not require any manual feature engineering, generalizes well to hundreds of types and achieves near-linear scaling on Big Graphs containing many millions of triples and instances by virtue of an incremental execution. We demonstrate the utility of the embeddings on a type recommendation task, outperforming a non-parametric feature-agnostic baseline while achieving 15x speedup and near-constant memory usage on a full partition of DBpedia. Using state-of-the-art visualization, we illustrate the agreement of our extensionally derived DBpedia type embeddings with the manually curated domain ontology. Finally, we use the embeddings to probabilistically cluster about 4 million DBpedia instances into 415 types in the DBpedia ontology.
We consider the problem of a robot learning the mechanical properties of objects through physical interaction with the object, and introduce a practical, data-efficient approach for identifying the motion models of these objects. The proposed method utilizes a physics engine, where the robot seeks to identify the inertial and friction parameters of the object by simulating its motion under different values of the parameters and identifying those that result in a simulation which matches the observed real motions. The problem is solved in a Bayesian optimization framework. The same framework is used for both identifying the model of an object online and searching for a policy that would minimize a given cost function according to the identified model. Experimental results both in simulation and using a real robot indicate that the proposed method outperforms state-of-the-art model-free reinforcement learning approaches.
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems. However, the behavior of deep networks is yet to be fully understood and is still an active area of research. In this work, we present an intriguing behavior: pre-trained CNNs can be made to improve their predictions by structurally perturbing the input. We observe that these perturbations - referred as Guided Perturbations - enable a trained network to improve its prediction performance without any learning or change in network weights. We perform various ablative experiments to understand how these perturbations affect the local context and feature representations. Furthermore, we demonstrate that this idea can improve performance of several existing approaches on semantic segmentation and scene labeling tasks on the PASCAL VOC dataset and supervised classification tasks on MNIST and CIFAR10 datasets.
Recently, research on accelerated stochastic gradient descent methods (e.g., SVRG) has made exciting progress (e.g., linear convergence for strongly convex problems). However, the best-known methods (e.g., Katyusha) requires at least two auxiliary variables and two momentum parameters. In this paper, we propose a fast stochastic variance reduction gradient (FSVRG) method, in which we design a novel update rule with the Nesterov's momentum and incorporate the technique of growing epoch size. FSVRG has only one auxiliary variable and one momentum weight, and thus it is much simpler and has much lower per-iteration complexity. We prove that FSVRG achieves linear convergence for strongly convex problems and the optimal $\mathcal{O}(1/T^2)$ convergence rate for non-strongly convex problems, where $T$ is the number of outer-iterations. We also extend FSVRG to directly solve the problems with non-smooth component functions, such as SVM. Finally, we empirically study the performance of FSVRG for solving various machine learning problems such as logistic regression, ridge regression, Lasso and SVM. Our results show that FSVRG outperforms the state-of-the-art stochastic methods, including Katyusha.
Many efforts have been dedicated to identifying restrictions on ontologies expressed as tuple-generating dependencies (tgds), a.k.a. existential rules, that lead to the decidability for the problem of answering ontology-mediated queries (OMQs). This has given rise to three families of formalisms: guarded, non-recursive, and sticky sets of tgds. In this work, we study the containment problem for OMQs expressed in such formalisms, which is a key ingredient for solving static analysis tasks associated with them. Our main contribution is the development of specially tailored techniques for OMQ containment under the classes of tgds stated above. This enables us to obtain sharp complexity bounds for the problems at hand, which in turn allow us to delimitate its practical applicability. We also apply our techniques to pinpoint the complexity of problems associated with two emerging applications of OMQ containment: distribution over components and UCQ rewritability of OMQs.
We present the first treatment of the arc length of the Gaussian Process (GP) with more than a single output dimension. GPs are commonly used for tasks such as trajectory modelling, where path length is a crucial quantity of interest. Previously, only paths in one dimension have been considered, with no theoretical consideration of higher dimensional problems. We fill the gap in the existing literature by deriving the moments of the arc length for a stationary GP with multiple output dimensions. A new method is used to derive the mean of a one-dimensional GP over a finite interval, by considering the distribution of the arc length integrand. This technique is used to derive an approximate distribution over the arc length of a vector valued GP in $\mathbb{R}^n$ by moment matching the distribution. Numerical simulations confirm our theoretical derivations.
Knowledge bases (KBs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform knowledge base completion or link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This article serves as a brief overview of embedding models of entities and relationships for knowledge base completion, summarizing up-to-date experimental results on standard benchmark datasets FB15k, WN18, FB15k-237, WN18RR, FB13 and WN11.
In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN). Our method is designed to work in both transductive and inductive settings while explicitly alleviating noise effects from outliers. Experimental results on various datasets drawn from the web, text and image domains demonstrate the advantages of SEANO over state-of-the-art methods in semi-supervised classification under transductive as well as inductive settings. We also show that a subset of parameters in SEANO is interpretable as outlier score and can significantly outperform baseline methods when applied for detecting network outliers. Finally, we present the use of SEANO in a challenging real-world setting -- flood mapping of satellite images and show that it is able to outperform modern remote sensing algorithms for this task.
This paper presents a statistical method for use in music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals. Because performed note durations can deviate largely from score-indicated values, previous methods had the problem of not being able to accurately estimate offset score times (or note values) and thus could only output incomplete musical scores. Based on observations that the pitch context and onset score times are influential on the configuration of note values, we construct a context-tree model that provides prior distributions of note values using these features and combine it with a performance model in the framework of Markov random fields. Evaluation results show that our method reduces the average error rate by around 40 percent compared to existing/simple methods. We also confirmed that, in our model, the score model plays a more important role than the performance model, and it automatically captures the voice structure by unsupervised learning.
As a general and thus popular model for autonomous systems, partially observable Markov decision process (POMDP) can capture uncertainties from different sources like sensing noises, actuation errors, and uncertain environments. However, its comprehensiveness makes the planning and control in POMDP difficult. Traditional POMDP planning problems target to find the optimal policy to maximize the expectation of accumulated rewards. But for safety critical applications, guarantees of system performance described by formal specifications are desired, which motivates us to consider formal methods to synthesize supervisor for POMDP. With system specifications given by Probabilistic Computation Tree Logic (PCTL), we propose a supervisory control framework with a type of deterministic finite automata (DFA), za-DFA, as the controller form. While the existing work mainly relies on optimization techniques to learn fixed-size finite state controllers (FSCs), we develop an $L^*$ learning based algorithm to determine both space and transitions of za-DFA. Membership queries and different oracles for conjectures are defined. The learning algorithm is sound and complete. An example is given in detailed steps to illustrate the supervisor synthesis algorithm.
A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method which we call Smart Augmentation and we show how to use it to increase the accuracy and reduce overfitting on a target network. Smart Augmentation works by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart Augmentation has shown the potential to increase accuracy by demonstrably significant measures on all datasets tested. In addition, it has shown potential to achieve similar or improved performance levels with significantly smaller network sizes in a number of tested cases.
We extend the $ASPIC^+$ framework for structured argumentation so as to allow applications of the reasoning by cases inference scheme for defeasible arguments. Given an argument with conclusion `$A$ or $B$', an argument based on $A$ with conclusion $C$, and an argument based on $B$ with conclusion $C$, we allow the construction of an argument with conclusion $C$. We show how our framework leads to different results than other approaches in non-monotonic logic for dealing with disjunctive information, such as disjunctive default theory or approaches based on the OR-rule (which allows to derive a defeasible rule `If ($A$ or $B$) then $C$', given two defeasible rules `If $A$ then $C$' and `If $B$ then $C$'). We raise new questions regarding the subtleties of reasoning defeasibly with disjunctive information, and show that its formalization is more intricate than one would presume.
Although information workers may complain about meetings, they are an essential part of their work life. Consequently, busy people spend a significant amount of time scheduling meetings. We present Calendar.help, a system that provides fast, efficient scheduling through structured workflows. Users interact with the system via email, delegating their scheduling needs to the system as if it were a human personal assistant. Common scheduling scenarios are broken down using well-defined workflows and completed as a series of microtasks that are automated when possible and executed by a human otherwise. Unusual scenarios fall back to a trained human assistant who executes them as unstructured macrotasks. We describe the iterative approach we used to develop Calendar.help, and share the lessons learned from scheduling thousands of meetings during a year of real-world deployments. Our findings provide insight into how complex information tasks can be broken down into repeatable components that can be executed efficiently to improve productivity.
Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task. Here, we propose a method, i.e. incremental moment matching (IMM), to resolve this problem. IMM incrementally matches the moment of the posterior distribution of the neural network which is trained on the first and the second task, respectively. To make the search space of posterior parameter smooth, the IMM procedure is complemented by various transfer learning techniques including weight transfer, L2-norm of the old and the new parameter, and a variant of dropout with the old parameter. We analyze our approach on a variety of datasets including the MNIST, CIFAR-10, Caltech-UCSD-Birds, and Lifelog datasets. The experimental results show that IMM achieves state-of-the-art performance by balancing the information between an old and a new network.
Whether teaching in a classroom or a Massive Online Open Course it is crucial to present the material in a way that benefits the audience as a whole. We identify two important tasks to solve towards this objective, 1 group students so that they can maximally benefit from peer interaction and 2 find an optimal schedule of the educational material for each group. Thus, in this paper, we solve the problem of team formation and content scheduling for education. Given a time frame d, a set of students S with their required need to learn different activities T and given k as the number of desired groups, we study the problem of finding k group of students. The goal is to teach students within time frame d such that their potential for learning is maximized and find the best schedule for each group. We show this problem to be NP-hard and develop a polynomial algorithm for it. We show our algorithm to be effective both on synthetic as well as a real data set. For our experiments, we use real data on students' grades in a Computer Science department. As part of our contribution, we release a semi-synthetic dataset that mimics the properties of the real data.
Recognizing arbitrary objects in the wild has been a challenging problem due to the limitations of existing classification models and datasets. In this paper, we propose a new task that aims at parsing scenes with a large and open vocabulary, and several evaluation metrics are explored for this problem. Our proposed approach to this problem is a joint image pixel and word concept embeddings framework, where word concepts are connected by semantic relations. We validate the open vocabulary prediction ability of our framework on ADE20K dataset which covers a wide variety of scenes and objects. We further explore the trained joint embedding space to show its interpretability.
The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal. Expert demonstrations provided by humans, however, often show significant variability due to latent factors that are typically not explicitly modeled. In this paper, we propose a new algorithm that can infer the latent structure of expert demonstrations in an unsupervised way. Our method, built on top of Generative Adversarial Imitation Learning, can not only imitate complex behaviors, but also learn interpretable and meaningful representations of complex behavioral data, including visual demonstrations. In the driving domain, we show that a model learned from human demonstrations is able to both accurately reproduce a variety of behaviors and accurately anticipate human actions using raw visual inputs. Compared with various baselines, our method can better capture the latent structure underlying expert demonstrations, often recovering semantically meaningful factors of variation in the data.
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules. However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.
In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as general-purpose music representation. In the experiments, a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks. The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as low- and high-level music features.
Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR application. Even though DL-based approaches now outperform the state-of-the-art in a number of recognitions tasks of the field, yet substantial challenges remain. Most prominently, issues with real-life datasets, typically including imbalanced datasets and problematic data quality, still limit the effectiveness of activity recognition using wearables. In this paper we tackle such challenges through Ensembles of deep Long Short Term Memory (LSTM) networks. We have developed modified training procedures for LSTM networks and combine sets of diverse LSTM learners into classifier collectives. We demonstrate, both formally and empirically, that Ensembles of deep LSTM learners outperform the individual LSTM networks. Through an extensive experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition capabilities of our approach and its potential for real-life applications of human activity recognition.
Multiple different approaches of generating adversarial examples have been proposed to attack deep neural networks. These approaches involve either directly computing gradients with respect to the image pixels, or directly solving an optimization on the image pixels. In this work, we present a fundamentally new method for generating adversarial examples that is fast to execute and provides exceptional diversity of output. We efficiently train feed-forward neural networks in a self-supervised manner to generate adversarial examples against a target network or set of networks. We call such a network an Adversarial Transformation Network (ATN). ATNs are trained to generate adversarial examples that minimally modify the classifier's outputs given the original input, while constraining the new classification to match an adversarial target class. We present methods to train ATNs and analyze their effectiveness targeting a variety of MNIST classifiers as well as the latest state-of-the-art ImageNet classifier Inception ResNet v2.
The exponential explosion of the set of patterns is one of the main challenges in pattern mining. This challenge is approached by introducing a constraint for pattern selection. One of the first constraints proposed in pattern mining is support (frequency) of a pattern in a dataset. Frequency is an anti-monotonic function, i.e., given an infrequent pattern, all its superpatterns are not frequent. However, many other constraints for pattern selection are neither monotonic nor anti-monotonic, which makes it difficult to generate patterns satisfying these constraints. In order to deal with nonmonotonic constraints we introduce the notion of "projection antimonotonicity" and SOFIA algorithm that allow generating best patterns for a class of nonmonotonic constraints. Cosine interest, robustness, stability of closed itemsets, and the associated delta-measure are among these constraints. SOFIA starts from light descriptions of transactions in dataset (a small set of items in the case of itemset description) and then iteratively adds more information to these descriptions (more items with indication of tidsets they describe).
While humor has been historically studied from a psychological, cognitive and linguistic standpoint, its study from a computational perspective is an area yet to be explored in Computational Linguistics. There exist some previous works, but a characterization of humor that allows its automatic recognition and generation is far from being specified. In this work we build a crowdsourced corpus of labeled tweets, annotated according to its humor value, letting the annotators subjectively decide which are humorous. A humor classifier for Spanish tweets is assembled based on supervised learning, reaching a precision of 84% and a recall of 69%.
Inverse reinforcement learning (IRL) aims to explain observed strategic behavior by fitting reinforcement learning models to behavioral data. However, traditional IRL methods are only applicable when the observations are in the form of state-action paths. This assumption may not hold in many real-world modelling settings, where only partial observations are available. In general, we may assume that there is a summarizing function $\sigma$, which acts as a filter between us and the true state-action paths that constitute the demonstration. Some initial approaches to extending IRL to such situations have been presented, but with very specific assumptions about the structure of $\sigma$, such as that only certain state observations are missing. This paper instead focuses on the most general case of the problem, where no assumptions are made about the summarizing function, except that it can be evaluated. We demonstrate that inference is still possible. The paper presents exact and approximate inference algorithms that allow full posterior inference, which is particularly important for assessing parameter uncertainty in this challenging inference situation. Empirical scalability is demonstrated to reasonably sized problems, and practical applicability is demonstrated by estimating the posterior for a cognitive science RL model based on observed user's task completion time only.
This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We design several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high quality texture images with desired perceptual properties.
The recently launched LinkedIn Salary product has been designed with the goal of providing compensation insights to the world's professionals and thereby helping them optimize their earning potential. We describe the overall design and architecture of the statistical modeling system underlying this product. We focus on the unique data mining challenges while designing and implementing the system, and describe the modeling components such as Bayesian hierarchical smoothing that help to compute and present robust compensation insights to users. We report on extensive evaluation with nearly one year of de-identified compensation data collected from over one million LinkedIn users, thereby demonstrating the efficacy of the statistical models. We also highlight the lessons learned through the deployment of our system at LinkedIn.
Semantic segmentation requires a detailed labeling of image pixels by object category. Information derived from local image patches is necessary to describe the detailed shape of individual objects. However, this information is ambiguous and can result in noisy labels. Global inference of image content can instead capture the general semantic concepts present. We advocate that holistic inference of image concepts provides valuable information for detailed pixel labeling. We propose a generic framework to leverage holistic information in the form of a LabelBank for pixel-level segmentation. We show the ability of our framework to improve semantic segmentation performance in a variety of settings. We learn models for extracting a holistic LabelBank from visual cues, attributes, and/or textual descriptions. We demonstrate improvements in semantic segmentation accuracy on standard datasets across a range of state-of-the-art segmentation architectures and holistic inference approaches.
Self-paced learning (SPL) is a new methodology that simulates the learning principle of humans/animals to start learning easier aspects of a learning task, and then gradually take more complex examples into training. This new-coming learning regime has been empirically substantiated to be effective in various computer vision and pattern recognition tasks. Recently, it has been proved that the SPL regime has a close relationship to a implicit self-paced objective function. While this implicit objective could provide helpful interpretations to the effectiveness, especially the robustness, insights under the SPL paradigms, there are still no theoretical results strictly proved to verify such relationship. To this issue, in this paper, we provide some convergence results on this implicit objective of SPL. Specifically, we prove that the learning process of SPL always converges to critical points of this implicit objective under some mild conditions. This result verifies the intrinsic relationship between SPL and this implicit objective, and makes the previous robustness analysis on SPL complete and theoretically rational.
In this paper, we address the problem of how automated situation-awareness can be achieved by learning real-world situations from ubiquitously generated mobility data. Without semantic input about the time and space where situations take place, this turns out to be a fundamental challenging problem. Uncertainties also introduce technical challenges when data is generated in irregular time intervals, being mixed with noise, and errors. Purely relying on temporal patterns observable in mobility data, in this paper, we propose Spaceprint, a fully automated algorithm for finding the repetitive pattern of similar situations in spaces. We evaluate this technique by showing how the latent variables describing the category, and the actual identity of a space can be discovered from the extracted situation patterns. Doing so, we use different real-world mobility datasets with data about the presence of mobile entities in a variety of spaces. We also evaluate the performance of this technique by showing its robustness against uncertainties.
We present a novel approach to deformable object manipulation that does not rely on highly-accurate modeling. The key contribution of this paper is to formulate the task as a Multi-Armed Bandit problem, with each arm representing a model of the deformable object. To "pull" an arm and evaluate its utility, we use the arm's model to generate a velocity command for the gripper(s) holding the object and execute it. As the task proceeds and the object deforms, the utility of each model can change. Our framework estimates these changes and balances exploration of the model set with exploitation of high-utility models. We also propose an approach based on Kalman Filtering for Non-stationary Multi-armed Normal Bandits (KF-MANB) to leverage the coupling between models to learn more from each arm pull. We demonstrate that our method outperforms previous methods on synthetic trials, and performs competitively on several manipulation tasks in simulation.
Robots and autonomous systems that operate around humans will likely always rely on kill switches that stop their execution and allow them to be remote-controlled for the safety of humans or to prevent damage to the system. It is theoretically possible for an autonomous system with sufficient sensor and effector capability and using reinforcement learning to learn that the kill switch deprives it of long-term reward and learn to act to disable the switch or otherwise prevent a human operator from using the switch. This is referred to as the big red button problem. We present a technique which prevents a reinforcement learning agent from learning to disable the big red button. Our technique interrupts the agent or robot by placing it in a virtual simulation where it continues to receive reward. We illustrate our technique in a simple grid world environment.
Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [17], and a more efficient variant TransE- AdaGrad [10] validate that ParTrans-X can speed up the training process by more than an order of magnitude.
We present a novel heuristic approach that defines fuzzy geographical descriptors using data gathered from a survey with human subjects. The participants were asked to provide graphical interpretations of the descriptors `north' and `south' for the Galician region (Spain). Based on these interpretations, our approach builds fuzzy descriptors that are able to compute membership degrees for geographical locations. We evaluated our approach in terms of efficiency and precision. The fuzzy descriptors are meant to be used as the cornerstones of a geographical referring expression generation algorithm that is able to linguistically characterize geographical locations and regions. This work is also part of a general research effort that intends to establish a methodology which reunites the empirical studies traditionally practiced in data-to-text and the use of fuzzy sets to model imprecision and vagueness in words and expressions for text generation purposes.
While strong progress has been made in image captioning over the last years, machine and human captions are still quite distinct. A closer look reveals that this is due to the deficiencies in the generated word distribution, vocabulary size, and strong bias in the generators towards frequent captions. Furthermore, humans -- rightfully so -- generate multiple, diverse captions, due to the inherent ambiguity in the captioning task which is not considered in today's systems. To address these challenges, we change the training objective of the caption generator from reproducing groundtruth captions to generating a set of captions that is indistinguishable from human generated captions. Instead of handcrafting such a learning target, we employ adversarial training in combination with an approximate Gumbel sampler to implicitly match the generated distribution to the human one. While our method achieves comparable performance to the state-of-the-art in terms of the correctness of the captions, we generate a set of diverse captions, that are significantly less biased and match the word statistics better in several aspects.
This paper introduces a new approach to the long-term tracking of an object in a challenging environment. The object is a cow and the environment is an enclosure in a cowshed. Some of the key challenges in this domain are a cluttered background, low contrast and high similarity between moving objects which greatly reduces the efficiency of most existing approaches, including those based on background subtraction. Our approach is split into object localization, instance segmentation, learning and tracking stages. Our solution is compared to a range of semi-supervised object tracking algorithms and we show that the performance is strong and well suited to subsequent analysis. We present our solution as a first step towards broader tracking and behavior monitoring for cows in precision agriculture with the ultimate objective of early detection of lameness.
With the popularity of massive open online courses, grading through crowdsourcing has become a prevalent approach towards large scale classes. However, for getting grades for complex tasks, which require specific skills and efforts for grading, crowdsourcing encounters a restriction of insufficient knowledge of the workers from the crowd. Due to knowledge limitation of the crowd graders, grading based on partial perspectives becomes a big challenge for evaluating complex tasks through crowdsourcing. Especially for those tasks which not only need specific knowledge for grading, but also should be graded as a whole instead of being decomposed into smaller and simpler subtasks. We propose a framework for grading complex tasks via multiple views, which are different grading perspectives defined by experts for the task, to provide uniformity. Aggregation algorithm based on graders variances are used to combine the grades for each view. We also detect bias patterns of the graders, and debias them regarding each view of the task. Bias pattern determines how the behavior is biased among graders, which is detected by a statistical technique. The proposed approach is analyzed on a synthetic data set. We show that our model gives more accurate results compared to the grading approaches without different views and debiasing algorithm.
Decision-makers are faced with the challenge of estimating what is likely to happen when they take an action. For instance, if I choose not to treat this patient, are they likely to die? Practitioners commonly use supervised learning algorithms to fit predictive models that help decision-makers reason about likely future outcomes, but we show that this approach is unreliable, and sometimes even dangerous. The key issue is that supervised learning algorithms are highly sensitive to the policy used to choose actions in the training data, which causes the model to capture relationships that do not generalize. We propose using a different learning objective that predicts counterfactuals instead of predicting outcomes under an existing action policy as in supervised learning. To support decision-making in temporal settings, we introduce the Counterfactual Gaussian Process (CGP) to predict the counterfactual future progression of continuous-time trajectories under sequences of future actions. We demonstrate the benefits of the CGP on two important decision-support tasks: risk prediction and "what if?" reasoning for individualized treatment planning.
While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved by introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence in discourse-level decision-making.
Data quality assessment and data cleaning are context-dependent activities. Motivated by this observation, we propose the Ontological Multidimensional Data Model (OMD model), which can be used to model and represent contexts as logic-based ontologies. The data under assessment is mapped into the context, for additional analysis, processing, and quality data extraction. The resulting contexts allow for the representation of dimensions, and multidimensional data quality assessment becomes possible. At the core of a multidimensional context we include a generalized multidimensional data model and a Datalog+/- ontology with provably good properties in terms of query answering. These main components are used to represent dimension hierarchies, dimensional constraints, dimensional rules, and define predicates for quality data specification. Query answering relies upon and triggers navigation through dimension hierarchies, and becomes the basic tool for the extraction of quality data. The OMD model is interesting per se, beyond applications to data quality. It allows for a logic-based, and computationally tractable representation of multidimensional data, extending previous multidimensional data models with additional expressive power and functionalities.
Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.
An important goal of computer vision is to build systems that learn visual representations over time that can be applied to many tasks. In this paper, we investigate a vision-language embedding as a core representation and show that it leads to better cross-task transfer than standard multi-task learning. In particular, the task of visual recognition is aligned to the task of visual question answering by forcing each to use the same word-region embeddings. We show this leads to greater inductive transfer from recognition to VQA than standard multitask learning. Visual recognition also improves, especially for categories that have relatively few recognition training labels but appear often in the VQA setting. Thus, our paper takes a small step towards creating more general vision systems by showing the benefit of interpretable, flexible, and trainable core representations.
General human action recognition requires understanding of various visual cues. In this paper, we propose a network architecture that computes and integrates the most important visual cues for action recognition: pose, motion, and the raw images. For the integration, we introduce a Markov chain model which adds cues successively. The resulting approach is efficient and applicable to action classification as well as to spatial and temporal action localization. The two contributions clearly improve the performance over respective baselines. The overall approach achieves state-of-the-art action classification performance on HMDB51, J-HMDB and NTU RGB+D datasets. Moreover, it yields state-of-the-art spatio-temporal action localization results on UCF101 and J-HMDB.
Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Cluster-aware Generative Model, that uses unlabelled information to infer a latent representation that models the natural clustering of the data, and additional labelled data points to refine this clustering. The generative performances of the model significantly improve when labelled information is exploited, obtaining a log-likelihood of -79.38 nats on permutation invariant MNIST, while also achieving competitive semi-supervised classification accuracies. The model can also be trained fully unsupervised, and still improve the log-likelihood performance with respect to related methods.
We consider tackling a single-agent RL problem by distributing it to $n$ learners. These learners, called advisors, endeavour to solve the problem from a different focus. Their advice, taking the form of action values, is then communicated to an aggregator, which is in control of the system. We show that the local planning method for the advisors is critical and that none of the ones found in the literature is flawless: the egocentric planning overestimates values of states where the other advisors disagree, and the agnostic planning is inefficient around danger zones. We introduce a novel approach called empathic and discuss its theoretical aspects. We empirically examine and validate our theoretical findings on a fruit collection task.
The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In this work, we present a density compiler for a probabilistic language with failure and both discrete and continuous distributions, and provide a proof of its soundness. The compiler greatly reduces the development effort of domain experts, which we demonstrate by solving inference problems from various scientific applications, such as modelling the global carbon cycle, using a standard Markov chain Monte Carlo framework.
This paper presents a design of a non-player character (AI) for promoting balancedness in use of body segments when engaging in full-body motion gaming. In our experiment, we settle a battle between the proposed AI and a player by using FightingICE, a fighting game platform for AI development. A middleware called UKI is used to allow the player to control the game by using body motion instead of the keyboard and mouse. During gameplay, the proposed AI analyze health states of the player; it determines its next action by predicting how each candidate action, recommended by a Monte-Carlo tree search algorithm, will induce the player to move, and how the player's health tends to be affected. Our result demonstrates successful improvement in balancedness in use of body segments on 4 out of 5 subjects.
The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain knowledge, (II) structure, and standardize where needed, orbital data and terminology, and (III) foster semantic interoperability and data-sharing. In doing so I hope to (IV) contribute to solving the orbital debris problem, improving peaceful global SSA, and ensuring safe space travel for future generations.
Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.
Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach for searching structured data based on probabilistic programming and nonparametric Bayes. Users specify queries in a probabilistic language that combines standard SQL database search operators with an information theoretic ranking function called predictive relevance. Predictive relevance can be calculated by a fast sparse matrix algorithm based on posterior samples from CrossCat, a nonparametric Bayesian model for high-dimensional, heterogeneously-typed data tables. The result is a flexible search technique that applies to a broad class of information retrieval problems, which we integrate into BayesDB, a probabilistic programming platform for probabilistic data analysis. This paper demonstrates applications to databases of US colleges, global macroeconomic indicators of public health, and classic cars. We found that human evaluators often prefer the results from probabilistic search to results from a standard baseline.
Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets. In this paper, we offer contributions in both these areas to enable similar progress in audio modeling. First, we detail a powerful new WaveNet-style autoencoder model that conditions an autoregressive decoder on temporal codes learned from the raw audio waveform. Second, we introduce NSynth, a large-scale and high-quality dataset of musical notes that is an order of magnitude larger than comparable public datasets. Using NSynth, we demonstrate improved qualitative and quantitative performance of the WaveNet autoencoder over a well-tuned spectral autoencoder baseline. Finally, we show that the model learns a manifold of embeddings that allows for morphing between instruments, meaningfully interpolating in timbre to create new types of sounds that are realistic and expressive.
It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult. That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data.
End-to-end training of deep learning-based models allows for implicit learning of intermediate representations based on the final task loss. However, the end-to-end approach ignores the useful domain knowledge encoded in explicit intermediate-level supervision. We hypothesize that using intermediate representations as auxiliary supervision at lower levels of deep networks may be a good way of combining the advantages of end-to-end training and more traditional pipeline approaches. We present experiments on conversational speech recognition where we use lower-level tasks, such as phoneme recognition, in a multitask training approach with an encoder-decoder model for direct character transcription. We compare multiple types of lower-level tasks and analyze the effects of the auxiliary tasks. Our results on the Switchboard corpus show that this approach improves recognition accuracy over a standard encoder-decoder model on the Eval2000 test set.
A landmark based heuristic is investigated for reducing query phase run-time of the probabilistic roadmap (\PRM) motion planning method. The heuristic is generated by storing minimum spanning trees from a small number of vertices within the \PRM graph and using these trees to approximate the cost of a shortest path between any two vertices of the graph. The intermediate step of preprocessing the graph increases the time and memory requirements of the classical motion planning technique in exchange for speeding up individual queries making the method advantageous in multi-query applications. This paper investigates these trade-offs on \PRM graphs constructed in randomized environments as well as a practical manipulator simulation.We conclude that the method is preferable to Dijkstra's algorithm or the ${\rm A}^*$ algorithm with conventional heuristics in multi-query applications.
Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and domain independent. However, there is a lack of negative examples. Existing works circumvent this problem by making various assumptions regarding the unconsumed items, which fail to hold when the user did not consume an item because she was unaware of it. In this paper we propose Conformative Filtering (CoF) as a novel method for addressing the lack of negative examples in implicit feedback. The motivation is that if there is a large group of users who share the same taste and none of them consumed an item, then it is highly likely that the item is irrelevant to this taste. We use Hierarchical Latent Tree Analysis (HLTA) to identify taste-based user groups, and make recommendations for a user based on her memberships in the groups. Experiments on real-world datasets from different domains show that CoF has superior performance compared to other baselines and more than 10% improvement in Recall@5 and Recall@10 is observed.
This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision systems face in this context is catastrophic forgetting: as they tend to adapt to the most recently seen task, they lose performance on the tasks that were learned previously. Our method aims at preserving the knowledge of the previous tasks while learning a new one by using autoencoders. For each task, an under-complete autoencoder is learned, capturing the features that are crucial for its achievement. When a new task is presented to the system, we prevent the reconstructions of the features with these autoencoders from changing, which has the effect of preserving the information on which the previous tasks are mainly relying. At the same time, the features are given space to adjust to the most recent environment as only their projection into a low dimension submanifold is controlled. The proposed system is evaluated on image classification tasks and shows a reduction of forgetting over the state-of-the-art
In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.
Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous environment simulators from high-dimensional pixel observations by introducing recurrent neural networks that are able to make temporally and spatially coherent predictions for hundreds of time-steps into the future. We present an in-depth analysis of the factors affecting performance, providing the most extensive attempt to advance the understanding of the properties of these models. We address the issue of computationally inefficiency with a model that does not need to generate a high-dimensional image at each time-step. We show that our approach can be used to improve exploration and is adaptable to many diverse environments, namely 10 Atari games, a 3D car racing environment, and complex 3D mazes.
In this paper we propose the Augmented-UCB (AugUCB) algorithm for a fixed-budget version of the thresholding bandit problem (TBP), where the objective is to identify a set of arms whose quality is above a threshold. A key feature of AugUCB is that it uses both mean and variance estimates to eliminate arms that have been sufficiently explored; to the best of our knowledge this is the first algorithm to employ such an approach for the considered TBP. Theoretically, we obtain an upper bound on the loss (probability of mis-classification) incurred by AugUCB. Although UCBEV in literature provides a better guarantee, it is important to emphasize that UCBEV has access to problem complexity (whose computation requires arms' mean and variances), and hence is not realistic in practice; this is in contrast to AugUCB whose implementation does not require any such complexity inputs. We conduct extensive simulation experiments to validate the performance of AugUCB. Through our simulation work, we establish that AugUCB, owing to its utilization of variance estimates, performs significantly better than the state-of-the-art APT, CSAR and other non variance-based algorithms.
Sentence simplification reduces semantic complexity to benefit people with language impairments. Previous simplification studies on the sentence level and word level have achieved promising results but also meet great challenges. For sentence-level studies, sentences after simplification are fluent but sometimes are not really simplified. For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification. In this paper, we propose a two-step simplification framework by combining both the word-level and the sentence-level simplifications, making use of their corresponding advantages. Based on the two-step framework, we implement a novel constrained neural generation model to simplify sentences given simplified words. The final results on Wikipedia and Simple Wikipedia aligned datasets indicate that our method yields better performance than various baselines.
The paper presents a novel view of the Dempster-Shafer belief function as a measure of diversity in relational data bases. It is demonstrated that under the interpretation The Dempster rule of evidence combination corresponds to the join operator of the relational database theory. This rough-set based interpretation is qualitative in nature and can represent a number of belief function operators. The interpretation has the property that Given a definition of the belief measure of objects in the interpretation domain we can perform operations in this domain and the measure of the resulting object is derivable from measures of component objects via belief operator. We demonstrated this property for Dempster rule of combination, marginalization, Shafer's conditioning, independent variables, Shenoy's notion of conditional independence of variables. The interpretation is based on rough sets (in connection with decision tables), but differs from previous interpretations of this type in that it counts the diversity rather than frequencies in a decision table.
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data. Once learned, causal graphs can be used for classification, feature selection and hypothesis generation, while revealing the underlying causal network structure and thus allowing for arbitrary likelihood queries over the data. However, current algorithms for learning sparse directed graphs are generally designed to handle only one type of data (continuous-only or discrete-only), which limits their applicability to a large class of multi-modal biological datasets that include mixed type variables. To address this issue, we developed new methods that modify and combine existing methods for finding undirected graphs with methods for finding directed graphs. These hybrid methods are not only faster, but also perform better than the directed graph estimation methods alone for a variety of parameter settings and data set sizes. Here, we describe a new conditional independence test for learning directed graphs over mixed data types and we compare performances of different graph learning strategies on synthetic data.
Many modern computer vision and machine learning applications rely on solving difficult optimization problems that involve non-differentiable objective functions and constraints. The alternating direction method of multipliers (ADMM) is a widely used approach to solve such problems. Relaxed ADMM is a generalization of ADMM that often achieves better performance, but its efficiency depends strongly on algorithm parameters that must be chosen by an expert user. We propose an adaptive method that automatically tunes the key algorithm parameters to achieve optimal performance without user oversight. Inspired by recent work on adaptivity, the proposed adaptive relaxed ADMM (ARADMM) is derived by assuming a Barzilai-Borwein style linear gradient. A detailed convergence analysis of ARADMM is provided, and numerical results on several applications demonstrate fast practical convergence.
Text normalization techniques based on rules, lexicons or supervised training requiring large corpora are not scalable nor domain interchangeable, and this makes them unsuitable for normalizing user-generated content (UGC). Current tools available for Brazilian Portuguese make use of such techniques. In this work we propose a technique based on distributed representation of words (or word embeddings). It generates continuous numeric vectors of high-dimensionality to represent words. The vectors explicitly encode many linguistic regularities and patterns, as well as syntactic and semantic word relationships. Words that share semantic similarity are represented by similar vectors. Based on these features, we present a totally unsupervised, expandable and language and domain independent method for learning normalization lexicons from word embeddings. Our approach obtains high correction rate of orthographic errors and internet slang in product reviews, outperforming the current available tools for Brazilian Portuguese.
Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks. Our approach brings together some of the strengths of intrinsic motivation and hierarchical methods: the learning of useful skill is guided by a single proxy reward, the design of which requires very minimal domain knowledge about the downstream tasks. Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks. To efficiently pre-train a large span of skills, we use Stochastic Neural Networks combined with an information-theoretic regularizer. Our experiments show that this combination is effective in learning a wide span of interpretable skills in a sample-efficient way, and can significantly boost the learning performance uniformly across a wide range of downstream tasks.
The role of semantics in zero-shot learning is considered. The effectiveness of previous approaches is analyzed according to the form of supervision provided. While some learn semantics independently, others only supervise the semantic subspace explained by training classes. Thus, the former is able to constrain the whole space but lacks the ability to model semantic correlations. The latter addresses this issue but leaves part of the semantic space unsupervised. This complementarity is exploited in a new convolutional neural network (CNN) framework, which proposes the use of semantics as constraints for recognition.Although a CNN trained for classification has no transfer ability, this can be encouraged by learning an hidden semantic layer together with a semantic code for classification. Two forms of semantic constraints are then introduced. The first is a loss-based regularizer that introduces a generalization constraint on each semantic predictor. The second is a codeword regularizer that favors semantic-to-class mappings consistent with prior semantic knowledge while allowing these to be learned from data. Significant improvements over the state-of-the-art are achieved on several datasets.
For computer vision applications, prior works have shown the efficacy of reducing the numeric precision of model parameters (network weights) in deep neural networks but also that reducing the precision of activations hurts model accuracy much more than reducing the precision of model parameters. We study schemes to train networks from scratch using reduced-precision activations without hurting the model accuracy. We reduce the precision of activation maps (along with model parameters) using a novel quantization scheme and increase the number of filter maps in a layer, and find that this scheme compensates or surpasses the accuracy of the baseline full-precision network. As a result, one can significantly reduce the dynamic memory footprint, memory bandwidth, computational energy and speed up the training and inference process with appropriate hardware support. We call our scheme WRPN - wide reduced-precision networks. We report results using our proposed schemes and show that our results are better than previously reported accuracies on ILSVRC-12 dataset while being computationally less expensive compared to previously reported reduced-precision networks.
As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions. In recent years, a number of image saliency methods have been developed to summarize where highly complex neural networks "look" in an image for evidence for their predictions. However, these techniques are limited by their heuristic nature and architectural constraints. In this paper, we make two main contributions: First, we propose a general framework for learning different kinds of explanations for any black box algorithm. Second, we specialise the framework to find the part of an image most responsible for a classifier decision. Unlike previous works, our method is model-agnostic and testable because it is grounded in explicit and interpretable image perturbations.
Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of granularity, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and then use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs.
In this paper, we develop a novel paradigm, namely hypergraph shift, to find robust graph modes by probabilistic voting strategy, which are semantically sound besides the self-cohesiveness requirement in forming graph modes. Unlike the existing techniques to seek graph modes by shifting vertices based on pair-wise edges (i.e, an edge with $2$ ends), our paradigm is based on shifting high-order edges (hyperedges) to deliver graph modes. Specifically, we convert the problem of seeking graph modes as the problem of seeking maximizers of a novel objective function with the aim to generate good graph modes based on sifting edges in hypergraphs. As a result, the generated graph modes based on dense subhypergraphs may more accurately capture the object semantics besides the self-cohesiveness requirement. We also formally prove that our technique is always convergent. Extensive empirical studies on synthetic and real world data sets are conducted on clustering and graph matching. They demonstrate that our techniques significantly outperform the existing techniques.
Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on stigmergy. By using stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015.
This paper is devoted to expressiveness of hypergraphs for which uncertainty propagation by local computations via Shenoy/Shafer method applies. It is demonstrated that for this propagation method for a given joint belief distribution no valuation of hyperedges of a hypergraph may provide with simpler hypergraph structure than valuation of hyperedges by conditional distributions. This has vital implication that methods recovering belief networks from data have no better alternative for finding the simplest hypergraph structure for belief propagation. A method for recovery tree-structured belief networks has been developed and specialized for Dempster-Shafer belief functions
This paper describes three variants of a counterexample guided inductive optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT) solvers. In particular, CEGIO relies on iterative executions to constrain a verification procedure, in order to perform inductive generalization, based on counterexamples extracted from SMT solvers. CEGIO is able to successfully optimize a wide range of functions, including non-linear and non-convex optimization problems based on SMT solvers, in which data provided by counterexamples are employed to guide the verification engine, thus reducing the optimization domain. The present algorithms are evaluated using a large set of benchmarks typically employed for evaluating optimization techniques. Experimental results show the efficiency and effectiveness of the proposed algorithms, which find the optimal solution in all evaluated benchmarks, while traditional techniques are usually trapped by local minima.
Pairwise association measure is an important operation in data analytics. Kendall's tau coefficient is one widely used correlation coefficient identifying non-linear relationships between ordinal variables. In this paper, we investigated a parallel algorithm accelerating all-pairs Kendall's tau coefficient computation via single instruction multiple data (SIMD) vectorized sorting on Intel Xeon Phis by taking advantage of many processing cores and 512-bit SIMD vector instructions. To facilitate workload balancing and overcome on-chip memory limitation, we proposed a generic framework for symmetric all-pairs computation by building provable bijective functions between job identifier and coordinate space. Performance evaluation demonstrated that our algorithm on one 5110P Phi achieves two orders-of-magnitude speedups over 16-threaded MATLAB and three orders-of-magnitude speedups over sequential R, both running on high-end CPUs. Besides, our algorithm exhibited rather good distributed computing scalability with respect to number of Phis. Source code and datasets are publicly available at http://lightpcc.sourceforge.net.
Image captioning is a challenging problem owing to the complexity in understanding the image content and diverse ways of describing it in natural language. Recent advances in deep neural networks have substantially improved the performance of this task. Most state-of-the-art approaches follow an encoder-decoder framework, which generates captions using a sequential recurrent prediction model. However, in this paper, we introduce a novel decision-making framework for image captioning. We utilize a "policy network" and a "value network" to collaboratively generate captions. The policy network serves as a local guidance by providing the confidence of predicting the next word according to the current state. Additionally, the value network serves as a global and lookahead guidance by evaluating all possible extensions of the current state. In essence, it adjusts the goal of predicting the correct words towards the goal of generating captions similar to the ground truth captions. We train both networks using an actor-critic reinforcement learning model, with a novel reward defined by visual-semantic embedding. Extensive experiments and analyses on the Microsoft COCO dataset show that the proposed framework outperforms state-of-the-art approaches across different evaluation metrics.
Multi-armed bandits are a quintessential machine learning problem requiring the balancing of exploration and exploitation. While there has been progress in developing algorithms with strong theoretical guarantees, there has been less focus on practical near-optimal finite-time performance. In this paper, we propose an algorithm for Bayesian multi-armed bandits that utilizes value-function-driven online planning techniques. Building on previous work on UCB and Gittins index, we introduce linearly-separable value functions that take both the expected return and the benefit of exploration into consideration to perform n-step lookahead. The algorithm enjoys a sub-linear performance guarantee and we present simulation results that confirm its strength in problems with structured priors. The simplicity and generality of our approach makes it a strong candidate for analyzing more complex multi-armed bandit problems.
Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to make model trained in virtual environment be workable in real world. The proposed network can convert non-realistic virtual image input into a realistic one with similar scene structure. Given realistic frames as input, driving policy trained by reinforcement learning can nicely adapt to real world driving. Experiments show that our proposed virtual to real (VR) reinforcement learning (RL) works pretty well. To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.
This paper considers a general data-fitting problem over a networked system, in which many computing nodes are connected by an undirected graph. This kind of problem can find many real-world applications and has been studied extensively in the literature. However, existing solutions either need a central controller for information sharing or requires slot synchronization among different nodes, which increases the difficulty of practical implementations, especially for a very large and heterogeneous system. As a contrast, in this paper, we treat the data-fitting problem over the network as a stochastic programming problem with many constraints. By adapting the results in a recent paper, we design a fully distributed and asynchronized stochastic gradient descent (SGD) algorithm. We show that our algorithm can achieve global optimality and consensus asymptotically by only local computations and communications. Additionally, we provide a sharp lower bound for the convergence speed in the regular graph case. This result fits the intuition and provides guidance to design a `good' network topology to speed up the convergence. Also, the merit of our design is validated by experiments on both synthetic and real-world datasets.
We propose a partially learned approach for the solution of ill posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularization theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularizing functional. The method results in a gradient-like iterative scheme, where the "gradient" component is learned using a convolutional network that includes the gradients of the data discrepancy and regularizer as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against FBP and TV reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the TV reconstruction while being significantly faster, giving reconstructions of 512 x 512 volumes in about 0.4 seconds using a single GPU.
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.
Extracting per-frame features using convolutional neural networks for real-time processing of video data is currently mainly performed on powerful GPU-accelerated workstations and compute clusters. However, there are many applications such as smart surveillance cameras that require or would benefit from on-site processing. To this end, we propose and evaluate a novel algorithm for change-based evaluation of CNNs for video data recorded with a static camera setting, exploiting the spatio-temporal sparsity of pixel changes. We achieve an average speed-up of 8.6x over a cuDNN baseline on a realistic benchmark with a negligible accuracy loss of less than 0.1% and no retraining of the network. The resulting energy efficiency is 10x higher than that of per-frame evaluation and reaches an equivalent of 328 GOp/s/W on the Tegra X1 platform.
We present DAPIP, a Programming-By-Example system that learns to program with APIs to perform data transformation tasks. We design a domain-specific language (DSL) that allows for arbitrary concatenations of API outputs and constant strings. The DSL consists of three family of APIs: regular expression-based APIs, lookup APIs, and transformation APIs. We then present a novel neural synthesis algorithm to search for programs in the DSL that are consistent with a given set of examples. The search algorithm uses recently introduced neural architectures to encode input-output examples and to model the program search in the DSL. We show that synthesis algorithm outperforms baseline methods for synthesizing programs on both synthetic and real-world benchmarks.
Chinese discourse coherence modeling remains a challenge taskin Natural Language Processing field.Existing approaches mostlyfocus on the need for feature engineering, whichadoptthe sophisticated features to capture the logic or syntactic or semantic relationships acrosssentences within a text.In this paper, we present an entity-drivenrecursive deep modelfor the Chinese discourse coherence evaluation based on current English discourse coherenceneural network model. Specifically, to overcome the shortage of identifying the entity(nouns) overlap across sentences in the currentmodel, Our combined modelsuccessfully investigatesthe entities information into the recursive neural network freamework.Evaluation results on both sentence ordering and machine translation coherence rating task show the effectiveness of the proposed model, which significantly outperforms the existing strong baseline.
Nowadays, robots become a companion in everyday life. To be well-accepted by humans, robots should efficiently understand meanings of their partners' motions and body language, and respond accordingly. Learning concepts by imitation brings them this ability in a user-friendly way. This paper presents a fast and robust model for Incremental Learning of Concepts by Imitation (ILoCI). In ILoCI, observed multimodal spatio-temporal demonstrations are incrementally abstracted and generalized based on both their perceptual and functional similarities during the imitation. In this method, perceptually similar demonstrations are abstracted by a dynamic model of mirror neuron system. An incremental method is proposed to learn their functional similarities through a limited number of interactions with the teacher. Learning all concepts together by the proposed memory rehearsal enables robot to utilize the common structural relations among concepts which not only expedites the learning process especially at the initial stages, but also improves the generalization ability and the robustness against discrepancies between observed demonstrations. Performance of ILoCI is assessed using standard LASA handwriting benchmark data set. The results show efficiency of ILoCI in concept acquisition, recognition and generation in addition to its robustness against variability in demonstrations.
Coreference evaluation metrics are hard to optimize directly as they are non-differentiable functions, not easily decomposable into elementary decisions. Consequently, most approaches optimize objectives only indirectly related to the end goal, resulting in suboptimal performance. Instead, we propose a differentiable relaxation that lends itself to gradient-based optimisation, thus bypassing the need for reinforcement learning or heuristic modification of cross-entropy. We show that by modifying the training objective of a competitive neural coreference system, we obtain a substantial gain in performance. This suggests that our approach can be regarded as a viable alternative to using reinforcement learning or more computationally expensive imitation learning.
Vision and language understanding has emerged as a subject undergoing intense study in Artificial Intelligence. Among many tasks in this line of research, visual question answering (VQA) has been one of the most successful ones, where the goal is to learn a model that understands visual content at region-level details and finds their associations with pairs of questions and answers in the natural language form. Despite the rapid progress in the past few years, most existing work in VQA have focused primarily on images. In this paper, we focus on extending VQA to the video domain and contribute to the literature in three important ways. First, we propose three new tasks designed specifically for video VQA, which require spatio-temporal reasoning from videos to answer questions correctly. Next, we introduce a new large-scale dataset for video VQA named TGIF-QA that extends existing VQA work with our new tasks. Finally, we propose a dual-LSTM based approach with both spatial and temporal attention, and show its effectiveness over conventional VQA techniques through empirical evaluations.
In this work we present a new reinforcement learning agent, called Reactor (for Retrace-actor), based on an off-policy multi-step return actor-critic architecture. The agent uses a deep recurrent neural network for function approximation. The network outputs a target policy {\pi} (the actor), an action-value Q-function (the critic) evaluating the current policy {\pi}, and an estimated behavioral policy {\hat \mu} which we use for off-policy correction. The agent maintains a memory buffer filled with past experiences. The critic is trained by the multi-step off-policy Retrace algorithm and the actor is trained by a novel {\beta}-leave-one-out policy gradient estimate (which uses both the off-policy corrected return and the estimated Q-function). The Reactor is sample-efficient thanks to the use of memory replay, and numerical efficient since it uses multi-step returns. Also both acting and learning can be parallelized. We evaluated our algorithm on 57 Atari 2600 games and demonstrate that it achieves state-of-the-art performance.
We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students' ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at https://github.com/qizhex/RACE_AR_baselines.
The rise of robotic applications has led to the generation of a huge volume of unstructured data, whereas the current cloud infrastructure was designed to process limited amounts of structured data. To address this problem, we propose a learn-memorize-recall-reduce paradigm for robotic cloud computing. The learning stage converts incoming unstructured data into structured data; the memorization stage provides effective storage for the massive amount of data; the recall stage provides efficient means to retrieve the raw data; while the reduction stage provides means to make sense of this massive amount of unstructured data with limited computing resources.
This work presents a new multi-chemical experimental platform for molecular communication where the transmitter can release different chemicals. This platform is designed to be inexpensive and accessible, and it can be expanded to simulate different environments including the cardiovascular system and complex network of pipes in industrial complexes and city infrastructures. To demonstrate the capabilities of the platform, we implement a time-slotted binary communication system where a bit-0 is represented by an acid pulse, a bit-1 by a base pulse, and information is carried via pH signals. The channel model for this system, which is nonlinear and has long memories, is unknown. Therefore, we devise novel detection algorithms that use techniques from machine learning and deep learning to train a maximum-likelihood detector. Using these algorithms the bit error rate improves by an order of magnitude relative to the approach used in previous works. Moreover, our system achieves a data rate that is an order of magnitude higher than any of the previous molecular communication platforms.
We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for data integration, based on combining multiple matrix factorisation methods, that can be used for in- and out-of-matrix prediction of missing values. The model is very general and can be used to integrate many datasets across different entity types, including repeated experiments, similarity matrices, and very sparse datasets. We apply our method on two biological applications, and extensively compare it to state-of-the-art machine learning and matrix factorisation models. For in-matrix predictions on drug sensitivity datasets we obtain consistently better performances than existing methods. This is especially the case when we increase the sparsity of the datasets. Furthermore, we perform out-of-matrix predictions on methylation and gene expression datasets, and obtain the best results on two of the three datasets, especially when the predictivity of datasets is high.
Morpheo is a transparent and secure machine learning platform collecting and analysing large datasets. It aims at building state-of-the art prediction models in various fields where data are sensitive. Indeed, it offers strong privacy of data and algorithm, by preventing anyone to read the data, apart from the owner and the chosen algorithms. Computations in Morpheo are orchestrated by a blockchain infrastructure, thus offering total traceability of operations. Morpheo aims at building an attractive economic ecosystem around data prediction by channelling crypto-money from prediction requests to useful data and algorithms providers. Morpheo is designed to handle multiple data sources in a transfer learning approach in order to mutualize knowledge acquired from large datasets for applications with smaller but similar datasets.
The advent of the Big Data hype and the consistent recollection of event logs and real-time data from sensors, monitoring software and machine configuration has generated a huge amount of time-varying data in about every sector of the industry. Rule-based processing of such data has ceased to be relevant in many scenarios where anomaly detection and pattern mining have to be entirely accomplished by the machine. Since the early 2000s, the de-facto standard for representing time series has been the Symbolic Aggregate approXimation (SAX).In this document, we present a few algorithms using this representation for anomaly detection and motif discovery, also known as pattern mining, in such data. We propose a benchmark of anomaly detection algorithms using data from Cloud monitoring software.
Information systems experience an ever-growing volume of unstructured data, particularly in the form of textual materials. This represents a rich source of information from which one can create value for people, organizations and businesses. For instance, recommender systems can benefit from automatically understanding preferences based on user reviews or social media. However, it is difficult for computer programs to correctly infer meaning from narrative content. One major challenge is negations that invert the interpretation of words and sentences. As a remedy, this paper proposes a novel learning strategy to detect negations: we apply reinforcement learning to find a policy that replicates the human perception of negations based on an exogenous response, such as a user rating for reviews. Our method yields several benefits, as it eliminates the former need for expensive and subjective manual labeling in an intermediate stage. Moreover, the inferred policy can be used to derive statistical inferences and implications regarding how humans process and act on negations.
Lattice-theoretic ideals have been used to define and generate non granular rough approximations over general approximation spaces over the last few years by few authors. The goal of these studies, in relation based rough sets, have been to obtain nice properties comparable to those of classical rough approximations. In this research paper, these ideas are generalized in a severe way by the present author and associated semantic features are investigated by her. Granules are used in the construction of approximations in implicit ways and so a concept of co-granularity is introduced. Knowledge interpretation associable with the approaches is also investigated. This research will be of relevance for a number of logico-algebraic approaches to rough sets that proceed from point-wise definitions of approximations and also for using alternative approximations in spatial mereological contexts involving actual contact relations. The antichain based semantics invented in earlier papers by the present author also applies to the contexts considered.
Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media. In this work, we aim to drastically reduce the data requirement for personality modeling and develop a model that is applicable to most users on Twitter. Our model integrates Word Embedding features with Gaussian Processes regression. Based on the evaluation of over 1.3K users on Twitter, we find that our model achieves comparable or better accuracy than state of the art techniques with 8 times fewer data.
In this work, we propose a method for learning driver models that account for variables that cannot be observed directly. When trained on a synthetic dataset, our models are able to learn encodings for vehicle trajectories that distinguish between four distinct classes of driver behavior. Such encodings are learned without any knowledge of the number of driver classes or any objective that directly requires the models to learn encodings for each class. We show that driving policies trained with knowledge of latent variables are more effective than baseline methods at imitating the driver behavior that they are trained to replicate. Furthermore, we demonstrate that the actions chosen by our policy are heavily influenced by the latent variable settings that are provided to them.
Extracting geographical tags from webpages is a well-motivated application in many domains. In illicit domains with unusual language models, like human trafficking, extracting geotags with both high precision and recall is a challenging problem. In this paper, we describe a geotag extraction framework in which context, constraints and the openly available Geonames knowledge base work in tandem in an Integer Linear Programming (ILP) model to achieve good performance. In preliminary empirical investigations, the framework improves precision by 28.57% and F-measure by 36.9% on a difficult human trafficking geotagging task compared to a machine learning-based baseline. The method is already being integrated into an existing knowledge base construction system widely used by US law enforcement agencies to combat human trafficking.
While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date such knowledge has only been used to answer simple questions with retrieval-based methods. We overcome this limitation by presenting a method for reasoning with Open IE knowledge, allowing more complex questions to be handled. Using a recently proposed support graph optimization framework for QA, we develop a new inference model for Open IE, in particular one that can work effectively with multiple short facts, noise, and the relational structure of tuples. Our model significantly outperforms a state-of-the-art structured solver on complex questions of varying difficulty, while also removing the reliance on manually curated knowledge.
We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm research and for training and evaluating systems for sarcasm detection. The corpus has 1.3 million sarcastic statements -- 10 times more than any previous dataset -- and many times more instances of non-sarcastic statements, allowing for learning in both balanced and unbalanced label regimes. Each statement is furthermore self-annotated -- sarcasm is labeled by the author, not an independent annotator -- and provided with user, topic, and conversation context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm detection, and evaluate baseline methods.
In this paper, we propose an OCR (optical character recognition)-based localization system called OCRAPOSE II, which is applicable in a number of indoor scenarios including office buildings, parkings, airports, grocery stores, etc. In these scenarios, characters (i.e. texts or numbers) can be used as suitable distinctive landmarks for localization. The proposed system takes advantage of OCR to read these characters in the query still images and provides a rough location estimate using a floor plan. Then, it finds depth and angle-of-view of the query using the information provided by the OCR engine in order to refine the location estimate. We derive novel formulas for the query angle-of-view and depth estimation using image line segments and the OCR box information. We demonstrate the applicability and effectiveness of the proposed system through experiments in indoor scenarios. It is shown that our system demonstrates better performance compared to the state-of-the-art benchmarks in terms of location recognition rate and average localization error specially under sparse database condition.
While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise added to the input that was generated specifically to fool the system while being quasi-imperceptible for humans. More severely, there even exist universal perturbations that are input-agnostic but fool the network on the majority of inputs. While recent work has focused on image classification, this work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial perturbations that make the network yield a desired target segmentation as output. We show empirically that there exist barely perceptible universal noise patterns which result in nearly the same predicted segmentation for arbitrary inputs. Furthermore, we also show the existence of universal noise which removes a target class (e.g., all pedestrians) from the segmentation while leaving the segmentation mostly unchanged otherwise.
Variational inference approximates the posterior distribution of a probabilistic model with a parameterized density by maximizing a lower bound for the model evidence. Modern solutions fit a flexible approximation with stochastic gradient descent, using Monte Carlo approximation for the gradients. This enables variational inference for arbitrary differentiable probabilistic models, and consequently makes variational inference feasible for probabilistic programming languages. In this work we develop more efficient inference algorithms for the task by considering importance sampling estimates for the gradients. We show how the gradient with respect to the approximation parameters can often be evaluated efficiently without needing to re-compute gradients of the model itself, and then proceed to derive practical algorithms that use importance sampled estimates to speed up computation.We present importance sampled stochastic gradient descent that outperforms standard stochastic gradient descent by a clear margin for a range of models, and provide a justifiable variant of stochastic average gradients for variational inference.
We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts. Given any CNN model, the proposed method draws on a broad data set of visual concepts to score the semantics of hidden units at each intermediate convolutional layer. The units with semantics are given labels across a range of objects, parts, scenes, textures, materials, and colors. We use the proposed method to test the hypothesis that interpretability of units is equivalent to random linear combinations of units, then we apply our method to compare the latent representations of various networks when trained to solve different supervised and self-supervised training tasks. We further analyze the effect of training iterations, compare networks trained with different initializations, examine the impact of network depth and width, and measure the effect of dropout and batch normalization on the interpretability of deep visual representations. We demonstrate that the proposed method can shed light on characteristics of CNN models and training methods that go beyond measurements of their discriminative power.
Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, \emph{ITransF}, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets---WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information.
Media is full of false claims. Even Oxford Dictionaries named "post-truth" as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the kind of discourse there is around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics - each having their own families of claims and replies - and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.
For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using flurodeoxyglucose and florbetapir positron emission tomography (PET). The architecture of the network only relies on baseline PET studies of AD and normal subjects as the training dataset. Feature extraction and complicated image preprocessing including nonlinear warping are unnecessary for our approach. Accuracy of prediction (84.2%) for conversion to AD in MCI patients outperformed conventional feature-based quantification approaches. ROC analyses revealed that performance of CNN-based approach was significantly higher than that of the conventional quantification methods (p < 0.05). Output scores of the network were strongly correlated with the longitudinal change in cognitive measurements. These results show the feasibility of deep learning as a tool for predicting disease outcome using brain images.
We consider the problem of diagnosis where a set of simple observations are used to infer a potentially complex hidden hypothesis. Finding the optimal subset of observations is intractable in general, thus we focus on the problem of active diagnosis, where the agent selects the next most-informative observation based on the results of previous observations. We show that under the assumption of uniform observation entropy, one can build an implication model which directly predicts the outcome of the potential next observation conditioned on the results of past observations, and selects the observation with the maximum entropy. This approach enjoys reduced computation complexity by bypassing the complicated hypothesis space, and can be trained on observation data alone, learning how to query without knowledge of the hidden hypothesis.
Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different hierarchies of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. Experimental results evidence that our approach achieves not only outstanding relation detection performance, but more importantly, it helps our KBQA system to achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.
Singleton arc consistency is an important type of local consistency which has been recently shown to solve all constraint satisfaction problems (CSPs) over constraint languages of bounded width. We aim to characterise all classes of CSPs defined by a forbidden pattern that are solved by singleton arc consistency and closed under removing constraints. We identify five new patterns whose absence ensures solvability by singleton arc consistency, four of which are provably maximal and three of which generalise 2-SAT. Combined with simple counter-examples for other patterns, we make significant progress towards a complete classification.
Many Natural Language Processing and Computational Linguistics applications involves the generation of new texts based on some existing texts, such as summarization, text simplification and machine translation. However, there has been a serious problem haunting these applications for decades, that is, how to automatically and accurately assess quality of these applications. In this paper, we will present some preliminary results on one especially useful and challenging problem in NLP system evaluation: how to pinpoint content differences of two text passages (especially for large pas-sages such as articles and books). Our idea is intuitive and very different from existing approaches. We treat one text passage as a small knowledge base, and ask it a large number of questions to exhaustively identify all content points in it. By comparing the correctly answered questions from two text passages, we will be able to compare their content precisely. The experiment using 2007 DUC summarization corpus clearly shows promising results.
The management of invasive mechanical ventilation, and the regulation of sedation and analgesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechanical ventilation and premature extubation are associated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ventilator varies. This work aims to develop a decision support tool that uses available patient information to predict time-to-extubation readiness and to recommend a personalized regime of sedation dosage and ventilator support. To this end, we use off-policy reinforcement learning algorithms to determine the best action at a given patient state from sub-optimal historical ICU data. We compare treatment policies from fitted Q-iteration with extremely randomized trees and with feedforward neural networks, and demonstrate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintubation and regulating physiological stability.
This dissertation is motivated by the need, in today's globalist world, for a precise way to enable governments, organisations and other regulatory bodies to evaluate the constraints they place on themselves and others. An organisation's modus operandi is enacting and fulfilling contracts between itself and its participants. Yet, organisational contracts should respect external laws, such as those setting out data privacy rights and liberties. Contracts can only be enacted by following contract law processes, which often require bilateral agreement and consideration. Governments need to legislate whilst understanding today's context of national and international governance hierarchy where law makers shun isolationism and seek to influence one another. Governments should avoid punishment by respecting constraints from international treaties and human rights charters. Governments can only enact legislation by following their own, pre-existing, law making procedures. In other words, institutions, such as laws and contracts are designed and enacted under constraints.
In this work we present a method for using Deep Q-Networks (DQNs) in multi-objective environments. Deep Q-Networks provide remarkable performance in single objective problems learning from high-level visual state representations. However, in many scenarios (e.g in robotics, games), the agent needs to pursue multiple objectives simultaneously. We propose an architecture in which separate DQNs are used to control the agent's behaviour with respect to particular objectives. In this architecture we introduce decision values to improve the scalarization of multiple DQNs into a single action. Our architecture enables the decomposition of the agent's behaviour into controllable and replaceable sub-behaviours learned by distinct modules. Moreover, it allows to change the priorities of particular objectives post-learning, while preserving the overall performance of the agent. To evaluate our solution we used a game-like simulator in which an agent - provided with high-level visual input - pursues multiple objectives in a 2D world.
Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we describe the terminology of this field as well as mandatory background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and their targets. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.
While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative. However, there is little attention paid to population initialization techniques in the setting of general real-time video games. Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus with multiple evolution parameter values (population size and individual length). An in-depth analysis is carried out between the results of the seeding methods and the vanilla Rolling Horizon Evolution. In addition, the paper presents a comparison to a Monte Carlo Tree Search algorithm. The results are promising, with seeding able to boost performance significantly over baseline evolution and even match the high level of play obtained by the Monte Carlo Tree Search.
Our goal is to create a convenient natural language interface for performing well-specified but complex actions such as analyzing data, manipulating text, and querying databases. However, existing natural language interfaces for such tasks are quite primitive compared to the power one wields with a programming language. To bridge this gap, we start with a core programming language and allow users to "naturalize" the core language incrementally by defining alternative, more natural syntax and increasingly complex concepts in terms of compositions of simpler ones. In a voxel world, we show that a community of users can simultaneously teach a common system a diverse language and use it to build hundreds of complex voxel structures. Over the course of three days, these users went from using only the core language to using the naturalized language in 85.9\% of the last 10K utterances.
Agent modelling involves considering how other agents will behave, in order to influence your own actions. In this paper, we explore the use of agent modelling in the hidden-information, collaborative card game Hanabi. We implement a number of rule-based agents, both from the literature and of our own devising, in addition to an Information Set Monte Carlo Tree Search (IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new, predictor version that uses a model of the agents with which it is paired. We observe a significant improvement in game-playing strength from this agent in comparison to IS-MCTS, resulting from its consideration of what the other agents in a game would do. In addition, we create a flawed rule-based agent to highlight the predictor's capabilities with such an agent.
Monte Carlo Tree Search techniques have generally dominated General Video Game Playing, but recent research has started looking at Evolutionary Algorithms and their potential at matching Tree Search level of play or even outperforming these methods. Online or Rolling Horizon Evolution is one of the options available to evolve sequences of actions for planning in General Video Game Playing, but no research has been done up to date that explores the capabilities of the vanilla version of this algorithm in multiple games. This study aims to critically analyse the different configurations regarding population size and individual length in a set of 20 games from the General Video Game AI corpus. Distinctions are made between deterministic and stochastic games, and the implications of using superior time budgets are studied. Results show that there is scope for the use of these techniques, which in some configurations outperform Monte Carlo Tree Search, and also suggest that further research in these methods could boost their performance.
This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge of rumour stance classification, which involves identifying the attitude of Twitter users towards the truthfulness of the rumour they are discussing. Stance classification is considered to be an important step towards rumour verification, therefore performing well in this task is expected to be useful in debunking false rumours. In this work we classify a set of Twitter posts discussing rumours into either supporting, denying, questioning or commenting on the underlying rumours. We propose a LSTM-based sequential model that, through modelling the conversational structure of tweets, which achieves an accuracy of 0.784 on the RumourEval test set outperforming all other systems in Subtask A.
To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene. In this paper, we study the agreement between bottom-up saliency-based visual attention and object referrals in scene description constructs. We investigate the properties of human-written descriptions and machine-generated ones. We then propose a saliency-boosted image captioning model in order to investigate benefits from low-level cues in language models. We learn that (1) humans mention more salient objects earlier than less salient ones in their descriptions, (2) the better a captioning model performs, the better attention agreement it has with human descriptions, (3) the proposed saliency-boosted model, compared to its baseline form, does not improve significantly on the MS COCO database, indicating explicit bottom-up boosting does not help when the task is well learnt and tuned on a data, (4) a better generalization is, however, observed for the saliency-boosted model on unseen data.
Video captioning, the task of describing the content of a video, has seen some promising improvements in recent years with sequence-to-sequence models, but accurately learning the temporal and logical dynamics involved in the task still remains a challenge, especially given the lack of sufficient annotated data. We improve video captioning by sharing knowledge with two related directed-generation tasks: a temporally-directed unsupervised video prediction task to learn richer context-aware video encoder representations, and a logically-directed language entailment generation task to learn better video-entailed caption decoder representations. For this, we present a many-to-many multi-task learning model that shares parameters across the encoders and decoders of the three tasks. We achieve significant improvements and the new state-of-the-art on several standard video captioning datasets using diverse automatic and human evaluations. We also show mutual multi-task improvements on the entailment generation task.
To date, developing a good model for early intensive care unit (ICU) mortality prediction is still challenging. This paper presents a patient based predictive modeling framework (PPMF) to improve the performance of ICU mortality prediction using data collected during the first 48 hours of ICU admission. PPMF consists of three main components verifying three related research hypotheses. The first component captures dynamic changes of patients status in the ICU using their time series data (e.g., vital signs and laboratory tests). The second component is a local approximation algorithm that classifies patients based on their similarities. The third component is a Gradient Decent wrapper that updates feature weights according to the classification feedback. Experiments using data from MIMICIII show that PPMF significantly outperforms: (1) the severity score systems, namely SASP III, APACHE IV, and MPM0III, (2) the aggregation based classifiers that utilize summarized time series, and (3) baseline feature selection methods.
There is a wide gap between symbolic reasoning and deep learning. In this research, we explore the possibility of using deep learning to improve symbolic reasoning. Briefly, in a reasoning system, a deep feedforward neural network is used to guide rewriting processes after learning from algebraic reasoning examples produced by humans. To enable the neural network to recognise patterns of algebraic expressions with non-deterministic sizes, reduced partial trees are used to represent the expressions. Also, to represent both top-down and bottom-up information of the expressions, a centralisation technique is used to improve the reduced partial trees. Besides, symbolic association vectors and rule application records are used to improve the rewriting processes. Experimental results reveal that the algebraic reasoning examples can be accurately learnt only if the feedforward neural network has enough hidden layers. Also, the centralisation technique, the symbolic association vectors and the rule application records can reduce error rates of reasoning. In particular, the above approaches have led to 4.6% error rate of reasoning on a dataset of linear equations, differentials and integrals.
Path planning for multiple robots is well studied in the AI and robotics communities. For a given discretized environment, robots need to find collision-free paths to a set of specified goal locations. Robots can be fully anonymous, non-anonymous, or organized in groups. Although powerful solvers for this abstract problem exist, they make simplifying assumptions by ignoring kinematic constraints, making it difficult to use the resulting plans on actual robots. In this paper, we present a solution which takes kinematic constraints, such as maximum velocities, into account, while guaranteeing a user-specified minimum safety distance between robots. We demonstrate our approach in simulation and on real robots in 2D and 3D environments.
In emotion recognition, it is difficult to recognize human's emotional states using just a single modality. Besides, the annotation of physiological emotional data is particularly expensive. These two aspects make the building of effective emotion recognition model challenging. In this paper, we first build a multi-view deep generative model to simulate the generative process of multi-modality emotional data. By imposing a mixture of Gaussians assumption on the posterior approximation of the latent variables, our model can learn the shared deep representation from multiple modalities. To solve the labeled-data-scarcity problem, we further extend our multi-view model to semi-supervised learning scenario by casting the semi-supervised classification problem as a specialized missing data imputation task. Our semi-supervised multi-view deep generative framework can leverage both labeled and unlabeled data from multiple modalities, where the weight factor for each modality can be learned automatically. Compared with previous emotion recognition methods, our method is more robust and flexible. The experiments conducted on two real multi-modal emotion datasets have demonstrated the superiority of our framework over a number of competitors.
This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.
We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary.
Vehicle climate control systems aim to keep passengers thermally comfortable. However, current systems control temperature rather than thermal comfort and tend to be energy hungry, which is of particular concern when considering electric vehicles. This paper poses energy-efficient vehicle comfort control as a Markov Decision Process, which is then solved numerically using Sarsa({\lambda}) and an empirically validated, single-zone, 1D thermal model of the cabin. The resulting controller was tested in simulation using 200 randomly selected scenarios and found to exceed the performance of bang-bang, proportional, simple fuzzy logic, and commercial controllers with 23%, 43%, 40%, 56% increase, respectively. Compared to the next best performing controller, energy consumption is reduced by 13% while the proportion of time spent thermally comfortable is increased by 23%. These results indicate that this is a viable approach that promises to translate into substantial comfort and energy improvements in the car.
Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct result, while not being misled by spurious programs: incorrect programs that coincidentally output the correct result. We connect two common learning paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML), and then present a new learning algorithm that combines the strengths of both. The new algorithm guards against spurious programs by combining the systematic search traditionally employed in MML with the randomized exploration of RL, and by updating parameters such that probability is spread more evenly across consistent programs. We apply our learning algorithm to a new neural semantic parser and show significant gains over existing state-of-the-art results on a recent context-dependent semantic parsing task.
The $L_1$-regularized models are widely used for sparse regression or classification tasks. In this paper, we propose the orthant-wise passive descent algorithm (OPDA) for optimizing $L_1$-regularized models, as an improved substitute of proximal algorithms, which are the standard tools for optimizing the models nowadays. OPDA uses a stochastic variance-reduced gradient (SVRG) to initialize the descent direction, then apply a novel alignment operator to encourage each element keeping the same sign after one iteration of update, so the parameter remains in the same orthant as before. It also explicitly suppresses the magnitude of each element to impose sparsity. The quasi-Newton update can be utilized to incorporate curvature information and accelerate the speed. We prove a linear convergence rate for OPDA on general smooth and strongly-convex loss functions. By conducting experiments on $L_1$-regularized logistic regression and convolutional neural networks, we show that OPDA outperforms state-of-the-art stochastic proximal algorithms, implying a wide range of applications in training sparse models.
While the optimization problem behind deep neural networks is highly non-convex, it is frequently observed in practice that training deep networks seems possible without getting stuck in suboptimal points. It has been argued that this is the case as all local minima are close to being globally optimal. We show that this is (almost) true, in fact almost all local minima are globally optimal, for a fully connected network with squared loss and analytic activation function given that the number of hidden units of one layer of the network is larger than the number of training points and the network structure from this layer on is pyramidal.
We propose a development of the Analytic Hierarchy Process (AHP) permitting to use the methodology also in cases of decision problems with a very large number of alternatives evaluated with respect to several criteria. While the application of the original AHP method involves many pairwise comparisons between alternatives and criteria, our proposal is composed of three steps: (i) direct evaluation of the alternatives at hand on the considered criteria, (ii) selection of some reference evaluations; (iii) application of the original AHP method to reference evaluations; (iv) revision of the direct evaluation on the basis of the prioritization supplied by AHP on reference evaluations. The new proposal has been tested and validated in an experiment conducted on a sample of university students. The new methodology has been therefore applied to a real world problem involving the evaluation of 21 Social Housing initiatives sited in the Piedmont region (Italy). To take into account interaction between criteria, the Choquet integral preference model has been considered within a Non Additive Robust Ordinal Regression approach.
This paper introduces a generalization of Convolutional Neural Networks (CNNs) from low-dimensional grid data, such as images, to graph-structured data. We propose a novel spatial convolution utilizing a random walk to uncover the relations within the input, analogous to the way the standard convolution uses the spatial neighborhood of a pixel on the grid. The convolution has an intuitive interpretation, is efficient and scalable and can also be used on data with varying graph structure. Furthermore, this generalization can be applied to many standard regression or classification problems, by learning the the underlying graph. We empirically demonstrate the performance of the proposed CNN on MNIST, and challenge the state-of-the-art on Merck molecular activity data set.
Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases. Instead of collecting a large number of annotated images of each city of interest to train or refine the segmenter, we propose an unsupervised learning approach to adapt road scene segmenters across different cities. By utilizing Google Street View and its time-machine feature, we can collect unannotated images for each road scene at different times, so that the associated static-object priors can be extracted accordingly. By advancing a joint global and class-specific domain adversarial learning framework, adaptation of pre-trained segmenters to that city can be achieved without the need of any user annotation or interaction. We show that our method improves the performance of semantic segmentation in multiple cities across continents, while it performs favorably against state-of-the-art approaches requiring annotated training data.
One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data. This task is complicated by the huge search space of possible solutions and turned out to be a well-known NP-hard problem and, hence, approximations are required. However, to the best of our knowledge, a quantitative analysis of the performance and characteristics of the different heuristics to solve this problem has never been done before. For this reason, in this work, we provide a detailed study of the different state-of-the-arts methods for structural learning on simulated data considering both BNs with discrete and continuous variables, and with different rates of noise in the data. In particular, we investigate the characteristics of different widespread scores proposed for the inference and the statistical pitfalls within them.
We introduce Parseval networks, a form of deep neural networks in which the Lipschitz constant of linear, convolutional and aggregation layers is constrained to be smaller than 1. Parseval networks are empirically and theoretically motivated by an analysis of the robustness of the predictions made by deep neural networks when their input is subject to an adversarial perturbation. The most important feature of Parseval networks is to maintain weight matrices of linear and convolutional layers to be (approximately) Parseval tight frames, which are extensions of orthogonal matrices to non-square matrices. We describe how these constraints can be maintained efficiently during SGD. We show that Parseval networks match the state-of-the-art in terms of accuracy on CIFAR-10/100 and Street View House Numbers (SVHN) while being more robust than their vanilla counterpart against adversarial examples. Incidentally, Parseval networks also tend to train faster and make a better usage of the full capacity of the networks.
We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i.e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use. We show that SuperPivot performs well for the crosslingual analysis of the linguistic phenomenon of tense. We produce analysis results for more than 1000 languages, conducting - to the best of our knowledge - the largest crosslingual computational study performed to date. We extend existing methodology for leveraging parallel corpora for typological analysis by overcoming a limiting assumption of earlier work: We only require that a linguistic feature is overtly marked in a few of thousands of languages as opposed to requiring that it be marked in all languages under investigation.
Neural conversational models require substantial amounts of dialogue data for their parameter estimation and are therefore usually learned on large corpora such as chat forums or movie subtitles. These corpora are, however, often challenging to work with, notably due to their frequent lack of turn segmentation and the presence of multiple references external to the dialogue itself. This paper shows that these challenges can be mitigated by adding a weighting model into the architecture. The weighting model, which is itself estimated from dialogue data, associates each training example to a numerical weight that reflects its intrinsic quality for dialogue modelling. At training time, these sample weights are included into the empirical loss to be minimised. Evaluation results on retrieval-based models trained on movie and TV subtitles demonstrate that the inclusion of such a weighting model improves the model performance on unsupervised metrics.
We study automatic question generation for sentences from text passages in reading comprehension. We introduce an attention-based sequence learning model for the task and investigate the effect of encoding sentence- vs. paragraph-level information. In contrast to all previous work, our model does not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead trainable end-to-end via sequence-to-sequence learning. Automatic evaluation results show that our system significantly outperforms the state-of-the-art rule-based system. In human evaluations, questions generated by our system are also rated as being more natural (i.e., grammaticality, fluency) and as more difficult to answer (in terms of syntactic and lexical divergence from the original text and reasoning needed to answer).
In this paper we show how the defense relation among abstract arguments can be used to encode the reasons for accepting arguments. After introducing a novel notion of defenses and defense graphs, we propose a defense semantics together with a new notion of defense equivalence of argument graphs, and compare defense equivalence with standard equivalence and strong equivalence, respectively. Then, based on defense semantics, we define two kinds of reasons for accepting arguments, i.e., direct reasons and root reasons, and a notion of root equivalence of argument graphs. Finally, we show how the notion of root equivalence can be used in argumentation summarization.
Open world games present players with more freedom than games with linear progression structures. However, without clearly-defined objectives, they often leave players without a sense of purpose. Most of the time, quests and objectives are hand-authored and overlaid atop an open world's mechanics. But what if they could be generated organically from the gameplay itself? The goal of our project was to develop a model of the mechanics in Minecraft that could be used to determine the ideal placement of objectives in an open world setting. We formalized the game logic of Minecraft in terms of logical rules that can be manipulated in two ways: they may be executed to generate graphs representative of the player experience when playing an open world game with little developer direction; and they may be statically analyzed to determine dependency orderings, feedback loops, and bottlenecks. These analyses may then be used to place achievements on gameplay actions algorithmically.
Semi-supervised learning plays an important role in large-scale machine learning. Properly using additional unlabeled data (largely available nowadays) often can improve the machine learning accuracy. However, if the machine learning model is misspecified for the underlying true data distribution, the model performance could be seriously jeopardized. This issue is known as model misspecification. To address this issue, we focus on generative models and propose a criterion to detect the onset of model misspecification by measuring the performance difference between models obtained using supervised and semi-supervised learning. Then, we propose to automatically modify the generative models during model training to achieve an unbiased generative model. Rigorous experiments were carried out to evaluate the proposed method using two image classification data sets PASCAL VOC'07 and MIR Flickr. Our proposed method has been demonstrated to outperform a number of state-of-the-art semi-supervised learning approaches for the classification task.
We propose a software architecture designed to ease the implementation of dialogue systems. The Modular Architecture for Conversational Agents (MACA) uses a plug-n-play style that allows quick prototyping, thereby facilitating the development of new techniques and the reproduction of previous work. The architecture separates the domain of the conversation from the agent's dialogue strategy, and as such can be easily extended to multiple domains. MACA provides tools to host dialogue agents on Amazon Mechanical Turk (mTurk) for data collection and allows processing of other sources of training data. The current version of the framework already incorporates several domains and existing dialogue strategies from the recent literature.
The increase of connectivity and the impact it has in every day life is raising new and existing security problems that are becoming important for social good. We introduce two particular problems: cyber attack attribution and regulatory data sharing. For both problems, decisions about which rules to apply, should be taken under incomplete and context dependent information. The solution we propose is based on argumentation reasoning, that is a well suited technique for implementing decision making mechanisms under conflicting and incomplete information. Our proposal permits us to identify the attacker of a cyber attack and decide the regulation rule that should be used while using and sharing data. We illustrate our solution through concrete examples.
Logical theories have been developed which have allowed temporal reasoning about eventualities (a la Galton) such as states, processes, actions, events, processes and complex eventualities such as sequences and recurrences of other eventualities. This paper presents the problem of coincidence within the framework of a first order logical theory formalising temporal multiple recurrence of two sequences of fixed duration eventualities and presents a solution to it The coincidence problem is described as: if two complex eventualities (or eventuality sequences) consisting respectively of component eventualities x0, x1,....,xr and y0, y1, ..,ys both recur over an interval k and all eventualities are of fixed durations, is there a sub-interval of k over which the incidence xt and yu for t between 0..r and s between 0..s coincide. The solution presented here formalises the intuition that a solution can be found by temporal projection over a cycle of the multiple recurrence of both sequences.
It is not rare that the performance of one metaheuristic algorithm can be improved by incorporating ideas taken from another. In this article we present how Simulated Annealing (SA) can be used to improve the efficiency of the Ant Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering Problem (SOP). Moreover, we show how the very same ideas can be applied to improve the convergence of a dedicated local search, i.e. the SOP-3-exchange algorithm. A statistical analysis of the proposed algorithms both in terms of finding suitable parameter values and the quality of the generated solutions is presented based on a series of computational experiments conducted on SOP instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed ACS-SA and EACS-SA algorithms often generate solutions of better quality than the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with the proposed SOP-3-exchange-SA local search was able to find 10 new best solutions for the SOP instances from the SOPLIB2006 repository, thus improving the state-of-the-art results as known from the literature. Overall, the best known or improved solutions were found in 41 out of 48 cases.
Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.
Cognitive arithmetic studies the mental processes used in solving math problems. This area of research explores the retrieval mechanisms and strategies used by people during a common cognitive task. Past research has shown that human performance in arithmetic operations is correlated to the numerical size of the problem. Past research on cognitive arithmetic has pinpointed this trend to either retrieval strength, error checking, or strategy-based approaches when solving equations. This paper describes a rule-based computational model that performs the four major arithmetic operations (addition, subtraction, multiplication and division) on two operands. We then evaluated our model to probe its validity in representing the prevailing concepts observed in psychology experiments from the related works. The experiments specifically explore the problem size effect, an activation-based model for fact retrieval, backup strategies when retrieval fails, and finally optimization strategies when faced with large operands. From our experimental results, we concluded that our model's response times were comparable to results observed when people performed similar tasks during psychology experiments. The fit of our model in reproducing these results and incorporating accuracy into our model are discussed.
The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider lifelong learning problem to mimic "human learning", i.e., endowing a new capability to the learned metric for a new task from new online samples and incorporating previous experiences and knowledge. Therefore, we propose a new metric learning framework: lifelong metric learning (LML), which only utilizes the data of the new task to train the metric model while preserving the original capabilities. More specifically, the proposed LML maintains a common subspace for all learned metrics, named lifelong dictionary, transfers knowledge from the common subspace to each new metric task with task-specific idiosyncrasy, and redefines the common subspace over time to maximize performance across all metric tasks. For model optimization, we apply online passive aggressive optimization algorithm to solve the proposed LML framework, where the lifelong dictionary and task-specific partition are optimized alternatively and consecutively. Finally, we evaluate our approach by analyzing several multi-task metric learning datasets. Extensive experimental results demonstrate effectiveness and efficiency of the proposed framework.
We present an approach for the verification of feed-forward neural networks in which all nodes have a piece-wise linear activation function. Such networks are often used in deep learning and have been shown to be hard to verify for modern satisfiability modulo theory (SMT) and integer linear programming (ILP) solvers. The starting point of our approach is the addition of a global linear approximation of the overall network behavior to the verification problem that helps with SMT-like reasoning over the network behavior. We present a specialized verification algorithm that employs this approximation in a search process in which it infers additional node phases for the non-linear nodes in the network from partial node phase assignments, similar to unit propagation in classical SAT solving. We also show how to infer additional conflict clauses and safe node fixtures from the results of the analysis steps performed during the search. The resulting approach is evaluated on collision avoidance and handwritten digit recognition case studies.
Non-stationary domains, where unforeseen changes happen, present a challenge for agents to find an optimal policy for a sequential decision making problem. This work investigates a solution to this problem that combines Markov Decision Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming (ASP) in a method we call ASP(RL). In this method, Answer Set Programming is used to find the possible trajectories of an MDP, from where Reinforcement Learning is applied to learn the optimal policy of the problem. Results show that ASP(RL) is capable of efficiently finding the optimal solution of an MDP representing non-stationary domains.
The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms. The interpretation of independence and the way it is utilized, however, varies across these methods. Our aim in this paper is to propose a group theoretic framework for ICM to unify and generalize these approaches. In our setting, the cause-mechanism relationship is assessed by comparing it against a null hypothesis through the application of random generic group transformations. We show that the group theoretic view provides a very general tool to study the structure of data generating mechanisms with direct applications to machine learning.
Application of models to data is fraught. Data-generating collaborators often only have a very basic understanding of the complications of collating, processing and curating data. Challenges include: poor data collection practices, missing values, inconvenient storage mechanisms, intellectual property, security and privacy. All these aspects obstruct the sharing and interconnection of data, and the eventual interpretation of data through machine learning or other approaches. In project reporting, a major challenge is in encapsulating these problems and enabling goals to be built around the processing of data. Project overruns can occur due to failure to account for the amount of time required to curate and collate. But to understand these failures we need to have a common language for assessing the readiness of a particular data set. This position paper proposes the use of data readiness levels: it gives a rough outline of three stages of data preparedness and speculates on how formalisation of these levels into a common language for data readiness could facilitate project management.
Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the \textit{analogical} properties of the embedded entities and relations. By formulating the learning objective in a differentiable fashion, our model enjoys both theoretical power and computational scalability, and significantly outperformed a large number of representative baseline methods on benchmark datasets. Furthermore, the model offers an elegant unification of several well-known methods in multi-relational embedding, which can be proven to be special instantiations of our framework.
This article describes their biopolitical implications for design from psychological, cultural, legal, functional and aesthetic/perceptive ways, in the framework of Hyperconnectivity: the condition according to which person-to-person, person-to-machine and machine-to-machine communication progressively shift to networked and digital means. A definition is given for the terms of "interface biopolitics" and "data biopolitics", as well as evidence supporting these definitions and a description of the technological, theoretical and practice-based innovations bringing them into meaningful existence. Interfaces, algorithms, artificial intelligences of various types, the tendency in quantified self and the concept of "information bubbles" will be examined in terms of interface and data biopolitics, from the point of view of design, and for their implications in terms of freedoms, transparency, justice and accessibility to human rights. A working hypothesis is described for technologically relevant design practices and education processes, in order to confront with these issues in critical, ethical and inclusive ways.
Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.
We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have been previously employed for consistent learning of (passive) latent variable models such as hidden Markov models, POMDPs are more challenging since the learner interacts with the environment and possibly changes the future observations in the process. We devise a learning algorithm running through epochs, in each epoch we employ spectral techniques to learn the POMDP parameters from a trajectory generated by a fixed policy. At the end of the epoch, an optimization oracle returns the optimal memoryless planning policy which maximizes the expected reward based on the estimated POMDP model. We prove an order-optimal regret bound with respect to the optimal memoryless policy and efficient scaling with respect to the dimensionality of observation and action spaces.
How to handle uncertainty in medical diagnosis is an open issue. In this paper, a new decision making methodology based on Z-numbers is presented. Firstly, the experts' opinions are represented by Z-numbers. Z-number is an ordered pair of fuzzy numbers denoted as Z = (A, B). Then, a new method for ranking fuzzy numbers is proposed. And based on the proposed fuzzy number ranking method, a novel method is presented to transform the Z-numbers into Basic Probability Assignment (BPA). As a result, the information from different sources is combined by the Dempster' combination rule. The final decision making is more reasonable due to the advantage of information fusion. Finally, two experiments, risk analysis and medical diagnosis, are illustrated to show the efficiency of the proposed methodology.
Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose TrajectoryNet-a neural network architecture for point-based trajectory classification to infer real world human transportation modes from GPS traces. To overcome the challenge of capturing the underlying latent factors in the low-dimensional and heterogeneous feature space imposed by GPS data, we develop a novel representation that embeds the original feature space into another space that can be understood as a form of basis expansion. We also enrich the feature space via segment-based information and use Maxout activations to improve the predictive power of Recurrent Neural Networks (RNNs). We achieve over 98% classification accuracy when detecting four types of transportation modes, outperforming existing models without additional sensory data or location-based prior knowledge.
Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers' purchasing decisions. However, the proliferation of non-credible reviews -- either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased -- entails the problem of identifying credible reviews. Prior works involve classifiers harnessing rich information about items/users -- which might not be readily available in several domains -- that provide only limited interpretability as to why a review is deemed non-credible. This paper presents a novel approach to address the above issues. We utilize latent topic models leveraging review texts, item ratings, and timestamps to derive consistency features without relying on item/user histories, unavailable for "long-tail" items/users. We develop models, for computing review credibility scores to provide interpretable evidence for non-credible reviews, that are also transferable to other domains -- addressing the scarcity of labeled data. Experiments on real-world datasets demonstrate improvements over state-of-the-art baselines.
Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends. Recent work has shown that recommender systems benefit from explicit consideration of user experience. However, prior work assumes a fixed number of discrete experience levels, whereas in reality users gain experience and mature continuously over time. This paper presents a new model that captures the continuous evolution of user experience, and the resulting language model in reviews and other posts. Our model is unsupervised and combines principles of Geometric Brownian Motion, Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal progression of user experience and language model respectively. We develop practical algorithms for estimating the model parameters from data and for inference with our model (e.g., to recommend items). Extensive experiments with five real-world datasets show that our model not only fits data better than discrete-model baselines, but also outperforms state-of-the-art methods for predicting item ratings.
Wearable computing is one of the fastest growing technologies today. Smart watches are poised to take over at least of half the wearable devices market in the near future. Smart watch screen size, however, is a limiting factor for growth, as it restricts practical text input. On the other hand, wearable devices have some features, such as consistent user interaction and hands-free, heads-up operations, which pave the way for gesture recognition methods of text entry. This paper proposes a new text input method for smart watches, which utilizes motion sensor data and machine learning approaches to detect letters written in the air by a user. This method is less computationally intensive and less expensive when compared to computer vision approaches. It is also not affected by lighting factors, which limit computer vision solutions. The AirDraw system prototype developed to test this approach is presented. Additionally, experimental results close to 71% accuracy are presented.
Consumers often react expressively to products such as food samples, perfume, jewelry, sunglasses, and clothing accessories. This research discusses a multimodal affect recognition system developed to classify whether a consumer likes or dislikes a product tested at a counter or kiosk, by analyzing the consumer's facial expression, body posture, hand gestures, and voice after testing the product. A depth-capable camera and microphone system - Kinect for Windows - is utilized. An emotion identification engine has been developed to analyze the images and voice to determine affective state of the customer. The image is segmented using skin color and adaptive threshold. Face, body and hands are detected using the Haar cascade classifier. Canny edges are identified and the lip, body and hand contours are extracted using spatial filtering. Edge count and orientation around the mouth, cheeks, eyes, shoulders, fingers and the location of the edges are used as features. Classification is done by an emotion template mapping algorithm and training a classifier using support vector machines. The real-time performance, accuracy and feasibility for multimodal affect recognition in feedback assessment are evaluated.
We study the problem of finding a small subset of items that is \emph{agreeable} to all agents, meaning that all agents value the subset at least as much as its complement. Previous work has shown worst-case bounds, over all instances with a given number of agents and items, on the number of items that may need to be included in such a subset. Our goal in this paper is to efficiently compute an agreeable subset whose size approximates the size of the smallest agreeable subset for a given instance. We consider three well-known models for representing the preferences of the agents: ordinal preferences on single items, the value oracle model, and additive utilities. In each of these models, we establish virtually tight bounds on the approximation ratio that can be obtained by algorithms running in polynomial time.
The character information in natural scene images contains various personal information, such as telephone numbers, home addresses, etc. It is a high risk of leakage the information if they are published. In this paper, we proposed a scene text erasing method to properly hide the information via an inpainting convolutional neural network (CNN) model. The input is a scene text image, and the output is expected to be text erased image with all the character regions filled up the colors of the surrounding background pixels. This work is accomplished by a CNN model through convolution to deconvolution with interconnection process. The training samples and the corresponding inpainting images are considered as teaching signals for training. To evaluate the text erasing performance, the output images are detected by a novel scene text detection method. Subsequently, the same measurement on text detection is utilized for testing the images in benchmark dataset ICDAR2013. Compared with direct text detection way, the scene text erasing process demonstrates a drastically decrease on the precision, recall and f-score. That proves the effectiveness of proposed method for erasing the text in natural scene images.
Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other. One of the main contributions of this paper is to reveal a unified geometric structure in GAN and its variants. Specifically, we show that the adversarial generative model training can be decomposed into three geometric steps: separating hyperplane search, discriminator parameter update away from the separating hyperplane, and the generator update along the normal vector direction of the separating hyperplane. This geometric intuition reveals the limitations of the existing approaches and leads us to propose a new formulation called geometric GAN using SVM separating hyperplane that maximizes the margin. Our theoretical analysis shows that the geometric GAN converges to a Nash equilibrium between the discriminator and generator. In addition, extensive numerical results show that the superior performance of geometric GAN.
Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic.
In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames. Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games. Nevertheless, it is possible to first approximate a solution for the whole game and then improve it by solving individual subgames. This is referred to as subgame solving. We introduce subgame-solving techniques that outperform prior methods both in theory and practice. We also show how to adapt them, and past subgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the prior state-of-the-art approach, action translation. Finally, we show that subgame solving can be repeated as the game progresses down the game tree, leading to far lower exploitability. These techniques were a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold'em poker.
Word and phrase tables are key inputs to machine translations, but costly to produce. New unsupervised learning methods represent words and phrases in a high-dimensional vector space, and these monolingual embeddings have been shown to encode syntactic and semantic relationships between language elements. The information captured by these embeddings can be exploited for bilingual translation by learning a transformation matrix that allows to match relative positions across two monolingual vector spaces. This method aims to identify high-quality candidates for word and phrase translation more cost-effectively from unlabeled data. This paper expands the scope of previous attempts of bilingual translation to four languages (English, German, Spanish, and French). It shows how to process the source data, train a neural network to learn the high-dimensional embeddings for individual languages and expands the framework for testing their quality beyond the English language. Furthermore, it shows how to learn bilingual transformation matrices and obtain candidates for word and phrase translation, and assess their quality.
This paper proposes a path planning strategy for an Autonomous Ground Vehicle (AGV) navigating in a partially known environment. Global path planning is performed by first using a spatial database of the region to be traversed containing selected attributes such as height data and soil information from a suitable spatial database. The database is processed using a biomimetic swarm algorithm that is inspired by the nest building strategies followed by termites. Local path planning is performed online utilizing information regarding contingencies that affect the safe navigation of the AGV from various sensors. The simulation discussed has been implemented on the open source Player-Stage-Gazebo platform.
In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed throughout the years, no research has been done to validate whether these measures achieve the intended aim of quantifying over-approximation in a consistent way for all models and logs. This paper fills this gap by postulating a number of axioms for quantifying precision consistently for any log and any model. Further, we show through counter-examples that none of the existing measures consistently quantifies precision.
We propose a logic of asynchronous announcements, where truthful announcements are publicly sent but individually received by agents. Additional to epistemic modalities, the logic therefore contains two types of dynamic modalities, for sending messages and for receiving messages. The semantics defines truth relative to the current state of reception of messages for all agents. This means that knowledge need not be truthful, because some messages may not have been received by the knowing agent. Messages that are announcements may also result in partial synchronization, namely when an agent learns from receiving an announcement that other announcements must already have been received by other agents. We give detailed examples of the semantics, and prove several semantic results, including that: after an announcement an agent knows that a proposition is true, if and only if on condition of the truth of that announcement, the agent knows that after that announcement and after any number of other agents also receiving it, the proposition is true. We show that on multi-agent epistemic models, each formula in asynchronous announcement logic is equivalent to a formula in epistemic logic.
Spoken Language Understanding (SLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally SLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Sequential Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.
In most common settings of Markov Decision Process (MDP), an agent evaluate a policy based on expectation of (discounted) sum of rewards. However in many applications this criterion might not be suitable from two perspective: first, in risk aversion situation expectation of accumulated rewards is not robust enough, this is the case when distribution of accumulated reward is heavily skewed; another issue is that many applications naturally take several objective into consideration when evaluating a policy, for instance in autonomous driving an agent needs to balance speed and safety when choosing appropriate decision. In this paper, we consider evaluating a policy based on a sequence of quantiles it induces on a set of target states, our idea is to reformulate the original problem into a multi-objective MDP problem with lexicographic preference naturally defined. For computation of finding an optimal policy, we proposed an algorithm \textbf{FLMDP} that could solve general multi-objective MDP with lexicographic reward preference.
It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism. A potential problem of this approach, however, is that neural models can only learn abstract rules, while poem generation is a highly creative process that involves not only rules but also innovations for which pure statistical models are not appropriate in principle. This work proposes a memory-augmented neural model for Chinese poem generation, where the neural model and the augmented memory work together to balance the requirements of linguistic accordance and aesthetic innovation, leading to innovative generations that are still rule-compliant. In addition, it is found that the memory mechanism provides interesting flexibility that can be used to generate poems with different styles.
We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommender systems and attention modeling. Herein, we adapt the standard multi-armed bandit algorithm known as Thompson Sampling to take advantage of our restricted context setting, and propose two novel algorithms, called the Thompson Sampling with Restricted Context(TSRC) and the Windows Thompson Sampling with Restricted Context(WTSRC), for handling stationary and nonstationary environments, respectively. Our empirical results demonstrate advantages of the proposed approaches on several real-life datasets
Visual question answering (or VQA) is a new and exciting problem that combines natural language processing and computer vision techniques. We present a survey of the various datasets and models that have been used to tackle this task. The first part of the survey details the various datasets for VQA and compares them along some common factors. The second part of this survey details the different approaches for VQA, classified into four types: non-deep learning models, deep learning models without attention, deep learning models with attention, and other models which do not fit into the first three. Finally, we compare the performances of these approaches and provide some directions for future work.
This paper explores the use of Answer Set Programming (ASP) in solving Distributed Constraint Optimization Problems (DCOPs). The paper provides the following novel contributions: (1) It shows how one can formulate DCOPs as logic programs; (2) It introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (3) It experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative programming counterpart) as well as solve some problems that DPOP fails to solve, due to memory limitations; and (4) It demonstrates the applicability of ASP in a wide array of multi-agent problems currently modeled as DCOPs. Under consideration in Theory and Practice of Logic Programming (TPLP).
Critical node problems involve identifying a subset of critical nodes from an undirected graph whose removal results in optimizing a pre-defined measure over the residual graph. As useful models for a variety of practical applications, these problems are computational challenging. In this paper, we study the classic critical node problem (CNP) and introduce an effective memetic algorithm for solving CNP. The proposed algorithm combines a double backbone-based crossover operator (to generate promising offspring solutions), a component-based neighborhood search procedure (to find high-quality local optima) and a rank-based pool updating strategy (to guarantee a healthy population). Specially, the component-based neighborhood search integrates two key techniques, i.e., two-phase node exchange strategy and node weighting scheme. The double backbone-based crossover extends the idea of general backbone-based crossovers. Extensive evaluations on 42 synthetic and real-world benchmark instances show that the proposed algorithm discovers 21 new upper bounds and matches 18 previous best-known upper bounds. We also demonstrate the relevance of our algorithm for effectively solving a variant of the classic CNP, called the cardinality-constrained critical node problem. Finally, we investigate the usefulness of each key algorithmic component.
Solving algebraic word problems requires executing a series of arithmetic operations---a program---to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.
In this paper we present the first empirical study of the emphatic temporal-difference learning algorithm (ETD), comparing it with conventional temporal-difference learning, in particular, with linear TD(0), on on-policy and off-policy variations of the Mountain Car problem. The initial motivation for developing ETD was that it has good convergence properties under off-policy training (Sutton, Mahmood and White 2016), but it is also a new algorithm for the on-policy case. In both our on-policy and off-policy experiments, we found that each method converged to a characteristic asymptotic level of error, with ETD better than TD(0). TD(0) achieved a still lower error level temporarily before falling back to its higher asymptote, whereas ETD never showed this kind of "bounce". In the off-policy case (in which TD(0) is not guaranteed to converge), ETD was significantly slower.
Humans make complex inferences on faces, ranging from objective properties (gender, ethnicity, expression, age, identity, etc) to subjective judgments (facial attractiveness, trustworthiness, sociability, friendliness, etc). While the objective aspects of face perception have been extensively studied, relatively fewer computational models have been developed for the social impressions of faces. Bridging this gap, we develop a method to predict human impressions of faces in 40 subjective social dimensions, using deep representations from state-of-the-art neural networks. We find that model performance grows as the human consensus on a face trait increases, and that model predictions outperform human groups in correlation with human averages. This illustrates the learnability of subjective social perception of faces, especially when there is high human consensus. Our system can be used to decide which photographs from a personal collection will make the best impression. The results are significant for the field of social robotics, demonstrating that robots can learn the subjective judgments defining the underlying fabric of human interaction.
The pancake puzzle is a classic optimization problem that has become a standard benchmark for heuristic search algorithms. In this paper, we provide full proofs regarding the local search topology of the gap heuristic for the pancake puzzle. First, we show that in any non-goal state in which there is no move that will decrease the number of gaps, there is a move that will keep the number of gaps constant. We then classify any state in which the number of gaps cannot be decreased in a single action into two groups: those requiring 2 actions to decrease the number of gaps, and those which require 3 actions to decrease the number of gaps.
Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using only generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).
The brain's self-monitoring of activities, including internal activities -- a functionality that we refer to as awareness -- has been suggested as a key element of consciousness. Here we investigate whether the presence of an inner-eye-like process (monitor) that supervises the activities of a number of subsystems (operative agents) engaged in the solution of a problem can improve the problem-solving efficiency of the system. The problem is to find the global maximum of a NK fitness landscape and the performance is measured by the time required to find that maximum. The operative agents explore blindly the fitness landscape and the monitor provides them with feedback on the quality (fitness) of the proposed solutions. This feedback is then used by the operative agents to bias their searches towards the fittest regions of the landscape. We find that a weak feedback between the monitor and the operative agents improves the performance of the system, regardless of the difficulty of the problem, which is gauged by the number of local maxima in the landscape. For easy problems (i.e., landscapes without local maxima), the performance improves monotonically as the feedback strength increases, but for difficult problems, there is an optimal value of the feedback strength beyond which the system performance degrades very rapidly.
It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponentially large number of bins over which policies would have to be learned. In this paper, we draw inspiration from the recent success of sequence-to-sequence models for structured prediction problems to develop policies over discretized spaces. Central to this method is the realization that complex functions over high dimensional spaces can be modeled by neural networks that use next step prediction. Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions. With this parameterization, it is possible to both leverage the compositional structure of action spaces during learning, as well as compute maxima over action spaces (approximately). On a simple example task we demonstrate empirically that our method can perform global search, which effectively gets around the local optimization issues that plague DDPG and NAF. We apply the technique to off-policy (Q-learning) methods and show that our method can achieve the state-of-the-art for off-policy methods on several continuous control tasks.
Penetration testing is a well-established practical concept for the identification of potentially exploitable security weaknesses and an important component of a security audit. Providing a holistic security assessment for networks consisting of several hundreds hosts is hardly feasible though without some sort of mechanization. Mitigation, prioritizing counter- measures subject to a given budget, currently lacks a solid theoretical understanding and is hence more art than science. In this work, we propose the first approach for conduct- ing comprehensive what-if analyses in order to reason about mitigation in a conceptually well-founded manner. To evaluate and compare mitigation strategies, we use simulated penetration testing, i.e., automated attack-finding, based on a network model to which a subset of a given set of mitigation actions, e.g., changes to the network topology, system updates, configuration changes etc. is applied. We determine optimal combinations that minimize the maximal attacker success (similar to a Stackelberg game), and thus provide a well-founded basis for a holistic mitigation strategy. We show that these what-if analysis models can largely be derived from network scan, public vulnerability databases and manual inspection with various degrees of automation and detail, and we simulate mitigation analysis on networks of different size and vulnerability.
User opinions expressed in the form of ratings can influence an individual's view of an item. However, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings the importance different users place on different aspects of an item. We propose a probabilistic modeling of the observed aspect ratings to infer (i) each user's aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.
Massive public resume data emerging on the WWW indicates individual-related characteristics in terms of profile and career experiences. Resume Analysis (RA) provides opportunities for many applications, such as talent seeking and evaluation. Existing RA studies based on statistical analyzing have primarily focused on talent recruitment by identifying explicit attributes. However, they failed to discover the implicit semantic information, i.e., individual career progress patterns and social-relations, which are vital to comprehensive understanding of career development. Besides, how to visualize them for better human cognition is also challenging. To tackle these issues, we propose a visual analytics system ResumeVis to mine and visualize resume data. Firstly, a text-mining based approach is presented to extract semantic information. Then, a set of visualizations are devised to represent the semantic information in multiple perspectives. By interactive exploration on ResumeVis performed by domain experts, the following tasks can be accomplished: to trace individual career evolving trajectory; to mine latent social-relations among individuals; and to hold the full picture of massive resumes' collective mobility. Case studies with over 2500 online officer resumes demonstrate the effectiveness of our system. We provide a demonstration video.
In this paper, we propose a single-agent logic of goal-directed knowing how extending the standard epistemic logic of knowing that with a new knowing how operator. The semantics of the new operator is based on the idea that knowing how to achieve $\phi$ means that there exists a (uniform) strategy such that the agent knows that it can make sure $\phi$. We give an intuitive axiomatization of our logic and prove the soundness, completeness, and decidability of the logic. The crucial axioms relating knowing that and knowing how illustrate our understanding of knowing how in this setting. This logic can be used in representing both knowledge-that and knowledge-how.
Local consistencies stronger than arc consistency have received a lot of attention since the early days of CSP research. %because of the strong pruning they can achieve. However, they have not been widely adopted by CSP solvers. This is because applying such consistencies can sometimes result in considerably smaller search tree sizes and therefore in important speed-ups, but in other cases the search space reduction may be small, causing severe run time penalties. Taking advantage of recent advances in parallelization, we propose a novel approach for the application of strong local consistencies (SLCs) that can improve their performance by largely preserving the speed-ups they offer in cases where they are successful, and eliminating the run time penalties in cases where they are unsuccessful. This approach is presented in the form of two search algorithms. Both algorithms consist of a master search process, which is a typical CSP solver, and a number of slave processes, with each one implementing a SLC method. The first algorithm runs the different SLCs synchronously at each node of the search tree explored in the master process, while the second one can run them asynchronously at different nodes of the search tree. Experimental results demonstrate the benefits of the proposed method.
We develop the theory and practice of an approach to modelling and probabilistic inference in causal networks that is suitable when application-specific or analysis-specific constraints should inform such inference or when little or no data for the learning of causal network structure or probability values at nodes are available. Constrained Bayesian Networks generalize a Bayesian Network such that probabilities can be symbolic, arithmetic expressions and where the meaning of the network is constrained by finitely many formulas from the theory of the reals. A formal semantics for constrained Bayesian Networks over first-order logic of the reals is given, which enables non-linear and non-convex optimisation algorithms that rely on decision procedures for this logic, and supports the composition of several constrained Bayesian Networks. A non-trivial case study in arms control, where few or no data are available to assess the effectiveness of an arms inspection process, evaluates our approach. An open-access prototype implementation of these foundations and their algorithms uses the SMT solver Z3 as decision procedure, leverages an open-source package for Bayesian inference to symbolic computation, and is evaluated experimentally.
Although learning-based methods have great potential for robotics, one concern is that a robot that updates its parameters might cause large amounts of damage before it learns the optimal policy. We formalize the idea of safe learning in a probabilistic sense by defining an optimization problem: we desire to maximize the expected return while keeping the expected damage below a given safety limit. We study this optimization for the case of a robot manipulator with safety-based torque limits. We would like to ensure that the damage constraint is maintained at every step of the optimization and not just at convergence. To achieve this aim, we introduce a novel method which predicts how modifying the torque limit, as well as how updating the policy parameters, might affect the robot's safety. We show through a number of experiments that our approach allows the robot to improve its performance while ensuring that the expected damage constraint is not violated during the learning process.
There has recently been significant interest in hard attention models for tasks such as object recognition, visual captioning and speech recognition. Hard attention can offer benefits over soft attention such as decreased computational cost, but training hard attention models can be difficult because of the discrete latent variables they introduce. Previous work used REINFORCE and Q-learning to approach these issues, but those methods can provide high-variance gradient estimates and be slow to train. In this paper, we tackle the problem of learning hard attention for a sequential task using variational inference methods, specifically the recently introduced VIMCO and NVIL. Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We demonstrate our method on a phoneme recognition task in clean and noisy environments and show that our method outperforms REINFORCE, with the difference being greater for a more complicated task.
Update rules for learning in dynamic time warping spaces are based on optimal warping paths between parameter and input time series. In general, optimal warping paths are not unique resulting in adverse effects in theory and practice. Under the assumption of squared error local costs, we show that no two warping paths have identical costs almost everywhere in a measure-theoretic sense. Two direct consequences of this result are: (i) optimal warping paths are unique almost everywhere, and (ii) the set of all pairs of time series with multiple equal-cost warping paths coincides with the union of exponentially many zero sets of quadratic forms. One implication of the proposed results is that typical distance-based cost functions such as the k-means objective are differentiable almost everywhere and can be minimized by subgradient methods.
Knowledge bases (KBs) have attracted increasing attention due to its great success in various areas, such as Web and mobile search.Existing KBs are restricted to objective factual knowledge, such as city population or fruit shape, whereas,subjective knowledge, such as big city, which is commonly mentioned in Web and mobile queries, has been neglected. Subjective knowledge differs from objective knowledge in that it has no documented or observed ground truth. Instead, the truth relies on people's dominant opinion. Thus, we can use the crowdsourcing technique to get opinion from the crowd. In our work, we propose a system, called crowdsourced subjective knowledge acquisition (CoSKA),for subjective knowledge acquisition powered by crowdsourcing and existing KBs. The acquired knowledge can be used to enrich existing KBs in the subjective dimension which bridges the gap between existing objective knowledge and subjective queries.The main challenge of CoSKA is the conflict between large scale knowledge facts and limited crowdsourcing resource. To address this challenge, in this work, we define knowledge inference rules and then select the seed knowledge judiciously for crowdsourcing to maximize the inference power under the resource constraint. Our experimental results on real knowledge base and crowdsourcing platform verify the effectiveness of CoSKA system.
The availability of large scale event data with time stamps has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over time in such dynamic knowledge graphs is not yet well understood. To this end, we present Know-Evolve, a novel deep evolutionary knowledge network that learns non-linearly evolving entity representations over time. The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings. We demonstrate significantly improved performance over various relational learning approaches on two large scale real-world datasets. Further, our method effectively predicts occurrence or recurrence time of a fact which is novel compared to prior reasoning approaches in multi-relational setting.
This paper describes a method for identification of the informative variables in the information system with discrete decision variables. It is targeted specifically towards discovery of the variables that are non-informative when considered alone, but are informative when the synergistic interactions between multiple variables are considered. To this end, the mutual entropy of all possible k-tuples of variables with decision variable is computed. Then, for each variable the maximal information gain due to interactions with other variables is obtained. For non-informative variables this quantity conforms to the well known statistical distributions. This allows for discerning truly informative variables from non-informative ones. For demonstration of the approach, the method is applied to several synthetic datasets that involve complex multidimensional interactions between variables. It is capable of identifying most important informative variables, even in the case when the dimensionality of the analysis is smaller than the true dimensionality of the problem. What is more, the high sensitivity of the algorithm allows for detection of the influence of nuisance variables on the response variable.
Latent features learned by deep learning approaches have proven to be a powerful tool for machine learning. They serve as a data abstraction that makes learning easier by capturing regularities in data explicitly. Their benefits motivated their adaptation to relational learning context. In our previous work, we introduce an approach that learns relational latent features by means of clustering instances and their relations. The major drawback of latent representations is that they are often black-box and difficult to interpret. This work addresses these issues and shows that (1) latent features created by clustering are interpretable and capture interesting properties of data; (2) they identify local regions of instances that match well with the label, which partially explains their benefit; and (3) although the number of latent features generated by this approach is large, often many of them are highly redundant and can be removed without hurting performance much.
The city has proven to be the most successful form of human agglomeration and provides wide employment opportunities for its dwellers. As advances in robotics and artificial intelligence revive concerns about the impact of automation on jobs, a question looms: How will automation affect employment in cities? Here, we provide a comparative picture of the impact of automation across U.S. urban areas. Small cities will undertake greater adjustments, such as worker displacement and job content substitutions. We demonstrate that large cities exhibit increased occupational and skill specialization due to increased abundance of managerial and technical professions. These occupations are not easily automatable, and, thus, reduce the potential impact of automation in large cities. Our results pass several robustness checks including potential errors in the estimation of occupational automation and sub-sampling of occupations. Our study provides the first empirical law connecting two societal forces: urban agglomeration and automation's impact on employment.
Based on Alan Turing's proposition on AI and computing machinery, which shaped Computing as we know it today, the new AI computing machinery should comprise a universal computer and a universal learning machine. The later should understand linear algebra natively to overcome the slowdown of Moore's law. In such a universal learnig machine, a computing unit does not need to keep the legacy of a universal computing core. The data can be distributed to the computing units, and the results can be collected from them through Collective Streaming, reminiscent of Collective Communication in Supercomputing. It is not necessary to use a GPU-like deep memory hierarchy, nor a TPU-like fine-grain mesh.
The sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require incorporating haptic feedback. However, manually designing a feedback mechanism can be extremely challenging. In this work, we consider manipulation tasks that need to incorporate tactile sensor feedback in order to modify a provided nominal plan. To incorporate partial observation, we present a new framework that models the task as a partially observable Markov decision process (POMDP) and learns an appropriate representation of haptic feedback which can serve as the state for a POMDP model. The model, that is parametrized by deep recurrent neural networks, utilizes variational Bayes methods to optimize the approximate posterior. Finally, we build on deep Q-learning to be able to select the optimal action in each state without access to a simulator. We test our model on a PR2 robot for multiple tasks of turning a knob until it clicks.
In this work, we present a methodology that enables an agent to make efficient use of its exploratory actions by autonomously identifying possible objectives in its environment and learning them in parallel. The identification of objectives is achieved using an online and unsupervised adaptive clustering algorithm. The identified objectives are learned (at least partially) in parallel using Q-learning. Using a simulated agent and environment, it is shown that the converged or partially converged value function weights resulting from off-policy learning can be used to accumulate knowledge about multiple objectives without any additional exploration. We claim that the proposed approach could be useful in scenarios where the objectives are initially unknown or in real world scenarios where exploration is typically a time and energy intensive process. The implications and possible extensions of this work are also briefly discussed.
Reinforcement learning is a powerful technique to train an agent to perform a task. However, an agent that is trained using reinforcement learning is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations. Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing. We use a generator network to propose tasks for the agent to try to achieve, specified as goal states. The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent. Our method thus automatically produces a curriculum of tasks for the agent to learn. We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment. Our method can also learn to achieve tasks with sparse rewards, which traditionally pose significant challenges.
In Machine Learning, the parent set identification problem is to find a set of random variables that best explain selected variable given the data and some predefined scoring function. This problem is a critical component to structure learning of Bayesian networks and Markov blankets discovery, and thus has many practical applications, ranging from fraud detection to clinical decision support. In this paper, we introduce a new distributed memory approach to the exact parent sets assignment problem. To achieve scalability, we derive theoretical bounds to constraint the search space when MDL scoring function is used, and we reorganize the underlying dynamic programming such that the computational density is increased and fine-grain synchronization is eliminated. We then design efficient realization of our approach in the Apache Spark platform. Through experimental results, we demonstrate that the method maintains strong scalability on a 500-core standalone Spark cluster, and it can be used to efficiently process data sets with 70 variables, far beyond the reach of the currently available solutions.
We introduce a package service model where trucks as well as drones can deliver packages. Drones can travel on trucks or fly; but while flying, drones can only carry one package at a time and have to return to a truck to charge after each delivery. We present a heuristic algorithm to solve the problem of finding a good schedule for all drones and trucks. The algorithm is based on two nested local searches, thus the definition of suitable neighbourhoods of solutions is crucial for the algorithm. Empirical tests show that our algorithm performs significantly better than a natural Greedy algorithm. Moreover, the savings compared to solutions without drones turn out to be substantial, suggesting that delivery systems might considerably benefit from using drones in addition to trucks.
Statistical Relational Learning (SRL) methods for anomaly detection are introduced via a security-related application. Operational requirements for online learning stability are outlined and compared to mathematical definitions as applied to the learning process of a representative SRL method - Bayesian Logic Programs (BLP). Since a formal proof of online stability appears to be impossible, tentative common sense requirements are formulated and tested by theoretical and experimental analysis of a simple and analytically tractable BLP model. It is found that learning algorithms in initial stages of online learning can lock on unstable false predictors that nevertheless comply with our tentative stability requirements and thus masquerade as bona fide solutions. The very expressiveness of SRL seems to cause significant stability issues in settings with many variables and scarce data. We conclude that reliable anomaly detection with SRL-methods requires monitoring by an overarching framework that may involve a comprehensive context knowledge base or human supervision.
The sure thing principle and the law of total probability are basic laws in classic probability theory. A disjunction fallacy leads to the violation of these two classical laws. In this paper, an Evidential Markov (EM) decision making model based on Dempster-Shafer (D-S) evidence theory and Markov modelling is proposed to address this issue and model the real human decision-making process. In an evidential framework, the states are extended by introducing an uncertain state which represents the hesitance of a decision maker. The classical Markov model can not produce the disjunction effect, which assumes that a decision has to be certain at one time. However, the state is allowed to be uncertain in the EM model before the final decision is made. An extra uncertainty degree parameter is defined by a belief entropy, named Deng entropy, to assignment the basic probability assignment of the uncertain state, which is the key to predict the disjunction effect. A classical categorization decision-making experiment is used to illustrate the effectiveness and validity of EM model. The disjunction effect can be well predicted and the free parameters are less compared with the existing models.
As mobile devices have become indispensable in modern life, mobile security is becoming much more important. Traditional password or PIN-like point-of-entry security measures score low on usability and are vulnerable to brute force and other types of attacks. In order to improve mobile security, an adaptive neuro-fuzzy inference system(ANFIS)-based implicit authentication system is proposed in this paper to provide authentication in a continuous and transparent manner.To illustrate the applicability and capability of ANFIS in our implicit authentication system, experiments were conducted on behavioural data collected for up to 12 weeks from different Android users. The ability of the ANFIS-based system to detect an adversary is also tested with scenarios involving an attacker with varying levels of knowledge. The results demonstrate that ANFIS is a feasible and efficient approach for implicit authentication with an average of 95% user recognition rate. Moreover, the use of ANFIS-based system for implicit authentication significantly reduces manual tuning and configuration tasks due to its selflearning capability.
Motivated by the common academic problem of allocating papers to referees for conference reviewing we propose a novel mechanism for solving the assignment problem when we have a two sided matching problem with preferences from one side (the agents/reviewers) over the other side (the objects/papers) and both sides have capacity constraints. The assignment problem is a fundamental problem in both computer science and economics with application in many areas including task and resource allocation. We draw inspiration from multi-criteria decision making and voting and use order weighted averages (OWAs) to propose a novel and flexible class of algorithms for the assignment problem. We show an algorithm for finding a $\Sigma$-OWA assignment in polynomial time, in contrast to the NP-hardness of finding an egalitarian assignment. Inspired by this setting we observe an interesting connection between our model and the classic proportional multi-winner election problem in social choice.
We propose an efficient method to estimate the accuracy of classifiers using only unlabeled data. We consider a setting with multiple classification problems where the target classes may be tied together through logical constraints. For example, a set of classes may be mutually exclusive, meaning that a data instance can belong to at most one of them. The proposed method is based on the intuition that: (i) when classifiers agree, they are more likely to be correct, and (ii) when the classifiers make a prediction that violates the constraints, at least one classifier must be making an error. Experiments on four real-world data sets produce accuracy estimates within a few percent of the true accuracy, using solely unlabeled data. Our models also outperform existing state-of-the-art solutions in both estimating accuracies, and combining multiple classifier outputs. The results emphasize the utility of logical constraints in estimating accuracy, thus validating our intuition.
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kind of weighted logical formulas, which make them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can contain many formulas that interact in non-trivial ways and weights do not always have an intuitive meaning. To address this, we propose a new SRL method which uses possibilistic logic to encode relational models. Learned models are then essentially stratified classical theories, which explicitly encode what can be derived with a given level of certainty. Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.
Ontology-based data access (OBDA) is a popular approach for integrating and querying multiple data sources by means of a shared ontology. The ontology is linked to the sources using mappings, which assign views over the data to ontology predicates. Motivated by the need for OBDA systems supporting database-style aggregate queries, we propose a bag semantics for OBDA, where duplicate tuples in the views defined by the mappings are retained, as is the case in standard databases. We show that bag semantics makes conjunctive query answering in OBDA coNP-hard in data complexity. To regain tractability, we consider a rather general class of queries and show its rewritability to a generalisation of the relational calculus to bags.
Many different methods to train deep generative models have been introduced in the past. In this paper, we propose to extend the variational auto-encoder (VAE) framework with a new type of prior which we call "Variational Mixture of Posteriors" prior, or VampPrior for short. The VampPrior consists of a mixture distribution (e.g., a mixture of Gaussians) with components given by variational posteriors conditioned on learnable pseudo-inputs. We further extend this prior to a two layer hierarchical model and show that this architecture with a coupled prior and posterior, learns significantly better models. The model also avoids the usual local optima issues related to useless latent dimensions that plague VAEs. We provide empirical studies on six datasets, namely, static and binary MNIST, OMNIGLOT, Caltech 101 Silhouettes, Frey Faces and Histopathology patches, and show that applying the hierarchical VampPrior delivers state-of-the-art results on all datasets in the unsupervised permutation invariant setting and the best results or comparable to SOTA methods for the approach with convolutional networks.
Action planning using learned and differentiable forward models of the world is a general approach which has a number of desirable properties, including improved sample complexity over model-free RL methods, reuse of learned models across different tasks, and the ability to perform efficient gradient-based optimization in continuous action spaces. However, this approach does not apply straightforwardly when the action space is discrete. In this work, we show that it is in fact possible to effectively perform planning via backprop in discrete action spaces, using a simple paramaterization of the actions vectors on the simplex combined with input noise when training the forward model. Our experiments show that this approach can match or outperform model-free RL and discrete planning methods on gridworld navigation tasks in terms of performance and/or planning time while using limited environment interactions, and can additionally be used to perform model-based control in a challenging new task where the action space combines discrete and continuous actions. We furthermore propose a policy distillation approach which yields a fast policy network which can be used at inference time, removing the need for an iterative planning procedure.
We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions. We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hypothesize the existence of undesirable local equilibria in this non-convex game to be responsible for mode collapse. We observe that these local equilibria often exhibit sharp gradients of the discriminator function around some real data points. We demonstrate that these degenerate local equilibria can be avoided with a gradient penalty scheme called DRAGAN. We show that DRAGAN enables faster training, achieves improved stability with fewer mode collapses, and leads to generator networks with better modeling performance across a variety of architectures and objective functions.
Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key problem faced by practitioners is measuring the accuracy of an approximate inference algorithm on a specific data set. This paper introduces the auxiliary inference divergence estimator (AIDE), an algorithm for measuring the accuracy of approximate inference algorithms. AIDE is based on the observation that inference algorithms can be treated as probabilistic models and the random variables used within the inference algorithm can be viewed as auxiliary variables. This view leads to a new estimator for the symmetric KL divergence between the approximating distributions of two inference algorithms. The paper illustrates application of AIDE to algorithms for inference in regression, hidden Markov, and Dirichlet process mixture models. The experiments show that AIDE captures the qualitative behavior of a broad class of inference algorithms and can detect failure modes of inference algorithms that are missed by standard heuristics.
In this paper, we extend an attention-based neural machine translation (NMT) model by allowing it to access an entire training set of parallel sentence pairs even after training. The proposed approach consists of two stages. In the first stage--retrieval stage--, an off-the-shelf, black-box search engine is used to retrieve a small subset of sentence pairs from a training set given a source sentence. These pairs are further filtered based on a fuzzy matching score based on edit distance. In the second stage--translation stage--, a novel translation model, called translation memory enhanced NMT (TM-NMT), seamlessly uses both the source sentence and a set of retrieved sentence pairs to perform the translation. Empirical evaluation on three language pairs (En-Fr, En-De, and En-Es) shows that the proposed approach significantly outperforms the baseline approach and the improvement is more significant when more relevant sentence pairs were retrieved.
A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model - where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.
Word embeddings improve the performance of NLP systems by revealing the hidden structural relationships between words. Despite their success in many applications, word embeddings have seen very little use in computational social science NLP tasks, presumably due to their reliance on big data, and to a lack of interpretability. I propose a probabilistic model-based word embedding method which can recover interpretable embeddings, without big data. The key insight is to leverage mixed membership modeling, in which global representations are shared, but individual entities (i.e. dictionary words) are free to use these representations to uniquely differing degrees. I show how to train the model using a combination of state-of-the-art training techniques for word embeddings and topic models. The experimental results show an improvement in predictive language modeling of up to 63% in MRR over the skip-gram, and demonstrate that the representations are beneficial for supervised learning. I illustrate the interpretability of the models with computational social science case studies on State of the Union addresses and NIPS articles.
The stochastic shortest path problem (SSP) is a highly expressive model for probabilistic planning. The computational hardness of SSPs has sparked interest in determinization-based planners that can quickly solve large problems. However, existing methods employ a simplistic approach to determinization. In particular, they ignore the possibility of tailoring the determinization to the specific characteristics of the target domain. In this work we examine this question, by showing that learning a good determinization for a planning domain can be done efficiently and can improve performance. Moreover, we show how to directly incorporate probabilistic reasoning into the planning problem when a good determinization is not sufficient by itself. Based on these insights, we introduce a planner, FF-LAO*, that outperforms state-of-the-art probabilistic planners on several well-known competition benchmarks.
Answer Set Programming (ASP) is a powerful modeling formalism for combinatorial problems. However, writing ASP models is not trivial. We propose a novel method, called Sketched Answer Set Programming (SkASP), aiming at supporting the user in resolving this issue. The user writes an ASP program while marking uncertain parts open with question marks. In addition, the user provides a number of positive and negative examples of the desired program behaviour. The sketched model is rewritten into another ASP program, which is solved by traditional methods. As a result, the user obtains a functional and reusable ASP program modelling her problem. We evaluate our approach on 21 well known puzzles and combinatorial problems inspired by Karp's 21 NP-complete problems and demonstrate a use-case for a database application based on ASP.
We present a novel method for frequentist statistical inference in $M$-estimation problems, based on stochastic gradient descent (SGD) with a fixed step size: we demonstrate that the average of such SGD sequences can be used for statistical inference, after proper scaling. An intuitive analysis using the Ornstein-Uhlenbeck process suggests that such averages are asymptotically normal. From a practical perspective, our SGD-based inference procedure is a first order method, and is well-suited for large scale problems. To show its merits, we apply it to both synthetic and real datasets, and demonstrate that its accuracy is comparable to classical statistical methods, while requiring potentially far less computation.
We propose a general framework for entropy-regularized average-reward reinforcement learning in Markov decision processes (MDPs). Our approach is based on extending the linear-programming formulation of policy optimization in MDPs to accommodate convex regularization functions. Our key result is showing that using the conditional entropy of the joint state-action distributions as regularization yields a dual optimization problem closely resembling the Bellman optimality equations. This result enables us to formalize a number of state-of-the-art entropy-regularized reinforcement learning algorithms as approximate variants of Mirror Descent or Dual Averaging, and thus to argue about the convergence properties of these methods. In particular, we show that the exact version of the TRPO algorithm of Schulman et al. (2015) actually converges to the optimal policy, while the entropy-regularized policy gradient methods of Mnih et al. (2016) may fail to converge to a fixed point. Finally, we illustrate empirically the effects of using various regularization techniques on learning performance in a simple reinforcement learning setup.
We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering. We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer. The reformulation system is trained end-to-end to maximize answer quality using policy gradient. We evaluate on SearchQA, a dataset of complex questions extracted from Jeopardy!. The agent outperforms a state-of-the-art base model, playing the role of the environment, and other benchmarks. We also analyze the language that the agent has learned while interacting with the question answering system. We find that successful question reformulations look quite different from natural language paraphrases. The agent is able to discover non-trivial reformulation strategies that resemble classic information retrieval techniques such as term re-weighting (tf-idf) and stemming.
Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.
We propose a new algorithm for training generative adversarial networks that jointly learns latent codes for both identities (e.g. individual humans) and observations (e.g. specific photographs). By fixing the identity portion of the latent codes, we can generate diverse images of the same subject, and by fixing the observation portion, we can traverse the manifold of subjects while maintaining contingent aspects such as lighting and pose. Our algorithm features a pairwise training scheme in which each sample from the generator consists of two images with a common identity code. Corresponding samples from the real dataset consist of two distinct photographs of the same subject. In order to fool the discriminator, the generator must produce pairs that are photorealistic, distinct, and appear to depict the same individual. We augment both the DCGAN and BEGAN approaches with Siamese discriminators to facilitate pairwise training. Experiments with human judges and an off-the-shelf face verification system demonstrate our algorithm's ability to generate convincing, identity-matched photographs.
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this property. For this purpose, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an n-dimensional Poincar\'e ball. Due to the underlying hyperbolic geometry, this allows us to learn parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. We introduce an efficient algorithm to learn the embeddings based on Riemannian optimization and show experimentally that Poincar\'e embeddings outperform Euclidean embeddings significantly on data with latent hierarchies, both in terms of representation capacity and in terms of generalization ability.
The design and analysis of communication systems typically rely on the development of mathematical models that describe the underlying communication channel, which dictates the relationship between the transmitted and the received signals. However, in some systems, such as molecular communication systems where chemical signals are used for transfer of information, it is not possible to accurately model this relationship. In these scenarios, because of the lack of mathematical channel models, a completely new approach to design and analysis is required. In this work, we focus on one important aspect of communication systems, the detection algorithms, and demonstrate that by borrowing tools from deep learning, it is possible to train detectors that perform well, without any knowledge of the underlying channel models. We evaluate these algorithms using experimental data that is collected by a chemical communication platform, where the channel model is unknown and difficult to model analytically. We show that deep learning algorithms perform significantly better than a simple detector that was used in previous works, which also did not assume any knowledge of the channel.
Applying deep reinforcement learning (RL) on real systems suffers from slow data sampling. We propose an enhanced generative adversarial network (EGAN) to initialize an RL agent in order to achieve faster learning. The EGAN utilizes the relation between states and actions to enhance the quality of data samples generated by a GAN. Pre-training the agent with the EGAN shows a steeper learning curve with a 20% improvement of training time in the beginning of learning, compared to no pre-training, and an improvement compared to training with GAN by about 5% with smaller variations. For real time systems with sparse and slow data sampling the EGAN could be used to speed up the early phases of the training process.
Explaining and reasoning about processes which underlie observed black-box phenomena enables the discovery of causal mechanisms, derivation of suitable abstract representations and the formulation of more robust predictions. We propose to learn high level functional programs in order to represent abstract models which capture the invariant structure in the observed data. We introduce the $\pi$-machine (program-induction machine) -- an architecture able to induce interpretable LISP-like programs from observed data traces. We propose an optimisation procedure for program learning based on backpropagation, gradient descent and A* search. We apply the proposed method to three problems: system identification of dynamical systems, explaining the behaviour of a DQN agent and learning by demonstration in a human-robot interaction scenario. Our experimental results show that the $\pi$-machine can efficiently induce interpretable programs from individual data traces.
No real-world reward function is perfect. Sensory errors and software bugs may result in RL agents observing higher (or lower) rewards than they should. For example, a reinforcement learning agent may prefer states where a sensory error gives it the maximum reward, but where the true reward is actually small. We formalise this problem as a generalised Markov Decision Problem called Corrupt Reward MDP. Traditional RL methods fare poorly in CRMDPs, even under strong simplifying assumptions and when trying to compensate for the possibly corrupt rewards. Two ways around the problem are investigated. First, by giving the agent richer data, such as in inverse reinforcement learning and semi-supervised reinforcement learning, reward corruption stemming from systematic sensory errors may sometimes be completely managed. Second, by using randomisation to blunt the agent's optimisation, reward corruption can be partially managed under some assumptions.
LTLf synthesis is the process of finding a strategy that satisfies a linear temporal specification over finite traces. An existing solution to this problem relies on a reduction to a DFA game. In this paper, we propose a symbolic framework for LTLf synthesis based on this technique, by performing the computation over a representation of the DFA as a boolean formula rather than as an explicit graph. This approach enables strategy generation by utilizing the mechanism of boolean synthesis. We implement this symbolic synthesis method in a tool called Syft, and demonstrate by experiments on scalable benchmarks that the symbolic approach scales better than the explicit one.
The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this paper, we introduce an efficient algorithm to address the above problem in a fully decentralized (peer-to-peer) and asynchronous fashion, with provable convergence rate. We show how to make the algorithm differentially private to protect against the disclosure of information about the personal datasets, and formally analyze the trade-off between utility and privacy. Our experiments show that our approach dramatically outperforms previous work in the non-private case, and that under privacy constraints, we can significantly improve over models learned in isolation.
Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small adversarial change of an originally with high confidence correctly classified input leads to a wrong classification again with high confidence. This raises concerns that such classifiers are vulnerable to attacks and calls into question their usage in safety-critical systems. We show in this paper for the first time formal guarantees on the robustness of a classifier by giving instance-specific lower bounds on the norm of the input manipulation required to change the classifier decision. Based on this analysis we propose the Cross-Lipschitz regularization functional. We show that using this form of regularization in kernel methods resp. neural networks improves the robustness of the classifier without any loss in prediction performance.
We introduce second-order vector representations of words, induced from nearest neighborhood topological features in pre-trained contextual word embeddings. We then analyze the effects of using second-order embeddings as input features in two deep natural language processing models, for named entity recognition and recognizing textual entailment, as well as a linear model for paraphrase recognition. Surprisingly, we find that nearest neighbor information alone is sufficient to capture most of the performance benefits derived from using pre-trained word embeddings. Furthermore, second-order embeddings are able to handle highly heterogeneous data better than first-order representations, though at the cost of some specificity. Additionally, augmenting contextual embeddings with second-order information further improves model performance in some cases. Due to variance in the random initializations of word embeddings, utilizing nearest neighbor features from multiple first-order embedding samples can also contribute to downstream performance gains. Finally, we identify intriguing characteristics of second-order embedding spaces for further research, including much higher density and different semantic interpretations of cosine similarity.
Randomized experiments have been used to assist decision-making in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods.
Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs). These techniques can potentially significantly improve DNNs prediction performance by trading-off coverage. In this paper we propose a method to construct a selective classifier given a trained neural network. Our method allows a user to set a desired risk level. At test time, the classifier rejects instances as needed, to grant the desired risk (with high probability). Empirical results over CIFAR and ImageNet convincingly demonstrate the viability of our method, which opens up possibilities to operate DNNs in mission-critical applications. For example, using our method an unprecedented 2% error in top-5 ImageNet classification can be guaranteed with probability 99.9%, and almost 60% test coverage.
The explosive growth of the location-enabled devices coupled with the increasing use of Internet services has led to an increasing awareness of the importance and usage of geospatial information in many applications. The navigation apps (often called Maps), use a variety of available data sources to calculate and predict the travel time as well as several options for routing in public transportation, car or pedestrian modes. This paper evaluates the pedestrian mode of Maps apps in three major smartphone operating systems (Android, iOS and Windows Phone). In the paper, we will show that the Maps apps on iOS, Android and Windows Phone in pedestrian mode, predict travel time without learning from the individual's movement profile. In addition, we will exemplify that those apps suffer from a specific data quality issue which relates to the absence of information about location and type of pedestrian crossings. Finally, we will illustrate learning from movement profile of individuals using various predictive analytics models to improve the accuracy of travel time estimation.
A major challenge in designing neural network (NN) systems is to determine the best structure and parameters for the network given the data for the machine learning problem at hand. Examples of parameters are the number of layers and nodes, the learning rates, and the dropout rates. Typically, these parameters are chosen based on heuristic rules and manually fine-tuned, which may be very time-consuming, because evaluating the performance of a single parametrization of the NN may require several hours. This paper addresses the problem of choosing appropriate parameters for the NN by formulating it as a box-constrained mathematical optimization problem, and applying a derivative-free optimization tool that automatically and effectively searches the parameter space. The optimization tool employs a radial basis function model of the objective function (the prediction accuracy of the NN) to accelerate the discovery of configurations yielding high accuracy. Candidate configurations explored by the algorithm are trained to a small number of epochs, and only the most promising candidates receive full training. The performance of the proposed methodology is assessed on benchmark sets and in the context of predicting drug-drug interactions, showing promising results. The optimization tool used in this paper is open-source.
Large-scale kernel approximation is an important problem in machine learning research. Approaches using random Fourier features have become increasingly popular [Rahimi and Recht, 2007], where kernel approximation is treated as empirical mean estimation via Monte Carlo (MC) or Quasi-Monte Carlo (QMC) integration [Yang et al., 2014]. A limitation of the current approaches is that all the features receive an equal weight summing to 1. In this paper, we propose a novel shrinkage estimator from "Stein effect", which provides a data-driven weighting strategy for random features and enjoys theoretical justifications in terms of lowering the empirical risk. We further present an efficient randomized algorithm for large-scale applications of the proposed method. Our empirical results on six benchmark data sets demonstrate the advantageous performance of this approach over representative baselines in both kernel approximation and supervised learning tasks.
Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explore all possible actions, which may be harmful for real-world systems. As a consequence, learning algorithms are rarely applied on safety-critical systems in the real world. In this paper, we present a learning algorithm that explicitly considers safety, defined in terms of stability guarantees. Specifically, we extend control-theoretic results on Lyapunov stability verification and show how to use statistical models of the dynamics to obtain high-performance control policies with provable stability certificates. Moreover, under additional regularity assumptions in terms of a Gaussian process prior, we prove that one can effectively and safely collect data in order to learn about the dynamics and thus both improve control performance and expand the safe region of the state space. In our experiments, we show how the resulting algorithm can safely optimize a neural network policy on a simulated inverted pendulum, without the pendulum ever falling down.
Semi-supervised learning methods using Generative Adversarial Networks (GANs) have shown promising empirical success recently. Most of these methods use a shared discriminator/classifier which discriminates real examples from fake while also predicting the class label. Motivated by the ability of the GANs generator to capture the data manifold well, we propose to estimate the tangent space to the data manifold using GANs and employ it to inject invariances into the classifier. In the process, we propose enhancements over existing methods for learning the inverse mapping (i.e., the encoder) which greatly improves in terms of semantic similarity of the reconstructed sample with the input sample. We observe considerable empirical gains in semi-supervised learning over baselines, particularly in the cases when the number of labeled examples is low. We also provide insights into how fake examples influence the semi-supervised learning procedure.
Adversarial learning of probabilistic models has recently emerged as a promising alternative to maximum likelihood. Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum likelihood. Yet, GANs sidestep the characterization of an explicit density which makes quantitative evaluations challenging. To bridge this gap, we propose Flow-GANs, a generative adversarial network for which we can perform exact likelihood evaluation, thus supporting both adversarial and maximum likelihood training. When trained adversarially, Flow-GANs generate high-quality samples but attain extremely poor log-likelihood scores, inferior even to a mixture model memorizing the training data; the opposite is true when trained by maximum likelihood. Results on MNIST and CIFAR-10 demonstrate that hybrid training can attain high held-out likelihoods while retaining visual fidelity in the generated samples.
Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing and the coordination of autonomous vehicles. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent's action, while keeping the other agents' actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actor-critic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.
Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tailored to their domain. As such, the policies they learn do not generalize even to similar domains. To address this issue, we develop a framework through which a deep RL agent learns to generalize policies from smaller, simpler domains to more complex ones using a recurrent attention mechanism. The task is presented to the agent as an image and an instruction specifying the goal. This meta-controller guides the agent towards its goal by designing a sequence of smaller subtasks on the part of the state space within the attention, effectively decomposing it. As a baseline, we consider a setup without attention as well. Our experiments show that the meta-controller learns to create subgoals within the attention.
We propose a probabilistic framework for domain adaptation that blends both generative and discriminative modeling in a principled way. Under this framework, generative and discriminative models correspond to specific choices of the prior over parameters. This provides us a very general way to interpolate between generative and discriminative extremes through different choices of priors. By maximizing both the marginal and the conditional log-likelihoods, models derived from this framework can use both labeled instances from the source domain as well as unlabeled instances from both source and target domains. Under this framework, we show that the popular reconstruction loss of autoencoder corresponds to an upper bound of the negative marginal log-likelihoods of unlabeled instances, where marginal distributions are given by proper kernel density estimations. This provides a way to interpret the empirical success of autoencoders in domain adaptation and semi-supervised learning. We instantiate our framework using neural networks, and build a concrete model, DAuto. Empirically, we demonstrate the effectiveness of DAuto on text, image and speech datasets, showing that it outperforms related competitors when domain adaptation is possible.
Incremental methods for structure learning of pairwise Markov random fields (MRFs), such as grafting, improve scalability to large systems by avoiding inference over the entire feature space in each optimization step. Instead, inference is performed over an incrementally grown active set of features. In this paper, we address the computational bottlenecks that current techniques still suffer by introducing online edge grafting, an incremental, structured method that activates edges as groups of features in a streaming setting. The framework is based on reservoir sampling of edges that satisfy a necessary activation condition, approximating the search for the optimal edge to activate. Online edge grafting performs an informed edge search set reorganization using search history and structure heuristics. Experiments show a significant computational speedup for structure learning and a controllable trade-off between the speed and the quality of learning.
In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of the solution in the agents environment. When humans are learning to complete tasks, we regularly utilize alternative sources that guide our understanding of the problem. Such task representations allow one to specify goals on their own terms, thus providing specifications that can be appropriately interpreted across various environments. This motivates our own work, in which we represent goals in environments that are different from the agents. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned rewards that represent the visual similarity between an agents state and a cross-domain goal image. We report results for learning the CDPRs with a deep neural network and using them to solve two tasks with deep reinforcement learning.
In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias, we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluation across different tasks and datasets shows that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.
We study the notion of robustness in stable matching problems. We first define robustness by introducing (a,b)-supermatches. An $(a,b)$-supermatch is a stable matching in which if $a$ pairs break up it is possible to find another stable matching by changing the partners of those $a$ pairs and at most $b$ other pairs. In this context, we define the most robust stable matching as a $(1,b)$-supermatch where b is minimum. We show that checking whether a given stable matching is a $(1,b)$-supermatch can be done in polynomial time. Next, we use this procedure to design a constraint programming model, a local search approach, and a genetic algorithm to find the most robust stable matching. Our empirical evaluation on large instances show that local search outperforms the other approaches.
Recurrent neural networks have achieved remarkable success at generating sequences with complex structures, thanks to advances that include richer embeddings of input and cures for vanishing gradients. Trained only on sequences from a known grammar, though, they can still struggle to learn rules and constraints of the grammar. Neural Attribute Machines (NAMs) are equipped with a logical machine that represents the underlying grammar, which is used to teach the constraints to the neural machine by (i) augmenting the input sequence, and (ii) optimizing a custom loss function. Unlike traditional RNNs, NAMs are exposed to the grammar, as well as samples from the language of the grammar. During generation, NAMs make significantly fewer violations of the constraints of the underlying grammar than RNNs trained only on samples from the language of the grammar.
When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter's estimator of the marginal likelihood, the filtering variational objectives (FIVOs). FIVOs take the same arguments as the ELBO, but can exploit a model's sequential structure to form tighter bounds. We present results that relate the tightness of FIVO's bound to the variance of the particle filter's estimator by considering the generic case of bounds defined as log-transformed likelihood estimators. Experimentally, we show that training with FIVO results in substantial improvements over training the same model architecture with the ELBO on sequential data.
The existence of a coalition strategy to achieve a goal does not necessarily mean that the coalition has enough information to know how to follow the strategy. Neither does it mean that the coalition knows that such a strategy exists. The article studies an interplay between the distributed knowledge, coalition strategies, and coalition "know-how" strategies. The main technical result is a sound and complete trimodal logical system that describes the properties of this interplay.
We study Robust Subspace Recovery (RSR) in distributed settings. We consider a huge dataset in an ad hoc network without a central processor, where each node has access only to one chunk of the dataset. We assume that part of the whole dataset lies around a low-dimensional subspace and the other part is composed of outliers that lie away from that subspace. The goal is to recover the underlying subspace for the whole dataset, without transferring the data itself between the nodes. We apply the Consensus-Based Gradient method for the Geometric Median Subspace algorithm for RSR. We propose an iterative solution for the local dual minimization problem and establish its $r$-linear convergence. We also explain how to distributedly implement the Reaper and Fast Median Subspace algorithms for RSR. We demonstrate the competitive performance of our algorithms for both synthetic and real data.
Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependence? And, how can we efficiently discover the optimal or $\alpha$-approximate top-$k$ dependencies? These are exactly the questions we answer in this paper. As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity.
Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research.
Online music services are increasing in popularity. They enable us to analyze people's music listening behavior based on play logs. Although it is known that people listen to music based on topic (e.g., rock or jazz), we assume that when a user is addicted to an artist, s/he chooses the artist's songs regardless of topic. Based on this assumption, in this paper, we propose a probabilistic model to analyze people's music listening behavior. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling music listening behavior by taking into account the influence of addiction to artists. Second, by using real-world datasets of play logs, we showed the effectiveness of our proposed model. Third, we carried out qualitative experiments and showed that taking addiction into account enables us to analyze music listening behavior from a new viewpoint in terms of how people listen to music according to the time of day, how an artist's songs are listened to by people, etc. We also discuss the possibility of applying the analysis results to applications such as artist similarity computation and song recommendation.
This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems. In this study, the SLU task consists in automatically extracting, from ASR transcriptions , semantic concepts and concept/values pairs in a e.g touristic information system. An approach is proposed for enriching the set of semantic labels with error specific labels and by using a recently proposed neural approach based on word embeddings to compute well calibrated ASR confidence measures. Experimental results are reported showing that it is possible to decrease significantly the Concept/Value Error Rate with a state of the art system, outperforming previously published results performance on the same experimental data. It also shown that combining an SLU approach based on conditional random fields with a neural encoder/decoder attention based architecture , it is possible to effectively identifying confidence islands and uncertain semantic output segments useful for deciding appropriate error handling actions by the dialogue manager strategy .
The quadratic unconstrained binary optimization (QUBO) problem arises in diverse optimization applications ranging from Ising spin problems to classical problems in graph theory and binary discrete optimization. The use of preprocessing to transform the graph representing the QUBO problem into a smaller equivalent graph is important for improving solution quality and time for both exact and metaheuristic algorithms and is a step towards mapping large scale QUBO to hardware graphs used in quantum annealing computers. In an earlier paper (Lewis and Glover, 2016) a set of rules was introduced that achieved significant QUBO reductions as verified through computational testing. Here this work is extended with additional rules that provide further reductions that succeed in exactly solving 10% of the benchmark QUBO problems. An algorithm and associated data structures to efficiently implement the entire set of rules is detailed and computational experiments are reported that demonstrate their efficacy.
The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space. We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary. Through a systematic empirical investigation, we show that state-of-the-art deep nets learn connected classification regions, and that the decision boundary in the vicinity of datapoints is flat along most directions. We further draw an essential connection between two seemingly unrelated properties of deep networks: their sensitivity to additive perturbations in the inputs, and the curvature of their decision boundary. The directions where the decision boundary is curved in fact remarkably characterize the directions to which the classifier is the most vulnerable. We finally leverage a fundamental asymmetry in the curvature of the decision boundary of deep nets, and propose a method to discriminate between original images, and images perturbed with small adversarial examples. We show the effectiveness of this purely geometric approach for detecting small adversarial perturbations in images, and for recovering the labels of perturbed images.
Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we propose the first quantitative analysis of the robustness of classifiers to universal perturbations, and draw a formal link between the robustness to universal perturbations, and the geometry of the decision boundary. Specifically, we establish theoretical bounds on the robustness of classifiers under two decision boundary models (flat and curved models). We show in particular that the robustness of deep networks to universal perturbations is driven by a key property of their curvature: there exists shared directions along which the decision boundary of deep networks is systematically positively curved. Under such conditions, we prove the existence of small universal perturbations. Our analysis further provides a novel geometric method for computing universal perturbations, in addition to explaining their properties.
Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. We present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs. Within this framework, we use stochastic gradient Hamiltonian Monte Carlo to marginalize the weights of the generator and discriminator networks. The resulting approach is straightforward and obtains good performance without any standard interventions such as feature matching, or mini-batch discrimination. By exploring an expressive posterior over the parameters of the generator, the Bayesian GAN avoids mode-collapse, produces interpretable and diverse candidate samples, and provides state-of-the-art quantitative results for semi-supervised learning on benchmarks including SVHN, CelebA, and CIFAR-10, outperforming DCGAN, Wasserstein GANs, and DCGAN ensembles.
Autonomous systems can substantially enhance a human's efficiency and effectiveness in complex environments. Machines, however, are often unable to observe the preferences of the humans that they serve. Despite the fact that the human's and machine's objectives are aligned, asymmetric information, along with heterogeneous sensitivities to risk by the human and machine, make their joint optimization process a game with strategic interactions. We propose a framework based on risk-sensitive dynamic games; the human seeks to optimize her risk-sensitive criterion according to her true preferences, while the machine seeks to adaptively learn the human's preferences and at the same time provide a good service to the human. We develop a class of performance measures for the proposed framework based on the concept of regret. We then evaluate their dependence on the risk-sensitivity and the degree of uncertainty. We present applications of our framework to self-driving taxis, and robo-financial advising.
The Quadratic Unconstrained Binary Optimization problem (QUBO) has become a unifying model for representing a wide range of combinatorial optimization problems, and for linking a variety of disciplines that face these problems. A new class of quantum annealing computer that maps QUBO onto a physical qubit network structure with specific size and edge density restrictions is generating a growing interest in ways to transform the underlying QUBO structure into an equivalent graph having fewer nodes and edges. In this paper we present rules for reducing the size of the QUBO matrix by identifying variables whose value at optimality can be predetermined. We verify that the reductions improve both solution quality and time to solution and, in the case of metaheuristic methods where optimal solutions cannot be guaranteed, the quality of solutions obtained within reasonable time limits. We discuss the general QUBO structural characteristics that can take advantage of these reduction techniques and perform careful experimental design and analysis to identify and quantify the specific characteristics most affecting reduction. The rules make it possible to dramatically improve solution times on a new set of problems using both the exact Cplex solver and a tabu search metaheuristic.
In this work we present strategies for (optimal) measurement selection in model-based sequential diagnosis. In particular, assuming a set of leading diagnoses being given, we show how queries (sets of measurements) can be computed and optimized along two dimensions: expected number of queries and cost per query. By means of a suitable decoupling of two optimizations and a clever search space reduction the computations are done without any inference engine calls. For the full search space, we give a method requiring only a polynomial number of inferences and guaranteeing query properties existing methods cannot provide. Evaluation results using real-world problems indicate that the new method computes (virtually) optimal queries instantly independently of the size and complexity of the considered diagnosis problems.
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, we call this process Role Playing Learning, which is formulated under a reinforcement learning (RL) framework. The NN policy is optimized end-to-end using Trust Region Policy Optimization (TRPO), with consideration of the imperfectness of robot's sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of our method.
Recent progress in variational inference has paid much attention to the flexibility of variational posteriors. One promising direction is to use implicit distributions, i.e., distributions without tractable densities as the variational posterior. However, existing methods on implicit posteriors still face challenges of noisy estimation and computational infeasibility when applied to models with high-dimensional latent variables. In this paper, we present a new approach named Kernel Implicit Variational Inference that addresses these challenges. As far as we know, for the first time implicit variational inference is successfully applied to Bayesian neural networks, which shows promising results on both regression and classification tasks.
There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. Though these are methods that typically operate separately, we combine evolutionary adaptation and machine learning into one approach. Our focus is on machines that can learn during their lifetime, but instead of equipping them with a machine learning algorithm we aim to let them evolve their ability to learn by themselves. We use evolvable networks of probabilistic and deterministic logic gates, known as Markov Brains, as our computational model organism. The ability of Markov Brains to learn is augmented by a novel adaptive component that can change its computational behavior based on feedback. We show that Markov Brains can indeed evolve to incorporate these feedback gates to improve their adaptability to variable environments. By combining these two methods, we now also implemented a computational model that can be used to study the evolution of learning.
We introduce contextual explanation networks (CENs)---a class of models that learn to predict by generating and leveraging intermediate explanations. CENs are deep networks that generate parameters for context-specific probabilistic graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularization and boosts performance in low-resource settings. We prove that local approximations to the decision boundary of our networks are consistent with the generated explanations. Our results on image and text classification and survival analysis tasks demonstrate that CENs are competitive with the state-of-the-art while offering additional insights behind each prediction, valuable for decision support.
Multisensory polices are known to enhance both state estimation and target tracking. However, in the space of end-to-end sensorimotor control, this multi-sensor outlook has received limited attention. Moreover, systematic ways to make policies robust to partial sensor failure are not well explored. In this work, we propose a specific customization of Dropout, called \textit{Sensor Dropout}, to improve multisensory policy robustness and handle partial failure in the sensor-set. We also introduce an additional auxiliary loss on the policy network in order to reduce variance in the band of potential multi- and uni-sensory policies to reduce jerks during policy switching triggered by an abrupt sensor failure or deactivation/activation. Finally, through the visualization of gradients, we show that the learned policies are conditioned on the same latent states representation despite having diverse observations spaces - a hallmark of true sensor-fusion. Simulation results of the multisensory policy, as visualized in TORCS racing game, can be seen here: https://youtu.be/QAK2lcXjNZc.
Despite the current interest in Open Data publishing, a formal and comprehensive methodology supporting an organization in deciding which data to publish and carrying out precise procedures for publishing high-quality data, is still missing. In this paper we argue that the Ontology-based Data Management paradigm can provide a formal basis for a principled approach to publish high quality, semantically annotated Open Data. We describe two main approaches to using an ontology for this endeavor, and then we present some technical results on one of the approaches, called bottom-up, where the specification of the data to be published is given in terms of the sources, and specific techniques allow deriving suitable annotations for interpreting the published data under the light of the ontology.
Deep neural networks trained on large supervised datasets have led to impressive results in image classification and other tasks. However, well-annotated datasets can be time-consuming and expensive to collect, lending increased interest to larger but noisy datasets that are more easily obtained. In this paper, we show that deep neural networks are capable of generalizing from training data for which true labels are massively outnumbered by incorrect labels. We demonstrate remarkably high test performance after training on corrupted data from MNIST, CIFAR, and ImageNet. For example, on MNIST we obtain test accuracy above 90 percent even after each clean training example has been diluted with 100 randomly-labeled examples. Such behavior holds across multiple patterns of label noise, even when erroneous labels are biased towards confusing classes. We show that training in this regime requires a significant but manageable increase in dataset size that is related to the factor by which correct labels have been diluted. Finally, we provide an analysis of our results that shows how increasing noise decreases the effective batch size.
Human trafficking is one of the most atrocious crimes and among the challenging problems facing law enforcement which demands attention of global magnitude. In this study, we leverage textual data from the website "Backpage"- used for classified advertisement- to discern potential patterns of human trafficking activities which manifest online and identify advertisements of high interest to law enforcement. Due to the lack of ground truth, we rely on a human analyst from law enforcement, for hand-labeling a small portion of the crawled data. We extend the existing Laplacian SVM and present S3VM-R, by adding a regularization term to exploit exogenous information embedded in our feature space in favor of the task at hand. We train the proposed method using labeled and unlabeled data and evaluate it on a fraction of the unlabeled data, herein referred to as unseen data, with our expert's further verification. Results from comparisons between our method and other semi-supervised and supervised approaches on the labeled data demonstrate that our learner is effective in identifying advertisements of high interest to law enforcement
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans, by contrast, can easily detect such errors by acquiring intuition for the correct morphology of objects. Biological neurons have complicated and variable shapes, which are challenging to learn, and merge errors take a multitude of different forms. We present an algorithm, MergeNet, that shows 3D ConvNets can, in fact, detect merge errors from high-level neuronal morphology. MergeNet follows unsupervised training and operates across datasets. We demonstrate the performance of MergeNet both on a variety of connectomics data and on a dataset created from merged MNIST images.
Clause Learning is one of the most important components of a conflict driven clause learning (CDCL) SAT solver that is effective on industrial instances. Since the number of learned clauses is proved to be exponential in the worse case, it is necessary to identify the most relevant clauses to maintain and delete the irrelevant ones. As reported in the literature, several learned clauses deletion strategies have been proposed. However the diversity in both the number of clauses to be removed at each step of reduction and the results obtained with each strategy creates confusion to determine which criterion is better. Thus, the problem to select which learned clauses are to be removed during the search step remains very challenging. In this paper, we propose a novel approach to identify the most relevant learned clauses without favoring or excluding any of the proposed measures, but by adopting the notion of dominance relationship among those measures. Our approach bypasses the problem of the diversity of results and reaches a compromise between the assessments of these measures. Furthermore, the proposed approach also avoids another non-trivial problem which is the amount of clauses to be deleted at each reduction of the learned clause database.
Representing symbolic knowledge into a connectionist network is the key element for the integration of scalable learning and sound reasoning. Most of the previous studies focus on discriminative neural networks which unnecessarily require a separation of input/output variables. Recent development of generative neural networks such as restricted Boltzmann machines (RBMs) has shown a capability of learning semantic abstractions directly from data, posing a promise for general symbolic learning and reasoning. Previous work on Penalty logic show a link between propositional logic and symmetric connectionist networks, however it is not applicable to RBMs. This paper proposes a novel method to represent propositional formulas into RBMs/stack of RBMs where Gibbs sampling can be seen as maximising satisfiability. It also shows a promising use of RBMs to learn symbolic knowledge through maximum likelihood estimation.
Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.
Partially observable environments present an important open challenge in the domain of sequential control learning with delayed rewards. Despite numerous attempts during the two last decades, the majority of reinforcement learning algorithms and associated approximate models, applied to this context, still assume Markovian state transitions. In this paper, we explore the use of a recently proposed attention-based model, the Gated End-to-End Memory Network, for sequential control. We call the resulting model the Gated End-to-End Memory Policy Network. More precisely, we use a model-free value-based algorithm to learn policies for partially observed domains using this memory-enhanced neural network. This model is end-to-end learnable and it features unbounded memory. Indeed, because of its attention mechanism and associated non-parametric memory, the proposed model allows us to define an attention mechanism over the observation stream unlike recurrent models. We show encouraging results that illustrate the capability of our attention-based model in the context of the continuous-state non-stationary control problem of stock trading. We also present an OpenAI Gym environment for simulated stock exchange and explain its relevance as a benchmark for the field of non-Markovian decision process learning.
Recent progress in Reinforcement Learning (RL), fueled by its combination, with Deep Learning has enabled impressive results in learning to interact with complex virtual environments, yet real-world applications of RL are still scarce. A key limitation is data efficiency, with current state-of-the-art approaches requiring millions of training samples. A promising way to tackle this problem is to augment RL with learning from human demonstrations. However, human demonstration data is not yet readily available. This hinders progress in this direction. The present work addresses this problem as follows. We (i) collect and describe a large dataset of human Atari 2600 replays -- the largest and most diverse such data set publicly released to date, (ii) illustrate an example use of this dataset by analyzing the relation between demonstration quality and imitation learning performance, and (iii) outline possible research directions that are opened up by our work.
We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.
Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game and find that it amounts to maximizing the uncertainty of inferring the detrimental factor given the representation while maximizing the certainty of making task-specific predictions. On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance.
Multi-start algorithms are a common and effective tool for metaheuristic searches. In this paper we amplify multi-start capabilities by employing the parallel processing power of the graphics processer unit (GPU) to quickly generate a diverse starting set of solutions for the Unconstrained Binary Quadratic Optimization Problem which are evaluated and used to implement screening methods to select solutions for further optimization. This method is implemented as an initial high quality solution generation phase prior to a secondary steepest ascent search and a comparison of results to best known approaches on benchmark unconstrained binary quadratic problems demonstrates that GPU-enabled diversified multi-start with screening quickly yields very good results.
In [1], we introduced mechanical learning and proposed 2 approaches to mechanical learning. Here, we follow one such approach to well describe the objects and the processes of learning. We discuss 2 kinds of patterns: objective and subjective pattern. Subjective pattern is crucial for learning machine. We prove that for any objective pattern we can find a proper subjective pattern based upon least base patterns to express the objective pattern well. X-form is algebraic expression for subjective pattern. Collection of X-forms form internal representation space, which is center of learning machine. We discuss learning by teaching and without teaching. We define data sufficiency by X-form. We then discussed some learning strategies. We show, in each strategy, with sufficient data, and with certain capabilities, learning machine indeed can learn any pattern (universal learning machine). In appendix, with knowledge of learning machine, we try to view deep learning from a different angle, i.e. its internal representation space and its learning dynamics.
Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free energy-based reinforcement learning (FERL) as an application of quantum hardware. We propose a method for processing a quantum annealer's measured qubit spin configurations in approximating the free energy of a quantum Boltzmann machine (QBM). We then apply this method to perform reinforcement learning on the grid-world problem using the D-Wave 2000Q quantum annealer. The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.
We introduce a diversified top-k partial MaxSAT problem, a combination of partial MaxSAT problem and enumeration problem. Given a partial MaxSAT formula F and a positive integer k, the diversified top-k partial MaxSAT is to find k maximal solutions for F such that the k maximal solutions satisfy the maximum number of soft clauses of F. This problem can be widely used in many applications including community detection, sensor place, motif discovery, and combinatorial testing. We prove the problem is NP-hard and propose an approach for solving the problem. The concrete idea of the approach is to design an encoding EE which reduces diversified top-k partial MaxSAT problem into partial MaxSAT problem, and then solve the resulting problem with state-of-art solvers. In addition, we present an algorithm MEMKC exactly solving the diversified top-k partial MaxSAT. Through several experiments we show that our approach can be successfully applied to the interesting problem.
Robots will eventually be part of every household. It is thus critical to enable algorithms to learn from and be guided by non-expert users. In this paper, we bring a human in the loop, and enable a human teacher to give feedback to a learning agent in the form of natural language. We argue that a descriptive sentence can provide a much stronger learning signal than a numeric reward in that it can easily point to where the mistakes are and how to correct them. We focus on the problem of image captioning in which the quality of the output can easily be judged by non-experts. We propose a hierarchical phrase-based captioning model trained with policy gradients, and design a feedback network that provides reward to the learner by conditioning on the human-provided feedback. We show that by exploiting descriptive feedback our model learns to perform better than when given independently written human captions.
Feature engineering is one of the most important and time consuming tasks in predictive analytics projects. It involves understanding domain knowledge and data exploration to discover relevant hand-crafted features from raw data. In this paper, we introduce a system called One Button Machine, or OneBM for short, which automates feature discovery in relational databases. OneBM automatically performs a key activity of data scientists, namely, joining of database tables and applying advanced data transformations to extract useful features from data. We validated OneBM in Kaggle competitions in which OneBM achieved performance as good as top 16% to 24% data scientists in three Kaggle competitions. More importantly, OneBM outperformed the state-of-the-art system in a Kaggle competition in terms of prediction accuracy and ranking on Kaggle leaderboard. The results show that OneBM can be useful for both data scientists and non-experts. It helps data scientists reduce data exploration time allowing them to try and error many ideas in short time. On the other hand, it enables non-experts, who are not familiar with data science, to quickly extract value from their data with a little effort, time and cost.
As robots begin to cohabit with humans in semi-structured environments, the need arises to understand instructions involving rich variability---for instance, learning to ground symbols in the physical world. Realistically, this task must cope with small datasets consisting of a particular users' contextual assignment of meaning to terms. We present a method for processing a raw stream of cross-modal input---i.e., linguistic instructions, visual perception of a scene and a concurrent trace of 3D eye tracking fixations---to produce the segmentation of objects with a correspondent association to high-level concepts. To test our framework we present experiments in a table-top object manipulation scenario. Our results show our model learns the user's notion of colour and shape from a small number of physical demonstrations, generalising to identifying physical referents for novel combinations of the words.
The growing adoption of IT-systems for modeling and executing (business) processes or services has thrust the scientific investigation towards techniques and tools which support more complex forms of process analysis. Many of them, such as conformance checking, process alignment, mining and enhancement, rely on complete observation of past (tracked and logged) executions. In many real cases, however, the lack of human or IT-support on all the steps of process execution, as well as information hiding and abstraction of model and data, result in incomplete log information of both data and activities. This paper tackles the issue of automatically repairing traces with missing information by notably considering not only activities but also data manipulated by them. Our technique recasts such a problem in a reachability problem and provides an encoding in an action language which allows to virtually use any state-of-the-art planning to return solutions.
Topic models have been widely explored as probabilistic generative models of documents. Traditional inference methods have sought closed-form derivations for updating the models, however as the expressiveness of these models grows, so does the difficulty of performing fast and accurate inference over their parameters. This paper presents alternative neural approaches to topic modelling by providing parameterisable distributions over topics which permit training by backpropagation in the framework of neural variational inference. In addition, with the help of a stick-breaking construction, we propose a recurrent network that is able to discover a notionally unbounded number of topics, analogous to Bayesian non-parametric topic models. Experimental results on the MXM Song Lyrics, 20NewsGroups and Reuters News datasets demonstrate the effectiveness and efficiency of these neural topic models.
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialised cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialised vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.
We introduce the relational ontology log, or relational olog, a knowledge representation system based on the category of sets and relations. It is inspired by Spivak and Kent's olog, a recent categorical framework for knowledge representation. Relational ologs interpolate between ologs and description logic, the dominant formalism for knowledge representation today. In this paper, we investigate relational ologs both for their own sake and to gain insight into the relationship between the algebraic and logical approaches to knowledge representation. On a practical level, we show by example that relational ologs have a friendly and intuitive--yet fully precise--graphical syntax, derived from the string diagrams of monoidal categories. We explain several other useful features of relational ologs not possessed by most description logics, such as a type system and a rich, flexible notion of instance data. In a more theoretical vein, we draw on categorical logic to show how relational ologs can be translated to and from logical theories in a fragment of first-order logic. Although we make extensive use of categorical language, this paper is designed to be self-contained and has considerable expository content. The only prerequisites are knowledge of first-order logic and the rudiments of category theory.
Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned internal mechanisms that contribute to such effective behaviors or, more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs. The computed "attention masks" support improved interpretability by highlighting which input attributes are critical in determining output. We demonstrate the effectiveness of our framework on a variety of deep neural network architectures in domains from computer vision, natural language processing, and reinforcement learning. The primary contribution of our approach is an interpretable visualization of attention that provides unique insights into the network's underlying decision-making process irrespective of the data modality.
While several matrix factorization (MF) and tensor factorization (TF) models have been proposed for knowledge base (KB) inference, they have rarely been compared across various datasets. Is there a single model that performs well across datasets? If not, what characteristics of a dataset determine the performance of MF and TF models? Is there a joint TF+MF model that performs robustly on all datasets? We perform an extensive evaluation to compare popular KB inference models across popular datasets in the literature. In addition to answering the questions above, we remove a limitation in the standard evaluation protocol for MF models, propose an extension to MF models so that they can better handle out-of-vocabulary (OOV) entity pairs, and develop a novel combination of TF and MF models. We also analyze and explain the results based on models and dataset characteristics. Our best model is robust, and obtains strong results across all datasets.
In many robotic applications, some aspects of the system dynamics can be modeled accurately while others are difficult to obtain or model. We present a novel reinforcement learning (RL) method for continuous state and action spaces that learns with partial knowledge of the system and without active exploration. It solves linearly-solvable Markov decision processes (L-MDPs), which are well suited for continuous state and action spaces, based on an actor-critic architecture. Compared to previous RL methods for L-MDPs and path integral methods which are model based, the actor-critic learning does not need a model of the uncontrolled dynamics and, importantly, transition noise levels; however, it requires knowing the control dynamics for the problem. We evaluate our method on two synthetic test problems, and one real-world problem in simulation and using real traffic data. Our experiments demonstrate improved learning and policy performance.
Pathfinding is a very popular area in computer game development. While two-dimensional (2D) pathfinding is widely applied in most of the popular game engines, little implementation of real three-dimensional (3D) pathfinding can be found. This research presents a dynamic search space optimization algorithm which can be applied to tessellate 3D search space unevenly, significantly reducing the total number of resulting nodes. The algorithm can be used with popular pathfinding algorithms in 3D game engines. Furthermore, a simplified standalone 3D pathfinding algorithm is proposed in this paper. The proposed algorithm relies on ray-casting or line vision to generate a feasible path during runtime without requiring division of the search space into a 3D grid. Both of the proposed algorithms are simulated on Unreal Engine to show innerworkings and resultant path comparison with A*. The advantages and shortcomings of the proposed algorithms are also discussed along with future directions.
Non-stationary domains, that change in unpredicted ways, are a challenge for agents searching for optimal policies in sequential decision-making problems. This paper presents a combination of Markov Decision Processes (MDP) with Answer Set Programming (ASP), named {\em Online ASP for MDP} (oASP(MDP)), which is a method capable of constructing the set of domain states while the agent interacts with a changing environment. oASP(MDP) updates previously obtained policies, learnt by means of Reinforcement Learning (RL), using rules that represent the domain changes observed by the agent. These rules represent a set of domain constraints that are processed as ASP programs reducing the search space. Results show that oASP(MDP) is capable of finding solutions for problems in non-stationary domains without interfering with the action-value function approximation process.
Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries. However, many cases, such as the ones with high-dimensional inputs, may require a much larger number of observations for optimization. Despite an abundance of observations thanks to parallel experiments, current BO techniques have been limited to merely a few thousand observations. In this paper, we propose ensemble Bayesian optimization (EBO) to address three current challenges in BO simultaneously: (1) large-scale observations; (2) high dimensional input spaces; and (3) selections of batch queries that balance quality and diversity. The key idea of EBO is to operate on an ensemble of additive Gaussian process models, each of which possesses a randomized strategy to divide and conquer. We show unprecedented, previously impossible results of scaling up BO to tens of thousands of observations within minutes of computation.
A significant amount of search queries originate from some real world information need or tasks. In order to improve the search experience of the end users, it is important to have accurate representations of tasks. As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions, for better recommendations, for satisfaction prediction, and for improved personalization in terms of tasks. Most existing task extraction methodologies focus on representing tasks as flat structures. However, tasks often tend to have multiple subtasks associated with them and a more naturalistic representation of tasks would be in terms of a hierarchy, where each task can be composed of multiple (sub)tasks. To this end, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks \& subtasks. We evaluate our method based on real world query log data both through quantitative and crowdsourced experiments and highlight the importance of considering task/subtask hierarchies.
Multi-task learning (MTL) is a supervised learning paradigm in which the prediction models for several related tasks are learned jointly to achieve better generalization performance. When there are only a few training examples per task, MTL considerably outperforms the traditional Single task learning (STL) in terms of prediction accuracy. In this work we develop an MTL based approach for classifying documents that are archived within dual concept hierarchies, namely, DMOZ and Wikipedia. We solve the multi-class classification problem by defining one-versus-rest binary classification tasks for each of the different classes across the two hierarchical datasets. Instead of learning a linear discriminant for each of the different tasks independently, we use a MTL approach with relationships between the different tasks across the datasets established using the non-parametric, lazy, nearest neighbor approach. We also develop and evaluate a transfer learning (TL) approach and compare the MTL (and TL) methods against the standard single task learning and semi-supervised learning approaches. Our empirical results demonstrate the strength of our developed methods that show an improvement especially when there are fewer number of training examples per classification task.
In this paper, we explore SPPIM-based text classification method, and the experiment reveals that the SPPIM method is equal to or even superior than SGNS method in text classification task on three international and standard text datasets, namely 20newsgroups, Reuters52 and WebKB. Comparing to SGNS, although SPPMI provides a better solution, it is not necessarily better than SGNS in text classification tasks. Based on our analysis, SGNS takes into the consideration of weight calculation during decomposition process, so it has better performance than SPPIM in some standard datasets. Inspired by this, we propose a WL-SPPIM semantic model based on SPPIM model, and experiment shows that WL-SPPIM approach has better classification and higher scalability in the text classification task compared with LDA, SGNS and SPPIM approaches.
Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually.
Epistemic logic with non-standard knowledge operators, especially the "knowing-value" operator, has recently gathered much attention. With the "knowing-value" operator, we can express knowledge of individual variables, but not of the relations between them in general. In this paper, we propose a new operator Kf to express knowledge of the functional dependencies between variables. The semantics of this Kf operator uses a function domain which imposes a constraint on what counts as a functional dependency relation. By adjusting this function domain, different interesting logics arise, and in this paper we axiomatize three such logics in a single agent setting. Then we show how these three logics can be unified by allowing the function domain to vary relative to different agents and possible worlds. A multiagent axiomatization is given in this case.
Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the discovery of such models. However, the focus is often on the representation of the individual artifacts rather than their interactions. Based on event data we can automatically discover composite state machines representing artifact-centric processes. Moreover, we provide ways of visualizing and quantifying interactions among different artifacts. For example, we are able to highlight strongly correlated behaviours in different artifacts. The approach has been fully implemented as a ProM plug-in; the CSM Miner provides an interactive artifact-centric process discovery tool focussing on interactions. The approach has been evaluated using real life data sets, including the personal loan and overdraft process of a Dutch financial institution.
In the context of solving large distributed constraint optimization problems (DCOP), belief-propagation and approximate inference algorithms are candidates of choice. However, in general, when the factor graph is very loopy (i.e. cyclic), these solution methods suffer from bad performance, due to non-convergence and many exchanged messages. As to improve performances of the Max-Sum inference algorithm when solving loopy constraint optimization problems, we propose here to take inspiration from the belief-propagation-guided dec-imation used to solve sparse random graphs (k-satisfiability). We propose the novel DeciMaxSum method, which is parameterized in terms of policies to decide when to trigger decimation, which variables to decimate, and which values to assign to decimated variables. Based on an empirical evaluation on a classical BP benchmark (the Ising model), some of these combinations of policies exhibit better performance than state-of-the-art competitors.
Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occluded objects. We combined psychophysics, physiology and computational models to test the hypothesis that pattern completion is implemented by recurrent computations and present three pieces of evidence that are consistent with this hypothesis. First, subjects robustly recognized objects even when rendered <15% visible, but recognition was largely impaired when processing was interrupted by backward masking. Second, invasive physiological responses along the human ventral cortex exhibited visually selective responses to partially visible objects that were delayed compared to whole objects, suggesting the need for additional computations. These physiological delays were correlated with the effects of backward masking. Third, state-of-the-art feed-forward computational architectures were not robust to partial visibility. However, recognition performance was recovered when the model was augmented with attractor-based recurrent connectivity. These results provide a strong argument of plausibility for the role of recurrent computations in making visual inferences from partial information.
As we know, some global optimization problems cannot be solved using analytic methods, so numeric/algorithmic approaches are used to find near to the optimal solutions for them. A stochastic global optimization algorithm (SGoal) is an iterative algorithm that generates a new population (a set of candidate solutions) from a previous population using stochastic operations. Although some research works have formalized SGoals using Markov kernels, such formalization is not general and sometimes is blurred. In this paper, we propose a comprehensive and systematic formal approach for studying SGoals. First, we present the required theory of probability (\sigma-algebras, measurable functions, kernel, markov chain, products, convergence and so on) and prove that some algorithmic functions like swapping and projection can be represented by kernels. Then, we introduce the notion of join-kernel as a way of characterizing the combination of stochastic methods. Next, we define the optimization space, a formal structure (a set with a \sigma-algebra that contains strict \epsilon-optimal states) for studying SGoals, and we develop kernels, like sort and permutation, on such structure. Finally, we present some popular SGoals in terms of the developed theory, we introduce sufficient conditions for convergence of a SGoal, and we prove convergence of some popular SGoals.
It has been previously observed that variational autoencoders tend to ignore the latent code when combined with a decoding distribution that is too flexible. This undermines the purpose of unsupervised representation learning. In this paper, we additionally show that existing training criteria can lead to extremely poor amortized inference distributions and overestimation of the posterior variance, even when trained to optimality. We identify the reason for both short-comings in the regularization term used in the ELBO criterion to match the variational posterior to the latent prior distribution. We propose a class of training criteria termed InfoVAE that solves the two problems. We show that these models maximize the mutual information between input and latent features, make effective use of the latent features regardless of the flexibility of the decoding distribution, and avoid the variance over-estimation problem. Through extensive qualitative and quantitative analyses, we demonstrate that our models do not suffer from these problems, and outperform models trained with ELBO on multiple metrics of performance.
Can computers overcome human capabilities? This is a paradoxical and controversial question, particularly because there are many hidden assumptions. This article focuses on that issue putting on evidence some misconception related with future generations of machines and the understanding of the brain. It will be discussed to what extent computers might reach human capabilities, and how it could be possible only if the computer is a conscious machine. However, it will be shown that if the computer is conscious, an interference process due to consciousness would affect the information processing of the system. Therefore, it might be possible to make conscious machines to overcome human capabilities, which will have limitations as well as humans. In other words, trying to overcome human capabilities with computers implies the paradoxical conclusion that a computer will never overcome human capabilities at all, or if the computer does, it should not be considered as a computer anymore.
We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We then present an adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multi-agent coordination. Additionally, we introduce a training regimen utilizing an ensemble of policies for each agent that leads to more robust multi-agent policies. We show the strength of our approach compared to existing methods in cooperative as well as competitive scenarios, where agent populations are able to discover various physical and informational coordination strategies.
In this paper, we introduce a generalized value iteration network (GVIN), which is an end-to-end neural network planning module. GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs. We propose three novel differentiable kernels as graph convolution operators and show that the embedding based kernel achieves the best performance. We further propose episodic Q-learning, an improvement upon traditional n-step Q-learning that stabilizes training for networks that contain a planning module. Lastly, we evaluate GVIN on planning problems in 2D mazes, irregular graphs, and real-world street networks, showing that GVIN generalizes well for both arbitrary graphs and unseen graphs of larger scale and outperforms a naive generalization of VIN (discretizing a spatial graph into a 2D image).
Automating statistical modelling is a challenging problem in artificial intelligence. The Automatic Statistician takes a first step in this direction, by employing a kernel search algorithm with Gaussian Processes (GP) to provide interpretable statistical models for regression problems. However this does not scale due to its $O(N^3)$ running time for the model selection. We propose Scalable Kernel Composition (SKC), a scalable kernel search algorithm that extends the Automatic Statistician to bigger data sets. In doing so, we derive a cheap upper bound on the GP marginal likelihood that sandwiches the marginal likelihood with the variational lower bound . We show that the upper bound is significantly tighter than the lower bound and thus useful for model selection.
The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method.
Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora. We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models. A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the task-specific inputs. Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach. Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.
Digital games are one of the major and most important fields on the entertainment domain, which also involves cinema and music. Numerous attempts have been done to improve the quality of the games including more realistic artistic production and computer science. Assessing the player's behavior, a task known as player modeling, is currently the need of the hour which leads to possible improvements in terms of: (i) better game interaction experience, (ii) better exploitation of the relationship between players, and (iii) increasing/maintaining the number of players interested in the game. In this paper we model players using the basic four behaviors proposed in \cite{BartleArtigo}, namely: achiever, explorer, socializer and killer. Our analysis is carried out using data obtained from the game "World of Warcraft" over 3 years (2006 $-$ 2009). We employ a semi-supervised learning technique in order to find out characteristics that possibly impact player's behavior.
We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. The Euclidean distance between any two nodes in this space approximates the length of the shortest path between them in the given graph. Later, at runtime, a shortest path between any two nodes can be computed with A* search using the Euclidean distances as heuristic. Our preprocessing algorithm, called FastMap, is inspired by the data mining algorithm of the same name and runs in near-linear time. Hence, FastMap is orders of magnitude faster than competing approaches that produce a Euclidean embedding using Semidefinite Programming. FastMap also produces admissible and consistent heuristics and therefore guarantees the generation of shortest paths. Moreover, FastMap applies to general undirected graphs for which many traditional heuristics, such as the Manhattan Distance heuristic, are not well defined. Empirically, we demonstrate that A* search using the FastMap heuristic is competitive with A* search using other state-of-the-art heuristics, such as the Differential heuristic.
We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an absolute concept and so we define it relative to a target model, which may or may not be a human. We define a framework that allows for comparing interpretable procedures by linking it to important practical aspects such as accuracy and robustness. We characterize many of the current state-of-the-art interpretable methods in our framework portraying its general applicability. Finally, principled interpretable strategies are proposed and empirically evaluated on synthetic data, as well as on the largest public olfaction dataset that was made recently available \cite{olfs}. We also experiment on MNIST with a simple target model and different oracle models of varying complexity. This leads to the insight that the improvement in the target model is not only a function of the oracle models performance, but also its relative complexity with respect to the target model.
On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundamental PE ratios are often estimated only by subjective expert opinions. The purpose of this research is to formalize a process of fundamental PE estimation by employing advanced dynamic Bayesian network (DBN) methodology. The estimated PE ratio from our model can be used either as a information support for an expert to make investment decisions, or as an automatic trading system illustrated in experiments. Forward-backward inference and EM parameter estimation algorithms are derived with respect to the proposed DBN structure. Unlike existing works in literatures, the economic interpretation of our DBN model is well-justified by behavioral finance evidences of volatility. A simple but practical trading strategy is invented based on the result of Bayesian inference. Extensive experiments show that our trading strategy equipped with the inferenced PE ratios consistently outperforms standard investment benchmarks.
In this paper we explore methods to exploit symmetries for ensuring sample efficiency in reinforcement learning (RL), this problem deserves ever increasing attention with the recent advances in the use of deep networks for complex RL tasks which require large amount of training data. We introduce a novel method to detect symmetries using reward trails observed during episodic experience and prove its completeness. We also provide a framework to incorporate the discovered symmetries for functional approximation. Finally we show that the use of potential based reward shaping is especially effective for our symmetry exploitation mechanism. Experiments on various classical problems show that our method improves the learning performance significantly by utilizing symmetry information.
In this research, we investigate the subject of path-finding. A pruned version of visibility graph based on Candidate Vertices is formulated, followed by a new visibility check technique. Such combination enables us to quickly identify the useful vertices and thus find the optimal path more efficiently. The algorithm proposed is demonstrated on various path-finding cases. The performance of the new technique on visibility graphs is compared to the traditional A* on Grids, Theta* and A* on Visibility Graphs in terms of path length, number of nodes evaluated, as well as computational time. The key algorithmic contribution is that the new approach combines the merits of grid-based method and visibility graph-based method and thus yields better overall performance.
We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn't aid our model to perform better, while it hurts the performance of the skip-thought model.
Most existing matching algorithms are one-off algorithms, i.e., they usually measure the distance between the two image feature representation vectors for only one time. In contrast, human's vision system achieves this task, i.e., image matching, by recursively looking at specific/related parts of both images and then making the final judgement. Towards this end, we propose a novel loopy recurrent neural network (Loopy RNN), which is capable of aggregating relationship information of two input images in a progressive/iterative manner and outputting the consolidated matching score in the final iteration. A Loopy RNN features two uniqueness. First, built on conventional long short-term memory (LSTM) nodes, it links the output gate of the tail node to the input gate of the head node, thus it brings up symmetry property required for matching. Second, a monotonous loss designed for the proposed network guarantees increasing confidence during the recursive matching process. Extensive experiments on several image matching benchmarks demonstrate the great potential of the proposed method.
Communication is a critical factor for the big multi-agent world to stay organized and productive. Typically, most previous multi-agent "learning-to-communicate" studies try to predefine the communication protocols or use technologies such as tabular reinforcement learning and evolutionary algorithm, which can not generalize to changing environment or large collection of agents. In this paper, we propose an Actor-Coordinator-Critic Net (ACCNet) framework for solving "learning-to-communicate" problem. The ACCNet naturally combines the powerful actor-critic reinforcement learning technology with deep learning technology. It can efficiently learn the communication protocols even from scratch under partially observable environment. We demonstrate that the ACCNet can achieve better results than several baselines under both continuous and discrete action space environments. We also analyse the learned protocols and discuss some design considerations.
We consider the task of evaluating a policy for a Markov decision process (MDP). The standard unbiased technique for evaluating a policy is to deploy the policy and observe its performance. We show that the data collected from deploying a different policy, commonly called the behavior policy, can be used to produce unbiased estimates with lower mean squared error than this standard technique. We derive an analytic expression for the optimal behavior policy --- the behavior policy that minimizes the mean squared error of the resulting estimates. Because this expression depends on terms that are unknown in practice, we propose a novel policy evaluation sub-problem, behavior policy search: searching for a behavior policy that reduces mean squared error. We present a behavior policy search algorithm and empirically demonstrate its effectiveness in lowering the mean squared error of policy performance estimates.
Hyperparameter tuning is one of the most time-consuming workloads in deep learning. State-of-the-art optimizers, such as AdaGrad, RMSProp and Adam, reduce this labor by adaptively tuning an individual learning rate for each variable. Recently researchers have shown renewed interest in simpler methods like momentum SGD as they may yield better test metrics. Motivated by this trend, we ask: can simple adaptive methods based on SGD perform as well or better? We revisit the momentum SGD algorithm and show that hand-tuning a single learning rate and momentum makes it competitive with Adam. We then analyze its robustness to learning rate misspecification and objective curvature variation. Based on these insights, we design YellowFin, an automatic tuner for momentum and learning rate in SGD. YellowFin optionally uses a negative-feedback loop to compensate for the momentum dynamics in asynchronous settings on the fly. We empirically show that YellowFin can converge in fewer iterations than Adam on ResNets and LSTMs for image recognition, language modeling and constituency parsing, with a speedup of up to 3.28x in synchronous and up to 2.69x in asynchronous settings.
Factoid question answering (QA) has recently benefited from the development of deep learning (DL) systems. Neural network models outperform traditional approaches in domains where large datasets exist, such as SQuAD (ca. 100,000 questions) for Wikipedia articles. However, these systems have not yet been applied to QA in more specific domains, such as biomedicine, because datasets are generally too small to train a DL system from scratch. For example, the BioASQ dataset for biomedical QA comprises less then 900 factoid (single answer) and list (multiple answers) QA instances. In this work, we adapt a neural QA system trained on a large open-domain dataset (SQuAD, source) to a biomedical dataset (BioASQ, target) by employing various transfer learning techniques. Our network architecture is based on a state-of-the-art QA system, extended with biomedical word embeddings and a novel mechanism to answer list questions. In contrast to existing biomedical QA systems, our system does not rely on domain-specific ontologies, parsers or entity taggers, which are expensive to create. Despite this fact, our systems achieve state-of-the-art results on factoid questions and competitive results on list questions.
For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than one percent of our agent's interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any that have been previously learned from human feedback.
Unsupervised learning of low-dimensional, semantic representations of words and entities has recently gained attention. In this paper we describe the Semantic Entity Retrieval Toolkit (SERT) that provides implementations of our previously published entity representation models. The toolkit provides a unified interface to different representation learning algorithms, fine-grained parsing configuration and can be used transparently with GPUs. In addition, users can easily modify existing models or implement their own models in the framework. After model training, SERT can be used to rank entities according to a textual query and extract the learned entity/word representation for use in downstream algorithms, such as clustering or recommendation.
Measurement error in the observed values of the variables can greatly change the output of various causal discovery methods. This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance. In this paper, we study precise sufficient identifiability conditions for the measurement-error-free causal model and show what information of the causal model can be recovered from observed data. In particular, we present two different sets of identifiability conditions, based on the second-order statistics and higher-order statistics of the data, respectively. The former was inspired by the relationship between the generating model of the measurement-error-contaminated data and the factor analysis model, and the latter makes use of the identifiability result of the over-complete independent component analysis problem.
The population in Sweden is growing rapidly due to immigration. In this light, the issue of infrastructure upgrades to provide telecommunication services is of importance. New antennas can be installed at hot spots of user demand, which will require an investment, and/or the clientele expansion can be carried out in a planned manner to promote the exploitation of the infrastructure in the less loaded geographical zones. In this paper, we explore the second alternative. Informally speaking, the term Infrastructure-Stressing describes a user who stays in the zones of high demand, which are prone to produce service failures, if further loaded. We have studied the Infrastructure-Stressing population in the light of their correlation with geo-demographic segments. This is motivated by the fact that specific geo-demographic segments can be targeted via marketing campaigns. Fuzzy logic is applied to create an interface between big data, numeric methods for processing big data and a manager.
A major investment made by a telecom operator goes into the infrastructure and its maintenance, while business revenues are proportional to how big and good the customer base is. We present a data-driven analytic strategy based on combinatorial optimization and analysis of historical data. The data cover historical mobility of the users in one region of Sweden during a week. Applying the proposed method to the case study, we have identified the optimal proportion of geo-demographic segments in the customer base, developed a functionality to assess the potential of a planned marketing campaign, and explored the problem of an optimal number and types of the geo-demographic segments to target through marketing campaigns. With the help of fuzzy logic, the conclusions of data analysis are automatically translated into comprehensible recommendations in a natural language.
Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.
In this paper we provide a first analysis of the research questions that arise when dealing with the problem of communicating pieces of formal argumentation through natural language interfaces. It is a generally held opinion that formal models of argumentation naturally capture human argument, and some preliminary studies have focused on justifying this view. Unfortunately, the results are not only inconclusive, but seem to suggest that explaining formal argumentation to humans is a rather articulated task. Graphical models for expressing argumentation-based reasoning are appealing, but often humans require significant training to use these tools effectively. We claim that natural language interfaces to formal argumentation systems offer a real alternative, and may be the way forward for systems that capture human argument.
We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relation-extraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-specific crowd-sourced questions with distant supervision, and even (3) do zero-shot learning by extracting new relation types that are only specified at test-time, for which we have no labeled training examples. Experiments on a Wikipedia slot-filling task demonstrate that the approach can generalize to new questions for known relation types with high accuracy, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels, setting the bar for future work on this task.
A reinforcement algorithm solves a classical optimization problem by introducing a feedback to the system which slowly changes the energy landscape and converges the algorithm to an optimal solution in the configuration space. Here, we use this strategy to concentrate (localize) preferentially the wave function of a quantum particle, which explores the configuration space of the problem, on an optimal configuration. We examine the method by solving numerically the equations governing the evolution of the system, which are similar to the nonlinear Schr\"odinger equations, for small problem sizes. In particular, we observe that reinforcement increases the minimal energy gap of the system in a quantum annealing algorithm. Our numerical simulations and the latter observation show that such kind of quantum feedbacks might be helpful in solving a computationally hard optimization problem by a quantum reinforcement algorithm.
In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge.
Mapping in the GPS-denied environment is an important and challenging task in the field of robotics. In the large environment, mapping can be significantly accelerated by multiple robots exploring different parts of the environment. Accordingly, a key problem is how to integrate these local maps built by different robots into a single global map. In this paper, we propose an approach for simultaneous merging of multiple grid maps by the robust motion averaging. The main idea of this approach is to recover all global motions for map merging from a set of relative motions. Therefore, it firstly adopts the pair-wise map merging method to estimate relative motions for grid map pairs. To obtain as many reliable relative motions as possible, a graph-based sampling scheme is utilized to efficiently remove unreliable relative motions obtained from the pair-wise map merging. Subsequently, the accurate global motions can be recovered from the set of reliable relative motions by the motion averaging. Experimental results carried on real robot data sets demonstrate that proposed approach can achieve simultaneous merging of multiple grid maps with good performances.
We propose a two-stage neural model to tackle question generation from documents. Our model first estimates the probability that word sequences in a document compose "interesting" answers using a neural model trained on a question-answering corpus. We thus take a data-driven approach to interestingness. Predicted key phrases then act as target answers that condition a sequence-to-sequence question generation model with a copy mechanism. Empirically, our neural key phrase detection model significantly outperforms an entity-tagging baseline system and existing rule-based approaches. We demonstrate that the question generator formulates good quality natural language questions from extracted key phrases, and a human study indicates that our system's generated question-answer pairs are competitive with those of an earlier approach. We foresee our system being used in an educational setting to assess reading comprehension and also as a data augmentation technique for semi-supervised learning.
Many existing global constraints can be encoded as a conjunction of among constraints. An among constraint holds if the number of the variables in its scope whose value belongs to a prespecified set, which we call its range, is within some given bounds. It is known that domain filtering algorithms can benefit from reasoning about the interaction of among constraints so that values can be filtered out taking into consideration several among constraints simultaneously. The present pa- per embarks into a systematic investigation on the circumstances under which it is possible to obtain efficient and complete domain filtering algorithms for conjunctions of among constraints. We start by observing that restrictions on both the scope and the range of the among constraints are necessary to obtain meaningful results. Then, we derive a domain flow-based filtering algorithm and present several applications. In particular, it is shown that the algorithm unifies and generalizes several previous existing results.
As a step towards developing zero-shot task generalization capabilities in reinforcement learning (RL), we introduce a new RL problem where the agent should learn to execute sequences of instructions after learning useful skills that solve subtasks. In this problem, we consider two types of generalizations: to previously unseen instructions and to longer sequences of instructions. For generalization over unseen instructions, we propose a new objective which encourages learning correspondences between similar subtasks by making analogies. For generalization over sequential instructions, we present a hierarchical architecture where a meta controller learns to use the acquired skills for executing the instructions. To deal with delayed reward, we propose a new neural architecture in the meta controller that learns when to update the subtask, which makes learning more efficient. Experimental results on a stochastic 3D domain show that the proposed ideas are crucial for generalization to longer instructions as well as unseen instructions.
Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.
Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other's reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available (https://github.com/facebookresearch/end-to-end-negotiator).
We consider the question of extending propositional logic to a logic of plausible reasoning, and posit four requirements that any such extension should satisfy. Each is a requirement that some property of classical propositional logic be preserved in the extended logic; as such, the requirements are simpler and less problematic than those used in Cox's Theorem and its variants. As with Cox's Theorem, our requirements imply that the extended logic must be isomorphic to (finite-set) probability theory. We also obtain specific numerical values for the probabilities, recovering the classical definition of probability as a theorem, with truth assignments that satisfy the premise playing the role of the "possible cases."
We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large combined action and observation spaces. In the fully centralized and decentralized approaches, we find the problem of spurious rewards and a phenomenon we call the "lazy agent" problem, which arises due to partial observability. We address these problems by training individual agents with a novel value decomposition network architecture, which learns to decompose the team value function into agent-wise value functions. We perform an experimental evaluation across a range of partially-observable multi-agent domains and show that learning such value-decompositions leads to superior results, in particular when combined with weight sharing, role information and information channels.
Adaptive gradient methods have become recently very popular, in particular as they have been shown to be useful in the training of deep neural networks. In this paper we have analyzed RMSProp, originally proposed for the training of deep neural networks, in the context of online convex optimization and show $\sqrt{T}$-type regret bounds. Moreover, we propose two variants SC-Adagrad and SC-RMSProp for which we show logarithmic regret bounds for strongly convex functions. Finally, we demonstrate in the experiments that these new variants outperform other adaptive gradient techniques or stochastic gradient descent in the optimization of strongly convex functions as well as in training of deep neural networks.
Many tourist applications provide a personalized tourist agenda with the list of recommended activities to the user. These applications must undoubtedly deal with the constraints and preferences that define the user interests. Among these preferences, we can find those that define the travel style of the user, such as the rhythm of the trip, the number of visits to include in the tour or the priority to visits of special interest for the user. In this paper, we deal with the task of creating a customized tourist agenda as a planning and scheduling application capable of conveniently scheduling the most appropriate goals (visits) so as to maximize the user satisfaction with the tourist route. This paper makes an analysis of the meaning of the travel style preferences and compares the quality of the solutions obtained by two different solvers, a PDDL-based planner and a Constraint Satisfaction Problem solver. We also define several quality metrics and perform extensive experiments in order to evaluate the results obtained with both solvers.
Diffusions and related random walk procedures are of central importance in many areas of machine learning, data analysis, and applied mathematics. Because they spread mass agnostically at each step in an iterative manner, they can sometimes spread mass "too aggressively," thereby failing to find the "right" clusters. We introduce a novel Capacity Releasing Diffusion (CRD) Process, which is both faster and stays more local than the classical spectral diffusion process. As an application, we use our CRD Process to develop an improved local algorithm for graph clustering. Our local graph clustering method can find local clusters in a model of clustering where one begins the CRD Process in a cluster whose vertices are connected better internally than externally by an $O(\log^2 n)$ factor, where $n$ is the number of nodes in the cluster. Thus, our CRD Process is the first local graph clustering algorithm that is not subject to the well-known quadratic Cheeger barrier. Our result requires a certain smoothness condition, which we expect to be an artifact of our analysis. Our empirical evaluation demonstrates improved results, in particular for realistic social graphs where there are moderately good---but not very good---clusters.
This work proposes a new algorithm for training a re-weighted L2 Support Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Cand\`es et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In particular, the margin required for each training vector is set independently, defining a new weighted SVM model. These weights are selected to be binary, and they are automatically adapted during the training of the model, resulting in a variation of the Frank-Wolfe optimization algorithm with essentially the same computational complexity as the original algorithm. As shown experimentally, this algorithm is computationally cheaper to apply since it requires less iterations to converge, and it produces models with a sparser representation in terms of support vectors and which are more stable with respect to the selection of the regularization hyper-parameter.
The use of semi-autonomous and autonomous robotic assistants to aid in care of the elderly is expected to ease the burden on human caretakers, with small-stage testing already occurring in a variety of countries. Yet, it is likely that these robots will need to request human assistance via teleoperation when domain expertise is needed for a specific task. As deployment of robotic assistants moves to scale, mapping these requests for human aid to the teleoperators themselves will be a difficult online optimization problem. In this paper, we design a system that allocates requests to a limited number of teleoperators, each with different specialities, in an online fashion. We generalize a recent model of online job scheduling with a worst-case competitive-ratio bound to our setting. Next, we design a scalable machine-learning-based teleoperator-aware task scheduling algorithm and show, experimentally, that it performs well when compared to an omniscient optimal scheduling algorithm.
Integer Linear Programming (ILP) has a broad range of applications in various areas of artificial intelligence. Yet in spite of recent advances, we still lack a thorough understanding of which structural restrictions make ILP tractable. Here we study ILP instances consisting of a small number of "global" variables and/or constraints such that the remaining part of the instance consists of small and otherwise independent components; this is captured in terms of a structural measure we call fracture backdoors which generalizes, for instance, the well-studied class of N -fold ILP instances. Our main contributions can be divided into three parts. First, we formally develop fracture backdoors and obtain exact and approximation algorithms for computing these. Second, we exploit these backdoors to develop several new parameterized algorithms for ILP; the performance of these algorithms will naturally scale based on the number of global variables or constraints in the instance. Finally, we complement the developed algorithms with matching lower bounds. Altogether, our results paint a near-complete complexity landscape of ILP with respect to fracture backdoors.
As a means of human-based computation, crowdsourcing has been widely used to annotate large-scale unlabeled datasets. One of the obvious challenges is how to aggregate these possibly noisy labels provided by a set of heterogeneous annotators. Another challenge stems from the difficulty in evaluating the annotator reliability without even knowing the ground truth, which can be used to build incentive mechanisms in crowdsourcing platforms. When each instance is associated with many possible labels simultaneously, the problem becomes even harder because of its combinatorial nature. In this paper, we present new flexible Bayesian models and efficient inference algorithms for multi-label annotation aggregation by taking both annotator reliability and label dependency into account. Extensive experiments on real-world datasets confirm that the proposed methods outperform other competitive alternatives, and the model can recover the type of the annotators with high accuracy. Besides, we empirically find that the mixture of multiple independent Bernoulli distribution is able to accurately capture label dependency in this unsupervised multi-label annotation aggregation scenario.
In this report, we provide a comparative analysis of different techniques for user intent classification towards the task of app recommendation. We analyse the performance of different models and architectures for multi-label classification over a dataset with a relative large number of classes and only a handful examples of each class. We focus, in particular, on memory network architectures, and compare how well the different versions perform under the task constraints. Since the classifier is meant to serve as a module in a practical dialog system, it needs to be able to work with limited training data and incorporate new data on the fly. We devise a 1-shot learning task to test the models under the above constraint. We conclude that relatively simple versions of memory networks perform better than other approaches. Although, for tasks with very limited data, simple non-parametric methods perform comparably, without needing the extra training data.
We propose a simple yet effective technique for neural network learning. The forward propagation is computed as usual. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-$k$ elements (in terms of magnitude) are kept. As a result, only $k$ rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction ($k$ divided by the vector dimension) in the computational cost. Surprisingly, experimental results demonstrate that we can update only 1--4\% of the weights at each back propagation pass. This does not result in a larger number of training iterations. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The code is available at https://github.com/jklj077/meProp
Human conversation is inherently complex, often spanning many different topics/domains. This makes policy learning for dialogue systems very challenging. Standard flat reinforcement learning methods do not provide an efficient framework for modelling such dialogues. In this paper, we focus on the under-explored problem of multi-domain dialogue management. First, we propose a new method for hierarchical reinforcement learning using the option framework. Next, we show that the proposed architecture learns faster and arrives at a better policy than the existing flat ones do. Moreover, we show how pretrained policies can be adapted to more complex systems with an additional set of new actions. In doing that, we show that our approach has the potential to facilitate policy optimisation for more sophisticated multi-domain dialogue systems.
Generative adversarial nets (GANs) are a promising technique for modeling a distribution from samples. It is however well known that GAN training suffers from instability due to the nature of its maximin formulation. In this paper, we explore ways to tackle the instability problem by dualizing the discriminator. We start from linear discriminators in which case conjugate duality provides a mechanism to reformulate the saddle point objective into a maximization problem, such that both the generator and the discriminator of this 'dualing GAN' act in concert. We then demonstrate how to extend this intuition to non-linear formulations. For GANs with linear discriminators our approach is able to remove the instability in training, while for GANs with nonlinear discriminators our approach provides an alternative to the commonly used GAN training algorithm.
In "The Logic of Campaigning", Dean and Parikh consider a candidate making campaign statements to appeal to the voters. They model these statements as Boolean formulas over variables that represent stances on the issues, and study optimal candidate strategies under three proposed models of voter preferences based on the assignments that satisfy these formulas. We prove that voter utility evaluation is computationally hard under these preference models (in one case, #P-hard), along with certain problems related to candidate strategic reasoning. Our results raise questions about the desirable characteristics of a voter preference model and to what extent a polynomial-time-evaluable function can capture them.
This paper presents preliminary results of our work with a major financial company, where we try to use methods of plan recognition in order to investigate the interactions of a costumer with the company's online interface. In this paper, we present the first steps of integrating a plan recognition algorithm in a real-world application for detecting and analyzing the interactions of a costumer. It uses a novel approach for plan recognition from bare-bone UI data, which reasons about the plan library at the lowest recognition level in order to define the relevancy of actions in our domain, and then uses it to perform plan recognition. We present preliminary results of inference on three different use-cases modeled by domain experts from the company, and show that this approach manages to decrease the overload of information required from an analyst to evaluate a costumer's session - whether this is a malicious or benign session, whether the intended tasks were completed, and if not - what actions are expected next.
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a high-dimensional space and concepts are represented by convex regions in this space. After pointing out a problem with the convexity requirement, we propose a formalization of conceptual spaces based on fuzzy star-shaped sets. Our formalization uses a parametric definition of concepts and extends the original framework by adding means to represent correlations between different domains in a geometric way. Moreover, we define computationally efficient operations on concepts (intersection, union, and projection onto a subspace) and show that these operations can support both learning and reasoning processes.
Inspired by the recent evolution of deep neural networks (DNNs) in machine learning, we explore their application to PL-related topics. This paper is the first step towards this goal; we propose a proof-synthesis method for the negation-free propositional logic in which we use a DNN to obtain a guide of proof search. The idea is to view the proof-synthesis problem as a translation from a proposition to its proof. We train seq2seq, which is a popular network in neural machine translation, so that it generates a proof encoded as a $\lambda$-term of a given proposition. We implement the whole framework and empirically observe that a generated proof term is close to a correct proof in terms of the tree edit distance of AST. This observation justifies using the output from a trained seq2seq model as a guide for proof search.
The neural network is a powerful computing framework that has been exploited by biological evolution and by humans for solving diverse problems. Although the computational capabilities of neural networks are determined by their structure, the current understanding of the relationships between a neural network's architecture and function is still primitive. Here we reveal that neural network's modular architecture plays a vital role in determining the neural dynamics and memory performance of the network. In particular, we demonstrate that there exists an optimal modularity for memory performance, where a balance between local cohesion and global connectivity is established, allowing optimally modular networks to remember longer. Our results suggest that insights from dynamical analysis of neural networks and information spreading processes can be leveraged to better design neural networks and may shed light on the brain's modular organization.
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference. Thus, we expand the scope of the HiP-MDP to applications with higher dimensions and more complex dynamics.
This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc retrieval. In this work, the query and documents are modeled by word-based representations and entity-based representations. Ranking features are generated by the interactions between the two representations, incorporating information from the word space, the entity space, and the cross-space connections through the knowledge graph. To handle the uncertainties from the automatically constructed entity representations, an attention-based ranking model AttR-Duet is developed. With back-propagation from ranking labels, the model learns simultaneously how to demote noisy entities and how to rank documents with the word-entity duet. Evaluation results on TREC Web Track ad-hoc task demonstrate that all of the four-way interactions in the duet are useful, the attention mechanism successfully steers the model away from noisy entities, and together they significantly outperform both word-based and entity-based learning to rank systems.
This paper addresses the design and implementation of complex Reinforcement Learning (RL) behaviors where multi-dimensional action spaces are involved, as well as the need to execute the behaviors in real-time using robotic platforms with limited computational resources and training times. For this purpose, we propose the use of decentralized RL, in combination with finite support basis functions as alternatives to Gaussian RBF, in order to alleviate the effects of the curse of dimensionality on the action and state spaces respectively, and to reduce the computation time. As testbed, a RL based controller for the in-walk kick in NAO robots, a challenging and critical problem for soccer robotics, is used. The reported experiments show empirically that our solution saves up to 99.94% of execution time and 98.82% of memory consumption during execution, without diminishing performance compared to classical approaches.
In this paper, we try to solve the problem of temporal link prediction in information networks. This implies predicting the time it takes for a link to appear in the future, given its features that have been extracted at the current network snapshot. To this end, we introduce a probabilistic non-parametric approach, called "Non-Parametric Generalized Linear Model" (NP-GLM), which infers the hidden underlying probability distribution of the link advent time given its features. We then present a learning algorithm for NP-GLM and an inference method to answer time-related queries. Extensive experiments conducted on both synthetic data and real-world Sina Weibo social network demonstrate the effectiveness of NP-GLM in solving temporal link prediction problem vis-a-vis competitive baselines.
Motor adaptation displays a structure-learning effect: adaptation to a new perturbation occurs more quickly when the subject has prior exposure to perturbations with related structure. Although this `learning-to-learn' effect is well documented, its underlying computational mechanisms are poorly understood. We present a new model of motor structure learning, approaching it from the point of view of deep reinforcement learning. Previous work outside of motor control has shown how recurrent neural networks can account for learning-to-learn effects. We leverage this insight to address motor learning, by importing it into the setting of model-based reinforcement learning. We apply the resulting processing architecture to empirical findings from a landmark study of structure learning in target-directed reaching (Braun et al., 2009), and discuss its implications for a wider range of learning-to-learn phenomena.
In this work, we present Web-STAR, an online platform for story understanding built on top of the STAR (STory comprehension through ARgumentation) reasoning engine. This platform includes a web-based IDE, integration with the STAR system and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of "social" features like public story sharing with a built-in commenting system, a public repository for sharing stories with the community and collaboration tools that can be used from both project team members for development and educators for teaching. Moreover, we discuss the ongoing work on adding new features and functionality to this platform.
We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution. We conducted experiments to compare the response of human subjects to the generated art with their response to art created by artists. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art fairs. Human subjects even rated the generated images higher on various scales.
Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.
To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment. This problem is called task-oriented language grounding. We propose an end-to-end trainable neural architecture for task-oriented language grounding in 3D environments which assumes no prior linguistic or perceptual knowledge and requires only raw pixels from the environment and the natural language instruction as input. The proposed model combines the image and text representations using a Gated-Attention mechanism and learns a policy to execute the natural language instruction using standard reinforcement and imitation learning methods. We show the effectiveness of the proposed model on unseen instructions as well as unseen maps, both quantitatively and qualitatively. We also introduce a novel environment based on a 3D game engine to simulate the challenges of task-oriented language grounding over a rich set of instructions and environment states.
Technological advancement in Wireless Sensor Networks (WSN) has made it become an invaluable component of a reliable environmental monitoring system; they form the digital skin' through which to 'sense' and collect the context of the surroundings and provides information on the process leading to complex events such as drought. However, these environmental properties are measured by various heterogeneous sensors of different modalities in distributed locations making up the WSN, using different abstruse terms and vocabulary in most cases to denote the same observed property, causing data heterogeneity. Adding semantics and understanding the relationships that exist between the observed properties, and augmenting it with local indigenous knowledge is necessary for an accurate drought forecasting system. In this paper, we propose the framework for the semantic representation of sensor data and integration with indigenous knowledge on drought using a middleware for an efficient drought forecasting system.
Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised domain adaptation. The NET reduces cross-domain disparity through nonlinear domain alignment. It also embeds the domain-aligned data such that similar data points are clustered together. This results in enhanced classification. To determine the parameters in the NET model (and in other unsupervised domain adaptation models), we introduce a validation procedure by sampling source data points that are similar in distribution to the target data. We test the NET and the validation procedure using popular image datasets and compare the classification results across competitive procedures for unsupervised domain adaptation.
Existing Markov Chain Monte Carlo (MCMC) methods are either based on general-purpose and domain-agnostic schemes which can lead to slow convergence, or hand-crafting of problem-specific proposals by an expert. We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties. First, we propose an efficient likelihood-free adversarial training method to train a Markov chain and mimic a given data distribution. Then, we leverage flexible volume preserving flows to obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to train efficient Markov chains to sample from a prescribed posterior distribution by iteratively improving the quality of both the model and the samples. A-NICE-MC provides the first framework to automatically design efficient domain-specific MCMC proposals. Empirical results demonstrate that A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of deep neural networks, and is able to significantly outperform competing methods such as Hamiltonian Monte Carlo.
This work aims at the goal whether the artificial intelligence can recognize phase transition without the prior human knowledge. If this becomes successful, it can be applied to, for instance, analyze data from quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark of this approach. In this work, we feed the compute with data generated by the classical Monte Carlo simulation for the XY model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principle component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principle component analysis with kernel tricks and the neural network method.
In this paper, we present a toolbox for a specific optimization problem that frequently arises in bioinformatics or genomics. In this specific optimisation problem, the state space is a set of words of specified length over a finite alphabet. To each word is associated a score. The overall objective is to find the words which have the lowest possible score. This type of general optimization problem is encountered in e.g 3D conformation optimisation for protein structure prediction, or largest core genes subset discovery based on best supported phylogenetic tree for a set of species. In order to solve this problem, we propose a toolbox that can be easily launched using MPI and embeds 3 well-known metaheuristics. The toolbox is fully parametrized and well documented. It has been specifically designed to be easy modified and possibly improved by the user depending on the application, and does not require to be a computer scientist. We show that the toolbox performs very well on two difficult practical problems.
In Markov Decision Processes (MDPs), the reward obtained in a state depends on the properties of the last state and action. This state dependency makes it difficult to reward more interesting long-term behaviors, such as always closing a door after it has been opened, or providing coffee only following a request. Extending MDPs to handle such non-Markovian reward function was the subject of two previous lines of work, both using variants of LTL to specify the reward function and then compiling the new model back into a Markovian model. Building upon recent progress in the theories of temporal logics over finite traces, we adopt LDLf for specifying non-Markovian rewards and provide an elegant automata construction for building a Markovian model, which extends that of previous work and offers strong minimality and compositionality guarantees.
In this paper, random forests are proposed for operating devices diagnostics in the presence of a variable number of features. In various contexts, like large or difficult-to-access monitored areas, wired sensor networks providing features to achieve diagnostics are either very costly to use or totally impossible to spread out. Using a wireless sensor network can solve this problem, but this latter is more subjected to flaws. Furthermore, the networks' topology often changes, leading to a variability in quality of coverage in the targeted area. Diagnostics at the sink level must take into consideration that both the number and the quality of the provided features are not constant, and that some politics like scheduling or data aggregation may be developed across the network. The aim of this article is ($1$) to show that random forests are relevant in this context, due to their flexibility and robustness, and ($2$) to provide first examples of use of this method for diagnostics based on data provided by a wireless sensor network.
We propose a simple and efficient approach to learning sparse models. Our approach consists of (1) projecting the data into a lower dimensional space, (2) learning a dense model in the lower dimensional space, and then (3) recovering the sparse model in the original space via compressive sensing. We apply this approach to Non-negative Matrix Factorization (NMF), tensor decomposition and linear classification---showing that it obtains $10\times$ compression with negligible loss in accuracy on real data, and obtains up to $5\times$ speedups. Our main theoretical contribution is to show the following result for NMF: if the original factors are sparse, then their projections are the sparsest solutions to the projected NMF problem. This explains why our method works for NMF and shows an interesting new property of random projections: they can preserve the solutions of non-convex optimization problems such as NMF.
Finding solution values for unknowns in Boolean equations was a principal reasoning mode in the Algebra of Logic of the 19th century. Schr\"oder investigated it as "Aufl\"osungsproblem" ("solution problem"). It is closely related to the modern notion of Boolean unification. Today it is commonly presented in an algebraic setting, but seems potentially useful also in knowledge representation based on predicate logic. We show that it can be modeled on the basis of first-order logic extended by second-order quantification. A wealth of classical results transfers, foundations for algorithms unfold, and connections with second-order quantifier elimination and Craig interpolation show up. Although for first-order inputs the set of solutions is recursively enumerable, the development of constructive methods remains a challenge. We identify some cases that allow constructions, most of them based on Craig interpolation, and show a method to take vocabulary restrictions on solution components into account.
Generative encoder-decoder models offer great promise in developing domain-general dialog systems. However, they have mainly been applied to open-domain conversations. This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models. This framework enables encoder-decoder models to accomplish slot-value independent decision-making and interact with external databases. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slot-filling system for better out-of-domain recovery. The models were trained on both real-user data from a bus information system and human-human chat data. Results show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users.
A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, all learned without any human supervision! In this paper, using a Task and Tell reference game between two agents as a testbed, we present a sequence of 'negative' results culminating in a 'positive' one -- showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional. In essence, we find that natural language does not emerge 'naturally', despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate.
This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-of-the-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.
Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual implementation errors can be extremely difficult. We demonstrate a methodology in which developers use an interactive proof assistant to both implement their system and to state a formal theorem defining what it means for their system to be correct. The process of proving this theorem interactively in the proof assistant exposes all implementation errors since any error in the program would cause the proof to fail. As a case study, we implement a new system, Certigrad, for optimizing over stochastic computation graphs, and we generate a formal (i.e. machine-checkable) proof that the gradients sampled by the system are unbiased estimates of the true mathematical gradients. We train a variational autoencoder using Certigrad and find the performance comparable to training the same model in TensorFlow.
Over the years complexity theorists have proposed many structural parameters to explain the surprising efficiency of conflict-driven clause-learning (CDCL) SAT solvers on a wide variety of large industrial Boolean instances. While some of these parameters have been studied empirically, until now there has not been a unified comparative study of their explanatory power on a comprehensive benchmark. We correct this state of affairs by conducting a large-scale empirical evaluation of CDCL SAT solver performance on nearly 7000 industrial and crafted formulas against several structural parameters such as backdoors, treewidth, backbones, and community structure. Our study led us to several results. First, we show that while such parameters only weakly correlate with CDCL solving time, certain combinations of them yield much better regression models. Second, we show how some parameters can be used as a "lens" to better understand the efficiency of different solving heuristics. Finally, we propose a new complexity-theoretic parameter, which we call learning-sensitive with restarts (LSR) backdoors, that extends the notion of learning-sensitive (LS) backdoors to incorporate restarts and discuss algorithms to compute them. We mathematically prove that for certain class of instances minimal LSR-backdoors are exponentially smaller than minimal-LS backdoors.
Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained on diverse time series in an unsupervised manner using sequence to sequence (seq2seq) models to extract features from time series. Rather than relying on data from the problem domain, TimeNet attempts to generalize time series representation across domains by ingesting time series from several domains simultaneously. Once trained, TimeNet can be used as a generic off-the-shelf feature extractor for time series. The representations or embeddings given by a pre-trained TimeNet are found to be useful for time series classification (TSC). For several publicly available datasets from UCR TSC Archive and an industrial telematics sensor data from vehicles, we observe that a classifier learned over the TimeNet embeddings yields significantly better performance compared to (i) a classifier learned over the embeddings given by a domain-specific RNN, as well as (ii) a nearest neighbor classifier based on Dynamic Time Warping.
One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better understand this issue, we study the problem of continual learning, where the model observes, once and one by one, examples concerning a sequence of tasks. First, we propose a set of metrics to evaluate models learning over a continuum of data. These metrics characterize models not only by their test accuracy, but also in terms of their ability to transfer knowledge across tasks. Second, we propose a model for continual learning, called Gradient Episodic Memory (GEM) that alleviates forgetting, while allowing beneficial transfer of knowledge to previous tasks. Our experiments on variants of the MNIST and CIFAR-100 datasets demonstrate the strong performance of GEM when compared to the state-of-the-art.
We present a deep, fully convolutional neural network that learns to route a circuit layout net with appropriate choice of metal tracks and wire class combinations. Inputs to the network are the encoded layouts containing spatial location of pins to be routed. After 15 fully convolutional stages followed by a score comparator, the network outputs 8 layout layers (corresponding to 4 route layers, 3 via layers and an identity-mapped pin layer) which are then decoded to obtain the routed layouts. We formulate this as a binary segmentation problem on a per-pixel per-layer basis, where the network is trained to correctly classify pixels in each layout layer to be 'on' or 'off'. To demonstrate learnability of layout design rules, we train the network on a dataset of 50,000 train and 10,000 validation samples that we generate based on certain pre-defined layout constraints. Precision, recall and $F_1$ score metrics are used to track the training progress. Our network achieves $F_1\approx97\%$ on the train set and $F_1\approx92\%$ on the validation set. We use PyTorch for implementing our model. Code is made publicly available at https://github.com/sjain-stanford/deep-route .
Additively separable hedonic games and fractional hedonic games have received considerable attention. They are coalition forming games of selfish agents based on their mutual preferences. Most of the work in the literature characterizes the existence and structure of stable outcomes (i.e., partitions in coalitions), assuming that preferences are given. However, there is little discussion on this assumption. In fact, agents receive different utilities if they belong to different partitions, and thus it is natural for them to declare their preferences strategically in order to maximize their benefit. In this paper we consider strategyproof mechanisms for additively separable hedonic games and fractional hedonic games, that is, partitioning methods without payments such that utility maximizing agents have no incentive to lie about their true preferences. We focus on social welfare maximization and provide several lower and upper bounds on the performance achievable by strategyproof mechanisms for general and specific additive functions. In most of the cases we provide tight or asymptotically tight results. All our mechanisms are simple and can be computed in polynomial time. Moreover, all the lower bounds are unconditional, that is, they do not rely on any computational or complexity assumptions.
A descriptive approach for automatic generation of visual blends is presented. The implemented system, the Blender, is composed of two components: the Mapper and the Visual Blender. The approach uses structured visual representations along with sets of visual relations which describe how the elements (in which the visual representation can be decomposed) relate among each other. Our system is a hybrid blender, as the blending process starts at the Mapper (conceptual level) and ends at the Visual Blender (visual representation level). The experimental results show that the Blender is able to create analogies from input mental spaces and produce well-composed blends, which follow the rules imposed by its base-analogy and its relations. The resulting blends are visually interesting and some can be considered as unexpected.
In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network). Unlike MLE directly maximizing the conditional likelihood, the bridge extends the point-wise ground truth to a bridge distribution conditioned on it, and the generator is optimized to minimize their KL-divergence. Three different GBNs, namely uniform GBN, language-model GBN and coaching GBN, are proposed to penalize confidence, enhance language smoothness and relieve learning burden. Experiments conducted on two recognized sequence prediction tasks (machine translation and abstractive text summarization) show that our proposed GBNs can yield significant improvements over strong baselines. Furthermore, by analyzing samples drawn from different bridges, expected influences on the generator are verified.
Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate "where" and "what" processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos. The first layer of attention discards the majority of background by selecting a region containing the object of interest, while the subsequent layers tune in on visual features particular to the tracked object. This framework is fully differentiable and can be trained in a purely data driven fashion by gradient methods. To improve training convergence, we augment the loss function with terms for a number of auxiliary tasks relevant for tracking. Evaluation of the proposed model is performed on two datasets: pedestrian tracking on the KTH activity recognition dataset and the more difficult KITTI object tracking dataset.
This paper presents a collection of path planning algorithms for real-time movement of multiple robots across a Robotic Mobile Fulfillment System (RMFS). Robots are assigned to move storage units to pickers at working stations instead of requiring pickers to go to the storage area. Path planning algorithms aim to find paths for the robots to fulfill the requests without collisions or deadlocks. The state-of-the-art path planning algorithms, including WHCA*, FAR, BCP, OD&ID and CBS, were adapted to suit path planning in RMFS and integrated within a simulation tool to guide the robots from their starting points to their destinations during the storage and retrieval processes. Ten different layouts with a variety of numbers of robots, floors, pods, stations and the sizes of storage areas were considered in the simulation study. Performance metrics of throughput, path length and search time were monitored. Simulation results demonstrate the best algorithm based on each performance metric.
A vibrant theoretical research area are efficient exact parameterized algorithms. Very recent solving competitions such as the PACE challenge show that there is also increasing practical interest in the parameterized algorithms community. An important research question is whether dedicated parameterized exact algorithms exhibit certain practical relevance and one can even beat well-established problem solvers. We consider the logic-based declarative modeling language and problem solving framework Answer Set Programming (ASP). State-of-the-art ASP solvers rely considerably on Sat-based algorithms. An ASP solver (DynASP2), which is based on a classical dynamic programming on tree decompositions, has been published very recently. Unfortunately, DynASP2 can outperform modern ASP solvers on programs of small treewidth only if the question of interest is to count the number of solutions. In this paper, we describe underlying concepts of our new implementation (DynASP2.5) that shows competitive behavior to state-of-the-art ASP solvers even for finding just one solution when solving problems as the Steiner tree problem that have been modeled in ASP on graphs with low treewidth. Our implementation is based on a novel approach that we call multi-pass dynamic programming (M-DPSINC).
In this paper, we study Reiter's propositional default logic when the treewidth of a certain graph representation (semi-primal graph) of the input theory is bounded. We establish a dynamic programming algorithm on tree decompositions that decides whether a theory has a consistent stable extension (Ext). Our algorithm can even be used to enumerate all generating defaults (ExtEnum) that lead to stable extensions. We show that our algorithm decides Ext in linear time in the input theory and triple exponential time in the treewidth (so-called fixed-parameter linear algorithm). Further, our algorithm solves ExtEnum with a pre-computation step that is linear in the input theory and triple exponential in the treewidth followed by a linear delay to output solutions.
We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments. To achieve this, we embed procedures mimicking that of traditional Simultaneous Localization and Mapping (SLAM) into the soft attention based addressing of external memory architectures, in which the external memory acts as an internal representation of the environment. This structure encourages the evolution of SLAM-like behaviors inside a completely differentiable deep neural network. We show that this approach can help reinforcement learning agents to successfully explore new environments where long-term memory is essential. We validate our approach in both challenging grid-world environments and preliminary Gazebo experiments. A video of our experiments can be found at: https://goo.gl/G2Vu5y.
In this paper, we introduce Path Integral Networks (PI-Net), a recurrent network representation of the Path Integral optimal control algorithm. The network includes both system dynamics and cost models, used for optimal control based planning. PI-Net is fully differentiable, learning both dynamics and cost models end-to-end by back-propagation and stochastic gradient descent. Because of this, PI-Net can learn to plan. PI-Net has several advantages: it can generalize to unseen states thanks to planning, it can be applied to continuous control tasks, and it allows for a wide variety learning schemes, including imitation and reinforcement learning. Preliminary experiment results show that PI-Net, trained by imitation learning, can mimic control demonstrations for two simulated problems; a linear system and a pendulum swing-up problem. We also show that PI-Net is able to learn dynamics and cost models latent in the demonstrations.
Research in UAV scheduling has obtained an emerging interest from scientists in the optimization field. When the scheduling itself has established a strong root since the 19th century, works on UAV scheduling in indoor environment has come forth in the latest decade. Several works on scheduling UAV operations in indoor (two and three dimensional) and outdoor environments are reported. In this paper, a further study on UAV scheduling in three dimensional indoor environment is investigated. Dealing with indoor environment\textemdash where humans, UAVs, and other elements or infrastructures are likely to coexist in the same space\textemdash draws attention towards the safety of the operations. In relation to the battery level, a preserved battery level leads to safer operations, promoting the UAV to have a decent remaining power level. A methodology which consists of a heuristic approach based on Restful Task Assignment Algorithm, incorporated with Particle Swarm Optimization Algorithm, is proposed. The motivation is to preserve the battery level throughout the operations, which promotes less possibility in having failed UAVs on duty. This methodology is tested with 54 benchmark datasets stressing on 4 different aspects: geographical distance, number of tasks, number of predecessors, and slack time. The test results and their characteristics in regard to the proposed methodology are discussed and presented.
It is well known that speaker identification performs extremely well in the neutral talking environments; however, the identification performance is declined sharply in the shouted talking environments. This work aims at proposing, implementing and testing a new approach to enhance the declined performance in the shouted talking environments. The new proposed approach is based on gender-dependent speaker identification using Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. This proposed approach has been tested on two different and separate speech databases: our collected database and the Speech Under Simulated and Actual Stress (SUSAS) database. The results of this work show that gender-dependent speaker identification based on SPHMMs outperforms gender-independent speaker identification based on the same models and gender-dependent speaker identification based on Hidden Markov Models (HMMs) by about 6% and 8%, respectively. The results obtained based on the proposed approach are close to those obtained in subjective evaluation by human judges.
Virtual reality simulation is becoming popular as a training platform in surgical education. However, one important aspect of simulation-based surgical training that has not received much attention is the provision of automated real-time performance feedback to support the learning process. Performance feedback is actionable advice that improves novice behaviour. In simulation, automated feedback is typically extracted from prediction models trained using data mining techniques. Existing techniques suffer from either low effectiveness or low efficiency resulting in their inability to be used in real-time. In this paper, we propose a random forest based method that finds a balance between effectiveness and efficiency. Experimental results in a temporal bone surgery simulation show that the proposed method is able to extract highly effective feedback at a high level of efficiency.
The challenge of sharing and communicating information is crucial in complex human-robot interaction (HRI) scenarios. Ontologies and symbolic reasoning are the state-of-the-art approaches for a natural representation of knowledge, especially within the Semantic Web domain. In such a context, scripted paradigms have been adopted to achieve high expressiveness. Nevertheless, since symbolic reasoning is a high complexity problem, optimizing its performance requires a careful design of the knowledge. Specifically, a robot architecture requires the integration of several components implementing different behaviors and generating a series of beliefs. Most of the components are expected to access, manipulate, and reason upon a run-time generated semantic representation of knowledge grounding robot behaviors and perceptions through formal axioms, with soft real-time requirements.
In this paper the elements of the CAPTCHA usability are analyzed. CAPTCHA, as a time progressive element in computer science, has been under constant interest of ordinary, professional as well as the scientific users of the Internet. The analysis is given based on the usability elements of CAPTCHA which are abbreviated as user-centric approach to the CAPTCHA. To demonstrate it, the specific type of Dice CAPTCHA is used in the experiment. The experiment is conducted on 190 Internet users with different demographic characteristics on laptop and tablet computers. The obtained results are statistically processed. At the end, the results are compared and conclusion of their use is drawn.
The influence maximization is the problem of finding a set of social network users, called influencers, that can trigger a large cascade of propagation. Influencers are very beneficial to make a marketing campaign goes viral through social networks for example. In this paper, we propose an influence measure that combines many influence indicators. Besides, we consider the reliability of each influence indicator and we present a distance-based process that allows to estimate the reliability of each indicator. The proposed measure is defined under the framework of the theory of belief functions. Furthermore, the reliability-based influence measure is used with an influence maximization model to select a set of users that are able to maximize the influence in the network. Finally, we present a set of experiments on a dataset collected from Twitter. These experiments show the performance of the proposed solution in detecting social influencers with good quality.
It is widely observed that deep learning models with learned parameters generalize well, even with much more model parameters than the number of training samples. We systematically investigate the underlying reasons why deep neural networks often generalize well, and reveal the difference between the minima (with the same training error) that generalize well and those they don't. We show that it is the characteristics the landscape of the loss function that explains the good generalization capability. For the landscape of loss function for deep networks, the volume of basin of attraction of good minima dominates over that of poor minima, which guarantees optimization methods with random initialization to converge to good minima. We theoretically justify our findings through analyzing 2-layer neural networks; and show that the low-complexity solutions have a small norm of Hessian matrix with respect to model parameters. For deeper networks, extensive numerical evidence helps to support our arguments.
The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.
Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.
Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation. However, they suffer from a poor performance in the early stages of learning. This is especially problematic for on-line learning with real users. Two approaches are introduced to tackle this problem. Firstly, to speed up the learning process, two sample-efficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER) are presented. For TRACER, the trust region helps to control the learning step size and avoid catastrophic model changes. For eNACER, the natural gradient identifies the steepest ascent direction in policy space to speed up the convergence. Both models employ off-policy learning with experience replay to improve sample-efficiency. Secondly, to mitigate the cold start issue, a corpus of demonstration data is utilised to pre-train the models prior to on-line reinforcement learning. Combining these two approaches, we demonstrate a practical approach to learn deep RL-based dialogue policies and demonstrate their effectiveness in a task-oriented information seeking domain.
We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic curriculum learning, where the Student tries to learn a complex task and the Teacher automatically chooses subtasks from a given set for the Student to train on. We describe a family of Teacher algorithms that rely on the intuition that the Student should practice more those tasks on which it makes the fastest progress, i.e. where the slope of the learning curve is highest. In addition, the Teacher algorithms address the problem of forgetting by also choosing tasks where the Student's performance is getting worse. We demonstrate that TSCL matches or surpasses the results of carefully hand-crafted curricula in two tasks: addition of decimal numbers with LSTM and navigation in Minecraft. Using our automatically generated curriculum enabled to solve a Minecraft maze that could not be solved at all when training directly on solving the maze, and the learning was an order of magnitude faster than uniform sampling of subtasks.
In this work, we formulated a real-world problem related to sewer pipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer pipeline to offer safe and non-hazardous access to sewer pipeline workers so that the human fatalities, which occurs due to the toxic exposure of sewer gas components, can be avoided. The dataset acquired through laboratory tests, experiments, and various literature sources was organized to design a predictive model that was able to identify/classify hazardous and non-hazardous situation of sewer pipeline. To design such prediction model, several classification algorithms were used and their performances were evaluated and compared, both empirically and statistically, over the collected dataset. In addition, the performances of several ensemble methods were analyzed to understand the extent of improvement offered by these methods. The result of this comprehensive study showed that the instance-based learning algorithm performed better than many other algorithms such as multilayer perceptron, radial basis function network, support vector machine, reduced pruning tree. Similarly, it was observed that multi-scheme ensemble approach enhanced the performance of base predictors.
We propose a novel approach for group elevator scheduling by formulating it as the maximization of submodular function under a matroid constraint. In particular, we propose to model the total waiting time of passengers using a quadratic Boolean function. The unary and pairwise terms in the function denote the waiting time for single and pairwise allocation of passengers to elevators, respectively. We show that this objective function is submodular. The matroid constraints ensure that every passenger is allocated to exactly one elevator. We use a greedy algorithm to maximize the submodular objective function, and derive provable guarantees on the optimality of the solution. We tested our algorithm using Elevate 8, a commercial-grade elevator simulator that allows simulation with a wide range of elevator settings. We achieve significant improvement over the existing algorithms.
We present Solrex,an automated solver for the game of Reverse Hex.Reverse Hex, also known as Rex, or Misere Hex, is the variant of the game of Hex in which the player who joins her two sides loses the game. Solrex performs a mini-max search of the state space using Scalable Parallel Depth First Proof Number Search, enhanced by the pruning of inferior moves and the early detection of certain winning strategies. Solrex is implemented on the same code base as the Hex program Solver, and can solve arbitrary positions on board sizes up to 6x6, with the hardest position taking less than four hours on four threads.
Modeling preference time in triathlons means predicting the intermediate times of particular sports disciplines by a given overall finish time in a specific triathlon course for the athlete with the known personal best result. This is a hard task for athletes and sport trainers due to a lot of different factors that need to be taken into account, e.g., athlete's abilities, health, mental preparations and even their current sports form. So far, this process was calculated manually without any specific software tools or using the artificial intelligence. This paper presents the new solution for modeling preference time in middle distance triathlons based on particle swarm optimization algorithm and archive of existing sports results. Initial results are presented, which suggest the usefulness of proposed approach, while remarks for future improvements and use are also emphasized.
A popular testbed for deep learning has been multimodal recognition of human activity or gesture involving diverse inputs such as video, audio, skeletal pose and depth images. Deep learning architectures have excelled on such problems due to their ability to combine modality representations at different levels of nonlinear feature extraction. However, designing an optimal architecture in which to fuse such learned representations has largely been a non-trivial human engineering effort. We treat fusion structure optimization as a hyper-parameter search and cast it as a discrete optimization problem under the Bayesian optimization framework. We propose a novel graph-induced kernel to compute structural similarities in the search space of tree-structured multimodal architectures and demonstrate its effectiveness using two challenging multimodal human activity recognition datasets.
This paper addresses the challenge of viewing and navigating Bayesian networks as their structural size and complexity grow. Starting with a review of the state of the art of visualizing Bayesian networks, an area which has largely been passed over, we improve upon existing visualizations in three ways. First, we apply a disciplined approach to the graphic design of the basic elements of the Bayesian network. Second, we propose a technique for direct, visual comparison of posterior distributions resulting from alternative evidence sets. Third, we leverage a central mathematical tool in information theory, to assist the user in finding variables of interest in the network, and to reduce visual complexity where unimportant. We present our methods applied to two modestly large Bayesian networks constructed from real-world data sets. Results suggest the new techniques can be a useful tool for discovering information flow phenomena, and also for qualitative comparisons of different evidence configurations, especially in large probabilistic networks.
We present a conditional generative model that maps low-dimensional embeddings of multiple modalities of data to a common latent space hence extracting semantic relationships between them. The embedding specific to a modality is first extracted and subsequently a constrained optimization procedure is performed to project the two embedding spaces to a common manifold. The individual embeddings are generated back from this common latent space. However, in order to enable independent conditional inference for separately extracting the corresponding embeddings from the common latent space representation, we deploy a proxy variable trick - wherein, the single shared latent space is replaced by the respective separate latent spaces of each modality. We design an objective function, such that, during training we can force these separate spaces to lie close to each other, by minimizing the distance between their probability distribution functions. Experimental results demonstrate that the learned joint model can generalize to learning concepts of double MNIST digits with additional attributes of colors,from both textual and speech input.
The set-based concept approach has been suggested as a means to simultaneously explore different design concepts, which are meaningful sub-sets of the entire set of solutions. Previous efforts concerning the suggested approach focused on either revealing the global front (s-Pareto front), of all the concepts, or on finding the concepts' fronts, within a relaxation zone. In contrast, here the aim is to reveal which of the concepts have at least one solution with a performance vector within a pre-defined window-of-interest (WOI). This paper provides the rational for this new concept-based exploration problem, and suggests a WOI-based rather than Pareto-based multi-objective evolutionary algorithm. The proposed algorithm, which simultaneously explores different concepts, is tested using a recently suggested concept-based benchmarking approach. The numerical study of this paper shows that the algorithm can cope with various numerical difficulties in a simultaneous way, which outperforms a sequential exploration approach.
A network of driven nonlinear oscillators without dissipation has recently been proposed for solving combinatorial optimization problems via quantum adiabatic evolution through its bifurcation point. Here we investigate the behavior of the quantum bifurcation machine in the presence of dissipation. Our numerical study suggests that the output probability distribution of the dissipative quantum bifurcation machine is Boltzmann-like, where the energy in the Boltzmann distribution corresponds to the cost function of the optimization problem. We explain the Boltzmann distribution by generalizing the concept of quantum heating in a single oscillator to the case of multiple coupled oscillators. The present result also suggests that such driven dissipative nonlinear oscillator networks can be applied to Boltzmann sampling, which is used, e.g., for Boltzmann machine learning in the field of artificial intelligence.
We propose Black Box Explanations through Transparent Approximations (BETA), a novel model agnostic framework for explaining the behavior of any black-box classifier by simultaneously optimizing for fidelity to the original model and interpretability of the explanation. To this end, we develop a novel objective function which allows us to learn (with optimality guarantees), a small number of compact decision sets each of which explains the behavior of the black box model in unambiguous, well-defined regions of feature space. Furthermore, our framework also is capable of accepting user input when generating these approximations, thus allowing users to interactively explore how the black-box model behaves in different subspaces that are of interest to the user. To the best of our knowledge, this is the first approach which can produce global explanations of the behavior of any given black box model through joint optimization of unambiguity, fidelity, and interpretability, while also allowing users to explore model behavior based on their preferences. Experimental evaluation with real-world datasets and user studies demonstrates that our approach can generate highly compact, easy-to-understand, yet accurate approximations of various kinds of predictive models compared to state-of-the-art baselines.
Unsupervised rank aggregation on score-based permutations, which is widely used in many applications, has not been deeply explored yet. This work studies the use of submodular optimization for rank aggregation on score-based permutations in an unsupervised way. Specifically, we propose an unsupervised approach based on the Lovasz Bregman divergence for setting up linear structured convex and nested structured concave objective functions. In addition, stochastic optimization methods are applied in the training process and efficient algorithms for inference can be guaranteed. The experimental results from Information Retrieval, Combining Distributed Neural Networks, Influencers in Social Networks, and Distributed Automatic Speech Recognition tasks demonstrate the effectiveness of the proposed methods.
Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying the presence of sentiment in the text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is a presence of sentiment in the text, it has a source (people, group of people or any entity) and the sentiment is directed towards some entity, object, event or person. Sentiment analysis tasks aim to determine the subject, the target and the polarity or valence of the sentiment. In our work, we try to automatically extract sentiment (positive or negative) from Facebook posts using a machine learning approach.While some works have been done in code-mixed social media data and in sentiment analysis separately, our work is the first attempt (as of now) which aims at performing sentiment analysis of code-mixed social media text. We have used extensive pre-processing to remove noise from raw text. Multilayer Perceptron model has been used to determine the polarity of the sentiment. We have also developed the corpus for this task by manually labeling Facebook posts with their associated sentiments.
Various measures can be used to estimate bias or unfairness in a predictor. Previous work has already established that some of these measures are incompatible with each other. Here we show that, when groups differ in prevalence of the predicted event, several intuitive, reasonable measures of fairness (probability of positive prediction given occurrence or non-occurrence; probability of occurrence given prediction or non-prediction; and ratio of predictions over occurrences for each group) are all mutually exclusive: if one of them is equal among groups, the other two must differ. The only exceptions are for perfect, or trivial (always-positive or always-negative) predictors. As a consequence, any non-perfect, non-trivial predictor must necessarily be "unfair" under two out of three reasonable sets of criteria. This result readily generalizes to a wide range of well-known statistical quantities (sensitivity, specificity, false positive rate, precision, etc.), all of which can be divided into three mutually exclusive groups. Importantly, The results applies to all predictors, whether algorithmic or human. We conclude with possible ways to handle this effect when assessing and designing prediction methods.
Modeling users for the purpose of identifying their preferences and then personalizing services on the basis of these models is a complex task, primarily due to the need to take into consideration various explicit and implicit signals, missing or uncertain information, contextual aspects, and more. In this study, a novel generic approach for uncovering latent preference patterns from user data is proposed and evaluated. The approach relies on representing the data using graphs, and then systematically extracting graph-based features and using them to enrich the original user models. The extracted features encapsulate complex relationships between users, items, and metadata. The enhanced user models can then serve as an input to any recommendation algorithm. The proposed approach is domain-independent (demonstrated on data from movies, music, and business recommender systems), and is evaluated using several state-of-the-art machine learning methods, on different recommendation tasks, and using different evaluation metrics. The results show a unanimous improvement in the recommendation accuracy across tasks and domains. In addition, the evaluation provides a deeper analysis regarding the performance of the approach in special scenarios, including high sparsity and variability of ratings.
Causation discovery without manipulation is considered a crucial problem to a variety of applications. The state-of-the-art solutions are applicable only when large numbers of samples are available or the problem domain is sufficiently small. Motivated by the observations of the local sparsity properties on causal structures, we propose a general Split-and-Merge framework, named SADA, to enhance the scalability of a wide class of causation discovery algorithms. In SADA, the variables are partitioned into subsets, by finding causal cut on the sparse causal structure over the variables. By running mainstream causation discovery algorithms as basic causal solvers on the subproblems, complete causal structure can be reconstructed by combining the partial results. SADA benefits from the recursive division technique, since each small subproblem generates more accurate result under the same number of samples. We theoretically prove that SADA always reduces the scales of problems without sacrifice on accuracy, under the condition of local causal sparsity and reliable conditional independence tests. We also present sufficient condition to accuracy enhancement by SADA, even when the conditional independence tests are vulnerable. Extensive experiments on both simulated and real-world datasets verify the improvements on scalability and accuracy by applying SADA together with existing causation discovery algorithms.
In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment. In this paper, we extend this setting by considering the environment is not given, but controllable and learnable through its interaction with the agent at the same time. Theoretically, we find a dual Markov decision process (MDP) w.r.t. the environment to that w.r.t. the agent, and solving the dual MDP-policy pair yields a policy gradient solution to optimizing the parametrized environment. Furthermore, environments with discontinuous parameters are addressed by a proposed general generative framework. While the idea is illustrated by an extended two-agent rock-paper-scissors game, our experiments on a Maze game design task show the effectiveness of the proposed algorithm in generating diverse and challenging Mazes against different agents with various settings.
Content-invariance in mapping codes learned by GAEs is a useful feature for various relation learning tasks. In this paper we show that the content-invariance of mapping codes for images of 2D and 3D rotated objects can be substantially improved by extending the standard GAE loss (symmetric reconstruction error) with a regularization term that penalizes the symmetric cross-reconstruction error. This error term involves reconstruction of pairs with mapping codes obtained from other pairs exhibiting similar transformations. Although this would principally require knowledge of the transformations exhibited by training pairs, our experiments show that a bootstrapping approach can sidestep this issue, and that the regularization term can effectively be used in an unsupervised setting.
Many practical problems are characterized by a preference relation over admissible solutions, where preferred solutions are minimal in some sense. For example, a preferred diagnosis usually comprises a minimal set of reasons that is sufficient to cause the observed anomaly. Alternatively, a minimal correction subset comprises a minimal set of reasons whose deletion is sufficient to eliminate the observed anomaly. Circumscription formalizes such preference relations by associating propositional theories with minimal models. The resulting enumeration problem is addressed here by means of a new algorithm taking advantage of unsatisfiable core analysis. Empirical evidence of the efficiency of the algorithm is given by comparing the performance of the resulting solver, CIRCUMSCRIPTINO, with HCLASP, CAMUS MCS, LBX and MCSLS on the enumeration of minimal models for problems originating from practical applications. This paper is under consideration for acceptance in TPLP.
Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering. It can be combined with an arbitrary off-policy RL algorithm and may be seen as a form of implicit curriculum. We demonstrate our approach on the task of manipulating objects with a robotic arm. In particular, we run experiments on three different tasks: pushing, sliding, and pick-and-place, in each case using only binary rewards indicating whether or not the task is completed. Our ablation studies show that Hindsight Experience Replay is a crucial ingredient which makes training possible in these challenging environments. We show that our policies trained on a physics simulation can be deployed on a physical robot and successfully complete the task.
Over the past few years, ride-sharing has emerged as an effective way to relieve traffic congestion. A key problem for these platforms is to come up with a revenue-optimal (or GMV-optimal) pricing scheme and an induced vehicle dispatching policy that incorporate geographic and temporal information. In this paper, we aim to tackle this problem via an economic approach. Modeled naively, the underlying optimization problem may be non-convex and thus hard to compute. To this end, we use a so-called "ironing" technique to convert the problem into an equivalent convex optimization one via a clean Markov decision process (MDP) formulation, where the states are the driver distributions and the decision variables are the prices for each pair of locations. Our main finding is an efficient algorithm that computes the exact revenue-optimal (or GMV-optimal) randomized pricing schemes. We characterize the optimal solution of the MDP by a primal-dual analysis of a corresponding convex program. We also conduct empirical evaluations of our solution through real data of a major ride-sharing platform and show its advantages over fixed pricing schemes as well as several prevalent surge-based pricing schemes.
This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems. We study the impact of different pretrained CNN feature extractors on the problem of image set clustering for object classification as well as fine-grained classification. We propose a rather straightforward pipeline combining deep-feature extraction using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of images. This approach is compared to state-of-the-art algorithms in image-clustering and provides better results. These results strengthen the belief that supervised training of deep CNN on large datasets, with a large variability of classes, extracts better features than most carefully designed engineering approaches, even for unsupervised tasks. We also validate our approach on a robotic application, consisting in sorting and storing objects smartly based on clustering.
Automatic image description systems are commonly trained and evaluated on large image description datasets. Recently, researchers have started to collect such datasets for languages other than English. An unexplored question is how different these datasets are from English and, if there are any differences, what causes them to differ. This paper provides a cross-linguistic comparison of Dutch, English, and German image descriptions. We find that these descriptions are similar in many respects, but the familiarity of crowd workers with the subjects of the images has a noticeable influence on description specificity.
Trust region methods, such as TRPO, are often used to stabilize policy optimization algorithms in reinforcement learning (RL). While current trust region strategies are effective for continuous control, they typically require a prohibitively large amount of on-policy interaction with the environment. To address this problem, we propose an off-policy trust region method, Trust-PCL. The algorithm is the result of observing that the optimal policy and state values of a maximum reward objective with a relative-entropy regularizer satisfy a set of multi-step pathwise consistencies along any path. Thus, Trust-PCL is able to maintain optimization stability while exploiting off-policy data to improve sample efficiency. When evaluated on a number of continuous control tasks, Trust-PCL improves the solution quality and sample efficiency of TRPO.
Question answering is an important and difficult task in the natural language processing domain, because many basic natural language processing tasks can be cast into a question answering task. Several deep neural network architectures have been developed recently, which employ memory and inference components to memorize and reason over text information, and generate answers to questions. However, a major drawback of many such models is that they are capable of only generating single-word answers. In addition, they require large amount of training data to generate accurate answers. In this paper, we introduce the Long-Term Memory Network (LTMN), which incorporates both an external memory module and a Long Short-Term Memory (LSTM) module to comprehend the input data and generate multi-word answers. The LTMN model can be trained end-to-end using back-propagation and requires minimal supervision. We test our model on two synthetic data sets (based on Facebook's bAbI data set) and the real-world Stanford question answering data set, and show that it can achieve state-of-the-art performance.
We introduce a graphical framework for fair division in cake cutting, where comparisons between agents are limited by an underlying network structure. We generalize the classical fairness notions of envy-freeness and proportionality to this graphical setting. Given a simple undirected graph G, an allocation is envy-free on G if no agent envies any of her neighbor's share, and is proportional on G if every agent values her own share no less than the average among her neighbors, with respect to her own measure. These generalizations open new research directions in developing simple and efficient algorithms that can produce fair allocations under specific graph structures. On the algorithmic frontier, we first propose a moving-knife algorithm that outputs an envy-free allocation on trees. The algorithm is significantly simpler than the discrete and bounded envy-free algorithm recently designed by Aziz and Mackenzie for complete graphs. Next, we give a discrete and bounded algorithm for computing a proportional allocation on descendant graphs, a class of graphs by taking a rooted tree and connecting all its ancestor-descendant pairs.
The concept of leader--follower (or Stackelberg) equilibrium plays a central role in a number of real--world applications of game theory. While the case with a single follower has been thoroughly investigated, results with multiple followers are only sporadic and the problem of designing and evaluating computationally tractable equilibrium-finding algorithms is still largely open. In this work, we focus on the fundamental case where multiple followers play a Nash equilibrium once the leader has committed to a strategy---as we illustrate, the corresponding equilibrium finding problem can be easily shown to be $\mathcal{FNP}$--hard and not in Poly--$\mathcal{APX}$ unless $\mathcal{P} = \mathcal{NP}$ and therefore it is one among the hardest problems to solve and approximate. We propose nonconvex mathematical programming formulations and global optimization methods to find both exact and approximate equilibria, as well as a heuristic black box algorithm. All the methods and formulations that we introduce are thoroughly evaluated computationally.
Automated documentation of programming source code and automated code generation from natural language are challenging tasks of both practical and scientific interest. Progress in these areas has been limited by the low availability of parallel corpora of code and natural language descriptions, which tend to be small and constrained to specific domains. In this work we introduce a large and diverse parallel corpus of a hundred thousands Python functions with their documentation strings ("docstrings") generated by scraping open source repositories on GitHub. We describe baseline results for the code documentation and code generation tasks obtained by neural machine translation. We also experiment with data augmentation techniques to further increase the amount of training data. We release our datasets and processing scripts in order to stimulate research in these areas.
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a high-dimensional space and concepts are represented by regions in this space. Our recent mathematical formalization of this framework is capable of representing correlations between different domains in a geometric way. In this paper, we extend our formalization by providing quantitative mathematical definitions for the notions of concept size, subsethood, implication, similarity, and betweenness. This considerably increases the representational power of our formalization by introducing measurable ways of describing relations between concepts.
Diversity is one of the fundamental properties for the survival of species, populations, and organizations. Recent advances in deep learning allow for the rapid and automatic assessment of organizational diversity and possible discrimination by race, sex, age and other parameters. Automating the process of assessing the organizational diversity using the deep neural networks and eliminating the human factor may provide a set of real-time unbiased reports to all stakeholders. In this pilot study we applied the deep-learned predictors of race and sex to the executive management and board member profiles of the 500 largest companies from the 2016 Forbes Global 2000 list and compared the predicted ratios to the ratios within each company's country of origin and ranked them by the sex-, age- and race- diversity index (DI). While the study has many limitations and no claims are being made concerning the individual companies, it demonstrates a method for the rapid and impartial assessment of organizational diversity using deep neural networks.
State-of-the-art slot filling models for goal-oriented human/machine conversational language understanding systems rely on deep learning methods. While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems. This paper proposes a deep learning based approach that can utilize only the slot description in context without the need for any labeled or unlabeled in-domain examples, to quickly bootstrap a new domain. The main idea of this paper is to leverage the encoding of the slot names and descriptions within a multi-task deep learned slot filling model, to implicitly align slots across domains. The proposed approach is promising for solving the domain scaling problem and eliminating the need for any manually annotated data or explicit schema alignment. Furthermore, our experiments on multiple domains show that this approach results in significantly better slot-filling performance when compared to using only in-domain data, especially in the low data regime.
For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learning- and optimization-based approaches in a two-layer hierarchical structure. The first layer, defined as the "policy layer", is established by a neural network which learns the long-term optimal driving policy generated by MPC. The second layer, called the "execution layer", is a short-term optimization-based controller that tracks the reference trajecotries given by the "policy layer" with guaranteed short-term safety and feasibility. Moreover, with efficient and highly-representative features, a small-size neural network is sufficient in the "policy layer" to handle many complicated driving scenarios. This renders online imitation learning with Dataset Aggregation (DAgger) so that the performance of the "policy layer" can be improved rapidly and continuously online. Several exampled driving scenarios are demonstrated to verify the effectiveness and efficiency of the proposed framework.
The current study applies deep learning to herbalism. Toward the goal, we acquired the de-identified health insurance reimbursements that were claimed in a 10-year period from 2004 to 2013 in the National Health Insurance Database of Taiwan, the total number of reimbursement records equaling 340 millions. Two artificial intelligence techniques were applied to the dataset: residual convolutional neural network multitask classifier and attention-based recurrent neural network. The former works to translate from herbal prescriptions to diseases; and the latter from diseases to herbal prescriptions. Analysis of the classification results indicates that herbal prescriptions are specific to: anatomy, pathophysiology, sex and age of the patient, and season and year of the prescription. Further analysis identifies temperature and gross domestic product as the meteorological and socioeconomic factors that are associated with herbal prescriptions. Analysis of the neural machine transitional result indicates that the recurrent neural network learnt not only syntax but also semantics of diseases and herbal prescriptions.
Every year at the United Nations, member states deliver statements during the General Debate discussing major issues in world politics. These speeches provide invaluable information on governments' perspectives and preferences on a wide range of issues, but have largely been overlooked in the study of international politics. This paper introduces a new dataset consisting of over 7,701 English-language country statements from 1970-2016. We demonstrate how the UN General Debate Corpus (UNGDC) can be used to derive country positions on different policy dimensions using text analytic methods. The paper provides applications of these estimates, demonstrating the contribution the UNGDC can make to the study of international politics.
Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a classifier at hand. An attacker introduces specially crafted adversarial samples to a deployed classifier, which are being mis-classified by the classifier. However, the samples are perceived to be drawn from entirely different classes and thus it becomes hard to detect the adversarial samples. Most of the prior works have been focused on synthesizing adversarial samples in the image domain. In this paper, we propose a new method of crafting adversarial text samples by modification of the original samples. Modifications of the original text samples are done by deleting or replacing the important or salient words in the text or by introducing new words in the text sample. Our algorithm works best for the datasets which have sub-categories within each of the classes of examples. While crafting adversarial samples, one of the key constraint is to generate meaningful sentences which can at pass off as legitimate from language (English) viewpoint. Experimental results on IMDB movie review dataset for sentiment analysis and Twitter dataset for gender detection show the efficiency of our proposed method.
The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.
In this paper, we propose a multi-task learning from demonstration method that works using raw images as input to autonomously accomplish a wide variety of tasks in the real world using a low-cost robotic arm. The controller is a single recurrent neural network that can generate robot arm trajectories to perform different manipulation tasks. In order to learn complex skills from relatively few demonstrations, we share parameters across different tasks. Our network also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction for improved data efficiency. Our results show that weight sharing and reconstruction substantially improve generalization and robustness, and that training on multiple tasks simultaneously greatly improves the success rate on all of the tasks. Our experiments, performed on a real-world low-cost Lynxmotion arm, illustrate a variety of picking and placing tasks, as well as non-prehensile manipulation.
We investigate a generalisation of the coherent choice functions considered by Seidenfeld et al. (2010), by sticking to the convexity axiom but imposing no Archimedeanity condition. We define our choice functions on vector spaces of options, which allows us to incorporate as special cases both Seidenfeld et al.'s (2010) choice functions on horse lotteries and sets of desirable gambles (Quaeghebeur, 2014), and to investigate their connections. We show that choice functions based on sets of desirable options (gambles) satisfy Seidenfeld's convexity axiom only for very particular types of sets of desirable options, which are in a one-to-one relationship with the lexicographic probabilities. We call them lexicographic choice functions. Finally, we prove that these choice functions can be used to determine the most conservative convex choice function associated with a given binary relation.
Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc. However, recent research has shown that machine learning models are venerable to attacks by adversaries at all phases of machine learning (eg, training data collection, training, operation). All model classes of machine learning systems can be misled by providing carefully crafted inputs making them wrongly classify inputs. Maliciously created input samples can affect the learning process of a ML system by either slowing down the learning process, or affecting the performance of the learned mode, or causing the system make error(s) only in attacker's planned scenario. Because of these developments, understanding security of machine learning algorithms and systems is emerging as an important research area among computer security and machine learning researchers and practitioners. We present a survey of this emerging area in machine learning.
Recently, many variance reduced stochastic alternating direction method of multipliers (ADMM) methods (e.g.\ SAG-ADMM, SDCA-ADMM and SVRG-ADMM) have made exciting progress such as linear convergence rates for strongly convex problems. However, the best known convergence rate for general convex problems is O(1/T) as opposed to O(1/T^2) of accelerated batch algorithms, where $T$ is the number of iterations. Thus, there still remains a gap in convergence rates between existing stochastic ADMM and batch algorithms. To bridge this gap, we introduce the momentum acceleration trick for batch optimization into the stochastic variance reduced gradient based ADMM (SVRG-ADMM), which leads to an accelerated (ASVRG-ADMM) method. Then we design two different momentum term update rules for strongly convex and general convex cases. We prove that ASVRG-ADMM converges linearly for strongly convex problems. Besides having a low per-iteration complexity as existing stochastic ADMM methods, ASVRG-ADMM improves the convergence rate on general convex problems from O(1/T) to O(1/T^2). Our experimental results show the effectiveness of ASVRG-ADMM.
While general game playing is an active field of research, the learning of game design has tended to be either a secondary goal of such research or it has been solely the domain of humans. We propose a field of research, Automated Game Design Learning (AGDL), with the direct purpose of learning game designs directly through interaction with games in the mode that most people experience games: via play. We detail existing work that touches the edges of this field, describe current successful projects in AGDL and the theoretical foundations that enable them, point to promising applications enabled by AGDL, and discuss next steps for this exciting area of study. The key moves of AGDL are to use game programs as the ultimate source of truth about their own design, and to make these design properties available to other systems and avenues of inquiry.
We propose and evaluate a new technique for learning hybrid automata automatically by observing the runtime behavior of a dynamical system. Working from a sequence of continuous state values and predicates about the environment, CHARDA recovers the distinct dynamic modes, learns a model for each mode from a given set of templates, and postulates causal guard conditions which trigger transitions between modes. Our main contribution is the use of information-theoretic measures (1)~as a cost function for data segmentation and model selection to penalize over-fitting and (2)~to determine the likely causes of each transition. CHARDA is easily extended with different classes of model templates, fitting methods, or predicates. In our experiments on a complex videogame character, CHARDA successfully discovers a reasonable over-approximation of the character's true behaviors. Our results also compare favorably against recent work in automatically learning probabilistic timed automata in an aircraft domain: CHARDA exactly learns the modes of these simpler automata.
Foreign policy analysis has been struggling to find ways to measure policy preferences and paradigm shifts in international political systems. This paper presents a novel, potential solution to this challenge, through the application of a neural word embedding (Word2vec) model on a dataset featuring speeches by heads of state or government in the United Nations General Debate. The paper provides three key contributions based on the output of the Word2vec model. First, it presents a set of policy attention indices, synthesizing the semantic proximity of political speeches to specific policy themes. Second, it introduces country-specific semantic centrality indices, based on topological analyses of countries' semantic positions with respect to each other. Third, it tests the hypothesis that there exists a statistical relation between the semantic content of political speeches and UN voting behavior, falsifying it and suggesting that political speeches contain information of different nature then the one behind voting outcomes. The paper concludes with a discussion of the practical use of its results and consequences for foreign policy analysis, public accountability, and transparency.
This paper proposes a novel deep reinforcement learning (RL) architecture, called Value Prediction Network (VPN), which integrates model-free and model-based RL methods into a single neural network. In contrast to typical model-based RL methods, VPN learns a dynamics model whose abstract states are trained to make option-conditional predictions of future values (discounted sum of rewards) rather than of future observations. Our experimental results show that VPN has several advantages over both model-free and model-based baselines in a stochastic environment where careful planning is required but building an accurate observation-prediction model is difficult. Furthermore, VPN outperforms Deep Q-Network (DQN) on several Atari games even with short-lookahead planning, demonstrating its potential as a new way of learning a good state representation.
Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.
The Semantic Web began to emerge as its standards and technologies developed rapidly in the recent years. The continuing development of Semantic Web technologies has facilitated publishing explicit semantics with data on the Web in RDF data model. This study proposes a semantic search framework to support efficient keyword-based semantic search on RDF data utilizing near neighbor explorations. The framework augments the search results with the resources in close proximity by utilizing the entity type semantics. Along with the search results, the system generates a relevance confidence score measuring the inferred semantic relatedness of returned entities based on the degree of similarity. Furthermore, the evaluations assessing the effectiveness of the framework and the accuracy of the results are presented.
Models that can execute natural language instructions for situated robotic tasks such as assembly and navigation have several useful applications in homes, offices, and remote scenarios. We study the semantics of spatially-referred configuration and arrangement instructions, based on the challenging Bisk-2016 blank-labeled block dataset. This task involves finding a source block and moving it to the target position (mentioned via a reference block and offset), where the blocks have no names or colors and are just referred to via spatial location features. We present novel models for the subtasks of source block classification and target position regression, based on joint-loss language and spatial-world representation learning, as well as CNN-based and dual attention models to compute the alignment between the world blocks and the instruction phrases. For target position prediction, we compare two inference approaches: annealed sampling via policy gradient versus expectation inference via supervised regression. Our models achieve the new state-of-the-art on this task, with an improvement of 47% on source block accuracy and 22% on target position distance.
Several approaches of structuring (factorization, decomposition) of Dempster-Shafer joint belief functions from literature are reviewed with special emphasis on their capability to capture independence from the point of view of the claim that belief functions generalize bayes notion of probability. It is demonstrated that Zhu and Lee's {Zhu:93} logical networks and Smets' {Smets:93} directed acyclic graphs are unable to capture statistical dependence/independence of bayesian networks {Pearl:88}. On the other hand, though Shenoy and Shafer's hypergraphs can explicitly represent bayesian network factorization of bayesian belief functions, they disclaim any need for representation of independence of variables in belief functions. Cano et al. {Cano:93} reject the hypergraph representation of Shenoy and Shafer just on grounds of missing representation of variable independence, but in their frameworks some belief functions factorizable in Shenoy/Shafer framework cannot be factored. The approach in {Klopotek:93f} on the other hand combines the merits of both Cano et al. and of Shenoy/Shafer approach in that for Shenoy/Shafer approach no simpler factorization than that in {Klopotek:93f} approach exists and on the other hand all independences among variables captured in Cano et al. framework and many more are captured in {Klopotek:93f} approach.%
Mathematical Theory of Evidence called also Dempster-Shafer Theory (DST) is known as a foundation for reasoning when knowledge is expressed at various levels of detail. Though much research effort has been committed to this theory since its foundation, many questions remain open. One of the most important open questions seems to be the relationship between frequencies and the Mathematical Theory of Evidence. The theory is blamed to leave frequencies outside (or aside of) its framework. The seriousness of this accusation is obvious: (1) no experiment may be run to compare the performance of DST-based models of real world processes against real world data, (2) data may not serve as foundation for construction of an appropriate belief model. In this paper we develop a frequentist interpretation of the DST bringing to fall the above argument against DST. An immediate consequence of it is the possibility to develop algorithms acquiring automatically DST belief models from data. We propose three such algorithms for various classes of belief model structures: for tree structured belief networks, for poly-tree belief networks and for general type belief networks.
Game maps are useful for human players, general-game-playing agents, and data-driven procedural content generation. These maps are generally made by hand-assembling manually-created screenshots of game levels. Besides being tedious and error-prone, this approach requires additional effort for each new game and level to be mapped. The results can still be hard for humans or computational systems to make use of, privileging visual appearance over semantic information. We describe a software system, Mappy, that produces a good approximation of a linked map of rooms given a Nintendo Entertainment System game program and a sequence of button inputs exploring its world. In addition to visual maps, Mappy outputs grids of tiles (and how they change over time), positions of non-tile objects, clusters of similar rooms that might in fact be the same room, and a set of links between these rooms. We believe this is a necessary step towards developing larger corpora of high-quality semantically-annotated maps for PCG via machine learning and other applications.
Engineers widely use Gaussian process regression framework to construct surrogate models aimed to replace computationally expensive physical models while exploring design space. Thanks to Gaussian process properties we can use both samples generated by a high fidelity function (an expensive and accurate representation of a physical phenomenon) and a low fidelity function (a cheap and coarse approximation of the same physical phenomenon) while constructing a surrogate model. However, if samples sizes are more than few thousands of points, computational costs of the Gaussian process regression become prohibitive both in case of learning and in case of prediction calculation. We propose two approaches to circumvent this computational burden: one approach is based on the Nystr\"om approximation of sample covariance matrices and another is based on an intelligent usage of a blackbox that can evaluate a~low fidelity function on the fly at any point of a design space. We examine performance of the proposed approaches using a number of artificial and real problems, including engineering optimization of a rotating disk shape.
Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample complexity, or the number of samples required for successful learning. In this study, we ask whether sample complexity can be reduced for systems where the structure of the domain is unknown beforehand, and the structure and parameters must both be learned from the data. We show that sample complexity reduction through learning structure is possible for at least two simple cases. In studying these cases, we also gain insight into how this might be done for more complex domains.
Programming by Optimization tools perform automatic software configuration according to the specification supplied by a software developer. Developers specify design spaces for program components, and the onerous task of determining which configuration best suits a given use case is determined using automated analysis tools and optimization heuristics. However, in current approaches to Programming by Optimization, design space specification and exploration relies on external configuration algorithms, executable wrappers and fragile, preprocessed programming language extensions. Here we show that the architectural pattern of Dependency Injection provides a superior alternative to the traditional Programming by Optimization pipeline. We demonstrate that configuration tools based on Dependency Injection fit naturally into the software development process, while requiring less overhead than current wrapper-based mechanisms. Furthermore, the structural correspondence between Dependency Injection and context-free grammars yields a new class of evolutionary metaheuristics for automated algorithm configuration. We found that the new heuristics significantly outperform existing configuration algorithms on many problems of interest (in one case by two orders of magnitude). We anticipate that these developments will make Programming by Optimization immediately applicable to a large number of enterprise software projects.
Answer Set Programming (ASP) is a well-established declarative paradigm. One of the successes of ASP is the availability of efficient systems. State-of-the-art systems are based on the ground+solve approach. In some applications this approach is infeasible because the grounding of one or few constraints is expensive. In this paper, we systematically compare alternative strategies to avoid the instantiation of problematic constraints, that are based on custom extensions of the solver. Results on real and synthetic benchmarks highlight some strengths and weaknesses of the different strategies. (Under consideration for acceptance in TPLP, ICLP 2017 Special Issue.)
Youtube-8M dataset enhances the development of large-scale video recognition technology as ImageNet dataset has encouraged image classification, recognition and detection of artificial intelligence fields. For this large video dataset, it is a challenging task to classify a huge amount of multi-labels. By change of perspective, we propose a novel method by regarding labels as words. In details, we describe online learning approaches to multi-label video classification that are guided by deep recurrent neural networks for video to sentence translator. We designed the translator based on LSTMs and found out that a stochastic gating before the input of each LSTM cell can help us to design the structural details. In addition, we adopted batch normalizations into our models to improve our LSTM models. Since our models are feature extractors, they can be used with other classifiers. Finally we report improved validation results of our models on large-scale Youtube-8M datasets and discussions for the further improvement.
Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often do not converge well without careful hyperparameter tuning and proper initialization. We investigate whether turning the adversarial min-max problem into an optimization problem by replacing the maximization part with its dual improves the quality of the resulting alignment and explore its connections to Maximum Mean Discrepancy. Our empirical results suggest that using the dual formulation for the restricted family of linear discriminators results in a more stable convergence to a desirable solution when compared with the performance of a primal min-max GAN-like objective and an MMD objective under the same restrictions. We test our hypothesis on the problem of aligning two synthetic point clouds on a plane and on a real-image domain adaptation problem on digits. In both cases, the dual formulation yields an iterative procedure that gives more stable and monotonic improvement over time.
The recent series 5 of the ASP system clingo provides generic means to enhance basic Answer Set Programming (ASP) with theory reasoning capabilities. We instantiate this framework with different forms of linear constraints, discuss the respective implementations, and present techniques of how to use these constraints in a reactive context. More precisely, we introduce extensions to clingo with difference and linear constraints over integers and reals, respectively, and realize them in complementary ways. Finally, we empirically evaluate the resulting clingo derivatives clingo[dl] and clingo[lp] on common fragments and contrast them to related ASP systems. This paper is under consideration for acceptance in TPLP.
Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many different approaches for many different IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR research. It covers key architectures, as well as the most promising future directions.
In this paper, we describe the Lithium Natural Language Processing (NLP) system - a resource-constrained, high- throughput and language-agnostic system for information extraction from noisy user generated text on social media. Lithium NLP extracts a rich set of information including entities, topics, hashtags and sentiment from text. We discuss several real world applications of the system currently incorporated in Lithium products. We also compare our system with existing commercial and academic NLP systems in terms of performance, information extracted and languages supported. We show that Lithium NLP is at par with and in some cases, outperforms state- of-the-art commercial NLP systems.
Modern software systems in many application areas offer to the user a multitude of parameters, switches and other customisation hooks. Humans tend to have difficulties determining the best configurations for particular applications. Modern optimising compilers are an example of such software systems; their many parameters need to be tuned for optimal performance, but are often left at the default values for convenience. In this work, we automatically determine compiler parameter settings that result in optimised performance for particular applications. Specifically, we apply a state-of-the-art automated parameter configuration procedure based on cutting-edge machine learning and optimisation techniques to two prominent JavaScript compilers and demonstrate that significant performance improvements, more than 35% in some cases, can be achieved over the default parameter settings on a diverse set of benchmarks.
This paper verifies a result of {Shenoy:94} concerning graphoidal structure of Shenoy's notion of independence for Dempster-Shafer theory of belief functions. Shenoy proved that his notion of independence has graphoidal properties for positive normal valuations. The requirement of strict positive normal valuations as prerequisite for application of graphoidal properties excludes a wide class of DS belief functions. It excludes especially so-called probabilistic belief functions. It is demonstrated that the requirement of positiveness of valuation may be weakened in that it may be required that commonality function is non-zero for singleton sets instead, and the graphoidal properties for independence of belief function variables are then preserved. This means especially that probabilistic belief functions with all singleton sets as focal points possess graphoidal properties for independence.
Learners regularly abandon online coding tutorials when they get bored or frustrated, but there are few techniques for anticipating this abandonment to intervene. In this paper, we examine the feasibility of predicting abandonment with machine-learned classifiers. Using interaction logs from an online programming game, we extracted a collection of features that are potentially related to learner abandonment and engagement, then developed classifiers for each level. Across the first five levels of the game, our classifiers successfully predicted 61% to 76% of learners who did not complete the next level, achieving an average AUC of 0.68. In these classifiers, features negatively associated with abandonment included account activation and help-seeking behaviors, whereas features positively associated with abandonment included features indicating difficulty and disengagement. These findings highlight the feasibility of providing timely intervention to learners likely to quit.
We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables. By using a series of code transformations, the evidence of any probabilistic program, and therefore of any graphical model, can be optimized with respect to an arbitrary subset of its sampled variables. To carry out this optimization, we develop the first Bayesian optimization package to directly exploit the source code of its target, leading to innovations in problem-independent hyperpriors, unbounded optimization, and implicit constraint satisfaction; delivering significant performance improvements over prominent existing packages. We present applications of our method to a number of tasks including engineering design and parameter optimization.
Freeway merging in congested traffic is a significant challenge toward fully automated driving. Merging vehicles need to decide not only how to merge into a spot, but also where to merge. We present a method for the freeway merging based on multi-policy decision making with a reinforcement learning method called {\em passive actor-critic} (pAC), which learns with less knowledge of the system and without active exploration. The method selects a merging spot candidate by using the state value learned with pAC. We evaluate our method using real traffic data. Our experiments show that pAC achieves 92\% success rate to merge into a freeway, which is comparable to human decision making.
Reliability assessment of distribution system, based on historical data and probabilistic methods, leads to an unreliable estimation of reliability indices since the data for the distribution components are usually inaccurate or unavailable. Fuzzy logic is an efficient method to deal with the uncertainty in reliability inputs. In this paper, the ENS index along with other commonly used indices in reliability assessment are evaluated for the distribution system using fuzzy logic. Accordingly, the influential variables on the failure rate and outage duration time of the distribution components, which are natural or human-made, are explained using proposed fuzzy membership functions. The reliability indices are calculated and compared for different cases of the system operations by simulation on the IEEE RBTS Bus 2. The results of simulation show how utilities can significantly improve the reliability of their distribution system by considering the risk of the influential variables.
VAEs (Variational AutoEncoders) have proved to be powerful in the context of density modeling and have been used in a variety of contexts for creative purposes. In many settings, the data we model possesses continuous attributes that we would like to take into account at generation time. We propose in this paper GLSR-VAE, a Geodesic Latent Space Regularization for the Variational AutoEncoder architecture and its generalizations which allows a fine control on the embedding of the data into the latent space. When augmenting the VAE loss with this regularization, changes in the learned latent space reflects changes of the attributes of the data. This deeper understanding of the VAE latent space structure offers the possibility to modulate the attributes of the generated data in a continuous way. We demonstrate its efficiency on a monophonic music generation task where we manage to generate variations of discrete sequences in an intended and playful way.
It is quite exceptional, if it ever happens, that a new conceptual domain be built from scratch. Usually, it is developed and mastered in interaction, both positive and negative, with other more operational existing domains. Few reasoning mechanisms have been proposed to account for the interplay of different conceptual domains and the transfer of information from one to another. Analogical reasoning is one, blending is another. This paper presents a new mechanism, called 'tunnel effect', that may explain, in part, how scientists and students reason while constructing a new conceptual domain. One experimental study with high school students and analyses from the history of science, particularly about the birth of classical thermodynamics, provide evidence and illustrate this mechanism. The knowledge organization, processes and conditions for its appearance are detailed and put into the perspective of a computational model. Specifically, we put forward the hypothesis that two levels of knowledge, notional and conceptual, cooperate in the scientific discovery process when a new conceptual domain is being built. The type of conceptual learning that can be associated with tunnel effect is discussed and a thorough comparison is made with analogical reasoning in order to underline the main features of the new proposed mechanism.
Management of chronic diseases such as heart failure (HF) is a major public health problem. A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that all physicians should follow. Due to their complexity, these guidelines are difficult to implement and are adopted slowly by the medical community at large. We have developed a physician advisory system that codes the entire set of clinical practice guidelines for managing HF using answer set programming(ASP). In this paper we show how abductive reasoning can be deployed to find missing symptoms and conditions that the patient must exhibit in order for a treatment prescribed by a physician to work effectively. Thus, if a physician does not make an appropriate recommendation or makes a non-adherent recommendation, our system will advise the physician about symptoms and conditions that must be in effect for that recommendation to apply. It is under consideration for acceptance in TPLP.
We introduce a novel variant of the multi-armed bandit problem, in which bandits are streamed one at a time to the player, and at each point, the player can either choose to pull the current bandit or move on to the next bandit. Once a player has moved on from a bandit, they may never visit it again, which is a crucial difference between our problem and classic multi-armed bandit problems. In this online context, we study Bernoulli bandits (bandits with payout Ber($p_i$) for some underlying mean $p_i$) with underlying means drawn i.i.d. from various distributions, including the uniform distribution, and in general, all distributions that have a CDF satisfying certain differentiability conditions near zero. In all cases, we suggest several strategies and investigate their expected performance. Furthermore, we bound the performance of any optimal strategy and show that the strategies we have suggested are indeed optimal up to a constant factor. We also investigate the case where the distribution from which the underlying means are drawn is not known ahead of time. We again, are able to suggest algorithms that are optimal up to a constant factor for this case, given certain mild conditions on the universe of distributions.
An approach for coalition formation of multi-agent pursuit based on neural network and AGRMF model is proposed.This paper constructs a novel neural work called AGRMF-ANN which consists of feature extraction part and group generation part. On one hand,The convolutional layers of feature extraction part can abstract the features of agent group role membership function(AGRMF) for all of the groups,on the other hand,those features will be fed to the group generation part based on self-organizing map(SOM) layer which is used to group the pursuers with similar features in the same group. Besides, we also come up the group attractiveness function(GAF) to evaluate the quality of groups and the pursuers contribution in order to adjust the main ability indicators of AGRMF and other weight of all neural network. The simulation experiment showed that this proposal can improve the effectiveness of coalition formation for multi-agent pursuit and ability to adopt pursuit-evasion problem with the scale of pursuer team growing.
Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics tasks such as graph classification and clustering require representing entire graphs as fixed length feature vectors. While the aforementioned approaches are naturally unequipped to learn such representations, graph kernels remain as the most effective way of obtaining them. However, these graph kernels use handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are hampered by problems such as poor generalization. To address this limitation, in this work, we propose a neural embedding framework named graph2vec to learn data-driven distributed representations of arbitrary sized graphs. graph2vec's embeddings are learnt in an unsupervised manner and are task agnostic. Hence, they could be used for any downstream task such as graph classification, clustering and even seeding supervised representation learning approaches. Our experiments on several benchmark and large real-world datasets show that graph2vec achieves significant improvements in classification and clustering accuracies over substructure representation learning approaches and are competitive with state-of-the-art graph kernels.
Sequential Constraint Grammar (SCG) (Karlsson, 1990) and its extensions have lacked clear connections to formal language theory. The purpose of this article is to lay a foundation for these connections by simplifying the definition of strings processed by the grammar and by showing that Nonmonotonic SCG is undecidable and that derivations similar to the Generative Phonology exist. The current investigations propose resource bounds that restrict the generative power of SCG to a subset of context sensitive languages and present a strong finite-state condition for grammars as wholes. We show that a grammar is equivalent to a finite-state transducer if it is implemented with a Turing machine that runs in o(n log n) time. This condition opens new finite-state hypotheses and avenues for deeper analysis of SCG instances in the way inspired by Finite-State Phonology.
Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient's symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogenous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.
Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines. So far, most existing methods only work for classification and are not designed to alter the true performance measure of the problem at hand. We introduce a novel flexible approach named Houdini for generating adversarial examples specifically tailored for the final performance measure of the task considered, be it combinatorial and non-decomposable. We successfully apply Houdini to a range of applications such as speech recognition, pose estimation and semantic segmentation. In all cases, the attacks based on Houdini achieve higher success rate than those based on the traditional surrogates used to train the models while using a less perceptible adversarial perturbation.
In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification. While approaches based on the use of either model exist (e.g., for the task of image captioning), training such existing network architectures typically require pre-defined label sequences. For multi-label classification, it would be desirable to have a robust inference process, so that the prediction error would not propagate and thus affect the performance. Our proposed model uniquely integrates attention and Long Short Term Memory (LSTM) models, which not only addresses the above problem but also allows one to identify visual objects of interests with varying sizes without the prior knowledge of particular label ordering. More importantly, label co-occurrence information can be jointly exploited by our LSTM model. Finally, by advancing the technique of beam search, prediction of multiple labels can be efficiently achieved by our proposed network model.
The human language is one of the most natural interfaces for humans to interact with robots. This paper presents a robot system that retrieves everyday objects with unconstrained natural language descriptions. A core issue for the system is semantic and spatial grounding, which is to infer objects and their spatial relationships from images and natural language expressions. We introduce a two-stage neural-network grounding pipeline that maps natural language referring expressions directly to objects in the images. The first stage uses visual descriptions in the referring expressions to generate a candidate set of relevant objects. The second stage examines all pairwise relationships between the candidates and predicts the most likely referred object according to the spatial descriptions in the referring expressions. A key feature of our system is that by leveraging a large dataset of images labeled with text descriptions, it allows unrestricted object types and natural language referring expressions. Preliminary results indicate that our system outperforms a near state-of-the-art object comprehension system on standard benchmark datasets. We also present a robot system that follows voice commands to pick and place previously unseen objects.
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power system, and to help the customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using Deep Reinforcement Learning, a hybrid type of methods that combines Reinforcement Learning with Deep Learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and Deep Policy Gradient, both of them being extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly-dimensional database includes information about photovoltaic power generation, electric vehicles as well as buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide real-time feedback to consumers to encourage more efficient use of electricity.
Existing region-based object detectors are limited to regions with fixed box geometry to represent objects, even if those are highly non-rectangular. In this paper we introduce DP-FCN, a deep model for object detection which explicitly adapts to shapes of objects with deformable parts. Without additional annotations, it learns to focus on discriminative elements and to align them, and simultaneously brings more invariance for classification and geometric information to refine localization. DP-FCN is composed of three main modules: a Fully Convolutional Network to efficiently maintain spatial resolution, a deformable part-based RoI pooling layer to optimize positions of parts and build invariance, and a deformation-aware localization module explicitly exploiting displacements of parts to improve accuracy of bounding box regression. We experimentally validate our model and show significant gains. DP-FCN achieves state-of-the-art performances of 83.1% and 80.9% on PASCAL VOC 2007 and 2012 with VOC data only.
For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score. We target the task of constructing the collection of candidate parent sets when the score of choice is the Bayesian Information Criterion (BIC). We provide new non-trivial results that can be used to prune the search space of candidate parent sets of each node. We analyze how these new results relate to previous ideas in the literature both theoretically and empirically. We show in experiments with UCI data sets that gains can be significant. Since the new pruning rules are easy to implement and have low computational costs, they can be promptly integrated into all state-of-the-art methods for structure learning of Bayesian networks.
We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a learned environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. I2As show improved data efficiency, performance, and robustness to model misspecification compared to several baselines.
We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at http://allenai.org/data.html). We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.
Pairwise comparison data arises in many domains, including tournament rankings, web search, and preference elicitation. Given noisy comparisons of a fixed subset of pairs of items, we study the problem of estimating the underlying comparison probabilities under the assumption of strong stochastic transitivity (SST). We also consider the noisy sorting subclass of the SST model. We show that when the assignment of items to the topology is arbitrary, these permutation-based models, unlike their parametric counterparts, do not admit consistent estimation for most comparison topologies used in practice. We then demonstrate that consistent estimation is possible when the assignment of items to the topology is randomized, thus establishing a dichotomy between worst-case and average-case designs. We propose two estimators in the average-case setting and analyze their risk, showing that it depends on the comparison topology only through the degree sequence of the topology. The rates achieved by these estimators are shown to be optimal for a large class of graphs. Our results are corroborated by simulations on multiple comparison topologies.
Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, speaker's sarcastic intent is not always obvious without additional context. Focusing on social media discussions, we investigate two issues: (1) does modeling of conversation context help in sarcasm detection and (2) can we understand what part of conversation context triggered the sarcastic reply. To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the sarcastic response. We show that the conditional LSTM network (Rocktaschel et al., 2015) and LSTM networks with sentence level attention on context and response outperform the LSTM model that reads only the response. To address the second issue, we present a qualitative analysis of attention weights produced by the LSTM models with attention and discuss the results compared with human performance on the task.
LPMLN is a recent addition to probabilistic logic programming languages. Its main idea is to overcome the rigid nature of the stable model semantics by assigning a weight to each rule in a way similar to Markov Logic is defined. We present two implementations of LPMLN, $\text{LPMLN2ASP}$ and $\text{LPMLN2MLN}$. System $\text{LPMLN2ASP}$ translates LPMLN programs into the input language of answer set solver $\text{CLINGO}$, and using weak constraints and stable model enumeration, it can compute most probable stable models as well as exact conditional and marginal probabilities. System $\text{LPMLN2MLN}$ translates LPMLN programs into the input language of Markov Logic solvers, such as $\text{ALCHEMY}$, $\text{TUFFY}$, and $\text{ROCKIT}$, and allows for performing approximate probabilistic inference on LPMLN programs. We also demonstrate the usefulness of the LPMLN systems for computing other languages, such as ProbLog and Pearl's Causal Models, that are shown to be translatable into LPMLN. (Under consideration for acceptance in TPLP)
Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination. In some settings, the structure of a problem allows a distributed solution with limited communication. Here, we consider a scenario where no communication is available, and instead we learn local policies for all agents that collectively mimic the solution to a centralized multi-agent static optimization problem. Our main contribution is an information theoretic framework based on rate distortion theory which facilitates analysis of how well the resulting fully decentralized policies are able to reconstruct the optimal solution. Moreover, this framework provides a natural extension that addresses which nodes an agent should communicate with to improve the performance of its individual policy.
Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question. However, the existing visual question answering approaches mainly tackle the problem of static image question, which may be ineffectively for video question answering due to the insufficiency of modeling the temporal dynamics of video contents. In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism. We propose the attribute-augmented attention network learning framework that enables the joint frame-level attribute detection and unified video representation learning for video question answering. We then incorporate the multi-step reasoning process for our proposed attention network to further improve the performance. We construct a large-scale video question answering dataset. We conduct the experiments on both multiple-choice and open-ended video question answering tasks to show the effectiveness of the proposed method.
Bayesian Filtering for plan and activity recognition is challenging for scenarios that contain many observation equivalent entities (i.e. entities that produce the same observations). This is due to the combinatorial explosion in the number of hypotheses that need to be tracked. However, this class of problems exhibits a certain symmetry that can be exploited for state space representation and inference. We analyze current state of the art methods and find that none of them completely fits the requirements arising in this problem class. We sketch a novel inference algorithm that provides a solution by incorporating concepts from Lifted Inference algorithms, Probabilistic Multiset Rewriting Systems, and Computational State Space Models. Two experiments confirm that this novel algorithm has the potential to perform efficient probabilistic inference on this problem class.
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level task performance on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN and training the joint pipeline from end to end, we witness a large improvement of the object detection performance on hazy images.
We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.
We describe a generalization of the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) which is able to encode prior information that state transitions are more likely between "nearby" states. This is accomplished by defining a similarity function on the state space and scaling transition probabilities by pair-wise similarities, thereby inducing correlations among the transition distributions. We present an augmented data representation of the model as a Markov Jump Process in which: (1) some jump attempts fail, and (2) the probability of success is proportional to the similarity between the source and destination states. This augmentation restores conditional conjugacy and admits a simple Gibbs sampler. We evaluate the model and inference method on a speaker diarization task and a "harmonic parsing" task using four-part chorale data, as well as on several synthetic datasets, achieving favorable comparisons to existing models.
Answer Set Programming (ASP) is a well-established formalism for nonmonotonic reasoning. An ASP program can have no answer set due to cyclic default negation. In this case, it is not possible to draw any conclusion, even if this is not intended. Recently, several paracoherent semantics have been proposed that address this issue, and several potential applications for these semantics have been identified. However, paracoherent semantics have essentially been inapplicable in practice, due to the lack of efficient algorithms and implementations. In this paper, this lack is addressed, and several different algorithms to compute semi-stable and semi-equilibrium models are proposed and implemented into an answer set solving framework. An empirical performance comparison among the new algorithms on benchmarks from ASP competitions is given as well.
In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation of this return, or value. Although there is an established body of literature studying the value distribution, thus far it has always been used for a specific purpose such as implementing risk-aware behaviour. We begin with theoretical results in both the policy evaluation and control settings, exposing a significant distributional instability in the latter. We then use the distributional perspective to design a new algorithm which applies Bellman's equation to the learning of approximate value distributions. We evaluate our algorithm using the suite of games from the Arcade Learning Environment. We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning. Finally, we combine theoretical and empirical evidence to highlight the ways in which the value distribution impacts learning in the approximate setting.
Answer Set Programming (ASP) is a well-established declarative problem solving paradigm which became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR), especially for its high expressiveness and the ability to deal also with incomplete knowledge. Recently, thanks to the availability of a number of robust and efficient implementations, ASP has been increasingly employed in a number of different domains, and used for the development of industrial-level and enterprise applications. This made clear the need for proper development tools and interoperability mechanisms for easing interaction and integration with external systems in the widest range of real-world scenarios, including mobile applications and educational contexts. In this work we present a framework for integrating the KRR capabilities of ASP into generic applications. We show the use of the framework by illustrating proper specializations for some relevant ASP systems over different platforms, including the mobile setting; furthermore, the potential of the framework for educational purposes is illustrated by means of the development of several ASP-based applications.
Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.'
Because preferences naturally arise and play an important role in many real-life decisions, they are at the backbone of various fields. In particular preferences are increasingly used in almost all matching procedures-based applications. In this work we highlight the benefit of using AI insights on preferences in a large scale application, namely the French Admission Post-Baccalaureat Platform (APB). Each year APB allocates hundreds of thousands first year applicants to universities. This is done automatically by matching applicants preferences to university seats. In practice, APB can be unable to distinguish between applicants which leads to the introduction of random selection. This has created frustration in the French public since randomness, even used as a last mean does not fare well with the republican egalitarian principle. In this work, we provide a solution to this problem. We take advantage of recent AI Preferences Theory results to show how to enhance APB in order to improve expressiveness of applicants preferences and reduce their exposure to random decisions.
Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful. We propose a framework for training latent variable models that explicitly balances two goals: recovery of faithful generative explanations of high-dimensional data, and accurate prediction of associated semantic labels. Existing approaches fail to achieve these goals due to an incomplete treatment of a fundamental asymmetry: the intended application is always predicting labels from data, not data from labels. Our prediction-constrained objective for training generative models coherently integrates loss-based supervisory signals while enabling effective semi-supervised learning from partially labeled data. We derive learning algorithms for semi-supervised mixture and topic models using stochastic gradient descent with automatic differentiation. We demonstrate improved prediction quality compared to several previous supervised topic models, achieving predictions competitive with high-dimensional logistic regression on text sentiment analysis and electronic health records tasks while simultaneously learning interpretable topics.
Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve. Yet, current neural machine translation training focuses on expensive human-generated reference translations. We describe a reinforcement learning algorithm that improves neural machine translation systems from simulated human feedback. Our algorithm combines the advantage actor-critic algorithm (Mnih et al., 2016) with the attention-based neural encoder-decoder architecture (Luong et al., 2015). This algorithm (a) is well-designed for problems with a large action space and delayed rewards, (b) effectively optimizes traditional corpus-level machine translation metrics, and (c) is robust to skewed, high-variance, granular feedback modeled after actual human behaviors.
We present a simple method for assessing the quality of generated images in Generative Adversarial Networks (GANs). The method can be applied in any kind of GAN without interfering with the learning procedure or affecting the learning objective. The central idea is to define a likelihood function that correlates with the quality of the generated images. In particular, we derive a Gaussian likelihood function from the distribution of the embeddings (hidden activations) of the real images in the discriminator, and based on this, define two simple measures of how likely it is that the embeddings of generated images are from the distribution of the embeddings of the real images. This yields a simple measure of fitness for generated images, for all varieties of GANs. Empirical results on CIFAR-10 demonstrate a strong correlation between the proposed measures and the perceived quality of the generated images.
We propose a methodology that adapts graph embedding techniques (DeepWalk (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as cross-lingual vector space mapping approaches (Least Squares and Canonical Correlation Analysis) in order to merge the corpus and ontological sources of lexical knowledge. We also perform comparative analysis of the used algorithms in order to identify the best combination for the proposed system. We then apply this to the task of enhancing the coverage of an existing word embedding's vocabulary with rare and unseen words. We show that our technique can provide considerable extra coverage (over 99%), leading to consistent performance gain (around 10% absolute gain is achieved with w2v-gn-500K cf.\S 3.3) on the Rare Word Similarity dataset.
Deep neural networks have become a primary tool for solving problems in many fields. They are also used for addressing information retrieval problems and show strong performance in several tasks. Training these models requires large, representative datasets and for most IR tasks, such data contains sensitive information from users. Privacy and confidentiality concerns prevent many data owners from sharing the data, thus today the research community can only benefit from research on large-scale datasets in a limited manner. In this paper, we discuss privacy preserving mimic learning, i.e., using predictions from a privacy preserving trained model instead of labels from the original sensitive training data as a supervision signal. We present the results of preliminary experiments in which we apply the idea of mimic learning and privacy preserving mimic learning for the task of document re-ranking as one of the core IR tasks. This research is a step toward laying the ground for enabling researchers from data-rich environments to share knowledge learned from actual users' data, which should facilitate research collaborations.
There have been some works that learn a lexicon together with the corpus to improve the word embeddings. However, they either model the lexicon separately but update the neural networks for both the corpus and the lexicon by the same likelihood, or minimize the distance between all of the synonym pairs in the lexicon. Such methods do not consider the relatedness and difference of the corpus and the lexicon, and may not be the best optimized. In this paper, we propose a novel method that considers the relatedness and difference of the corpus and the lexicon. It trains word embeddings by learning the corpus to predicate a word and its corresponding synonym under the context at the same time. For polysemous words, we use a word sense disambiguation filter to eliminate the synonyms that have different meanings for the context. To evaluate the proposed method, we compare the performance of the word embeddings trained by our proposed model, the control groups without the filter or the lexicon, and the prior works in the word similarity tasks and text classification task. The experimental results show that the proposed model provides better embeddings for polysemous words and improves the performance for text classification.
Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the development of precision medicine approaches at scale. A main EHR use-case is defining phenotyping algorithms that identify disease status, onset and severity. Phenotyping algorithms utilize diagnoses, prescriptions, laboratory tests, symptoms and other elements in order to identify patients with or without a specific trait. No common standardized, structured, computable format exists for storing phenotyping algorithms. The majority of algorithms are stored as human-readable descriptive text documents making their translation to code challenging due to their inherent complexity and hinders their sharing and re-use across the community. In this paper, we evaluate the two key Semantic Web Technologies, the Web Ontology Language and the Resource Description Framework, for enabling computable representations of EHR-driven phenotyping algorithms.
The number of scientific articles has grown rapidly over the years and there are no signs that this growth will slow down in the near future. Because of this, it becomes increasingly difficult to keep up with the latest developments in a scientific field. To address this problem, we present here an approach to help researchers learn about the latest developments and findings by extracting in a normalized form core claims from scientific articles. This normalized representation is a controlled natural language of English sentences called AIDA, which has been proposed in previous work as a method to formally structure and organize scientific findings and discourse. We show how such AIDA sentences can be automatically extracted by detecting the core claim of an article, checking for AIDA compliance, and - if necessary - transforming it into a compliant sentence. While our algorithm is still far from perfect, our results indicate that the different steps are feasible and they support the claim that AIDA sentences might be a promising approach to improve scientific communication in the future.
The quest for better data analysis and artificial intelligence has lead to more and more data being collected and stored. As a consequence, more data are exposed to malicious entities. This paper examines the problem of privacy in machine learning for classification. We utilize the Ridge Discriminant Component Analysis (RDCA) to desensitize data with respect to a privacy label. Based on five experiments, we show that desensitization by RDCA can effectively protect privacy (i.e. low accuracy on the privacy label) with small loss in utility. On HAR and CMU Faces datasets, the use of desensitized data results in random guess level accuracies for privacy at a cost of 5.14% and 0.04%, on average, drop in the utility accuracies. For Semeion Handwritten Digit dataset, accuracies of the privacy-sensitive digits are almost zero, while the accuracies for the utility-relevant digits drop by 7.53% on average. This presents a promising solution to the problem of privacy in machine learning for classification.
Training robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress. While sample complexity can be reduced by training policies in simulation, such policies can perform sub-optimally on the real platform given imperfect calibration of model dynamics. We present an approach -- supplemental to fine tuning on the real robot -- to further benefit from parallel access to a simulator during training and reduce sample requirements on the real robot. The developed approach harnesses auxiliary rewards to guide the exploration for the real world agent based on the proficiency of the agent in simulation and vice versa. In this context, we demonstrate empirically that the reciprocal alignment for both agents provides further benefit as the agent in simulation can adjust to optimize its behaviour for states commonly visited by the real-world agent.
Entity retrieval is the task of finding entities such as people or products in response to a query, based solely on the textual documents they are associated with. Recent semantic entity retrieval algorithms represent queries and experts in finite-dimensional vector spaces, where both are constructed from text sequences. We investigate entity vector spaces and the degree to which they capture structural regularities. Such vector spaces are constructed in an unsupervised manner without explicit information about structural aspects. For concreteness, we address these questions for a specific type of entity: experts in the context of expert finding. We discover how clusterings of experts correspond to committees in organizations, the ability of expert representations to encode the co-author graph, and the degree to which they encode academic rank. We compare latent, continuous representations created using methods based on distributional semantics (LSI), topic models (LDA) and neural networks (word2vec, doc2vec, SERT). Vector spaces created using neural methods, such as doc2vec and SERT, systematically perform better at clustering than LSI, LDA and word2vec. When it comes to encoding entity relations, SERT performs best.
Processing and publishing the data of the historical sciences in the semantic web is an interesting challenge in which the representation of temporal aspects plays a key role. We propose in this paper a model of temporal knowledge representation adapted to work on historical documents. This model is based on the notion of fluent that is represented in RDF graphs. We show how this model allows to represent the knowledge necessary to the historians and how it can be used to reason on this knowledge using the SWRL and SPARQL languages. This model is being used in a project to digitize, study and publish the manuscripts of linguist Ferdinand de Saussure.
Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user. However, such items are not necessarily the ones that maximize the utility of the service provider (e.g., an online retailer) in terms of the business value, such as profit. One approach to increasing the providers' utility is to incorporate purchase-oriented information, e.g., the price, sales probabilities, and the resulting profit, into the recommendation algorithms. In this paper we specifically focus on price- and profit-aware recommender systems. We provide a brief overview of the relevant literature and use numerical simulations to illustrate the potential business benefit of such approaches.
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL. First, we classify different MTL algorithms into several categories: feature learning approach, low-rank approach, task clustering approach, task relation learning approach, dirty approach, multi-level approach and deep learning approach. In order to compare different approaches, we discuss the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, batch MTL models are difficult to handle this situation and online, parallel and distributed MTL models as well as feature hashing are reviewed to reveal the computational and storage advantages. Many real-world applications use MTL to boost their performance and we introduce some representative works. Finally, we present theoretical analyses and discuss several future directions for MTL.
In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand or two hands to do so. In Experiment~2, we asked participants online to judge whether they think the person in the lab used one or two hands. The results revealed a close correspondence between participants' actions in the lab, and the mental simulations of participants online. To explain participants' actions and mental simulations, we develop a model that plans over a symbolic representation of the situation, executes the plan using a geometric solver, and checks the plan's feasibility by taking into account the physical constraints of the scene. Our model explains participants' actions and judgments to a high degree of quantitative accuracy.
Manufacturers of safety-critical systems must make the case that their product is sufficiently safe for public deployment. Much of this case often relies upon critical event outcomes from real-world testing, requiring manufacturers to be strategic about how they allocate testing resources in order to maximize their chances of demonstrating system safety. This work frames the partially observable and belief-dependent problem of test scheduling as a Markov decision process, which can be solved efficiently to yield closed-loop manufacturer testing policies. By solving for policies over a wide range of problem formulations, we are able to provide high-level guidance for manufacturers and regulators on issues relating to the testing of safety-critical systems. This guidance spans an array of topics, including circumstances under which manufacturers should continue testing despite observed incidents, when manufacturers should test aggressively, and when regulators should increase or reduce the real-world testing requirements for an autonomous vehicle.
Nowadays, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others take use of both the graph structure and the node attributes. In real-world networks, there are many uncertain and noisy attributes in the graph. In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step. The numerical, probabilistic as well as evidential attributes are generated according to the graph structure. In the second step, some noise will be added to the attributes. We perform experiments on graphs with different types of attributes and compare the detection results in terms of the Normalized Mutual Information (NMI) values. The experimental results show that the clustering with evidential attributes gives better results comparing to those with probabilistic and numerical attributes. This illustrates the advantages of evidential attributes.
We introduce $\mathcal{DLR}^+$, an extension of the n-ary propositionally closed description logic $\mathcal{DLR}$ to deal with attribute-labelled tuples (generalising the positional notation), projections of relations, and global and local objectification of relations, able to express inclusion, functional, key, and external uniqueness dependencies. The logic is equipped with both TBox and ABox axioms. We show how a simple syntactic restriction on the appearance of projections sharing common attributes in a $\mathcal{DLR}^+$ knowledge base makes reasoning in the language decidable with the same computational complexity as $\mathcal{DLR}$. The obtained $\mathcal{DLR}^\pm$ n-ary description logic is able to encode more thoroughly conceptual data models such as EER, UML, and ORM.
Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a setting where data is readily available, with the hope that it generalises well to the target domain. We propose a new multi-stage RL agent, DARLA (DisentAngled Representation Learning Agent), which learns to see before learning to act. DARLA's vision is based on learning a disentangled representation of the observed environment. Once DARLA can see, it is able to acquire source policies that are robust to many domain shifts - even with no access to the target domain. DARLA significantly outperforms conventional baselines in zero-shot domain adaptation scenarios, an effect that holds across a variety of RL environments (Jaco arm, DeepMind Lab) and base RL algorithms (DQN, A3C and EC).
In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and state-action information. We then use this learned model to guide agent exploration using a modified version of policy shaping to make it more effective at learning in unseen environments. We evaluate this technique using the popular arcade game, Frogger, under ideal and non-ideal conditions. This evaluation shows that our modified policy shaping algorithm improves over a Q-learning agent as well as a baseline version of policy shaping.
Matching 3D rigid point clouds in complex environments robustly and accurately is still a core technique used in many applications. This paper proposes a new architecture combining error estimation from sample covariances and dual global probability alignment based on the convolution of adaptive Gaussian Mixture Models (GMM) from point clouds. Firstly, a novel adaptive GMM is defined using probability distributions from the corresponding points. Then rigid point cloud alignment is performed by maximizing the global probability from the convolution of dual adaptive GMMs in the whole 2D or 3D space, which can be efficiently optimized and has a large zone of accurate convergence. Thousands of trials have been conducted on 200 models from public 2D and 3D datasets to demonstrate superior robustness and accuracy in complex environments with unpredictable noise, outliers, occlusion, initial rotation, shape and missing points.
Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two categories: those that specify a goal condition or target state, and those that specify explicit actions, or how to perform a given task. Recent approaches have used reward functions as a semantic representation of goal-based commands, which allows for the use of a state-of-the-art planner to find a policy for the given task. However, these reward functions cannot be directly used to represent action-oriented commands. We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding Network (DRAGGN), for task grounding and execution that handles natural language from either category as input, and generalizes to unseen environments. Our robot-simulation results demonstrate that a system successfully interpreting both goal-oriented and action-oriented task specifications brings us closer to robust natural language understanding for human-robot interaction.
Gossip protocols aim at arriving, by means of point-to-point or group communications, at a situation in which all the agents know each other secrets. Recently a number of authors studied distributed epistemic gossip protocols. These protocols use as guards formulas from a simple epistemic logic, which makes their analysis and verification substantially easier. We study here common knowledge in the context of such a logic. First, we analyze when it can be reduced to iterated knowledge. Then we show that the semantics and truth for formulas without nested common knowledge operator are decidable. This implies that implementability, partial correctness and termination of distributed epistemic gossip protocols that use non-nested common knowledge operator is decidable, as well. Given that common knowledge is equivalent to an infinite conjunction of nested knowledge, these results are non-trivial generalizations of the corresponding decidability results for the original epistemic logic, established in (Apt & Wojtczak, 2016). K. R. Apt & D. Wojtczak (2016): On Decidability of a Logic of Gossips. In Proc. of JELIA 2016, pp. 18-33, doi:10.1007/ 978-3-319-48758-8_2.
An abstract argumentation framework can be used to model the argumentative stance of an agent at a high level of abstraction, by indicating for every pair of arguments that is being considered in a debate whether the first attacks the second. When modelling a group of agents engaged in a debate, we may wish to aggregate their individual argumentation frameworks to obtain a single such framework that reflects the consensus of the group. Even when agents disagree on many details, there may well be high-level agreement on important semantic properties, such as the acceptability of a given argument. Using techniques from social choice theory, we analyse under what circumstances such semantic properties agreed upon by the individual agents can be preserved under aggregation.
While there have been many attempts, going back to BAN logic, to base reasoning about security protocols on epistemic notions, they have not been all that successful. Arguably, this has been due to the particular logics chosen. We present a simple logic based on the well-understood modal operators of knowledge, time, and probability, and show that it is able to handle issues that have often been swept under the rug by other approaches, while being flexible enough to capture all the higher- level security notions that appear in BAN logic. Moreover, while still assuming that the knowledge operator allows for unbounded computation, it can handle the fact that a computationally bounded agent cannot decrypt messages in a natural way, by distinguishing strings and message terms. We demonstrate that our logic can capture BAN logic notions by providing a translation of the BAN operators into our logic, capturing belief by a form of probabilistic knowledge.
We introduce an axiomatic approach to group recommendations, in line of previous work on the axiomatic treatment of trust-based recommendation systems, ranking systems, and other foundational work on the axiomatic approach to internet mechanisms in social choice settings. In group recommendations we wish to recommend to a group of agents, consisting of both opinionated and undecided members, a joint choice that would be acceptable to them. Such a system has many applications, such as choosing a movie or a restaurant to go to with a group of friends, recommending games for online game players, & other communal activities. Our method utilizes a given social graph to extract information on the undecided, relying on the agents influencing them. We first show that a set of fairly natural desired requirements (a.k.a axioms) leads to an impossibility, rendering mutual satisfaction of them unreachable. However, we also show a modified set of axioms that fully axiomatize a group variant of the random-walk recommendation system, expanding a previous result from the individual recommendation case.
This paper combines two studies: a topological semantics for epistemic notions and abstract argumentation theory. In our combined setting, we use a topological semantics to represent the structure of an agent's collection of evidence, and we use argumentation theory to single out the relevant sets of evidence through which a notion of beliefs grounded on arguments is defined. We discuss the formal properties of this newly defined notion, providing also a formal language with a matching modality together with a sound and complete axiom system for it. Despite the fact that our agent can combine her evidence in a 'rational' way (captured via the topological structure), argument-based beliefs are not closed under conjunction. This illustrates the difference between an agent's reasoning abilities (i.e. the way she is able to combine her available evidence) and the closure properties of her beliefs. We use this point to argue for why the failure of closure under conjunction of belief should not bear the burden of the failure of rationality.
Legal probabilism (LP) claims the degrees of conviction in juridical fact-finding are to be modeled exactly the way degrees of beliefs are modeled in standard bayesian epistemology. Classical legal probabilism (CLP) adds that the conviction is justified if the credence in guilt given the evidence is above an appropriate guilt probability threshold. The views are challenged on various counts, especially by the proponents of the so-called narrative approach, on which the fact-finders' decision is the result of a dynamic interplay between competing narratives of what happened. I develop a way a bayesian epistemologist can make sense of the narrative approach. I do so by formulating a probabilistic framework for evaluating competing narrations in terms of formal explications of the informal evaluation criteria used in the narrative approach.
Recent years witnessed a growing interest in non-standard epistemic logics of knowing whether, knowing how, knowing what, knowing why and so on. The new epistemic modalities introduced in those logics all share, in their semantics, the general schema of $\exists x \Box \phi$, e.g., knowing how to achieve $\phi$ roughly means that there exists a way such that you know that it is a way to ensure that $\phi$. Moreover, the resulting logics are decidable. Inspired by those particular logics, in this work, we propose a very general and powerful framework based on quantifier-free predicate language extended by a new modality $\Box^x$, which packs exactly $\exists x \Box$ together. We show that the resulting language, though much more expressive, shares many good properties of the basic propositional modal logic over arbitrary models, such as finite-tree-model property and van Benthem-like characterization w.r.t.\ first-order modal logic. We axiomatize the logic over S5 frames with intuitive axioms to capture the interaction between $\Box^x$ and know-that operator in an epistemic setting.
We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards. We build upon the Deep Deterministic Policy Gradient (DDPG) algorithm to use demonstrations. Both demonstrations and actual interactions are used to fill a replay buffer and the sampling ratio between demonstrations and transitions is automatically tuned via a prioritized replay mechanism. Typically, carefully engineered shaping rewards are required to enable the agents to efficiently explore on high dimensional control problems such as robotics. They are also required for model-based acceleration methods relying on local solvers such as iLQG (e.g. Guided Policy Search and Normalized Advantage Function). The demonstrations replace the need for carefully engineered rewards, and reduce the exploration problem encountered by classical RL approaches in these domains. Demonstrations are collected by a robot kinesthetically force-controlled by a human demonstrator. Results on four simulated insertion tasks show that DDPG from demonstrations out-performs DDPG, and does not require engineered rewards. Finally, we demonstrate the method on a real robotics task consisting of inserting a clip (flexible object) into a rigid object.
Deep residual learning (ResNet) is a new method for training very deep neural networks using identity map-ping for shortcut connections. ResNet has won the ImageNet ILSVRC 2015 classification task, and achieved state-of-the-art performances in many computer vision tasks. However, the effect of residual learning on noisy natural language processing tasks is still not well understood. In this paper, we design a novel convolutional neural network (CNN) with residual learning, and investigate its impacts on the task of distantly supervised noisy relation extraction. In contradictory to popular beliefs that ResNet only works well for very deep networks, we found that even with 9 layers of CNNs, using identity mapping could significantly improve the performance for distantly-supervised relation extraction.
In this article we study the transfer learning model of action advice under a budget. We focus on reinforcement learning teachers providing action advice to heterogeneous students playing the game of Pac-Man under a limited advice budget. First, we examine several critical factors affecting advice quality in this setting, such as the average performance of the teacher, its variance and the importance of reward discounting in advising. The experiments show the non-trivial importance of the coefficient of variation (CV) as a statistic for choosing policies that generate advice. The CV statistic relates variance to the corresponding mean. Second, the article studies policy learning for distributing advice under a budget. Whereas most methods in the relevant literature rely on heuristics for advice distribution we formulate the problem as a learning one and propose a novel RL algorithm capable of learning when to advise, adapting to the student and the task at hand. Furthermore, we argue that learning to advise under a budget is an instance of a more generic learning problem: Constrained Exploitation Reinforcement Learning.
Machine comprehension(MC) style question answering is a representative problem in natural language processing. Previous methods rarely spend time on the improvement of encoding layer, especially the embedding of syntactic information and name entity of the words, which are very crucial to the quality of encoding. Moreover, existing attention methods represent each query word as a vector or use a single vector to represent the whole query sentence, neither of them can handle the proper weight of the key words in query sentence. In this paper, we introduce a novel neural network architecture called Multi-layer Embedding with Memory Network(MEMEN) for machine reading task. In the encoding layer, we employ classic skip-gram model to the syntactic and semantic information of the words to train a new kind of embedding layer. We also propose a memory network of full-orientation matching of the query and passage to catch more pivotal information. Experiments show that our model has competitive results both from the perspectives of precision and efficiency in Stanford Question Answering Dataset(SQuAD) among all published results and achieves the state-of-the-art results on TriviaQA dataset.
A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for uncertainty modeling. A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure. The outer optimization problem follows a two-stage stochastic programming approach to optimize the expected objective across different data classes; adaptive robust optimization is nested as the inner problem to ensure the robustness of the solution while maintaining computational tractability. A decomposition-based algorithm is further developed to solve the resulting multi-level optimization problem efficiently. Case studies on process network design and planning are presented to demonstrate the applicability of the proposed framework and algorithm.
We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models. We demonstrate that the recurrent Ladder is able to handle a wide variety of complex learning tasks that benefit from iterative inference and temporal modeling. The architecture shows close-to-optimal results on temporal modeling of video data, competitive results on music modeling, and improved perceptual grouping based on higher order abstractions, such as stochastic textures and motion cues. We present results for fully supervised, semi-supervised, and unsupervised tasks. The results suggest that the proposed architecture and principles are powerful tools for learning a hierarchy of abstractions, learning iterative inference and handling temporal information.
Topological models of empirical and formal inquiry are increasingly prevalent. They have emerged in such diverse fields as domain theory [1, 16], formal learning theory [18], epistemology and philosophy of science [10, 15, 8, 9, 2], statistics [6, 7] and modal logic [17, 4]. In those applications, open sets are typically interpreted as hypotheses deductively verifiable by true propositional information that rules out relevant possibilities. However, in statistical data analysis, one routinely receives random samples logically compatible with every statistical hypothesis. We bridge the gap between propositional and statistical data by solving for the unique topology on probability measures in which the open sets are exactly the statistically verifiable hypotheses. Furthermore, we extend that result to a topological characterization of learnability in the limit from statistical data.
We present an approach to synthesizing photographic images conditioned on semantic layouts. Given a semantic label map, our approach produces an image with photographic appearance that conforms to the input layout. The approach thus functions as a rendering engine that takes a two-dimensional semantic specification of the scene and produces a corresponding photographic image. Unlike recent and contemporaneous work, our approach does not rely on adversarial training. We show that photographic images can be synthesized from semantic layouts by a single feedforward network with appropriate structure, trained end-to-end with a direct regression objective. The presented approach scales seamlessly to high resolutions; we demonstrate this by synthesizing photographic images at 2-megapixel resolution, the full resolution of our training data. Extensive perceptual experiments on datasets of outdoor and indoor scenes demonstrate that images synthesized by the presented approach are considerably more realistic than alternative approaches. The results are shown in the supplementary video at https://youtu.be/0fhUJT21-bs
A device which contains number of symbol input keys, where the number of available keys is less than the number of symbols of an alphabet of any given language, screen, and dynamic reordering table of the symbols which are mapped onto those keys, according to a disambiguation method based on the previously entered symbols. The device incorporates a previously entered keystrokes tracking mechanism, and the key selected by the user detector, as well as a mechanism to select the dynamic symbol reordering mapped onto this key according to the information contained to the reordering table. The reordering table occurs from a disambiguation method which reorders the symbol appearance. The reordering information occurs from Bayesian Belief network construction and training from text corpora of the specific language.
In this work we present a novel system for PET estimation using CT scans. We explore the use of fully convolutional networks (FCN) and conditional generative adversarial networks (GAN) to export PET data from CT data. Our dataset includes 25 pairs of PET and CT scans where 17 were used for training and 8 for testing. The system was tested for detection of malignant tumors in the liver region. Initial results look promising showing high detection performance with a TPR of 92.3% and FPR of 0.25 per case. Future work entails expansion of the current system to the entire body using a much larger dataset. Such a system can be used for tumor detection and drug treatment evaluation in a CT-only environment instead of the expansive and radioactive PET-CT scan.
Precision medicine requires the precision disease risk prediction models. In literature, there have been a lot well-established (inter-)national risk models, but when applying them into the local population, the prediction performance becomes unsatisfactory. To address the localization issue, this paper exploits the way to develop knowledge-enhanced localized risk models. On the one hand, we tune models by learning from regional Electronic Health Record (EHR) repositories, and on the other hand, we propose knowledge injection into the EHR data learning process. For experiments, we leverage the Pooled Cohort Equations (PCE, as recommended in ACC/AHA guidelines to estimate the risk of ASCVD) to develop a localized ASCVD risk prediction model in diabetes. The experimental results show that, if directly using the PCE algorithm on our cohort, the AUC is only 0.653, while our knowledge-enhanced localized risk model can achieve higher prediction performance with AUC of 0.723 (improved by 10.7%).
Convolutional Neural Networks have been highly successful in performing a host of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis. In 2015, Gatys et. al [7] show how the style of a painter can be extracted from an image of the painting and applied to another normal photograph, thus recreating the photo in the style of the painter. The method has been successfully applied to a wide range of images and has since spawned multiple applications and mobile apps. In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes. We construct an approach to personalize and generate new custom clothes based on a users preference and by learning the users fashion choices from a limited set of clothes from their closet. The approach is evaluated by analyzing the generated images of clothes and how well they align with the users fashion style.
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the texture were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep convolutional neural network (CNN) as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserves the detail texture of the original images.
Computer vision has benefited from initializing multiple deep layers with weights pretrained on large supervised training sets like ImageNet. Natural language processing (NLP) typically sees initialization of only the lowest layer of deep models with pretrained word vectors. In this paper, we use a deep LSTM encoder from an attentional sequence-to-sequence model trained for machine translation (MT) to contextualize word vectors. We show that adding these context vectors (CoVe) improves performance over using only unsupervised word and character vectors on a wide variety of common NLP tasks: sentiment analysis (SST, IMDb), question classification (TREC), entailment (SNLI), and question answering (SQuAD). For fine-grained sentiment analysis and entailment, CoVe improves performance of our baseline models to the state of the art.
The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature.
Projective Simulation was introduced as a novel approach to Artificial Intelligence. It involves a deliberation procedure that consists of a random walk on a graph of clips and allows for the learning agent to project itself into the future before committing to an action. Here we study and analyze a quantum mechanical version in which the random walk is performed by two kinds of Hamiltonians. The first kind is implemented by naively embedding the classical model in a quantum model by turning the clips into qubits. The other allows for storing clips in superpositions of qubits allowing for a potentially purely quantum mechanical learning procedure in which the perception of the environment is purely quantum mechanical but the action is classical. We lastly introduce the concept of interacting projective agents for both the classical and quantum mechanical case.
Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings. Jointly modeling these two facets is helpful for designing a better recommendation system. While some existing models integrate text information such as item specifications or user reviews into user and item latent factors for improving the rating prediction, no existing works consider tips for improving recommendation quality. We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. For abstractive tips generation, gated recurrent neural networks are employed to "translate" user and item latent representations into a concise sentence. Extensive experiments on benchmark datasets from different domains show that NRT achieves significant improvements over the state-of-the-art methods. Moreover, the generated tips can vividly predict the user experience and feelings.
Exemplar-based face sketch synthesis methods usually meet the challenging problem that input photos are captured in different lighting conditions from training photos. The critical step causing the failure is the search of similar patch candidates for an input photo patch. Conventional illumination invariant patch distances are adopted rather than directly relying on pixel intensity difference, but they will fail when local contrast within a patch changes. In this paper, we propose a fast preprocessing method named Bidirectional Luminance Remapping (BLR), which interactively adjust the lighting of training and input photos. Our method can be directly integrated into state-of-the-art exemplar-based methods to improve their robustness with ignorable computational cost.
Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. They only need a small set of training samples from the initial frame to generate an appearance model. However, existing DCFs learn the filters separately from feature extraction, and update these filters using a moving average operation with an empirical weight. These DCF trackers hardly benefit from the end-to-end training. In this paper, we propose the CREST algorithm to reformulate DCFs as a one-layer convolutional neural network. Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training. To reduce model degradation during online update, we apply residual learning to take appearance changes into account. Extensive experiments on the benchmark datasets demonstrate that our CREST tracker performs favorably against state-of-the-art trackers.
Explaining and reasoning about processes which underlie observed black-box phenomena enables the discovery of causal mechanisms, derivation of suitable abstract representations and the formulation of more robust predictions. We propose to learn high level functional programs in order to represent abstract models which capture the invariant structure in the observed data. We introduce the $\pi$-machine (program-induction machine) -- an architecture able to induce interpretable LISP-like programs from observed data traces. We propose an optimisation procedure for program learning based on backpropagation, gradient descent and A* search. We apply the proposed method to two problems: system identification of dynamical systems and explaining the behaviour of a DQN agent. Our results show that the $\pi$-machine can efficiently induce interpretable programs from individual data traces.
Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge. We present a novel algorithm for subtask discovery, based on the recently introduced multitask linearly-solvable Markov decision process (MLMDP) framework. The MLMDP can perform never-before-seen tasks by representing them as a linear combination of a previously learned basis set of tasks. In this setting, the subtask discovery problem can naturally be posed as finding an optimal low-rank approximation of the set of tasks the agent will face in a domain. We use non-negative matrix factorization to discover this minimal basis set of tasks, and show that the technique learns intuitive decompositions in a variety of domains. Our method has several qualitatively desirable features: it is not limited to learning subtasks with single goal states, instead learning distributed patterns of preferred states; it learns qualitatively different hierarchical decompositions in the same domain depending on the ensemble of tasks the agent will face; and it may be straightforwardly iterated to obtain deeper hierarchical decompositions.
As technology become more advanced, those who design, use and are otherwise affected by it want to know that it will perform correctly, and understand why it does what it does, and how to use it appropriately. In essence they want to be able to trust the systems that are being designed. In this survey we present assurances that are the method by which users can understand how to trust this technology. Trust between humans and autonomy is reviewed, and the implications for the design of assurances are highlighted. A survey of research that has been performed with respect to assurances is presented, and several key ideas are extracted in order to refine the definition of assurances. Several directions for future research are identified and discussed.
We explain that the difficulties of training deep neural networks come from a syndrome of three consistency issues. This paper describes our efforts in their analysis and treatment. The first issue is the training speed inconsistency in different layers. We propose to address it with an intuitive, simple-to-implement, low footprint second-order method. The second issue is the scale inconsistency between the layer inputs and the layer residuals. We explain how second-order information provides favorable convenience in removing this roadblock. The third and most challenging issue is the inconsistency in residual propagation. Based on the fundamental theorem of linear algebra, we provide a mathematical characterization of the famous vanishing gradient problem. Thus, an important design principle for future optimization and neural network design is derived. We conclude this paper with the construction of a novel contractive neural network.
Algorithms learned from data are increasingly used for deciding many aspects in our life: from movies we see, to prices we pay, or medicine we get. Yet there is growing evidence that decision making by inappropriately trained algorithms may unintentionally discriminate people. For example, in automated matching of candidate CVs with job descriptions, algorithms may capture and propagate ethnicity related biases. Several repairs for selected algorithms have already been proposed, but the underlying mechanisms how such discrimination happens from the computational perspective are not yet scientifically understood. We need to develop theoretical understanding how algorithms may become discriminatory, and establish fundamental machine learning principles for prevention. We need to analyze machine learning process as a whole to systematically explain the roots of discrimination occurrence, which will allow to devise global machine learning optimization criteria for guaranteed prevention, as opposed to pushing empirical constraints into existing algorithms case-by-case. As a result, the state-of-the-art will advance from heuristic repairing, to proactive and theoretically supported prevention. This is needed not only because law requires to protect vulnerable people. Penetration of big data initiatives will only increase, and computer science needs to provide solid explanations and accountability to the public, before public concerns lead to unnecessarily restrictive regulations against machine learning.
While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance , with results comparable to those previously obtained with a content-based approach.
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.
High-dimensional representations, such as radial basis function networks or tile coding, are common choices for policy evaluation in reinforcement learning. Learning with such high-dimensional representations, however, can be expensive, particularly for matrix methods, such as least-squares temporal difference learning or quasi-Newton methods that approximate matrix step-sizes. In this work, we explore the utility of sketching for these two classes of algorithms. We highlight issues with sketching the high-dimensional features directly, which can incur significant bias. As a remedy, we demonstrate how to use sketching more sparingly, with only a left-sided sketch, that can still enable significant computational gains and the use of these matrix-based learning algorithms that are less sensitive to parameters. We empirically investigate these algorithms, in four domains with a variety of representations. Our aim is to provide insights into effective use of sketching in practice.
We describe the University of Maryland machine translation systems submitted to the WMT17 German-English Bandit Learning Task. The task is to adapt a translation system to a new domain, using only bandit feedback: the system receives a German sentence to translate, produces an English sentence, and only gets a scalar score as feedback. Targeting these two challenges (adaptation and bandit learning), we built a standard neural machine translation system and extended it in two ways: (1) robust reinforcement learning techniques to learn effectively from the bandit feedback, and (2) domain adaptation using data selection from a large corpus of parallel data.
Agent-based modeling and simulation tools provide a mature platform for development of complex simulations. They however, have not been applied much in the domain of mainstream modeling and simulation of computer networks. In this article, we evaluate how and if these tools can offer any value-addition in the modeling & simulation of complex networks such as pervasive computing, large-scale peer-to-peer systems, and networks involving considerable environment and human/animal/habitat interaction. Specifically, we demonstrate the effectiveness of NetLogo - a tool that has been widely used in the area of agent-based social simulation.
In the real world, agents or entities are in a continuous state of interactions. These inter- actions lead to various types of complexity dynamics. One key difficulty in the study of complex agent interactions is the difficulty of modeling agent communication on the basis of rewards. Game theory offers a perspective of analysis and modeling these interactions. Previously, while a large amount of literature is available on game theory, most of it is from specific domains and does not cater for the concepts from an agent- based perspective. Here in this paper, we present a comprehensive multidisciplinary state-of-the-art review and taxonomy of game theory models of complex interactions between agents.
The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data. Inspired by the nature of human perception of 3D shapes as a collection of simple parts, we explore such an abstract shape representation based on primitives. Given a single depth image of an object, we present 3D-PRNN, a generative recurrent neural network that synthesizes multiple plausible shapes composed of a set of primitives. Our generative model encodes symmetry characteristics of common man-made objects, preserves long-range structural coherence, and describes objects of varying complexity with a compact representation. We also propose a method based on Gaussian Fields to generate a large scale dataset of primitive-based shape representations to train our network. We evaluate our approach on a wide range of examples and show that it outperforms nearest-neighbor based shape retrieval methods and is on-par with voxel-based generative models while using a significantly reduced parameter space.
Variational inference is a popular technique to approximate a possibly intractable Bayesian posterior with a more tractable one. Recently, boosting variational inference has been proposed as a new paradigm to approximate the posterior by a mixture of densities by greedily adding components to the mixture. However, as is the case with many other variational inference algorithms, its theoretical properties have not been studied. In the present work, we study the convergence properties of this approach from a modern optimization viewpoint by establishing connections to the classic Frank-Wolfe algorithm. Our analyses yields novel theoretical insights regarding the sufficient conditions for convergence, explicit rates, and algorithmic simplifications. Since a lot of focus in previous works for variational inference has been on tractability, our work is especially important as a much needed attempt to bridge the gap between probabilistic models and their corresponding theoretical properties.
In this paper we present a new dataset and user simulator e-QRAQ (explainable Query, Reason, and Answer Question) which tests an Agent's ability to read an ambiguous text; ask questions until it can answer a challenge question; and explain the reasoning behind its questions and answer. The User simulator provides the Agent with a short, ambiguous story and a challenge question about the story. The story is ambiguous because some of the entities have been replaced by variables. At each turn the Agent may ask for the value of a variable or try to answer the challenge question. In response the User simulator provides a natural language explanation of why the Agent's query or answer was useful in narrowing down the set of possible answers, or not. To demonstrate one potential application of the e-QRAQ dataset, we train a new neural architecture based on End-to-End Memory Networks to successfully generate both predictions and partial explanations of its current understanding of the problem. We observe a strong correlation between the quality of the prediction and explanation.
In this work we introduce declarative statistics, a suite of declarative modelling tools for statistical analysis. Statistical constraints represent the key building block of declarative statistics. First, we introduce a range of relevant counting and matrix constraints and associated decompositions, some of which novel, that are instrumental in the design of statistical constraints. Second, we introduce a selection of novel statistical constraints and associated decompositions, which constitute a self-contained toolbox that can be used to tackle a wide range of problems typically encountered by statisticians. Finally, we deploy these statistical constraints to a wide range of application areas drawn from classical statistics and we contrast our framework against established practices.
We methodologically address the problem of Q-value overestimation in deep reinforcement learning to handle high-dimensional state spaces efficiently. By adapting concepts from information theory, we introduce an intrinsic penalty signal encouraging reduced Q-value estimates. The resultant algorithm encompasses a wide range of learning outcomes containing deep Q-networks as a special case. Different learning outcomes can be demonstrated by tuning a Lagrange multiplier accordingly. We furthermore propose a novel scheduling scheme for this Lagrange multiplier to ensure efficient and robust learning. In experiments on Atari games, our algorithm outperforms other algorithms (e.g. deep and double deep Q-networks) in terms of both game-play performance and sample complexity.
The vanishing gradient problem was a major obstacle for the success of deep learning. In recent years it was gradually alleviated through multiple different techniques. However the problem was not really overcome in a fundamental way, since it is inherent to neural networks with activation functions based on dot products. In a series of papers, we are going to analyze alternative neural network structures which are not based on dot products. In this first paper, we revisit neural networks built up of layers based on distance measures and Gaussian activation functions. These kinds of networks were only sparsely used in the past since they are hard to train when using plain stochastic gradient descent methods. We show that by using Root Mean Square Propagation (RMSProp) it is possible to efficiently learn multi-layer neural networks. Furthermore we show that when appropriately initialized these kinds of neural networks suffer much less from the vanishing and exploding gradient problem than traditional neural networks even for deep networks.
From scientific experiments to online A/B testing, the previously observed data often affects how future experiments are performed, which in turn affects which data will be collected. Such adaptivity introduces complex correlations between the data and the collection procedure. In this paper, we prove that when the data collection procedure satisfies natural conditions, then sample means of the data have systematic \emph{negative} biases. As an example, consider an adaptive clinical trial where additional data points are more likely to be tested for treatments that show initial promise. Our surprising result implies that the average observed treatment effects would underestimate the true effects of each treatment. We quantitatively analyze the magnitude and behavior of this negative bias in a variety of settings. We also propose a novel debiasing algorithm based on selective inference techniques. In experiments, our method can effectively reduce bias and estimation error.
We release a dataset of 65646 StarCraft replays that contains 1535 million frames and 496 million player actions. We provide full game state data along with the original replays that can be viewed in StarCraft. The game state data was recorded every 3 frames which ensures suitability for a wide variety of machine learning tasks such as strategy classification, inverse reinforcement learning, imitation learning, forward modeling, partial information extraction, and others. We use TorchCraft to extract and store the data, which standardizes the data format for both reading from replays and reading directly from the game. Furthermore, the data can be used on different operating systems and platforms. The dataset contains valid, non-corrupted replays only and its quality and diversity was ensured by a number of heuristics. We illustrate the diversity of the data with various statistics and provide examples of tasks that benefit from the dataset. We make the dataset available at https://github.com/TorchCraft/StarData . En Taro Adun!
In this work we focus on the following question: how important was the i-th feature in determining the outcome for a given datapoint? We identify a family of influence measures; functions that, given a datapoint x, assign a value phi_i(x) to every feature i, which roughly corresponds to that i's importance in determining the outcome for x. This family is uniquely derived from a set of axioms: desirable properties that any reasonable influence measure should satisfy. Departing from prior work on influence measures, we assume no knowledge of - or access to - the underlying classifier labelling the dataset. In other words, our influence measures are based on the dataset alone, and do not make any queries to the classifier. While this requirement naturally limits the scope of explanations we provide, we show that it is effective on real datasets.
Questions play a prominent role in social interactions, performing rhetorical functions that go beyond that of simple informational exchange. The surface form of a question can signal the intention and background of the person asking it, as well as the nature of their relation with the interlocutor. While the informational nature of questions has been extensively examined in the context of question-answering applications, their rhetorical aspects have been largely understudied. In this work we introduce an unsupervised methodology for extracting surface motifs that recur in questions, and for grouping them according to their latent rhetorical role. By applying this framework to the setting of question sessions in the UK parliament, we show that the resulting typology encodes key aspects of the political discourse---such as the bifurcation in questioning behavior between government and opposition parties---and reveals new insights into the effects of a legislator's tenure and political career ambitions.
Generative statistical models of chord sequences play crucial roles in music processing. To capture syntactic similarities among certain chords (e.g. in C major key, between G and G7 and between F and Dm), we study hidden Markov models and probabilistic context-free grammar models with latent variables describing syntactic categories of chord symbols and their unsupervised learning techniques for inducing the latent grammar from data. Surprisingly, we find that these models often outperform conventional Markov models in predictive power, and the self-emergent categories often correspond to traditional harmonic functions. This implies the need for chord categories in harmony models from the informatics perspective.
In a robotised warehouse a major issue is the safety of human operators in case of intervention in the work area of the robots. The current solution is to shut down every robot but it causes a loss of productivity, especially for large robotised warehouses. In order to avoid this loss we need to ensure the operator's security during his/her intervention in the warehouse without powering off the robots. The human operator needs to be localised in the warehouse and the trajectories of the robots have to be modified so that they do not interfere with the human. The purpose of this paper is to demonstrate a visual localisation method with visual elements that are already available in the current warehouse setup.
Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training. First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), achieving significant improvements over the baseline, based on both automatic metrics and human evaluation on multiple datasets. Next, we propose a novel entailment-enhanced reward (CIDEnt) that corrects phrase-matching based metrics (such as CIDEr) to only allow for logically-implied partial matches and avoid contradictions, achieving further significant improvements over the CIDEr-reward model. Overall, our CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.
We present a simple sequential sentence encoder for multi-domain natural language inference. Our encoder is based on stacked bidirectional LSTM-RNNs with shortcut connections and fine-tuning of word embeddings. The overall supervised model uses the above encoder to encode two input sentences into two vectors, and then uses a classifier over the vector combination to label the relationship between these two sentences as that of entailment, contradiction, or neural. Our Shortcut-Stacked sentence encoders achieve strong improvements over existing encoders on matched and mismatched multi-domain natural language inference (top non-ensemble single-model result in the EMNLP RepEval 2017 Shared Task (Nangia et al., 2017)). Moreover, they achieve the new state-of-the-art encoding result on the original SNLI dataset (Bowman et al., 2015).
In this paper, we propose a secure multibiometric system that uses deep neural networks and error-correction coding. We present a feature-level fusion framework to generate a secure multibiometric template from each user's multiple biometrics. Two fusion architectures, fully connected architecture and bilinear architecture, are implemented to develop a robust multibiometric shared representation. The shared representation is used to generate a cancelable biometric template that involves the selection of a different set of reliable and discriminative features for each user. This cancelable template is a binary vector and is passed through an appropriate error-correcting decoder to find a closest codeword and this codeword is hashed to generate the final secure template. The efficacy of the proposed approach is shown using a multimodal database where we achieve state-of-the-art matching performance, along with cancelability and security.
Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a variety of domain-specific approaches and applications. However, while cas researchers are inher-ently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains. In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers to adopt a suitable framework level on the basis of available data types, their research study objectives and expected outcomes, thus allowing them to better plan and conduct their respective re-search case studies.
Recently software development companies started to embrace Machine Learning (ML) techniques for introducing a series of advanced functionality in their products such as personalisation of the user experience, improved search, content recommendation and automation. The technical challenges for tackling these problems are heavily researched in literature. A less studied area is a pragmatic approach to the role of humans in a complex modern industrial environment where ML based systems are developed. Key stakeholders affect the system from inception and up to operation and maintenance. Product managers want to embed "smart" experiences for their users and drive the decisions on what should be built next; software engineers are challenged to build or utilise ML software tools that require skills that are well outside of their comfort zone; legal and risk departments may influence design choices and data access; operations teams are requested to maintain ML systems which are non-stationary in their nature and change behaviour over time; and finally ML practitioners should communicate with all these stakeholders to successfully build a reliable system. This paper discusses some of the challenges we faced in Atlassian as we started investing more in the ML space.
Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limited and moreover, the performance of heuristics varies between datasets. To address these shortcomings, we introduce a novel formulation by reframing the active learning as a reinforcement learning problem and explicitly learning a data selection policy, where the policy takes the role of the active learning heuristic. Importantly, our method allows the selection policy learned using simulation on one language to be transferred to other languages. We demonstrate our method using cross-lingual named entity recognition, observing uniform improvements over traditional active learning.
Many stochastic optimization algorithms work by estimating the gradient of the cost function on the fly by sampling datapoints uniformly at random from a training set. However, the estimator might have a large variance, which inadvertently slows down the convergence rate of the algorithms. One way to reduce this variance is to sample the datapoints from a carefully selected non-uniform distribution. In this work, we propose a novel non-uniform sampling approach that uses the multi-armed bandit framework. Theoretically, we show that our algorithm asymptotically approximates the optimal variance within a factor of 3. Empirically, we show that using this datapoint-selection technique results in a significant reduction in the convergence time and variance of several stochastic optimization algorithms such as SGD, SVRG and SAGA. This approach for sampling datapoints is general, and can be used in conjunction with any algorithm that uses an unbiased gradient estimation -- we expect it to have broad applicability beyond the specific examples explored in this work.
This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge. VQA is a task of significant importance for research in artificial intelligence, given its multimodal nature, clear evaluation protocol, and potential real-world applications. The performance of deep neural networks for VQA is very dependent on choices of architectures and hyperparameters. To help further research in the area, we describe in detail our high-performing, though relatively simple model. Through a massive exploration of architectures and hyperparameters representing more than 3,000 GPU-hours, we identified tips and tricks that lead to its success, namely: sigmoid outputs, soft training targets, image features from bottom-up attention, gated tanh activations, output embeddings initialized using GloVe and Google Images, large mini-batches, and smart shuffling of training data. We provide a detailed analysis of their impact on performance to assist others in making an appropriate selection.
Reinforcement learning is a proven technique for an agent to learn a task. However, when learning a task using reinforcement learning, the agent cannot distinguish the characteristics of the environment from those of the task. This makes it harder to transfer skills between tasks in the same environment. Furthermore, this does not reduce risk when training for a new task. In this paper, we introduce an approach to decouple the environment characteristics from the task-specific ones, allowing an agent to develop a sense of survival. We evaluate our approach in an environment where an agent must learn a sequence of collection tasks, and show that decoupled learning allows for a safer utilization of prior knowledge.
We discuss memory models which are based on tensor decompositions using latent representations of entities and events. We show how episodic memory and semantic memory can be realized and discuss how new memory traces can be generated from sensory input: Existing memories are the basis for perception and new memories are generated via perception. We relate our mathematical approach to the hippocampal memory indexing theory. We describe the first detailed mathematical models for the complete processing pipeline from sensory input and its semantic decoding, i.e., perception, to the formation of episodic and semantic memories and their declarative semantic decodings. Our main hypothesis is that perception includes an active semantic decoding process, which relies on latent representations of entities and predicates, and that episodic and semantic memories depend on the same decoding process. We contribute to the debate between the leading memory consolidation theories, i.e., the standard consolidation theory (SCT) and the multiple trace theory (MTT). The latter is closely related to the complementary learning systems (CLS) framework. In particular, we show explicitly how episodic memory can teach the neocortex to form a semantic memory, which is a core issue in MTT and CLS.
Data Mining is best-known for its analytical and prediction capabilities. It is used in several areas such as fraud detection, predicting client behavior, money market behavior, bankruptcy prediction. It can also help in establishing an educational ecosystem, which discovers useful knowledge, and assist educators to take proactive decisions to boost student performance and employability. This paper presents an empirical study that compares varied classification algorithms on two datasets of MCA (Masters in Computer Applications) students collected from various affiliated colleges of a reputed state university in India. One dataset includes only primary attributes, whereas other dataset is feeded with secondary psychometric attributes in it. The results showcase that solely primary academic attributes do not lead to smart prediction accuracy of students employability, once they square measure within the initial year of their education. The study analyzes and stresses the role of secondary psychometric attributes for better prediction accuracy and analysis of students performance. Timely prediction and analysis of students performance can help Management, Teachers and Students to work on their gray areas for better results and employment opportunities.
Hierarchically structured agent plans are important for efficient planning and acting, and they also serve (among other things) to produce "richer" classical plans, composed not just of a sequence of primitive actions, but also "abstract" ones representing the supplied hierarchies. A crucial step for this and other approaches is deriving precondition and effect "summaries" from a given plan hierarchy. This paper provides mechanisms to do this for more pragmatic and conventional hierarchies than in the past. To this end, we formally define the notion of a precondition and an effect for a hierarchical plan; we present data structures and algorithms for automatically deriving this information; and we analyse the properties of the presented algorithms. We conclude the paper by detailing how our algorithms may be used together with a classical planner in order to obtain abstract plans.
Facing an unknown situation, a person may not be able to firmly elicit his/her preferences over different alternatives, so he/she tends to express uncertain preferences. Given a community of different persons expressing their preferences over certain alternatives under uncertainty, to get a collective representative opinion of the whole community, a preference fusion process is required. The aim of this work is to propose a preference fusion method that copes with uncertainty and escape from the Condorcet paradox. To model preferences under uncertainty, we propose to develop a model of preferences based on belief function theory that accurately describes and captures the uncertainty associated with individual or collective preferences. This work improves and extends the previous results. This work improves and extends the contribution presented in a previous work. The benefits of our contribution are twofold. On the one hand, we propose a qualitative and expressive preference modeling strategy based on belief-function theory which scales better with the number of sources. On the other hand, we propose an incremental distance-based algorithm (using Jousselme distance) for the construction of the collective preference order to avoid the Condorcet Paradox.
Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but are severely constrained when evaluating complex dependency relations and other logic-based operations that are a strength of knowledge graphs. We describe the VKG structure that helps unify knowledge graphs and vector representation of entities, and enables powerful inference methods and search capabilities that combine their complementary strengths. We analogize this to thinking `fast' in vector space along with thinking 'slow' and `deeply' by reasoning over the knowledge graph. We have created a query processing engine that takes complex queries and decomposes them into subqueries optimized to run on the respective knowledge graph or vector view of a VKG. We show that the VKG structure can process specific queries that are not efficiently handled by vector spaces or knowledge graphs alone. We also demonstrate and evaluate the VKG structure and the query processing engine by developing a system called Cyber-All-Intel for knowledge extraction, representation and querying in an end-to-end pipeline grounded in the cybersecurity informatics domain.
There exist two main approaches to automatically extract affective orientation: lexicon-based and corpus-based. In this work, we argue that these two methods are compatible and show that combining them can improve the accuracy of emotion classifiers. In particular, we introduce a novel variant of the Label Propagation algorithm that is tailored to distributed word representations, we apply batch gradient descent to accelerate the optimization of label propagation and to make the optimization feasible for large graphs, and we propose a reproducible method for emotion lexicon expansion. We conclude that label propagation can expand an emotion lexicon in a meaningful way and that the expanded emotion lexicon can be leveraged to improve the accuracy of an emotion classifier.
We describe and evaluate a novel optimization-based off-line path planning algorithm for mobile robots based on the Counterexample-Guided Inductive Optimization (CEGIO) technique. CEGIO iteratively employs counterexamples generated from Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solvers, in order to guide the optimization process and to ensure global optimization. This paper marks the first application of CEGIO for planning mobile robot path. In particular, CEGIO has been successfully applied to obtain optimal two-dimensional paths for autonomous mobile robots using off-the-shelf SAT and SMT solvers.
Robotic manipulation in complex open-world scenarios requires both reliable physical manipulation skills and effective and generalizable perception. In this paper, we propose a method where general purpose pretrained visual models serve as an object-centric prior for the perception system of a learned policy. We devise an object-level attentional mechanism that can be used to determine relevant objects from a few trajectories or demonstrations, and then immediately incorporate those objects into a learned policy. A task-independent meta-attention locates possible objects in the scene, and a task-specific attention identifies which objects are predictive of the trajectories. The scope of the task-specific attention is easily adjusted by showing demonstrations with distractor objects or with diverse relevant objects. Our results indicate that this approach exhibits good generalization across object instances using very few samples, and can be used to learn a variety of manipulation tasks using reinforcement learning.
We study motion planning problems where agents move inside environments that are not fully observable and subject to uncertainties. The goal is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications. Such problems are naturally modelled by partially observable Markov decision processes (POMDPs). Because of the potentially huge or even infinite belief space of POMDPs, verification and strategy synthesis is in general computationally intractable. We tackle this difficulty by exploiting typical structural properties of such scenarios; for instance, we assume that agents have the ability to observe their own positions inside an environment. Ambiguity in the state of the environment is abstracted into non-deterministic choices over the possible states of the environment. Technically, this abstraction transforms POMDPs into probabilistic two-player games (PGs). For these PGs, efficient verification tools are able to determine strategies that approximate certain measures on the POMDP. If an approximation is too coarse to provide guarantees, an abstraction refinement scheme further resolves the belief space of the POMDP. We demonstrate that our method improves the state of the art by orders of magnitude compared to a direct solution of the POMDP.
As demand drives systems to generalize to various domains and problems, the study of multitask, transfer and lifelong learning has become an increasingly important pursuit. In discrete domains, performance on the Atari game suite has emerged as the de facto benchmark for assessing multitask learning. However, in continuous domains there is a lack of agreement on standard multitask evaluation environments which makes it difficult to compare different approaches fairly. In this work, we describe a benchmark set of tasks that we have developed in an extendable framework based on OpenAI Gym. We run a simple baseline using Trust Region Policy Optimization and release the framework publicly to be expanded and used for the systematic comparison of multitask, transfer, and lifelong learning in continuous domains.
Learning representation for graph classification turns a variable-size graph into a fixed-size vector (or matrix). Such a representation works nicely with algebraic manipulations. Here we introduce a simple method to augment an attributed graph with a virtual node that is bidirectionally connected to all existing nodes. The virtual node represents the latent aspects of the graph, which are not immediately available from the attributes and local connectivity structures. The expanded graph is then put through any node representation method. The representation of the virtual node is then the representation of the entire graph. In this paper, we use the recently introduced Column Network for the expanded graph, resulting in a new end-to-end graph classification model dubbed Virtual Column Network (VCN). The model is validated on two tasks: (i) predicting bio-activity of chemical compounds, and (ii) finding software vulnerability from source code. Results demonstrate that VCN is competitive against well-established rivals.
In this paper, we consider a novel machine learning problem, that is, learning a classifier from noisy label distributions. In this problem, each instance with a feature vector belongs to at least one group. Then, instead of the true label of each instance, we observe the label distribution of the instances associated with a group, where the label distribution is distorted by an unknown noise. Our goals are to (1) estimate the true label of each instance, and (2) learn a classifier that predicts the true label of a new instance. We propose a probabilistic model that considers true label distributions of groups and parameters that represent the noise as hidden variables. The model can be learned based on a variational Bayesian method. In numerical experiments, we show that the proposed model outperforms existing methods in terms of the estimation of the true labels of instances.
Debate summarization is one of the novel and challenging research areas in automatic text summarization which has been largely unexplored. In this paper, we develop a debate summarization pipeline to summarize key topics which are discussed or argued in the two opposing sides of online debates. We view that the generation of debate summaries can be achieved by clustering, cluster labeling, and visualization. In our work, we investigate two different clustering approaches for the generation of the summaries. In the first approach, we generate the summaries by applying purely term-based clustering and cluster labeling. The second approach makes use of X-means for clustering and Mutual Information for labeling the clusters. Both approaches are driven by ontologies. We visualize the results using bar charts. We think that our results are a smooth entry for users aiming to receive the first impression about what is discussed within a debate topic containing waste number of argumentations.
Stochastic gradient descent (SGD) is a popular stochastic optimization method in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel SGD, often require all nodes to have the same performance or to consume equal quantities of data. However, these requirements are difficult to satisfy when the parallel SGD algorithms run in a heterogeneous computing environment; low-performance nodes will exert a negative influence on the final result. In this paper, we propose an algorithm called weighted parallel SGD (WP-SGD). WP-SGD combines weighted model parameters from different nodes in the system to produce the final output. WP-SGD makes use of the reduction in standard deviation to compensate for the loss from the inconsistency in performance of nodes in the cluster, which means that WP-SGD does not require that all nodes consume equal quantities of data. We also analyze the theoretical feasibility of running two other parallel SGD algorithms combined with WP-SGD in a heterogeneous environment. The experimental results show that WP-SGD significantly outperforms the traditional parallel SGD algorithms on distributed training systems with an unbalanced workload.
This paper continues the research that considers a new cognitive model based strongly on the human brain. In particular, it considers the neural binding structure of an earlier paper. It also describes some new methods in the areas of image processing and behaviour simulation. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with 'full linking'. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared to. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.
Sum-product networks (SPNs) are a class of probabilistic graphical models that allow tractable marginal inference. However, the maximum a posteriori (MAP) inference in SPNs is NP-hard. We investigate MAP inference in SPNs from both theoretical and algorithmic perspectives. For the theoretical part, we reduce general MAP inference to its special case without evidence and hidden variables; we also show that it is NP-hard to approximate the MAP problem to $2^{n^\epsilon}$ for fixed $0 \leq \epsilon < 1$, where $n$ is the input size. For the algorithmic part, we first present an exact MAP solver that runs reasonably fast and could handle SPNs with up to 1k variables and 150k arcs in our experiments. We then present a new approximate MAP solver with a good balance between speed and accuracy, and our comprehensive experiments on real-world datasets show that it has better overall performance than existing approximate solvers.
Cross-modal data retrieval has been the basis of various creative tasks performed by Artificial Intelligence (AI). One such highly challenging task for AI is to convert a book into its corresponding movie, which most of the creative film makers do as of today. In this research, we take the first step towards it by visualizing the content of a book using its corresponding movie visuals. Given a set of sentences from a book or even a fan-fiction written in the same universe, we employ deep learning models to visualize the input by stitching together relevant frames from the movie. We studied and compared three different types of setting to match the book with the movie content: (i) Dialog model: using only the dialog from the movie, (ii) Visual model: using only the visual content from the movie, and (iii) Hybrid model: using the dialog and the visual content from the movie. Experiments on the publicly available MovieBook dataset shows the effectiveness of the proposed models.
T-distributed stochastic neighbor embedding (tSNE) is a popular and prize-winning approach for dimensionality reduction and visualizing high-dimensional data. However, tSNE is non-parametric: once visualization is built, tSNE is not designed to incorporate additional data into existing representation. It highly limits the applicability of tSNE to the scenarios where data are added or updated over time (like dashboards or series of data snapshots). In this paper we propose, analyze and evaluate LION-tSNE (Local Interpolation with Outlier coNtrol) - a novel approach for incorporating new data into tSNE representation. LION-tSNE is based on local interpolation in the vicinity of training data, outlier detection and a special outlier mapping algorithm. We show that LION-tSNE method is robust both to outliers and to new samples from existing clusters. We also discuss multiple possible improvements for special cases. We compare LION-tSNE to a comprehensive list of possible benchmark approaches that include multiple interpolation techniques, gradient descent for new data, and neural network approximation.
Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment, a significant number of attributes remain largely unexplored. In this paper, we propose a joint attribute-preserving embedding model for cross-lingual entity alignment. It jointly embeds the structures of two KBs into a unified vector space and further refines it by leveraging attribute correlations in the KBs. Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.
Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of points away from the training data. The computation of the SVDD classifier requires a kernel function, and the Gaussian kernel is a common choice for the kernel function. The Gaussian kernel has a bandwidth parameter, whose value is important for good results. A small bandwidth leads to overfitting, and the resulting SVDD classifier overestimates the number of anomalies. A large bandwidth leads to underfitting, and the classifier fails to detect many anomalies. In this paper we present a new automatic, unsupervised method for selecting the Gaussian kernel bandwidth. The selected value can be computed quickly, and it is competitive with existing bandwidth selection methods.
The cognitive framework of conceptual spaces [3] provides geometric means for representing knowledge. A conceptual space is a high-dimensional space whose dimensions are partitioned into so-called domains. Within each domain, the Euclidean metric is used to compute distances. Distances in the overall space are computed by applying the Manhattan metric to the intra-domain distances. Instances are represented as points in this space and concepts are represented by regions. In this paper, we derive a formula for the size of a hyperball under the combined metric of a conceptual space. One can think of such a hyperball as the set of all points having a certain minimal similarity to the hyperball's center.
Knowledge is useless without structure. While the classification of knowledge has been an enduring philosophical enterprise, it recently found applications in computer science, notably for artificial intelligence. The availability of large databases allowed for complex ontologies to be built automatically, for example by extracting structured content from Wikipedia. However, this approach is subject to manual categorization decisions made by online editors. Here we show that an implicit classification hierarchy emerges spontaneously on Wikipedia. We study the network of first links between articles, and find that it centers on a core cycle involving concepts of fundamental classifying importance. We argue that this structure is rooted in cultural history. For European languages, articles like Philosophy and Science are central, whereas Human and Earth dominate for East Asian languages. This reflects the differences between ancient Greek thought and Chinese tradition. Our results reveal the powerful influence of culture on the intrinsic architecture of complex data sets.
Among the local consistency techniques used for solving constraint networks, path-consistency (PC) has received a great deal of attention. However, enforcing PC is computationally expensive and sometimes even unnecessary. Directional path-consistency (DPC) is a weaker notion of PC that considers a given variable ordering and can thus be enforced more efficiently than PC. This paper shows that DPC (the DPC enforcing algorithm of Dechter and Pearl) decides the constraint satisfaction problem (CSP) of a constraint language if it is complete and has the variable elimination property (VEP). However, we also show that no complete VEP constraint language can have a domain with more than 2 values. We then present a simple variant of the DPC algorithm, called DPC*, and show that the CSP of a constraint language can be decided by DPC* if it is closed under a majority operation. In fact, DPC* is sufficient for guaranteeing backtrack-free search for such constraint networks. Examples of majority-closed constraint classes include the classes of connected row-convex (CRC) constraints and tree-preserving constraints, which have found applications in various domains, such as scene labeling, temporal reasoning, geometric reasoning, and logical filtering. Our experimental evaluations show that DPC* significantly outperforms the state-of-the-art algorithms for solving majority-closed constraints.
We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information. The agent is based on an asynchronous stochastic variant of DQN (Deep Q Network) named DASQN. The inputs of the agent are plain-text descriptions of states of a game of incomplete information, i.e. real-time large scale online auctions, and the rewards are auction profits of very large scale. We apply the agent to an essential portion of JD's online RTB (real-time bidding) advertising business and find that it easily beats the former state-of-the-art bidding policy that had been carefully engineered and calibrated by human experts: during JD.com's June 18th anniversary sale, the agent increased the company's ads revenue from the portion by more than 50%, while the advertisers' ROI (return on investment) also improved significantly.
Long Short-Term Memory (LSTM) is the primary recurrent neural networks architecture for acoustic modeling in automatic speech recognition systems. Residual learning is an efficient method to help neural networks converge easier and faster. In this paper, we propose several types of residual LSTM methods for our acoustic modeling. Our experiments indicate that, compared with classic LSTM, our architecture shows more than 8% relative reduction in Phone Error Rate (PER) on TIMIT tasks. At the same time, our residual fast LSTM approach shows 4% relative reduction in PER on the same task. Besides, we find that all this architecture could have good results on THCHS-30, Librispeech and Switchboard corpora.
One of the most crucial issues in data mining is to model human behaviour in order to provide personalisation, adaptation and recommendation. This usually involves implicit or explicit knowledge, either by observing user interactions, or by asking users directly. But these sources of information are always subject to the volatility of human decisions, making utilised data uncertain to a particular extent. In this contribution, we elaborate on the impact of this human uncertainty when it comes to comparative assessments of different data mining approaches. In particular, we reveal two problems: (1) biasing effects on various metrics of model-based prediction and (2) the propagation of uncertainty and its thus induced error probabilities for algorithm rankings. For this purpose, we introduce a probabilistic view and prove the existence of those problems mathematically, as well as provide possible solution strategies. We exemplify our theory mainly in the context of recommender systems along with the metric RMSE as a prominent example of precision quality measures.
This article constructs a Turing Machine which can solve for $\beta^{'}$ which is RE-complete. Such a machine is only possible if there is something wrong with the foundations of computer science and mathematics. We therefore check our work by looking very closely at Cantor's diagonalization and construct a novel formal language as an Abelian group which allows us, through equivalence relations, to provide a non-trivial counterexample to Cantor's argument. As if that wasn't enough, we then discover that the impredicative nature of G\"odel's diagonalization lemma leads to logical tautology, invalidating any meaning behind the method, leaving no doubt that diagonalization is flawed. Our discovery in regards to these foundational arguments opens the door to solving the P vs NP problem.
Data analytics helps basketball teams to create tactics. However, manual data collection and analytics are costly and ineffective. Therefore, we applied a deep bidirectional long short-term memory (BLSTM) and mixture density network (MDN) approach. This model is not only capable of predicting a basketball trajectory based on real data, but it also can generate new trajectory samples. It is an excellent application to help coaches and players decide when and where to shoot. Its structure is particularly suitable for dealing with time series problems. BLSTM receives forward and backward information at the same time, while stacking multiple BLSTMs further increases the learning ability of the model. Combined with BLSTMs, MDN is used to generate a multi-modal distribution of outputs. Thus, the proposed model can, in principle, represent arbitrary conditional probability distributions of output variables. We tested our model with two experiments on three-pointer datasets from NBA SportVu data. In the hit-or-miss classification experiment, the proposed model outperformed other models in terms of the convergence speed and accuracy. In the trajectory generation experiment, eight model-generated trajectories at a given time closely matched real trajectories.
Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep $Q$-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.
In this paper we present a novel Formal Agent-Based Simulation framework (FABS). FABS uses formal specification as a means of clear description of wireless sensor networks (WSN) sensing a Complex Adaptive Environment. This specification model is then used to develop an agent-based model of both the wireless sensor network as well as the environment. As proof of concept, we demonstrate the application of FABS to a boids model of self-organized flocking of animals monitored by a random deployment of proximity sensors.
In this paper, a new type of 3D bin packing problem (BPP) is proposed, in which a number of cuboid-shaped items must be put into a bin one by one orthogonally. The objective is to find a way to place these items that can minimize the surface area of the bin. This problem is based on the fact that there is no fixed-sized bin in many real business scenarios and the cost of a bin is proportional to its surface area. Our research shows that this problem is NP-hard. Based on previous research on 3D BPP, the surface area is determined by the sequence, spatial locations and orientations of items. Among these factors, the sequence of items plays a key role in minimizing the surface area. Inspired by recent achievements of deep reinforcement learning (DRL) techniques, especially Pointer Network, on combinatorial optimization problems such as TSP, a DRL-based method is applied to optimize the sequence of items to be packed into the bin. Numerical results show that the method proposed in this paper achieve about 5% improvement than heuristic method.
The methodology of Software-Defined Robotics hierarchical-based and stand-alone framework can be designed and implemented to program and control different sets of robots, regardless of their manufacturers' parameters and specifications, with unified commands and communications. This framework approach will increase the capability of (re)programming a specific group of robots during the runtime without affecting the others as desired in the critical missions and industrial operations, expand the shared bandwidth, enhance the reusability of code, leverage the computational processing power, decrease the unnecessary analyses of vast supplemental electrical components for each robot, as well as get advantages of the most state-of-the-art industrial trends in the cloud-based computing, Virtual Machines (VM), and Robot-as-a-Service (RaaS) technologies.
Handwritten character recognition has been the center of research and a benchmark problem in the sector of pattern recognition and artificial intelligence, and it continues to be a challenging research topic. Due to its enormous application many works have been done in this field focusing on different languages. Arabic, being a diversified language has a huge scope of research with potential challenges. A convolutional neural network model for recognizing handwritten numerals in Arabic language is proposed in this paper, where the dataset is subject to various augmentation in order to add robustness needed for deep learning approach. The proposed method is empowered by the presence of dropout regularization to do away with the problem of data overfitting. Moreover, suitable change is introduced in activation function to overcome the problem of vanishing gradient. With these modifications, the proposed system achieves an accuracy of 99.4\% which performs better than every previous work on the dataset.
Training large vocabulary Neural Network Language Models (NNLMs) is a difficult task due to the explicit requirement of the output layer normalization, which typically involves the evaluation of the full softmax function over the complete vocabulary. This paper proposes a Batch Noise Contrastive Estimation (B-NCE) approach to alleviate this problem. This is achieved by reducing the vocabulary, at each time step, to the target words in the batch and then replacing the softmax by the noise contrastive estimation approach, where these words play the role of targets and noise samples at the same time. In doing so, the proposed approach can be fully formulated and implemented using optimal dense matrix operations. Applying B-NCE to train different NNLMs on the Large Text Compression Benchmark (LTCB) and the One Billion Word Benchmark (OBWB) shows a significant reduction of the training time with no noticeable degradation of the models performance. This paper also presents a new baseline comparative study of different standard NNLMs on the large OBWB on a single Titan-X GPU.
The increasing availability of affect-rich multimedia resources has bolstered interest in understanding sentiment and emotions in and from visual content. Adjective-noun pairs (ANP) are a popular mid-level semantic construct for capturing affect via visually detectable concepts such as "cute dog" or "beautiful landscape". Current state-of-the-art methods approach ANP prediction by considering each of these compound concepts as individual tokens, ignoring the underlying relationships in ANPs. This work aims at disentangling the contributions of the `adjectives' and `nouns' in the visual prediction of ANPs. Two specialised classifiers, one trained for detecting adjectives and another for nouns, are fused to predict 553 different ANPs. The resulting ANP prediction model is more interpretable as it allows us to study contributions of the adjective and noun components. Source code and models are available at https://imatge-upc.github.io/affective-2017-musa2/ .
Efficient Monte Carlo inference often requires manual construction of model-specific proposals. We propose an approach to automated proposal construction by training neural networks to provide fast approximations to block Gibbs conditionals. The learned proposals generalize to occurrences of common structural motifs both within a given model and across models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required. We explore several applications including open-universe Gaussian mixture models, in which our learned proposals outperform a hand-tuned sampler, and a real-world named entity recognition task, in which our sampler's ability to escape local modes yields higher final F1 scores than single-site Gibbs.
In this era of digitization, knowing the user's sociolect aspects have become essential features to build the user specific recommendation systems. These sociolect aspects could be found by mining the user's language sharing in the form of text in social media and reviews. This paper describes about the experiment that was performed in PAN Author Profiling 2017 shared task. The objective of the task is to find the sociolect aspects of the users from their tweets. The sociolect aspects considered in this experiment are user's gender and native language information. Here user's tweets written in a different language from their native language are represented as Document - Term Matrix with document frequency as the constraint. Further classification is done using the Support Vector Machine by taking gender and native language as target classes. This experiment attains the average accuracy of 73.42% in gender prediction and 76.26% in the native language identification task.
In this paper we present a comprehensive view of prominent causal discovery algorithms, categorized into two main categories (1) assuming acyclic and no latent variables, and (2) allowing both cycles and latent variables, along with experimental results comparing them from three perspectives: (a) structural accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual inference. For (b) and (c) we train causal Bayesian networks with structures as predicted by each causal discovery technique to carry out counterfactual or standard predictive inference. We compare causal algorithms on two pub- licly available and one simulated datasets having different sample sizes: small, medium and large. Experiments show that structural accuracy of a technique does not necessarily correlate with higher accuracy of inferencing tasks. Fur- ther, surveyed structure learning algorithms do not perform well in terms of structural accuracy in case of datasets having large number of variables.
Word embeddings have been found to capture a surprisingly rich amount of syntactic and semantic knowledge. However, it is not yet sufficiently well-understood how the relational knowledge that is implicitly encoded in word embeddings can be extracted in a reliable way. In this paper, we propose two probabilistic models to address this issue. The first model is based on the common relations-as-translations view, but is cast in a probabilistic setting. Our second model is based on the much weaker assumption that there is a linear relationship between the vector representations of related words. Compared to existing approaches, our models lead to more accurate predictions, and they are more explicit about what can and cannot be extracted from the word embedding.
The nursing literature shows that cultural competence is an important requirement for effective healthcare. We claim that personal assistive robots should likewise be culturally competent, that is, they should be aware of general cultural characteristics and of the different forms they take in different individuals, and take these into account while perceiving, reasoning, and acting. The CARESSES project is an Europe-Japan collaborative effort that aims at designing, developing and evaluating culturally competent assistive robots. These robots will be able to adapt the way they behave, speak and interact to the cultural identity of the person they assist. This paper describes the approach taken in the CARESSES project, its initial steps, and its future plans.
Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection to evaluate network representations from data, focusing on fundamental predictive tasks on networks. We present a modular methodology using general, interpretable network models, task neighborhood functions found across domains, and several criteria for robust model selection. We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude in our experiments.
In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of safety assurance --- what are the minimal requirements that every self-driving car must satisfy, and how can we verify these requirements. The second parameter is scalability --- engineering solutions that lead to unleashed costs will not scale to millions of cars, which will push interest in this field into a niche academic corner, and drive the entire field into a "winter of autonomous driving". In the first part of the paper we propose a white-box, interpretable, mathematical model for safety assurance, which we call Responsibility-Sensitive Safety (RSS). In the second part we describe a design of a system that adheres to our safety assurance requirements and is scalable to millions of cars.
Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability. While hierarchy and partial observability are usually tackled separately (for instance by combining recurrent neural networks and options), we show that addressing both problems simultaneously is simpler and more efficient in many cases. More specifically, we make the initiation set of options conditional on the previously-executed option, and show that options with such Option-Observation Initiation Sets (OOIs) are at least as expressive as Finite State Controllers (FSCs), a state-of-the-art approach for learning in POMDPs. OOIs are easy to design based on an intuitive description of the task, lead to explainable policies and keep the top-level and option policies memoryless. Our experiments show that OOIs allow agents to learn optimal policies in challenging POMDPs, while being much more sample-efficient than a recurrent neural network over options.
Human action recognition involves the characterization of human actions through the automated analysis of video data and is integral in the development of smart computer vision systems. However, several challenges like dynamic backgrounds, camera stabilization, complex actions, occlusions etc. make action recognition in a real time and robust fashion difficult. Several complex approaches exist but are computationally intensive. This paper presents a novel approach of using a combination of good features along with iterative optical flow algorithm to compute feature vectors which are classified using a multilayer perceptron (MLP) network. The use of multiple features for motion descriptors enhances the quality of tracking. Resilient backpropagation algorithm is used for training the feedforward neural network reducing the learning time. The overall system accuracy is improved by optimizing the various parameters of the multilayer perceptron network.
Knowledge graphs are large, useful, but incomplete knowledge repositories. They encode knowledge through entities and relations which define each other through the connective structure of the graph. This has inspired methods for the joint embedding of entities and relations in continuous low-dimensional vector spaces, that can be used to induce new edges in the graph, i.e., link prediction in knowledge graphs. Learning these representations relies on contrasting positive instances with negative ones. Knowledge graphs include only positive relation instances, leaving the door open for a variety of methods for selecting negative examples. In this paper we present an empirical study on the impact of negative sampling on the learned embeddings, assessed through the task of link prediction. We use state-of-the-art knowledge graph embeddings -- \rescal , TransE, DistMult and ComplEX -- and evaluate on benchmark datasets -- FB15k and WN18. We compare well known methods for negative sampling and additionally propose embedding based sampling methods. We note a marked difference in the impact of these sampling methods on the two datasets, with the "traditional" corrupting positives method leading to best results on WN18, while embedding based methods benefiting the task on FB15k.
The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from previous studies, our approach embraces a double-annotated dataset and strays away from obscure "black-box" models to comprehensive deep learning models. In this paper, we present a novel neural attention mechanism that not only classifies clinically important findings. Specifically, convolutional neural networks (CNN) with attention analysis are used to classify radiology head computed tomography reports based on five categories that radiologists would account for in assessing acute and communicable findings in daily practice. The experiments show that our CNN attention models outperform non-neural models, especially when trained on a larger dataset. Our attention analysis demonstrates the intuition behind the classifier's decision by generating a heatmap that highlights attended terms used by the CNN model; this is valuable when potential downstream medical decisions are to be performed by human experts or the classifier information is to be used in cohort construction such as for epidemiological studies.
Anytime predictors first produce crude results quickly, and then continuously refine them until the test-time computational budget is depleted. Such predictors are used in real-time vision systems and streaming-data processing to efficiently utilize varying test-time budgets, and to reduce average prediction cost via early-exits. However, anytime prediction algorithms have difficulties utilizing the accurate predictions of deep neural networks (DNNs), because DNNs are often computationally expensive without competitive intermediate results. In this work, we propose to add auxiliary predictions in DNNs to generate anytime predictions, and optimize these predictions simultaneously by minimizing a carefully constructed weighted sum of losses, where the weights also oscillate during training. The proposed anytime neural networks (ANNs) produce reasonable anytime predictions without sacrificing the final performance or incurring noticeable extra computation. This enables us to assemble a sequence of exponentially deepening ANNs, and it achieves, both theoretically and practically, near-optimal anytime predictions at every budget after spending a constant fraction of extra cost. The proposed methods are shown to produce anytime predictions at the state-of-the-art level on visual recognition data-sets, including ILSVRC2012.
Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models. Source code is publicly available at https://imatge-upc.github.io/skiprnn-2017-telecombcn/ .
The past decade has seen a significant interest in learning tractable probabilistic representations. Arithmetic circuits (ACs) were among the first proposed tractable representations, with some subsequent representations being instances of ACs with weaker or stronger properties. In this paper, we provide a formal basis under which variants on ACs can be compared, and where the precise roles and semantics of their various properties can be made more transparent. This allows us to place some recent developments on ACs in a clearer perspective and to also derive new results for ACs. This includes an exponential separation between ACs with and without determinism; completeness and incompleteness results; and tractability results (or lack thereof) when computing most probable explanations (MPEs).
The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by the computational complexity of extended dynamic mode decomposition; this requires a combinatorially large basis set to adequately describe many nonlinear systems of interest, e.g. cyber-physical infrastructure systems, biological networks, social systems, and fluid dynamics. Often the dictionaries generated for these problems are manually curated, requiring domain-specific knowledge and painstaking tuning. In this paper we introduce a deep learning framework for learning Koopman operators of nonlinear dynamical systems. We show that this novel method automatically selects efficient deep dictionaries, outperforming state-of-the-art methods. We benchmark this method on partially observed nonlinear systems, including the glycolytic oscillator and show it is able to predict quantitatively 100 steps into the future, using only a single timepoint, and qualitative oscillatory behavior 400 steps into the future.
During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend the convolution technique to non-linear forms. Typical convolutional layers are linear systems, hence their expressiveness is limited. To overcome this, various non-linearities have been used as activation functions inside CNNs, while also many pooling strategies have been applied. We address the issue of developing a convolution method in the context of a computational model of the visual cortex, exploring quadratic forms through the Volterra kernels. Such forms, constituting a more rich function space, are used as approximations of the response profile of visual cells. Our proposed second-order convolution is tested on CIFAR-10 and CIFAR-100. We show that a network which combines linear and non-linear filters in its convolutional layers, can outperform networks that use standard linear filters with the same architecture, yielding results competitive with the state-of-the-art on these datasets.
The goal of continuous emotion recognition is to assign an emotion value to every frame in a sequence of acoustic features. We show that incorporating long-term temporal dependencies is critical for continuous emotion recognition tasks. To this end, we first investigate architectures that use dilated convolutions. We show that even though such architectures outperform previously reported systems, the output signals produced from such architectures undergo erratic changes between consecutive time steps. This is inconsistent with the slow moving ground-truth emotion labels that are obtained from human annotators. To deal with this problem, we model a downsampled version of the input signal and then generate the output signal through upsampling. Not only does the resulting downsampling/upsampling network achieve good performance, it also generates smooth output trajectories. Our method yields the best known audio-only performance on the RECOLA dataset.
Image relighting is to change the illumination of an image to a target illumination effect without known the original scene geometry, material information and illumination condition. We propose a novel outdoor scene relighting method, which needs only a single reference image and is based on material constrained layer decomposition. Firstly, the material map is extracted from the input image. Then, the reference image is warped to the input image through patch match based image warping. Lastly, the input image is relit using material constrained layer decomposition. The experimental results reveal that our method can produce similar illumination effect as that of the reference image on the input image using only a single reference image.
In this article, we present a survey of recent advances in passive human behaviour recognition in indoor areas using the channel state information (CSI) of commercial WiFi systems. Movement of human body causes a change in the wireless signal reflections, which results in variations in the CSI. By analyzing the data streams of CSIs for different activities and comparing them against stored models, human behaviour can be recognized. This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers. The techniques from the literature that are presented herein have great performances, however, instead of the machine learning techniques employed in these works, we propose to use deep learning techniques such as long-short term memory (LSTM) recurrent neural network (RNN), and show the improved performance. We also discuss about different challenges such as environment change, frame rate selection, and multi-user scenario, and suggest possible directions for future work.
Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.
An exhaustive study on neural network language modeling (NNLM) is performed in this paper. Different architectures of basic neural network language models are described and examined. A number of different improvements over basic neural network language models, including importance sampling, word classes, caching and bidirectional recurrent neural network (BiRNN), are studied separately, and the advantages and disadvantages of every technique are evaluated. Then, the limits of neural network language modeling are explored from the aspects of model architecture and knowledge representation. Part of the statistical information from a word sequence will loss when it is processed word by word in a certain order, and the mechanism of training neural network by updating weight matrixes and vectors imposes severe restrictions on any significant enhancement of NNLM. For knowledge representation, the knowledge represented by neural network language models is the approximate probabilistic distribution of word sequences from a certain training data set rather than the knowledge of a language itself or the information conveyed by word sequences in a natural language. Finally, some directions for improving neural network language modeling further is discussed.
We consider the problem of learning for planning, where knowledge acquired while planning is reused to plan faster in new problem instances. For robotic tasks, among others, plan execution can be captured as a sequence of visual images. For such domains, we propose to use deep neural networks in learning for planning, based on learning a reactive policy that imitates execution traces produced by a planner. We investigate architectural properties of deep networks that are suitable for learning long-horizon planning behavior, and explore how to learn, in addition to the policy, a heuristic function that can be used with classical planners or search algorithms such as A*. Our results on the challenging Sokoban domain show that, with a suitable network design, complex decision making policies and powerful heuristic functions can be learned through imitation.
Proportional representation (PR) is often discussed in voting settings as a major desideratum. For the past century or so, it is common both in practice and in the academic literature to jump to STV (Single Transferable Vote) as the solution for achieving PR. Some of the most prominent electoral reform movements around the globe are pushing for the adoption of STV. It has been termed a major open problem to design a voting rule that satisfies the same PR properties as STV and better monotonicity properties. We present a rule called EAR (Expanding Approvals Rule) that satisfies properties stronger than the central PR axiom satisfied by STV, can handle indifferences in a convenient and computationally efficient manner, and also satisfies better candidate monotonicity properties. In view of this, our proposed rule seems to be a compelling solution for achieving proportional representation in voting settings.
We study the problem of allocating impressions to sellers in e-commerce websites, such as Amazon, eBay or Taobao, aiming to maximize the total revenue generated by the platform. We employ a general framework of reinforcement mechanism design, which uses deep reinforcement learning to design efficient algorithms, taking the strategic behaviour of the sellers into account. Specifically, we model the impression allocation problem as a Markov decision process, where the states encode the history of impressions, prices, transactions and generated revenue and the actions are the possible impression allocations in each round. To tackle the problem of continuity and high-dimensionality of states and actions, we adopt the ideas of the DDPG algorithm to design an actor-critic policy gradient algorithm which takes advantage of the problem domain in order to achieve convergence and stability. We evaluate our proposed algorithm, coined IA(GRU), by comparing it against DDPG, as well as several natural heuristics, under different rationality models for the sellers - we assume that sellers follow well-known no-regret type strategies which may vary in their degree of sophistication. We find that IA(GRU) outperforms all algorithms in terms of the total revenue.
Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images. However, these models were applied in a black-box manner with no information provided about which facial features are actually used for prediction and how these features depend on image preprocessing, model initialization and architecture choice. We present a study investigating these different effects. In detail, our work compares four popular neural network architectures, studies the effect of pretraining, evaluates the robustness of the considered alignment preprocessings via cross-method test set swapping and intuitively visualizes the model's prediction strategies in given preprocessing conditions using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our evaluations on the challenging Adience benchmark show that suitable parameter initialization leads to a holistic perception of the input, compensating artefactual data representations. With a combination of simple preprocessing steps, we reach state of the art performance in gender recognition.
We consider the problem of minimizing the difference in the demand and the supply of power using microgrids. We setup multiple microgrids, that provide electricity to a village. They have access to the batteries that can store renewable power and also the electrical lines from the main grid. During each time period, these microgrids need to take decision on the amount of renewable power to be used from the batteries as well as the amount of power needed from the main grid. We formulate this problem in the framework of Markov Decision Process (MDP), similar to the one discussed in [1]. The power allotment to the village from the main grid is fixed and bounded, whereas the renewable energy generation is uncertain in nature. Therefore we adapt a distributed version of the popular Reinforcement learning technique, Multi-Agent Q-Learning to the problem. Finally, we also consider a variant of this problem where the cost of power production at the main site is taken into consideration. In this scenario the microgrids need to minimize the demand-supply deficit, while maintaining the desired average cost of the power production.
In recent years, many deep-learning based models are proposed for text classification. This kind of models well fits the training set from the statistical point of view. However, it lacks the capacity of utilizing instance-level information from individual instances in the training set. In this work, we propose to enhance neural network models by allowing them to leverage information from $k$-nearest neighbor (kNN) of the input text. Our model employs a neural network that encodes texts into text embeddings. Moreover, we also utilize $k$-nearest neighbor of the input text as an external memory, and utilize it to capture instance-level information from the training set. The final prediction is made based on features from both the neural network encoder and the kNN memory. Experimental results on several standard benchmark datasets show that our model outperforms the baseline model on all the datasets, and it even beats a very deep neural network model (with 29 layers) in several datasets. Our model also shows superior performance when training instances are scarce, and when the training set is severely unbalanced. Our model also leverages techniques such as semi-supervised training and transfer learning quite well.
In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike the existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid by filling in the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets show that the proposed 3D-RecGAN significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects. Our code and data are available at: https://github.com/Yang7879/3D-RecGAN.
We consider the problem of tracking an intruder using a network of wireless sensors. For tracking the intruder at each instant, the optimal number and the right configuration of sensors has to be powered. As powering the sensors consumes energy, there is a trade off between accurately tracking the position of the intruder at each instant and the energy consumption of sensors. This problem has been formulated in the framework of Partially Observable Markov Decision Process (POMDP). Even for the state-of-the-art algorithm in the literature, the curse of dimensionality renders the problem intractable. In this paper, we formulate the Intrusion Detection (ID) problem with a suitable state-action space in the framework of POMDP and develop a Reinforcement Learning (RL) algorithm utilizing the Upper Confidence Tree Search (UCT) method to solve the ID problem. Through simulations, we show that our algorithm performs and scales well with the increasing state and action spaces.
Traditional tools for configuring cloud services can run much slower than the workflows they are trying to optimize. For example, in the case studies reported here, we find cases where (using traditional methods) it takes hours to find ways to make a workflow terminate in tens of seconds. Such slow optimizers are a poor choice of tools for reacting to changing operational environmental conditions. Hence, they are unsuited for cloud services that support rapidly changing workflows, e.g., scientific workflows or workflows from the media or telecommunication industries. To solve this problem, this paper presents RIOT (Randomized Instance Order Types), a new configuration tool. RIOT has a very low optimization overhead-- often, less than 10\% of the system runtime, especially for every complex workflow. Instead of simulating many configurations, RIOT uses a novel surrogate sampling method to quickly find promising solutions. As shown by this paper, RIOT achieves comparable results to the other approaches but does so in a fraction of the time.
Today, the practice of returning entities from a knowledge base in response to search queries has become widespread. One of the distinctive characteristics of entities is that they are typed, i.e., assigned to some hierarchically organized type system (type taxonomy). The primary objective of this paper is to gain a better understanding of how entity type information can be utilized in entity retrieval. We perform this investigation in an idealized "oracle" setting, assuming that we know the distribution of target types of the relevant entities for a given query. We perform a thorough analysis of three main aspects: (i) the choice of type taxonomy, (ii) the representation of hierarchical type information, and (iii) the combination of type-based and term-based similarity in the retrieval model. Using a standard entity search test collection based on DBpedia, we find that type information proves most useful when using large type taxonomies that provide very specific types. We provide further insights on the extensional coverage of entities and on the utility of target types.
The areas of machine learning and communication technology are converging. Today's communications systems generate a huge amount of traffic data, which can help to significantly enhance the design and management of networks and communication components when combined with advanced machine learning methods. Furthermore, recently developed end-to-end training procedures offer new ways to jointly optimize the components of a communication system. Also in many emerging application fields of communication technology, e.g., smart cities or internet of things, machine learning methods are of central importance. This paper gives an overview over the use of machine learning in different areas of communications and discusses two exemplar applications in wireless networking. Furthermore, it identifies promising future research topics and discusses their potential impact.
Ubiquitous bio-sensing for personalized health monitoring is slowly becoming a reality with the increasing availability of small, diverse, robust, high fidelity sensors. This oncoming flood of data begs the question of how we will extract useful information from it. In this paper we explore the use of a variety of representations and machine learning algorithms applied to the task of seizure detection in high resolution, multichannel EEG data. We explore classification accuracy, computational complexity and memory requirements with a view toward understanding which approaches are most suitable for such tasks as the number of people involved and the amount of data they produce grows to be quite large. In particular, we show that layered learning approaches such as Deep Belief Networks excel along these dimensions.
Natural disasters can have catastrophic impacts on the functionality of infrastructure systems and cause severe physical and socio-economic losses. Given budget constraints, it is crucial to optimize decisions regarding mitigation, preparedness, response, and recovery practices for these systems. This requires accurate and efficient means to evaluate the infrastructure system reliability. While numerous research efforts have addressed and quantified the impact of natural disasters on infrastructure systems, typically using the Monte Carlo approach, they still suffer from high computational cost and, thus, are of limited applicability to large systems. This paper presents a deep learning framework for accelerating infrastructure system reliability analysis. In particular, two distinct deep neural network surrogates are constructed and studied: (1) A classifier surrogate which speeds up the connectivity determination of networks, and (2) An end-to-end surrogate that replaces a number of components such as roadway status realization, connectivity determination, and connectivity averaging. The proposed approach is applied to a simulation-based study of the two-terminal connectivity of a California transportation network subject to extreme probabilistic earthquake events. Numerical results highlight the effectiveness of the proposed approach in accelerating the transportation system two-terminal reliability analysis with extremely high prediction accuracy.
Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic. To this end, given the temporal logic specification that is to be obeyed by the learning system, we propose to synthesize a reactive system called a shield. The shield is introduced in the traditional learning process in two alternative ways, depending on the location at which the shield is implemented. In the first one, the shield acts each time the learning agent is about to make a decision and provides a list of safe actions. In the second way, the shield is introduced after the learning agent. The shield monitors the actions from the learner and corrects them only if the chosen action causes a violation of the specification. We discuss which requirements a shield must meet to preserve the convergence guarantees of the learner. Finally, we demonstrate the versatility of our approach on several challenging reinforcement learning scenarios.
We introduce a new class of graphical models that generalizes Lauritzen-Wermuth-Frydenberg chain graphs by relaxing the semi-directed acyclity constraint so that only directed cycles are forbidden. Moreover, up to two edges are allowed between any pair of nodes. Specifically, we present local, pairwise and global Markov properties for the new graphical models and prove their equivalence. We also present an equivalent factorization property. Finally, we present a causal interpretation of the new models.
In this paper, we build the case that 5G and concomitant emerging technologies (such as IoT, big data, artificial intelligence, and machine learning) will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of AI and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system, and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution.
Generative models are widely used for unsupervised learning with various applications, including data compression and signal restoration. Training methods for such systems focus on the generality of the network given limited amount of training data. A less researched type of techniques concerns generation of only a single type of input. This is useful for applications such as constraint handling, noise reduction and anomaly detection. In this paper we present a technique to limit the generative capability of the network using negative learning. The proposed method searches the solution in the gradient direction for the desired input and in the opposite direction for the undesired input. One of the application can be anomaly detection where the undesired inputs are the anomalous data. In the results section we demonstrate the features of the algorithm using MNIST handwritten digit dataset and latter apply the technique to a real-world obstacle detection problem. The results clearly show that the proposed learning technique can significantly improve the performance for anomaly detection.
Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives. There is broad consensus that understanding a narrative requires identifying and tracking the goals and desires of the characters and their narrative outcomes. However, to date, there has been limited work on computational models for this problem. We introduce a new dataset, DesireDB, which includes gold-standard labels for identifying statements of desire, textual evidence for desire fulfillment, and annotations for whether the stated desire is fulfilled given the evidence in the narrative context. We report experiments on tracking desire fulfillment using different methods, and show that LSTM Skip-Thought model achieves F-measure of 0.7 on our corpus.
The optimization of functions to find the best solution according to one or several objectives has a central role in many engineering and research fields. Recently, a new family of optimization algorithms, named Quality-Diversity optimization, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, Quality-Diversity algorithms are searching for a large collection of both diverse and high-performing solutions. The role of this collection is to cover the range of possible solution types as much as possible, and to contain the best solution for each type. The contribution of this paper is threefold. Firstly, we present a unifying framework of Quality-Diversity optimization algorithms that covers the two main algorithms of this family (Multi-dimensional Archive of Phenotypic Elites and the Novelty Search with Local Competition), and that highlights the large variety of variants that can be investigated within this family. Secondly, we propose algorithms with a new selection mechanism for Quality-Diversity algorithms that outperforms all the algorithms tested in this paper. Lastly, we present a new collection management that overcomes the erosion issues observed when using unstructured collections. These three contributions are supported by extensive experimental comparisons of Quality-Diversity algorithms on three different experimental scenarios.
The aim of the current work is to assess the challenges that gamification in education are facing nowadays. Benefits and disadvantages of using gamification in classroom are both discussed to offer a clearer view on the impact of using gamification within learning process. Exploratory study cases are provided to investigate the relation between motivation and engagement of the students and gamification in training. Following this idea, a survey was conducted to assess how students behavior and motivation is affected by introducing a single, specific gamification element during a semester learning process. To stimulate competition among students, a ranking type plugin was introduced within the university learning management system used for extramural education. The results prove that motivation decreases by comparison to the previous semester.
We develop an end-to-end training algorithm for whole-image breast cancer diagnosis based on mammograms. It requires lesion annotations only at the first stage of training. After that, a whole image classifier can be trained using only image level labels. This greatly reduced the reliance on lesion annotations. Our approach is implemented using an all convolutional design that is simple yet provides superior performance in comparison with the previous methods. On DDSM, our best single-model achieves a per-image AUC score of 0.88 and three-model averaging increases the score to 0.91. On INbreast, our best single-model achieves a per-image AUC score of 0.96. Using DDSM as benchmark, our models compare favorably with the current state-of-the-art. We also demonstrate that a whole image model trained on DDSM can be easily transferred to INbreast without using its lesion annotations and using only a small amount of training data. Code and model availability: https://github.com/lishen/end2end-all-conv
Much of the user-generated content on social media is provided by ordinary people telling stories about their daily lives. We develop and test a novel method for learning fine-grained common-sense knowledge from these stories about contingent (causal and conditional) relationships between everyday events. This type of knowledge is useful for text and story understanding, information extraction, question answering, and text summarization. We test and compare different methods for learning contingency relation, and compare what is learned from topic-sorted story collections vs. general-domain stories. Our experiments show that using topic-specific datasets enables learning finer-grained knowledge about events and results in significant improvement over the baselines. An evaluation on Amazon Mechanical Turk shows 82% of the relations between events that we learn from topic-sorted stories are judged as contingent.
Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a particular story or macro-event, along with its storyline. In this position paper, we focus on knowledge acquisition approach and claim that newswire is a relatively poor source for learning fine-grained causal relations between everyday events. We describe experiments using an unsupervised method to learn causal relations between events in the narrative genres of first-person narratives and film scene descriptions. We show that our method learns fine-grained causal relations, judged by humans as likely to be causal over 80% of the time. We also demonstrate that the learned event pairs do not exist in publicly available event-pair datasets extracted from newswire.
Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today's scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead. In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible. We introduce a family of algorithms for focused reading, including an intuitive, strong baseline, and a second approach which uses a reinforcement learning (RL) framework that learns when to explore (widen the search) or exploit (narrow it). We demonstrate that the RL approach is capable of answering more queries than the baseline, while being more efficient, i.e., reading fewer documents.
Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, researchers use neural language models and encoder-decoder frameworks for table-to-text generation. However, these neural network-based approaches do not model the order of contents during text generation. When a human writes a summary based on a given table, he or she would probably consider the content order before wording. In a biography, for example, the nationality of a person is typically mentioned before occupation in a biography. In this paper, we propose an order-planning text generation model to capture the relationship between different fields and use such relationship to make the generated text more fluent and smooth. We conducted experiments on the WikiBio dataset and achieve significantly higher performance than previous methods in terms of BLEU, ROUGE, and NIST scores.
We study the problem of inferring the type of a networked device in a home network by leveraging low level traffic activity indicators seen at commodity home gateways. We analyze a dataset of detailed device network activity obtained from 240 subscriber homes of a large European ISP and extract a number of traffic and spatial fingerprints for individual devices. We develop a two level taxonomy to describe devices onto which we map individual devices using a number of heuristics. We leverage the heuristically derived labels to train classifiers that distinguish device classes based on the traffic and spatial fingerprints of a device. Our results show an accuracy level up to 91% for the coarse level category and up to 84% for the fine grained category. By incorporating information from other sources (e.g., MAC OUI), we are able to further improve accuracy to above 97% and 92%, respectively. Finally, we also extract a set of simple and human-readable rules that concisely capture the behaviour of these distinct device categories.
Traditionally psychometric tests were used for profiling incoming workers. These methods use DISC profiling method to classify people into distinct personality types, which are further used to predict if a person may be a possible fit to the organizational culture. This concept is taken further by introducing a novel technique to predict if a particular pair of an incoming worker and the manager being assigned are compatible at a psychological scale. This is done using multilayer perceptron neural network which can be adaptively trained to showcase the true nature of the compatibility index. The proposed prototype model is used to quantify the relevant attributes, use them to train the prediction engine, and to define the data pipeline required for it.
We propose two multimodal deep learning architectures that allow for cross-modal dataflow (XFlow) between the feature extractors, thereby extracting more interpretable features and obtaining a better representation than through unimodal learning, for the same amount of training data. These models can usefully exploit correlations between audio and visual data, which have a different dimensionality and are therefore nontrivially exchangeable. Our work improves on existing multimodal deep learning metholodogies in two essential ways: (1) it presents a novel method for performing cross-modality (before features are learned from individual modalities) and (2) extends the previously proposed cross-connections, which only transfer information between streams that process compatible data. Both cross-modal architectures outperformed their baselines (by up to 7.5%) when evaluated on the AVletters dataset.
Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community's ability to develop better theories, as well as good off the shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic independent way. Our results show that our theoretically motivated features achieve 66% accuracy, an improvement over a unigram baseline of an absolute 6%.
Semantics based knowledge representations such as ontologies are found to be very useful in automatically generating meaningful factual questions. Determining the difficulty level of these system generated questions is helpful to effectively utilize them in various educational and professional applications. The existing approaches for finding the difficulty level of factual questions are very simple and are limited to a few basic principles. We propose a new methodology for this problem by considering an educational theory called Item Response Theory (IRT). In the IRT, knowledge proficiency of end users (learners) are considered for assigning difficulty levels, because of the assumptions that a given question is perceived differently by learners of various proficiencies. We have done a detailed study on the features (factors) of a question statement which could possibly determine its difficulty level for three learner categories (experts, intermediates and beginners). We formulate ontology based metrics for the same. We then train three logistic regression models to predict the difficulty level corresponding to the three learner categories.
Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.
Mobile applications are being used every day by more than half of the world's population to perform a great variety of tasks. With the increasingly widespread usage of these applications, the need arises for efficient techniques to test them. Many frameworks allow automating the process of application testing, however existing frameworks mainly rely on the application developer for providing testing scripts for each developed application, thus preventing reuse of these tests for similar applications. In this paper, we present a novel approach for the automation of testing Android applications by leveraging machine learning techniques and reusing popular test scenarios. We discuss and demonstrate the potential benefits of our approach in an empirical study where we show that our developed testing tool, based on the proposed approach, outperforms standard methods in realistic settings.
We explain how the prototype automatic chess problem composer, Chesthetica, successfully composed a rare and interesting chess problem using the new Digital Synaptic Neural Substrate (DSNS) computational creativity approach. This problem represents a greater challenge from a creative standpoint because the checkmate is not always clear and the method of winning even less so. Creating a decisive chess problem of this type without the aid of an omniscient 7-piece endgame tablebase (and one that also abides by several chess composition conventions) would therefore be a challenge for most human players and composers working on their own. The fact that a small computer with relatively low processing power and memory was sufficient to compose such a problem using the DSNS approach in just 10 days is therefore noteworthy. In this report we document the event and result in some detail. It lends additional credence to the DSNS as a viable new approach in the field of computational creativity. In particular, in areas where human-like creativity is required for targeted or specific problems with no clear path to the solution.
We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as joint distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validated in synthetic data and real-world applications.
We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions. The decision-making process of a quantum learning agent within the projective simulation paradigm for machine learning is implemented in a system of two qubits. The latter are realized using hyperfine states of two frequency-addressed atomic ions exposed to a static magnetic field gradient. We show that the deliberation time of this quantum learning agent is quadratically improved with respect to comparable classical learning agents. The performance of this quantum-enhanced learning agent highlights the potential of scalable quantum processors taking advantage of machine learning.
A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points. First-order methods often get stuck at saddle points, greatly deteriorating their performance. Typically, to escape from saddles one has to use second-order methods. However, most works on second-order methods rely extensively on expensive Hessian-based computations, making them impractical in large-scale settings. To tackle this challenge, we introduce a generic framework that minimizes Hessian based computations while at the same time provably converging to second-order critical points. Our framework carefully alternates between a first-order and a second-order subroutine, using the latter only close to saddle points, and yields convergence results competitive to the state-of-the-art. Empirical results suggest that our strategy also enjoys a good practical performance.
Many efforts have been made to use various forms of domain knowledge in malware detection. Currently there exist two common approaches to malware detection without domain knowledge, namely byte n-grams and strings. In this work we explore the feasibility of applying neural networks to malware detection and feature learning. We do this by restricting ourselves to a minimal amount of domain knowledge in order to extract a portion of the Portable Executable (PE) header. By doing this we show that neural networks can learn from raw bytes without explicit feature construction, and perform even better than a domain knowledge approach that parses the PE header into explicit features.
Fine-tuning of a deep convolutional neural network (CNN) is often desired. This paper provides an overview of our publicly available py-faster-rcnn-ft software library that can be used to fine-tune the VGG_CNN_M_1024 model on custom subsets of the Microsoft Common Objects in Context (MS COCO) dataset. For example, we improved the procedure so that the user does not have to look for suitable image files in the dataset by hand which can then be used in the demo program. Our implementation randomly selects images that contain at least one object of the categories on which the model is fine-tuned.
We develop a second order primal-dual method for optimization problems in which the objective function is given by the sum of a strongly convex twice differentiable term and a possibly nondifferentiable convex regularizer. After introducing an auxiliary variable, we utilize the proximal operator of the nonsmooth regularizer to transform the associated augmented Lagrangian into a function that is once, but not twice, continuously differentiable. The saddle point of this function corresponds to the solution of the original optimization problem. We employ a generalization of the Hessian to define second order updates on this function and prove global exponential stability of the corresponding differential inclusion. Furthermore, we develop a globally convergent customized algorithm that utilizes the primal-dual augmented Lagrangian as a merit function. We show that the search direction can be computed efficiently and prove quadratic/superlinear asymptotic convergence. We use the $\ell_1$-regularized least squares problem and the problem of designing a distributed controller for a spatially-invariant system to demonstrate the merits and the effectiveness of our method.
This paper presents the EACare project, an ambitious multi-disciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e.g., due to Alzheimer's disease. The system will use methods from Machine Learning and Social Robotics, and be trained with examples of recorded clinician-patient interactions. The interaction will be developed using a participatory design approach. We describe the scope and method of the project, and report on a first Wizard of Oz prototype.
In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to unsafe areas and distort the model being learnt. In this paper, we address this problem and present a novel method that allows BO to consider localisation uncertainty by applying a Gaussian process model for uncertain inputs as a prior. We evaluate the proposed method in simulation and in experiments with a real robot navigating over rough terrain and compare it against standard BO methods.
In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriate for modeling complex and noisy human behaviors. The proposed uncertainty acquisition can be done with a single forward path without Monte Carlo sampling and is suitable for real-time robotics applications. The properties of the proposed uncertainty measure are analyzed through three different synthetic examples, absence of data, heavy measurement noise, and composition of functions scenarios. We show that each case can be distinguished using the proposed uncertainty measure and presented an uncertainty-aware learn- ing from demonstration method of an autonomous driving using this property. The proposed uncertainty-aware learning from demonstration method outperforms other compared methods in terms of safety using a complex real-world driving dataset.
We build a model using Gaussian processes to infer a spatio-temporal vector field from observed agent trajectories. Significant landmarks or influence points in agent surroundings are jointly derived through vector calculus operations that indicate presence of sources and sinks. We evaluate these influence points by using the Kullback-Leibler divergence between the posterior and prior Laplacian of the inferred spatio-temporal vector field. Through locating significant features that influence trajectories, our model aims to give greater insight into underlying causal utility functions that determine agent decision-making. A key feature of our model is that it infers a joint Gaussian process over the observed trajectories, the time-varying vector field of utility and canonical vector calculus operators. We apply our model to both synthetic data and lion GPS data collected at the Bubye Valley Conservancy in southern Zimbabwe.
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects training label quality, but is difficult to learn without any ground truth labels. We instead rely on these weak supervision sources having some structure by virtue of being encoded programmatically. We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus reducing the data required to learn structure significantly. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations found, improving over the standard sample complexity, which is exponential in $n$ for identifying $n^{\textrm{th}}$ degree relations. Experimentally, Coral matches or outperforms traditional structure learning approaches by up to 3.81 F1 points. Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3.07 accuracy points when heuristics are used to label radiology data without ground truth labels.
This paper contains analysis and extension of exploiters-based knowledge extraction methods, which allow generation of new knowledge, based on the basic ones. The main achievement of the paper is useful features of some universal exploiters proof, which allow extending set of basic classes and set of basic relations by finite set of new classes of objects and relations among them, which allow creating of complete lattice. Proposed approach gives an opportunity to compute quantity of new classes, which can be generated using it, and quantity of different types, which each of obtained classes describes; constructing of defined hierarchy of classes with determined subsumption relation; avoidance of some problems of inheritance and more efficient restoring of basic knowledge within the database.
The ability to rapidly identify symmetry and anti-symmetry is an essential attribute of intelligence. Symmetry perception is a central process in human vision and may be key to human 3D visualization. While previous work in understanding neuron symmetry perception has concentrated on the neuron as an integrator, here we show how the coincidence detecting property of the spiking neuron can be used to reveal symmetry density in spatial data. We develop a method for synchronizing symmetry-identifying spiking artificial neural networks to enable layering and feedback in the network. We show a method for building a network capable of identifying symmetry density between sets of data and present a digital logic implementation demonstrating an 8x8 leaky-integrate-and-fire symmetry detector in a field programmable gate array. Our results show that the efficiencies of spiking neural networks can be harnessed to rapidly identify symmetry in spatial data with applications in image processing, 3D computer vision, and robotics.
A new approach to the study of Generalized Graphs as semantic data structures using machine learning techniques is presented. We show how vector representations maintaining semantic characteristics of the original data can be obtained from a given graph using neural encoding architectures and considering the topological properties of the graph. Semantic features of these new representations are tested by using some machine learning tasks and new directions on efficient link discovery, entitity retrieval and long distance query methodologies on large relational datasets are investigated using real datasets. ---- En este trabajo se presenta un nuevo enfoque en el contexto del aprendizaje autom\'atico multi-relacional para el estudio de Grafos Generalizados. Se muestra c\'omo se pueden obtener representaciones vectoriales que mantienen caracter\'isticas sem\'anticas del grafo original utilizando codificadores neuronales y considerando las propiedades topol\'ogicas del grafo. Adem\'as, se eval\'uan las caracter\'isticas sem\'anticas capturadas por estas nuevas representaciones y se investigan nuevas metodolog\'ias eficientes relacionadas con Link Discovery, Entity Retrieval y consultas a larga distancia en grandes conjuntos de datos relacionales haciendo uso de bases de datos reales.
Social dilemmas have been regarded as the essence of evolution game theory, in which the prisoner's dilemma game is the most famous metaphor for the problem of cooperation. Recent findings revealed people's behavior violated the Sure Thing Principle in such games. Classic probability methodologies have difficulty explaining the underlying mechanisms of people's behavior. In this paper, a novel quantum-like Bayesian Network was proposed to accommodate the paradoxical phenomenon. The special network can take interference into consideration, which is likely to be an efficient way to describe the underlying mechanism. With the assistance of belief entropy, named as Deng entropy, the paper proposes Belief Distance to render the model practical. Tested with empirical data, the proposed model is proved to be predictable and effective.
In this paper, we propose: (a) a restart schedule for an adaptive simulated annealer, and (b) parallel simulated annealing, with an adaptive and parameter-free annealing schedule. The foundation of our approach is the Modified Lam annealing schedule, which adaptively controls the temperature parameter to track a theoretically ideal rate of acceptance of neighboring states. A sequential implementation of Modified Lam simulated annealing is almost parameter-free. However, it requires prior knowledge of the annealing length. We eliminate this parameter using restarts, with an exponentially increasing schedule of annealing lengths. We then extend this restart schedule to parallel implementation, executing several Modified Lam simulated annealers in parallel, with varying initial annealing lengths, and our proposed parallel annealing length schedule. To validate our approach, we conduct experiments on an NP-Hard scheduling problem with sequence-dependent setup constraints. We compare our approach to fixed length restarts, both sequentially and in parallel. Our results show that our approach can achieve substantial performance gains, throughout the course of the run, demonstrating our approach to be an effective anytime algorithm.
We discuss that how the majority of traditional modeling approaches are following the idealism point of view in scientific modeling, which follow the set theoretical notions of models based on abstract universals. We show that while successful in many classical modeling domains, there are fundamental limits to the application of set theoretical models in dealing with complex systems with many potential aspects or properties depending on the perspectives. As an alternative to abstract universals, we propose a conceptual modeling framework based on concrete universals that can be interpreted as a category theoretical approach to modeling. We call this modeling framework pre-specific modeling. We further, discuss how a certain group of mathematical and computational methods, along with ever-growing data streams are able to operationalize the concept of pre-specific modeling.
In this paper, we tackle the problem of extracting frequent opinions from uncertain databases. We introduce the foundation of an opinion mining approach with the definition of pattern and support measure. The support measure is derived from the commitment definition. A new algorithm called OpMiner that extracts the set of frequent opinions modelled as a mass functions is detailed. Finally, we apply our approach on a real-world biomedical database that stores opinions of experts to evaluate the reliability level of biomedical data. Performance analysis showed a better quality patterns for our proposed model in comparison with literature-based methods.
Current metropolises largely depend on a functioning transport infrastructure and the increasing demand can only be satisfied by a well organized mass transit. One example for a crucial mass transit system is New York City's Staten Island Ferry, connecting the two boroughs of Staten Island and Manhattan with a regular passenger service. Today's demand already exceeds 2500 passengers for a single cycle during peek hours, and future projections suggest that it will further increase. One way to appraise how the system will cope with future demand is by simulation. This contribution proposes an integrated simulation approach to evaluate the system performance with respect to future demand. The simulation relies on a multiscale modeling approach where the terminal buildings are simulated by a microscopic and quantitatively valid cellular automata (CA) and the journeys of the ferries themselves are modeled by a mesoscopic queue simulation approach. Based on the simulation results recommendations with respect to the future demand are given.
We describe a novel approach to monitoring high level behaviors using concepts from AI planning. Our goal is to understand what a program is doing based on its system call trace. This ability is particularly important for detecting malware. We approach this problem by building an abstract model of the operating system using the STRIPS planning language, casting system calls as planning operators. Given a system call trace, we simulate the corresponding operators on our model and by observing the properties of the state reached, we learn about the nature of the original program and its behavior. Thus, unlike most statistical detection methods that focus on syntactic features, our approach is semantic in nature. Therefore, it is more robust against obfuscation techniques used by malware that change the outward appearance of the trace but not its effect. We demonstrate the efficacy of our approach by evaluating it on actual system call traces.
This paper introduces a novel activity dataset which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions. The dataset presents a set of videos of actors performing everyday activities in a natural and unscripted manner. The dataset was recorded using a static Kinect 2 sensor which is commonly used on many robotic platforms. The dataset comprises of RGB-D images, point cloud data, automatically generated skeleton tracks in addition to crowdsourced annotations. Furthermore, we also describe the methodology used to acquire annotations through crowdsourcing. Finally some activity recognition benchmarks are presented using current state-of-the-art techniques. We believe that this dataset is particularly suitable as a testbed for activity recognition research but it can also be applicable for other common tasks in robotics/computer vision research such as object detection and human skeleton tracking.
Vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has been attracting a lot of attention in recent studies. It has been shown that for many state of the art DNNs performing image classification there exist universal adversarial perturbations --- image-agnostic perturbations mere addition of which to natural images with high probability leads to their misclassification. In this work we propose a new algorithm for constructing such universal perturbations. Our approach is based on computing the so-called $(p, q)$-singular vectors of the Jacobian matrices of hidden layers of a network. Resulting perturbations present interesting visual patterns, and by using only 64 images we were able to construct universal perturbations with more than 60 \% fooling rate on the dataset consisting of 50000 images. We also investigate a correlation between the maximal singular value of the Jacobian matrix and the fooling rate of the corresponding singular vector, and show that the constructed perturbations generalize across networks.
Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that correctly customize treatment assignment base on subject characteristics. The problem of constructing such models from randomized experiments data is known as Uplift Modeling in the literature. Many algorithms have been proposed for uplift modeling and some have generated promising results on various data sets. Yet little is known about the theoretical properties of these algorithms. In this paper, we propose a new tree-based ensemble algorithm for uplift modeling. Experiments show that our algorithm can achieve competitive results on both synthetic and industry-provided data. In addition, by properly tuning the "node size" parameter, our algorithm is proved to be consistent under mild regularity conditions. This is the first consistent algorithm for uplift modeling that we are aware of.
In this paper, we propose a recurrent neural network (RNN) with residual attention (RRA) to learn long-range dependencies from sequential data. We propose to add residual connections across timesteps to RNN, which explicitly enhances the interaction between current state and hidden states that are several timesteps apart. This also allows training errors to be directly back-propagated through residual connections and effectively alleviates gradient vanishing problem. We further reformulate an attention mechanism over residual connections. An attention gate is defined to summarize the individual contribution from multiple previous hidden states in computing the current state. We evaluate RRA on three tasks: the adding problem, pixel-by-pixel MNIST classification and sentiment analysis on the IMDB dataset. Our experiments demonstrate that RRA yields better performance, faster convergence and more stable training compared to a standard LSTM network. Furthermore, RRA shows highly competitive performance to the state-of-the-art methods.
The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been explored yet. In this paper, we propose an End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal consistency between consecutive video frames. A thorough study has been conducted over a number of structure options, to identify the best temporal fusion strategy. Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model. The resulting augmented end-to-end pipeline has demonstrated much more stable and accurate detection results in hazy video.
Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by proposing three novel ways to incorporate affective/emotional aspects into long short term memory (LSTM) encoder-decoder neural conversation models: (1) affective word embeddings, which are cognitively engineered, (2) affect-based objective functions that augment the standard cross-entropy loss, and (3) affectively diverse beam search for decoding. Experiments show that these techniques improve the open-domain conversational prowess of encoder-decoder networks by enabling them to produce emotionally rich responses that are more interesting and natural.
We describe a method to use discrete human feedback to enhance the performance of deep learning agents in virtual three-dimensional environments by extending deep-reinforcement learning to model the confidence and consistency of human feedback. This enables deep reinforcement learning algorithms to determine the most appropriate time to listen to the human feedback, exploit the current policy model, or explore the agent's environment. Managing the trade-off between these three strategies allows DRL agents to be robust to inconsistent or intermittent human feedback. Through experimentation using a synthetic oracle, we show that our technique improves the training speed and overall performance of deep reinforcement learning in navigating three-dimensional environments using Minecraft. We further show that our technique is robust to highly innacurate human feedback and can also operate when no human feedback is given.
Although neural machine translation (NMT) with the encoder-decoder framework has achieved great success in recent times, it still suffers from some drawbacks: RNNs tend to forget old information which is often useful and the encoder only operates through words without considering word relationship. To solve these problems, we introduce a relation networks (RN) into NMT to refine the encoding representations of the source. In our method, the RN first augments the representation of each source word with its neighbors and reasons all the possible pairwise relations between them. Then the source representations and all the relations are fed to the attention module and the decoder together, keeping the main encoder-decoder architecture unchanged. Experiments on two Chinese-to-English data sets in different scales both show that our method can outperform the competitive baselines significantly.
Crowdfunding has emerged as a prominent way for entrepreneurs to secure funding without sophisticated intermediation. In crowdfunding, an entrepreneur often has to decide how to disclose the campaign status in order to collect as many contributions as possible. Such decisions are difficult to make primarily due to incomplete information. We propose information design as a tool to help the entrepreneur to improve revenue by influencing backers' beliefs. We introduce a heuristic algorithm to dynamically compute information-disclosure policies for the entrepreneur, followed by an empirical evaluation to demonstrate its competitiveness over the widely-adopted immediate-disclosure policy. Our results demonstrate that the immediate-disclosure policy is not optimal when backers follow thresholding policies despite its ease of implementation. With appropriate heuristics, an entrepreneur can benefit from dynamic information disclosure. Our work sheds light on information design in a dynamic setting where agents make decisions using thresholding policies.
Recurrent neural networks (RNNs) are widely used to model sequential data but their non-linear dependencies between sequence elements prevent parallelizing training over sequence length. We show the training of RNNs with only linear sequential dependencies can be parallelized over the sequence length using the parallel scan algorithm, leading to rapid training on long sequences even with small minibatch size. We develop a parallel linear recurrence CUDA kernel and show that it can be applied to immediately speed up training and inference of several state of the art RNN architectures by up to 9x. We abstract recent work on linear RNNs into a new framework of linear surrogate RNNs and develop a linear surrogate model for the long short-term memory unit, the GILR-LSTM, that utilizes parallel linear recurrence. We extend sequence learning to new extremely long sequence regimes that were previously out of reach by successfully training a GILR-LSTM on a synthetic sequence classification task with a one million timestep dependency.
Multi-scanner Antivirus systems provide insightful information on the nature of a suspect application; however there is often a lack of consensus and consistency between different Anti-Virus engines. In this article, we analyze more than 250 thousand malware signatures generated by 61 different Anti-Virus engines after analyzing 82 thousand different Android malware applications. We identify 41 different malware classes grouped into three major categories, namely Adware, Harmful Threats and Unknown or Generic signatures. We further investigate the relationships between such 41 classes using community detection algorithms from graph theory to identify similarities between them; and we finally propose a Structure Equation Model to identify which Anti-Virus engines are more powerful at detecting each macro-category. As an application, we show how such models can help in identifying whether Unknown malware applications are more likely to be of Harmful or Adware type.
We compare Tetrad (Java) algorithms to the other public software packages BNT (Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \vanilla" task of recovering DAG structure to the extent possible from data generated recursively from linear, Gaussian structure equation models (SEMs) with no latent variables, for random graphs, with no additional knowledge of variable order or adjacency structure, and without additional specification of intervention information. Each one of the above packages offers at least one implementation suitable to this purpose. We compare them on adjacency and orientation accuracy as well as time performance, for fixed datasets. We vary the number of variables, the number of samples, and the density of graph, for a total of 27 combinations, averaging all statistics over 10 runs, for a total of 270 datasets. All runs are carried out on the same machine and on their native platforms. An interactive visualization tool is provided for the reader who wishes to know more than can be documented explicitly in this report.
In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems. By mimicking the relational structure of planning problems, ASNets are able to adopt a weight-sharing scheme which allows the network to be applied to any problem from a given planning domain. This allows the cost of training the network to be amortised over all problems in that domain. Further, we propose a training method which balances exploration and supervised training on small problems to produce a policy which remains robust when evaluated on larger problems. In experiments, we show that ASNet's learning capability allows it to significantly outperform traditional non-learning planners in several challenging domains.
As the use of cloud computing continues to rise, controlling cost becomes increasingly important. Yet there is evidence that 30\% - 45\% of cloud spend is wasted. Existing tools for cloud provisioning typically rely on highly trained human experts to specify what to monitor, thresholds for triggering action, and actions. In this paper we explore the use of reinforcement learning (RL) to acquire policies to balance performance and spend, allowing humans to specify what they want as opposed to how to do it, minimizing the need for cloud expertise. Empirical results with tabular, deep, and dueling double deep Q-learning with the CloudSim simulator show the utility of RL and the relative merits of the approaches. We also demonstrate effective policy transfer learning from an extremely simple simulator to CloudSim, with the next step being transfer from CloudSim to an Amazon Web Services physical environment.
Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent successes of nonlinear models in machine learning, it is natural to wonder whether ex-tending WFA to the nonlinear setting would be beneficial. In this paper, we propose a novel model of neural network based nonlinearWFA model (NL-WFA) along with a learning algorithm. Our learning algorithm is inspired by the spectral learning algorithm for WFAand relies on a nonlinear decomposition of the so-called Hankel matrix, by means of an auto-encoder network. The expressive power of NL-WFA and the proposed learning algorithm are assessed on both synthetic and real-world data, showing that NL-WFA can lead to smaller model sizes and infer complex grammatical structures from data.
In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.
In this paper, we introduce the problem of denoting and deriving the complexity of workflows (plans, schedules) in collaborative, planner-assisted settings where humans and agents are trying to jointly solve a task. The interactions -- and hence the workflows that connect the human and the agents -- may differ according to the domain and the kind of agents. We adapt insights from prior work in human-agent teaming and workflow analysis to suggest metrics for workflow complexity. The main motivation behind this work is to highlight metrics for human comprehensibility of plans and schedules. The planning community has seen its fair share of work on the synthesis of plans that take diversity into account -- what value do such plans hold if their generation is not guided at least in part by metrics that reflect the ease of engaging with and using those plans?
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry. Since a reaction may involve hundreds of atoms, fully exploring the space of possible transformations is intractable. The current solution utilizes reaction templates to limit the space, but it suffers from coverage and efficiency issues. In this paper, we propose a template-free approach to efficiently explore the space of product molecules by first pinpointing the reaction center -- the set of nodes and edges where graph edits occur. Since only a small number of atoms contribute to reaction center, we can directly enumerate candidate products. The generated candidates are scored by a Weisfeiler-Lehman Difference Network that models high-order interactions between changes occurring at nodes across the molecule. Our framework outperforms the top-performing template-based approach with a 10\% margin, while running orders of magnitude faster. Finally, we demonstrate that the model accuracy rivals the performance of domain experts.
Reinforcement learning (RL), while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. The autonomous decomposition of tasks and use of hierarchical methods hold the potential to significantly speed up learning in such domains. This paper proposes a novel practical method that can autonomously decompose tasks, by leveraging association rule mining, which discovers hidden relationship among entities in data mining. We introduce a novel method called ARM-HSTRL (Association Rule Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning). It extracts temporal and structural relationships of sub-goals in RL, and multi-task RL. In particular,it finds sub-goals and relationship among them. It is shown the significant efficiency and performance of the proposed method in two main topics of RL.
Random walks are at the heart of many existing deep learning algorithms for graph data. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable to transfer to new nodes and graphs as they are tied to node identity. In this work, we introduce the notion of attributed random walks which serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many others that leverage random walks. Our proposed framework enables these methods to be more widely applicable for both transductive and inductive learning as well as for use on graphs with attributes (if available). This is achieved by learning functions that generalize to new nodes and graphs. We show that our proposed framework is effective with an average AUC improvement of 16.1% while requiring on average 853 times less space than existing methods on a variety of graphs from several domains.
The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI community has recently developed several algorithm configuration (AC) methods to automatically address this problem. While all existing AC methods start the configuration process of an algorithm A from scratch for each new type of benchmark instances, here we propose to exploit information about A's performance on previous benchmarks in order to warmstart its configuration on new types of benchmarks. We introduce two complementary ways in which we can exploit this information to warmstart AC methods based on a predictive model. Experiments for optimizing a very flexible modern SAT solver on twelve different instance sets show that our methods often yield substantial speedups over existing AC methods (up to 165-fold) and can also find substantially better configurations given the same compute budget.
We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. We have made the data sets available for further research. We also investigate the impact numerical features have on the KB completion performance of KBLRN.
We present a novel method to solve image analogy problems : it allows to learn the relation between paired images present in training data, and then generalize and generate images that correspond to the relation, but were never seen in the training set. Therefore, we call the method Conditional Analogy Generative Adversarial Network (CAGAN), as it is based on adversarial training and employs deep convolutional neural networks. An especially interesting application of that technique is automatic swapping of clothing on fashion model photos. Our work has the following contributions. First, the definition of the end-to-end trainable CAGAN architecture, which implicitly learns segmentation masks without expensive supervised labeling data. Second, experimental results show plausible segmentation masks and often convincing swapped images, given the target article. Finally, we discuss the next steps for that technique: neural network architecture improvements and more advanced applications.
Despite the recent developments that allowed neural networks to achieve impressive performance on a variety of applications, these models are intrinsically affected by the problem of overgeneralization, due to their partitioning of the full input space into the fixed set of target classes used during training. Thus it is possible for novel inputs belonging to categories unknown during training or even completely unrecognizable to humans to fool the system into classifying them as one of the known classes, even with a high degree of confidence. Solving this problem may help improve the security of such systems in critical applications, and may further lead to applications in the context of open set recognition and 1-class recognition. This paper presents a novel way to compute a confidence score using denoising autoencoders and shows that such confidence score can correctly identify the regions of the input space close to the training distribution by approximately identifying its local maxima.
High-dimensional data requires scalable algorithms. We propose and analyze three scalable and related algorithms for semi-supervised discriminant analysis (SDA). These methods are based on Krylov subspace methods which exploit the data sparsity and the shift-invariance of Krylov subspaces. In addition, the problem definition was improved by adding centralization to the semi-supervised setting. The proposed methods are evaluated on a industry-scale data set from a pharmaceutical company to predict compound activity on target proteins. The results show that SDA achieves good predictive performance and our methods only require a few seconds, significantly improving computation time on previous state of the art.
In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity models such as deep neural networks can enable a robot to represent complex skills, but learning each skill from scratch then becomes infeasible. In this work, we present a meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new skills from just a single demonstration. Unlike prior methods for one-shot imitation, our method can scale to raw pixel inputs and requires data from significantly fewer prior tasks for effective learning of new skills. Our experiments on both simulated and real robot platforms demonstrate the ability to learn new tasks, end-to-end, from a single visual demonstration.
Deep Reinforcement Learning has been able to achieve amazing successes in a variety of domains from video games to continuous control by trying to maximize the cumulative reward. However, most of these successes rely on algorithms that require a large amount of data to train in order to obtain results on par with human-level performance. This is not feasible if we are to deploy these systems on real world tasks and hence there has been an increased thrust in exploring data efficient algorithms. To this end, we propose the Shared Learning framework aimed at making $Q$-ensemble algorithms data-efficient. For achieving this, we look into some principles of transfer learning which aim to study the benefits of information exchange across tasks in reinforcement learning and adapt transfer to learning our value function estimates in a novel manner. In this paper, we consider the special case of transfer between the value function estimates in the $Q$-ensemble architecture of BootstrappedDQN. We further empirically demonstrate how our proposed framework can help in speeding up the learning process in $Q$-ensembles with minimum computational overhead on a suite of Atari 2600 Games.
Today's general-purpose deep convolutional neural networks (CNN) for image classification and object detection are trained offline on large static datasets. Some applications, however, will require training in real-time on live video streams with a human-in-the-loop. We refer to this class of problem as Time-ordered Online Training (ToOT) - these problems will require a consideration of not only the quantity of incoming training data, but the human effort required to tag and use it. In this paper, we define training benefit as a metric to measure the effectiveness of a sequence in using each user interaction. We demonstrate and evaluate a system tailored to performing ToOT in the field, capable of training an image classifier on a live video stream through minimal input from a human operator. We show that by exploiting the time-ordered nature of the video stream through optical flow-based object tracking, we can increase the effectiveness of human actions by about 8 times.
In this paper, we introduce Query-based Attention CNN(QACNN) for Text Similarity Map, an end-to-end neural network for question answering. This network is composed of compare mechanism, two-staged CNN architecture with attention mechanism, and a prediction layer. First, the compare mechanism compares between the given passage, query, and multiple answer choices to build similarity maps. Then, the two-staged CNN architecture extracts features through word-level and sentence-level. At the same time, attention mechanism helps CNN focus more on the important part of the passage based on the query information. Finally, the prediction layer find out the most possible answer choice. We conduct this model on the MovieQA dataset using Plot Synopses only, and achieve 79.99% accuracy which is the state of the art on the dataset.
Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCALVOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some smallobjects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS. Keywords: small object detection, feature fusion, real-time, single shot multi-box detector
Information Cascades Model captures dynamical properties of user activity in a social network. In this work, we develop a novel framework for activity shaping under the Continuous-Time Information Cascades Model which allows the administrator for local control actions by allocating targeted resources that can alter the spread of the process. Our framework employs the optimization of the spectral radius of the Hazard matrix, a quantity that has been shown to drive the maximum influence in a network, while enjoying a simple convex relaxation when used to minimize the influence of the cascade. In addition, use-cases such as quarantine and node immunization are discussed to highlight the generality of the proposed activity shaping framework. Finally, we present the NetShape influence minimization method which is compared favorably to baseline and state-of-the-art approaches through simulations on real social networks.
We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms. We demonstrate the versatility of our framework via simple agnostic bounds on unsupervised problems. In the context of clustering, our approach helps choose the number of clusters and the clustering algorithm, remove the outliers, and provably circumvent the Kleinberg's impossibility result. Experimental results across hundreds of problems demonstrate improved performance on unsupervised data with simple algorithms, despite the fact that our problems come from heterogeneous domains. Additionally, our framework lets us leverage deep networks to learn common features from many such small datasets, and perform zero shot learning.
In this paper, we propose a novel explanation module to explain the predictions made by a deep network. The explanation module works by embedding a high-dimensional deep network layer nonlinearly into a low-dimensional explanation space while retaining faithfulness, so that the original deep learning predictions can be constructed from the few concepts extracted by the explanation module. We then visualize such concepts for human to learn about the high-level concepts that deep learning is using to make decisions. We propose an algorithm called Sparse Reconstruction Autoencoder (SRAE) for learning the embedding to the explanation space. SRAE aims to reconstruct part of the original feature space while retaining faithfulness. A pull-away term is applied to SRAE to make the explanation space more orthogonal. A visualization system is then introduced for human understanding of the features in the explanation space. The proposed method is applied to explain CNN models in image classification tasks, and several novel metrics are introduced to evaluate the performance of explanations quantitatively without human involvement. Experiments show that the proposed approach generates interesting explanations of the mechanisms CNN use for making predictions.
We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the expected value at subsequent time-steps. In this paper we consider a similar uncertainty Bellman equation (UBE), which connects the uncertainty at any time-step to the expected uncertainties at subsequent time-steps, thereby extending the potential exploratory benefit of a policy beyond individual time-steps. We prove that the unique fixed point of the UBE yields an upper bound on the variance of the estimated value of any fixed policy. This bound can be much tighter than traditional count-based bonuses that compound standard deviation rather than variance. Importantly, and unlike several existing approaches to optimism, this method scales naturally to large systems with complex generalization. Substituting our UBE-exploration strategy for $\epsilon$-greedy improves DQN performance on 51 out of 57 games in the Atari suite.
Cross-view video understanding is an important yet under-explored area in computer vision. In this paper, we introduce a joint parsing framework that integrates view-centric proposals into scene-centric parse graphs that represent a coherent scene-centric understanding of cross-view scenes. Our key observations are that overlapping fields of views embed rich appearance and geometry correlations and that knowledge fragments corresponding to individual vision tasks are governed by consistency constraints available in commonsense knowledge. The proposed joint parsing framework represents such correlations and constraints explicitly and generates semantic scene-centric parse graphs. Quantitative experiments show that scene-centric predictions in the parse graph outperform view-centric predictions.
Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.
In this work, we develop an end-to-end Reinforcement Learning based architecture for a conversational search agent to assist users in searching on an e-commerce marketplace for digital assets. Our approach caters to a search task fundamentally different from the ones which have limited search modalities where the user can express his preferences objectively. The system interacts with the users to display search results to the queries, and gauges user's intent and context of the conversation to choose the next action and reply. To train the agent in the absence of true conversation data, a virtual user is constructed to model a human user using the query and session logs from a major stock photography and digital assets marketplace. The system provides an alternative that is more engaging than the traditional search while maintaining similar effectiveness. This work provides a mechanism to build and deploy bootstrapped version of an effective conversational agent from readily available query log data. The system can then be used to acquire true conversational data and be fine-tuned further. The methodology discussed in this paper can be extended to e-commerce domains in general.
Recently, digital music libraries have been developed and can be plainly accessed. Latest research showed that current organization and retrieval of music tracks based on album information are inefficient. Moreover, they demonstrated that people use emotion tags for music tracks in order to search and retrieve them. In this paper, we discuss separability of a set of emotional labels, proposed in the categorical emotion expression, using Fisher's separation theorem. We determine a set of adjectives to tag music parts: happy, sad, relaxing, exciting, epic and thriller. Temporal, frequency and energy features have been extracted from the music parts. It could be seen that the maximum separability within the extracted features occurs between relaxing and epic music parts. Finally, we have trained a classifier using Support Vector Machines to automatically recognize and generate emotional labels for a music part. Accuracy for recognizing each label has been calculated; where the results show that epic music can be recognized more accurately (77.4%), comparing to the other types of music.
In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal human guidance. At its core, AI Programmer uses genetic algorithms (GA) coupled with a tightly constrained programming language that minimizes the overhead of its ML search space. Part of AI Programmer's novelty stems from (i) its unique system design, including an embedded, hand-crafted interpreter for efficiency and security and (ii) its augmentation of GAs to include instruction-gene randomization bindings and programming language-specific genome construction and elimination techniques. We provide a detailed examination of AI Programmer's system design, several examples detailing how the system works, and experimental data demonstrating its software generation capabilities and performance using only mainstream CPUs.
A modular method is proposed to learn and transfer visuo-motor policies from simulation to the real world in an efficient manner by combining domain randomization and adaptation. The feasibility of the approach is demonstrated in a table-top object reaching task where a 7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter through visual observations. The learned visuo-motor policies are robust to novel (not seen in training) objects in clutter and even a moving target, achieving a 93.3% success rate and 2.2 cm control accuracy.
To aide simultaneous localization and mapping (SLAM), future perception systems will incorporate forms of scene understanding. In a step towards fully integrated probabilistic geometric scene understanding, localization and mapping we propose the first direction-aware semi-dense SLAM system. It jointly infers the directional Stata Center World (SCW) segmentation and a surfel-based semi-dense map while performing real-time camera tracking. The joint SCW map model connects a scene-wide Bayesian nonparametric Dirichlet Process von-Mises-Fisher mixture model (DP-vMF) prior on surfel orientations with the local surfel locations via a conditional random field (CRF). Camera tracking leverages the SCW segmentation to improve efficiency via guided observation selection. Results demonstrate improved SLAM accuracy and tracking efficiency at state of the art performance.
In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals. We study this problem in a relational setting and make the following contributions. First, we compare two different notions of relational marginals. Second, we show a duality between the resulting relational marginal problems and the maximum likelihood estimation of the parameters of relational models, which generalizes a well-known duality from the propositional setting. Third, by exploiting the relational marginal formulation, we present a statistically sound method to learn the parameters of relational models that will be applied in settings where the number of constants differs between the training and test data. Furthermore, based on a relational generalization of marginal polytopes, we characterize cases where the standard estimators based on feature's number of true groundings needs to be adjusted and we quantitatively characterize the consequences of these adjustments. Fourth, we prove bounds on expected errors of the estimated parameters, which allows us to lower-bound, among other things, the effective sample size of relational training data.
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan is featured for its deep root into Bayesian inference, thus supporting various kinds of probabilistic models, including both the traditional hierarchical Bayesian models and recent deep generative models. We use running examples to illustrate the probabilistic programming on ZhuSuan, including Bayesian logistic regression, variational auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural networks.
Cross-correlator plays a significant role in many visual perception tasks, such as object detection and tracking. Beyond the linear cross-correlator, this paper proposes a kernel cross-correlator (KCC) that breaks traditional limitations. First, by introducing the kernel trick, the KCC extends the linear cross-correlation to non-linear space, which is more robust to signal noises and distortions. Second, the connection to the existing works shows that KCC provides a unified solution for correlation filters. Third, KCC is applicable to any kernel function and is not limited to circulant structure on training data, thus it is able to predict affine transformations with customized properties. Last, by leveraging the fast Fourier transform (FFT), KCC eliminates direct calculation of kernel vectors, thus achieves better performance yet still with a reasonable computational cost. Comprehensive experiments on visual tracking and human activity recognition using wearable devices demonstrate its robustness, flexibility, and efficiency. The source codes of both experiments are released at https://github.com/wang-chen/KCC
What if $\{$a tourist, a train addict, Dr. Sheldon Cooper, somebody who likes to waste time$\}$ wants to visit all metro lines or carriages in a given network in a minimum number of steps? We study this problem with an application to the metro network of Paris and Tokyo, proposing optimal solutions thanks to mathematical programming tools. Quite surprisingly, it appears that you can visit all 16 Parisian metro lines in only 26 steps (we denote by a step the act of taking the metro from one station to an adjacent one). Perhaps even more surprisingly, adding the 5 RER lines to these 16 lines does not increase the size of the best solution. It is also possible to visit the 13 lines of (the dense network of) Tokyo with only 15 steps.
Graph classification is a problem with practical applications in many different domains. Most of the existing methods take the entire graph into account when calculating graph features. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or sub-graphs. In the real-world, however, graphs can be both large and noisy with discriminative patterns confined to certain regions in the graph only. In this work, we study the problem of attentional processing for graph classification. The use of attention allows us to focus on small but informative parts of the graph, avoiding noise in the rest of the graph. We present a novel RNN model, called the Graph Attention Model (GAM), that processes only a portion of the graph by adaptively selecting a sequence of "interesting" nodes. The model is equipped with an external memory component which allows it to integrate information gathered from different parts of the graph. We demonstrate the effectiveness of the model through various experiments.
In this paper we focus on the linear algebra theory behind feedforward (FNN) and recurrent (RNN) neural networks. We review backward propagation, including backward propagation through time (BPTT). Also, we obtain a new exact expression for Hessian, which represents second order effects. We show that for $t$ time steps the weight gradient can be expressed as a rank-$t$ matrix, while the weight Hessian is as a sum of $t^{2}$ Kronecker products of rank-$1$ and $W^{T}AW$ matrices, for some matrix $A$ and weight matrix $W$. Also, we show that for a mini-batch of size $r$, the weight update can be expressed as a rank-$rt$ matrix. Finally, we briefly comment on the eigenvalues of the Hessian matrix.
We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that are restricted to standard Gaussian input. We show that (stochastic) gradient descent with random initialization can learn the convolutional filter in polynomial time and the convergence rate depends on the smoothness of the input distribution and the closeness of patches. To the best of our knowledge, this is the first recovery guarantee of gradient-based algorithms for convolutional filter on non-Gaussian input distributions. Our theory also justifies the two-stage learning rate strategy in deep neural networks. While our focus is theoretical, we also present experiments that illustrate our theoretical findings.
While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by continually aggregating training data from both the expert and novice policies, but does not consider the impact of safety. We present a probabilistic extension to DAgger, which uses the distribution over actions provided by the novice policy, for a given observation. Our method, which we call DropoutDAgger, uses dropout to train the novice as a Bayesian neural network that provides insight to its confidence. Using the distribution over the novice's actions, we estimate a probabilistic measure of safety with respect to the expert action, tuned to balance exploration and exploitation. The utility of this approach is evaluated on the MuJoCo HalfCheetah and in a simple driving experiment, demonstrating improved performance and safety compared to other DAgger variants and classic imitation learning.
Robust Stable Marriage (RSM) is a variant of the classical Stable Marriage problem, where the robustness of a given stable matching is measured by the number of modifications required for repairing it in case an unforeseen event occurs. We focus on the complexity of finding an (a,b)-supermatch. An (a,b)-supermatch is defined as a stable matching in which if any 'a' (non-fixed) men/women break up it is possible to find another stable matching by changing the partners of those 'a' men/women and also the partners of at most 'b' other couples. In order to show deciding if there exists an (a,b)-supermatch is NP-Complete, we first introduce a SAT formulation that is NP-Complete by using Schaefer's Dichotomy Theorem. Then, we show the equivalence between the SAT formulation and finding a (1,1)-supermatch on a specific family of instances.
Online solvers for partially observable Markov decision processes have been applied to problems with large discrete state spaces, but continuous state, action, and observation spaces remain a challenge. This paper begins by investigating double progressive widening (DPW) as a solution to this challenge. However, we prove that this modification alone is not sufficient because the belief representations in the search tree collapse to a single particle causing the algorithm to converge to a policy that is suboptimal regardless of the computation time. This paper proposes and evaluates two new algorithms, POMCPOW and PFT-DPW, that overcome this deficiency by using weighted particle filtering. Simulation results show that these modifications allow the algorithms to be successful where previous approaches fail.
The hospitality industry is one of the data-rich industries that receives huge Volumes of data streaming at high Velocity with considerably Variety, Veracity, and Variability. These properties make the data analysis in the hospitality industry a big data problem. Meeting the customers' expectations is a key factor in the hospitality industry to grasp the customers' loyalty. To achieve this goal, marketing professionals in this industry actively look for ways to utilize their data in the best possible manner and advance their data analytic solutions, such as identifying a unique market segmentation clustering and developing a recommendation system. In this paper, we present a comprehensive literature review of existing big data clustering algorithms and their advantages and disadvantages for various use cases. We implement the existing big data clustering algorithms and provide a quantitative comparison of the performance of different clustering algorithms for different scenarios. We also present our insights and recommendations regarding the suitability of different big data clustering algorithms for different use cases. These recommendations will be helpful for hoteliers in selecting the appropriate market segmentation clustering algorithm for different clustering datasets to improve the customer experience and maximize the hotel revenue.
A value learning system has incentives to follow shutdown instructions, assuming the shutdown instruction provides information (in the technical sense) about which actions lead to valuable outcomes. However, this assumption is not robust to model mis-specification (e.g., in the case of programmer errors). We demonstrate this by presenting some Supervised POMDP scenarios in which errors in the parameterized reward function remove the incentive to follow shutdown commands. These difficulties parallel those discussed by Soares et al. (2015) in their paper on corrigibility. We argue that it is important to consider systems that follow shutdown commands under some weaker set of assumptions (e.g., that one small verified module is correctly implemented; as opposed to an entire prior probability distribution and/or parameterized reward function). We discuss some difficulties with simple ways to attempt to attain these sorts of guarantees in a value learning framework.
In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyze the optimality condition of a sparse MDP. Then, we propose a sparse value iteration method which solves a sparse MDP and then prove the convergence and optimality of sparse value iteration using the Banach fixed point theorem. The proposed sparse MDP is compared to soft MDPs which utilize causal entropy regularization. We show that the performance error of a sparse MDP has a constant bound, while the error of a soft MDP increases logarithmically with respect to the number of actions, where this performance error is caused by the introduced regularization term. In experiments, we apply sparse MDPs to reinforcement learning problems. The proposed method outperforms existing methods in terms of the convergence speed and performance.
Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for interactive and creative usages such as interactive music generation. This paper introduces a novel architecture called Anticipation-RNN which possesses the assets of the RNN-based generative models while allowing to enforce user-defined positional constraints. We demonstrate its efficiency on the task of generating melodies satisfying positional constraints in the style of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using the Anticipation-RNN is of the same order of complexity than sampling from the traditional RNN model. This fast and interactive generation of musical sequences opens ways to devise real-time systems that could be used for creative purposes.
Learning to remember long sequences remains a challenging task for recurrent neural networks. Register memory and attention mechanisms were both proposed to resolve the issue with either high computational cost to retain memory differentiability, or by discounting the RNN representation learning towards encoding shorter local contexts than encouraging long sequence encoding. Associative memory, which studies the compression of multiple patterns in a fixed size memory, were rarely considered in recent years. Although some recent work tries to introduce associative memory in RNN and mimic the energy decay process in Hopfield nets, it inherits the shortcoming of rule-based memory updates, and the memory capacity is limited. This paper proposes a method to learn the memory update rule jointly with task objective to improve memory capacity for remembering long sequences. Also, we propose an architecture that uses multiple such associative memory for more complex input encoding. We observed some interesting facts when compared to other RNN architectures on some well-studied sequence learning tasks.
Generative adversarial networks (GANs) are an exciting alternative to algorithms for solving density estimation problems---using data to assess how likely samples are to be drawn from the same distribution. Instead of explicitly computing these probabilities, GANs learn a generator that can match the given probabilistic source. This paper looks particularly at this matching capability in the context of problems with one-dimensional outputs. We identify a class of function decompositions with properties that make them well suited to the critic role in a leading approach to GANs known as Wasserstein GANs. We show that Taylor and Fourier series decompositions belong to our class, provide examples of these critics outperforming standard GAN approaches, and suggest how they can be scaled to higher dimensional problems in the future.
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.
Understanding properties of deep neural networks is an important challenge in deep learning. In this paper, we take a step in this direction by proposing a rigorous way of verifying properties of a popular class of neural networks, Binarized Neural Networks, using the well-developed means of Boolean satisfiability. Our main contribution is a construction that creates a representation of a binarized neural network as a Boolean formula. Our encoding is the first exact Boolean representation of a deep neural network. Using this encoding, we leverage the power of modern SAT solvers along with a proposed counterexample-guided search procedure to verify various properties of these networks. A particular focus will be on the critical property of robustness to adversarial perturbations. For this property, our experimental results demonstrate that our approach scales to medium-size deep neural networks used in image classification tasks. To the best of our knowledge, this is the first work on verifying properties of deep neural networks using an exact Boolean encoding of the network.
Representing the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, knowledge base completion and relational information retrieval. A simple, yet surprisingly accurate method for representing a relation between two words is to compute the vector offset (\PairDiff) between their corresponding word embeddings. Despite the empirical success, it remains unclear as to whether \PairDiff is the best operator for obtaining a relational representation from word embeddings. We conduct a theoretical analysis of generalised bilinear operators that can be used to measure the $\ell_{2}$ relational distance between two word-pairs. We show that, if the word embeddings are standardised and uncorrelated, such an operator will be independent of bilinear terms, and can be simplified to a linear form, where \PairDiff is a special case. For numerous word embedding types, we empirically verify the uncorrelation assumption, demonstrating the general applicability of our theoretical result. Moreover, we experimentally discover \PairDiff from the bilinear relation composition operator on several benchmark analogy datasets.
Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for intricate tasks. Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations. Yet in reality, the corpus of demonstrations may contain trajectories arising from a diverse set of underlying reward functions rather than a single one. Thus, in inverse reinforcement learning, it is useful to consider such a decomposition. The options framework in reinforcement learning is specifically designed to decompose policies in a similar light. We therefore extend the options framework and propose a method to simultaneously recover reward options in addition to policy options. We leverage adversarial methods to learn joint reward-policy options using only observed expert states. We show that this approach works well in both simple and complex continuous control tasks and shows significant performance increases in one-shot transfer learning.
We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice. In a nutshell, we propose to learn a model of societal preferences, and, when faced with a specific ethical dilemma at runtime, efficiently aggregate those preferences to identify a desirable choice. We provide a concrete algorithm that instantiates our approach; some of its crucial steps are informed by a new theory of swap-dominance efficient voting rules. Finally, we implement and evaluate a system for ethical decision making in the autonomous vehicle domain, using preference data collected from 1.3 million people through the Moral Machine website.
Recently, evolving networks are becoming a suitable form to model many real-world complex systems, due to their peculiarities to represent the systems and their constituting entities, the interactions between the entities and the time-variability of their structure and properties. Designing computational models able to analyze evolving networks becomes relevant in many applications. The goal of this research project is to evaluate the possible contribution of temporal pattern mining techniques in the analysis of evolving networks. In particular, we aim at exploiting available snapshots for the recognition of valuable and potentially useful knowledge about the temporal dynamics exhibited by the network over the time, without making any prior assumption about the underlying evolutionary schema. Pattern-based approaches of temporal pattern mining can be exploited to detect and characterize changes exhibited by a network over the time, starting from observed snapshots.
This paper presents an evaluation of deep neural networks for recognition of digits entered by users on a smartphone touchscreen. A new large dataset of Arabic numerals was collected for training and evaluation of the network. The dataset consists of spatial and temporal touch data recorded for 80 digits entered by 260 users. Two neural network models were investigated. The first model was a 2D convolutional neural (ConvNet) network applied to bitmaps of the glpyhs created by interpolation of the sensed screen touches and its topology is similar to that of previously published models for offline handwriting recognition from scanned images. The second model used a 1D ConvNet architecture but was applied to the sequence of polar vectors connecting the touch points. The models were found to provide accuracies of 98.50% and 95.86%, respectively. The second model was much simpler, providing a reduction in the number of parameters from 1,663,370 to 287,690. The dataset has been made available to the community as an open source resource.
One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or from previous tasks) to accelerate learning on a robot. In this paper, we are interested in situations for which several priors exist but we do not know in advance which one fits best the current situation. We tackle this problem by introducing a novel acquisition function, called Most Likely Expected Improvement (MLEI), that combines the likelihood of the priors and the expected improvement. We evaluate this new acquisition function on a transfer learning task for a 5-DOF planar arm and on a possibly damaged, 6-legged robot that has to learn to walk on flat ground and on stairs, with priors corresponding to different stairs and different kinds of damages. Our results show that MLEI effectively identifies and exploits the priors, even when there is no obvious match between the current situations and the priors.
Deep reinforcement learning yields great results for a large array of problems, but models are generally retrained anew for each new problem to be solved. Prior learning and knowledge are difficult to incorporate when training new models, requiring increasingly longer training as problems become more complex. This is especially problematic for problems with sparse rewards. We provide a solution to these problems by introducing Concept Network Reinforcement Learning (CNRL), a framework which allows us to decompose problems using a multi-level hierarchy. Concepts in a concept network are reusable, and flexible enough to encapsulate feature extractors, skills, or other concept networks. With this hierarchical learning approach, deep reinforcement learning can be used to solve complex tasks in a modular way, through problem decomposition. We demonstrate the strength of CNRL by training a model to grasp a rectangular prism and precisely stack it on top of a cube using a gripper on a Kinova JACO arm, simulated in MuJoCo. Our experiments show that our use of hierarchy results in a 45x reduction in environment interactions compared to the state-of-the-art on this task.
State-of-the-art knowledge compilers generate deterministic subsets of DNNF, which have been recently shown to be exponentially less succinct than DNNF. In this paper, we propose a new method to compile DNNFs without enforcing determinism necessarily. Our approach is based on compiling deterministic DNNFs with the addition of auxiliary variables to the input formula. These variables are then existentially quantified from the deterministic structure in linear time, which would lead to a DNNF that is equivalent to the input formula and not necessarily deterministic. On the theoretical side, we show that the new method could generate exponentially smaller DNNFs than deterministic ones, even by adding a single auxiliary variable. Further, we show that various existing techniques that introduce auxiliary variables to the input formulas can be employed in our framework. On the practical side, we empirically demonstrate that our new method can significantly advance DNNF compilation on certain benchmarks.
Operationalizing machine learning based security detections is extremely challenging, especially in a continuously evolving cloud environment. Conventional anomaly detection does not produce satisfactory results for analysts that are investigating security incidents in the cloud. Model evaluation alone presents its own set of problems due to a lack of benchmark datasets. When deploying these detections, we must deal with model compliance, localization, and data silo issues, among many others. We pose the problem of "attack disruption" as a way forward in the security data science space. In this paper, we describe the framework, challenges, and open questions surrounding the successful operationalization of machine learning based security detections in a cloud environment and provide some insights on how we have addressed them.
Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly enumerates the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.
Deep learning algorithms offer a powerful means to automatically analyze the content of medical images. However, many biological samples of interest are primarily transparent to visible light and contain features that are difficult to resolve with a standard optical microscope. Here, we use a convolutional neural network (CNN) not only to classify images, but also to optimize the physical layout of the imaging device itself. We increase the classification accuracy of a microscope's recorded images by merging an optical model of image formation into the pipeline of a CNN. The resulting network simultaneously determines an ideal illumination arrangement to highlight important sample features during image acquisition, along with a set of convolutional weights to classify the detected images post-capture. We demonstrate our joint optimization technique with an experimental microscope configuration that automatically identifies malaria-infected cells with 5-10% higher accuracy than standard and alternative microscope lighting designs.
We study the problem of learning description logic (DL) ontologies in Angluin et al.'s framework of exact learning via queries. We admit membership queries ("is a given subsumption entailed by the target ontology?") and equivalence queries ("is a given ontology equivalent to the target ontology?"). We present three main results: (1) ontologies formulated in (two relevant versions of) the description logic DL-Lite can be learned with polynomially many queries of polynomial size; (2) this is not the case for ontologies formulated in the description logic EL, even when only acyclic ontologies are admitted; and (3) ontologies formulated in a fragment of EL related to the web ontology language OWL 2 RL can be learned in polynomial time. We also show that neither membership nor equivalence queries alone are sufficient in cases (1) and (3).
We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures. We train a Recurrent Neural Network controller to generate a string in a domain specific language that describes a mathematical update equation based on a list of primitive functions, such as the gradient, running average of the gradient, etc. The controller is trained with Reinforcement Learning to maximize the performance of a model after a few epochs. On CIFAR-10, our method discovers several update rules that are better than many commonly used optimizers, such as Adam, RMSProp, or SGD with and without Momentum on a ConvNet model. We introduce two new optimizers, named PowerSign and AddSign, which we show transfer well and improve training on a variety of different tasks and architectures, including ImageNet classification and Google's neural machine translation system.
In the smart grid, the intent is to use flexibility in demand, both to balance demand and supply as well as to resolve potential congestion. A first prominent example of such flexible demand is the charging of electric vehicles, which do not necessarily need to be charged as soon as they are plugged in. The problem of optimally scheduling the charging demand of electric vehicles within the constraints of the electricity infrastructure is called the charge scheduling problem. The models of the charging speed, horizon, and charging demand determine the computational complexity of the charge scheduling problem. For about 20 variants, we show, using a dynamic programming approach, that the problem is either in P or weakly NP-hard. We also show that about 10 variants of the problem are strongly NP-hard, presenting a potentially significant obstacle to their use in practical situations of scale.
In this paper we focus on the unconstrained binary quadratic optimization model, maximize x^t Qx, x binary, and consider the problem of identifying optimal solutions that are robust with respect to perturbations in the Q matrix.. We are motivated to find robust, or stable, solutions because of the uncertainty inherent in the big data origins of Q and limitations in computer numerical precision, particularly in a new class of quantum annealing computers. Experimental design techniques are used to generate a diverse subset of possible scenarios, from which robust solutions are identified. An illustrative example with practical application to business decision making is examined. The approach presented also generates a surface response equation which is used to estimate upper bounds in constant time for Q instantiations within the scenario extremes. In addition, a theoretical framework for the robustness of individual x_i variables is considered by examining the range of Q values over which the x_i are predetermined.
Many AI systems have a black box nature that makes it difficult to understand how they make their recommendations. This can be unsettling, as the designer cannot be certain how the system will respond to novelty. To penetrate our Na\"ive Bayes recommender's black box, we first asked, what do we want to know from our system, and how can it be obtained? The answers led us to recursively define a common lexicon with the AI, a lingua franca, using the very items that the system ranks to create meta-symbols recognized by the system, and enabling us to understand the system's knowledge in plain terms and at different levels of abstraction. As one bonus, using its existing knowledge, the lingua franca can enable the system to extend recommendations to related, but entirely new areas, ameliorating the cold start problem. We also supplement the lingua franca with techniques for visualizing the system's knowledge state, develop metrics for evaluating the meaningfulness of terms in the lingua franca, and generalize the requirements for developing a similar lingua franca in other applications.
Local search is a basic building block in memetic algorithms. Guided Local Search (GLS) can improve the efficiency of local search. By changing the guide function, GLS guides a local search to escape from locally optimal solutions and find better solutions. The key component of GLS is its penalizing mechanism which determines which feature is selected to penalize when the search is trapped in a locally optimal solution. The original GLS penalizing mechanism only makes use of the cost and the current penalty value of each feature. It is well known that many combinatorial optimization problems have a big valley structure, i.e., the better a solution is, the more the chance it is closer to a globally optimal solution. This paper proposes to use big valley structure assumption to improve the GLS penalizing mechanism. An improved GLS algorithm called Elite Biased GLS (EB-GLS) is proposed. EB-GLS records and maintains an elite solution as an estimate of the globally optimal solutions, and reduces the chance of penalizing the features in this solution. We have systematically tested the proposed algorithm on the symmetric traveling salesman problem. Experimental results show that EB-GLS is significantly better than GLS.
Automatic mesh-based shape generation is of great interest across a wide range of disciplines, from industrial design to gaming, computer graphics and various other forms of digital art. While most traditional methods focus on primitive based model generation, advances in deep learning made it possible to learn 3-dimensional geometric shape representations in an end-to-end manner. However, most current deep learning based frameworks focus on the representation and generation of voxel and point-cloud based shapes, making it not directly applicable to design and graphics communities. This study addresses the needs for automatic generation of mesh-based geometries, and propose a novel framework that utilizes signed distance function representation that generates detail preserving three-dimensional surface mesh by a deep learning based approach.
We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL). In particular, we describe an algorithm using Conditional Choice Probabilities (CCP), which are maximum likelihood estimates of the policy estimated from expert demonstrations, to solve the IRL problem. Using the language of structural econometrics, we re-frame the optimal decision problem and introduce an alternative representation of value functions due to (Hotz and Miller 1993). In addition to presenting the theoretical connections that bridge the IRL literature between Economics and Robotics, the use of CCPs also has the practical benefit of reducing the computational cost of solving the IRL problem. Specifically, under the CCP representation, we show how one can avoid repeated calls to the dynamic programming subroutine typically used in IRL. We show via extensive experimentation on standard IRL benchmarks that CCP-IRL is able to outperform MaxEnt-IRL, with as much as a 5x speedup and without compromising on the quality of the recovered reward function.
Many state-of-the-art algorithms for solving hard combinatorial problems include elements of stochasticity that lead to high variations in runtime, even for a fixed problem instance, across runs with different pseudo-random number seeds. Knowledge about the runtime distributions (RTDs) of algorithms on given problem instances can be exploited in various meta-algorithmic procedures, such as algorithm selection, portfolios, and randomized restarts. Previous work has shown that machine learning can be used to individually predict mean, median and variance of RTDs. To establish a new state-of-the-art in predicting RTDs, we demonstrate that the parameters of an RTD should be learned jointly and that neural networks can do this well by directly optimizing the likelihood of an RTD given runtime observations. In an empirical study involving four algorithms for SAT solving and AI planning, we show that our neural networks predict the true RTDs of unseen instances better than previous methods. As an exemplary application of RTD predictions, we show that our RTD models also yield good predictions of running these algorithms in parallel.
Appropriate comments of code snippets provide insight for code functionality, which are helpful for program comprehension. However, due to the great cost of authoring with the comments, many code projects do not contain adequate comments. Automatic comment generation techniques have been proposed to generate comments from pieces of code in order to alleviate the human efforts in annotating the code. Most existing approaches attempt to exploit certain correlations (usually manually given) between code and generated comments, which could be easily violated if the coding patterns change and hence the performance of comment generation declines. In this paper, we first build C2CGit, a large dataset from open projects in GitHub, which is more than 20$\times$ larger than existing datasets. Then we propose a new attention module called Code Attention to translate code to comments, which is able to utilize the domain features of code snippets, such as symbols and identifiers. We make ablation studies to determine effects of different parts in Code Attention. Experimental results demonstrate that the proposed module has better performance over existing approaches in both BLEU and METEOR.
While deep reinforcement learning techniques have recently produced considerable achievements on many decision-making problems, their use in robotics has largely been limited to simulated worlds or restricted motions, since unconstrained trial-and-error interactions in the real world can have undesirable consequences for the robot or its environment. To overcome such limitations, we propose a novel reinforcement learning architecture, OptLayer, that takes as inputs possibly unsafe actions predicted by a neural network and outputs the closest actions that satisfy chosen constraints. While learning control policies often requires carefully crafted rewards and penalties while exploring the range of possible actions, OptLayer ensures that only safe actions are actually executed and unsafe predictions are penalized during training. We demonstrate the effectiveness of our approach on robot reaching tasks, both simulated and in the real world.
The "Loving AI" project involves developing software enabling humanoid robots to interact with people in loving and compassionate ways, and to promote people' self-understanding and self-transcendence. Currently the project centers on the Hanson Robotics robot "Sophia" -- specifically, on supplying Sophia with personality content and cognitive, linguistic, perceptual and behavioral content aimed at enabling loving interactions supportive of human self-transcendence. In September 2017 a small pilot study was conducted, involving the Sophia robot leading human subjects through dialogues and exercises focused on meditation, visualization and relaxation. The pilot was an apparent success, qualitatively demonstrating the viability of the approach and the ability of appropriate human-robot interaction to increase human well-being and advance human consciousness.
This paper stands in the context of reinforcement learning with partial observability and limited data. In this setting, we focus on the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data), and theoretically show that while potentially increasing the asymptotic bias, a smaller state representation decreases the risk of overfitting. Our analysis relies on expressing the quality of a state representation by bounding L1 error terms of the associated belief states. Theoretical results are empirically illustrated when the state representation is a truncated history of observations. Finally, we also discuss and empirically illustrate how using function approximators and adapting the discount factor may enhance the tradeoff between asymptotic bias and overfitting.
We propose a method to build quantum memristors in quantum photonic platforms. We firstly design an effective beam splitter, which is tunable in real-time, by means of a Mach-Zehnder-type array with two equal 50:50 beam splitters and a tunable retarder, which allows us to control its reflectivity. Then, we show that this tunable beam splitter, when equipped with weak measurements and classical feedback, behaves as a quantum memristor. Indeed, in order to prove its quantumness, we show how to codify quantum information in the coherent beams. Moreover, we estimate the memory capability of the quantum memristor. Finally, we show the feasibility of the proposed setup in integrated quantum photonics.
We propose a protocol to perform generalized quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits [L. Lamata, Sci. Rep. 7, 1609 (2017)], in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.
Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution with regard to a control variable. That is, when the distribution of this control variable is the same, or almost the same, as over the whole data. We formalise this objective function and give an efficient algorithm to compute its tight optimistic estimator for the case of a numeric target and a binary control variable. This enables us to use the branch-and-bound framework to efficiently discover the top-$k$ subgroups that are both exceptional as well as representative. Experimental evaluation on a wide range of datasets shows that with this algorithm we discover meaningful representative patterns and are up to orders of magnitude faster in terms of node evaluations as well as time.
Models based on Convolutional Neural Networks (CNNs) have been proven very successful for semantic segmentation and object parsing that yield hierarchies of features. Our key insight is to build convolutional networks that take input of arbitrary size and produce object parsing output with efficient inference and learning. In this work, we focus on the task of instance segmentation and parsing which recognizes and localizes objects down to a pixel level base on deep CNN. Therefore, unlike some related work, a pixel cannot belong to multiple instances and parsing. Our model is based on a deep neural network trained for object masking that supervised with input image and follow incorporates a Conditional Random Field (CRF) with end-to-end trainable piecewise order potentials based on object parsing outputs. In each CRF unit we designed terms to capture the short range and long range dependencies from various neighbors. The accurate instance-level segmentation that our network produce is reflected by the considerable improvements obtained over previous work at high APr thresholds. We demonstrate the effectiveness of our model with extensive experiments on challenging dataset subset of PASCAL VOC2012.
This paper contains analysis of concept of a class within different object-oriented knowledge representation models. The main attention is paid to structure of the class and its efficiency in the context of data storage, using object-relational mapping. The main achievement of the paper is extension of concept of homogeneous class of objects by introducing concepts of single-core and multi-core inhomogeneous classes of objects, which allow simultaneous defining of a few different types within one class of objects, avoiding duplication of properties and methods in representation of types, decreasing sizes of program codes and providing more efficient information storage in the databases. In addition, the paper contains results of experiment, which show that data storage in relational database, using proposed extensions of the class, in some cases is more efficient in contrast to usage of homogeneous classes of objects.
The new era of the Web is known as the semantic Web or the Web of data. The semantic Web depends on ontologies that are seen as one of its pillars. The bigger these ontologies, the greater their exploitation. However, when these ontologies become too big other problems may appear, such as the complexity to charge big files in memory, the time it needs to download such files and especially the time it needs to make reasoning on them. We discuss in this paper approaches for segmenting such big Web ontologies as well as its usefulness. The segmentation method extracts from an existing ontology a segment that represents a layer or a generation in the existing ontology; i.e. a horizontally extraction. The extracted segment should be itself an ontology.
To resolve conflicts among norms, various nonmonotonic formalisms can be used to perform prioritized normative reasoning. Meanwhile, formal argumentation provides a way to represent nonmonotonic logics. In this paper, we propose a representation of prioritized normative reasoning by argumentation. Using hierarchical abstract normative systems, we define three kinds of prioritized normative reasoning approaches, called Greedy, Reduction, and Optimization. Then, after formulating an argumentation theory for a hierarchical abstract normative system, we show that for a totally ordered hierarchical abstract normative system, Greedy and Reduction can be represented in argumentation by applying the weakest link and the last link principles respectively, and Optimization can be represented by introducing additional defeats capturing the idea that for each argument that contains a norm not belonging to the maximal obeyable set then this argument should be rejected.
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) architectures, including a novel cross-modal LSTM (X-LSTM), and demonstrate their superiority over baseline approaches. The X-LSTM improves parameter efficiency by processing each modality separately and allowing for information flow between them by way of recurrent cross-connections. We present a general hyperparameter optimisation technique for X-LSTMs, which allows us to significantly improve on the LSTM and a prior state-of-the-art cross-modal approach, using a comparable number of parameters. Finally, we visualise the model's predictions, revealing implications about latent variables in this task.
Self-supervised learning (SSL) is a reliable learning mechanism in which a robot enhances its perceptual capabilities. Typically, in SSL a trusted, primary sensor cue provides supervised training data to a secondary sensor cue. In this article, a theoretical analysis is performed on the fusion of the primary and secondary cue in a minimal model of SSL. A proof is provided that determines the specific conditions under which it is favorable to perform fusion. In short, it is favorable when (i) the prior on the target value is strong or (ii) the secondary cue is sufficiently accurate. The theoretical findings are validated with computational experiments. Subsequently, a real-world case study is performed to investigate if fusion in SSL is also beneficial when assumptions of the minimal model are not met. In particular, a flying robot learns to map pressure measurements to sonar height measurements and then fuses the two, resulting in better height estimation. Fusion is also beneficial in the opposite case, when pressure is the primary cue. The analysis and results are encouraging to study SSL fusion also for other robots and sensors.
We present a probabilistic model of an intrusion in a marked renewal process. Given a process and a sequence of events, an intrusion is a subsequence of events that is not produced by the process. Applications of the model are, for example, online payment fraud with the fraudster taking over a user's account and performing payments on the user's behalf, or unexpected equipment failures due to unintended use. We adopt Bayesian approach to infer the probability of an intrusion in a sequence of events, a MAP subsequence of events constituting the intrusion, and the marginal probability of each event in a sequence to belong to the intrusion. We evaluate the model for intrusion detection on synthetic data, as well as on anonymized data from an online payment system.
Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source policies during the learning process is of great importance yet challenging. There has been little theoretical analysis of this problem. In this paper, we develop an optimal online method to select source policies for reinforcement learning. This method formulates online source policy selection as a multi-armed bandit problem and augments Q-learning with policy reuse. We provide theoretical guarantees of the optimal selection process and convergence to the optimal policy. In addition, we conduct experiments on a grid-based robot navigation domain to demonstrate its efficiency and robustness by comparing to the state-of-the-art transfer learning method.
While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints. In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the policy by a convolutional neural network. We develop a hierarchical approach that combines a model-free policy gradient method with a conventional feedback proportional-integral-derivative (PID) controller to enable stable learning without catastrophic failure. The neural network is trained by a combination of supervised learning from raw images and reinforcement learning from games of self-play. We show that the proposed approach can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to the DJI quadrotor platform for real-world UAV control.
The continually increasing number of documents produced each year necessitates ever improving information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supervised learning. Recently the performance of these traditional classifiers has degraded as the number of documents has increased. This is because along with this growth in the number of documents has come an increase in the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy.
We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expressed in terms of arbitrary, dynamic graphs. While many approaches to structured prediction place strict constraints on the interactions between inferred variables, many real-world problems can be only characterized using complex graph structures of varying size, often contaminated with noise when obtained from real data. Here, we focus on one such problem in the domain of robotics. We demonstrate how GraphSPNs can be used to bolster inference about semantic, conceptual place descriptions using noisy topological relations discovered by a robot exploring large-scale office spaces. Through experiments, we show that GraphSPNs consistently outperform the traditional approach based on undirected graphical models, successfully disambiguating information in global semantic maps built from uncertain, noisy local evidence. We further exploit the probabilistic nature of the model to infer marginal distributions over semantic descriptions of as yet unexplored places and detect spatial environment configurations that are novel and incongruent with the known evidence.
Persuasivenes is a creative art aimed at making people believe in certain set of beliefs. Many a times, such creativity is about adapting richness of one domain into another to strike a chord with the target audience. In this research, we present PersuAIDE! - A persuasive system based on linguistic creativity to transform given sentence to generate various forms of persuading sentences. These various forms cover multiple focus of persuasion such as memorability and sentiment. For a given simple product line, the algorithm is composed of several steps including: (i) select an appropriate well-known expression for the target domain to add memorability, (ii) identify keywords and entities in the given sentence and expression and transform it to produce creative persuading sentence, and (iii) adding positive or negative sentiment for further persuasion. The persuasive conversion were manually verified using qualitative results and the effectiveness of the proposed approach is empirically discussed.
Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a DCNN, is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of contemporary data compendiums, hence feeding the model cognition, a methodology for the procedural generation of synthetic cryptographic binaries is defined, utilising core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesis combinable variants which are automatically fed in its core model. Converging at 91% accuracy, CryptoKnight is successfully able to classify the sample algorithms with minimal loss.
Graph based semi-supervised learning (GSSL) has intuitive representation and can be improved by exploiting the matrix calculation. However, it has to perform iterative optimization to achieve a preset objective, which usually leads to low efficiency. Another inconvenience lying in GSSL is that when new data come, the graph construction and the optimization have to be conducted all over again. We propose a sound assumption, arguing that: the neighboring data points are not in peer-to-peer relation, but in a partial-ordered relation induced by the local density and distance between the data; and the label of a center can be regarded as the contribution of its followers. Starting from the assumption, we develop a highly efficient non-iterative label propagation algorithm based on a novel data structure named as optimal leading forest (LaPOLeaF). The major weaknesses of the traditional GSSL are addressed by this study. We further scale LaPOLeaF to accommodate big data by utilizing block distance matrix technique, parallel computing, and Locality-Sensitive Hashing (LSH). Experiments on large datasets have shown the promising results of the proposed methods.
In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach. The work is based on the deep deterministic policy gradient (DDPG) algorithm, proven to be very successful in simple simulation environments. The algorithm works in an end-to-end fashion in order to control the continuous position of the flippers. This end-to-end approach makes it easy to apply the controller to a wide array of circumstances, but the huge flexibility comes to the cost of an increased difficulty of solution. The complexity of the task is enlarged even more by the fact that real multi-terrain robots move in partially observable environments. Notwithstanding these complications, being able to smoothly control a multi-terrain robot can produce huge benefits in impaired people daily lives or in search and rescue situations.
We study the quantum synchronization between a pair of two-level systems inside two coupledcavities. Using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in this digital-analog block decomposition the fundamental elements of a quantum machine learning protocol, in which the agent and the environment (learning units) interact through a mediating system, namely, the register. If we can additionally equip this algorithm with a classical feedback mechanism, which consists of projective measurements in the register, reinitialization of the register state and local conditional operations on the agent and register subspace, a powerful and flexible quantum machine learning protocol emerges. Indeed, numerical simulations show that this protocol enhances the synchronization process, even when every subsystem experience different loss/decoherence mechanisms, and give us flexibility to choose the synchronization state. Finally, we propose an implementation based on current technologies in superconducting circuits.
The growing importance and utilization of measuring brain waves (e.g. EEG signals of eye state) in brain-computer interface (BCI) applications highlighted the need for suitable classification methods. In this paper, a comparison between three of well-known classification methods (i.e. support vector machine (SVM), hidden Markov map (HMM), and radial basis function (RBF)) for EEG based eye state classification was achieved. Furthermore, a suggested method that is based on ensemble model was tested. The suggested (ensemble system) method based on a voting algorithm with two kernels: random forest (RF) and Kstar classification methods. The performance was tested using three measurement parameters: accuracy, mean absolute error (MAE), and confusion matrix. Results showed that the proposed method outperforms the other tested methods. For instance, the suggested method's performance was 97.27% accuracy and 0.13 MAE.
Daily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken. We describe an approach that uses Machine Learning for the automated system to monitor data quality, which is based on partial use of data qualified manually by detector experts. The system automatically classifies marginal cases: both of good an bad data, and use human expert decision to classify remaining "grey area" cases. This study uses collision data collected by the CMS experiment at LHC in 2010. We demonstrate that proposed workflow is able to automatically process at least 20\% of samples without noticeable degradation of the result.
Adversarial attacks are known to succeed on classifiers, but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial examples for vision and language models, which incorporate natural language understanding and complex structures such as attention, localization, and modular architectures. In particular, we investigate attacks on a dense captioning model and on two visual question answering (VQA) models. Our evaluation shows that we can generate adversarial examples with a high success rate (i.e., > 90%) for these models. Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks. These observations will inform future work towards building effective defenses.
The process of building ontologies is a difficult task that involves collaboration between ontology developers and domain experts and requires an ongoing interaction between them. This collaboration is made more difficult, because they tend to use different tool sets, which can hamper this interaction. In this paper, we propose to decrease this distance between domain experts and ontology developers by creating more readable forms of ontologies, and further to enable editing in normal office environments. Building on a programmatic ontology development environment, such as Tawny-OWL, we are now able to generate these readable/editable from the raw ontological source and its embedded comments. We have this translation to HTML for reading; this environment provides rich hyperlinking as well as active features such as hiding the source code in favour of comments. We are now working on translation to a Word document that also enables editing. Taken together this should provide a significant new route for collaboration between the ontologist and domain specialist.
This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.
Question processing is a fundamental step in a question answering (QA) application, and its quality impacts the performance of QA application. The major challenging issue in processing question is how to extract semantic of natural language questions (NLQs). A human language is ambiguous. Ambiguity may occur at two levels; lexical and syntactic. In this paper, we propose a new approach for resolving lexical ambiguity problem by integrating context knowledge and concepts knowledge of a domain, into shallow natural language processing (SNLP) techniques. Concepts knowledge is modeled using ontology, while context knowledge is obtained from WordNet, and it is determined based on neighborhood words in a question. The approach will be applied to a university QA system.
Resource allocation is still a difficult issue to deal with in wireless networks. The unstable channel condition and traffic demand for Quality of Service (QoS) raise some barriers that interfere with the process. It is significant that an optimal policy takes into account some resources available to each traffic class while considering the spectral efficiency and other related channel issues. Reinforcement learning is a dynamic and effective method to support the accomplishment of resource allocation properly maintaining QoS levels for applications. The technique can track the system state as feedback to enhance the performance of a given task. Herein, it is proposed a simple reinforcement learning mechanism introduced in LTE-A networks and aimed to choose and limit the number of resources allocated for each traffic class, regarding the QoS Class Identifier (QCI), at each Transmission Time Interval (TTI) along the scheduling procedure. The proposed mechanism implements a Markov Decision Process (MDP) solved by the Q-Learning algorithm to find an optimal action-state decision policy. The results obtained from simulation exhibit good performance, especially for the real-time Video application.
Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations(wider than adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method.
In the context of the Electronic Health Record, automated diagnosis coding of patient notes is a useful task, but a challenging one due to the large number of codes and the length of patient notes. We investigate four models for assigning multiple ICD codes to discharge summaries taken from both MIMIC II and III. We present Hierarchical Attention-GRU (HA-GRU), a hierarchical approach to tag a document by identifying the sentences relevant for each label. HA-GRU achieves state-of-the art results. Furthermore, the learned sentence-level attention layer highlights the model decision process, allows easier error analysis, and suggests future directions for improvement.
Reward engineering is an important aspect of reinforcement learning. Whether or not the user's intentions can be correctly encapsulated in the reward function can significantly impact the learning outcome. Current methods rely on manually crafted reward functions that often require parameter tuning to obtain the desired behavior. This operation can be expensive when exploration requires systems to interact with the physical world. In this paper, we explore the use of temporal logic (TL) to specify tasks in reinforcement learning. TL formula can be translated to a real-valued function that measures its level of satisfaction against a trajectory. We take advantage of this function and propose temporal logic policy search (TLPS), a model-free learning technique that finds a policy that satisfies the TL specification. A set of simulated experiments are conducted to evaluate the proposed approach.
Named Entity Recognition (NER) is one of the most common tasks of the natural language processing. The purpose of NER is to find and classify tokens in text documents into predefined categories called tags, such as person names, quantity expressions, percentage expressions, names of locations, organizations, as well as expression of time, currency and others. Although there is a number of approaches have been proposed for this task in Russian language, it still has a substantial potential for the better solutions. In this work, we studied several deep neural network models starting from vanilla Bi-directional Long Short-Term Memory (Bi-LSTM) then supplementing it with Conditional Random Fields (CRF) as well as highway networks and finally adding external word embeddings. All models were evaluated across three datasets: Gareev's dataset, Person-1000, FactRuEval-2016. We found that extension of Bi-LSTM model with CRF significantly increased the quality of predictions. Encoding input tokens with external word embeddings reduced training time and allowed to achieve state of the art for the Russian NER task.
Goal recognition is the problem of inferring the goal of an agent, based on its observed actions. An inspiring approach - plan recognition by planning (PRP) - uses off-the-shelf planners to dynamically generate plans for given goals, eliminating the need for the traditional plan library. However, existing PRP formulation is inherently inefficient in online recognition, and cannot be used with motion planners for continuous spaces. In this paper, we utilize a different PRP formulation which allows for online goal recognition, and for application in continuous spaces. We present an online recognition algorithm, where two heuristic decision points may be used to improve run-time significantly over existing work. We specify heuristics for continuous domains, prove guarantees on their use, and empirically evaluate the algorithm over hundreds of experiments in both a 3D navigational environment and a cooperative robotic team task.
The reliable measurement of confidence in classifiers' predictions is very important for many applications and is, therefore, an important part of classifier design. Yet, although deep learning has received tremendous attention in recent years, not much progress has been made in quantifying the prediction confidence of neural network classifiers. Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with prohibitive computational costs. In this paper we propose a simple, scalable method to achieve a reliable confidence score, based on the data embedding derived from the penultimate layer of the network. We investigate two ways to achieve desirable embeddings, by using either a distance-based loss or Adversarial Training. We then test the benefits of our method when used for classification error prediction, weighting an ensemble of classifiers, and novelty detection. In all tasks we show significant improvement over traditional, commonly used confidence scores.
We report on an extensive study of the current benefits and limitations of deep learning approaches to robot vision and introduce a novel dataset used for our investigation. To avoid the biases in currently available datasets, we consider a human-robot interaction setting to design a data-acquisition protocol for visual object recognition on the iCub humanoid robot. Considering the performance of off-the-shelf models trained on off-line large-scale image retrieval datasets, we show the necessity for knowledge transfer. Indeed, we analyze different ways in which this last step can be done, and identify the major bottlenecks in robotics scenarios. By studying both object categorization and identification tasks, we highlight the key differences between object recognition in robotics and in image retrieval tasks, for which the considered deep learning approaches have been originally designed. In a nutshell, our results confirm also in the considered setting the remarkable improvements yield by deep learning, while pointing to specific open challenges that need to be addressed for seamless deployment in robotics.
The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of floating point operations. Several works have been developed to compress a pre-trained deep network to reduce memory footprint and, possibly, computation. Instead of compressing a pre-trained network, in this work, we propose a generic neural network layer structure employing multilinear projection as the primary feature extractor. The proposed architecture requires several times less memory as compared to the traditional Convolutional Neural Networks (CNN), while inherits the similar design principles of a CNN. In addition, the proposed architecture is equipped with two computation schemes that enable computation reduction or scalability. Experimental results show the effectiveness of our compact projection that outperforms traditional CNN, while requiring far fewer parameters.
One of the key challenges for operations researchers solving real-world problems is designing and implementing high-quality heuristics to guide their search procedures. In the past, machine learning techniques have failed to play a major role in operations research approaches, especially in terms of guiding branching and pruning decisions. We integrate deep neural networks into a heuristic tree search procedure to decide which branch to choose next and to estimate a bound for pruning the search tree of an optimization problem. We call our approach Deep Learning assisted heuristic Tree Search (DLTS) and apply it to a well-known problem from the container terminals literature, the container pre-marshalling problem (CPMP). Our approach is able to learn heuristics customized to the CPMP solely through analyzing the solutions to CPMP instances, and applies this knowledge within a heuristic tree search to produce the highest quality heuristic solutions to the CPMP to date.
Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform multiple tasks in human-centric environments. However, effectively controlling them remains challenging due to their high dimensionality and large number of potential contacts. Deep reinforcement learning (DRL) provides a model-agnostic approach to control complex dynamical systems, but has not been shown to scale to high-dimensional dexterous manipulation. Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency. Thus, the success of DRL in robotics has thus far been limited to simpler manipulators and tasks. In this work, we show that model-free DRL with natural policy gradients can effectively scale up to complex manipulation tasks with a high-dimensional 24-DoF hand, and solve them from scratch in simulated experiments. Furthermore, with the use of a small number of human demonstrations, the sample complexity can be significantly reduced, and enable learning within the equivalent of a few hours of robot experience. We demonstrate successful policies for multiple complex tasks: object relocation, in-hand manipulation, tool use, and door opening.
Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL). Many tasks are natural to specify with a sparse reward, and manually shaping a reward function can result in suboptimal performance. However, finding a non-zero reward is exponentially more difficult with increasing task horizon or action dimensionality. This puts many real-world tasks out of practical reach of RL methods. In this work, we use demonstrations to overcome the exploration problem and successfully learn to perform long-horizon, multi-step robotics tasks with continuous control such as stacking blocks with a robot arm. Our method, which builds on top of Deep Deterministic Policy Gradients and Hindsight Experience Replay, provides an order of magnitude of speedup over RL on simulated robotics tasks. It is simple to implement and makes only the additional assumption that we can collect a small set of demonstrations. Furthermore, our method is able to solve tasks not solvable by either RL or behavior cloning alone, and often ends up outperforming the demonstrator policy.
This paper proposes a novel neural machine reading model for open-domain question answering at scale. Existing machine comprehension models typically assume that a short piece of relevant text containing answers is already identified and given to the models, from which the models are designed to extract answers. This assumption, however, is not realistic for building a large-scale open-domain question answering system which requires both deep text understanding and identifying relevant text from corpus simultaneously. In this paper, we introduce Neural Comprehensive Ranker (NCR) that integrates both passage ranking and answer extraction in one single framework. A Q&A system based on this framework allows users to issue an open-domain question without needing to provide a piece of text that must contain the answer. Experiments show that the unified NCR model is able to outperform the states-of-the-art in both retrieval of relevant text and answer extraction.
The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network. In recent years, several techniques have been proposed for increasing robustness to adversarial examples --- and yet most of these have been quickly shown to be vulnerable to future attacks. For example, over half of the defenses proposed by papers accepted at ICLR 2018 have already been broken. We propose to address this difficulty through formal verification techniques. We show how to construct provably minimally distorted adversarial examples: given an arbitrary neural network and input sample, we can construct adversarial examples which we prove are of minimal distortion. Using this approach, we demonstrate that one of the recent ICLR defense proposals, adversarial retraining, provably succeeds at increasing the distortion required to construct adversarial examples by a factor of 4.2.
We present a multi-modal dialogue system for interactive learning of perceptually grounded word meanings from a human tutor. The system integrates an incremental, semantic parsing/generation framework - Dynamic Syntax and Type Theory with Records (DS-TTR) - with a set of visual classifiers that are learned throughout the interaction and which ground the meaning representations that it produces. We use this system in interaction with a simulated human tutor to study the effects of different dialogue policies and capabilities on the accuracy of learned meanings, learning rates, and efforts/costs to the tutor. We show that the overall performance of the learning agent is affected by (1) who takes initiative in the dialogues; (2) the ability to express/use their confidence level about visual attributes; and (3) the ability to process elliptical and incrementally constructed dialogue turns. Ultimately, we train an adaptive dialogue policy which optimises the trade-off between classifier accuracy and tutoring costs.
We present a novel framework for the automatic discovery and recognition of human motion primitives from motion capture data. Human motion primitives are discovered by optimizing the 'motion flux', a quantity which depends on the motion of a group of skeletal joints. Models of each primitive category are computed via non-parametric Bayes methods and recognition is performed based on their geometric properties. A normalization of the primitives is proposed in order to make them invariant with respect to anatomical variations and data sampling rate. Using our framework we build a publicly available dataset of human motion primitives based on motion capture sequences taken from well-known datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields related to Robotics including human inspired motion generation, learning by demonstration, and intuitive human-robot interaction.
Execution monitor of high-level robot actions can be effectively improved by visual monitoring the state of the world in terms of preconditions and postconditions that hold before and after the execution of an action. Furthermore a policy for searching where to look at, either for verifying the relations that specify the pre and postconditions or to refocus in case of a failure, can tremendously improve the robot execution in an uncharted environment. It is now possible to strongly rely on visual perception in order to make the assumption that the environment is observable, by the amazing results of deep learning. In this work we present visual execution monitoring for a robot executing tasks in an uncharted Lab environment. The execution monitor interacts with the environment via a visual stream that uses two DCNN for recognizing the objects the robot has to deal with and manipulate, and a non-parametric Bayes estimation to discover the relations out of the DCNN features. To recover from lack of focus and failures due to missed objects we resort to visual search policies via deep reinforcement learning.
In this paper we study the personalized text search problem. The keyword based search method in conventional algorithms has a low efficiency in understanding users' intention since the semantic meaning, user profile, user interests are not always considered. Firstly, we propose a novel text search algorithm using a inverse filtering mechanism that is very efficient for label based item search. Secondly, we adopt the Bayesian network to implement the user interest prediction for an improved personalized search. According to user input, it searches the related items using keyword information, predicted user interest. Thirdly, the word vectorization is used to discover potential targets according to the semantic meaning. Experimental results show that the proposed search engine has an improved efficiency and accuracy and it can operate on embedded devices with very limited computational resources.
Deep reinforcement learning for multi-agent cooperation and competition has been a hot topic recently. This paper focuses on cooperative multi-agent problem based on actor-critic methods under local observations settings. Multi agent deep deterministic policy gradient obtained state of art results for some multi-agent games, whereas, it cannot scale well with growing amount of agents. In order to boost scalability, we propose a parameter sharing deterministic policy gradient method with three variants based on neural networks, including actor-critic sharing, actor sharing and actor sharing with partially shared critic. Benchmarks from rllab show that the proposed method has advantages in learning speed and memory efficiency, well scales with growing amount of agents, and moreover, it can make full use of reward sharing and exchangeability if possible.
Selecting an optimal event representation is essential for event classification in real world contexts. In this paper, we investigate the application of qualitative spatial reasoning (QSR) frameworks for classification of human-object interaction in three dimensional space, in comparison with the use of quantitative feature extraction approaches for the same purpose. In particular, we modify QSRLib, a library that allows computation of Qualitative Spatial Relations and Calculi, and employ it for feature extraction, before inputting features into our neural network models. Using an experimental setup involving motion captures of human-object interaction as three dimensional inputs, we observe that the use of qualitative spatial features significantly improves the performance of our machine learning algorithm against our baseline, while quantitative features of similar kinds fail to deliver similar improvement. We also observe that sequential representations of QSR features yield the best classification performance. A result of our learning method is a simple approach to the qualitative representation of 3D activities as compositions of 2D actions that can be visualized and learned using 2-dimensional QSR.
We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards. Recent approaches have shown great successes in many Atari 2600 domains. However, domains with long horizons and sparse rewards, such as Montezuma's Revenge and Venture, remain challenging for existing methods. Methods using abstraction (Dietterich 2000; Sutton, Precup, and Singh 1999) have shown to be useful in tackling long-horizon problems. We combine recent techniques of deep reinforcement learning with existing model-based approaches using an expert-provided state abstraction. We construct toy domains that elucidate the problem of long horizons, sparse rewards and high-dimensional inputs, and show that our algorithm significantly outperforms previous methods on these domains. Our abstraction-based approach outperforms Deep Q-Networks (Mnih et al. 2015) on Montezuma's Revenge and Venture, and exhibits backtracking behavior that is absent from previous methods.
Speech recognition is largely taking advantage of deep learning, showing that substantial benefits can be obtained by modern Recurrent Neural Networks (RNNs). The most popular RNNs are Long Short-Term Memory (LSTMs), which typically reach state-of-the-art performance in many tasks thanks to their ability to learn long-term dependencies and robustness to vanishing gradients. Nevertheless, LSTMs have a rather complex design with three multiplicative gates, that might impair their efficient implementation. An attempt to simplify LSTMs has recently led to Gated Recurrent Units (GRUs), which are based on just two multiplicative gates. This paper builds on these efforts by further revising GRUs and proposing a simplified architecture potentially more suitable for speech recognition. The contribution of this work is two-fold. First, we suggest to remove the reset gate in the GRU design, resulting in a more efficient single-gate architecture. Second, we propose to replace tanh with ReLU activations in the state update equations. Results show that, in our implementation, the revised architecture reduces the per-epoch training time with more than 30% and consistently improves recognition performance across different tasks, input features, and noisy conditions when compared to a standard GRU.
Partially observable Markov decision processes (POMDPs) are widely used in probabilistic planning problems in which an agent interacts with an environment using noisy and imprecise sensors. We study a setting in which the sensors are only partially defined and the goal is to synthesize "weakest" additional sensors, such that in the resulting POMDP, there is a small-memory policy for the agent that almost-surely (with probability~1) satisfies a reachability objective. We show that the problem is NP-complete, and present a symbolic algorithm by encoding the problem into SAT instances. We illustrate trade-offs between the amount of memory of the policy and the number of additional sensors on a simple example. We have implemented our approach and consider three classical POMDP examples from the literature, and show that in all the examples the number of sensors can be significantly decreased (as compared to the existing solutions in the literature) without increasing the complexity of the policies.
Automatic feature learning algorithms are at the forefront of modern day machine learning research. We present a novel algorithm, supervised Q-walk, which applies Q-learning to generate random walks on graphs such that the walks prove to be useful for learning node features suitable for tackling with the node classification problem. We present another novel algorithm, k-hops neighborhood based confidence values learner, which learns confidence values of labels for unlabelled nodes in the network without first learning the node embedding. These confidence values aid in learning an apt reward function for Q-learning. We demonstrate the efficacy of supervised Q-walk approach over existing state-of-the-art random walk based node embedding learners in solving the single / multi-label multi-class node classification problem using several real world datasets. Summarising, our approach represents a novel state-of-the-art technique to learn features, for nodes in networks, tailor-made for dealing with the node classification problem.
Deep Neural Networks (DNNs) require very large amounts of computation both for training and for inference when deployed in the field. Many different algorithms have been proposed to implement the most computationally expensive layers of DNNs. Further, each of these algorithms has a large number of variants, which offer different trade-offs of parallelism, data locality, memory footprint, and execution time. In addition, specific algorithms operate much more efficiently on specialized data layouts and formats. We state the problem of optimal primitive selection in the presence of data format transformations, and show that it is NP-hard by demonstrating an embedding in the Partitioned Boolean Quadratic Assignment problem (PBQP). We propose an analytic solution via a PBQP solver, and evaluate our approach experimentally by optimizing several popular DNNs using a library of more than 70 DNN primitives, on an embedded platform and a general purpose platform. We show experimentally that significant gains are possible versus the state of the art vendor libraries by using a principled analytic solution to the problem of layout selection in the presence of data format transformations.
Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications. One way to construct these embeddings is to acquire estimates of similarity from the crowd. However, similarity is a multi-dimensional concept that varies from individual to individual. Existing models for learning embeddings from the crowd typically make simplifying assumptions such as all individuals estimate similarity using the same criteria, the list of criteria is known in advance, or that the crowd workers are not influenced by the data that they see. To overcome these limitations we introduce Context Embedding Networks (CENs). In addition to learning interpretable embeddings from images, CENs also model worker biases for different attributes along with the visual context i.e. the visual attributes highlighted by a set of images. Experiments on two noisy crowd annotated datasets show that modeling both worker bias and visual context results in more interpretable embeddings compared to existing approaches.
In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well to- wards unseen tasks with increasing lengths, variable topologies, and changing objectives.
A current challenge for data management systems is to support the construction and maintenance of machine learning models over data that is large, multi-dimensional, and evolving. While systems that could support these tasks are emerging, the need to scale to distributed, streaming data requires new models and algorithms. In this setting, as well as computational scalability and model accuracy, we also need to minimize the amount of communication between distributed processors, which is the chief component of latency. We study Bayesian networks, the workhorse of graphical models, and present a communication-efficient method for continuously learning and maintaining a Bayesian network model over data that is arriving as a distributed stream partitioned across multiple processors. We show a strategy for maintaining model parameters that leads to an exponential reduction in communication when compared with baseline approaches to maintain the exact MLE (maximum likelihood estimation). Meanwhile, our strategy provides similar prediction errors for the target distribution and for classification tasks.
Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks. While previous work has shown that using LRNNs can lead to state-of-the-art results in various ILP tasks, these results depended on hand-crafted rules. In this paper, we extend the framework of LRNNs with structure learning, thus enabling a fully automated learning process. Similarly to many ILP methods, our structure learning algorithm proceeds in an iterative fashion by top-down searching through the hypothesis space of all possible Horn clauses, considering the predicates that occur in the training examples as well as invented soft concepts entailed by the best weighted rules found so far. In the experiments, we demonstrate the ability to automatically induce useful hierarchical soft concepts leading to deep LRNNs with a competitive predictive power.
In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its corresponding posterior. First, we decompose the learning of VAEs into layerwise density estimation, and argue that having a flexible prior is beneficial to both sample generation and inference. Second, we analyze the family of inverse autoregressive flows (inverse AF) and show that with further improvement, inverse AF could be used as universal approximation to any complicated posterior. Our analysis results in a unified approach to parameterizing a VAE, without the need to restrict ourselves to use factorial Gaussians in the latent real space.
Recurrent neural networks have shown remarkable success in modeling sequences. However low resource situations still adversely affect the generalizability of these models. We introduce a new family of models, called Lattice Recurrent Units (LRU), to address the challenge of learning deep multi-layer recurrent models with limited resources. LRU models achieve this goal by creating distinct (but coupled) flow of information inside the units: a first flow along time dimension and a second flow along depth dimension. It also offers a symmetry in how information can flow horizontally and vertically. We analyze the effects of decoupling three different components of our LRU model: Reset Gate, Update Gate and Projected State. We evaluate this family on new LRU models on computational convergence rates and statistical efficiency. Our experiments are performed on four publicly-available datasets, comparing with Grid-LSTM and Recurrent Highway networks. Our results show that LRU has better empirical computational convergence rates and statistical efficiency values, along with learning more accurate language models.
We present a hybrid neural network and rule-based system that generates pop music. Music produced by pure rule-based systems often sounds mechanical. Music produced by machine learning sounds better, but still lacks hierarchical temporal structure. We restore temporal hierarchy by augmenting machine learning with a temporal production grammar, which generates the music's overall structure and chord progressions. A compatible melody is then generated by a conditional variational recurrent autoencoder. The autoencoder is trained with eight-measure segments from a corpus of 10,000 MIDI files, each of which has had its melody track and chord progressions identified heuristically. The autoencoder maps melody into a multi-dimensional feature space, conditioned by the underlying chord progression. A melody is then generated by feeding a random sample from that space to the autoencoder's decoder, along with the chord progression generated by the grammar. The autoencoder can make musically plausible variations on an existing melody, suitable for recurring motifs. It can also reharmonize a melody to a new chord progression, keeping the rhythm and contour. The generated music compares favorably with that generated by other academic and commercial software designed for the music-as-a-service industry.
The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance.
We present an approach for mobile robots to learn to navigate in dynamic environments with pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we adopt a generative adversarial imitation learning (GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our approach overcomes the disadvantages of previous methods, as they heavily depend on the full knowledge of the location and velocity information of nearby pedestrians, which not only requires specific sensors, but also the extraction of such state information from raw sensory input could consume much computation time. In this paper, our proposed GAIL-based model performs directly on raw depth inputs and plans in real-time. Experiments show that our GAIL-based approach greatly improves the safety and efficiency of the behavior of mobile robots from pure behavior cloning. The real-world deployment also shows that our method is capable of guiding autonomous vehicles to navigate in a socially compliant manner directly through raw depth inputs. In addition, we release a simulation plugin for modeling pedestrian behaviors based on the social force model.
Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been successfully used in the segmentation of images from a wide variety of sources. However, the traditional fuzzy segmentation algorithm fails to segment objects that are characterized by textures whose patterns cannot be successfully described by simple statistics computed over a very restricted area. In this paper, we propose an extension of the fuzzy segmentation algorithm that uses adaptive textural affinity functions to perform the segmentation of such objects on bidimensional images. The adaptive affinity functions compute their appropriate neighborhood size as they compute the texture descriptors surrounding the seed spels (spatial elements), according to the characteristics of the texture being processed. The algorithm then segments the image with an appropriate neighborhood for each object. We performed experiments on mosaic images that were composed using images from the Brodatz database, and compared our results with the ones produced by a recently published texture segmentation algorithm, showing the applicability of our method.
In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret. Our strategy is based on the value of information criterion. This criterion measures the trade-off between policy information and obtainable rewards. High amounts of policy information are associated with exploration-dominant searches of the space and yield high rewards. Low amounts of policy information favor the exploitation of existing knowledge. Information, in this criterion, is quantified by a parameter that can be varied during search. We demonstrate that a simulated-annealing-like update of this parameter, with a sufficiently fast cooling schedule, leads to an optimal regret that is logarithmic with respect to the number of episodes.
This paper presents the learning algorithm based on the Recurrent Network-based Deterministic Policy Gradient. The Long-Short Term Memory is utilized to enable the Partially Observed Markov Decision Process framework. The novelty are improvements of LSTM networks: update of multi-step temporal difference, removal of backpropagation through time on actor, initialisation of hidden state using past trajectory scanning, and injection of external experiences learned by other agents. Our methods benefit the reinforcement learning agent on inferring the desirable action by referring the trajectories of both past observations and actions. The proposed algorithm was implemented to solve the Bipedal-Walker challenge in OpenAI virtual environment where only partial state information is available. The validation on the extremely rugged terrain demonstrates the effectiveness of the proposed algorithm by achieving a new record of highest rewards in the challenge. The autonomous behaviors generated by our agent are highly adaptive to a variety of obstacles as shown in the simulation results.
Machine learning algorithms for prediction are increasingly being used in critical decisions affecting human lives. Various fairness formalizations, with no firm consensus yet, are employed to prevent such algorithms from systematically discriminating against people based on certain attributes protected by law. The aim of this article is to survey how fairness is formalized in the machine learning literature for the task of prediction and present these formalizations with their corresponding notions of distributive justice from the social sciences literature. We provide theoretical as well as empirical critiques of these notions from the social sciences literature and explain how these critiques limit the suitability of the corresponding fairness formalizations to certain domains. We also suggest two notions of distributive justice which address some of these critiques and discuss avenues for prospective fairness formalizations.
In this paper we propose a function space approach to Representation Learning and the analysis of the representation layers in deep learning architectures. We show how to compute a weak-type Besov smoothness index that quantifies the geometry of the clustering in the feature space. This approach was already applied successfully to improve the performance of machine learning algorithms such as the Random Forest and tree-based Gradient Boosting. Our experiments demonstrate that in well-known and well-performing trained networks, the Besov smoothness of the training set, measured in the corresponding hidden layer feature map representation, increases from layer to layer. We also contribute to the understanding of generalization by showing how the Besov smoothness of the representations, decreases as we add more mis-labeling to the training data. We hope this approach will contribute to the de-mystification of some aspects of deep learning.
Many applications infer the structure of a probabilistic graphical model from data to elucidate the relationships between variables. But how can we train graphical models on a massive data set? In this paper, we show how to construct coresets -compressed data sets which can be used as proxy for the original data and have provably bounded worst case error- for Gaussian dependency networks (DNs), i.e., cyclic directed graphical models over Gaussians, where the parents of each variable are its Markov blanket. Specifically, we prove that Gaussian DNs admit coresets of size independent of the size of the data set. Unfortunately, this does not extend to DNs over members of the exponential family in general. As we will prove, Poisson DNs do not admit small coresets. Despite this worst-case result, we will provide an argument why our coreset construction for DNs can still work well in practice on count data. To corroborate our theoretical results, we empirically evaluated the resulting Core DNs on real data sets. The results
Natural language place descriptions in everyday communication provide a rich source of spatial knowledge about places. An important step to utilize such knowledge in information systems is geo-referencing all the places referred to in these descriptions. Current techniques for geo-referencing places from text documents are using place name recognition and disambiguation; however, place descriptions often contain place references that are not known by gazetteers, or that are expressed in other, more flexible ways. Hence, the approach for geo-referencing presented in this paper starts from a place graph that contains the place references as well as spatial relationships extracted from place descriptions. Spatial relationships are used to constrain the locations of places and allow the later best-matching process for geo-referencing. The novel geo-referencing process results in higher precision and recall compared to state-of-art toponym resolution approaches on several tested place description datasets.
Reasoning about causes and effects naturally arises in the engineering of safety-critical systems. A classical example is Fault Tree Analysis, a deductive technique used for system safety assessment, whereby an undesired state is reduced to the set of its immediate causes. The design of fault management systems also requires reasoning on causality relationships. In particular, a fail-operational system needs to ensure timely detection and identification of faults, i.e. recognize the occurrence of run-time faults through their observable effects on the system. Even more complex scenarios arise when multiple faults are involved and may interact in subtle ways. In this work, we propose a formal approach to fault management for complex systems. We first introduce the notions of fault tree and minimal cut sets. We then present a formal framework for the specification and analysis of diagnosability, and for the design of fault detection and identification (FDI) components. Finally, we review recent advances in fault propagation analysis, based on the Timed Failure Propagation Graphs (TFPG) formalism.
How do we determine the mutational effects in exome sequencing data with little or no statistical evidence? Can protein structural information fill in the gap of not having enough statistical evidence? In this work, we answer the two questions with the goal towards determining pathogenic effects of rare variants in rare disease. We take the approach of determining the importance of point mutation loci focusing on protein structure features. The proposed structure-based features contain information about geometric, physicochemical, and functional information of mutation loci and those of structural neighbors of the loci. The performance of the structure-based features trained on 80\% of HumDiv and tested on 20\% of HumDiv and on ClinVar datasets showed high levels of discernibility in the mutation's pathogenic or benign effects: F score of 0.71 and 0.68 respectively using multi-layer perceptron. Combining structure- and sequence-based feature further improve the accuracy: F score of 0.86 (HumDiv) and 0.75 (ClinVar). Also, careful examination of the rare variants in rare diseases cases showed that structure-based features are important in discerning importance of variant loci.
In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an word-level to a chunk-level to effectively capture the entire meaning. In particular, by adapting the hierarchical structure, our model shows very small performance degradations in longer text comprehension while other state-of-the-art recurrent neural network models suffer from it. Additionally, the latent topic clustering module extracts semantic information from target samples. This clustering module is useful for any text related tasks by allowing each data sample to find its nearest topic cluster, thus helping the neural network model analyze the entire data. We evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic domain question answering dataset, which is related to Samsung products. The proposed model shows state-of-the-art results for ranking question-answer pairs.
In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant. While most semi-supervised approaches enforce restrictive assumptions on the data distribution, recent work has managed to learn semi-supervised models in a non-restrictive regime. However, so far such approaches have only been proposed for linear models. In this work, we introduce semi-supervised parameter learning for Sum-Product Networks (SPNs). SPNs are deep probabilistic models admitting inference in linear time in number of network edges. Our approach has several advantages, as it (1) allows generative and discriminative semi-supervised learning, (2) guarantees that adding unlabelled data can increase, but not degrade, the performance (safe), and (3) is computationally efficient and does not enforce restrictive assumptions on the data distribution. We show on a variety of data sets that safe semi-supervised learning with SPNs is competitive compared to state-of-the-art and can lead to a better generative and discriminative objective value than a purely supervised approach.
This work handles the inverse reinforcement learning (IRL) problem where only a small number of demonstrations are available from a demonstrator for each high-dimensional task, insufficient to estimate an accurate reward function. Observing that each demonstrator has an inherent reward for each state and the task-specific behaviors mainly depend on a small number of key states, we propose a meta IRL algorithm that first models the reward function for each task as a distribution conditioned on a baseline reward function shared by all tasks and dependent only on the demonstrator, and then finds the most likely reward function in the distribution that explains the task-specific behaviors. We test the method in a simulated environment on path planning tasks with limited demonstrations, and show that the accuracy of the learned reward function is significantly improved. We also apply the method to analyze the motion of a patient under rehabilitation.
Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the complexity of the environment. This suggests that a highly capable agent requires a complex environment for training. In this paper, we point out that a competitive multi-agent environment trained with self-play can produce behaviors that are far more complex than the environment itself. We also point out that such environments come with a natural curriculum, because for any skill level, an environment full of agents of this level will have the right level of difficulty. This work introduces several competitive multi-agent environments where agents compete in a 3D world with simulated physics. The trained agents learn a wide variety of complex and interesting skills, even though the environment themselves are relatively simple. The skills include behaviors such as running, blocking, ducking, tackling, fooling opponents, kicking, and defending using both arms and legs. A highlight of the learned behaviors can be found here: https://goo.gl/eR7fbX
The recent breakthroughs of deep reinforcement learning (DRL) technique in Alpha Go and playing Atari have set a good example in handling large state and actions spaces of complicated control problems. The DRL technique is comprised of (i) an offline deep neural network (DNN) construction phase, which derives the correlation between each state-action pair of the system and its value function, and (ii) an online deep Q-learning phase, which adaptively derives the optimal action and updates value estimates. In this paper, we first present the general DRL framework, which can be widely utilized in many applications with different optimization objectives. This is followed by the introduction of three specific applications: the cloud computing resource allocation problem, the residential smart grid task scheduling problem, and building HVAC system optimal control problem. The effectiveness of the DRL technique in these three cyber-physical applications have been validated. Finally, this paper investigates the stochastic computing-based hardware implementations of the DRL framework, which consumes a significant improvement in area efficiency and power consumption compared with binary-based implementation counterparts.
Steering a car through traffic is a complex task that is difficult to cast into algorithms. Therefore, researchers turn to training artificial neural networks from front-facing camera data stream along with the associated steering angles. Nevertheless, most existing solutions consider only the visual camera frames as input, thus ignoring the temporal relationship between frames. In this work, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (C-LSTM), that is end-to-end trainable, to learn both visual and dynamic temporal dependencies of driving. Additionally, We introduce posing the steering angle regression problem as classification while imposing a spatial relationship between the output layer neurons. Such method is based on learning a sinusoidal function that encodes steering angles. To train and validate our proposed methods, we used the publicly available Comma.ai dataset. Our solution improved steering root mean square error by 35% over recent methods, and led to a more stable steering by 87%.
We propose a deep semantic characterization of space and motion categorically from the viewpoint of grounding embodied human-object interactions. Our key focus is on an ontological model that would be adept to formalisation from the viewpoint of commonsense knowledge representation, relational learning, and qualitative reasoning about space and motion in cognitive robotics settings. We demonstrate key aspects of the space & motion ontology and its formalization as a representational framework in the backdrop of select examples from a dataset of everyday activities. Furthermore, focussing on human-object interaction data obtained from RGBD sensors, we also illustrate how declarative (spatio-temporal) reasoning in the (constraint) logic programming family may be performed with the developed deep semantic abstractions.
In this paper we demonstrate a new algorithm for sparse prestack azimuthal AVO inversion. A novel Euclidean prior model is developed to at once respect sparseness in the layered earth and smoothness in the model of reflectivity. Recognizing that methods of artificial intelligence and Bayesian computation are finding an every increasing role in augmenting the process of interpretation and analysis of geophysical data, we derive a generalized matrix-variate model of reflectivity in terms of orthogonal basis functions, subject to sparse constraints. This supports a direct application of machine learning methods, in a way that can be mapped back onto the physical principles known to govern reflection seismology. As a demonstration we present an application of these methods to the Marcellus shale. Attributes extracted using the azimuthal inversion are clustered using an unsupervised learning algorithm. Interpretation of the clusters is performed in the context of the Ruger model of azimuthal AVO.
We present a novel formalization of counterfactual conditionals in a quantified modal logic. Counterfactual conditionals play a vital role in ethical and moral reasoning. Prior work has shown that moral reasoning systems (and more generally, theory-of-mind reasoning systems) should be at least as expressive as first-order (quantified) modal logic (QML) to be well-behaved. While existing work on moral reasoning has focused on counterfactual-free QML moral reasoning, we present a fully specified and implemented formal system that includes counterfactual conditionals. We validate our model with two projects. In the first project, we demonstrate that our system can be used to model a complex moral principle, the doctrine of double effect. In the second project, we use the system to build a data-set with true and false counterfactuals as licensed by our theory, which we believe can be useful for other researchers. This project also shows that our model can be computationally feasible.
We present Synkhronos, an extension to Theano for multi-GPU computations leveraging data parallelism. Our framework provides automated execution and synchronization across devices, allowing users to continue to write serial programs without risk of race conditions. The NVIDIA Collective Communication Library is used for high-bandwidth inter-GPU communication. Further enhancements to the Theano function interface include input slicing (with aggregation) and input indexing, which perform common data-parallel computation patterns efficiently. One example use case is synchronous SGD, which has recently been shown to scale well for a growing set of deep learning problems. When training ResNet-50, we achieve a near-linear speedup of 7.5x on an NVIDIA DGX-1 using 8 GPUs, relative to Theano-only code running a single GPU in isolation. Yet Synkhronos remains general to any data-parallel computation programmable in Theano. By implementing parallelism at the level of individual Theano functions, our framework uniquely addresses a niche between manual multi-device programming and prescribed multi-GPU training routines.
Machine learning, the core of artificial intelligence and big data science, is one of today's most rapidly growing interdisciplinary fields. Recently, its tools and techniques have been adopted to tackle intricate quantum many-body problems. In this work, we introduce machine learning techniques to the detection of quantum nonlocality in many-body systems, with a focus on the restricted-Boltzmann-machine (RBM) architecture. Using reinforcement learning, we demonstrate that RBM is capable of finding the maximum quantum violations of multipartite Bell inequalities with given measurement settings. Our results build a novel bridge between computer-science-based machine learning and quantum many-body nonlocality, which will benefit future studies in both areas.
We propose Marve, a system for extracting measurement values, units, and related words from natural language text. Marve uses conditional random fields (CRF) to identify measurement values and units, followed by a rule-based system to find related entities, descriptors and modifiers within a sentence. Sentence tokens are represented by an undirected graphical model, and rules are based on part-of-speech and word dependency patterns connecting values and units to contextual words. Marve is unique in its focus on measurement context and early experimentation demonstrates Marve's ability to generate high-precision extractions with strong recall. We also discuss Marve's role in refining measurement requirements for NASA's proposed HyspIRI mission, a hyperspectral infrared imaging satellite that will study the world's ecosystems. In general, our work with HyspIRI demonstrates the value of semantic measurement extractions in characterizing quantitative discussion contained in large corpuses of natural language text. These extractions accelerate broad, cross-cutting research and expose scientists new algorithmic approaches and experimental nuances. They also facilitate identification of scientific opportunities enabled by HyspIRI leading to more efficient scientific investment and research.
Sentence vectors represent an appealing approach to meaning: learn an embedding that encompasses the meaning of a sentence in a single vector, that can be used for a variety of semantic tasks. Existing models for learning sentence embeddings either require extensive computational resources to train on large corpora, or are trained on costly, manually curated datasets of sentence relations. We observe that humans naturally annotate the relations between their sentences with discourse markers like "but" and "because". These words are deeply linked to the meanings of the sentences they connect. Using this natural signal, we automatically collect a classification dataset from unannotated text. Training a model to predict these discourse markers yields high quality sentence embeddings. Our model captures complementary information to existing models and achieves comparable generalization performance to state of the art models.
In practical analysis, domain knowledge about analysis target has often been accumulated, although, typically, such knowledge has been discarded in the statistical analysis stage, and the statistical tool has been applied as a black box. In this paper, we introduce sign constraints that are a handy and simple representation for non-experts in generic learning problems. We have developed two new optimization algorithms for the sign-constrained regularized loss minimization, called the sign-constrained Pegasos (SC-Pega) and the sign-constrained SDCA (SC-SDCA), by simply inserting the sign correction step into the original Pegasos and SDCA, respectively. We present theoretical analyses that guarantee that insertion of the sign correction step does not degrade the convergence rate for both algorithms. Two applications, where the sign-constrained learning is effective, are presented. The one is exploitation of prior information about correlation between explanatory variables and a target variable. The other is introduction of the sign-constrained to SVM-Pairwise method. Experimental results demonstrate significant improvement of generalization performance by introducing sign constraints in both applications.
Bayesian inference for models that have an intractable partition function is known as a doubly intractable problem, where standard Monte Carlo methods are not applicable. The past decade has seen the development of auxiliary variable Monte Carlo techniques (M{\o}ller et al., 2006; Murray et al., 2006) for tackling this problem; these approaches being members of the more general class of pseudo-marginal, or exact-approximate, Monte Carlo algorithms (Andrieu and Roberts, 2009), which make use of unbiased estimates of intractable posteriors. Everitt et al. (2017) investigated the use of exact-approximate importance sampling (IS) and sequential Monte Carlo (SMC) in doubly intractable problems, but focussed only on SMC algorithms that used data-point tempering. This paper describes SMC samplers that may use alternative sequences of distributions, and describes ways in which likelihood estimates may be improved adaptively as the algorithm progresses, building on ideas from Moores et al. (2015). This approach is compared with a number of alternative algorithms for doubly intractable problems, including approximate Bayesian computation (ABC), which we show is closely related to the method of M{\o}ller et al. (2006).
Understanding driving behaviors is essential for improving safety and mobility of our transportation systems. Data is usually collected via simulator-based studies or naturalistic driving studies. Those techniques allow for understanding relations between demographics, road conditions and safety. On the other hand, they are very costly and time consuming. Thanks to the ubiquity of smartphones, we have an opportunity to substantially complement more traditional data collection techniques with data extracted from phone sensors, such as GPS, accelerometer gyroscope and camera. We developed statistical models that provided insight into driver behavior in the San Francisco metro area based on tens of thousands of driver logs. We used novel data sources to support our work. We used cell phone sensor data drawn from five hundred drivers in San Francisco to understand the speed of traffic across the city as well as the maneuvers of drivers in different areas. Specifically, we clustered drivers based on their driving behavior. We looked at driver norms by street and flagged driving behaviors that deviated from the norm.
In Crowdfunding platforms, people turn their prototype ideas into real products by raising money from the crowd, or invest in someone else's projects. In reward-based crowdfunding platforms such as Kickstarter and Indiegogo, selecting accurate reward delivery duration becomes crucial for creators, backers, and platform providers to keep the trust between the creators and the backers, and the trust between the platform providers and users. According to Kickstarter, 35% backers did not receive rewards on time. Unfortunately, little is known about on-time and late reward delivery projects, and there is no prior work to estimate reward delivery duration. To fill the gap, in this paper, we (i) extract novel features that reveal latent difficulty levels of project rewards; (ii) build predictive models to identify whether a creator will deliver all rewards in a project on time or not; and (iii) build a regression model to estimate accurate reward delivery duration (i.e., how long it will take to produce and deliver all the rewards). Experimental results show that our models achieve good performance -- 82.5% accuracy, 78.1 RMSE, and 0.108 NRMSE at the first 5% of the longest reward delivery duration.
Recent developments within memory-augmented neural networks have solved sequential problems requiring long-term memory, which are intractable for traditional neural networks. However, current approaches still struggle to scale to large memory sizes and sequence lengths. In this paper we show how access to memory can be encoded geometrically through a HyperNEAT-based Neural Turing Machine (HyperENTM). We demonstrate that using the indirect HyperNEAT encoding allows for training on small memory vectors in a bit-vector copy task and then applying the knowledge gained from such training to speed up training on larger size memory vectors. Additionally, we demonstrate that in some instances, networks trained to copy bit-vectors of size 9 can be scaled to sizes of 1,000 without further training. While the task in this paper is simple, these results could open up the problems amendable to networks with external memories to problems with larger memory vectors and theoretically unbounded memory sizes.
We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork, $h$, is a neural network which learns to transform a simple noise distribution, $p(\epsilon) = \mathcal{N}(0,I)$, to a distribution $q(\theta) \doteq q(h(\epsilon))$ over the parameters $\theta$ of another neural network (the "primary network"). We train $q$ with variational inference, using an invertible $h$ to enable efficient estimation of the variational lower bound on the posterior $p(\theta | \mathcal{D})$ via sampling. In contrast to most methods for Bayesian deep learning, Bayesian hypernets can represent a complex multimodal approximate posterior with correlations between parameters, while enabling cheap i.i.d. sampling of $q(\theta)$. We demonstrate these qualitative advantages of Bayesian hypernets, which also achieve competitive performance on a suite of tasks that demonstrate the advantage of estimating model uncertainty, including active learning and anomaly detection.
With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, and from datasets and evaluation settings. We also overview related recognition tasks including one-shot and open set recognition which can be used as natural extensions of zero-shot recognition when limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. Importantly, we highlight the limitations of existing approaches and point out future research directions in this existing new research area.
This paper describes and motivates a new decision theory known as functional decision theory (FDT), as distinct from causal decision theory and evidential decision theory. Functional decision theorists hold that the normative principle for action is to treat one's decision as the output of a fixed mathematical function that answers the question, "Which output of this very function would yield the best outcome?" Adhering to this principle delivers a number of benefits, including the ability to maximize wealth in an array of traditional decision-theoretic and game-theoretic problems where CDT and EDT perform poorly. Using one simple and coherent decision rule, functional decision theorists (for example) achieve more utility than CDT on Newcomb's problem, more utility than EDT on the smoking lesion problem, and more utility than both in Parfit's hitchhiker problem. In this paper, we define FDT, explore its prescriptions in a number of different decision problems, compare it to CDT and EDT, and give philosophical justifications for FDT as a normative theory of decision-making.
Networks are fundamental models for data used in practically every application domain. In most instances, several implicit or explicit choices about the network definition impact the translation of underlying data to a network representation, and the subsequent question(s) about the underlying system being represented. Users of downstream network data may not even be aware of these choices or their impacts. We propose a task-focused network model selection methodology which addresses several key challenges. Our approach constructs network models from underlying data and uses minimum description length (MDL) criteria for selection. Our methodology measures efficiency, a general and comparable measure of the network's performance of a local (i.e. node-level) predictive task of interest. Selection on efficiency favors parsimonious (e.g. sparse) models to avoid overfitting and can be applied across arbitrary tasks and representations. We show stability, sensitivity, and significance testing in our methodology.
We study learning algorithms that are restricted to using a small amount of information from their input sample. We introduce a category of learning algorithms we term $d$-bit information learners, which are algorithms whose output conveys at most $d$ bits of information of their input. A central theme in this work is that such algorithms generalize. We focus on the learning capacity of these algorithms, and prove sample complexity bounds with tight dependencies on the confidence and error parameters. We also observe connections with well studied notions such as sample compression schemes, Occam's razor, PAC-Bayes and differential privacy. We discuss an approach that allows us to prove upper bounds on the amount of information that algorithms reveal about their inputs, and also provide a lower bound by showing a simple concept class for which every (possibly randomized) empirical risk minimizer must reveal a lot of information. On the other hand, we show that in the distribution-dependent setting every VC class has empirical risk minimizers that do not reveal a lot of information.
We present the Multi-vAlue Rule Set (MARS) model for interpretable classification with feature efficient presentations. MARS introduces a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than traditional single-valued rules in capturing and describing patterns in data. MARS mitigates the problem of dealing with continuous features and high-cardinality categorical features faced by rule-based models. Our formulation also pursues a higher efficiency of feature utilization, which reduces the cognitive load to understand the decision process. We propose an efficient inference method for learning a maximum a posteriori model, incorporating theoretically grounded bounds to iteratively reduce the search space to improve search efficiency. Experiments with synthetic and real-world data demonstrate that MARS models have significantly smaller complexity and fewer features, providing better interpretability while being competitive in predictive accuracy. We conducted a usability study with human subjects and results show that MARS is the easiest to use compared with other competing rule-based models, in terms of the correct rate and response time. Overall, MARS introduces a new approach to rule-based models that balance accuracy and interpretability with feature-efficient representations.
In this work, we propose an infinite restricted Boltzmann machine~(RBM), whose maximum likelihood estimation~(MLE) corresponds to a constrained convex optimization. We consider the Frank-Wolfe algorithm to solve the program, which provides a sparse solution that can be interpreted as inserting a hidden unit at each iteration, so that the optimization process takes the form of a sequence of finite models of increasing complexity. As a side benefit, this can be used to easily and efficiently identify an appropriate number of hidden units during the optimization. The resulting model can also be used as an initialization for typical state-of-the-art RBM training algorithms such as contrastive divergence, leading to models with consistently higher test likelihood than random initialization.
In his seminal paper that inaugurated abstract argumentation, Dung proved that the set of complete extensions forms a complete semilattice with respect to set inclusion. In this note we demonstrate that this proof is incorrect with counterexamples. We then trace the error in the proof and explain why it arose. We then examine the implications for the grounded extension. [Reason for withdrawal continued] Page 4, Example 2 is not a counterexample to Dung 1995 Theorem 25(3). It was believed to be a counter-example because the author misunderstood ``glb'' to be set-theoretic intersection. But in this case, ``glb'' is defined to be other than set-theoretic intersection such that Theorem 25(3) is true. The author was motivated to fully understand the lattice-theoretic claims of Dung 1995 in writing this note and was not aware that this issue is probably folklore; the author bears full responsibility for this error.
Policy evaluation or value function or Q-function approximation is a key procedure in reinforcement learning (RL). It is a necessary component of policy iteration and can be used for variance reduction in policy gradient methods. Therefore its quality has a significant impact on most RL algorithms. Motivated by manifold regularized learning, we propose a novel kernelized policy evaluation method that takes advantage of the intrinsic geometry of the state space learned from data, in order to achieve better sample efficiency and higher accuracy in Q-function approximation. Applying the proposed method in the Least-Squares Policy Iteration (LSPI) framework, we observe superior performance compared to widely used parametric basis functions on two standard benchmarks in terms of policy quality.
We introduce a novel generative model for interpretable subgroup analysis for causal inference applications, Causal Rule Sets (CRS). A CRS model uses a small set of short rules to capture a subgroup where the average treatment effect is elevated compared to the entire population. We present a Bayesian framework for learning a causal rule set. The Bayesian framework consists of a prior that favors simpler models and a Bayesian logistic regression that characterizes the relation between outcomes, attributes and subgroup membership. We find maximum a posteriori models using discrete Monte Carlo steps in the joint solution space of rules sets and parameters. We provide theoretically grounded heuristics and bounding strategies to improve search efficiency. Experiments show that the search algorithm can efficiently recover a true underlying subgroup and CRS shows consistently competitive performance compared to other state-of-the-art baseline methods.
Flow is a new computational framework, built to support a key need triggered by the rapid growth of autonomy in ground traffic: controllers for autonomous vehicles in the presence of complex nonlinear dynamics in traffic. Leveraging recent advances in deep Reinforcement Learning (RL), Flow enables the use of RL methods such as policy gradient for traffic control and enables benchmarking the performance of classical (including hand-designed) controllers with learned policies (control laws). Flow integrates traffic microsimulator SUMO with deep reinforcement learning library rllab and enables the easy design of traffic tasks, including different networks configurations and vehicle dynamics. We use Flow to develop reliable controllers for complex problems, such as controlling mixed-autonomy traffic (involving both autonomous and human-driven vehicles) in a ring road. For this, we first show that state-of-the-art hand-designed controllers excel when in-distribution, but fail to generalize; then, we show that even simple neural network policies can solve the stabilization task across density settings and generalize to out-of-distribution settings.
With a direct analysis of neural networks, this paper presents a mathematically tight generalization theory to partially address an open problem regarding the generalization of deep learning. Unlike previous bound-based theory, our main theory is quantitatively as tight as possible for every dataset individually, while producing qualitative insights competitively. Our results give insight into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, answering to an open question in the literature. We also discuss limitations of our results and propose additional open problems.
How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information? To tackle this challenge, this paper introduces a two-level hierarchical approach, which integrates model-free deep learning and model-based path planning. At the low level, a neural-network motion controller, called the intention-net, is trained end-to-end to provide robust local navigation. The intention-net maps images from a single monocular camera and "intentions" directly to robot controls. At the high level, a path planner uses a crude map, e.g., a 2-D floor plan, to compute a path from the robot's current location to the goal. The planned path provides intentions to the intention-net. Preliminary experiments suggest that the learned motion controller is robust against perceptual uncertainty and by integrating with a path planner, it generalizes effectively to new environments and goals.
Process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from event logs and by offering techniques to discover, monitor and enhance real processes. In the discovery of process models, retrieving a complex one, i.e., a hardly readable process model, can hinder the extraction of information. Even in well-structured process models, there is information that cannot be obtained with the current techniques. In this paper, we present WoMine, an algorithm to retrieve frequent behavioural patterns from the model. Our approach searches in process models extracting structures with sequences, selections, parallels and loops, which are frequently executed in the logs. This proposal has been validated with a set of process models, including some from BPI Challenges, and compared with the state of the art techniques. Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.
Optical Character Recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into its constituent characters. Despite decades of intense research, developing OCR with capabilities comparable to that of human still remains an open challenge. Due to this challenging nature, researchers from industry and academic circles have directed their attentions towards Optical Character Recognition. Over the last few years, the number of academic laboratories and companies involved in research on Character Recognition has increased dramatically. This research aims at summarizing the research so far done in the field of OCR. It provides an overview of different aspects of OCR and discusses corresponding proposals aimed at resolving issues of OCR.
Because of the increasing availability of spatiotemporal data, a variety of data-analytic applications have become possible. Characterizing driving context, where context may be thought of as a combination of location and time, is a new challenging application. An example of such a characterization is finding the correlation between driving behavior and traffic conditions. This contextual information enables analysts to validate observation-based hypotheses about the driving of an individual. In this paper, we present DriveContext, a novel framework to find the characteristics of a context, by extracting significant driving patterns (e.g., a slow-down), and then identifying the set of potential causes behind patterns (e.g., traffic congestion). Our experimental results confirm the feasibility of the framework in identifying meaningful driving patterns, with improvements in comparison with the state-of-the-art. We also demonstrate how the framework derives interesting characteristics for different contexts, through real-world examples.
We develop a method for policy architecture search and adaptation via gradient-free optimization which can learn to perform autonomous driving tasks. By learning from both demonstration and environmental reward we develop a model that can learn with relatively few early catastrophic failures. We first learn an architecture of appropriate complexity to perceive aspects of world state relevant to the expert demonstration, and then mitigate the effect of domain-shift during deployment by adapting a policy demonstrated in a source domain to rewards obtained in a target environment. We show that our approach allows safer learning than baseline methods, offering a reduced cumulative crash metric over the agent's lifetime as it learns to drive in a realistic simulated environment.
We present PubMed 200k RCT, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following classes: background, objective, method, result, or conclusion. The purpose of releasing this dataset is twofold. First, the majority of datasets for sequential short-text classification (i.e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task. Second, from an application perspective, researchers need better tools to efficiently skim through the literature. Automatically classifying each sentence in an abstract would help researchers read abstracts more efficiently, especially in fields where abstracts may be long, such as the medical field.
In this paper, an original heuristic algorithm of empty vehicles management in personal rapid transit network is presented. The algorithm is used for the delivery of empty vehicles for waiting passengers, for balancing the distribution of empty vehicles within the network, and for providing an empty space for vehicles approaching a station. Each of these tasks involves a decision on the trip that has to be done by a selected empty vehicle from its actual location to some determined destination. The decisions are based on a multi-parameter function involving a set of factors and thresholds. An important feature of the algorithm is that it does not use any central database of passenger input (demand) and locations of free vehicles. Instead, it is based on the local exchange of data between stations: on their states and on the vehicles they expect. Therefore, it seems well-tailored for a distributed implementation. The algorithm is uniform, meaning that the same basic procedure is used for multiple tasks using a task-specific set of parameters.
Recent universal-hashing based approaches to sampling and counting crucially depend on the runtime performance of SAT solvers on formulas expressed as the conjunction of both CNF constraints and variable-width XOR constraints (known as CNF-XOR formulas). In this paper, we present the first study of the runtime behavior of SAT solvers equipped with XOR-reasoning techniques on random CNF-XOR formulas. We empirically demonstrate that a state-of-the-art SAT solver scales exponentially on random CNF-XOR formulas across a wide range of XOR-clause densities, peaking around the empirical phase-transition location. On the theoretical front, we prove that the solution space of a random CNF-XOR formula 'shatters' at all nonzero XOR-clause densities into well-separated components, similar to the behavior seen in random CNF formulas known to be difficult for many SAT algorithms.
Many iterative procedures in stochastic optimization exhibit a transient phase followed by a stationary phase. During the transient phase the procedure converges towards a region of interest, and during the stationary phase the procedure oscillates in that region, commonly around a single point. In this paper, we develop a statistical diagnostic test to detect such phase transition in the context of stochastic gradient descent with constant learning rate. We present theory and experiments suggesting that the region where the proposed diagnostic is activated coincides with the convergence region. For a class of loss functions, we derive a closed-form solution describing such region. Finally, we suggest an application to speed up convergence of stochastic gradient descent by halving the learning rate each time stationarity is detected. This leads to a new variant of stochastic gradient descent, which in many settings is comparable to state-of-art.
Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi-task domain adaptation framework for instance grasping in cluttered scenes by utilizing simulated robot experiments. Our neural network takes monocular RGB images and the instance segmentation mask of a specified target object as inputs, and predicts the probability of successfully grasping the specified object for each candidate motor command. The proposed transfer learning framework trains a model for instance grasping in simulation and uses a domain-adversarial loss to transfer the trained model to real robots using indiscriminate grasping data, which is available both in simulation and the real world. We evaluate our model in real-world robot experiments, comparing it with alternative model architectures as well as an indiscriminate grasping baseline.
Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently there exist no resources to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence - effectively performing multi-hop (alias multi-step) inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information, as providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 42.9% compared to human performance at 74.0% - leaving ample room for improvement.
A key challenge in multi-robot and multi-agent systems is generating solutions that are robust to other self-interested or even adversarial parties who actively try to prevent the agents from achieving their goals. The practicality of existing works addressing this challenge is limited to only small-scale synchronous decision-making scenarios or a single agent planning its best response against a single adversary with fixed, procedurally characterized strategies. In contrast this paper considers a more realistic class of problems where a team of asynchronous agents with limited observation and communication capabilities need to compete against multiple strategic adversaries with changing strategies. This problem necessitates agents that can coordinate to detect changes in adversary strategies and plan the best response accordingly. Our approach first optimizes a set of stratagems that represent these best responses. These optimized stratagems are then integrated into a unified policy that can detect and respond when the adversaries change their strategies. The near-optimality of the proposed framework is established theoretically as well as demonstrated empirically in simulation and hardware.
Deep reinforcement learning (RL) has proven a powerful technique in many sequential decision making domains. However, Robotics poses many challenges for RL, most notably training on a physical system can be expensive and dangerous, which has sparked significant interest in learning control policies using a physics simulator. While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator. In this work, we exploit the full state observability in the simulator to train better policies which take as input only partial observations (RGBD images). We do this by employing an actor-critic training algorithm in which the critic is trained on full states while the actor (or policy) gets rendered images as input. We show experimentally on a range of simulated tasks that using these asymmetric inputs significantly improves performance. Finally, we combine this method with domain randomization and show real robot experiments for several tasks like picking, pushing, and moving a block. We achieve this simulation to real world transfer without training on any real world data.
Experience replay is a key technique behind many recent advances in deep reinforcement learning. Allowing the agent to learn from earlier memories can speed up learning and break undesirable temporal correlations. Despite its wide-spread application, very little is understood about the properties of experience replay. How does the amount of memory kept affect learning dynamics? Does it help to prioritize certain experiences? In this paper, we address these questions by formulating a dynamical systems ODE model of Q-learning with experience replay. We derive analytic solutions of the ODE for a simple setting. We show that even in this very simple setting, the amount of memory kept can substantially affect the agent's performance. Too much or too little memory both slow down learning. Moreover, we characterize regimes where prioritized replay harms the agent's learning. We show that our analytic solutions have excellent agreement with experiments. Finally, we propose a simple algorithm for adaptively changing the memory buffer size which achieves consistently good empirical performance.
We tackle highly nonconvex, nonsmooth composite optimization problems whose objectives comprise a Moreau-Yosida regularized term. Classical nonconvex proximal splitting algorithms, such as nonconvex ADMM, suffer from lack of convergence for such a problem class. To overcome this difficulty, in this work we consider a lifted variant of the Moreau-Yosida regularized model and propose a novel multiblock primal-dual algorithm that intrinsically stabilizes the dual block. We provide a complete convergence analysis of our algorithm and identify respective optimality qualifications under which stationarity of the original model is retrieved at convergence. Numerically, we demonstrate the relevance of Moreau-Yosida regularized models and the efficiency of our algorithm on robust regression as well as joint feature selection and semi-supervised learning.
Graph embedding has attracted increasing attention due to its critical application in social network analysis. Most existing algorithms for graph embedding only rely on the typology information and fail to use the copious information in nodes as well as edges. As a result, their performance for many tasks may not be satisfactory. In this paper, we proposed a novel and general framework of representation learning for graph with rich text information through constructing a bipartite heterogeneous network. Specially, we designed a biased random walk to explore the constructed heterogeneous network with the notion of flexible neighborhood. The efficacy of our method is demonstrated by extensive comparison experiments with several baselines on various datasets. It improves the Micro-F1 and Macro-F1 of node classification by 10% and 7% on Cora dataset.
The past decade has witnessed a successful application of deep learning to solving many challenging problems in machine learning and artificial intelligence. However, the loss functions of deep neural networks (especially nonlinear networks) are still far from being well understood from a theoretical aspect. In this paper, we enrich the current understanding of the landscape of the square loss functions for three types of neural networks. Specifically, when the parameter matrices are square, we provide an explicit characterization of the global minimizers for linear networks, linear residual networks, and nonlinear networks with one hidden layer. Then, we establish two quadratic types of landscape properties for the square loss of these neural networks, i.e., the gradient dominance condition within the neighborhood of their full rank global minimizers, and the regularity condition along certain directions and within the neighborhood of their global minimizers. These two landscape properties are desirable for the optimization around the global minimizers of the loss function for these neural networks.
While most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting with other humans. In this work, we propose a communication game where two agents, native speakers of their own respective languages, jointly learn to solve a visual referential task. We find that the ability to understand and translate a foreign language emerges as a means to achieve shared goals. The emergent translation is interactive and multimodal, and crucially does not require parallel corpora, but only monolingual, independent text and corresponding images. Our proposed translation model achieves this by grounding the source and target languages into a shared visual modality, and outperforms several baselines on both word-level and sentence-level translation tasks. Furthermore, we show that agents in a multilingual community learn to translate better and faster than in a bilingual communication setting.
Speech-based natural language question-answering interfaces to enterprise systems are gaining a lot of attention. General-purpose speech engines can be integrated with NLP systems to provide such interfaces. Usually, general-purpose speech engines are trained on large `general' corpus. However, when such engines are used for specific domains, they may not recognize domain-specific words well, and may produce erroneous output. Further, the accent and the environmental conditions in which the speaker speaks a sentence may induce the speech engine to inaccurately recognize certain words. The subsequent natural language question-answering does not produce the requisite results as the question does not accurately represent what the speaker intended. Thus, the speech engine's output may need to be adapted for a domain before further natural language processing is carried out. We present two mechanisms for such an adaptation, one based on evolutionary development and the other based on machine learning, and show how we can repair the speech-output to make the subsequent natural language question-answering better.
Social dilemmas, where mutual cooperation can lead to high payoffs but participants face incentives to cheat, are ubiquitous in multi-agent interaction. We wish to construct agents that cooperate with pure cooperators, avoid exploitation by pure defectors, and incentivize cooperation from the rest. However, often the actions taken by a partner are (partially) unobserved or the consequences of individual actions are hard to predict. We show that in a large class of games good strategies can be constructed by conditioning one's behavior solely on outcomes (ie. one's past rewards). We call this consequentialist conditional cooperation. We show how to construct such strategies using deep reinforcement learning techniques and demonstrate, both analytically and experimentally, that they are effective in social dilemmas beyond simple matrix games. We also show the limitations of relying purely on consequences and discuss the need for understanding both the consequences of and the intentions behind an action.
We use decision trees to build a helpdesk agent reference network to facilitate the on-the-job advising of junior or less experienced staff on how to better address telecommunication customer fault reports. Such reports generate field measurements and remote measurements which, when coupled with location data and client attributes, and fused with organization-level statistics, can produce models of how support should be provided. Beyond decision support, these models can help identify staff who can act as advisors, based on the quality, consistency and predictability of dealing with complex troubleshooting reports. Advisor staff models are then used to guide less experienced staff in their decision making; thus, we advocate the deployment of a simple mechanism which exploits the availability of staff with a sound track record at the helpdesk to act as dormant tutors.
In this study, we present Swift Linked Data Miner, an interruptible algorithm that can directly mine an online Linked Data source (e.g., a SPARQL endpoint) for OWL 2 EL class expressions to extend an ontology with new SubClassOf: axioms. The algorithm works by downloading only a small part of the Linked Data source at a time, building a smart index in the memory and swiftly iterating over the index to mine axioms. We propose a transformation function from mined axioms to RDF Data Shapes. We show, by means of a crowdsourcing experiment, that most of the axioms mined by Swift Linked Data Miner are correct and can be added to an ontology. We provide a ready to use Prot\'eg\'e plugin implementing the algorithm, to support ontology engineers in their daily modeling work.
We propose a two phase time dependent vehicle routing and scheduling optimization model that identifies the safest routes, as a substitute for the classical objectives given in the literature such as shortest distance or travel time, through (1) avoiding recurring congestions, and (2) selecting routes that have a lower probability of crash occurrences and non-recurring congestion caused by those crashes. In the first phase, we solve a mixed-integer programming model which takes the dynamic speed variations into account on a graph of roadway networks according to the time of day, and identify the routing of a fleet and sequence of nodes on the safest feasible paths. Second phase considers each route as an independent transit path (fixed route with fixed node sequences), and tries to avoid congestion by rescheduling the departure times of each vehicle from each node, and by adjusting the sub-optimal speed on each arc. A modified simulated annealing (SA) algorithm is formulated to solve both complex models iteratively, which is found to be capable of providing solutions in a considerably short amount of time.
This paper focuses on preserving the privacy of sensitive pat-terns when inducing decision trees. We adopt a record aug-mentation approach for hiding sensitive classification rules in binary datasets. Such a hiding methodology is preferred over other heuristic solutions like output perturbation or crypto-graphic techniques - which restrict the usability of the data - since the raw data itself is readily available for public use. In this paper, we propose a look ahead approach using linear Diophantine equations in order to add the appropriate number of instances while minimally disturbing the initial entropy of the nodes.
In this study we developed an automated system that evaluates speech and language features from audio recordings of neuropsychological examinations of 92 subjects in the Framingham Heart Study. A total of 265 features were used in an elastic-net regularized binomial logistic regression model to classify the presence of cognitive impairment, and to select the most predictive features. We compared performance with a demographic model from 6,258 subjects in the greater study cohort (0.79 AUC), and found that a system that incorporated both audio and text features performed the best (0.92 AUC), with a True Positive Rate of 29% (at 0% False Positive Rate) and a good model fit (Hosmer-Lemeshow test > 0.05). We also found that decreasing pitch and jitter, shorter segments of speech, and responses phrased as questions were positively associated with cognitive impairment.
We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. We scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on one single-GPU server.
The mathematical model underlying the Neural Engineering Framework (NEF) expresses neuronal input as a linear combination of synaptic currents. However, in biology, synapses are not perfect current sources and are thus nonlinear. Detailed synapse models are based on channel conductances instead of currents, which require independent handling of excitatory and inhibitory synapses. This, in particular, significantly affects the influence of inhibitory signals on the neuronal dynamics. In this technical report we first summarize the relevant portions of the NEF and conductance-based synapse models. We then discuss a na\"ive translation between populations of LIF neurons with current- and conductance-based synapses based on an estimation of an average membrane potential. Experiments show that this simple approach works relatively well for feed-forward communication channels, yet performance degrades for NEF networks describing more complex dynamics, such as integration.
In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction. False positives plague the fraud prediction industry. It is estimated that only 1 in 5 declared as fraud are actually fraud and roughly 1 in every 6 customers have had a valid transaction declined in the past year. To address this problem, we use the Deep Feature Synthesis algorithm to automatically derive behavioral features based on the historical data of the card associated with a transaction. We generate 237 features (>100 behavioral patterns) for each transaction, and use a random forest to learn a classifier. We tested our machine learning model on data from a large multinational bank and compared it to their existing solution. On an unseen data of 1.852 million transactions, we were able to reduce the false positives by 54% and provide a savings of 190K euros. We also assess how to deploy this solution, and whether it necessitates streaming computation for real time scoring. We found that our solution can maintain similar benefits even when historical features are computed once every 7 days.
The use of random perturbations of ground truth data, such as random translation or scaling of bounding boxes, is a common heuristic used for data augmentation that has been shown to prevent overfitting and improve generalization. Since the design of data augmentation is largely guided by reported best practices, it is difficult to understand if those design choices are optimal. To provide a more principled perspective, we develop a game-theoretic interpretation of data augmentation in the context of object detection. We aim to find an optimal adversarial perturbations of the ground truth data (i.e., the worst case perturbations) that forces the object bounding box predictor to learn from the hardest distribution of perturbed examples for better test-time performance. We establish that the game theoretic solution, the Nash equilibrium, provides both an optimal predictor and optimal data augmentation distribution. We show that our adversarial method of training a predictor can significantly improve test time performance for the task of object detection. On the ImageNet object detection task, our adversarial approach improves performance by over 16\% compared to the best performing data augmentation method
Identifying arbitrary topologies of power networks in real time is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. A new "Learning-to-Infer" variational inference method is developed for efficient inference of every line status in the network. Optimizing the variational model is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learning-to-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount fast and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time topology identification. The proposed methods are evaluated in the IEEE 30, 118 and 300 bus systems. Excellent performance in identifying arbitrary power network topologies in real time is achieved even with relatively simple variational models and a reasonably small amount of data.
Deep neural networks are powerful learning models that achieve state-of-the-art performance on many computer vision, speech, and language processing tasks. In this paper, we study a fundamental question that arises when designing deep network architectures: Given a target network architecture can we design a smaller network architecture that approximates the operation of the target network? The question is, in part, motivated by the challenge of parameter reduction (compression) in modern deep neural networks, as the ever increasing storage and memory requirements of these networks pose a problem in resource constrained environments. In this work, we focus on deep convolutional neural network architectures, and propose a novel randomized tensor sketching technique that we utilize to develop a unified framework for approximating the operation of both the convolutional and fully connected layers. By applying the sketching technique along different tensor dimensions, we design changes to the convolutional and fully connected layers that substantially reduce the number of effective parameters in a network. We show that the resulting smaller network can be trained directly, and has a classification accuracy that is comparable to the original network.
We study the never-worse relation (NWR) for Markov decision processes with an infinite-horizon reachability objective. A state q is never worse than a state p if the maximal probability of reaching the target set of states from p is at most the same value from q, regard- less of the probabilities labelling the transitions. Extremal-probability states, end components, and essential states are all special cases of the equivalence relation induced by the NWR. Using the NWR, states in the same equivalence class can be collapsed. Then, actions leading to sub- optimal states can be removed. We show the natural decision problem associated to computing the NWR is coNP-complete. Finally, we ex- tend a previously known incomplete polynomial-time iterative algorithm to under-approximate the NWR.
Apprenticeship learning (AL) is a class of "learning from demonstrations" techniques where the reward function of a Markov Decision Process (MDP) is unknown to the learning agent and the agent has to derive a good policy by observing an expert's demonstrations. In this paper, we study the problem of how to make AL algorithms inherently safe while still meeting its learning objective. We consider a setting where the unknown reward function is assumed to be a linear combination of a set of state features, and the safety property is specified in Probabilistic Computation Tree Logic (PCTL). By embedding probabilistic model checking inside AL, we propose a novel counterexample-guided approach that can ensure both safety and performance of the learnt policy. We demonstrate the effectiveness of our approach on several challenging AL scenarios where safety is essential.
In this semi-tutorial paper, we first review the information-theoretic approach to account for the computational costs incurred during the search for optimal actions in a sequential decision-making problem. The traditional (MDP) framework ignores computational limitations while searching for optimal policies, essentially assuming that the acting agent is perfectly rational and aims for exact optimality. Using the free-energy, a variational principle is introduced that accounts not only for the value of a policy alone, but also considers the cost of finding this optimal policy. The solution of the variational equations arising from this formulation can be obtained using familiar Bellman-like value iterations from dynamic programming (DP) and the Blahut-Arimoto (BA) algorithm from rate distortion theory. Finally, we demonstrate the utility of the approach for generating hierarchies of state abstractions that can be used to best exploit the available computational resources. A numerical example showcases these concepts for a path-planning problem in a grid world environment.
Since the study of deep convolutional neural network became prevalent, one of the important discoveries is that a feature map from a convolutional network can be extracted before going into the fully connected layer and can be used as a saliency map for object detection. Furthermore, the model can use features from each different layer for accurate object detection: the features from different layers can have different properties. As the model goes deeper, it has many latent skip connections and feature maps to elaborate object detection. Although there are many intermediate layers that we can use for semantic segmentation through skip connection, still the characteristics of each skip connection and the best skip connection for this task are uncertain. Therefore, in this study, we exhaustively research skip connections of state-of-the-art deep convolutional networks and investigate the characteristics of the features from each intermediate layer. In addition, this study would suggest how to use a recent deep neural network model for semantic segmentation and it would therefore become a cornerstone for later studies with the state-of-the-art network models.
We study transfer learning in convolutional network architectures applied to the task of recognizing audio, such as environmental sound events and speech commands. Our key finding is that not only is it possible to transfer representations from an unrelated task like environmental sound classification to a voice-focused task like speech command recognition, but also that doing so improves accuracies significantly. We also investigate the effect of increased model capacity for transfer learning audio, by first validating known results from the field of Computer Vision of achieving better accuracies with increasingly deeper networks on two audio datasets: UrbanSound8k and the newly released Google Speech Commands dataset. Then we propose a simple multiscale input representation using dilated convolutions and show that it is able to aggregate larger contexts and increase classification performance. Further, the models trained using a combination of transfer learning and multiscale input representations need only 40% of the training data to achieve similar accuracies as a freshly trained model with 100% of the training data. Finally, we demonstrate a positive interaction effect for the multiscale input and transfer learning, making a case for the joint application of the two techniques.
Health related social media mining is a valuable apparatus for the early recognition of the diverse antagonistic medicinal conditions. Mostly, the existing methods are based on machine learning with knowledge-based learning. This working note presents the Recurrent neural network (RNN) and Long short-term memory (LSTM) based embedding for automatic health text classification in the social media mining. For each task, two systems are built and that classify the tweet at the tweet level. RNN and LSTM are used for extracting features and non-linear activation function at the last layer facilitates to distinguish the tweets of different categories. The experiments are conducted on 2nd Social Media Mining for Health Applications Shared Task at AMIA 2017. The experiment results are considerable; however the proposed method is appropriate for the health text classification. This is primarily due to the reason that, it doesn't rely on any feature engineering mechanisms.
Serverless computing has emerged as a compelling paradigm for the development and deployment of a wide range of event based cloud applications. At the same time, cloud providers and enterprise companies are heavily adopting machine learning and Artificial Intelligence to either differentiate themselves, or provide their customers with value added services. In this work we evaluate the suitability of a serverless computing environment for the inferencing of large neural network models. Our experimental evaluations are executed on the AWS Lambda environment using the MxNet deep learning framework. Our experimental results show that while the inferencing latency can be within an acceptable range, longer delays due to cold starts can skew the latency distribution and hence risk violating more stringent SLAs.
The study of representations invariant to common transformations of the data is important to learning. Most techniques have focused on local approximate invariance implemented within expensive optimization frameworks lacking explicit theoretical guarantees. In this paper, we study kernels that are invariant to a unitary group while having theoretical guarantees in addressing the important practical issue of unavailability of transformed versions of labelled data. A problem we call the Unlabeled Transformation Problem which is a special form of semi-supervised learning and one-shot learning. We present a theoretically motivated alternate approach to the invariant kernel SVM based on which we propose Max-Margin Invariant Features (MMIF) to solve this problem. As an illustration, we design an framework for face recognition and demonstrate the efficacy of our approach on a large scale semi-synthetic dataset with 153,000 images and a new challenging protocol on Labelled Faces in the Wild (LFW) while out-performing strong baselines.
This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without any recurrent units. Recurrent neural network (RNN) has been a standard technique to model sequential data recently, and this technique has been used in some cutting-edge neural TTS techniques. However, training RNN component often requires a very powerful computer, or very long time typically several days or weeks. Recent other studies, on the other hand, have shown that CNN-based sequence synthesis can be much faster than RNN-based techniques, because of high parallelizability. The objective of this paper is to show an alternative neural TTS system, based only on CNN, that can alleviate these economic costs of training. In our experiment, the proposed Deep Convolutional TTS can be sufficiently trained only in a night (15 hours), using an ordinary gaming PC equipped with two GPUs, while the quality of the synthesized speech was almost acceptable.
Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectively exploited by those models. In recent years, several works have been using complex networks for data representation and analysis. However, no feature learning method has been proposed for such category of techniques. Here, we present an unsupervised feature learning mechanism that works on datasets with binary features. First, the dataset is mapped into a feature--sample network. Then, a multi-objective optimization process selects a set of new vertices to produce an enhanced version of the network. The new features depend on a nonlinear function of a combination of preexisting features. Effectively, the process projects the input data into a higher-dimensional space. To solve the optimization problem, we design two metaheuristics based on the lexicographic genetic algorithm and the improved strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced network contains more information and can be exploited to improve the performance of machine learning methods. The advantages and disadvantages of each optimization strategy are discussed.
A core business in the fashion industry is the understanding and prediction of customer needs and trends. Search engines and social networks are at the same time a fundamental bridge and a costly middleman between the customer's purchase intention and the retailer. To better exploit Europe's distinctive characteristics e.g., multiple languages, fashion and cultural differences, it is pivotal to reduce retailers' dependence to search engines. This goal can be achieved by harnessing various data channels (manufacturers and distribution networks, online shops, large retailers, social media, market observers, call centers, press/magazines etc.) that retailers can leverage in order to gain more insight about potential buyers, and on the industry trends as a whole. This can enable the creation of novel on-line shopping experiences, the detection of influencers, and the prediction of upcoming fashion trends. In this paper, we provide an overview of the main research challenges and an analysis of the most promising technological solutions that we are investigating in the FashionBrain project.
This paper presents Klout Topics, a lightweight ontology to describe social media users' topics of interest and expertise. Klout Topics is designed to: be human-readable and consumer-friendly; cover multiple domains of knowledge in depth; and promote data extensibility via knowledge base entities. We discuss why this ontology is well-suited for text labeling and interest modeling applications, and how it compares to available alternatives. We show its coverage against common social media interest sets, and examples of how it is used to model the interests of over 780M social media users on Klout.com. Finally, we open the ontology for external use.
Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-studied learning challenges that are also fundamental to infants learning their first words. While it is notable that models with no meaningful prior knowledge overcome these learning obstacles, AI researchers and practitioners currently lack a clear understanding of exactly how they do so. Here we address this question as a way of achieving a clearer general understanding of grounded language learning, both to inform future research and to improve confidence in model predictions. For maximum control and generality, we focus on a simple neural network-based language learning agent trained via policy-gradient methods to interpret synthetic linguistic instructions in a simulated 3D world. We apply experimental paradigms from developmental psychology to this agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel way to visualise and analyse semantic representation in grounded language learning agents that yields a plausible computational account of the observed effects.
Visual Analytics might be defined as data mining assisted by interactive visual interfaces. The field has been receiving prominent consideration by researchers, developers and the industry. The literature, however, is complex because it involves multiple fields of knowledge and is considerably recent. In this article we describe an initial tentative organization of the knowledge in the field as an OWL ontology and a SKOS vocabulary. This effort might be useful in many ways that include conceptual considerations and software implementations. Within the results and discussions, we expose a core and an example expansion of the conceptualization, and incorporate design issues that enhance the expressive power of the abstraction.
In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed long-term return. Traditionally, reinforcement learning algorithms average over this randomness to estimate the value function. In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean. That is, we examine methods of learning the value distribution instead of the value function. We give results that close a number of gaps between the theoretical and algorithmic results given by Bellemare, Dabney, and Munos (2017). First, we extend existing results to the approximate distribution setting. Second, we present a novel distributional reinforcement learning algorithm consistent with our theoretical formulation. Finally, we evaluate this new algorithm on the Atari 2600 games, observing that it significantly outperforms many of the recent improvements on DQN, including the related distributional algorithm C51.
We study multiwinner voting problems when there is an additional requirement that the selected committee should be fair with respect to attributes such as gender, ethnicity, or political parties. Every setting of an attribute gives rise to a group, and the goal is to ensure that each group is neither over nor under represented in the selected committee. Prior work has largely focused on designing specialized score functions that lead to a precise level of representation with respect to disjoint attributes (e.g., only political affiliation). Here we propose a general algorithmic framework that allows the use of any score function and can guarantee flexible notions of fairness with respect to multiple, non-disjoint attributes (e.g., political affiliation and gender). Technically, we study the complexity of this constrained multiwinner voting problem subject to group-fairness constraints for monotone submodular score functions. We present approximation algorithms and hardness of approximation results for various attribute set structures and score functions.
The literature on Multiple Criteria Decision Analysis (MCDA) proposes several methods in order to sort alternatives evaluated on several attributes into ordered classes. Non Compensatory Sorting models (NCS) assign alternatives to classes based on the way they compare to multicriteria profiles separating the consecutive classes. Previous works have proposed approaches to learn the parameters of a NCS model based on a learning set. Exact approaches based on mixed integer linear programming ensures that the learning set is best restored, but can only handle datasets of limited size. Heuristic approaches can handle large learning sets, but do not provide any guarantee about the inferred model. In this paper, we propose an alternative formulation to learn a NCS model. This formulation, based on a SAT problem, guarantees to find a model fully consistent with the learning set (whenever it exists), and is computationally much more efficient than existing exact MIP approaches.
Providing elderly and people with special needs, including those suffering from physical disabilities and chronic diseases, with the possibility of retaining their independence at best is one of the most important challenges our society is expected to face. Assistance models based on the home care paradigm are being adopted rapidly in almost all industrialized and emerging countries. Such paradigms hypothesize that it is necessary to ensure that the so-called Activities of Daily Living are correctly and regularly performed by the assisted person to increase the perception of an improved quality of life. This chapter describes the computational inference engine at the core of Arianna, a system able to understand whether an assisted person performs a given set of ADL and to motivate him/her in performing them through a speech-mediated motivational dialogue, using a set of nearables to be installed in an apartment, plus a wearable to be worn or fit in garments.
The ability to modulate vocal sounds and generate speech is one of the features which set humans apart from other living beings. The human voice can be characterized by several attributes such as pitch, timbre, loudness, and vocal tone. It has often been observed that humans express their emotions by varying different vocal attributes during speech generation. Hence, deduction of human emotions through voice and speech analysis has a practical plausibility and could potentially be beneficial for improving human conversational and persuasion skills. This paper presents an algorithmic approach for detection and analysis of human emotions with the help of voice and speech processing. The proposed approach has been developed with the objective of incorporation with futuristic artificial intelligence systems for improving human-computer interactions.
In this paper, we provide an approach to clustering relational matrices whose entries correspond to either similarities or dissimilarities between objects. Our approach is based on the value of information, a parameterized, information-theoretic criterion that measures the change in costs associated with changes in information. Optimizing the value of information yields a deterministic annealing style of clustering with many benefits. For instance, investigators avoid needing to a priori specify the number of clusters, as the partitions naturally undergo phase changes, during the annealing process, whereby the number of clusters changes in a data-driven fashion. The global-best partition can also often be identified.
Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-to-fine object categorization. Such a network has two branches to simultaneously deal with both coarse and fine-grained classification tasks. Specifically, we propose a layer-skipping mechanism that learns a gating network to predict which layers to skip in the testing stage. This layer-skipping mechanism endows the network with good flexibility and capability in practice. Evaluations are conducted on several widely used coarse-to-fine object categorization benchmarks, and promising results are achieved by our proposed network model.
Visual localization under large changes in scale is an important capability in many robotic mapping applications, such as localizing at low altitudes in maps built at high altitudes, or performing loop closure over long distances. Existing approaches, however, are robust only up to a 3x difference in scale between map and query images. We propose a novel combination of deep-learning-based object features and hand-engineered point-features that yields improved robustness to scale change, perspective change, and image noise. We conduct experiments in simulation and in real-world outdoor scenes exhibiting up to a 7x change in scale, and compare our approach against localization using state-of-the-art SIFT features. This technique is training-free and class-agnostic, and in principle can be deployed in any environment out-of-the-box.
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection. Recent random forests based methods exploit randomly sampled pixel comparison features to predict 3D world locations for 2D image locations to guide the camera pose optimization. However, these image features are only sampled randomly in the images, without considering the spatial structures or geometric information, leading to large errors or failure cases with the existence of poorly textured areas or in motion blur. Line segment features are more robust in these environments. In this work, we propose to jointly exploit points and lines within the framework of uncertainty driven regression forests. The proposed approach is thoroughly evaluated on three publicly available datasets against several strong state-of-the-art baselines in terms of several different error metrics. Experimental results prove the efficacy of our method, showing superior or on-par state-of-the-art performance.
This paper introduces a new routing problem referred to as the vehicle routing problem with vector profits. Given a network composed of nodes (depot/sites) and arcs connecting the nodes, the problem determines routes that depart from the depot, visit sites to collect profits, and return to the depot. There are multiple stakeholders interested in the mission and each site is associated with a vector whose k-th element represents the profit value for the k-th stakeholder. The objective of the problem is to maximize the profit sum for the least satisfied stakeholder, i.e., the stakeholder with the smallest total profit value. An approach based on the linear programming relaxation and column-generation to solve this max-min type routing problem was developed. Two cases studies - the planetary surface exploration and the Rome tour cases - were presented to demonstrate the effectiveness of the proposed problem formulation and solution methodology.
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.
Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNN is studied for the purpose of classification, under a Generalized Linear Model (GLM) probabilistic neural model that was previously considered within the computational neuroscience literature. Conventional classification rules for SNNs operate offline based on the number of output spikes at each output neuron. In contrast, a novel training method is proposed here for a first-to-spike decoding rule, whereby the SNN can perform an early classification decision once spike firing is detected at an output neuron. Numerical results bring insights into the optimal parameter selection for the GLM neuron and on the accuracy-complexity trade-off performance of conventional and first-to-spike decoding.
Generative Adversarial Network (GAN) and its variants exhibit state-of-the-art performance in the class of generative models. To capture higher-dimensional distributions, the common learning procedure requires high computational complexity and a large number of parameters. The problem of employing such massive framework arises when deploying it on a platform with limited computational power such as mobile phones. In this paper, we present a new generative adversarial framework by representing each layer as a tensor structure connected by multilinear operations, aiming to reduce the number of model parameters by a large factor while preserving the generative performance and sample quality. To learn the model, we employ an efficient algorithm which alternatively optimizes both discriminator and generator. Experimental outcomes demonstrate that our model can achieve high compression rate for model parameters up to $35$ times when compared to the original GAN for MNIST dataset.
Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.
Extreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated, the weights of the output layer can be analytically determined. ELM has been achieved good results for a large number of classification tasks. In this paper, a new extreme learning machine called rough extreme learning machine (RELM) was proposed. RELM uses rough set to divide data into upper approximation set and lower approximation set, and the two approximation sets are utilized to train upper approximation neurons and lower approximation neurons. In addition, an attribute reduction is executed in this algorithm to remove redundant attributes. The experimental results showed, comparing with the comparison algorithms, RELM can get a better accuracy and repeatability in most cases, RELM can not only maintain the advantages of fast speed, but also effectively cope with the classification task for high-dimensional data.
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).
We analyze the expressiveness and loss surface of practical deep convolutional neural networks (CNNs) with shared weights and max pooling layers. We show that such CNNs produce linearly independent features at a "wide" layer which has more neurons than the number of training samples. This condition holds e.g. for the VGG network. Furthermore, we provide for such wide CNNs necessary and sufficient conditions for global minima with zero training error. For the case where the wide layer is followed by a fully connected layer, we show that almost every critical point of the empirical loss is a global minimum with zero training error. Our analysis suggests that both depth and width are very important in deep learning. While depth brings more representational power and allows the network to learn high level features, width smoothes the optimization landscape of the loss function in the sense that a sufficiently wide network has a well-behaved loss surface with potentially no bad local minima.
In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal. In this work, we completely remove the need of parallel data and propose a novel method to train an NMT system in a completely unsupervised manner, relying on nothing but monolingual corpora. Our model builds upon the recent work on unsupervised embedding mappings, and consists of a slightly modified attentional encoder-decoder model that can be trained on monolingual corpora alone using a combination of denoising and backtranslation. Despite the simplicity of the approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014 French-to-English and German-to-English translation. The model can also profit from small parallel corpora, and attains 21.81 and 15.24 points when combined with 100,000 parallel sentences, respectively. Our implementation is released as an open source project.
Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning. However, autonomously learning effective sets of options is still a major challenge in the field. In this paper we focus on the recently introduced idea of using representation learning methods to guide the option discovery process. Specifically, we look at eigenoptions, options obtained from representations that encode diffusive information flow in the environment. We extend the existing algorithms for eigenoption discovery to settings with stochastic transitions and in which handcrafted features are not available. We propose an algorithm that discovers eigenoptions while learning non-linear state representations from raw pixels. It exploits recent successes in the deep reinforcement learning literature and the equivalence between proto-value functions and the successor representation. We use traditional tabular domains to provide intuition about our approach and Atari 2600 games to demonstrate its potential.
We focus on the problem of estimating the change in the dependency structures of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation in GGMs involve expensive and difficult non-smooth optimization. We propose a novel method, DIFFEE for estimating DIFFerential networks via an Elementary Estimator under a high-dimensional situation. DIFFEE is solved through a faster and closed form solution that enables it to work in large-scale settings. We conduct a rigorous statistical analysis showing that surprisingly DIFFEE achieves the same asymptotic convergence rates as the state-of-the-art estimators that are much more difficult to compute. Our experimental results on multiple synthetic datasets and one real-world data about brain connectivity show strong performance improvements over baselines, as well as significant computational benefits.
This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts. Then, we incorporate the discriminator as another critic into the advantage actor-critic (A2C) framework, to encourage the dialogue agent to explore state-action within the regions where the agent takes actions similar to those of the experts. Experimental results in a movie-ticket booking domain show that the proposed Adversarial A2C can accelerate policy exploration efficiently.
We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics. Specifically, we present a perceptual model for generating video frames from state with deep networks, and provide a framework for its use in tracking and prediction tasks. We show that our proposed model greatly outperforms standard deconvolutional methods and GANs for image generation, producing clear, photo-realistic images. We also develop a convolutional neural network model for state estimation and compare the result to an Extended Kalman Filter to estimate robot trajectories. We validate all models on a real robotic system.
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers.
It is commonly agreed that the use of relevant invariances as a good statistical bias is important in machine-learning. However, most approaches that explicitly incorporate invariances into a model architecture only make use of very simple transformations, such as translations and rotations. Hence, there is a need for methods to model and extract richer transformations that capture much higher-level invariances. To that end, we introduce a tool allowing to parametrize the set of filters of a trained convolutional neural network with the latent space of a generative adversarial network. We then show that the method can capture highly non-linear invariances of the data by visualizing their effect in the data space.
Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully observed state and must compensate for partial or non-Markovian observations by using finite-length frame-history observations or recurrent networks. In this work, we propose a new deep reinforcement learning algorithm based on counterfactual regret minimization that iteratively updates an approximation to a cumulative clipped advantage function and is robust to partially observed state. We demonstrate that on several partially observed reinforcement learning tasks, this new class of algorithms can substantially outperform strong baseline methods: on Pong with single-frame observations, and on the challenging Doom (ViZDoom) and Minecraft (Malm\"o) first-person navigation benchmarks.
This paper introduces a novel framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This framework, termed as physics-guided neural network (PGNN), leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network architecture. Further, this paper presents a novel framework for using physics-based loss functions in the learning objective of neural networks, to ensure that the model predictions not only show lower errors on the training set but are also scientifically consistent with the known physics on the unlabeled set. We illustrate the effectiveness of PGNN for the problem of lake temperature modeling, where physical relationships between the temperature, density, and depth of water are used to design a physics-based loss function. By using scientific knowledge to guide the construction and learning of neural networks, we are able to show that the proposed framework ensures better generalizability as well as scientific consistency of results.
In this paper we argue that crime drama exemplified in television programs such as CSI:Crime Scene Investigation is an ideal testbed for approximating real-world natural language understanding and the complex inferences associated with it. We propose to treat crime drama as a new inference task, capitalizing on the fact that each episode poses the same basic question (i.e., who committed the crime) and naturally provides the answer when the perpetrator is revealed. We develop a new dataset based on CSI episodes, formalize perpetrator identification as a sequence labeling problem, and develop an LSTM-based model which learns from multi-modal data. Experimental results show that an incremental inference strategy is key to making accurate guesses as well as learning from representations fusing textual, visual, and acoustic input.
Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs. Alternatively, a more recent approach to meta-learning aims to acquire deep representations that can be effectively fine-tuned, via standard gradient descent, to new tasks. In this paper, we consider the meta-learning problem from the perspective of universality, formalizing the notion of learning algorithm approximation and comparing the expressive power of the aforementioned recurrent models to the more recent approaches that embed gradient descent into the meta-learner. In particular, we seek to answer the following question: does deep representation combined with standard gradient descent have sufficient capacity to approximate any learning algorithm? We find that this is indeed true, and further find, in our experiments, that gradient-based meta-learning consistently leads to learning strategies that generalize more widely compared to those represented by recurrent models.
Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.
Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate. We propose a mixed objective that combines cross entropy loss with self-critical policy learning. The objective uses rewards derived from word overlap to solve the misalignment between evaluation metric and optimization objective. In addition to the mixed objective, we improve dynamic coattention networks (DCN) with a deep residual coattention encoder that is inspired by recent work in deep self-attention and residual networks. Our proposals improve model performance across question types and input lengths, especially for long questions that requires the ability to capture long-term dependencies. On the Stanford Question Answering Dataset, our model achieves state-of-the-art results with 75.1% exact match accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy and 86.0% F1.
Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared layers. The results indicate that a flexible ordering can enable more effective sharing, thus motivating the development of a soft ordering approach, which learns how shared layers are applied in different ways for different tasks. Deep MTL with soft ordering outperforms parallel ordering methods across a series of domains. These results suggest that the power of deep MTL comes from learning highly general building blocks that can be assembled to meet the demands of each task.
An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large amount of interactions with the environ- ment in order to master a skill. The learned skill usually generalizes poorly across domains and re-training is often necessary when presented with a new task. We present a framework that combines methods in formal methods with hierarchi- cal reinforcement learning (HRL). The set of techniques we provide allows for convenient specification of tasks with complex logic, learn hierarchical policies (meta-controller and low-level controllers) with well-defined intrinsic rewards us- ing any RL methods and is able to construct new skills from existing ones without additional learning. We evaluate the proposed methods in a simple grid world simulation as well as simulation on a Baxter robot.
We present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling encompasses a wide range of methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented in pomegranate are general mixture models, hidden Markov models, and Bayesian networks. A primary focus of pomegranate is to abstract away the complexities of training models from their definition. This allows users to focus on specifying the correct model for their application instead of being limited by their understanding of the underlying algorithms. An aspect of this focus involves the collection of additive sufficient statistics from data sets as a strategy for training models. This approach trivially enables many useful learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider how to partition data or modify the algorithms to handle these tasks themselves. pomegranate is written in Cython to speed up calculations and releases the global interpreter lock to allow for built-in multithreaded parallelism, making it competitive with---or outperform---other implementations of similar algorithms. This paper presents an overview of the design choices in pomegranate, and how they have enabled complex features to be supported by simple code.
Humans can understand and produce new utterances effortlessly, thanks to their compositional skills. Once a person learns the meaning of a new verb "dax," he or she can immediately understand the meaning of "dax twice" or "sing and dax." In this paper, we introduce the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences. We then test the zero-shot generalization capabilities of a variety of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence methods. We find that RNNs can make successful zero-shot generalizations when the differences between training and test commands are small, so that they can apply "mix-and-match" strategies to solve the task. However, when generalization requires systematic compositional skills (as in the "dax" example above), RNNs fail spectacularly. We conclude with a proof-of-concept experiment in neural machine translation, suggesting that lack of systematicity might be partially responsible for neural networks' notorious training data thirst.
It is often argued that an agent making decisions on behalf of two or more principals who have different utility functions should adopt a {\em Pareto-optimal} policy, i.e., a policy that cannot be improved upon for one agent without making sacrifices for another. A famous theorem of Harsanyi shows that, when the principals have a common prior on the outcome distributions of all policies, a Pareto-optimal policy for the agent is one that maximizes a fixed, weighted linear combination of the principals' utilities. In this paper, we show that Harsanyi's theorem does not hold for principals with different priors, and derive a more precise generalization which does hold, which constitutes our main result. In this more general case, the relative weight given to each principal's utility should evolve over time according to how well the agent's observations conform with that principal's prior. The result has implications for the design of contracts, treaties, joint ventures, and robots.
This paper introduces and addresses a wide class of stochastic bandit problems where the function mapping the arm to the corresponding reward exhibits some known structural properties. Most existing structures (e.g. linear, Lipschitz, unimodal, combinatorial, dueling, ...) are covered by our framework. We derive an asymptotic instance-specific regret lower bound for these problems, and develop OSSB, an algorithm whose regret matches this fundamental limit. OSSB is not based on the classical principle of "optimism in the face of uncertainty" or on Thompson sampling, and rather aims at matching the minimal exploration rates of sub-optimal arms as characterized in the derivation of the regret lower bound. We illustrate the efficiency of OSSB using numerical experiments in the case of the linear bandit problem and show that OSSB outperforms existing algorithms, including Thompson sampling.
As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process-structure-property relations throughout materials science, it seems logical that models that can leverage microstructural data would be more capable of predicting property information. While there have been some recent attempts to use convolutional neural networks to understand microstructural images, these early studies have focused only on which featurizations yield the highest machine learning model accuracy for a single data set. This paper explores the use of convolutional neural networks for classifying microstructure with a more holistic set of objectives in mind: generalization between data sets, number of features required, and interpretability.
This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software Development Kit (SDK) that enables developers to extend the capabilities of Amazon's virtual assistant, Alexa. At Amazon, the infrastructure powers over 25,000 skills deployed through the ASK, as well as AWS's Amazon Lex SLU Service. The ASK emphasizes flexibility, predictability and a rapid iteration cycle for third party developers. It imposes inductive biases that allow it to learn robust SLU models from extremely small and sparse datasets and, in doing so, removes significant barriers to entry for software developers and dialogue systems researchers.
Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc. We consider the problem of unsupervised learning of disentangled representations from large pool of unlabeled observations, and propose a variational inference based approach to infer disentangled latent factors. We introduce a regularizer on the expectation of the approximate posterior over observed data that encourages the disentanglement. We evaluate the proposed approach using several quantitative metrics and empirically observe significant gains over existing methods in terms of both disentanglement and data likelihood (reconstruction quality).
Dom/wdeg is one of the best performing heuristics for dynamic variable ordering in backtrack search [Boussemart et al., 2004]. As originally defined, this heuristic increments the weight of the constraint that causes a domain wipeout (i.e., a dead-end) when enforcing arc consistency during search. "The process of weighting constraints with dom/wdeg is not defined when more than one constraint lead to a domain wipeout [Vion et al., 2011]." In this paper, we investigate how weights should be updated in the context of two high-level consistencies, namely, singleton (POAC) and relational consistencies (RNIC). We propose, analyze, and empirically evaluate several strategies for updating the weights. We statistically compare the proposed strategies and conclude with our recommendations.
Program analysis is a technique to reason about programs without executing them, and it has various applications in compilers, integrated development environments, and security. In this work, we present a machine learning pipeline that induces a security analyzer for programs by example. The security analyzer determines whether a program is either secure or insecure based on symbolic rules that were deduced by our machine learning pipeline. The machine pipeline is two-staged consisting of a Recurrent Neural Networks (RNN) and an Extractor that converts an RNN to symbolic rules. To evaluate the quality of the learned symbolic rules, we propose a sampling-based similarity measurement between two infinite regular languages. We conduct a case study using real-world data. In this work, we discuss the limitations of existing techniques and possible improvements in the future. The results show that with sufficient training data and a fair distribution of program paths it is feasible to deducing symbolic security rules for the OpenJDK library with millions lines of code.
In meta-learning an agent extracts knowledge from observed tasks, aiming to facilitate learning of novel future tasks. Under the assumption that future tasks are 'related' to previous tasks, representations should be learned in a way which captures the common structure across learned tasks, while allowing the learner sufficient flexibility to adapt to novel aspects of new tasks. We present a framework for meta-learning that is based on generalization error bounds, allowing us to extend various PAC-Bayes bounds to meta-learning. Learning takes place through the construction of a distribution over hypotheses based on the observed tasks, and its utilization for learning a new task. Thus, prior knowledge is incorporated through setting an experience-dependent prior for novel tasks. We develop a gradient-based algorithm which minimizes an objective function derived from the bounds and demonstrate its effectiveness numerically with deep neural networks. In addition to establishing the improved performance available through meta-learning, we demonstrate the intuitive way by which prior information is manifested at different levels of the network.
Markov Logic Networks join probabilistic modeling with first-order logic and have been shown to integrate well with the Semantic Web foundations. While several approaches have been devised to tackle the subproblems of rule mining, grounding, and inference, no comprehensive workflow has been proposed so far. In this paper, we fill this gap by introducing a framework called Mandolin, which implements a workflow for knowledge discovery specifically on RDF datasets. Our framework imports knowledge from referenced graphs, creates similarity relationships among similar literals, and relies on state-of-the-art techniques for rule mining, grounding, and inference computation. We show that our best configuration scales well and achieves at least comparable results with respect to other statistical-relational-learning algorithms on link prediction.
This paper describes the design and development of a decentralized firewall system powered by a novel malware detection engine. The firewall is built using blockchain technology. The detection engine aims to classify Portable Executable (PE) files as malicious or benign. File classification is carried out using a deep belief neural network (DBN) as the detection engine. Our approach is to model the files as grayscale images and use the DBN to classify those images into the aforementioned two classes. An extensive data set of 10,000 files is used to train the DBN. Validation is carried out using 4,000 files previously unexposed to the network. The final result of whether to allow or block a file is obtained by arriving at a proof of work based consensus in the blockchain network.
Machine learning is usually defined in behaviourist terms, where external validation is the primary mechanism of learning. In this paper, I argue for a more holistic interpretation in which finding more probable, efficient and abstract representations is as central to learning as performance. In other words, machine learning should be extended with strategies to reason over its own learning process, leading to so-called meta-cognitive machine learning. As such, the de facto definition of machine learning should be reformulated in these intrinsically multi-objective terms, taking into account not only the task performance but also internal learning objectives. To this end, we suggest a "model entropy function" to be defined that quantifies the efficiency of the internal learning processes. It is conjured that the minimization of this model entropy leads to concept formation. Besides philosophical aspects, some initial illustrations are included to support the claims.
Individual Neurons in the nervous systems exploit various dynamics. To capture these dynamics for single neurons, we tune the parameters of an electrophysiological model of nerve cells, to fit experimental data obtained by calcium imaging. A search for the biophysical parameters of this model is performed by means of a genetic algorithm, where the model neuron is exposed to a predefined input current representing overall inputs from other parts of the nervous system. The algorithm is then constrained for keeping the ion-channel currents within reasonable ranges, while producing the best fit to a calcium imaging time series of the AVA interneuron, from the brain of the soil-worm, C. elegans. Our settings enable us to project a set of biophysical parameters to the the neuron kinetics observed in neuronal imaging.
Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the various proposed networks perform differently, with behaviour largely influenced by architectural choices and training settings. This paper explores Ensembles of Multiple Models and Architectures (EMMA) for robust performance through aggregation of predictions from a wide range of methods. The approach reduces the influence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database. EMMA can be seen as an unbiased, generic deep learning model which is shown to yield excellent performance, winning the first position in the BRATS 2017 competition among 50+ participating teams.
Rather than learning new control policies for each new task, it is possible, when tasks share some structure, to compose a "meta-policy" from previously learned policies. This paper reports results from experiments using Deep Reinforcement Learning on a continuous-state, discrete-action autonomous driving simulator. We explore how Deep Neural Networks can represent meta-policies that switch among a set of previously learned policies, specifically in settings where the dynamics of a new scenario are composed of a mixture of previously learned dynamics and where the state observation is possibly corrupted by sensing noise. We also report the results of experiments varying dynamics mixes, distractor policies, magnitudes/distributions of sensing noise, and obstacles. In a fully observed experiment, the meta-policy learning algorithm achieves 2.6x the reward achieved by the next best policy composition technique with 80% less exploration. In a partially observed experiment, the meta-policy learning algorithm converges after 50 iterations while a direct application of RL fails to converge even after 200 iterations.
The next leap on the internet has already started as Semantic Web. At its core, Semantic Web transforms the document oriented web to a data oriented web enriched with semantics embedded as metadata. This change in perspective towards the web offers numerous benefits for vast amount of data intensive industries that are bound to the web and its related applications. The industries are diverse as they range from Oil & Gas exploration to the investigative journalism, and everything in between. This paper discusses eight different industries which currently reap the benefits of Semantic Web. The paper also offers a future outlook into Semantic Web applications and discusses the areas in which Semantic Web would play a key role in the future.
We study the relationship between geometry and capacity measures for deep neural networks from an invariance viewpoint. We introduce a new notion of capacity --- the Fisher-Rao norm --- that possesses desirable invariance properties and is motivated by Information Geometry. We discover an analytical characterization of the new capacity measure, through which we establish norm-comparison inequalities and further show that the new measure serves as an umbrella for several existing norm-based complexity measures. We discuss upper bounds on the generalization error induced by the proposed measure. Extensive numerical experiments on CIFAR-10 support our theoretical findings. Our theoretical analysis rests on a key structural lemma about partial derivatives of multi-layer rectifier networks.
Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network can be increased by widening and adding layers. However, usually the former introduces additional parameters, while the latter increases the runtime. As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution. By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence. Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model.
A remarkable feature of human beings is their capacity for creative behaviour, referring to their ability to react to problems in ways that are novel, surprising, and useful. Transformational creativity is a form of creativity where the creative behaviour is induced by a transformation of the actor's conceptual space, that is, the representational system with which the actor interprets its environment. In this report, we focus on ways of adapting systems of learned representations as they switch from performing one task to performing another. We describe an experimental comparison of multiple strategies for adaptation of learned features, and evaluate how effectively each of these strategies realizes the adaptation, in terms of the amount of training, and in terms of their ability to cope with restricted availability of training data. We show, among other things, that across handwritten digits, natural images, and classical music, adaptive strategies are systematically more effective than a baseline method that starts learning from scratch.
Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their scripts. Specifically, we take a union of language-specific grapheme sets and train a grapheme-based sequence-to-sequence model jointly on data from all languages. We find that this model, which is not explicitly given any information about language identity, improves recognition performance by 21% relative compared to analogous sequence-to-sequence models trained on each language individually. By modifying the model to accept a language identifier as an additional input feature, we further improve performance by an additional 7% relative and eliminate confusion between different languages.
RoboCup is an international scientific robot competition in which teams of multiple robots compete against each other. Its different leagues provide many sources of robotics data, that can be used for further analysis and application of machine learning. This paper describes a large dataset from games of some of the top teams (from 2016 and 2017) in RoboCup Soccer Simulation League (2D), where teams of 11 robots (agents) compete against each other. Overall, we used 10 different teams to play each other, resulting in 45 unique pairings. For each pairing, we ran 25 matches (of 10mins), leading to 1125 matches or more than 180 hours of game play. The generated CSV files are 17GB of data (zipped), or 229GB (unzipped). The dataset is unique in the sense that it contains both the ground truth data (global, complete, noise-free information of all objects on the field), as well as the noisy, local and incomplete percepts of each robot. These data are made available as CSV files, as well as in the original soccer simulator formats.
We propose a neural language model capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and better language modeling. Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic information. On the other hand, tree-structured recursive networks usually require additional structural supervision at the cost of human expert annotation. In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model. In our model, the gradient can be directly back-propagated from the language model loss into the neural parsing network. Experiments show that the proposed model can discover the underlying syntactic structure and achieve state-of-the-art performance on word/character-level language model tasks.
Neural networks (NNs) have begun to have a pervasive impact on various applications of machine learning. However, the problem of finding an optimal NN architecture for large applications has remained open for several decades. Conventional approaches search for the optimal NN architecture through extensive trial-and-error. Such a procedure is quite inefficient. In addition, the generated NN architectures incur substantial redundancy. To address these problems, we propose an NN synthesis tool (NeST) that automatically generates very compact architectures for a given dataset. NeST starts with a seed NN architecture. It iteratively tunes the architecture with gradient-based growth and magnitude-based pruning of neurons and connections. Our experimental results show that NeST yields accurate yet very compact NNs with a wide range of seed architecture selection. For example, for the LeNet-300-100 (LeNet-5) NN architecture derived from the MNIST dataset, we reduce network parameters by 34.1x (74.3x) and floating-point operations (FLOPs) by 35.8x (43.7x). For the AlexNet NN architecture derived from the ImageNet dataset, we reduce network parameters by 15.7x and FLOPs by 4.6x. All these results are the current state-of-the-art for these architectures.
In this paper, we study the representational power of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions, based on PWL activation units such as rectifier or maxout. We investigate the complexity of such networks by studying the number of linear regions of the PWL function. Typically, a PWL function from a DNN can be seen as a large family of linear functions acting on millions of such regions. We directly build upon the work of Montufar et al. (2014), Montufar (2017) and Raghu et al. (2017) by refining the upper and lower bounds on the number of linear regions for rectified and maxout networks. In addition to achieving tighter bounds, we also develop a novel method to perform exact enumeration or counting of the number of linear regions with a mixed-integer linear formulation that maps the input space to output. We use this new capability to visualize how the number of linear regions change while training DNNs.
State-of-the-art results on neural machine translation often use attentional sequence-to-sequence models with some form of convolution or recursion. Vaswani et al. (2017) propose a new architecture that avoids recurrence and convolution completely. Instead, it uses only self-attention and feed-forward layers. While the proposed architecture achieves state-of-the-art results on several machine translation tasks, it requires a large number of parameters and training iterations to converge. We propose Weighted Transformer, a Transformer with modified attention layers, that not only outperforms the baseline network in BLEU score but also converges 15-40% faster. Specifically, we replace the multi-head attention by multiple self-attention branches that the model learns to combine during the training process. Our model improves the state-of-the-art performance by 0.5 BLEU points on the WMT 2014 English-to-German translation task and by 0.4 on the English-to-French translation task.
We present a novel technique for learning the mass matrices in samplers obtained from discretized dynamics that preserve some energy function. Existing adaptive samplers use Riemannian preconditioning techniques, where the mass matrices are functions of the parameters being sampled. This leads to significant complexities in the energy reformulations and resultant dynamics, often leading to implicit systems of equations and requiring inversion of high-dimensional matrices in the leapfrog steps. Our approach provides a simpler alternative, by using existing dynamics in the sampling step of a Monte Carlo EM framework, and learning the mass matrices in the M step with a novel online technique. We also propose a way to adaptively set the number of samples gathered in the E step, using sampling error estimates from the leapfrog dynamics. Along with a novel stochastic sampler based on Nos\'{e}-Poincar\'{e} dynamics, we use this framework with standard Hamiltonian Monte Carlo (HMC) as well as newer stochastic algorithms such as SGHMC and SGNHT, and show strong performance on synthetic and real high-dimensional sampling scenarios; we achieve sampling accuracies comparable to Riemannian samplers while being significantly faster.
Approximate algorithms for structured prediction problems---such as the popular alpha-expansion algorithm (Boykov et al. 2001) in computer vision---typically far exceed their theoretical performance guarantees on real-world instances. These algorithms often find solutions that are very close to optimal. The goal of this paper is to partially explain the performance of alpha-expansion on MAP inference in Ferromagnetic Potts models (FPMs). Our main results use the connection between energy minimization in FPMs and the Uniform Metric Labeling problem to give a stability condition under which the alpha-expansion algorithm provably recovers the optimal MAP solution. This theoretical result complements the numerous empirical observations of alpha-expansion's performance. Additionally, we give a different stability condition under which an LP-based algorithm recovers the optimal solution.
Deep reinforcement learning has achieved many recent successes, but our understanding of its strengths and limitations is hampered by the lack of rich environments in which we can fully characterize optimal behavior, and correspondingly diagnose individual actions against such a characterization. Here we consider a family of combinatorial games, arising from work of Erdos, Selfridge, and Spencer, and we propose their use as environments for evaluating and comparing different approaches to reinforcement learning. These games have a number of appealing features: they are challenging for current learning approaches, but they form (i) a low-dimensional, simply parametrized environment where (ii) there is a linear closed form solution for optimal behavior from any state, and (iii) the difficulty of the game can be tuned by changing environment parameters in an interpretable way. We use these Erdos-Selfridge-Spencer games not only to compare different algorithms, but test for generalization, make comparisons to supervised learning, analyse multiagent play, and even develop a self play algorithm.
We study the problem of learning overcomplete HMMs---those that have many hidden states but a small output alphabet. Despite having significant practical importance, such HMMs are poorly understood with no known positive or negative results for efficient learning. In this paper, we present several new results---both positive and negative---which help define the boundaries between the tractable and intractable settings. Specifically, we show positive results for a large subclass of HMMs whose transition matrices are sparse, well-conditioned, and have small probability mass on short cycles. On the other hand, we show that learning is impossible given only a polynomial number of samples for HMMs with a small output alphabet and whose transition matrices are random regular graphs with large degree. We also discuss these results in the context of learning HMMs which can capture long-term dependencies.
A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and truncated backpropagation through time is used as a heuristic. However, this usually leads to biased estimates of the gradient in which longer term dependencies are ignored. Addressing this issue, we propose an alternative algorithm, Sparse Attentive Backtracking, which might also be related to principles used by brains to learn long-term dependencies. Sparse Attentive Backtracking learns an attention mechanism over the hidden states of the past and selectively backpropagates through paths with high attention weights. This allows the model to learn long term dependencies while only backtracking for a small number of time steps, not just from the recent past but also from attended relevant past states.
The main challenge of online multi-object tracking is to reliably associate object trajectories with detections in each video frame based on their tracking history. In this work, we propose the Recurrent Autoregressive Network (RAN), a temporal generative modeling framework to characterize the appearance and motion dynamics of multiple objects over time. The RAN couples an external memory and an internal memory. The external memory explicitly stores previous inputs of each trajectory in a time window, while the internal memory learns to summarize long-term tracking history and associate detections by processing the external memory. We conduct experiments on the MOT 2015 and 2016 datasets to demonstrate the robustness of our tracking method in highly crowded and occluded scenes. Our method achieves top-ranked results on the two benchmarks.
Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse RNNs are easier to deploy on devices and high-end server processors. Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms. In order to address this issue, we investigate two different approaches to induce block sparsity in RNNs: pruning blocks of weights in a layer and using group lasso regularization to create blocks of weights with zeros. Using these techniques, we demonstrate that we can create block-sparse RNNs with sparsity ranging from 80% to 90% with small loss in accuracy. This allows us to reduce the model size by roughly 10x. Additionally, we can prune a larger dense network to recover this loss in accuracy while maintaining high block sparsity and reducing the overall parameter count. Our technique works with a variety of block sizes up to 32x32. Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.
We improve the performance of the American Fuzzy Lop (AFL) fuzz testing framework by using Generative Adversarial Network (GAN) models to reinitialize the system with novel seed files. We assess performance based on the temporal rate at which we produce novel and unseen code paths. We compare this approach to seed file generation from a random draw of bytes observed in the training seed files. The code path lengths and variations were not sufficiently diverse to fully replace AFL input generation. However, augmenting native AFL with these additional code paths demonstrated improvements over AFL alone. Specifically, experiments showed the GAN was faster and more effective than the LSTM and out-performed a random augmentation strategy, as measured by the number of unique code paths discovered. GAN helps AFL discover 14.23% more code paths than the random strategy in the same amount of CPU time, finds 6.16% more unique code paths, and finds paths that are on average 13.84% longer. Using GAN shows promise as a reinitialization strategy for AFL to help the fuzzer exercise deep paths in software.
Autonomous agents optimize the reward function we give them. What they don't know is how hard it is for us to design a reward function that actually captures what we want. When designing the reward, we might think of some specific training scenarios, and make sure that the reward will lead to the right behavior in those scenarios. Inevitably, agents encounter new scenarios (e.g., new types of terrain) where optimizing that same reward may lead to undesired behavior. Our insight is that reward functions are merely observations about what the designer actually wants, and that they should be interpreted in the context in which they were designed. We introduce inverse reward design (IRD) as the problem of inferring the true objective based on the designed reward and the training MDP. We introduce approximate methods for solving IRD problems, and use their solution to plan risk-averse behavior in test MDPs. Empirical results suggest that this approach can help alleviate negative side effects of misspecified reward functions and mitigate reward hacking.
Sparsity inducing regularization is an important part for learning over-complete visual representations. Despite the popularity of $\ell_1$ regularization, in this paper, we investigate the usage of non-convex regularizations in this problem. Our contribution consists of three parts. First, we propose the leaky capped norm regularization (LCNR), which allows model weights below a certain threshold to be regularized more strongly as opposed to those above, therefore imposes strong sparsity and only introduces controllable estimation bias. We propose a majorization-minimization algorithm to optimize the joint objective function. Second, our study over monocular 3D shape recovery and neural networks with LCNR outperforms $\ell_1$ and other non-convex regularizations, achieving state-of-the-art performance and faster convergence. Third, we prove a theoretical global convergence speed on the 3D recovery problem. To the best of our knowledge, this is the first convergence analysis of the 3D recovery problem.
This paper deals with unsupervised clustering with feature selection. The problem is to estimate both labels and a sparse projection matrix of weights. To address this combinatorial non-convex problem maintaining a strict control on the sparsity of the matrix of weights, we propose an alternating minimization of the Frobenius norm criterion. We provide a new efficient algorithm named K-sparse which alternates k-means with projection-gradient minimization. The projection-gradient step is a method of splitting type, with exact projection on the $\ell^1$ ball to promote sparsity. The convergence of the gradient-projection step is addressed, and a preliminary analysis of the alternating minimization is made. The Frobenius norm criterion converges as the number of iterates in Algorithm K-sparse goes to infinity. Experiments on Single Cell RNA sequencing datasets show that our method significantly improves the results of PCA k-means, spectral clustering, SIMLR, and Sparcl methods, and achieves a relevant selection of genes. The complexity of K-sparse is linear in the number of samples (cells), so that the method scales up to large datasets.
Roguelike games generally feature exploration problems as a critical, yet often repetitive element of gameplay. Automated approaches, however, face challenges in terms of optimality, as well as due to incomplete information, such as from the presence of secret doors. This paper presents an algorithmic approach to exploration of roguelike dungeon environments. Our design aims to minimize exploration time, balancing coverage and discovery of secret areas with resource cost. Our algorithm is based on the concept of occupancy maps popular in robotics, adapted to encourage efficient discovery of secret access points. Through extensive experimentation on NetHack maps we show that this technique is significantly more efficient than simpler greedy approaches. We further investigate optimized parameterization for the algorithm through a comprehensive data analysis. These results point towards better automation for players as well as heuristics applicable to fully automated gameplay.
We consider stochastic multi-armed bandit problems with graph feedback, where the decision maker is allowed to observe the neighboring actions of the chosen action. We allow the graph structure to vary with time and consider both deterministic and Erd\H{o}s-R\'enyi random graph models. For such a graph feedback model, we first present a novel analysis of Thompson sampling that leads to tighter performance bound than existing work. Next, we propose new Information Directed Sampling based policies that are graph-aware in their decision making. Under the deterministic graph case, we establish a Bayesian regret bound for the proposed policies that scales with the clique cover number of the graph instead of the number of actions. Under the random graph case, we provide a Bayesian regret bound for the proposed policies that scales with the ratio of the number of actions over the expected number of observations per iteration. To the best of our knowledge, this is the first analytical result for stochastic bandits with random graph feedback. Finally, using numerical evaluations, we demonstrate that our proposed IDS policies outperform existing approaches, including adaptions of upper confidence bound, $\epsilon$-greedy and Exp3 algorithms.
Cloze test is widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH, in which the questions were used in middle-school and high-school language exams. With the missing blanks carefully created by teachers and candidate choices purposely designed to be confusing, CLOTH requires a deeper language understanding and a wider attention span than previous automatically generated cloze datasets. We show humans outperform dedicated designed baseline models by a significant margin, even when the model is trained on sufficiently large external data. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending a long-term context to be the key bottleneck.
Computational models of decisionmaking must contend with the variance of context and any number of possible decisions that a defined strategic actor can make at a given time. Relying on cognitive science theory, the authors have created an algorithm that captures the orientation of the actor towards an object and arrays the possible decisions available to that actor based on their given intersubjective orientation. This algorithm, like a traditional K-means clustering algorithm, relies on a core-periphery structure that gives the likelihood of moves as those closest to the cluster's centroid. The result is an algorithm that enables unsupervised classification of an array of decision points belonging to an actor's present state and deeply rooted in cognitive science theory.
Program synthesis is a class of regression problems where one seeks a solution, in the form of a source-code program, mapping the inputs to their corresponding outputs exactly. Due to its precise and combinatorial nature, program synthesis is commonly formulated as a constraint satisfaction problem, where input-output examples are encoded as constraints and solved with a constraint solver. A key challenge of this formulation is scalability: while constraint solvers work well with a few well-chosen examples, a large set of examples can incur significant overhead in both time and memory. We describe a method to discover a subset of examples that is both small and representative: the subset is constructed iteratively, using a neural network to predict the probability of unchosen examples conditioned on the chosen examples in the subset, and greedily adding the least probable example. We empirically evaluate the representativeness of the subsets constructed by our method, and demonstrate such subsets can significantly improve synthesis time and stability.
Network integration studies try to assess the impact of future developments, such as the increase of Renewable Energy Sources or the introduction of Smart Grid Technologies, on large-scale network areas. Goals can be to support strategic alignment in the regulatory framework or to adapt the network planning principles of Distribution System Operators. This study outlines an approach for the automated distribution system planning that can calculate network reconfiguration, reinforcement and extension plans in a fully automated fashion. This allows the estimation of the expected cost in massive probabilistic simulations of large numbers of real networks and constitutes a core component of a framework for large-scale network integration studies. Exemplary case study results are presented that were performed in cooperation with different major distribution system operators. The case studies cover the estimation of expected network reinforcement costs, technical and economical assessment of smart grid technologies and structural network optimisation.
Ontology engineering is a hard and error-prone task, in which small changes may lead to errors, or even produce an inconsistent ontology. As ontologies grow in size, the need for automated methods for repairing inconsistencies while preserving as much of the original knowledge as possible increases. Most previous approaches to this task are based on removing a few axioms from the ontology to regain consistency. We propose a new method based on weakening these axioms to make them less restrictive, employing the use of refinement operators. We introduce the theoretical framework for weakening DL ontologies, propose algorithms to repair ontologies based on the framework, and provide an analysis of the computational complexity. Through an empirical analysis made over real-life ontologies, we show that our approach preserves significantly more of the original knowledge of the ontology than removing axioms.
Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace. To address these problems, Knowledge Graph Completion (KGC) has been proposed to improve KGs by filling in its missing connections. Unlike existing methods which hold a closed-world assumption, i.e., where KGs are fixed and new entities cannot be easily added, in the present work we relax this assumption and propose a new open-world KGC task. As a first attempt to solve this task we introduce an open-world KGC model called ConMask. This model learns embeddings of the entity's name and parts of its text-description to connect unseen entities to the KG. To mitigate the presence of noisy text descriptions, ConMask uses a relationship-dependent content masking to extract relevant snippets and then trains a fully convolutional neural network to fuse the extracted snippets with entities in the KG. Experiments on large data sets, both old and new, show that ConMask performs well in the open-world KGC task and even outperforms existing KGC models on the standard closed-world KGC task.
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an $n \times n$ positive definite matrix, and its derivatives - leading to prohibitive $\mathcal{O}(n^3)$ computations. We propose novel $\mathcal{O}(n)$ approaches to estimating these quantities from only fast matrix vector multiplications (MVMs). These stochastic approximations are based on Chebyshev, Lanczos, and surrogate models, and converge quickly even for kernel matrices that have challenging spectra. We leverage these approximations to develop a scalable Gaussian process approach to kernel learning. We find that Lanczos is generally superior to Chebyshev for kernel learning, and that a surrogate approach can be highly efficient and accurate with popular kernels.
Representing the semantics of words is a long-standing problem for the natural language processing community. Most methods compute word semantics given their textual context in large corpora. More recently, researchers attempted to integrate perceptual and visual features. Most of these works consider the visual appearance of objects to enhance word representations but they ignore the visual environment and context in which objects appear. We propose to unify text-based techniques with vision-based techniques by simultaneously leveraging textual and visual context to learn multimodal word embeddings. We explore various choices for what can serve as a visual context and present an end-to-end method to integrate visual context elements in a multimodal skip-gram model. We provide experiments and extensive analysis of the obtained results.
We propose a new splitting criterion for a meta-learning approach to multiclass classifier design that adaptively merges the classes into a tree-structured hierarchy of increasingly difficult binary classification problems. The classification tree is constructed from empirical estimates of the Henze-Penrose bounds on the pairwise Bayes misclassification rates that rank the binary subproblems in terms of difficulty of classification. The proposed empirical estimates of the Bayes error rate are computed from the minimal spanning tree (MST) of the samples from each pair of classes. Moreover, a meta-learning technique is presented for quantifying the one-vs-rest Bayes error rate for each individual class from a single MST on the entire dataset. Extensive simulations on benchmark datasets show that the proposed hierarchical method can often be learned much faster than competing methods, while achieving competitive accuracy.
This paper proposes a computational approach for analysis of strokes in line drawings by artists. We aim at developing an AI methodology that facilitates attribution of drawings of unknown authors in a way that is not easy to be deceived by forged art. The methodology used is based on quantifying the characteristics of individual strokes in drawings. We propose a novel algorithm for segmenting individual strokes. We designed and compared different hand-crafted and learned features for the task of quantifying stroke characteristics. We also propose and compare different classification methods at the drawing level. We experimented with a dataset of 300 digitized drawings with over 80 thousands strokes. The collection mainly consisted of drawings of Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of representative works of other artists. The experiments shows that the proposed methodology can classify individual strokes with accuracy 70%-90%, and aggregate over drawings with accuracy above 80%, while being robust to be deceived by fakes (with accuracy 100% for detecting fakes in most settings).
The multi-armed bandit problem has been extensively studied under the stationary assumption. However in reality, this assumption often does not hold because the distributions of rewards themselves may change over time. In this paper, we propose a change-detection (CD) based framework for multi-armed bandit problems under the piecewise-stationary setting, and study a class of change-detection based UCB (Upper Confidence Bound) policies, CD-UCB, that actively detects change points and restarts the UCB indices. We then develop CUSUM-UCB and PHT-UCB, that belong to the CD-UCB class and use cumulative sum (CUSUM) and Page-Hinkley Test (PHT) to detect changes. We show that CUSUM-UCB obtains the best known regret upper bound under mild assumptions. We also demonstrate the regret reduction of the CD-UCB policies over arbitrary Bernoulli rewards and Yahoo! datasets of webpage click-through rates.
With an abundance of research papers in deep learning, reproducibility or adoption of the existing works becomes a challenge. This is due to the lack of open source implementations provided by the authors. Further, re-implementing research papers in a different library is a daunting task. To address these challenges, we propose a novel extensible approach, DLPaper2Code, to extract and understand deep learning design flow diagrams and tables available in a research paper and convert them to an abstract computational graph. The extracted computational graph is then converted into execution ready source code in both Keras and Caffe, in real-time. An arXiv-like website is created where the automatically generated designs is made publicly available for 5,000 research papers. The generated designs could be rated and edited using an intuitive drag-and-drop UI framework in a crowdsourced manner. To evaluate our approach, we create a simulated dataset with over 216,000 valid design visualizations using a manually defined grammar. Experiments on the simulated dataset show that the proposed framework provide more than $93\%$ accuracy in flow diagram content extraction.
We study the performance of stochastically trained deep neural networks (DNNs) whose synaptic weights are implemented using emerging memristive devices that exhibit limited dynamic range, resolution, and variability in their programming characteristics. We show that a key device parameter to optimize the learning efficiency of DNNs is the variability in its programming characteristics. DNNs with such memristive synapses, even with dynamic range as low as $15$ and only $32$ discrete levels, when trained based on stochastic updates suffer less than $3\%$ loss in accuracy compared to floating point software baseline. We also study the performance of stochastic memristive DNNs when used as inference engines with noise corrupted data and find that if the device variability can be minimized, the relative degradation in performance for the Stochastic DNN is better than that of the software baseline. Hence, our study presents a new optimization corner for memristive devices for building large noise-immune deep learning systems.
Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data. In contrast to traditional fully supervised learning approaches, in this paper we propose learning intrinsic image decomposition by explaining the input image. Our model, the Rendered Intrinsics Network (RIN), joins together an image decomposition pipeline, which predicts reflectance, shape, and lighting conditions given a single image, with a recombination function, a learned shading model used to recompose the original input based off of intrinsic image predictions. Our network can then use unsupervised reconstruction error as an additional signal to improve its intermediate representations. This allows large-scale unlabeled data to be useful during training, and also enables transferring learned knowledge to images of unseen object categories, lighting conditions, and shapes. Extensive experiments demonstrate that our method performs well on both intrinsic image decomposition and knowledge transfer.
We introduce models for saliency prediction for mobile user interfaces. A mobile interface may include elements like buttons, text, etc. in addition to natural images which enable performing a variety of tasks. Saliency in natural images is a well studied area. However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency prediction for mobile interface images requires a fresh approach. Mobile interface design involves operating on elements, the building blocks of the interface. We first collected eye-gaze data from mobile devices for free viewing task. Using this data, we develop a novel autoencoder based multi-scale deep learning model that provides saliency prediction at the mobile interface element level. Compared to saliency prediction approaches developed for natural images, we show that our approach performs significantly better on a range of established metrics.
In this paper we deal with the problem of extending Zadeh's operators on fuzzy sets (FSs) to interval-valued (IVFSs), set-valued (SVFSs) and type-2 (T2FSs) fuzzy sets. Namely, it is known that seeing FSs as SVFSs, or T2FSs, whose membership degrees are singletons is not order-preserving. We then describe a family of lattice embeddings from FSs to SVFSs. Alternatively, if the former singleton viewpoint is required, we reformulate the intersection on hesitant fuzzy sets and introduce what we have called closed-valued fuzzy sets. This new type of fuzzy sets extends standard union and intersection on FSs. In addition, it allows handling together membership degrees of different nature as, for instance, closed intervals and finite sets. Finally, all these constructions are viewed as T2FSs forming a chain of lattices.
A temporally abstract action, or an option, is specified by a policy and a termination condition: the policy guides option behavior, and the termination condition roughly determines its length. Generally, learning with longer options (like learning with multi-step returns) is known to be more efficient. However, if the option set for the task is not ideal, and cannot express the primitive optimal policy exactly, shorter options offer more flexibility and can yield a better solution. Thus, the termination condition puts learning efficiency at odds with solution quality. We propose to resolve this dilemma by decoupling the behavior and target terminations, just like it is done with policies in off-policy learning. To this end, we give a new algorithm, Q(\beta), that learns the solution with respect to any termination condition, regardless of how the options actually terminate. We derive Q(\beta) by casting learning with options into a common framework with well-studied multi-step off-policy learning. We validate our algorithm empirically, and show that it holds up to its motivating claims.
In this work we propose a new method for the rhythm classification of short single-lead ECG records, using a set of high-level and clinically meaningful features provided by the abductive interpretation of the records. These features include morphological and rhythm-related features that are used to build two classifiers: one that evaluates the record globally, using aggregated values for each feature; and another one that evaluates the record as a sequence, using a Recurrent Neural Network fed with the individual features for each detected heartbeat. The two classifiers are finally combined using the stacking technique, providing an answer by means of four target classes: Normal sinus rhythm, Atrial fibrillation, Other anomaly, and Noisy. The approach has been validated against the 2017 Physionet/CinC Challenge dataset, obtaining a final score of 0.83 and ranking first in the competition.
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an end-to-end model trained via imitation learning, and an end-to-end model trained via reinforcement learning. The approaches are evaluated in controlled scenarios of increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform's utility for autonomous driving research. The supplementary video can be viewed at https://youtu.be/Hp8Dz-Zek2E
Rapid advances of hardware-based technologies during the past decades have opened up new possibilities for Life scientists to gather multimodal data in various application domains (e.g., Omics, Bioimaging, Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques. Overall, recent research in Deep learning (DL), Reinforcement learning (RL), and their combination (Deep RL) promise to revolutionize Artificial Intelligence. The growth in computational power accompanied by faster and increased data storage and declining computing costs have already allowed scientists in various fields to apply these techniques on datasets that were previously intractable for their size and complexity. This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data. In addition, we compare performances of DL techniques when applied to different datasets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.
To efficiently answer queries, datalog systems often materialise all consequences of a datalog program, so the materialisation must be updated whenever the input facts change. Several solutions to the materialisation update problem have been proposed. The Delete/Rederive (DRed) and the Backward/Forward (B/F) algorithms solve this problem for general datalog, but both contain steps that evaluate rules 'backwards' by matching their heads to a fact and evaluating the partially instantiated rule bodies as queries. We show that this can be a considerable source of overhead even on very small updates. In contrast, the Counting algorithm does not evaluate the rules 'backwards', but it can handle only nonrecursive rules. We present two hybrid approaches that combine DRed and B/F with Counting so as to reduce or even eliminate 'backward' rule evaluation while still handling arbitrary datalog programs. We show empirically that our hybrid algorithms are usually significantly faster than existing approaches, sometimes by orders of magnitude.
In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications).
Within-Class Covariance Normalization (WCCN) is a powerful post-processing method for normalizing the within-class covariance of a set of data points. WCCN projects the observations into a linear sub-space where the within-class variability is reduced. This property has proven to be beneficial in subsequent recognition tasks. The central idea of this paper is to reformulate the classic WCCN as a Deep Neural Network (DNN) compatible version. We propose the Deep WithinClass Covariance Analysis (DWCCA) which can be incorporated in a DNN architecture. This formulation enables us to exploit the beneficial properties of WCCN, and still allows for training with Stochastic Gradient Descent (SGD) in an end-to-end fashion. We investigate the advantages of DWCCA on deep neural networks with convolutional layers for supervised learning. Our results on Acoustic Scene Classification show that via DWCCA we can achieves equal or superior performance in a VGG-style deep neural network.
We search for digital biomarkers from Parkinson's Disease by observing approximate repetitive patterns matching hypothesized step and stride periodic cycles. These observations were modeled as a cycle of hidden states with randomness allowing deviation from a canonical pattern of transitions and emissions, under the hypothesis that the averaged features of hidden states would serve to informatively characterize classes of patients/controls. We propose a Hidden Semi-Markov Model (HSMM), a latent-state model, emitting 3D-acceleration vectors. Transitions and emissions are inferred from data. We fit separate models per unique device and training label. Hidden Markov Models (HMM) force geometric distributions of the duration spent at each state before transition to a new state. Instead, our HSMM allows us to specify the distribution of state duration. This modified version is more effective because we are interested more in each state's duration than the sequence of distinct states, allowing inclusion of these durations the feature vector.
Training a personalized dialogue system requires a lot of data, and the data collected for a single user is usually insufficient. One common practice for this problem is to share training dialogues between different users and train multiple sequence-to-sequence dialogue models together with transfer learning. However, current sequence-to-sequence transfer learning models operate on the entire sentence, which might cause negative transfer if different personal information from different users is mixed up. We propose a personalized decoder model to transfer finer granularity phrase-level knowledge between different users while keeping personal preferences of each user intact. A novel personal control gate is introduced, enabling the personalized decoder to switch between generating personalized phrases and shared phrases. The proposed personalized decoder model can be easily combined with various deep models and can be trained with reinforcement learning. Real-world experimental results demonstrate that the phrase-level personalized decoder improves the BLEU over multiple sentence-level transfer baseline models by as much as 7.5%.
Generating emotional language is a key step towards building empathetic natural language processing agents. However, a major challenge for this line of research is the lack of large-scale labeled training data, and previous studies are limited to only small sets of human annotated sentiment labels. Additionally, explicitly controlling the emotion and sentiment of generated text is also difficult. In this paper, we take a more radical approach: we exploit the idea of leveraging Twitter data that are naturally labeled with emojis. More specifically, we collect a large corpus of Twitter conversations that include emojis in the response, and assume the emojis convey the underlying emotions of the sentence. We then introduce a reinforced conditional variational encoder approach to train a deep generative model on these conversations, which allows us to use emojis to control the emotion of the generated text. Experimentally, we show in our quantitative and qualitative analyses that the proposed models can successfully generate high-quality abstractive conversation responses in accordance with designated emotions.
LocatedNear relation describes two typically co-located objects, which is a type of useful commonsense knowledge for computer vision, natural language understanding, machine comprehension, etc. We propose to automatically extract such relationship through a sentence-level classifier and aggregating the scores of entity pairs detected from a large number of sentences. To enable the research of these tasks, we release two benchmark datasets, one containing 5,000 sentences annotated with whether a mentioned entity pair has LocatedNear relation in the given sentence or not; the other containing 500 pairs of physical objects and whether they are commonly located nearby. We also propose some baseline methods for the tasks and compare the results with a state-of-the-art general-purpose relation classifier.
Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. Compared to tracking with a still camera, the images captured with a PTZ camera are highly dynamic in nature because the camera can perform large motion resulting in quickly changing capture conditions. Furthermore, tracking with a PTZ camera involves camera control to position the camera on the target. For successful tracking and camera control, the tracker must be fast enough, or has to be able to predict accurately the next position of the target. Therefore, standard benchmarks do not allow to assess properly the quality of a tracker for the PTZ scenario. In this work, we use a virtual PTZ framework to evaluate different tracking algorithms and compare their performances. We also extend the framework to add target position prediction for the next frame, accounting for camera motion and processing delays. By doing this, we can assess if predicting can make long-term tracking more robust as it may help slower algorithms for keeping the target in the field of view of the camera. Results confirm that both speed and robustness are required for tracking under the PTZ scenario.
The quest for algorithms that enable cognitive abilities is an important part of machine learning. A common trait in many recently investigated cognitive-like tasks is that they take into account different data modalities, such as visual and textual input. In this paper we propose a novel and generally applicable form of attention mechanism that learns high-order correlations between various data modalities. We show that high-order correlations effectively direct the appropriate attention to the relevant elements in the different data modalities that are required to solve the joint task. We demonstrate the effectiveness of our high-order attention mechanism on the task of visual question answering (VQA), where we achieve state-of-the-art performance on the standard VQA dataset.
Juba recently proposed a formulation of learning abductive reasoning from examples, in which both the relative plausibility of various explanations, as well as which explanations are valid, are learned directly from data. The main shortcoming of this formulation of the task is that it assumes access to full-information (i.e., fully specified) examples; relatedly, it offers no role for declarative background knowledge, as such knowledge is rendered redundant in the abduction task by complete information. In this work, we extend the formulation to utilize such partially specified examples, along with declarative background knowledge about the missing data. We show that it is possible to use implicitly learned rules together with the explicitly given declarative knowledge to support hypotheses in the course of abduction. We observe that when a small explanation exists, it is possible to obtain a much-improved guarantee in the challenging exception-tolerant setting. Such small, human-understandable explanations are of particular interest for potential applications of the task.
Neural networks have recently had a lot of success for many tasks. However, neural network architectures that perform well are still typically designed manually by experts in a cumbersome trial-and-error process. We propose a new method to automatically search for well-performing CNN architectures based on a simple hill climbing procedure whose operators apply network morphisms, followed by short optimization runs by cosine annealing. Surprisingly, this simple method yields competitive results, despite only requiring resources in the same order of magnitude as training a single network. E.g., on CIFAR-10, our method designs and trains networks with an error rate below 6% in only 12 hours on a single GPU; training for one day reduces this error further, to almost 5%.
In the paper, a parallel Tabu Search algorithm for the Resource Constrained Project Scheduling Problem is proposed. To deal with this NP-hard combinatorial problem many optimizations have been performed. For example, a resource evaluation algorithm is selected by a heuristic and an effective Tabu List was designed. In addition to that, a capacity-indexed resource evaluation algorithm was proposed and the GPU (Graphics Processing Unit) version uses a homogeneous model to reduce the required communication bandwidth. According to the experiments, the GPU version outperforms the optimized parallel CPU version with respect to the computational time and the quality of solutions. In comparison with other existing heuristics, the proposed solution often gives better quality solutions.
Training automatic speech recognition (ASR) systems requires large amounts of data in the target language in order to achieve good performance. Whereas large training corpora are readily available for languages like English, there exists a long tail of languages which do suffer from a lack of resources. One method to handle data sparsity is to use data from additional source languages and build a multilingual system. Recently, ASR systems based on recurrent neural networks (RNNs) trained with connectionist temporal classification (CTC) have gained substantial research interest. In this work, we extended our previous approach towards training CTC-based systems multilingually. Our systems feature a global phone set, based on the joint phone sets of each source language. We evaluated the use of different language combinations as well as the addition of Language Feature Vectors (LFVs). As contrastive experiment, we built systems based on graphemes as well. Systems having a multilingual phone set are known to suffer in performance compared to their monolingual counterparts. With our proposed approach, we could reduce the gap between these mono- and multilingual setups, using either graphemes or phonemes.
In this work, we focus on multilingual systems based on recurrent neural networks (RNNs), trained using the Connectionist Temporal Classification (CTC) loss function. Using a multilingual set of acoustic units poses difficulties. To address this issue, we proposed Language Feature Vectors (LFVs) to train language adaptive multilingual systems. Language adaptation, in contrast to speaker adaptation, needs to be applied not only on the feature level, but also to deeper layers of the network. In this work, we therefore extended our previous approach by introducing a novel technique which we call "modulation". Based on this method, we modulated the hidden layers of RNNs using LFVs. We evaluated this approach in both full and low resource conditions, as well as for grapheme and phone based systems. Lower error rates throughout the different conditions could be achieved by the use of the modulation.
This paper considers two important problems - on the supply-side and demand-side respectively and studies both in a unified framework. On the supply side, we study the problem of energy sharing among microgrids with the goal of maximizing profit obtained from selling power while meeting customer demand. On the other hand, under shortage of power, this problem becomes one of deciding the amount of power to be bought with dynamically varying prices. On the demand side, we consider the problem of optimally scheduling the time-adjustable demand - i.e., of loads with flexible time windows in which they can be scheduled. While previous works have treated these two problems in isolation, we combine these problems together and provide for the first time in the literature, a unified Markov decision process (MDP) framework for these problems. We then apply the Q-learning algorithm, a popular model-free reinforcement learning technique, to obtain the optimal policy. Through simulations, we show that our model outperforms the traditional power sharing models.
This article presents the use of Answer Set Programming (ASP) to mine sequential patterns. ASP is a high-level declarative logic programming paradigm for high level encoding combinatorial and optimization problem solving as well as knowledge representation and reasoning. Thus, ASP is a good candidate for implementing pattern mining with background knowledge, which has been a data mining issue for a long time. We propose encodings of the classical sequential pattern mining tasks within two representations of embeddings (fill-gaps vs skip-gaps) and for various kinds of patterns: frequent, constrained and condensed. We compare the computational performance of these encodings with each other to get a good insight into the efficiency of ASP encodings. The results show that the fill-gaps strategy is better on real problems due to lower memory consumption. Finally, compared to a constraint programming approach (CPSM), another declarative programming paradigm, our proposal showed comparable performance.
Recent industry reports assure the rise of web robots which comprise more than half of the total web traffic. They not only threaten the security, privacy and efficiency of the web but they also distort analytics and metrics, doubting the veracity of the information being promoted. In the academic publishing domain, this can cause articles to be faulty presented as prominent and influential. In this paper, we present our approach on detecting web robots in academic publishing websites. We use different supervised learning algorithms with a variety of characteristics deriving from both the log files of the server and the content served by the website. Our approach relies on the assumption that human users will be interested in specific domains or articles, while web robots crawl a web library incoherently. We experiment with features adopted in previous studies with the addition of novel semantic characteristics which derive after performing a semantic analysis using the Latent Dirichlet Allocation (LDA) algorithm. Our real-world case study shows promising results, pinpointing the significance of semantic features in the web robot detection problem.
A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer. Our models have achieved state-of-the-art results on three public open-domain QA datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8 percentage points of improvement over the former two datasets.
Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently for sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network. DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded. We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance. DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.
Humans process visual scenes selectively and sequentially using attention. Central to models of human visual attention is the saliency map. We propose a hierarchical visual architecture that operates on a saliency map and uses a novel attention mechanism to sequentially focus on salient regions and take additional glimpses within those regions. The architecture is motivated by human visual attention, and is used for multi-label image classification on a novel multiset task, demonstrating that it achieves high precision and recall while localizing objects with its attention. Unlike conventional multi-label image classification models, the model supports multiset prediction due to a reinforcement-learning based training process that allows for arbitrary label permutation and multiple instances per label.
Inspired by the magic sets for Datalog, we present a novel goal-driven approach for answering queries over terminating existential rules with equality (aka TGDs and EGDs). Our technique improves the performance of query answering by pruning the consequences that are not relevant for the query. This is challenging in our setting because equalities can potentially affect all predicates in a dataset. We address this problem by combining the existing singularization technique with two new ingredients: an algorithm for identifying the rules relevant to a query and a new magic sets algorithm. We show empirically that our technique can significantly improve the performance of query answering, and that it can mean the difference between answering a query in a few seconds or not being able to process the query at all.
Semantic parsers translate language utterances to programs, but are often trained from utterance-denotation pairs only. Consequently, parsers must overcome the problem of spuriousness at training time, where an incorrect program found at search time accidentally leads to a correct denotation. We propose that in small well-typed domains, we can semi-automatically generate an abstract representation for examples that facilitates information sharing across examples. This alleviates spuriousness, as the probability of randomly obtaining a correct answer from a program decreases across multiple examples. We test our approach on CNLVR, a challenging visual reasoning dataset, where spuriousness is central because denotations are either TRUE or FALSE, and thus random programs have high probability of leading to a correct denotation. We develop the first semantic parser for this task and reach 83.5% accuracy, a 15.7% absolute accuracy improvement compared to the best reported accuracy so far.
We study the problem of multiset prediction. The goal of multiset prediction is to train a predictor that maps an input to a multiset consisting of multiple items. Unlike existing problems in supervised learning, such as classification, ranking and sequence generation, there is no known order among items in a target multiset, and each item in the multiset may appear more than once, making this problem extremely challenging. In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making. The proposed multiset loss function is empirically evaluated on two families of datasets, one synthetic and the other real, with varying levels of difficulty, against various baseline loss functions including reinforcement learning, sequence, and aggregated distribution matching loss functions. The experiments reveal the effectiveness of the proposed loss function over the others.
We address the problem of learning vector representations for entities and relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This problem has received significant attention in the past few years and multiple methods have been proposed. Most of the existing methods in the literature use a predefined characteristic scoring function for evaluating the correctness of KG triples. These scoring functions distinguish correct triples (high score) from incorrect ones (low score). However, their performance vary across different datasets. In this work, we demonstrate that a simple neural network based score function can consistently achieve near start-of-the-art performance on multiple datasets. We also quantitatively demonstrate biases in standard benchmark datasets, and highlight the need to perform evaluation spanning various datasets.
Our team Hibikino-Musashi@Home was founded in 2010. It is based in Kitakyushu Science and Research Park, Japan. Since 2010, we have participated in the RoboCup@Home Japan open competition open-platform league every year. Currently, the Hibikino-Musashi@Home team has 24 members from seven different laboratories based in the Kyushu Institute of Technology. Our home-service robots are used as platforms for both education and implementation of our research outcomes. In this paper, we introduce our team and the technologies that we have implemented in our robots.
Model-Based Diagnosis deals with the identification of the real cause of a system's malfunction based on a formal system model and observations of the system behavior. When a malfunction is detected, there is usually not enough information available to pinpoint the real cause and one needs to discriminate between multiple fault hypotheses (called diagnoses). To this end, Sequential Diagnosis approaches ask an oracle for additional system measurements. This work presents strategies for (optimal) measurement selection in model-based sequential diagnosis. In particular, assuming a set of leading diagnoses being given, we show how queries (sets of measurements) can be computed and optimized along two dimensions: expected number of queries and cost per query. By means of a suitable decoupling of two optimizations and a clever search space reduction the computations are done without any inference engine calls. For the full search space, we give a method requiring only a polynomial number of inferences and show how query properties can be guaranteed which existing methods do not provide. Evaluation results using real-world problems indicate that the new method computes (virtually) optimal queries instantly independently of the size and complexity of the considered diagnosis problems and outperforms equally general methods not exploiting the proposed theory by orders of magnitude.
By introducing sign constraints on the weights, this paper proposes sign constrained rectifier networks (SCRNs), whose training can be solved efficiently by the well known majorization-minimization (MM) algorithms. We prove that the proposed two-hidden-layer SCRNs, which exhibit negative weights in the second hidden layer and negative weights in the output layer, are capable of separating any two (or more) disjoint pattern sets. Furthermore, the proposed two-hidden-layer SCRNs can decompose the patterns of each class into several clusters so that each cluster is convexly separable from all the patterns from the other classes. This provides a means to learn the pattern structures and analyse the discriminant factors between different classes of patterns.
We consider a reinforcement learning (RL) setting in which the agent interacts with a sequence of episodic MDPs. At the start of each episode the agent has access to some side-information or context that determines the dynamics of the MDP for that episode. Our setting is motivated by applications in healthcare where baseline measurements of a patient at the start of a treatment episode form the context that may provide information about how the patient might respond to treatment decisions. We propose algorithms for learning in such Contextual Markov Decision Processes (CMDPs) under an assumption that the unobserved MDP parameters vary smoothly with the observed context. We also give lower and upper PAC bounds under the smoothness assumption. Because our lower bound has an exponential dependence on the dimension, we consider a tractable linear setting where the context is used to create linear combinations of a finite set of MDPs. For the linear setting, we give a PAC learning algorithm based on KWIK learning techniques.
Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets.
In this paper, we consider the problem of optimizing the quantiles of the cumulative rewards of Markov Decision Processes (MDP), to which we refers as Quantile Markov Decision Processes (QMDP). Traditionally, the goal of a Markov Decision Process (MDP) is to maximize expected cumulative reward over a defined horizon (possibly to be infinite). In many applications, however, a decision maker may be interested in optimizing a specific quantile of the cumulative reward instead of its expectation. Our framework of QMDP provides analytical results characterizing the optimal QMDP solution and presents the algorithm for solving the QMDP. We provide analytical results characterizing the optimal QMDP solution and present the algorithms for solving the QMDP. We illustrate the model with two experiments: a grid game and a HIV optimal treatment experiment.
We investigate some well-known (and a few not-so-well-known) many-valued logics that have a small number (3 or 4) of truth values. For some of them we complain that they do not have any \emph{logical} use (despite their perhaps having some intuitive semantic interest) and we look at ways to add features so as to make them useful, while retaining their intuitive appeal. At the end, we show some surprising results in the system FDE, and its relationships with features of other logics. We close with some new examples of "synonymous logics." An Appendix contains a natural deduction system for our augmented FDE, and proofs of soundness and completeness.
Approximate Bayesian computation (ABC) and synthetic likelihood (SL) techniques have enabled the use of Bayesian inference for models that may be simulated, but for which the likelihood cannot be evaluated pointwise at values of an unknown parameter $\theta$. The main idea in ABC and SL is to, for different values of $\theta$ (usually chosen using a Monte Carlo algorithm), build estimates of the likelihood based on simulations from the model conditional on $\theta$. The quality of these estimates determines the efficiency of an ABC/SL algorithm. In standard ABC/SL, the only means to improve an estimated likelihood at $\theta$ is to simulate more times from the model conditional on $\theta$, which is infeasible in cases where the simulator is computationally expensive. In this paper we describe how to use bootstrapping as a means for improving SL estimates whilst using fewer simulations from the model, and also investigate its use in ABC. Further, we investigate the use of the bag of little bootstraps as a means for applying this approach to large datasets, yielding Monte Carlo algorithms that accurately approximate posterior distributions whilst only simulating subsamples of the full data. Examples of the approach applied to i.i.d., temporal and spatial data are given.
Field Programmable Gate Arrays (FPGAs) plays an increasingly important role in data sampling and processing industries due to its highly parallel architecture, low power consumption, and flexibility in custom algorithms. Especially, in the artificial intelligence field, for training and implement the neural networks and machine learning algorithms, high energy efficiency hardware implement and massively parallel computing capacity are heavily demanded. Therefore, many global companies have applied FPGAs into AI and Machine learning fields such as autonomous driving and Automatic Spoken Language Recognition (Baidu) [1] [2] and Bing search (Microsoft) [3]. Considering the FPGAs great potential in these fields, we tend to implement a general neural network hardware architecture on XILINX ZU9CG System On Chip (SOC) platform [4], which contains abundant hardware resource and powerful processing capacity. The general neural network architecture on the FPGA SOC platform can perform forward and backward algorithms in deep neural networks (DNN) with high performance and easily be adjusted according to the type and scale of the neural networks.
Knowledge bases (KB) constructed through information extraction from text play an important role in query answering and reasoning. In this work, we study a particular reasoning task, the problem of discovering causal relationships between entities, known as causal discovery. There are two contrasting types of approaches to discovering causal knowledge. One approach attempts to identify causal relationships from text using automatic extraction techniques, while the other approach infers causation from observational data. However, extractions alone are often insufficient to capture complex patterns and full observational data is expensive to obtain. We introduce a probabilistic method for fusing noisy extractions with observational data to discover causal knowledge. We propose a principled approach that uses the probabilistic soft logic (PSL) framework to encode well-studied constraints to recover long-range patterns and consistent predictions, while cheaply acquired extractions provide a proxy for unseen observations. We apply our method gene regulatory networks and show the promise of exploiting KB signals in causal discovery, suggesting a critical, new area of research.
We study the multi-armed bandit problem with multiple plays and a budget constraint for both the stochastic and the adversarial setting. At each round, exactly $K$ out of $N$ possible arms have to be played (with $1\leq K \leq N$). In addition to observing the individual rewards for each arm played, the player also learns a vector of costs which has to be covered with an a-priori defined budget $B$. The game ends when the sum of current costs associated with the played arms exceeds the remaining budget. Firstly, we analyze this setting for the stochastic case, for which we assume each arm to have an underlying cost and reward distribution with support $[c_{\min}, 1]$ and $[0, 1]$, respectively. We derive an Upper Confidence Bound (UCB) algorithm which achieves $O(NK^4 \log B)$ regret. Secondly, for the adversarial case in which the entire sequence of rewards and costs is fixed in advance, we derive an upper bound on the regret of order $O(\sqrt{NB\log(N/K)})$ utilizing an extension of the well-known $\texttt{Exp3}$ algorithm. We also provide upper bounds that hold with high probability and a lower bound of order $\Omega((1 - K/N)^2 \sqrt{NB/K})$.
Goal-conditional policies allow reinforcement learning agents to pursue specific goals during different episodes. In addition to their potential to generalize desired behavior to unseen goals, such policies may also help in defining options for arbitrary subgoals, enabling higher-level planning. While trying to achieve a specific goal, an agent may also be able to exploit information about the degree to which it has achieved alternative goals. Reinforcement learning agents have only recently been endowed with such capacity for hindsight, which is highly valuable in environments with sparse rewards. In this paper, we show how hindsight can be introduced to likelihood-ratio policy gradient methods, generalizing this capacity to an entire class of highly successful algorithms. Our preliminary experiments suggest that hindsight may increase the sample efficiency of policy gradient methods.
In order to automate verification process, regulatory rules written in natural language need to be translated into a format that machines can understand. However, none of the existing formalisms can fully represent the elements that appear in legal norms. For instance, most of these formalisms do not provide features to capture the behavior of deontic effects, which is an important aspect in automated compliance checking. This paper presents an approach for transforming legal norms represented using LegalRuleML to a variant of Modal Defeasible Logic (and vice versa) such that a legal statement represented using LegalRuleML can be transformed into a machine-readable format that can be understood and reasoned about depending upon the client's preferences.
We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system. We assume the training set to be labeled by human experts and that we have access only to a limited amount of data, typically noisy. We provide a systematic approach to synthesize both the syntactical structure and the parameters of the temporal logic formula using a two-steps procedure: first, we leverage a novel evolutionary algorithm for learning the structure of the formula; second, we perform the parameter synthesis operating on the statistical emulation of the average robustness for a candidate formula w.r.t. its parameters. We compare our results with our previous work [{BufoBSBLB14] and with a recently proposed decision-tree [bombara_decision_2016] based method. We present experimental results on two case studies: an anomalous trajectory detection problem of a naval surveillance system and the characterization of an Ineffective Respiratory effort, showing the usefulness of our work.
A major target of linguistics and cognitive science has been to understand what class of learning systems can acquire the key structures of natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire several of the key structures present natural language from positive evidence alone. The model successfully acquires regular (e.g. $(ab)^n$), context-free (e.g. $a^n b^n$, $x x^R$), and context-sensitive (e.g. $a^nb^nc^n$, $a^nb^mc^nd^m$, $xx$) formal languages. Our approach develops the concept of factorized programs in Bayesian program induction in order to help manage the complexity of representation. We show in learning, the model predicts several phenomena empirically observed in human grammar acquisition experiments.
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.
We introduce a data-driven approach to aid the repairing and conservation of archaeological objects: ORGAN, an object reconstruction generative adversarial network (GAN). By using an encoder-decoder 3D deep neural network on a GAN architecture, and combining two loss objectives: a completion loss and an Improved Wasserstein GAN loss, we can train a network to effectively predict the missing geometry of damaged objects. As archaeological objects can greatly differ between them, the network is conditioned on a variable, which can be a culture, a region or any metadata of the object. In our results, we show that our method can recover most of the information from damaged objects, even in cases where more than half of the voxels are missing, without producing many errors.
We present a method for explaining the image classification predictions of deep convolution neural networks, by highlighting the pixels in the image which influence the final class prediction. Our method requires the identification of a heuristic method to select parameters hypothesized to be most relevant in this prediction, and here we use Kullback-Leibler divergence to provide this focus. Overall, our approach helps in understanding and interpreting deep network predictions and we hope contributes to a foundation for such understanding of deep learning networks. In this brief paper, our experiments evaluate the performance of two popular networks in this context of interpretability.
Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy and prediction. Towards the latter, previous work has used match data from a variety of player ranks from hobbyist to professional players. However, professional players have been shown to behave differently than lower ranked players. Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters. Here we show that, although there is a slightly reduced accuracy, mixed-rank datasets can be used to predict the outcome of professional matches, with suitably optimized configurations.
Achieving superhuman playing level by AlphaGo corroborated the capabilities of convolutional neural architectures (CNNs) for capturing complex spatial patterns. This result was to a great extent due to several analogies between Go board states and 2D images CNNs have been designed for, in particular translational invariance and a relatively large board. In this paper, we verify whether CNN-based move predictors prove effective for Othello, a game with significantly different characteristics, including a much smaller board size and complete lack of translational invariance. We compare several CNN architectures and board encodings, augment them with state-of-the-art extensions, train on an extensive database of experts' moves, and examine them with respect to move prediction accuracy and playing strength. The empirical evaluation confirms high capabilities of neural move predictors and suggests a strong correlation between prediction accuracy and playing strength. The best CNNs not only surpass all other 1-ply Othello players proposed to date but defeat (2-ply) Edax, the best open-source Othello player.
The goal of point set registration is to find point-by-point correspondences between point sets, each of which characterizes the shape of an object. Because local preservation of object geometry is assumed, prevalent algorithms in the area can often elegantly solve the problems without using geometric information specific to the objects. This means that registration performance can be further improved by using prior knowledge of object geometry. In this paper, we propose a novel point set registration method using the Gaussian mixture model with prior shape information encoded as a statistical shape model. Our transformation model is defined as a combination of the similar transformation, motion coherence, and the statistical shape model. Therefore, the proposed method works effectively if the target point set includes outliers and missing regions, or if it is rotated. The computational cost can be reduced to linear, and therefore the method is scalable to large point sets. The effectiveness of the method will be verified through comparisons with existing algorithms using datasets concerning human body shapes, hands, and faces.
We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90% of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic.
Episodic control has been proposed as a third approach to reinforcement learning, besides model-free and model-based control, by analogy with the three types of human memory. i.e. episodic, procedural and semantic memory. But the theoretical properties of episodic control are not well investigated. Here I show that in deterministic tree Markov decision processes, episodic control is equivalent to a form of prioritized sweeping in terms of sample efficiency as well as memory and computation demands. For general deterministic and stochastic environments, prioritized sweeping performs better even when memory and computation demands are restricted to be equal to those of episodic control. These results suggest generalizations of prioritized sweeping to partially observable environments, its combined use with function approximation and the search for possible implementations of prioritized sweeping in brains.
Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to consider more human-realistic settings. Perhaps the most difficult of these settings is that of continual lifelong learning, where the model must learn online over a continuous stream of non-stationary data. A continual lifelong learning system must have three primary capabilities to succeed: it must learn and adapt over time, it must not forget what it has learned, and it must be efficient in both training time and memory. Recent techniques have focused their efforts largely on the first two capabilities while the third capability remains largely unexplored. In this paper, we consider the problem of efficient and effective storage of experiences over very large time-frames. In particular we consider the case where typical experiences are n bits and memories are limited to k bits for k << n. We present a novel scalable architecture and training algorithm in this challenging domain and provide an extensive evaluation of its performance. Our results show that we can achieve considerable gains on top of state-of-the-art methods such as GEM.
In this paper we introduce new types of square-piece jigsaw puzzles, where in addition to the unknown location and orientation of each piece, a piece might also need to be flipped. These puzzles, which are associated with a number of real world problems, are considerably harder, from a computational standpoint. Specifically, we present a novel generalized genetic algorithm (GA)-based solver that can handle puzzle pieces of unknown location and orientation (Type 2 puzzles) and (two-sided) puzzle pieces of unknown location, orientation, and face (Type 4 puzzles). To the best of our knowledge, our solver provides a new state-of-the-art, solving previously attempted puzzles faster and far more accurately, handling puzzle sizes that have never been attempted before, and assembling the newly introduced two-sided puzzles automatically and effectively. This paper also presents, among other results, the most extensive set of experimental results, compiled as of yet, on Type 2 puzzles.
This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents and show that agents who have social inhibition can converge to a Nash stable partition (i.e., social agreement) using our proposed decentralised algorithm within polynomial time. The algorithm is simple and executable based on local interactions with neighbour agents under a strongly-connected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of a converged solution's suboptimality and additionally show that 50 % of suboptimality can be minimally guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents. Through numerical experiments, it is confirmed that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation where some of the agents temporarily somehow do not operate during a mission.
In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible. The environment is computationally expensive, has a high-dimensional continuous action space and is stochastic. We benchmark state of the art policy-gradient methods and test several improvements, such as layer normalization, parameter noise, action and state reflecting, to stabilize training and improve its sample-efficiency. We found that the Deep Deterministic Policy Gradient method is the most efficient method for this environment and the improvements we have introduced help to stabilize training. Learned models are able to generalize to new physical scenarios, e.g. different obstacle courses.
We provide, to the best of our knowledge, the first computational study of extensive-form adversarial team games. These games are sequential, zero-sum games in which a team of players, sharing the same utility function, faces an adversary. We define three different scenarios according to the communication capabilities of the team. In the first, the teammates can communicate and correlate their actions both before and during the play. In the second, they can only communicate before the play. In the third, no communication is possible at all. We define the most suitable solution concepts, and we study the inefficiency caused by partial or null communication, showing that the inefficiency can be arbitrarily large in the size of the game tree. Furthermore, we study the computational complexity of the equilibrium-finding problem in the three scenarios mentioned above, and we provide, for each of the three scenarios, an exact algorithm. Finally, we empirically evaluate the scalability of the algorithms in random games and the inefficiency caused by partial or null communication.
There are many methodologies and techniques for easing the task of ontology building. Here we describe the intersection of two of these: ontology normalisation and fully programmatic ontology development. The first of these describes a standardized organisation for an ontology, with singly inherited self-standing entities, and a number of small taxonomies of refining entities. The former are described and defined in terms of the latter and used to manage the polyhierarchy of the self-standing entities. Fully programmatic development is a technique where an ontology is developed using a domain-specific language within a programming language, meaning that as well defining ontological entities, it is possible to add arbitrary patterns or new syntax within the same environment. We describe how new patterns can be used to enable a new style of ontology development that we call hypernormalisation.
This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. First, it puts forward a novel concept of "history of word" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation. Second, it introduces an improved attention scoring function that better utilizes the "history of word" concept. Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer. We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017). Meanwhile, we verify the generalization of FusionNet with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%.
The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and building point for much deep reinforcement learning research. However, replicating results for complex systems is often challenging since original scientific publications are not always able to describe in detail every important parameter setting and software engineering solution. In this paper, we present results from our work reproducing the results of the DQN paper. We highlight key areas in the implementation that were not covered in great detail in the original paper to make it easier for researchers to replicate these results, including termination conditions and gradient descent algorithms. Finally, we discuss methods for improving the computational performance and provide our own implementation that is designed to work with a range of domains, and not just the original Arcade Learning Environment [Bellemare et al., 2013].
Computer poetry generation is our first step towards computer writing. Writing must have a theme. The current approaches of using sequence-to-sequence models with attention often produce non-thematic poems. We present a novel conditional variational autoencoder with a hybrid decoder adding the deconvolutional neural networks to the general recurrent neural networks to fully learn topic information via latent variables. This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic. A proposed augmented word2vec model further improves the rhythm and symmetry. Tests show that the generated poems by our approach are mostly satisfying with regulated rules and consistent themes, and 73.42% of them receive an Overall score no less than 3 (the highest score is 5).
Temporal gates play a significant role in modern recurrent-based neural encoders, enabling fine-grained control over recursive compositional operations over time. In recurrent models such as the long short-term memory (LSTM), temporal gates control the amount of information retained or discarded over time, not only playing an important role in influencing the learned representations but also serving as a protection against vanishing gradients. This paper explores the idea of learning temporal gates for sequence pairs (question and answer), jointly influencing the learned representations in a pairwise manner. In our approach, temporal gates are learned via 1D convolutional layers and then subsequently cross applied across question and answer for joint learning. Empirically, we show that this conceptually simple sharing of temporal gates can lead to competitive performance across multiple benchmarks. Intuitively, what our network achieves can be interpreted as learning representations of question and answer pairs that are aware of what each other is remembering or forgetting, i.e., pairwise temporal gating. Via extensive experiments, we show that our proposed model achieves state-of-the-art performance on two community-based QA datasets and competitive performance on one factoid-based QA dataset.
Multimodal features play a key role in wearable sensor based Human Activity Recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the effectiveness of multimodal sensor data. In this regard, we propose a "collect fully and select wisely (Fullie and Wiselie)" principle as well as a dual-stream recurrent convolutional attention model, Recurrent Attention and Activity Frame (RAAF), to improve the recognition performance. We first collect modality features and the relations between each pair of features to generate activity frames, and then introduce an attention mechanism to select the most prominent regions from activity frames precisely. The selected frames not only maximize the utilization of valid features but also reduce the number of features to be computed effectively. We further analyze the hyper-parameters, accuracy, interpretability, and annotation dependency of the proposed model based on extensive experiments. The results show that RAAF achieves competitive performance on two benchmarked datasets and works well in real life scenarios.
The paper introduces the Hidden Tree Markov Network (HTN), a neuro-probabilistic hybrid fusing the representation power of generative models for trees with the incremental and discriminative learning capabilities of neural networks. We put forward a modular architecture in which multiple generative models of limited complexity are trained to learn structural feature detectors whose outputs are then combined and integrated by neural layers at a later stage. In this respect, the model is both deep, thanks to the unfolding of the generative models on the input structures, as well as wide, given the potentially large number of generative modules that can be trained in parallel. Experimental results show that the proposed approach can outperform state-of-the-art syntactic kernels as well as generative kernels built on the same probabilistic model as the HTN.
Hierarchical abstractions, also known as options -- a type of temporally extended action (Sutton et. al. 1999) that enables a reinforcement learning agent to plan at a higher level, abstracting away from the lower-level details. In this work, we learn reusable options whose parameters can vary, encouraging different behaviors, based on the current situation. In principle, these behaviors can include vigor, defence or even risk-averseness. These are some examples of what we refer to in the broader context as Situational Awareness (SA). We incorporate SA, in the form of vigor, into hierarchical RL by defining and learning situationally aware options in a Probabilistic Goal Semi-Markov Decision Process (PG-SMDP). This is achieved using our Situationally Aware oPtions (SAP) policy gradient algorithm which comes with a theoretical convergence guarantee. We learn reusable options in different scenarios in a RoboCup soccer domain (i.e., winning/losing). These options learn to execute with different levels of vigor resulting in human-like behaviours such as `time-wasting' in the winning scenario. We show the potential of the agent to exit bad local optima using reusable options in RoboCup. Finally, using SAP, the agent mitigates feature-based model misspecification in a Bottomless Pit of Death domain.
Peference elicitation is the task of suggesting a highly preferred configuration to a decision maker. The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects. In its constructive variant, new objects are synthesized "from scratch" by maximizing an estimate of the user utility over a combinatorial (possibly infinite) space of candidates. In the constructive setting, most existing elicitation techniques fail because they rely on exhaustive enumeration of the candidates. A previous solution explicitly designed for constructive tasks comes with no formal performance guarantees, and can be very expensive in (or unapplicable to) problems with non-Boolean attributes. We propose the Choice Perceptron, a Perceptron-like algorithm for learning user preferences from set-wise choice feedback over constructive domains and hybrid Boolean-numeric feature spaces. We provide a theoretical analysis on the attained regret that holds for a large class of query selection strategies, and devise a heuristic strategy that aims at optimizing the regret in practice. Finally, we demonstrate its effectiveness by empirical evaluation against existing competitors on constructive scenarios of increasing complexity.
Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spinal stimulation and rehabilitative robots (such as exoskeletons) in real time feedback. To study the potential for automated electromyography (EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients undergoing electrical spinal cord stimulation using both video and multi-channel surface EMG recordings during spinal stimulation therapy sessions. The quality of standing under different stimulation settings was quantified manually by experienced clinicians. By correlating features of the recorded EMG activity with the expert evaluations, we show that multi-channel EMG recording can provide accurate, fast, and robust estimation for the quality of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis shows that the total number of EMG channels needed to effectively predict standing quality can be reduced while maintaining high estimation accuracy, which provides more flexibility for rehabilitation robotic systems to incorporate EMG recordings.
We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge more traditional approaches elaborated over the years in fields like maths or physics. However, despite considerable successes in a variety of application domains, the machine learning field is not yet ready to handle the level of complexity required by such problems. Using an example application, namely Sea Surface Temperature Prediction, we show how general background knowledge gained from physics could be used as a guideline for designing efficient Deep Learning models. In order to motivate the approach and to assess its generality we demonstrate a formal link between the solution of a class of differential equations underlying a large family of physical phenomena and the proposed model. Experiments and comparison with series of baselines including a state of the art numerical approach is then provided.
Many current methods to interpret convolutional neural networks (CNNs) use visualization techniques and words to highlight concepts of the input seemingly relevant to a CNN's decision. The methods hypothesize that the recognition of these concepts are instrumental in the decision a CNN reaches, but the nature of this relationship has not been well explored. To address this gap, this paper examines the quality of a concept's recognition by a CNN and the degree to which the recognitions are associated with CNN decisions. The study considers a CNN trained for scene recognition over the ADE20k dataset. It uses a novel approach to find and score the strength of minimally distributed representations of input concepts (defined by objects in scene images) across late stage feature maps. Subsequent analysis finds evidence that concept recognition impacts decision making. Strong recognition of concepts frequently-occurring in few scenes are indicative of correct decisions, but recognizing concepts common to many scenes may mislead the network.
Humans possess an ability to abstractly reason about objects and their interactions, an ability not shared with state-of-the-art deep learning models. Relational networks, introduced by Santoro et al. (2017), add the capacity for relational reasoning to deep neural networks, but are limited in the complexity of the reasoning tasks they can address. We introduce recurrent relational networks which increase the suite of solvable tasks to those that require an order of magnitude more steps of relational reasoning. We use recurrent relational networks to solve Sudoku puzzles and achieve state-of-the-art results by solving 96.6% of the hardest Sudoku puzzles, where relational networks fail to solve any. We also apply our model to the BaBi textual QA dataset solving 19/20 tasks which is competitive with state-of-the-art sparse differentiable neural computers. The recurrent relational network is a general purpose module that can augment any neural network model with the capacity to do many-step relational reasoning.
Policy optimization methods have shown great promise in solving complex reinforcement and imitation learning tasks. While model-free methods are broadly applicable, they often require many samples to optimize complex policies. Model-based methods greatly improve sample-efficiency but at the cost of poor generalization, requiring a carefully handcrafted model of the system dynamics for each task. Recently, hybrid methods have been successful in trading off applicability for improved sample-complexity. However, these have been limited to continuous action spaces. In this work, we present a new hybrid method based on an approximation of the dynamics as an expectation over the next state under the current policy. This relaxation allows us to derive a novel hybrid policy gradient estimator, combining score function and pathwise derivative estimators, that is applicable to discrete action spaces. We show significant gains in sample complexity, ranging between $1.7$ and $25\times$, when learning parameterized policies on Cart Pole, Acrobot, Mountain Car and Hand Mass. Our method is applicable to both discrete and continuous action spaces, when competing pathwise methods are limited to the latter.
Stackelberg equilibria have become increasingly important as a solution concept in computational game theory, largely inspired by practical problems such as security settings. In practice, however, there is typically uncertainty regarding the model about the opponent. This paper is, to our knowledge, the first to investigate Stackelberg equilibria under uncertainty in extensive-form games, one of the broadest classes of game. We introduce robust Stackelberg equilibria, where the uncertainty is about the opponent's payoffs, as well as ones where the opponent has limited lookahead and the uncertainty is about the opponent's node evaluation function. We develop a new mixed-integer program for the deterministic limited-lookahead setting. We then extend the program to the robust setting for Stackelberg equilibrium under unlimited and under limited lookahead by the opponent. We show that for the specific case of interval uncertainty about the opponent's payoffs (or about the opponent's node evaluations in the case of limited lookahead), robust Stackelberg equilibria can be computed with a mixed-integer program that is of the same asymptotic size as that for the deterministic setting.
Action abstractions restrict the number of legal actions available during search in multi-unit real-time adversarial games, thus allowing algorithms to focus their search on a set of promising actions. Optimal strategies derived from un-abstracted spaces are guaranteed to be no worse than optimal strategies derived from action-abstracted spaces. In practice, however, due to real-time constraints and the state space size, one is only able to derive good strategies in un-abstracted spaces in small-scale games. In this paper we introduce search algorithms that use an action abstraction scheme we call asymmetric abstraction. Asymmetric abstractions retain the un-abstracted spaces' theoretical advantage over regularly abstracted spaces while still allowing the search algorithms to derive effective strategies, even in large-scale games. Empirical results on combat scenarios that arise in a real-time strategy game show that our search algorithms are able to substantially outperform state-of-the-art approaches.
The dynamics of infectious diseases spread is crucial in determining their risk and offering ways to contain them. We study sequential vaccination of individuals in networks. In the original (deterministic) version of the Firefighter problem, a fire breaks out at some node of a given graph. At each time step, b nodes can be protected by a firefighter and then the fire spreads to all unprotected neighbors of the nodes on fire. The process ends when the fire can no longer spread. We extend the Firefighter problem to a probabilistic setting, where the infection is stochastic. We devise a simple policy that only vaccinates neighbors of infected nodes and is optimal on regular trees and on general graphs for a sufficiently large budget. We derive methods for calculating upper and lower bounds of the expected number of infected individuals, as well as provide estimates on the budget needed for containment in expectation. We calculate these explicitly on trees, d-dimensional grids, and Erd\H{o}s R\'{e}nyi graphs. Finally, we construct a state-dependent budget allocation strategy and demonstrate its superiority over constant budget allocation on real networks following a first order acquaintance vaccination policy.
We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, while the preferences are learned itera tively by interacting with the user. Previous work has shown that Coactive Learning is a suitable method for learning user preferences in constructive scenarios. In Coactive Learning the user provides feedback to the algorithm in the form of an improvement to a suggested configuration. When the problem involves many decision variables and constraints, this type of interaction poses a significant cognitive burden on the user. We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations. This has the clear advantage of drastically reducing the user cognitive load. Additionally, part-wise inference can be (up to exponentially) less computationally demanding than inference over full configurations. We discuss the theoretical implications of working with parts and present promising empirical results on one synthetic and two realistic constructive problems.
A first step to reach Theory of Mind (ToM) abilities (attribution of beliefs to others) in synthetic agents through sensorimotor interactions, would be to tag sensory data with agent typology and action intentions: autonomous agent X moved an object under the box. We propose a dual arm robotic setup in which ToM could be probed. We then discuss what measures can be extracted from sensorimotor interaction data (based on a correlation analysis) in the proposed setup that allow to distinguish self than other and other/inanimate from other/active with intentions. We finally discuss what elements are missing in current cognitive architectures to be able to acquire ToM abilities in synthetic agents from sensorimotor interactions, bottom-up from reactive agent interaction behaviors and top-down from the optimization of social behaviour and cooperation.
Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and multi-modal-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research.
The discriminative approach to classification using deep neural networks has become the de-facto standard in various fields. Complementing recent reservations about safety against adversarial examples, we show that conventional discriminative methods can easily be fooled to provide incorrect labels with very high confidence to out of distribution examples. We posit that a generative approach is the natural remedy for this problem, and propose a method for classification using generative models. At training time, we learn a generative model for each class, while at test time, given an example to classify, we query each generator for its most similar generation, and select the class corresponding to the most similar one. Our approach is general and can be used with expressive models such as GANs and VAEs. At test time, our method accurately "knows when it does not know," and provides resilience to out of distribution examples while maintaining competitive performance for standard examples.
In this paper, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focussing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that manages to outperform the single best solver out of the portfolio by factor two. Acting on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications the model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial algorithm population so that feature costs become negligible.
The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.
We propose the cascade attribute learning network (CALNet), which can learn attributes in a control task separately and assemble them together. Our contribution is twofold: first we propose attribute learning in reinforcement learning (RL). Attributes used to be modeled using constraint functions or terms in the objective function, making it hard to transfer. Attribute learning, on the other hand, models these task properties as modules in the policy network. We also propose using novel cascading compensative networks in the CALNet to learn and assemble attributes. Using the CALNet, one can zero shoot an unseen task by separately learning all its attributes, and assembling the attribute modules. We have validated the capacity of our model on a wide variety of control problems with attributes in time, position, velocity and acceleration phases.
Adversarial decision making is a particular type of decision making problem where the gain a decision maker obtains as a result of his decisions is affected by the actions taken by others. Representation of alternatives' evaluations and methods to find the optimal alternative are two important aspects in the adversarial decision making. The aim of this study is to develop a general framework for solving the adversarial decision making issue under uncertain environment. By combining fuzzy set theory, game theory and D numbers theory (DNT), a DNT based game-theoretic framework for adversarial decision making under fuzzy environment is presented. Within the proposed framework or model, fuzzy set theory is used to model the uncertain evaluations of decision makers to alternatives, the non-exclusiveness among fuzzy evaluations are taken into consideration by using DNT, and the conflict of interests among decision makers is considered in a two-person non-constant sum game theory perspective. An illustrative application is given to demonstrate the effectiveness of the proposed model. This work, on one hand, has developed an effective framework for adversarial decision making under fuzzy environment; One the other hand, it has further improved the basis of DNT as a generalization of Dempster-Shafer theory for uncertainty reasoning.
We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. Our method generalizes Hamiltonian Monte Carlo and is trained to maximize expected squared jumped distance, a proxy for mixing speed. We demonstrate large empirical gains on a collection of simple but challenging distributions, for instance achieving a 106x improvement in effective sample size in one case, and mixing when standard HMC makes no measurable progress in a second. Finally, we show quantitative and qualitative gains on a real-world task: latent-variable generative modeling. We release an open source TensorFlow implementation of the algorithm.
In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries.
A common assumption in machine learning is that training data are i.i.d. samples from some distribution. Processes that generate i.i.d. samples are, in a sense, uninformative---they produce data without regard to how good this data is for learning. By contrast, cognitive science research has shown that when people generate training data for others (i.e., teaching), they deliberately select examples that are helpful for learning. Because the data is more informative, learning can require less data. Interestingly, such examples are most effective when learners know that the data were pedagogically generated (as opposed to randomly generated). We call this pedagogical learning---when a learner assumes that evidence comes from a helpful teacher. In this work, we ask how pedagogical learning might work for machine learning algorithms. Studying this question requires understanding how people actually teach complex concepts with examples, so we conducted a behavioral study examining how people teach regular expressions using example strings. We found that teachers' examples contain powerful clustering structure that can greatly facilitate learning. We then develop a model of teaching and show a proof of concept that using this model inside of a learner can improve performance.
We introduce a one-shot learning approach for video object tracking. The proposed algorithm requires seeing the object to be tracked only once, and employs an external memory to store and remember the evolving features of the foreground object as well as backgrounds over time during tracking. With the relevant memory retrieved and updated in each tracking, our tracking model is capable of maintaining long-term memory of the object, and thus can naturally deal with hard tracking scenarios including partial and total occlusion, motion changes and large scale and shape variations. In our experiments we use the ImageNet ILSVRC2015 video detection dataset to train and use the VOT-2016 benchmark to test and compare our Memory-Augmented Video Object Tracking (MAVOT) model. From the results, we conclude that given its oneshot property and simplicity in design, MAVOT is an attractive approach in visual tracking because it shows good performance on VOT-2016 benchmark and is among the top 5 performers in accuracy and robustness in occlusion, motion changes and empty target.
Interval Pairwise Comparison Matrices have been widely used to account for uncertain statements concerning the preferences of decision makers. Several approaches have been proposed in the literature, such as multiplicative and fuzzy interval matrices. In this paper, we propose a general unified approach to Interval Pairwise Comparison Matrices, based on Abelian linearly ordered groups. In this framework, we generalize some consistency conditions provided for multiplicative and/or fuzzy interval pairwise comparison matrices and provide inclusion relations between them. Then, we provide a concept of distance between intervals that, together with a notion of mean defined over real continuous Abelian linearly ordered groups, allows us to provide a consistency index and an indeterminacy index. In this way, by means of suitable isomorphisms between Abelian linearly ordered groups, we will be able to compare the inconsistency and the indeterminacy of different kinds of Interval Pairwise Comparison Matrices, e.g. multiplicative, additive, and fuzzy, on a unique Cartesian coordinate system.
Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we propose an approach to deduce treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Our model learns clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. The learned policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.
This paper proposes a new algorithm for controlling classification results by generating a small additive perturbation without changing the classifier network. Our work is inspired by existing works generating adversarial perturbation that worsens classification performance. In contrast to the existing methods, our work aims to generate perturbations that can enhance overall classification performance. To solve this performance enhancement problem, we newly propose a perturbation generation network (PGN) influenced by the adversarial learning strategy. In our problem, the information in a large external dataset is summarized by a small additive perturbation, which helps to improve the performance of the classifier trained with the target dataset. In addition to this performance enhancement problem, we show that the proposed PGN can be adopted to solve the classical adversarial problem without utilizing the information on the target classifier. The mentioned characteristics of our method are verified through extensive experiments on publicly available visual datasets.
In this paper, we present a hybrid model that combines a neural conversational model and a rule-based graph dialogue system that assists users in scheduling reminders through a chat conversation. The graph based system has high precision and provides a grammatically accurate response but has a low recall. The neural conversation model can cater to a variety of requests, as it generates the responses word by word as opposed to using canned responses. The hybrid system shows significant improvements over the existing baseline system of rule based approach and caters to complex queries with a domain-restricted neural model. Restricting the conversation topic and combination of graph based retrieval system with a neural generative model makes the final system robust enough for a real world application.
This work presents a content-based recommender system for machine learning classifier algorithms. Given a new data set, a recommendation of what classifier is likely to perform best is made based on classifier performance over similar known data sets. This similarity is measured according to a data set characterization that includes several state-of-the-art metrics taking into account physical structure, statis- tics, and information theory. A novelty with respect to prior work is the use of a robust approach based on permutation tests to directly assess whether a given learning algorithm is able to exploit the attributes in a data set to predict class labels, and compare it to the more commonly used F-score metric for evalu- ating classifier performance. To evaluate our approach, we have conducted an extensive experimentation including 8 of the main machine learning classification methods with varying configurations and 65 bi- nary data sets, leading to over 2331 experiments. Our results show that using the information from the permutation test clearly improves the quality of the recommendations.
Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the \texttt{WIKIBIO} dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on https://github.com/tyliupku/wiki2bio.
Deep neural networks have proved to be a very effective way to perform classification tasks. They excel when the input data is high dimensional, the relationship between the input and the output is complicated, and the number of labeled training examples is large. But it is hard to explain why a learned network makes a particular classification decision on a particular test case. This is due to their reliance on distributed hierarchical representations. If we could take the knowledge acquired by the neural net and express the same knowledge in a model that relies on hierarchical decisions instead, explaining a particular decision would be much easier. We describe a way of using a trained neural net to create a type of soft decision tree that generalizes better than one learned directly from the training data.
Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide attention and achievement in data mining, computer vision, signal processing, and neuroscience, etc. In this survey, we provide a modern overview of recent advances in tensor completion algorithms from the perspective of big data analytics characterized by diverse variety, large volume, and high velocity. Towards a better comprehension and comparison of vast existing advances, we summarize and categorize them into four groups including general tensor completion algorithms, tensor completion with auxiliary information (variety), scalable tensor completion algorithms (volume) and dynamic tensor completion algorithms (velocity). Besides, we introduce their applications on real-world data-driven problems and present an open-source package covering several widely used tensor decomposition and completion algorithms. Our goal is to summarize these popular methods and introduce them to researchers for promoting the research process in this field and give an available repository for practitioners. In the end, we also discuss some challenges and promising research directions in this community for future explorations.
Distributed training of deep neural networks has received significant research interest, and its major approaches include implementations on multiple GPUs and clusters. Parallelization can dramatically improve the efficiency of training deep and complicated models with large-scale data. A fundamental barrier against the speedup of DNN training, however, is the trade-off between computation and communication time. In other words, increasing the number of worker nodes decreases the time consumed in computation while simultaneously increasing communication overhead under constrained network bandwidth, especially in commodity hardware environments. To alleviate this trade-off, we suggest the idea of homomorphic parameter compression, which compresses parameters with the least expense and trains the DNN with the compressed representation. Although the specific method is yet to be discovered, we demonstrate that there is a high probability that the homomorphism can reduce the communication overhead, thanks to little compression and decompression times. We also provide theoretical speedup of homomorphic compression.
Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment. A significant issue with transferring this success to the robotics domain is that interaction with the real world is costly, but training on limited experience is prone to overfitting. We present a method for learning to navigate, to a fixed goal and in a known environment, on a mobile robot. The robot leverages an interactive world model built from a single traversal of the environment, a pre-trained visual feature encoder, and stochastic environmental augmentation, to demonstrate successful zero-shot transfer under real-world environmental variations without fine-tuning.
Quantitative CBA is a postprocessing algorithm for association rule classification algorithm CBA (Liu et al, 1998). QCBA uses original, undiscretized numerical attributes to optimize the discovered association rules, refining the boundaries of literals in the antecedent of the rules produced by CBA. Some rules as well as literals from the rules can consequently be removed, which makes the resulting classifier smaller. One-rule classification and crisp rules make CBA classification models possibly most comprehensible among all association rule classification algorithms. These viable properties are retained by QCBA. The postprocessing is conceptually fast, because it is performed on a relatively small number of rules that passed data coverage pruning in CBA. Benchmark of our QCBA approach on 22 UCI datasets shows average 53% decrease in the total size of the model as measured by the total number of conditions in all rules. Model accuracy remains on the same level as for CBA.
Learning an optimal policy from a multi-modal reward function is a challenging problem in reinforcement learning (RL). Hierarchical RL (HRL) tackles this problem by learning a hierarchical policy, where multiple option policies are in charge of different strategies corresponding to modes of a reward function and a gating policy selects the best option for a given context. Although HRL has been demonstrated to be promising, current state-of-the-art methods cannot still perform well in complex real-world problems due to the difficulty of identifying modes of the reward function. In this paper, we propose a novel method called hierarchical policy search via return-weighted density estimation (HPSDE), which can efficiently identify the modes through density estimation with return-weighted importance sampling. Our proposed method finds option policies corresponding to the modes of the return function and automatically determines the number and the location of option policies, which significantly reduces the burden of hyper-parameters tuning. Through experiments, we demonstrate that the proposed HPSDE successfully learns option policies corresponding to modes of the return function and that it can be successfully applied to a challenging motion planning problem of a redundant robotic manipulator.
This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. We provide concrete examples where the approach not only improves performance in a single task, but accelerates transfer to new tasks. We demonstrate the attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. We modify the Arcade Learning Environment [Bellemare et al., 2013] to support audio queries, and conduct evaluations of crossmodal learning in the Atari 2600 game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017], we open-source a fast hybrid CPU-GPU implementation of CASL.
Recent systems on structured prediction focus on increasing the level of structural dependencies within the model. However, our study suggests that complex structures entail high overfitting risks. To control the structure-based overfitting, we propose to conduct structure regularization decoding (SR decoding). The decoding of the complex structure model is regularized by the additionally trained simple structure model. We theoretically analyze the quantitative relations between the structural complexity and the overfitting risk. The analysis shows that complex structure models are prone to the structure-based overfitting. Empirical evaluations show that the proposed method improves the performance of the complex structure models by reducing the structure-based overfitting. On the sequence labeling tasks, the proposed method substantially improves the performance of the complex neural network models. The maximum F1 error rate reduction is 36.4% for the third-order model. The proposed method also works for the parsing task. The maximum UAS improvement is 5.5% for the tri-sibling model. The results are competitive with or better than the state-of-the-art results.
A supervised learning algorithm searches over a set of functions $A \to B$ parametrised by a space $P$ to find the best approximation to some ideal function $f\colon A \to B$. It does this by taking examples $(a,f(a)) \in A\times B$, and updating the parameter according to some rule. We define a category where these update rules may be composed, and show that gradient descent---with respect to a fixed step size and an error function satisfying a certain property---defines a monoidal functor from a category of parametrised functions to this category of update rules. This provides a structural perspective on backpropagation, as well as a broad generalisation of neural networks.
Demographic studies suggest that changes in the retinal vasculature geometry, especially in vessel width, are associated with the incidence or progression of eye-related or systemic diseases. To date, the main information source for width estimation from fundus images has been the intensity profile between vessel edges. However, there are many factors affecting the intensity profile: pathologies, the central light reflex and local illumination levels, to name a few. In this study, we introduce three information sources for width estimation. These are the probability profiles of vessel interior, centreline and edge locations generated by a deep network. The probability profiles provide direct access to vessel geometry and are used in the likelihood calculation for a Bayesian method, particle filtering. We also introduce a geometric model which can handle non-ideal conditions of the probability profiles. Our experiments conducted on the REVIEW dataset yielded consistent estimates of vessel width, even in cases when one of the vessel edges is difficult to identify. Moreover, our results suggest that the method is better than human observers at locating edges of low contrast vessels.
We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this two part treatise, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct classes of algorithms, namely continuous time and discrete time models. The resulting neural networks form a new class of data-efficient universal function approximators that naturally encode any underlying physical laws as prior information. In this first part, we demonstrate how these networks can be used to infer solutions to partial differential equations, and obtain physics-informed surrogate models that are fully differentiable with respect to all input coordinates and free parameters.
Incremental class learning involves sequentially learning classes in bursts of examples from the same class. This violates the assumptions that underlie methods for training standard deep neural networks, and will cause them to suffer from catastrophic forgetting. Arguably, the best method for incremental class learning is iCaRL, but it requires storing training examples for each class, making it challenging to scale. Here, we propose FearNet for incremental class learning. FearNet is a generative model that does not store previous examples, making it memory efficient. FearNet uses a brain-inspired dual-memory system in which new memories are consolidated from a network for recent memories inspired by the mammalian hippocampal complex to a network for long-term storage inspired by medial prefrontal cortex. Memory consolidation is inspired by mechanisms that occur during sleep. FearNet also uses a module inspired by the basolateral amygdala for determining which memory system to use for recall. FearNet achieves state-of-the-art performance at incremental class learning on image (CIFAR-100, CUB-200) and audio classification (AudioSet) benchmarks.
We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this second part of our two-part treatise, we focus on the problem of data-driven discovery of partial differential equations. Depending on whether the available data is scattered in space-time or arranged in fixed temporal snapshots, we introduce two main classes of algorithms, namely continuous time and discrete time models. The effectiveness of our approach is demonstrated using a wide range of benchmark problems in mathematical physics, including conservation laws, incompressible fluid flow, and the propagation of nonlinear shallow-water waves.
This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via some sort competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory. Particles are devised with Q learning algorithm for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced results are supportive to the algorithmic structures suggesting that a substantive collaboration can be build via proposed learning algorithm.
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on two basic abstractions, it offers flexible building blocks for probabilistic computation. Distributions provide fast, numerically stable methods for generating samples and computing statistics, e.g., log density. Bijectors provide composable volume-tracking transformations with automatic caching. Together these enable modular construction of high dimensional distributions and transformations not possible with previous libraries (e.g., pixelCNNs, autoregressive flows, and reversible residual networks). They are the workhorse behind deep probabilistic programming systems like Edward and empower fast black-box inference in probabilistic models built on deep-network components. TensorFlow Distributions has proven an important part of the TensorFlow toolkit within Google and in the broader deep learning community.
Predicting unseen weather phenomena is an important issue for disaster management. In this paper, we suggest a model for a convolutional sequence-to-sequence autoencoder for predicting undiscovered weather situations from previous satellite images. We also propose a symmetric skip connection between encoder and decoder modules to produce more comprehensive image predictions. To examine our model performance, we conducted experiments for each suggested model to predict future satellite images from historical satellite images. A specific combination of skip connection and sequence-to-sequence autoencoder was able to generate closest prediction from the ground truth image.
This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action. We identify two sources of uncertainty that are relevant for exploration. The first originates from limited data (parametric uncertainty), while the second originates from the distribution of the returns (return uncertainty). We identify methods to learn these distributions with deep neural networks, where we estimate parametric uncertainty with Bayesian drop-out, while return uncertainty is propagated through the Bellman equation as a Gaussian distribution. Then, we identify that both can be jointly estimated in one network, which we call the Double Uncertain Value Network. The policy is directly derived from the learned distributions based on Thompson sampling. Experimental results show that both types of uncertainty may vastly improve learning in domains with a strong exploration challenge.
This work explores attention models to weight the contribution of local convolutional representations for the instance search task. We present a retrieval framework based on bags of local convolutional features (BLCF) that benefits from saliency weighting to build an efficient image representation. The use of human visual attention models (saliency) allows significant improvements in retrieval performance without the need to conduct region analysis or spatial verification, and without requiring any feature fine tuning. We investigate the impact of different saliency models, finding that higher performance on saliency benchmarks does not necessarily equate to improved performance when used in instance search tasks. The proposed approach outperforms the state-of-the-art on the challenging INSTRE benchmark by a large margin, and provides similar performance on the Oxford and Paris benchmarks compared to more complex methods that use off-the-shelf representations. The source code used in this project is available at https://imatge-upc.github.io/salbow/
We propose a novel computational strategy based on deep and reinforcement learning techniques for de-novo design of molecules with desired properties. This strategy integrates two deep neural networks -generative and predictive - that are trained separately but employed jointly to generate novel chemical structures with the desired properties. Generative models are trained to produce chemically feasible SMILES, and predictive models are derived to forecast the desired compound properties. In the first phase of the method, generative and predictive models are separately trained with supervised learning algorithms. In the second phase, both models are trained jointly with reinforcement learning approach to bias newly generated chemical structures towards those with desired physical and biological properties. In this proof-of-concept study, we have employed this integrative strategy to design chemical libraries biased toward compounds with either maximal, minimal, or specific range of physical properties, such as melting point and hydrophobicity, as well as to develop novel putative inhibitors of JAK2. This new approach can find a general use for generating targeted chemical libraries optimized for a single desired property or multiple properties.
In the covariate shift learning scenario, the training and test covariate distributions differ, so that a predictor's average loss over the training and test distributions also differ. In this work, we explore the potential of extreme dimension reduction, i.e. to very low dimensions, in improving the performance of importance weighting methods for handling covariate shift, which fail in high dimensions due to potentially high train/test covariate divergence and the inability to accurately estimate the requisite density ratios. We first formulate and solve a problem optimizing over linear subspaces a combination of their predictive utility and train/test divergence within. Applying it to simulated and real data, we show extreme dimension reduction helps sometimes but not always, due to a bias introduced by dimension reduction.
Existing music recognition applications require a connection to a server that performs the actual recognition. In this paper we present a low-power music recognizer that runs entirely on a mobile device and automatically recognizes music without user interaction. To reduce battery consumption, a small music detector runs continuously on the mobile device's DSP chip and wakes up the main application processor only when it is confident that music is present. Once woken, the recognizer on the application processor is provided with a few seconds of audio which is fingerprinted and compared to the stored fingerprints in the on-device fingerprint database of tens of thousands of songs. Our presented system, Now Playing, has a daily battery usage of less than 1% on average, respects user privacy by running entirely on-device and can passively recognize a wide range of music.
We introduce a method for embedding words as probability densities in a low-dimensional space. Rather than assuming that a word embedding is fixed across the entire text collection, as in standard word embedding methods, in our Bayesian model we generate it from a word-specific prior density for each occurrence of a given word. Intuitively, for each word, the prior density encodes the distribution of its potential 'meanings'. These prior densities are conceptually similar to Gaussian embeddings. Interestingly, unlike the Gaussian embeddings, we can also obtain context-specific densities: they encode uncertainty about the sense of a word given its context and correspond to posterior distributions within our model. The context-dependent densities have many potential applications: for example, we show that they can be directly used in the lexical substitution task. We describe an effective estimation method based on the variational autoencoding framework. We also demonstrate that our embeddings achieve competitive results on standard benchmarks.
Modern social platforms are characterized by the presence of rich user-behavior data associated with the publication, sharing and consumption of textual content. Users interact with content and with each other in a complex and dynamic social environment while simultaneously evolving over time. In order to effectively characterize users and predict their future behavior in such a setting, it is necessary to overcome several challenges. Content heterogeneity and temporal inconsistency of behavior data result in severe sparsity at the user level. In this paper, we propose a novel mutual-enhancement framework to simultaneously partition and learn latent activity profiles of users. We propose a flexible user partitioning approach to effectively discover rare behaviors and tackle user-level sparsity. We extensively evaluate the proposed framework on massive datasets from real-world platforms including Q&A networks and interactive online courses (MOOCs). Our results indicate significant gains over state-of-the-art behavior models ( 15% avg ) in a varied range of tasks and our gains are further magnified for users with limited interaction data. The proposed algorithms are amenable to parallelization, scale linearly in the size of datasets, and provide flexibility to model diverse facets of user behavior.
Video captioning is the task of automatically generating a textual description of the actions in a video. Although previous work (e.g. sequence-to-sequence model) has shown promising results in abstracting a coarse description of a short video, it is still very challenging to caption a video containing multiple fine-grained actions with a detailed description. This paper aims to address the challenge by proposing a novel hierarchical reinforcement learning framework for video captioning, where a high-level Manager module learns to design sub-goals and a low-level Worker module recognizes the primitive actions to fulfill the sub-goal. With this compositional framework to reinforce video captioning at different levels, our approach significantly outperforms all the baseline methods on a newly introduced large-scale dataset for fine-grained video captioning. Furthermore, our non-ensemble model has already achieved the state-of-the-art results on the widely-used MSR-VTT dataset.
This paper develops a novel methodology for using symbolic knowledge in deep learning. From first principles, we derive a semantic loss function that bridges between neural output vectors and logical constraints. This loss function captures how close the neural network is to satisfying the constraints on its output. An experimental evaluation shows that our semantic loss function effectively guides the learner to achieve (near-)state-of-the-art results on semi-supervised multi-class classification. Moreover, it significantly increases the ability of the neural network to predict structured objects, such as rankings and paths. These discrete concepts are tremendously difficult to learn, and benefit from a tight integration of deep learning and symbolic reasoning methods.
This paper proposes a real-time embedded fall detection system using a DVS(Dynamic Vision Sensor) that has never been used for traditional fall detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal Network). The first contribution is building a DVS Falls Dataset, which made our network to recognize a much greater variety of falls than the existing datasets that existed before and solved privacy issues using the DVS. Secondly, we introduce the DVS-TN : optimized deep learning network to detect falls using DVS. Finally, we implemented a fall detection system which can run on low-computing H/W with real-time, and tested on DVS Falls Dataset that takes into account various falls situations. Our approach achieved 95.5% on the F1-score and operates at 31.25 FPS on NVIDIA Jetson TX1 board.
In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to control the magnitude of the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model.
ConvNets and Imagenet have driven the recent success of deep learning for image classification. However, the marked slowdown in performance improvement, the recent studies on the lack of robustness of neural networks to adversarial examples and their tendency to exhibit undesirable biases (e.g racial biases) questioned the reliability and the sustained development of these methods. This work investigates these questions from the perspective of the end-user by using human subject studies and explanations. We experimentally demonstrate that the accuracy and robustness of ConvNets measured on Imagenet are underestimated. We show that explanations can mitigate the impact of misclassified adversarial examples from the perspective of the end-user and we introduce a novel tool for uncovering the undesirable biases learned by a model. These contributions also show that explanations are a promising tool for improving our understanding of ConvNets' predictions and for designing more reliable models
This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar preferences with their social friends. Which, however, may not hold true due to various motivations of making online friends and dynamics of online social networks. Inspired by recent causal process based recommendations that first model user exposures towards items and then use these exposures to guide rating prediction, we utilize social information to capture user exposures rather than user preferences. We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality. Under this new assumption, in this paper, we present a novel recommendation approach (named SERec) to integrate social exposure into collaborative filtering. We propose two methods to implement SERec, namely social regularization and social boosting, each with different ways to construct social exposures. Experiments on four real-world datasets demonstrate that our methods outperform the state-of-the-art methods on top-N recommendations. Further study compares the robustness and scalability of the two proposed methods.
Determining semantic similarity between academic documents is crucial to many tasks such as plagiarism detection, automatic technical survey and semantic search. Current studies mostly focus on semantic similarity between concepts, sentences and short text fragments. However, document-level semantic matching is still based on statistical information in surface level, neglecting article structures and global semantic meanings, which may cause the deviation in document understanding. In this paper, we focus on the document-level semantic similarity issue for academic literatures with a novel method. We represent academic articles with topic events that utilize multiple information profiles, such as research purposes, methodologies and domains to integrally describe the research work, and calculate the similarity between topic events based on the domain ontology to acquire the semantic similarity between articles. Experiments show that our approach achieves significant performance compared to state-of-the-art methods.
We propose to use neural networks for simultaneous detection and localization of multiple sound sources in human-robot interaction. In contrast to conventional signal processing techniques, neural network-based sound source localization methods require fewer strong assumptions about the environment. Previous neural network-based methods have been focusing on localizing a single sound source, which do not extend to multiple sources in terms of detection and localization. In this paper, we thus propose a likelihood-based encoding of the network output, which naturally allows the detection of an arbitrary number of sources. In addition, we investigate the use of sub-band cross-correlation information as features for better localization in sound mixtures, as well as three different network architectures based on different motivations. Experiments on real data recorded from a robot show that our proposed methods significantly outperform the popular spatial spectrum-based approaches.
Reinforcement Learning and the Evolutionary Strategy are two major approaches in addressing complicated control problems. Both are strong contenders and have their own devotee communities. Both groups have been very active in developing new advances in their own domain and devising, in recent years, leading-edge techniques to address complex continuous control tasks. Here, in the context of Deep Reinforcement Learning, we formulate a parallelized version of the Proximal Policy Optimization method and a Deep Deterministic Policy Gradient method. Moreover, we conduct a thorough comparison between the state-of-the-art techniques in both camps fro continuous control; evolutionary methods and Deep Reinforcement Learning methods. The results show there is no consistent winner.
Recent progress in deep learning has been accompanied by a growing concern for whether models are fair for users, with equally good performance across different demographics. In computer vision research, such questions are relevant to face detection and the related task of face attribute detection, among others. We measure race and gender inclusion in the context of smiling detection, and introduce a method for improving smiling detection across demographic groups. Our method introduces several modifications over existing detection methods, leveraging twofold transfer learning to better model facial diversity. Results show that this technique improves accuracy against strong baselines for most demographic groups as well as overall. Our best-performing model defines a new state-of-the-art for smiling detection, reaching 91% on the Faces of the World dataset. The accompanying multi-head diversity classifier also defines a new state-of-the-art for gender classification, reaching 93.87% on the Faces of the World dataset. This research demonstrates the utility of modeling race and gender to improve a face attribute detection task, using a twofold transfer learning framework that allows for privacy towards individuals in a target dataset.
Learning Automata (LA) are considered as one of the most powerful tools in the field of reinforcement learning. The family of estimator algorithms is proposed to improve the convergence rate of LA and has made great achievements. However, the estimators perform poorly on estimating the reward probabilities of actions in the initial stage of the learning process of LA. In this situation, a lot of rewards would be added to the probabilities of non-optimal actions. Thus, a large number of extra iterations are needed to compensate for these wrong rewards. In order to improve the speed of convergence, we propose a new P-model absorbing learning automaton by utilizing a double competitive strategy which is designed for updating the action probability vector. In this way, the wrong rewards can be corrected instantly. Hence, the proposed Double Competitive Algorithm overcomes the drawbacks of existing estimator algorithms. A refined analysis is presented to show the $\epsilon-optimality$ of the proposed scheme. The extensive experimental results in benchmark environments demonstrate that our proposed learning automata perform more efficiently than the most classic LA $SE_{RI}$ and the current fastest LA $DGCPA^{*}$.
The task of decision-making under uncertainty is daunting, especially for problems which have significant complexity. Healthcare policy makers across the globe are facing problems under challenging constraints, with limited tools to help them make data driven decisions. In this work we frame the process of finding an optimal malaria policy as a stochastic multi-armed bandit problem, and implement three agent based strategies to explore the policy space. We apply a Gaussian Process regression to the findings of each agent, both for comparison and to account for stochastic results from simulating the spread of malaria in a fixed population. The generated policy spaces are compared with published results to give a direct reference with human expert decisions for the same simulated population. Our novel approach provides a powerful resource for policy makers, and a platform which can be readily extended to capture future more nuanced policy spaces.
Humans are able to identify a referred visual object in a complex scene via a few rounds of natural language communications. Success communication requires both parties to engage and learn to adapt for each other. In this paper, we introduce an interactive training method to improve the natural language conversation system for a visual grounding task. During interactive training, both agents are reinforced by the guidance from a common reward function. The parametrized reward function also cooperatively updates itself via interactions, and contribute to accomplishing the task. We evaluate the method on GuessWhat?! visual grounding task, and significantly improve the task success rate. However, we observe language drifting problem during training and propose to use reward engineering to improve the interpretability for the generated conversations. Our result also indicates evaluating goal-ended visual conversation tasks require semantic relevant metrics beyond task success rate.
This paper briefly elaborates on a development in (applied) fuzzy logic that has taken place in the last couple of decades, namely, the complementation or even replacement of the traditional knowledge-based approach to fuzzy rule-based systems design by a data-driven one. It is argued that the classical rule-based modeling paradigm is actually more amenable to the knowledge-based approach, for which it has originally been conceived, while being less apt to data-driven model design. An important reason that prevents fuzzy (rule-based) systems from being leveraged in large-scale applications is the flat structure of rule bases, along with the local nature of fuzzy rules and their limited ability to express complex dependencies between variables. This motivates alternative approaches to fuzzy systems modeling, in which functional dependencies can be represented more flexibly and more compactly in terms of hierarchical structures.
Programming trends suggest that software development will undergo a radical change in the future: the combination of machine learning, artificial intelligence, natural language processing, and code generation technologies will improve in such a way that machines, instead of humans, will write most of their own code by 2040. This poses a number of interesting challenges for scientific research, especially as the hardware on which this Machine Generated Code will run becomes extremely heterogeneous. Indeed, extreme heterogeneity may drive the creation of this technology because it will allow humans to cope with the difficulty of programming different devices efficiently and easily.
Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on multilayer perceptrons and Kohonen Self-Organized Map classifiers. We assume the classes of interest can be separated by hyperquadrics. Therefore, a 2-degree polynomial network is used to classify the original image, generating the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.
We consider the problem of learning a one-hidden-layer neural network with non-overlapping convolutional layer and ReLU activation function, i.e., $f(\mathbf{Z}; \mathbf{w}, \mathbf{a}) = \sum_j a_j\sigma(\mathbf{w}^\top\mathbf{Z}_j)$, in which both the convolutional weights $\mathbf{w}$ and the output weights $\mathbf{a}$ are parameters to be learned. We prove that with Gaussian input $\mathbf{Z}$, there is a spurious local minimum that is not a global mininum. Surprisingly, in the presence of local minimum, starting from randomly initialized weights, gradient descent with weight normalization can still be proven to recover the true parameters with constant probability (which can be boosted to arbitrarily high accuracy with multiple restarts). We also show that with constant probability, the same procedure could also converge to the spurious local minimum, showing that the local minimum plays a non-trivial role in the dynamics of gradient descent. Furthermore, a quantitative analysis shows that the gradient descent dynamics has two phases: it starts off slow, but converges much faster after several iterations.
The ability to learn at different resolutions in time may help overcome one of the main challenges in deep reinforcement learning -- sample efficiency. Hierarchical agents that operate at different levels of temporal abstraction can learn tasks more quickly because they can divide the work of learning behaviors among multiple policies and can also explore the environment at a higher level. In this paper, we present a novel approach to hierarchical reinforcement learning called Hierarchical Actor-Critic (HAC) that enables agents to learn to break down problems involving continuous action spaces into simpler subproblems belonging to different time scales. HAC has two key advantages over most existing hierarchical learning methods: (i) the potential for faster learning as agents learn short policies at each level of the hierarchy and (ii) an end-to-end approach. We demonstrate that HAC significantly accelerates learning in a series of tasks that require behavior over a relatively long time horizon and involve sparse rewards.
Performance appraisal (PA) is an important HR process to periodically measure and evaluate every employee's performance vis-a-vis the goals established by the organization. A PA process involves purposeful multi-step multi-modal communication between employees, their supervisors and their peers, such as self-appraisal, supervisor assessment and peer feedback. Analysis of the structured data and text produced in PA is crucial for measuring the quality of appraisals and tracking actual improvements. In this paper, we apply text mining techniques to produce insights from PA text. First, we perform sentence classification to identify strengths, weaknesses and suggestions of improvements found in the supervisor assessments and then use clustering to discover broad categories among them. Next we use multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance. Finally, we propose a short-text summarization technique to produce a summary of peer feedback comments for a given employee and compare it with manual summaries. All techniques are illustrated using a real-life dataset of supervisor assessment and peer feedback text produced during the PA of 4528 employees in a large multi-national IT company.
A correspondence between database tuples as causes for query answers in databases and tuple-based repairs of inconsistent databases with respect to denial constraints has already been established. In this work, answer-set programs that specify repairs of databases are used as a basis for solving computational and reasoning problems about causes. Here, causes are also introduced at the attribute level by appealing to a both null-based and attribute-based repair semantics. The corresponding repair programs are presented, and they are used as a basis for computation and reasoning about attribute-level causes.
Recently experience replay is widely used in various deep reinforcement learning (RL) algorithms, however in this paper we showcase that it is not as good as people think. To be more specific, experience replay will significantly hurt the learning process if the size of replay buffer is not well tuned. Although experience replay is a necessary component in modern deep RL algorithms to stabilize the network, we should be aware that the idea of experience replay itself is not as good as people think. The size of the replay buffer is an important hyper-parameter, which can significantly influence the performance and has unfortunately been underestimated in the community for a long time. In this paper we did a systematic empirical study of experience replay under various function representations. We showcase that a large replay buffer can significantly hurt the performance. Moreover, we propose a simple O(1) method to remedy the negative influence of a large replay buffer. We showcase its utility in both simple grid world and challenging domains like Atari games. Moreover, we visualize how a large replay buffer hurts the learning process.
Interactive systems have taken over the web and mobile space with increasing participation from users. Applications across every marketing domain can now be accessed through mobile or web where users can directly perform certain actions and reach a desired outcome. Actions of user on a system, though, can be representative of a certain intent. Ability to learn this intent through user's actions can help draw certain insight into the behavior of users on a system. In this paper, we present models to optimize interactive systems by learning and analyzing user intent through their actions on the system. We present a four phased model that uses time-series of interaction actions sequentially using a Long Short-Term Memory (LSTM) based sequence learning system that helps build a model for intent recognition. Our system then provides an objective specific maximization followed by analysis and contrasting methods in order to identify spaces of improvement in the interaction system. We discuss deployment scenarios for such a system and present results from evaluation on an online marketplace using user clickstream data.
In this work we propose a blackbox intervention method for visual dialog models, with the aim of assessing the contribution of individual linguistic or visual components. Concretely, we conduct structured or randomized interventions that aim to impair an individual component of the model, and observe changes in task performance. We reproduce a state-of-the-art visual dialog model and demonstrate that our methodology yields surprising insights, namely that both dialog and image information have minimal contributions to task performance. The intervention method presented here can be applied as a sanity check for the strength and robustness of each component in visual dialog systems.
Deriving event storylines is an effective summarization method to succinctly organize extensive information, which can significantly alleviate the pain of information overload. The critical challenge is the lack of widely recognized definition of storyline metric. Prior studies have developed various approaches based on different assumptions about users' interests. These works can extract interesting patterns, but their assumptions do not guarantee that the derived patterns will match users' preference. On the other hand, their exclusiveness of single modality source misses cross-modality information. This paper proposes a method, multimodal imitation learning via generative adversarial networks(MIL-GAN), to directly model users' interests as reflected by various data. In particular, the proposed model addresses the critical challenge by imitating users' demonstrated storylines. Our proposed model is designed to learn the reward patterns given user-provided storylines and then applies the learned policy to unseen data. The proposed approach is demonstrated to be capable of acquiring the user's implicit intent and outperforming competing methods by a substantial margin with a user study.
The search for increased trustworthiness of SAT solvers is very active and uses various methods. Some of these methods obtain a proof from the provers then check it, normally by replicating the search based on the proof's information. Because the certification process involves another nontrivial proof search, the trust we can place in it is decreased. Some attempts to amend this use certifiers which have been verified by proofs assistants such as Isabelle/HOL and Coq. Our approach is different because it is based on an extremely simplified certifier. This certifier enjoys a very high level of trust but is very inefficient. In this paper, we experiment with this approach and conclude that by placing some restrictions on the formats, one can mostly eliminate the need for search and in principle, can certify proofs of arbitrary size.
A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves linking mentions written in non-English documents to entries in the English Wikipedia: to compare textual clues across languages we need to compute similarity between textual fragments across languages. In this paper, we propose a neural EL model that trains fine-grained similarities and dissimilarities between the query and candidate document from multiple perspectives, combined with convolution and tensor networks. Further, we show that this English-trained system can be applied, in zero-shot learning, to other languages by making surprisingly effective use of multi-lingual embeddings. The proposed system has strong empirical evidence yielding state-of-the-art results in English as well as cross-lingual: Spanish and Chinese TAC 2015 datasets.
Attention-based sequence-to-sequence models for automatic speech recognition jointly train an acoustic model, language model, and alignment mechanism. Thus, the language model component is only trained on transcribed audio-text pairs. This leads to the use of shallow fusion with an external language model at inference time. Shallow fusion refers to log-linear interpolation with a separately trained language model at each step of the beam search. In this work, we investigate the behavior of shallow fusion across a range of conditions: different types of language models, different decoding units, and different tasks. On Google Voice Search, we demonstrate that the use of shallow fusion with a neural LM with wordpieces yields a 9.1% relative word error rate reduction (WERR) over our competitive attention-based sequence-to-sequence model, obviating the need for second-pass rescoring.
In this paper, we propose a new algorithm for learning general latent-variable probabilistic graphical models using the techniques of predictive state representation, instrumental variable regression, and reproducing-kernel Hilbert space embeddings of distributions. Under this new learning framework, we first convert latent-variable graphical models into corresponding latent-variable junction trees, and then reduce the hard parameter learning problem into a pipeline of supervised learning problems, whose results will then be used to perform predictive belief propagation over the latent junction tree during the actual inference procedure. We then give proofs of our algorithm's correctness, and demonstrate its good performance in experiments on one synthetic dataset and two real-world tasks from computational biology and computer vision - classifying DNA splice junctions and recognizing human actions in videos.
Attention mechanism has been used as an ancillary means to help RNN or CNN. However, the Transformer (Vaswani et al., 2017) recently recorded the state-of-the-art performance in machine translation with a dramatic reduction in training time by solely using attention. Motivated by the Transformer, Directional Self Attention Network (Shen et al., 2017), a fully attention-based sentence encoder, was proposed. It showed good performance with various data by using forward and backward directional information in a sentence. But in their study, not considered at all was the distance between words, an important feature when learning the local dependency to help understand the context of input text. We propose Distance-based Self-Attention Network, which considers the word distance by using a simple distance mask in order to model the local dependency without losing the ability of modeling global dependency which attention has inherent. Our model shows good performance with NLI data, and it records the new state-of-the-art result with SNLI data. Additionally, we show that our model has a strength in long sentences or documents.
An adversarial example is an example that has been adjusted to produce a wrong label when presented to a system at test time. To date, adversarial example constructions have been demonstrated for classifiers, but not for detectors. If adversarial examples that could fool a detector exist, they could be used to (for example) maliciously create security hazards on roads populated with smart vehicles. In this paper, we demonstrate a construction that successfully fools two standard detectors, Faster RCNN and YOLO. The existence of such examples is surprising, as attacking a classifier is very different from attacking a detector, and that the structure of detectors - which must search for their own bounding box, and which cannot estimate that box very accurately - makes it quite likely that adversarial patterns are strongly disrupted. We show that our construction produces adversarial examples that generalize well across sequences digitally, even though large perturbations are needed. We also show that our construction yields physical objects that are adversarial.
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable deep neural network for chemical property prediction that learns in a weak-supervised manner from large unlabeled chemical databases. When coupled with transfer learning approaches to predict other smaller datasets for chemical properties that it was not originally trained on, we show that ChemNet's accuracy outperforms contemporary DNN models that were trained using conventional supervised learning. Furthermore, we demonstrate that the ChemNet pre-training approach is equally effective on both CNN (Chemception) and RNN (SMILES2vec) models, indicating that this approach is network architecture agnostic and is effective across multiple data modalities. Our results indicate a pre-trained ChemNet that incorporates chemistry domain knowledge, enables the development of generalizable neural networks for more accurate prediction of novel chemical properties.
This paper is concerned with paraphrase detection. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Given two sentences, the objective is to detect whether they are semantically identical. An important insight from this work is that existing paraphrase systems perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts. Challenges with paraphrase detection on user generated short texts, such as Twitter, include language irregularity and noise. To cope with these challenges, we propose a novel deep neural network-based approach that relies on coarse-grained sentence modeling using a convolutional neural network and a long short-term memory model, combined with a specific fine-grained word-level similarity matching model. Our experimental results show that the proposed approach outperforms existing state-of-the-art approaches on user-generated noisy social media data, such as Twitter texts, and achieves highly competitive performance on a cleaner corpus.
Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL). Additionally, as companies accumulate massive quantities of dialog transcripts between customers and trained human agents, encoder-decoder methods have gained popularity as agent utterances can be directly treated as supervision without the need for utterance-level annotations. However, one potential drawback of such approaches is that they myopically generate the next agent utterance without regard for dialog-level considerations. To resolve this concern, this paper describes an offline RL method for learning from unannotated corpora that can optimize a goal-oriented policy at both the utterance and dialog level. We introduce a novel reward function and use both on-policy and off-policy policy gradient to learn a policy offline without requiring online user interaction or an explicit state space definition.
Recent advances with in-memory columnar database techniques have increased the performance of analytical queries on very large databases and data warehouses. At the same time, advances in artificial intelligence (AI) algorithms have increased the ability to analyze data. We use the term AI to encompass both Deep Learning (DL or neural network) and Machine Learning (ML aka Big Data analytics). Our exploration of the AI full stack has led us to a cross-stack columnar database innovation that efficiently creates features for AI analytics. The innovation is to create Augmented Dictionary Values (ADVs) to add to existing columnar database dictionaries in order to increase the efficiency of featurization by minimizing data movement and data duplication. We show how various forms of featurization (feature selection, feature extraction, and feature creation) can be efficiently calculated in a columnar database. The full stack AI investigation has also led us to propose an integrated columnar database and AI architecture. This architecture has information flows and feedback loops to improve the whole analytics cycle during multiple iterations of extracting data from the data sources, featurization, and analysis.
Coordinate descent methods minimize a cost function by updating a single decision variable (corresponding to one coordinate) at a time. Ideally, one would update the decision variable that yields the largest marginal decrease in the cost function. However, finding this coordinate would require checking all of them, which is not computationally practical. We instead propose a new adaptive method for coordinate descent. First, we define a lower bound on the decrease of the cost function when a coordinate is updated and, instead of calculating this lower bound for all coordinates, we use a multi-armed bandit algorithm to learn which coordinates result in the largest marginal decrease while simultaneously performing coordinate descent. We show that our approach improves the convergence of the coordinate methods (including parallel versions) both theoretically and experimentally.
In this paper, we describe and study the indicator mining problem in the online sex advertising domain. We present an in-development system, FlagIt (Flexible and adaptive generation of Indicators from text), which combines the benefits of both a lightweight expert system and classical semi-supervision (heuristic re-labeling) with recently released state-of-the-art unsupervised text embeddings to tag millions of sentences with indicators that are highly correlated with human trafficking. The FlagIt technology stack is open source. On preliminary evaluations involving five indicators, FlagIt illustrates promising performance compared to several alternatives. The system is being actively developed, refined and integrated into a domain-specific search system used by over 200 law enforcement agencies to combat human trafficking, and is being aggressively extended to mine at least six more indicators with minimal programming effort. FlagIt is a good example of a system that operates in limited label settings, and that requires creative combinations of established machine learning techniques to produce outputs that could be used by real-world non-technical analysts.
An outstanding challenge in nonlinear systems theory is identification or learning of a given nonlinear system's Koopman operator directly from data or models. Advances in extended dynamic mode decomposition approaches and machine learning methods have enabled data-driven discovery of Koopman operators, for both continuous and discrete-time systems. Since Koopman operators are often infinite-dimensional, they are approximated in practice using finite-dimensional systems. The fidelity and convergence of a given finite-dimensional Koopman approximation is a subject of ongoing research. In this paper we introduce a class of Koopman observable functions that confer an approximate closure property on their corresponding finite-dimensional approximations of the Koopman operator. We derive error bounds for the fidelity of this class of observable functions, as well as identify two key learning parameters which can be used to tune performance. We illustrate our approach on two classical nonlinear system models: the Van Der Pol oscillator and the bistable toggle switch.
Feature selection is an important preprocessing step for classification problems. It deals with selecting near optimal features in the original dataset. Feature selection is an NP-hard problem, so meta-heuristics can be more efficient than exact methods. In this work, Ant Lion Optimizer (ALO), which is a recent metaheuristic algorithm, is employed as a wrapper feature selection method. Six variants of ALO are proposed where each employ a transfer function to map a continuous search space to a discrete search space. The performance of the proposed approaches is tested on eighteen UCI datasets and compared to a number of existing approaches in the literature: Particle Swarm Optimization, Gravitational Search Algorithm, and two existing ALO-based approaches. Computational experiments show that the proposed approaches efficiently explore the feature space and select the most informative features, which help to improve the classification accuracy.
As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135.
While off-policy temporal difference methods have been broadly used in reinforcement learning due to their efficiency and simple implementation, their Bayesian counterparts have been relatively understudied. This is mainly because the max operator in the Bellman optimality equation brings non-linearity and inconsistent distributions over value function. In this paper, we introduce a new Bayesian approach to off-policy TD methods using Assumed Density Filtering, called ADFQ, which updates beliefs on action-values (Q) through an online Bayesian inference method. Uncertainty measures in the beliefs not only are used in exploration but they provide a natural regularization in the belief updates. We also present a connection between ADFQ and Q-learning. Our empirical results show the proposed ADFQ algorithms outperform comparing algorithms in several task domains. Moreover, our algorithms improve general drawbacks in BRL such as computational complexity, usage of uncertainty, and nonlinearity.
This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks. We show that even a naively engineered attack successfully degrades the performance of DRL algorithm. We further improve the attack using gradient information of an engineered loss function which leads to further degradation in performance. These attacks are then leveraged during training to improve the robustness of RL within robust control framework. We show that this adversarial training of DRL algorithms like Deep Double Q learning and Deep Deterministic Policy Gradients leads to significant increase in robustness to parameter variations for RL benchmarks such as Cart-pole, Mountain Car, Hopper and Half Cheetah environment.
In recent years, many techniques have been developed to improve the performance and efficiency of data center networks. While these techniques provide high accuracy, they are often designed using heuristics that leverage domain-specific properties of the workload or hardware. In this vision paper, we argue that many data center networking techniques, e.g., routing, topology augmentation, energy savings, with diverse goals actually share design and architectural similarity. We present a design for developing general intermediate representations of network topologies using deep learning that is amenable to solving classes of data center problems. We develop a framework, DeepConfig, that simplifies the processing of configuring and training deep learning agents that use the intermediate representation to learns different tasks. To illustrate the strength of our approach, we configured, implemented, and evaluated a DeepConfig-Agent that tackles the data center topology augmentation problem. Our initial results are promising --- DeepConfig performs comparably to the optimal.
We present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. The simulator leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites. We use MINOS to benchmark deep-learning-based navigation methods, to analyze the influence of environmental complexity on navigation performance, and to carry out a controlled study of multimodality in sensorimotor learning. The experiments show that current deep reinforcement learning approaches fail in large realistic environments. The experiments also indicate that multimodality is beneficial in learning to navigate cluttered scenes. MINOS is released open-source to the research community at http://minosworld.org . A video that shows MINOS can be found at https://youtu.be/c0mL9K64q84
Transfer Learning helps to build a system to recognize and apply knowledge and experience learned in previous tasks (source task) to new tasks or new domains (target task), which share some commonality. The two important factors that impact the performance of transfer learning models are: (a) the size of the target dataset and (b) the similarity in distribution between source and target domains. Thus far there has been little investigation into just how important these factors are. In this paper, we investigated the impact of target dataset size and source/target domain similarity on model performance through a series of experiments. We found that more data is always beneficial, and that model performance improved linearly with the log of data size, until we were out of data. As source/target domains differ, more data is required and fine tuning will render better performance than feature extraction. When source/target domains are similar and data size is small, fine tuning and feature extraction renders equivalent performance. We hope that our study inspires further work in transfer learning, which continues to be a very important technique for developing practical machine learning applications in business domains.
Modern virtual personal assistants provide a convenient interface for completing daily tasks via voice commands. An important consideration for these assistants is the ability to recover from automatic speech recognition (ASR) and natural language understanding (NLU) errors. In this paper, we focus on learning robust dialog policies to recover from these errors. To this end, we develop a user simulator which interacts with the assistant through voice commands in realistic scenarios with noisy audio, and use it to learn dialog policies through deep reinforcement learning. We show that dialogs generated by our simulator are indistinguishable from human generated dialogs, as determined by human evaluators. Furthermore, preliminary experimental results show that the learned policies in noisy environments achieve the same execution success rate with fewer dialog turns compared to fixed rule-based policies.
Eigenoptions (EOs) have been recently introduced as a promising idea for generating a diverse set of options through the graph Laplacian, having been shown to allow efficient exploration. Despite its initial promising results, a couple of issues in current algorithms limit its application, namely: (1) EO methods require two separate steps (eigenoption discovery and reward maximization) to learn a control policy, which can incur a significant amount of storage and computation; (2) EOs are only defined for problems with discrete state-spaces and; (3) it is not easy to take the environment's reward function into consideration when discovering EOs. To addresses these issues, we introduce an algorithm termed eigenoption-critic (EOC) based on the Option-critic (OC) framework [Bacon17], a general hierarchical reinforcement learning (RL) algorithm that allows learning the intra-option policies simultaneously with the policy over options. We also propose a generalization of EOC to problems with continuous state-spaces through the Nystr\"om approximation. EOC can also be seen as extending OC to nonstationary settings, where the discovered options are not tailored for a single task.
The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. Using a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using the sequential parameter optimization toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underling concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and response surface methodology. Many examples illustrate how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm's behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking.
In this paper, we present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of state-of-the-art dehazing algorithms, and suggest promising future directions.
Modeling and verifying real-world cyber-physical systems are challenging, especially so for complex systems where manually modeling is infeasible. In this work, we report our experience on combining model learning and abstraction refinement to analyze a challenging system, i.e., a real-world Secure Water Treatment (SWaT) system. Given a set of safety requirements, the objective is to either show that the system is safe with a high probability (so that a system shutdown is rarely triggered due to safety violation) or otherwise. As the system is too complicated to be manually modelled, we apply latest automatic model learning techniques to construct a set of Markov chains through abstraction and refinement, based on two long system execution logs (one for training and the other for testing). For each probabilistic property, we either report it does not hold with a certain level of probabilistic confidence, or report that it holds by showing the evidence in the form of an abstract Markov chain. The Markov chains can subsequently be implemented as runtime monitors in SWaT. This is the first case study of applying model learning techniques to a real-world system as far as we know.
Sequential pattern mining techniques extract patterns corresponding to frequent subsequences from a sequence database. A practical limitation of these techniques is that they overload the user with too many patterns. Local Process Model (LPM) mining is an alternative approach coming from the field of process mining. While in traditional sequential pattern mining, a pattern describes one subsequence, an LPM captures a set of subsequences. Also, while traditional sequential patterns only match subsequences that are observed in the sequence database, an LPM may capture subsequences that are not explicitly observed, but that are related to observed subsequences. In other words, LPMs generalize the behavior observed in the sequence database. These properties make it possible for a set of LPMs to cover the behavior of a much larger set of sequential patterns. Yet, existing LPM mining techniques still suffer from the pattern explosion problem because they produce sets of redundant LPMs. In this paper, we propose several heuristics to mine a set of non-redundant LPMs either from a set of redundant LPMs or from a set of sequential patterns. We empirically compare the proposed heuristics between them and against existing (local) process mining techniques in terms of coverage, precision, and complexity of the produced sets of LPMs.
The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deploy autonomously learned controllers if they are understandable and convenient to evaluate. Basic algebraic equations are supposed to meet these requirements, as long as they are restricted to an adequate complexity. Here we introduce the genetic programming for reinforcement learning (GPRL) approach based on model-based batch reinforcement learning and genetic programming, which autonomously learns policy equations from pre-existing default state-action trajectory samples. GPRL is compared to a straight-forward method which utilizes genetic programming for symbolic regression, yielding policies imitating an existing well-performing, but non-interpretable policy. Experiments on three reinforcement learning benchmarks, i.e., mountain car, cart-pole balancing, and industrial benchmark, demonstrate the superiority of our GPRL approach compared to the symbolic regression method. GPRL is capable of producing well-performing interpretable reinforcement learning policies from pre-existing default trajectory data.
In this paper, we will show that (1) the results about the fuzzy reasoning algoritm obtained in the paper "Computer Sciences Vol. 34, No.4, pp.145-148, 2007" according to the paper "IEEE Transactions On systems, Man and cybernetics, 18, pp.1049-1056, 1988" are correct; (2) example 2 in the paper "An Algorithm of General Fuzzy Inference With The Reductive Property" presented by He Ying-Si, Quan Hai-Jin and Deng Hui-Wen according to the paper "An approximate analogical reasoning approach based on similarity measures" presented by Tursken I.B. and Zhong zhao is incorrect; (3) the mistakes in their paper are modified and then a calculation example of FMT is supplemented.
Banking is one of the most significant adopters of cutting-edge information technologies. Since its modern era beginning in the form of paper based accounting maintained in the branch, adoption of computerized system made it possible to centralize the processing in data centre and improve customer experience by making a more available and efficient system. The latest twist in this evolution is adoption of natural language processing and speech recognition in the user interface between the human and the system and use of machine learning and advanced analytics, in general, for backend processing as well. The paper reviews the progress of technology adoption in the field and comments on the maturity level of solutions involving less studied or low-resource languages like Hindi and also other Indian, regional languages. Furthermore, it also provides an analysis from a prototype built by us. The future directions of this area are also highlighted.
We examine the issue of stability of probability in reasoning about complex systems with uncertainty in structure. Normally, propositions are viewed as probability functions on an abstract random graph where it is implicitly assumed that the nodes of the graph have stable properties. But what if some of the nodes change their characteristics? This is a situation that cannot be covered by abstractions of either static or dynamic sets when these changes take place at regular intervals. We propose the use of sets with elements that change, and modular forms are proposed to account for one type of such change. An expression for the dependence of the mean on the probability of the switching elements has been determined. The system is also analyzed from the perspective of decision between different hypotheses. Such sets are likely to be of use in complex system queries and in analysis of surveys.
This paper presents a framework for intrinsic point of interest discovery from trajectory databases. Intrinsic points of interest are regions of a geospatial area innately defined by the spatial and temporal aspects of trajectory data, and can be of varying size, shape, and resolution. Any trajectory database exhibits such points of interest, and hence are intrinsic, as compared to most other point of interest definitions which are said to be extrinsic, as they require trajectory metadata, external knowledge about the region the trajectories are observed, or other application-specific information. Spatial and temporal aspects are qualities of any trajectory database, making the framework applicable to data from any domain and of any resolution. The framework is developed under recent developments on the consistency of nonparametric hierarchical density estimators and enables the possibility of formal statistical inference and evaluation over such intrinsic points of interest. Comparisons of the POIs uncovered by the framework in synthetic truth data to thousands of parameter settings for common POI discovery methods show a marked improvement in fidelity without the need to tune any parameters by hand.
To harness the complexity of their high-dimensional bodies during sensorimotor development, infants are guided by patterns of freezing and freeing of degrees of freedom. For instance, when learning to reach, infants free the degrees of freedom in their arm proximodistally, i.e. from joints that are closer to the body to those that are more distant. Here, we formulate and study computationally the hypothesis that such patterns can emerge spontaneously as the result of a family of stochastic optimization processes (evolution strategies with covariance-matrix adaptation), without an innate encoding of a maturational schedule. In particular, we present simulated experiments with an arm where a computational learner progressively acquires reaching skills through adaptive exploration, and we show that a proximodistal organization appears spontaneously, which we denote PDFF (ProximoDistal Freezing and Freeing of degrees of freedom). We also compare this emergent organization between different arm morphologies -- from human-like to quite unnatural ones -- to study the effect of different kinematic structures on the emergence of PDFF. Keywords: human motor learning; proximo-distal exploration; stochastic optimization; modelling; evolution strategies; cross-entropy methods; policy search; morphology.}
This paper considers the integrated problem of quay crane assignment, quay crane scheduling, yard location assignment, and vehicle dispatching operations at a container terminal. The main objective is to minimize vessel turnover times and maximize the terminal throughput, which are key economic drivers in terminal operations. Due to their computational complexities, these problems are not optimized jointly in existing work. This paper revisits this limitation and proposes Mixed Integer Programming (MIP) and Constraint Programming (CP) models for the integrated problem, under some realistic assumptions. Experimental results show that the MIP formulation can only solve small instances, while the CP model finds optimal solutions in reasonable times for realistic instances derived from actual container terminal operations.
In this work, we propose a goal-driven collaborative task that contains vision, language, and action in a virtual environment as its core components. Specifically, we develop a collaborative `Image Drawing' game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects. Two players, Teller and Drawer, are involved. The Teller sees an abstract scene containing multiple clip arts in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip arts. The two players communicate via two-way communication using natural language. We collect the CoDraw dataset of ~10K dialogs consisting of 138K messages exchanged between a Teller and a Drawer from Amazon Mechanical Turk (AMT). We analyze our dataset and present three models to model the players' behaviors, including an attention model to describe and draw multiple clip arts at each round. The attention models are quantitatively compared to the other models to show how the conventional approaches work for this new task. We also present qualitative visualizations.
Recurrent neural networks with differentiable attention mechanisms have had success in generative and classification tasks. We show that the classification performance of such models can be enhanced by guiding a randomly initialized model to attend to salient regions of the input in early training iterations. We further show that, if explicit heuristics for guidance are unavailable, a model that is pretrained on an unsupervised reconstruction task can discover good attention policies without supervision. We demonstrate that increased efficiency of the attention mechanism itself contributes to these performance improvements. Based on these insights, we introduce bootstrapped glimpse mimicking, a simple, theoretically task-general method of more effectively training attention models. Our work draws inspiration from and parallels results on human learning of attention.
Inverse reinforcement learning (IRL) attempts to infer human rewards or preferences from observed behavior. Since human planning systematically deviates from rationality, several approaches have been tried to account for specific human shortcomings. However, there has been little analysis of the general problem of inferring the reward of a human of unknown rationality. The observed behavior can, in principle, be decomposed into two components: a reward function and a planning algorithm, both of which have to be inferred from behavior. This paper presents a No Free Lunch theorem, showing that, without making `normative' assumptions beyond the data, nothing about the human reward function can be deduced from human behavior. Unlike most No Free Lunch theorems, this cannot be alleviated by regularising with simplicity assumptions. We show that the simplest hypotheses which explain the data are generally degenerate.
As more robots act in physical proximity to people, it is essential to ensure they make decisions and execute actions that align with human values. To do so, robots need to understand the true intentions behind human-issued commands. In this paper, we define a safe robot as one that receives a natural-language command from humans, considers an action in response to that command, and accurately predicts how humans will judge that action if is executed in reality. Our contribution is two-fold: First, we introduce a web platform for human users to propose commands to simulated robots. The robots receive commands and act based on those proposed commands, and then the users provide positive and/or negative reinforcement. Next, we train a critic for each robot to predict the crowd's responses to one of the crowd-proposed commands. Second, we show that the morphology of a robot plays a role in the way it grounds language: The critics show that two of the robots used in the experiment achieve a lower prediction error than the others. Thus, those two robots are safer, according to our definition, since they ground the proposed command more accurately.
The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray---a distributed system to address them. Ray implements a dynamic task graph computation model that supports both the task-parallel and the actor programming models. To meet the performance requirements of AI applications, we propose an architecture that logically centralizes the system's control state using a sharded storage system and a novel bottom-up distributed scheduler. In our experiments, we demonstrate sub-millisecond remote task latencies and linear throughput scaling beyond 1.8 million tasks per second. We empirically validate that Ray speeds up challenging benchmarks and serves as both a natural and performant fit for an emerging class of reinforcement learning applications and algorithms.
Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning in general and deep learning in particular is ideally suited for representing quantum-mechanical interactions, enabling to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for \emph{molecules and materials} where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study of the quantum-mechanical properties of C$_{20}$-fullerene that would have been infeasible with regular ab initio molecular dynamics.
The visual explanation of learned representation of models helps to understand the fundamentals of learning. The attentional models of previous works used to visualize the attended regions over an image or text using their learned weights to confirm their intended mechanism. Kim et al. (2016) show that the Hadamard product in multimodal deep networks, which is well-known for the joint function of visual question answering tasks, implicitly performs an attentional mechanism for visual inputs. In this work, we extend their work to show that the Hadamard product in multimodal deep networks performs not only for visual inputs but also for textual inputs simultaneously using the proposed gradient-based visualization technique. The attentional effect of Hadamard product is visualized for both visual and textual inputs by analyzing the two inputs and an output of the Hadamard product with the proposed method and compared with learned attentional weights of a visual question answering model.
`Indifference' refers to a class of methods that are used to control a reward based agent. These methods of control work even if the implications of the agent's reward are otherwise not fully understood. Though they all come out of similar ideas, indifference techniques can be classified as way of achieving one or more of three distinct goals: rewards dependent on certain events (with no motivation for the agent to manipulate the probability of those events), effective disbelief that an event will ever occur, and seamless transition from one behaviour to another. This paper analyses methods of achieving these goals in the POMDP setting, and establishes their uses, strengths, and limitations. It aims to make the tools of indifference generally accessible and usable to agent designers.
We would like to learn latent representations that are low-dimensional and highly interpretable. A model that has these characteristics is the Gaussian Process Latent Variable Model. The benefits and negative of the GP-LVM are complementary to the Variational Autoencoder, the former provides interpretable low-dimensional latent representations while the latter is able to handle large amounts of data and can use non-Gaussian likelihoods. Our inspiration for this paper is to marry these two approaches and reap the benefits of both. In order to do so we will introduce a novel approximate inference scheme inspired by the GP-LVM and the VAE. We show experimentally that the approximation allows the capacity of the generative bottle-neck (Z) of the VAE to be arbitrarily large without losing a highly interpretable representation, allowing reconstruction quality to be unlimited by Z at the same time as a low-dimensional space can be used to perform ancestral sampling from as well as a means to reason about the embedded data.
We show that the forward and backward propagation can be formulated as a solution of lower and upper triangular systems of equations. For standard feedforward (FNNs) and recurrent neural networks (RNNs) the triangular systems are always block bi-diagonal, while for a general computation graph (directed acyclic graph) they can have a more complex triangular sparsity pattern. We discuss direct and iterative parallel algorithms that can be used for their solution and interpreted as different ways of performing model parallelism. Also, we show that for FNNs and RNNs with $k$ layers and $\tau$ time steps the backward propagation can be performed in parallel in O($\log k$) and O($\log k \log \tau$) steps, respectively. Finally, we outline the generalization of this technique using Jacobians that potentially allows us to handle arbitrary layers.
From our experiences in the past, we have seen that the growth of cities is very much dependent on the transportation networks. In mega cities, transportation networks determine to a significant extent as to where the people will move and houses will be built. Hence, transportation network data is crucial to an urban growth prediction system. Existing works have used manually derived distance based features based on the road networks to build models on urban growth. But due to the non-generic and laborious nature of the manual feature engineering process, we can shift to End-to-End systems which do not rely on manual feature engineering. In this paper, we propose a method to integrate road network data to an existing Rule based End-to-End framework without manual feature engineering. Our method employs recurrent neural networks to represent road networks in a structured way such that it can be plugged into the previously proposed End-to-End framework. The proposed approach enhances the performance in terms of Figure of Merit, Producer's accuracy, User's accuracy and Overall accuracy of the existing Rule based End-to-End framework.
For relational monadic formulas (the L\"owenheim class) second-order quantifier elimination, which is closely related to computation of uniform interpolants, projection and forgetting - operations that currently receive much attention in knowledge processing - always succeeds. The decidability proof for this class by Heinrich Behmann from 1922 explicitly proceeds by elimination with equivalence preserving formula rewriting. Here we reconstruct the results from Behmann's publication in detail and discuss related issues that are relevant in the context of modern approaches to second-order quantifier elimination in computational logic. In addition, an extensive documentation of the letters and manuscripts in Behmann's bequest that concern second-order quantifier elimination is given, including a commented register and English abstracts of the German sources with focus on technical material. In the late 1920s Behmann attempted to develop an elimination-based decision method for formulas with predicates whose arity is larger than one. His manuscripts and the correspondence with Wilhelm Ackermann show technical aspects that are still of interest today and give insight into the genesis of Ackermann's landmark paper "Untersuchungen \"uber das Eliminationsproblem der mathematischen Logik" from 1935, which laid the foundation of the two prevailing modern approaches to second-order quantifier elimination.
Nowadays many artificial intelligence systems rely on knowledge bases for enriching the information they process. Such Knowledge Bases are usually difficult to obtain and therefore they are crowdsourced: they are available for everyone on the internet to suggest edits and add new information. Unfortunately, they are sometimes targeted by vandals who put inaccurate or offensive information there. This is especially bad for the systems that use these Knowledge Bases: for them it is important to use reliable information to make correct inferences. One of such knowledge bases is Wikidata, and to fight vandals the organizers of WSDM Cup 2017 challenged participants to build a model for detecting mistrustful edits. In this paper we present the second place solution to the cup: we show that it is possible to achieve competitive performance with simple linear classification. With our approach we can achieve AU ROC of 0.938 on the test data. Additionally, compared to other approaches, ours is significantly faster. The solution is made available on GitHub.
A common goal in Reinforcement Learning is to derive a good strategy given a limited batch of data. In this paper, we adopt the safe policy improvement (SPI) approach: we compute a target policy guaranteed to perform at least as well as a given baseline policy. Our SPI strategy, inspired by the knows-what-it-knows paradigms, consists in bootstrapping the target policy with the baseline policy when it does not know. We develop two computationally efficient bootstrapping algorithms, a value-based and a policy-based, both accompanied with theoretical SPI bounds. Three algorithm variants are proposed. We empirically show the literature algorithms limits on a small stochastic gridworld problem, and then demonstrate that our five algorithms not only improve the worst case scenarios, but also the mean performance.
In the context of public transport modeling and simulation, we address the problem of mismatch between simulated transit trips and observed ones. We point to the weakness of the current travel demand modeling process; the trips it generates are over-optimistic and do not reflect the real passenger choices. We introduce the notion of mini activities the travelers do during the trips; they can explain the deviation of simulated trips from the observed trips. We propose to mine the smart card data to extract the mini activities. We develop a technique to integrate them in the generated trips and learn such an integration from two available sources, the trip history and trip planner recommendations. For an input travel demand, we build a Markov chain over the trip collection and apply the Monte Carlo Markov Chain algorithm to integrate mini activities in such a way that the selected characteristics converge to the desired distributions. We test our method in different settings on the passenger trip collection of Nancy, France. We report experimental results demonstrating a very important mismatch reduction.
This paper proposes a novel column generation framework for combinatorial software testing. In particular, it combines Mathematical Programming and Constraint Programming in a hybrid decomposition to generate covering arrays. The approach allows generating parameterized test cases with coverage guarantees between parameter interactions of a given application. Compared to exhaustive testing, combinatorial test case generation reduces the number of tests to run significantly. Our column generation algorithm is generic and can accommodate mixed coverage arrays over heterogeneous alphabets. The algorithm is realized in practice as a cloud service and recognized as one of the five winners of the company-wide cloud application challenge at Oracle. The service is currently helping software developers from a range of different product teams in their testing efforts while exposing declarative constraint models and hybrid optimization techniques to a broader audience.
The randomized-feature approach has been successfully employed in large-scale kernel approximation and supervised learning. The distribution from which the random features are drawn impacts the number of features required to efficiently perform a learning task. Recently, it has been shown that employing data-dependent randomization improves the performance in terms of the required number of random features. In this paper, we are concerned with the randomized-feature approach in supervised learning for good generalizability. We propose the Energy-based Exploration of Random Features (EERF) algorithm based on a data-dependent score function that explores the set of possible features and exploits the promising regions. We prove that the proposed score function with high probability recovers the spectrum of the best fit within the model class. Our empirical results on several benchmark datasets further verify that our method requires smaller number of random features to achieve a certain generalization error compared to the state-of-the-art while introducing negligible pre-processing overhead. EERF can be implemented in a few lines of code and requires no additional tuning parameters.
The emerging vehicular networks are expected to make everyday vehicular operation safer, greener, and more efficient, and pave the path to autonomous driving in the advent of the fifth generation (5G) cellular system. Machine learning, as a major branch of artificial intelligence, has been recently applied to wireless networks to provide a data-driven approach to solve traditionally challenging problems. In this article, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this emerging area. After a brief overview of the major concept of machine learning, we present some application examples of machine learning in solving problems arising in vehicular networks. We finally discuss and highlight several open issues that warrant further research.
Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to employ hierarchical policies that decide when to use a previously learned policy and when to learn a new skill. This enables agents to continually acquire new skills during different stages of training. Each learned task corresponds to a human language description. Because agents can only access previously learned skills through these descriptions, the agent can always provide a human-interpretable description of its choices. In order to help the agent learn the complex temporal dependencies necessary for the hierarchical policy, we provide it with a stochastic temporal grammar that modulates when to rely on previously learned skills and when to execute new skills. We validate our approach on Minecraft games designed to explicitly test the ability to reuse previously learned skills while simultaneously learning new skills.
Second-order methods for neural network optimization have several advantages over methods based on first-order gradient descent, including better scaling to large mini-batch sizes and fewer updates needed for convergence. But they are rarely applied to deep learning in practice because of high computational cost and the need for model-dependent algorithmic variations. We introduce a variant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix. Our method computes the curvature approximation matrix only for pairs of parameters from the same layer or block of the neural network and performs conjugate gradient updates independently for each block. Experiments on deep autoencoders, deep convolutional networks, and multilayer LSTMs demonstrate better convergence and generalization compared to the original Hessian-free approach and the Adam method.
Breast cancer is already one of the most common form of cancer worldwide. Mammography image analysis is still the most effective diagnostic method to promote the early detection of breast cancer. Accurately segmenting tumors in digital mammography images is important to improve diagnosis capabilities of health specialists and avoid misdiagnosis. In this work, we evaluate the feasibility of applying GrowCut to segment regions of tumor and we propose two GrowCut semi-supervised versions. All the analysis was performed by evaluating the application of segmentation techniques to a set of images obtained from the Mini-MIAS mammography image database. GrowCut segmentation was compared to Region Growing, Active Contours, Random Walks and Graph Cut techniques. Experiments showed that GrowCut, when compared to the other techniques, was able to acquire better results for the metrics analyzed. Moreover, the proposed semi-supervised versions of GrowCut was proved to have a clinically satisfactory quality of segmentation.
Gastric cancer is the second leading cause of cancer-related deaths worldwide, and the major hurdle in biomedical image analysis is the determination of the cancer extent. This assignment has high clinical relevance and would generally require vast microscopic assessment by pathologists. Recent advances in deep learning have produced inspiring results on biomedical image segmentation, while its outcome is reliant on comprehensive annotation. This requires plenty of labor costs, for the ground truth must be annotated meticulously by pathologists. In this paper, a reiterative learning framework was presented to train our network on partial annotated biomedical images, and superior performance was achieved without any pre-trained or further manual annotation. We eliminate the boundary error of patch-based model through our overlapped region forecast algorithm. Through these advisable methods, a mean intersection over union coefficient (IOU) of 0.883 and mean accuracy of 91.09% on the partial labeled dataset was achieved, which made us win the 2017 China Big Data & Artificial Intelligence Innovation and Entrepreneurship Competitions.
Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwide. The aim of the challenge is to converse "coherently and engagingly with humans on popular topics for 20 minutes". We describe our Alexa Prize system (called 'Alana') consisting of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose a system response. The ranker was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition.
Knowledge base (KB) completion aims to infer missing facts from existing ones in a KB. Among various approaches, path ranking (PR) algorithms have received increasing attention in recent years. PR algorithms enumerate paths between entity pairs in a KB and use those paths as features to train a model for missing fact prediction. Due to their good performances and high model interpretability, several methods have been proposed. However, most existing methods suffer from scalability (high RAM consumption) and feature explosion (trains on an exponentially large number of features) problems. This paper proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems by introducing a selective path exploration strategy. C-PR learns global semantics of entities in the KB using word embedding and leverages the knowledge of entity semantics to enumerate contextually relevant paths using bidirectional random walk. Experimental results on three large KBs show that the path features (fewer in number) discovered by C-PR not only improve predictive performance but also are more interpretable than existing baselines.
Approximate model counting for bit-vector SMT formulas (generalizing \#SAT) has many applications such as probabilistic inference and quantitative information-flow security, but it is computationally difficult. Adding random parity constraints (XOR streamlining) and then checking satisfiability is an effective approximation technique, but it requires a prior hypothesis about the model count to produce useful results. We propose an approach inspired by statistical estimation to continually refine a probabilistic estimate of the model count for a formula, so that each XOR-streamlined query yields as much information as possible. We implement this approach, with an approximate probability model, as a wrapper around an off-the-shelf SMT solver or SAT solver. Experimental results show that the implementation is faster than the most similar previous approaches which used simpler refinement strategies. The technique also lets us model count formulas over floating-point constraints, which we demonstrate with an application to a vulnerability in differential privacy mechanisms.
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from population biology to cosmology, low-level mechanistic components are composed to create complex generative models. These models lead to intractable likelihoods and are typically non-differentiable, which poses challenges for traditional approaches to inference. We extend previous work in "inference compilation", which combines universal probabilistic programming and deep learning methods, to large-scale scientific simulators, and introduce a C++ based probabilistic programming library called CPProb. We successfully use CPProb to interface with SHERPA, a large code-base used in particle physics. Here we describe the technical innovations realized and planned for this library.
Neural networks have been widely used to solve complex real-world problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the output behaviors of neural networks will be crucial for their applications in safety-critical systems.In this paper, the output reachable set computation and safety verification problems for a class of neural networks consisting of Rectified Linear Unit (ReLU) activation functions are addressed. A layer-by-layer approach is developed to compute output reachable set. The computation is formulated in the form of a set of manipulations for a union of polyhedra, which can be efficiently applied with the aid of polyhedron computation tools. Based on the output reachable set computation results, the safety verification for a ReLU neural network can be performed by checking the intersections of unsafe regions and output reachable set described by a union of polyhedra. A numerical example of a randomly generated ReLU neural network is provided to show the effectiveness of the approach developed in this paper.
The potential lack of fairness in the outputs of machine learning algorithms has recently gained attention both within the research community as well as in society more broadly. Surprisingly, there is no prior work developing tree-induction algorithms for building fair decision trees or fair random forests. These methods have widespread popularity as they are one of the few to be simultaneously interpretable, non-linear, and easy-to-use. In this paper we develop, to our knowledge, the first technique for the induction of fair decision trees. We show that our "Fair Forest" retains the benefits of the tree-based approach, while providing both greater accuracy and fairness than other alternatives, for both "group fairness" and "individual fairness.'" We also introduce new measures for fairness which are able to handle multinomial and continues attributes as well as regression problems, as opposed to binary attributes and labels only. Finally, we demonstrate a new, more robust evaluation procedure for algorithms that considers the dataset in its entirety rather than only a specific protected attribute.
We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape. The instructions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottom-up techniques for this shape parsing task rely on primitive detection and are inherently slow since the search space over possible primitive combinations is large. In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions. Our model is also more effective as a shape detector compared to existing state-of-the-art detection techniques. We finally demonstrate that our network can be trained on novel datasets without ground-truth program annotations through policy gradient techniques.
In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data). It proposes an instance-based approach whose principle consists in determining the minimal changes needed to alter a prediction: given a data point whose classification must be explained, the proposed method consists in identifying a close neighbour classified differently, where the closeness definition integrates a sparsity constraint. This principle is implemented using observation generation in the Growing Spheres algorithm. Experimental results on two datasets illustrate the relevance of the proposed approach that can be used to gain knowledge about the classifier.
Conditional preference networks (CP-nets) are a graphical representation of a person's (conditional) preferences over a set of discrete variables. In this paper, we introduce a novel method of quantifying preference for any given outcome based on a CP-net representation of a user's preferences. We demonstrate that these values are useful for reasoning about user preferences. In particular, they allow us to order (any subset of) the possible outcomes in accordance with the user's preferences. Further, these values can be used to improve the efficiency of outcome dominance testing. That is, given a pair of outcomes, we can determine which the user prefers more efficiently. We show that these results also hold for CP-nets that express indifference between variable values.
Discovery of an accurate causal Bayesian network structure from observational data can be useful in many areas of science. Often the discoveries are made under uncertainty, which can be expressed as probabilities. To guide the use of such discoveries, including directing further investigation, it is important that those probabilities be well-calibrated. In this paper, we introduce a novel framework to derive calibrated probabilities of causal relationships from observational data. The framework consists of three components: (1) an approximate method for generating initial probability estimates of the edge types for each pair of variables, (2) the availability of a relatively small number of the causal relationships in the network for which the truth status is known, which we call a calibration training set, and (3) a calibration method for using the approximate probability estimates and the calibration training set to generate calibrated probabilities for the many remaining pairs of variables. We also introduce a new calibration method based on a shallow neural network. Our experiments on simulated data support that the proposed approach improves the calibration of causal edge predictions. The results also support that the approach often improves the precision and recall of predictions.
Questions that require counting a variety of objects in images remain a major challenge in visual question answering (VQA). The most common approaches to VQA involve either classifying answers based on fixed length representations of both the image and question or summing fractional counts estimated from each section of the image. In contrast, we treat counting as a sequential decision process and force our model to make discrete choices of what to count. Specifically, the model sequentially selects from detected objects and learns interactions between objects that influence subsequent selections. A distinction of our approach is its intuitive and interpretable output, as discrete counts are automatically grounded in the image. Furthermore, our method outperforms the state of the art architecture for VQA on multiple metrics that evaluate counting.
In domains with high knowledge distribution a natural objective is to create principle foundations for collaborative interactive learning environments. We present a first mathematical characterization of a collaborative learning group, a consortium, based on closure systems of attribute sets and the well-known attribute exploration algorithm from formal concept analysis. To this end, we introduce (weak) local experts for subdomains of a given knowledge domain. These entities are able to refute and potentially accept a given (implicational) query for some closure system that is a restriction of the whole domain. On this we build up a consortial expert and show first insights about the ability of such an expert to answer queries. Furthermore, we depict techniques on how to cope with falsely accepted implications and on combining counterexamples. Using notions from combinatorial design theory we further expand those insights as far as providing first results on the decidability problem if a given consortium is able to explore some target domain. Applications in conceptual knowledge acquisition as well as in collaborative interactive ontology learning are at hand.
Deep Neural Networks are built to generalize outside of training set in mind by using techniques such as regularization, early stopping and dropout. But considerations to make them more resilient to adversarial examples are rarely taken. As deep neural networks become more prevalent in mission-critical and real-time systems, miscreants start to attack them by intentionally making deep neural networks to misclassify an object of one type to be seen as another type. This can be catastrophic in some scenarios where the classification of a deep neural network can lead to a fatal decision by a machine. In this work, we used GTSRB dataset to craft adversarial samples by Fast Gradient Sign Method and Jacobian Saliency Method, used those crafted adversarial samples to attack another Deep Convolutional Neural Network and built the attacked network to be more resilient against adversarial attacks by making it more robust by Defensive Distillation and Adversarial Training
As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities. PV systems provide online/dynamic peer-to-peer ride-sharing services with the goal of serving sufficient number of customers with minimum number of vehicles and lowest possible cost. A key component of the PV system is the online ride-sharing scheduling strategy. In this paper, we propose an efficient path planning strategy that focuses on a limited potential search area for each vehicle by filtering out the requests that violate passenger service quality level, so that the global search is reduced to local search. We analyze the performance of the proposed solution such as reduction ratio of computational complexity. Simulations based on the Manhattan taxi data set show that, the computing time is reduced by 22% compared with the exhaustive search method under the same service quality performance.
In the research of the impact of gestures using by a lecturer, one challenging task is to infer the attention of a group of audiences. Two important measurements that can help infer the level of attention are eye movement data and Electroencephalography (EEG) data. Under the fundamental assumption that a group of people would look at the same place if they all pay attention at the same time, we apply a method, "Time Warp Edit Distance", to calculate the similarity of their eye movement trajectories. Moreover, we also cluster eye movement pattern of audiences based on these pair-wised similarity metrics. Besides, since we don't have a direct metric for the "attention" ground truth, a visual assessment would be beneficial to evaluate the gesture-attention relationship. Thus we also implement a visualization tool.
While end-to-end neural conversation models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, they typically require an enormous amount of data due to the lack of modularity. Previous studies adopted a hybrid approach with knowledge-based components either to abstract out domain-specific information or to augment data to cover more diverse patterns. On the contrary, we propose to directly address the problem using recent developments in the space of continual learning for neural models. Specifically, we adopt a domain-independent neural conversational model and introduce a novel neural continual learning algorithm that allows a conversational agent to accumulate skills across different tasks in a data-efficient way. To the best of our knowledge, this is the first work that applies continual learning to conversation systems. We verified the efficacy of our method through a conversational skill transfer from either synthetic dialogs or human-human dialogs to human-computer conversations in a customer support domain.
The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads.
Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challenging machine learning task. Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase logarithmic loss for the target distribution while matching feature expectation constraints from the source distribution. However, employing RBA with insufficient feature constraints may result in high certainty predictions for much of the source data, while leaving too much uncertainty for target data predictions. To overcome this issue, we extend the representer theorem to the RBA setting, enabling minimization of regularized expected target risk by a reweighted kernel expectation under the source distribution. By applying kernel methods, we establish consistency guarantees and demonstrate better performance of the RBA classifier than competing methods on synthetically biased UCI datasets as well as datasets that have natural covariate shift.
Interior tomography for the region-of-interest (ROI) imaging has advantages of using a small detector and reducing X-ray radiation dose. However, standard analytic reconstruction suffers from severe cupping artifacts due to existence of null space in the truncated Radon transform. Existing penalized reconstruction methods may address this problem but they require extensive computations due to the iterative reconstruction. Inspired by the recent deep learning approaches to low-dose and sparse view CT, here we propose a deep learning architecture that removes null space signals from the FBP reconstruction. Experimental results have shown that the proposed method provides near-perfect reconstruction with about 7-10 dB improvement in PSNR over existing methods in spite of significantly reduced run-time complexity.
Hierarchies are of fundamental interest in both stochastic optimal control and biological control due to their facilitation of a range of desirable computational traits in a control algorithm and the possibility that they may form a core principle of sensorimotor and cognitive control systems. However, a theoretically justified construction of state-space hierarchies over all spatial resolutions and their evolution through a policy inference process remains elusive. Here, a formalism for deriving such normative representations of discrete Markov decision processes is introduced in the context of graphs. The resulting hierarchies correspond to a hierarchical policy inference algorithm approximating a discrete gradient flow between state-space trajectory densities generated by the prior and optimal policies.
Learning probability distributions on the weights of neural networks (NNs) has recently proven beneficial in many applications. Bayesian methods, such as Stein variational gradient descent (SVGD), offer an elegant framework to reason about NN model uncertainty. However, by assuming independent Gaussian priors for the individual NN weights (as often applied), SVGD does not impose prior knowledge that there is often structural information (dependence) among weights. We propose efficient posterior learning of structural weight uncertainty, within an SVGD framework, by employing matrix variate Gaussian priors on NN parameters. We further investigate the learned structural uncertainty in sequential decision-making problems, including contextual bandits and reinforcement learning. Experiments on several synthetic and real datasets indicate the superiority of our model, compared with state-of-the-art methods.
This paper presents a new deep learning architecture for Natural Language Inference (NLI). Firstly, we introduce a new compare-propagate architecture where alignments pairs are compared and then propagated to upper layers for enhanced representation learning. Secondly, we adopt novel factorization layers for efficient compression of alignment vectors into scalar valued features, which are then be used to augment the base word representations. The design of our approach is aimed to be conceptually simple, compact and yet powerful. We conduct experiments on three popular benchmarks, SNLI, MultiNLI and SciTail, achieving state-of-the-art performance on all. A lightweight parameterization of our model enjoys a $\approx 300\%$ reduction in parameter size compared to the ESIM and DIIN, while maintaining competitive performance. Visual analysis shows that our propagated features are highly interpretable, opening new avenues to explainability in neural NLI models.
Game-theoretic centrality is a flexible and sophisticated approach to identify the most important nodes in a network. It builds upon the methods from cooperative game theory and network theory. The key idea is to treat nodes as players in a cooperative game, where the value of each coalition is determined by certain graph-theoretic properties. Using solution concepts from cooperative game theory, it is then possible to measure how responsible each node is for the worth of the network. The literature on the topic is already quite large, and is scattered among game-theoretic and computer science venues. We review the main game-theoretic network centrality measures from both bodies of literature and organize them into two categories: those that are more focused on the connectivity of nodes, and those that are more focused on the synergies achieved by nodes in groups. We present and explain each centrality, with a focus on algorithms and complexity.
In this paper we present a neurally plausible model of robot reaching inspired by human infant reaching that is based on embodied artificial intelligence, which emphasizes the importance of the sensory-motor interaction of an agent and the world. This model encompasses both learning sensory-motor correlations through motor babbling and also arm motion planning using spreading activation. This model is organized in three layers of neural maps with parallel structures representing the same sensory-motor space. The motor babbling period shapes the structure of the three neural maps as well as the connections within and between them. We describe an implementation of this model and an investigation of this implementation using a simple reaching task on a humanoid robot. The robot has learned successfully to plan reaching motions from a test set with high accuracy and smoothness.
We propose a scalable divergence estimation method based on hashing. Consider two continuous random variables $X$ and $Y$ whose densities have bounded support. We consider a particular locality sensitive random hashing, and consider the ratio of samples in each hash bin having non-zero numbers of Y samples. We prove that the weighted average of these ratios over all of the hash bins converges to f-divergences between the two samples sets. We show that the proposed estimator is optimal in terms of both MSE rate and computational complexity. We derive the MSE rates for two families of smooth functions; the H\"{o}lder smoothness class and differentiable functions. In particular, it is proved that if the density functions have bounded derivatives up to the order $d/2$, where $d$ is the dimension of samples, the optimal parametric MSE rate of $O(1/N)$ can be achieved. The computational complexity is shown to be $O(N)$, which is optimal. To the best of our knowledge, this is the first empirical divergence estimator that has optimal computational complexity and achieves the optimal parametric MSE estimation rate.
Effective presentation skills can help to succeed in business, career and academy. This paper presents the design of speech assessment during the oral presentation and the algorithm for speech evaluation based on criteria of optimal intonation. As the pace of the speech and its optimal intonation varies from language to language, developing an automatic identification of language during the presentation is required. Proposed algorithm was tested with presentations delivered in Kazakh language. For testing purposes the features of Kazakh phonemes were extracted using MFCC and PLP methods and created a Hidden Markov Model (HMM) [5], [5] of Kazakh phonemes. Kazakh vowel formants were defined and the correlation between the deviation rate in fundamental frequency and the liveliness of the speech to evaluate intonation of the presentation was analyzed. It was established that the threshold value between monotone and dynamic speech is 0.16 and the error for intonation evaluation is 19%.
The number of optimization techniques in the combinatorial domain is large and diversified. Nevertheless, real-world based benchmarks for testing algorithms are few. This work creates an extensible real-world mail delivery benchmark to the Vehicle Routing Problem (VRP) in a planar graph embedded in the 2D Euclidean space. Such problem is multi-objective on a roadmap with up to 25 vehicles and 30,000 deliveries per day. Each instance models one generic day of mail delivery, allowing both comparison and validation of optimization algorithms for routing problems. The benchmark may be extended to model other scenarios.
Money laundering is a crime that makes it possible to finance other crimes, for this reason, it is important for criminal organizations and their combat is prioritized by nations around the world. The anti-money laundering process has not evolved as expected because it has prioritized only the signaling of suspicious transactions. The constant increasing in the volume of transactions has overloaded the indispensable human work of final evaluation of the suspicions. This article presents a multiagent system that aims to go beyond the capture of suspicious transactions, seeking to assist the human expert in the analysis of suspicions. The agents created use data mining techniques to create transactional behavioral profiles; apply rules generated in learning process in conjunction with specific rules based on legal aspects and profiles created to capture suspicious transactions; and analyze these suspicious transactions indicating to the human expert those that require more detailed analysis.
ViZDoom is a robust, first-person shooter reinforcement learning environment, characterized by a significant degree of latent state information. In this paper, double-Q learning and prioritized experience replay methods are tested under a certain ViZDoom combat scenario using a competitive deep recurrent Q-network (DRQN) architecture. In addition, an ensembling technique known as snapshot ensembling is employed using a specific annealed learning rate to observe differences in ensembling efficacy under these two methods. Annealed learning rates are important in general to the training of deep neural network models, as they shake up the status-quo and counter a model's tending towards local optima. While both variants show performance exceeding those of built-in AI agents of the game, the known stabilizing effects of double-Q learning are illustrated, and priority experience replay is again validated in its usefulness by showing immediate results early on in agent development, with the caveat that value overestimation is accelerated in this case. In addition, some unique behaviors are observed to develop for priority experience replay (PER) and double-Q (DDQ) variants, and snapshot ensembling of both PER and DDQ proves a valuable method for improving performance of the ViZDoom Marine.
Languages shared by people differ in different regions based on their accents, pronunciation and word usages. In this era sharing of language takes place mainly through social media and blogs. Every second swing of such a micro posts exist which induces the need of processing those micro posts, in-order to extract knowledge out of it. Knowledge extraction differs with respect to the application in which the research on cognitive science fed the necessities for the same. This work further moves forward such a research by extracting semantic information of streaming and batch data in applications like Named Entity Recognition and Author Profiling. In the case of Named Entity Recognition context of a single micro post has been utilized and context that lies in the pool of micro posts were utilized to identify the sociolect aspects of the author of those micro posts. In this work Conditional Random Field has been utilized to do the entity recognition and a novel approach has been proposed to find the sociolect aspects of the author (Gender, Age group).
A common problem in machine learning is to rank a set of n items based on pairwise comparisons. Here ranking refers to partitioning the items into sets of pre-specified sizes according to their scores, which includes identification of the top-k items as the most prominent special case. The score of a given item is defined as the probability that it beats a randomly chosen other item. Finding an exact ranking typically requires a prohibitively large number of comparisons, but in practice, approximate rankings are often adequate. Accordingly, we study the problem of finding approximate rankings from pairwise comparisons. We analyze an active ranking algorithm that counts the number of comparisons won, and decides whether to stop or which pair of items to compare next, based on confidence intervals computed from the data collected in previous steps. We show that this algorithm succeeds in recovering approximate rankings using a number of comparisons that is close to optimal up to logarithmic factors. We also present numerical results, showing that in practice, approximation can drastically reduce the number of comparisons required to estimate a ranking.
For homeland and transportation security applications, 2D X-ray explosive detection system (EDS) have been widely used, but they have limitations in recognizing 3D shape of the hidden objects. Among various types of 3D computed tomography (CT) systems to address this issue, this paper is interested in a stationary CT using fixed X-ray sources and detectors. However, due to the limited number of projection views, analytic reconstruction algorithms produce severe streaking artifacts. Inspired by recent success of deep learning approach for sparse view CT reconstruction, here we propose a novel image and sinogram domain deep learning architecture for 3D reconstruction from very sparse view measurement. The algorithm has been tested with the real data from a prototype 9-view dual energy stationary CT EDS carry-on baggage scanner developed by GEMSS Medical Systems, Korea, which confirms the superior reconstruction performance over the existing approaches.
Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning. Both of these challenges severely limit the applicability of such methods to complex, real-world domains. In this paper, we propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. In this framework, the actor aims to maximize expected reward while also maximizing entropy - that is, succeed at the task while acting as randomly as possible. Prior deep RL methods based on this framework have been formulated as Q-learning methods. By combining off-policy updates with a stable stochastic actor-critic formulation, our method achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off-policy methods. Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving very similar performance across different random seeds.
To choose a multi-winner rule, i.e., a voting rule that selects a subset of $k$ alternatives based on preferences of a certain population, is a hard and ambiguous task. Depending on the context, it varies widely what constitutes an "optimal" committee. In this paper, we offer a new perspective to measure the quality of committees and---consequently---multi-winner rules. We provide a quantitative analysis using methods from the theory of approximation algorithms and estimate how well multi-winner rules approximate two extreme objectives: diversity as captured by the (Approval) Chamberlin--Courant rule (CC) and individual excellence as captured by Approval Voting (AV). With both theoretical and experimental methods we establish a classification of multi-winner rules in terms of their quantitative alignment with these two opposing objectives.
Deep learning has achieved impressive results on many problems. However, it requires high degree of expertise or a lot of experience to tune well the hyperparameters, and such manual tuning process is likely to be biased. Moreover, it is not practical to try out as many different hyperparameter configurations in deep learning as in other machine learning scenarios, because evaluating each single hyperparameter configuration in deep learning would mean training a deep neural network, which usually takes quite long time. Hyperband algorithm achieves state-of-the-art performance on various hyperparameter optimization problems in the field of deep learning. However, Hyperband algorithm does not utilize history information of previous explored hyperparameter configurations, thus the solution found is suboptimal. We propose to combine Hyperband algorithm with Bayesian optimization (which does not ignore history when sampling next trial configuration). Experimental results show that our combination approach is superior to other hyperparameter optimization approaches including Hyperband algorithm.
In this paper, we use the witness-functions to analyze cryptographic protocols for secrecy under nonempty equational theories. The witness-functions are safe metrics used to compute security. An analysis with a witness-function consists in making sure that the security of every atomic message does not decrease during its lifecycle in the protocol. The analysis gets more difficult under nonempty equational theories. Indeed, the intruder can take advantage of the algebraic properties of the cryptographic primitives to derive secrets. These properties arise from the use of mathematical functions, such as multiplication, addition, exclusive-or or modular exponentiation in the cryptosystems and the protocols. Here, we show how to use the witness-functions under nonempty equational theories and we run an analysis on the Needham-Schroeder-Lowe protocol under the cipher homomorphism. This analysis reveals that although this protocol is proved secure under the perfect encryption assumption, its security collapses under the homomorphic primitives. We show how the witness-functions help to illustrate an attack scenario on it and we propose an amended version to fix it.
Evaluating pairwise comparisons breaks down complex decision problems into tractable ones. Pairwise comparison matrices (PCMs) are regularly used to solve multiple-criteria decision-making (MCDM) problems using Saaty's analytic hierarchy process (AHP) framework. There are two significant drawbacks of using PCMs. First, humans evaluate PCM in an inconsistent manner. Second, PCMs of large problems often have missing entries. We address these two issues by first establishing a novel connection between PCMs and time-irreversible Markov processes. Specifically, we show that every PCM induces a family of dissipative maximum path entropy random walks (MERW) over the set of alternatives. We show that only `consistent' PCMs correspond to detailed balanced MERWs. We identify the non-equilibrium entropy production in the induced MERWs as a metric of inconsistency of the underlying PCMs. Notably, the entropy production satisfies all of the recently laid out criteria for reasonable consistency indices. We also propose an approach to use incompletely filled PCMs in AHP. Potential future avenues are discussed as well.
The Graph Brain Project is an experiment in how the use of automated mathematical discovery software, databases, large collaboration, and systematic investigation provide a model for how mathematical research might proceed in the future. Our Project began with the development of a program that can be used to generate invariant-relation and property-relation conjectures in many areas of mathematics. This program can produce conjectures which are not implied by existing (published) theorems. Here we propose a new approach to push forward existing mathematical research goals---using automated mathematical discovery software. We suggest how to initiate and harness large-scale collaborative mathematics. We envision mathematical research labs similar to what exist in other sciences, new avenues for funding, new opportunities for training students, and a more efficient and effective use of published mathematical research. And our experiment in graph theory can be imitated in many other areas of mathematical research. Big Mathematics is the idea of large, systematic, collaborative research on problems of existing mathematical interest. What is possible when we put our skills, tools, and results together systematically?
The Semantic Web is becoming a large scale framework that enables data to be published, shared, and reused in the form of ontologies. The ontology which is considered as basic building block of semantic web consists of two layers including data and schema layer. With the current exponential development of ontologies in both data size and complexity of schemas, ontology understanding which is playing an important role in different tasks such as ontology engineering, ontology learning, etc., is becoming more difficult. Ontology summarization as a way to distill knowledge from an ontology and generate an abridge version to facilitate a better understanding is getting more attention recently. There are various approaches available for ontology summarization which are focusing on different measures in order to produce a proper summary for a given ontology. In this paper, we mainly focus on the common metrics which are using for ontology summarization and meet the state-of-the-art in ontology summarization.
Inductive inference is the process of extracting general rules from specific observations. This problem also arises in the analysis of biological networks, such as genetic regulatory networks, where the interactions are complex and the observations are incomplete. A typical task in these problems is to extract general interaction rules as combinations of Boolean covariates, that explain a measured response variable. The inductive inference process can be considered as an incompletely specified Boolean function synthesis problem. This incompleteness of the problem will also generate spurious inferences, which are a serious threat to valid inductive inference rules. Using random Boolean data as a null model, here we attempt to measure the competition between valid and spurious inductive inference rules from a given data set. We formulate two greedy search algorithms, which synthesize a given Boolean response variable in a sparse disjunct normal form, and respectively a sparse generalized algebraic normal form of the variables from the observation data, and we evaluate numerically their performance.
Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM) networks have demonstrated state-of-the-art accuracy in several emerging Artificial Intelligence tasks. However, the models are becoming increasingly demanding in terms of computational and memory load. Emerging latency-sensitive applications including mobile robots and autonomous vehicles often operate under stringent computation time constraints. In this paper, we address the challenge of deploying computationally demanding LSTMs at a constrained time budget by introducing an approximate computing scheme that combines iterative low-rank compression and pruning, along with a novel FPGA-based LSTM architecture. Combined in an end-to-end framework, the approximation method's parameters are optimised and the architecture is configured to address the problem of high-performance LSTM execution in time-constrained applications. Quantitative evaluation on a real-life image captioning application indicates that the proposed methods required up to 6.5x less time to achieve the same application-level accuracy compared to a baseline method, while achieving an average of 25x higher accuracy under the same computation time constraints.
Indian regional movie dataset is the first database of regional Indian movies, users and their ratings. It consists of movies belonging to 18 different Indian regional languages and metadata of users with varying demographics. Through this dataset, the diversity of Indian regional cinema and its huge viewership is captured. We analyze the dataset that contains roughly 10K ratings of 919 users and 2,851 movies using some supervised and unsupervised collaborative filtering techniques like Probabilistic Matrix Factorization, Matrix Completion, Blind Compressed Sensing etc. The dataset consists of metadata information of users like age, occupation, home state and known languages. It also consists of metadata of movies like genre, language, release year and cast. India has a wide base of viewers which is evident by the large number of movies released every year and the huge box-office revenue. This dataset can be used for designing recommendation systems for Indian users and regional movies, which do not, yet, exist. The dataset can be downloaded from \href{https://goo.gl/EmTPv6}{https://goo.gl/EmTPv6}.
Recent work in deep reinforcement learning has allowed algorithms to learn complex tasks such as Atari 2600 games just from the reward provided by the game, but these algorithms presently require millions of training steps in order to learn, making them approximately five orders of magnitude slower than humans. One reason for this is that humans build robust shared representations that are applicable to collections of problems, making it much easier to assimilate new variants. This paper first introduces the idea of automatically-generated game sets to aid in transfer learning research, and then demonstrates the utility of shared representations by showing that models can substantially benefit from the incorporation of relevant architectural priors. This technique affords a remarkable 50x positive transfer on a toy problem-set.
This paper investigates the following problem: how to find a GSMem malicious activity effectively. To this end, this paper puts forward a new method based on Artificial Intelligence (AI). At first, we use a large quantity of data in terms of frequencies and amplitudes of some electromagnetic waves to train our models. And then, we input a given frequency and amplitude into the obtained models, predicting that whether a GSMem malicious activity occurs or not. The simulated experiments show that the new method is potential to detect a GSMem one, with low False Positive Rates (FPR) and low False Negative Rates (FNR).
We present a micro-traffic simulation (named "DeepTraffic") where the perception, control, and planning systems for one of the cars are all handled by a single neural network as part of a model-free, off-policy reinforcement learning process. The primary goal of DeepTraffic is to make the hands-on study of deep reinforcement learning accessible to thousands of students, educators, and researchers in order to inspire and fuel the exploration and evaluation of DQN variants and hyperparameter configurations through large-scale, open competition. This paper investigates the crowd-sourced hyperparameter tuning of the policy network that resulted from the first iteration of the DeepTraffic competition where thousands of participants actively searched through the hyperparameter space with the objective of their neural network submission to make it onto the top-10 leaderboard.
We present a study in Distributed Deep Reinforcement Learning (DDRL) focused on scalability of a state-of-the-art Deep Reinforcement Learning algorithm known as Batch Asynchronous Advantage ActorCritic (BA3C). We show that using the Adam optimization algorithm with a batch size of up to 2048 is a viable choice for carrying out large scale machine learning computations. This, combined with careful reexamination of the optimizer's hyperparameters, using synchronous training on the node level (while keeping the local, single node part of the algorithm asynchronous) and minimizing the memory footprint of the model, allowed us to achieve linear scaling for up to 64 CPU nodes. This corresponds to a training time of 21 minutes on 768 CPU cores, as opposed to 10 hours when using a single node with 24 cores achieved by a baseline single-node implementation.
This paper demonstrates the development of ontology for satellite databases. First, I create a computational ontology for the Union of Concerned Scientists (UCS) Satellite Database (UCSSD for short), called the UCS Satellite Ontology (or UCSSO). Second, in developing UCSSO I show that The Space Situational Awareness Ontology (SSAO) (Rovetto and Kelso 2016)--an existing space domain reference ontology--and related ontology work by the author (Rovetto 2015, 2016) can be used either (i) with a database-specific local ontology such as UCSSO, or (ii) in its stead. In case (i), local ontologies such as UCSSO can reuse SSAO terms, perform term mappings, or extend it. In case (ii), the author's orbital space ontology work, such as the SSAO, is usable by the UCSSD and organizations with other space object catalogs, as a reference ontology suite providing a common semantically-rich domain model. The SSAO, UCSSO, and the broader Orbital Space Environment Domain Ontology project is online at http://purl.org/space-ontology and GitHub. This ontology effort aims, in part, to provide accurate formal representations of the domain for various applications. Ontology engineering has the potential to facilitate the sharing and integration of satellite data from federated databases and sensors for safer spaceflight.
We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. In a single framework, we integrated semantic data mapping and neural embedding technique to produce a text processing method that extracts relevant information from heterogeneous types of clinical notes in an unsupervised manner, and we designed a recurrent neural network to model the temporal dependency of the patient visits. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset (1818 patients). Our method achieved an area under the ROC curve (AUC) of 0.89. To provide explain-ability, we developed an interactive graphical tool that may improve physician understanding of the basis for the model's predictions. The high accuracy and explain-ability of the PPES-Met model may enable our model to be used as a decision support tool to personalize metastatic cancer treatment and provide valuable assistance to the physicians.
Experience replay allows a reinforcement learning agent to train on samples from a large amount of the most recent experiences. A simple in-RAM experience replay stores these most recent experiences in a list in RAM, and then copies sampled batches to the GPU for training. I moved this list to the GPU, thus creating an in-GPU experience replay, and a training step that no longer has inputs copied from the CPU. I trained an agent to play Super Smash Bros. Melee, using internal game memory values as inputs and outputting controller button presses. A single state in Melee contains 27 floats, so the full experience replay fits on a single GPU. For a batch size of 128, the in-GPU experience replay trained twice as fast as the in-RAM experience replay. As far as I know, this is the first in-GPU implementation of experience replay. Finally, I note a few ideas for fitting the experience replay inside the GPU when the environment state requires more memory.
We present a formalization and computational implementation of the second formulation of Kant's categorical imperative. This ethical principle requires an agent to never treat someone merely as a means but always also as an end. Here we interpret this principle in terms of how persons are causally affected by actions. We introduce Kantian causal agency models in which moral patients, actions, goals, and causal influence are represented, and we show how to formalize several readings of Kant's categorical imperative that correspond to Kant's concept of strict and wide duties towards oneself and others. Stricter versions handle cases where an action directly causally affects oneself or others, whereas the wide version maximizes the number of persons being treated as an end. We discuss limitations of our formalization by pointing to one of Kant's cases that the machinery cannot handle in a satisfying way.
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for each graph data while training. To efficiently learn the graph, a distance metric learning is proposed. Extensive experiments on nine graph-structured datasets have demonstrated the superior performance improvement on both convergence speed and predictive accuracy.
Current crowdsourcing platforms provide little support for worker feedback. Workers are sometimes invited to post free text describing their experience and preferences in completing tasks. They can also use forums such as Turker Nation1 to exchange preferences on tasks and requesters. In fact, crowdsourcing platforms rely heavily on observing workers and inferring their preferences implicitly. In this work, we believe that asking workers to indicate their preferences explicitly improve their experience in task completion and hence, the quality of their contributions. Explicit elicitation can indeed help to build more accurate worker models for task completion that captures the evolving nature of worker preferences. We design a worker model whose accuracy is improved iteratively by requesting preferences for task factors such as required skills, task payment, and task relevance. We propose a generic framework, develop efficient solutions in realistic scenarios, and run extensive experiments that show the benefit of explicit preference elicitation over implicit ones with statistical significance.
Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is known in advance and does not change during learning. This is an unrealistic assumption in many scenarios, because new evidence can reveal important information about what is possible, possibilities that the agent was not aware existed prior to learning. We present a model of an agent which both discovers and learns to exploit unforeseen possibilities using two sources of evidence: direct interaction with the world and communication with a domain expert. We use a combination of probabilistic and symbolic reasoning to estimate all components of the decision problem, including its set of random variables and their causal dependencies. Agent simulations show that the agent converges on optimal polices even when it starts out unaware of factors that are critical to behaving optimally.
We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far. Then, we synthesize new programs and add them to the priority queue by sampling from the RNN. We benchmark our algorithm, called priority queue training (or PQT), against genetic algorithm and reinforcement learning baselines on a simple but expressive Turing complete programming language called BF. Our experimental results show that our simple PQT algorithm significantly outperforms the baselines. By adding a program length penalty to the reward function, we are able to synthesize short, human readable programs.
Monte Carlo inference has asymptotic guarantees, but can be slow when using generic proposals. Handcrafted proposals that rely on user knowledge about the posterior distribution can be efficient, but are difficult to derive and implement. This paper proposes to let users express their posterior knowledge in the form of proposal programs, which are samplers written in probabilistic programming languages. One strategy for writing good proposal programs is to combine domain-specific heuristic algorithms with neural network models. The heuristics identify high probability regions, and the neural networks model the posterior uncertainty around the outputs of the algorithm. Proposal programs can be used as proposal distributions in importance sampling and Metropolis-Hastings samplers without sacrificing asymptotic consistency, and can be optimized offline using inference compilation. Support for optimizing and using proposal programs is easily implemented in a sampling-based probabilistic programming runtime. The paper illustrates the proposed technique with a proposal program that combines RANSAC and neural networks to accelerate inference in a Bayesian linear regression with outliers model.
Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined. We propose to evaluate dialog quality using topic-based metrics that describe the ability of a conversational bot to sustain coherent and engaging conversations on a topic, and the diversity of topics that a bot can handle. To detect conversation topics per utterance, we adopt Deep Average Networks (DAN) and train a topic classifier on a variety of question and query data categorized into multiple topics. We propose a novel extension to DAN by adding a topic-word attention table that allows the system to jointly capture topic keywords in an utterance and perform topic classification. We compare our proposed topic based metrics with the ratings provided by users and show that our metrics both correlate with and complement human judgment. Our analysis is performed on tens of thousands of real human-bot dialogs from the Alexa Prize competition and highlights user expectations for conversational bots.
In order to answer natural language questions over knowledge graphs, most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking has been performed either as dependent sequential tasks or independent parallel tasks. In this paper, we propose a framework called "EARL", which performs entity linking and relation linking as a joint single task. EARL uses a graph connection based solution to the problem. We model the linking task as an instance of the Generalised Travelling Salesman Problem (GTSP) and use GTSP approximate algorithm solutions. We later develop EARL which uses a pair-wise graph-distance based solution to the problem.The system determines the best semantic connection between all keywords of the question by referring to a knowledge graph. This is achieved by exploiting the "connection density" between entity candidates and relation candidates. The "connection density" based solution performs at par with the approximate GTSP solution.We have empirically evaluated the framework on a dataset with 5000 questions. Our system surpasses state-of-the-art scores for entity linking task by reporting an accuracy of 0.65 to 0.40 from the next best entity linker.
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a similarity space and concepts are represented by convex regions in this space. After pointing out a problem with the convexity requirement, we propose a formalization of conceptual spaces based on fuzzy star-shaped sets. Our formalization uses a parametric definition of concepts and extends the original framework by adding means to represent correlations between different domains in a geometric way. Moreover, we define various operations for our formalization, both for creating new concepts from old ones and for measuring relations between concepts. We present an illustrative toy-example and sketch a research project on concept formation that is based on both our formalization and its implementation.
Deep reinforcement learning has achieved great strides in solving challenging motion control tasks. Recently, there has been significant work on methods for exploiting the data gathered during training, but there has been less work on how to best generate the data to learn from. For continuous action domains, the most common method for generating exploratory actions involves sampling from a Gaussian distribution centred around the mean action output by a policy. Although these methods can be quite capable, they do not scale well with the dimensionality of the action space, and can be dangerous to apply on hardware. We consider learning a forward dynamics model to predict the result, ($x_{t+1}$), of taking a particular action, ($u$), given a specific observation of the state, ($x_{t}$). With this model we perform internal look-ahead predictions of outcomes and seek actions we believe have a reasonable chance of success. This method alters the exploratory action space, thereby increasing learning speed and enables higher quality solutions to difficult problems, such as robotic locomotion and juggling.
Learning of user preferences, as represented by, for example, Conditional Preference Networks (CP-nets), has become a core issue in AI research. Recent studies investigate learning of CP-nets from randomly chosen examples or from membership and equivalence queries. To assess the optimality of learning algorithms as well as to better understand the combinatorial structure of classes of CP-nets, it is helpful to calculate certain learning-theoretic information complexity parameters. This paper determines bounds on or exact values of some of the most central information complexity parameters, namely the VC dimension, the (recursive) teaching dimension, the self-directed learning complexity, and the optimal mistake bound, for classes of acyclic CP-nets. We further provide an algorithm that learns tree-structured CP-nets from membership queries. Using our results on complexity parameters, we assess the optimality of our algorithm as well as that of another query learning algorithm for acyclic CP-nets presented in the literature. Our algorithm is near-optimal, and can, under certain assumptions be adapted to the case when the membership oracle is faulty.
Trust is essential for human-robot collaboration and user adoption of autonomous systems, such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human behaviors, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table-clearing task in simulation (201 participants) and with a real robot (20 participants). The results show that the trust-POMDP improves human-robot team performance in this task. They further suggest that maximizing trust in itself may not improve team performance.
Neural programming involves training neural networks to learn programs from data. Previous works have failed to achieve good generalization performance, especially on programs with high complexity or on large domains. This is because they mostly rely either on black-box function evaluations that do not capture the structure of the program, or on detailed execution traces that are expensive to obtain, and hence the training data has poor coverage of the domain under consideration. We present a novel framework that utilizes black-box function evaluations, in conjunction with symbolic expressions that integrate relationships between the given functions. We employ tree LSTMs to incorporate the structure of the symbolic expression trees. We use tree encoding for numbers present in function evaluation data, based on their decimal representation. We present an evaluation benchmark for this task to demonstrate our proposed model combines symbolic reasoning and function evaluation in a fruitful manner, obtaining high accuracies in our experiments. Our framework generalizes significantly better to expressions of higher depth and is able to fill partial equations with valid completions.
We introduce a new computational model of moral decision making, drawing on a recent theory of commonsense moral learning via social dynamics. Our model describes moral dilemmas as a utility function that computes trade-offs in values over abstract moral dimensions, which provide interpretable parameter values when implemented in machine-led ethical decision-making. Moreover, characterizing the social structures of individuals and groups as a hierarchical Bayesian model, we show that a useful description of an individual's moral values - as well as a group's shared values - can be inferred from a limited amount of observed data. Finally, we apply and evaluate our approach to data from the Moral Machine, a web application that collects human judgments on moral dilemmas involving autonomous vehicles.
Automated decision making systems are increasingly being used in real-world applications. In these systems for the most part, the decision rules are derived by minimizing the training error on the available historical data. Therefore, if there is a bias related to a sensitive attribute such as gender, race, religion, etc. in the data, say, due to cultural/historical discriminatory practices against a certain demographic, the system could continue discrimination in decisions by including the said bias in its decision rule. We present an information theoretic framework for designing fair predictors from data, which aim to prevent discrimination against a specified sensitive attribute in a supervised learning setting. We use equalized odds as the criterion for discrimination, which demands that the prediction should be independent of the protected attribute conditioned on the actual label. To ensure fairness and generalization simultaneously, we compress the data to an auxiliary variable, which is used for the prediction task. This auxiliary variable is chosen such that it is decontaminated from the discriminatory attribute in the sense of equalized odds. The final predictor is obtained by applying a Bayesian decision rule to the auxiliary variable.
Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn a generative image model of all 1000 Imagenet classes in a single GAN with little hyperparameter tuning.
Apart from few exceptions, the mathematical runtime analysis of evolutionary algorithms is mostly concerned with expected runtimes. In this work, we argue that stochastic domination is a notion that should be used more frequently in this area. Stochastic domination allows to formulate much more informative performance guarantees than the expectation alone, it allows to decouple the algorithm analysis into the true algorithmic part of detecting a domination statement and probability theoretic part of deriving the desired probabilistic guarantees from this statement, and it allows simpler and more natural proofs. As particular results, we prove a fitness level theorem which shows that the runtime is dominated by a sum of independent geometric random variables, we prove tail bounds for several classic problems, and we give a short and natural proof for Witt's result that the runtime of any $(\mu,p)$ mutation-based algorithm on any function with unique optimum is subdominated by the runtime of a variant of the (1+1) evolutionary algorithm on the OneMax function.
ConvNets, through their architecture, only enforce invariance to translation. In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn \textit{general} invariances and symmetries directly from data. NPTNs are a natural generalization of ConvNets and can be optimized directly using gradient descent. Unlike almost all previous works in deep architectures, they make no assumption regarding the structure of the invariances present in the data and in that aspect are flexible and powerful. We also model ConvNets and NPTNs under a unified framework called Transformation Networks (TN), which yields a better understanding of the connection between the two. We demonstrate the efficacy of NPTNs on data such as MNIST and CIFAR10 where they outperform ConvNet baselines with the same number of parameters. We show it is more effective than ConvNets in modelling symmetries from data, without the explicit knowledge of the added arbitrary nuisance transformations. Finally, we replace ConvNets with NPTNs within Capsule Networks and show that this enables Capsule Nets to perform even better.
Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adhere to the false-positive rate constraint. In this paper, we propose a novel scoring-thresholding approach, tau-False Positive Learning (tau-FPL) to address this problem. We show the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed tau-FPL over existing approaches.
A large body of compelling evidence has been accumulated demonstrating that embodiment - the agent's physical setup, including its shape, materials, sensors and actuators - is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe.
We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with just a task schema and an API client from the dialogue system developer, but it is also customizable to cater to task-specific interactions. Compared to the Wizard-of-Oz approach for data collection, M2M achieves greater diversity and coverage of salient dialogue flows while maintaining the naturalness of individual utterances. In the first phase, a simulated user bot and a domain-agnostic system bot converse to exhaustively generate dialogue "outlines", i.e. sequences of template utterances and their semantic parses. In the second phase, crowd workers provide contextual rewrites of the dialogues to make the utterances more natural while preserving their meaning. The entire process can finish within a few hours. We propose a new corpus of 3,000 dialogues spanning 2 domains collected with M2M, and present comparisons with popular dialogue datasets on the quality and diversity of the surface forms and dialogue flows.
Topic modeling enables exploration and compact representation of a corpus. The CaringBridge (CB) dataset is a massive collection of journals written by patients and caregivers during a health crisis. Topic modeling on the CB dataset, however, is challenging due to the asynchronous nature of multiple authors writing about their health journeys. To overcome this challenge we introduce the Dynamic Author-Persona topic model (DAP), a probabilistic graphical model designed for temporal corpora with multiple authors. The novelty of the DAP model lies in its representation of authors by a persona --- where personas capture the propensity to write about certain topics over time. Further, we present a regularized variational inference algorithm, which we use to encourage the DAP model's personas to be distinct. Our results show significant improvements over competing topic models --- particularly after regularization, and highlight the DAP model's unique ability to capture common journeys shared by different authors.
We present extensive experiments training and testing hidden units in deep networks that emit only a predefined, static, number of discretized values. These units provide benefits in real-world deployment in systems in which memory and/or computation may be limited. Additionally, they are particularly well suited for use in large recurrent network models that require the maintenance of large amounts of internal state in memory. Surprisingly, we find that despite reducing the number of values that can be represented in the output activations from $2^{32}-2^{64}$ to between 64 and 256, there is little to no degradation in network performance across a variety of different settings. We investigate simple classification and regression tasks, as well as memorization and compression problems. We compare the results with more standard activations, such as tanh and relu. Unlike previous discretization studies which often concentrate only on binary units, we examine the effects of varying the number of allowed activation levels. Compared to existing approaches for discretization, the approach presented here is both conceptually and programatically simple, has no stochastic component, and allows the training, testing, and usage phases to be treated in exactly the same manner.
This paper proposes a novel adaptive algorithm for the automated short-term trading of financial instrument. The algorithm adopts a semantic sentiment analysis technique to inspect the Twitter posts and to use them to predict the behaviour of the stock market. Indeed, the algorithm is specifically developed to take advantage of both the sentiment and the past values of a certain financial instrument in order to choose the best investment decision. This allows the algorithm to ensure the maximization of the obtainable profits by trading on the stock market. We have conducted an investment simulation and compared the performance of our proposed with a well-known benchmark (DJTATO index) and the optimal results, in which an investor knows in advance the future price of a product. The result shows that our approach outperforms the benchmark and achieves the performance score close to the optimal result.
Feature engineering is one of the most important but tedious tasks in data science projects. This work studies automation of feature learning for relational data. We first theoretically proved that learning relevant features from relational data for a given predictive analytics problem is NP-hard. However, it is possible to empirically show that an efficient rule based approach predefining transformations as a priori based on heuristics can extract very useful features from relational data. Indeed, the proposed approach outperformed the state of the art solutions with a significant margin. We further introduce a deep neural network which automatically learns appropriate transformations of relational data into a representation that predicts the target variable well instead of being predefined as a priori by users. In an extensive experiment with Kaggle competitions, the proposed methods could win late medals. To the best of our knowledge, this is the first time an automation system could win medals in Kaggle competitions with complex relational data.
Control systems behavior can be analyzed taking into account a large number of parameters: performances, reliability, availability, security. Each control system presents various security vulnerabilities that affect in lower or higher measure its functioning. In this paper the authors present a method to assess the impact of security issues on the systems availability. A fuzzy model for estimating the availability of the system based on the security level and achieved availability coefficient (depending on MTBF and MTR) is developed and described. The results of the fuzzy inference system (FIS) are presented in the last section of the paper.
This paper concerns open-world classification, where the classifier not only needs to classify test examples into seen classes that have appeared in training but also reject examples from unseen or novel classes that have not appeared in training. Specifically, this paper focuses on discovering the hidden unseen classes of the rejected examples. Clearly, without prior knowledge this is difficult. However, we do have the data from the seen training classes, which can tell us what kind of similarity/difference is expected for examples from the same class or from different classes. It is reasonable to assume that this knowledge can be transferred to the rejected examples and used to discover the hidden unseen classes in them. This paper aims to solve this problem. It first proposes a joint open classification model with a sub-model for classifying whether a pair of examples belongs to the same or different classes. This sub-model can serve as a distance function for clustering to discover the hidden classes of the rejected examples. Experimental results show that the proposed model is highly promising.
Dempster-Shafer evidence theory has been widely used in various fields of applications. Besides, it has been proven that the quantum theory has powerful capabilities of solving the decision making problems. However, due to the inconsistency of the expression, the classical Dempster-Shafer evidence theory modelled by real numbers can not be integrated directly with the quantum theory modelled by complex numbers. The main contribution in this study is that, unlike the existing evidence theory, a mass function in the generalized Dempster-Shafer evidence theory is modelled by a complex number, called as a complex mass function. When the complex mass function is degenerated from complex numbers to real numbers, the generalized Dempster's combination rule degenerates to the classical evidence theory. This generalized Dempster-Shafer evidence theory provides a promising way to model and handle more uncertain information. Numerical examples are illustrated to show the efficiency of the generalized Dempster-Shafer evidence theory.
The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state of the art is limited to small systems. In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification.
With the demand for machine learning increasing, so does the demand for tools which make it easier to use. Automated machine learning (AutoML) tools have been developed to address this need, such as the Tree-Based Pipeline Optimization Tool (TPOT) which uses genetic programming to build optimal pipelines. We introduce Layered TPOT, a modification to TPOT which aims to create pipelines equally good as the original, but in significantly less time. This approach evaluates candidate pipelines on increasingly large subsets of the data according to their fitness, using a modified evolutionary algorithm to allow for separate competition between pipelines trained on different sample sizes. Empirical evaluation shows that, on sufficiently large datasets, Layered TPOT indeed finds better models faster.
We train multi-task autoencoders on linguistic tasks and analyze the learned hidden sentence representations. The representations change significantly when translation and part-of-speech decoders are added. The more decoders a model employs, the better it clusters sentences according to their syntactic similarity, as the representation space becomes less entangled. We explore the structure of the representation space by interpolating between sentences, which yields interesting pseudo-English sentences, many of which have recognizable syntactic structure. Lastly, we point out an interesting property of our models: The difference-vector between two sentences can be added to change a third sentence with similar features in a meaningful way.
A problem faced by many instructors is that of designing exams that accurately assess the abilities of the students. Typically these exams are prepared several days in advance, and generic question scores are used based on rough approximation of the question difficulty and length. For example, for a recent class taught by the author, there were 30 multiple choice questions worth 3 points, 15 true/false with explanation questions worth 4 points, and 5 analytical exercises worth 10 points. We describe a novel framework where algorithms from machine learning are used to modify the exam question weights in order to optimize the exam scores, using the overall class grade as a proxy for a student's true ability. We show that significant error reduction can be obtained by our approach over standard weighting schemes, and we make several new observations regarding the properties of the "good" and "bad" exam questions that can have impact on the design of improved future evaluation methods.
Training a task-completion dialogue agent with real users via reinforcement learning (RL) could be prohibitively expensive, because it requires many interactions with users. One alternative is to resort to a user simulator, while the discrepancy of between simulated and real users makes the learned policy unreliable in practice. This paper addresses these challenges by integrating planning into the dialogue policy learning based on Dyna-Q framework, and provides a more sample-efficient approach to learn the dialogue polices. The proposed agent consists of a planner trained on-line with limited real user experience that can generate large amounts of simulated experience to supplement with limited real user experience, and a policy model trained on these hybrid experiences. The effectiveness of our approach is validated on a movie-booking task in both a simulation setting and a human-in-the-loop setting.
Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure. Recently, an efficient quantum algorithm was proposed [Lloyd, Garnerone, Zanardi, Nat. Commun. 7, 10138 (2016)] for calculating Betti numbers of data points -- topological features that count the number of topological holes of various dimensions in a scatterplot. Here, we implement a proof-of-principle demonstration of this quantum algorithm by employing a six-photon quantum processor to successfully analyze the topological features of Betti numbers of a network including three data points, providing new insights into data analysis in the era of quantum computing.
Strict partial order is a mathematical structure commonly seen in relational data. One obstacle to extracting such type of relations at scale is the lack of large-scale labels for building effective data-driven solutions. We develop an active learning framework for mining such relations subject to a strict order. Our approach incorporates relational reasoning not only in finding new unlabeled pairs whose labels can be deduced from an existing label set, but also in devising new query strategies that consider the relational structure of labels. Our experiments on concept prerequisite relations show our proposed framework can substantially improve the classification performance with the same query budget compared to other baseline approaches.
Multi-view networks are ubiquitous in real-world applications. In order to extract knowledge or business value, it is of interest to transform such networks into representations that are easily machine-actionable. Meanwhile, network embedding has emerged as an effective approach to generate distributed network representations. Therefore, we are motivated to study the problem of multi-view network embedding, with a focus on the characteristics that are specific and important in embedding this type of networks. In our practice of embedding real-world multi-view networks, we identify two such characteristics, which we refer to as preservation and collaboration. We then explore the feasibility of achieving better embedding quality by simultaneously modeling preservation and collaboration, and propose the mvn2vec algorithms. With experiments on a series of synthetic datasets, an internal Snapchat dataset, and two public datasets, we further confirm the presence and importance of preservation and collaboration. These experiments also demonstrate that better embedding can be obtained by simultaneously modeling the two characteristics, while not over-complicating the model or requiring additional supervision.
We introduce a continuous-time analog solver for MaxSAT, a quintessential class of NP-hard discrete optimization problems, where the task is to find a truth assignment for a set of Boolean variables satisfying the maximum number of given logical constraints. We show that the scaling of an invariant of the solver's dynamics, the escape rate, as function of the number of unsatisfied clauses can predict the global optimum value, often well before reaching the corresponding state. We demonstrate the performance of the solver on hard MaxSAT competition problems. We then consider the two-color Ramsey number $R(m,m)$ problem, translate it to SAT, and apply our algorithm to the still unknown $R(5,5)$. We find edge colorings without monochromatic 5-cliques for complete graphs up to 42 vertices, while on 43 vertices we find colorings with only two monochromatic 5-cliques, the best coloring found so far, supporting the conjecture that $R(5,5) = 43$.
Displaying the large number of bands in a hyper spectral image on a trichromatic monitor has been an active research topic. The visualized image shall convey as much information as possible form the original data and facilitate image interpretation. Most existing methods display HSIs in false colors which contradict with human's experience and expectation. In this paper, we propose a nonlinear approach to visualize an input HSI with natural colors by taking advantage of a corresponding RGB image. Our approach is based on Moving Least Squares, an interpolation scheme for reconstructing a surface from a set of control points, which in our case is a set of matching pixels between the HSI and the corresponding RGB image. Based on MLS, the proposed method solves for each spectral signature a unique transformation so that the non linear structure of the HSI can be preserved. The matching pixels between a pair of HSI and RGB image can be reused to display other HSIs captured b the same imaging sensor with natural colors. Experiments show that the output image of the proposed method no only have natural colors but also maintain the visual information necessary for human analysis.
The seminal work of Chow and Liu (1968) shows that approximation of a finite probabilistic system by Markov trees can achieve the minimum information loss with the topology of a maximum spanning tree. Our current paper generalizes the result to Markov networks of tree width $\leq k$, for every fixed $k\geq 2$. In particular, we prove that approximation of a finite probabilistic system with such Markov networks has the minimum information loss when the network topology is achieved with a maximum spanning $k$-tree. While constructing a maximum spanning $k$-tree is intractable for even $k=2$, we show that polynomial algorithms can be ensured by a sufficient condition accommodated by many meaningful applications. In particular, we prove an efficient algorithm for learning the optimal topology of higher order correlations among random variables that belong to an underlying linear structure.
In this paper, we present a new approach to Transfer Learning (TL) in Reinforcement Learning (RL) for cross-domain tasks. Many of the available techniques approach the transfer architecture as a method of speeding up the target task learning. We propose to adapt and reuse the mapped source task optimal-policy directly in related domains. We show the optimal policy from a related source task can be near optimal in target domain provided an adaptive policy accounts for the model error between target and source. The main benefit of this policy augmentation is generalizing policies across multiple related domains without having to re-learn the new tasks. Our results show that this architecture leads to better sample efficiency in the transfer, reducing sample complexity of target task learning to target apprentice learning.
This paper is concerned with the sparsification of the input-hidden weights of ELM (Extreme Learning Machine). For ordinary feedforward neural networks, the sparsification is usually done by introducing certain regularization technique into the learning process of the network. But this strategy can not be applied for ELM, since the input-hidden weights of ELM are supposed to be randomly chosen rather than to be learned. To this end, we propose a modified ELM, called ELM-LC (ELM with local connections), which is designed for the sparsification of the input-hidden weights as follows: The hidden nodes and the input nodes are divided respectively into several corresponding groups, and an input node group is fully connected with its corresponding hidden node group, but is not connected with any other hidden node group. As in the usual ELM, the hidden-input weights are randomly given, and the hidden-output weights are obtained through a least square learning. In the numerical simulations on some benchmark problems, the new ELM-CL behaves better than the traditional ELM.
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes over the data. We show that optimal generalization error bounds can be retained for distributed SGM provided that the partition level is not too large. We then extend our results to spectral-regularization algorithms (SRA), including kernel ridge regression (KRR), kernel principal component analysis, and gradient methods. Our results are superior to the state-of-the-art theory. Particularly, our results show that distributed SGM has a smaller theoretical computational complexity, compared with distributed KRR and classic SGM. Moreover, even for non-distributed SRA, they provide the first optimal, capacity-dependent convergence rates, considering the case that the regression function may not be in the RKHS.
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i) condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.
This paper describes the application of comparison training (CT) for automatic feature weight tuning, with the final objective of improving the evaluation functions used in Chinese chess programs. First, we propose an n-tuple network to extract features, since n-tuple networks require very little expert knowledge through its large numbers of features, while simulta-neously allowing easy access. Second, we propose a novel evalua-tion method that incorporates tapered eval into CT. Experiments show that with the same features and the same Chinese chess program, the automatically tuned comparison training feature weights achieved a win rate of 86.58% against the weights that were hand-tuned. The above trained version was then improved by adding additional features, most importantly n-tuple features. This improved version achieved a win rate of 81.65% against the trained version without additional features.
We analyze the language learned by an agent trained with reinforcement learning as a component of the ActiveQA system [Buck et al., 2017]. In ActiveQA, question answering is framed as a reinforcement learning task in which an agent sits between the user and a black box question-answering system. The agent learns to reformulate the user's questions to elicit the optimal answers. It probes the system with many versions of a question that are generated via a sequence-to-sequence question reformulation model, then aggregates the returned evidence to find the best answer. This process is an instance of \emph{machine-machine} communication. The question reformulation model must adapt its language to increase the quality of the answers returned, matching the language of the question answering system. We find that the agent does not learn transformations that align with semantic intuitions but discovers through learning classical information retrieval techniques such as tf-idf re-weighting and stemming.
Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases. We present a framework for mitigating such biases by including a variable for the group of interest and simultaneously learning a predictor and an adversary. The input to the network X, here text or census data, produces a prediction Y, such as an analogy completion or income bracket, while the adversary tries to model a protected variable Z, here gender or zip code. The objective is to maximize the predictor's ability to predict Y while minimizing the adversary's ability to predict Z. Applied to analogy completion, this method results in accurate predictions that exhibit less evidence of stereotyping Z. When applied to a classification task using the UCI Adult (Census) Dataset, it results in a predictive model that does not lose much accuracy while achieving very close to equality of odds (Hardt, et al., 2016). The method is flexible and applicable to multiple definitions of fairness as well as a wide range of gradient-based learning models, including both regression and classification tasks.
Facial analysis technologies have recently measured up to the capabilities of expert clinicians in syndrome identification. To date, these technologies could only identify phenotypes of a few diseases, limiting their role in clinical settings where hundreds of diagnoses must be considered. We developed a facial analysis framework, DeepGestalt, using computer vision and deep learning algorithms, that quantifies similarities to hundreds of genetic syndromes based on unconstrained 2D images. DeepGestalt is currently trained with over 26,000 patient cases from a rapidly growing phenotype-genotype database, consisting of tens of thousands of validated clinical cases, curated through a community-driven platform. DeepGestalt currently achieves 91% top-10-accuracy in identifying over 215 different genetic syndromes and has outperformed clinical experts in three separate experiments. We suggest that this form of artificial intelligence is ready to support medical genetics in clinical and laboratory practices and will play a key role in the future of precision medicine.
Clustering is a fundamental machine learning method. The quality of its results is dependent on the data distribution. For this reason, deep neural networks can be used for learning better representations of the data. In this paper, we propose a systematic taxonomy for clustering with deep learning, in addition to a review of methods from the field. Based on our taxonomy, creating new methods is more straightforward. We also propose a new approach which is built on the taxonomy and surpasses some of the limitations of some previous work. Our experimental evaluation on image datasets shows that the method approaches state-of-the-art clustering quality, and performs better in some cases.
Based on the observation that semantic segmentation errors are partially predictable, we propose a compact formulation using confusion statistics of the trained classifier to refine (re-estimate) the initial pixel label hypotheses. The proposed strategy is contingent upon computing the classifier confusion probabilities for a given dataset and estimating a relevant prior on the object classes present in the image to be classified. We provide a procedure to robustly estimate the confusion probabilities and explore multiple prior definitions. Experiments are shown comparing performances on multiple challenging datasets using different priors to improve a state-of-the-art semantic segmentation classifier. This study demonstrates the potential to significantly improve semantic labeling and motivates future work for reliable label prior estimation from images.
Inspired by the matching of supply to demand in logistical problems, the optimal transportation (or Monge-Kantorovich) problem involves the matching of probability distributions defined over a geometric domain such as a surface or manifold. After discretization, optimal transportation becomes a large-scale linear program, which typically is infeasible to solve efficiently on triangle meshes, graphs, point clouds, and other domains encountered in graphics and machine learning. Recent breakthroughs in numerical optimal transportation enable scalability to orders-of-magnitude larger problems, solvable in a fraction of a second. In these lecture notes, we discuss advances in numerical optimal transport that leverage understanding of both discrete and smooth aspects of the problem. State-of-the-art techniques in discrete optimal transportation combine insight from partial differential equations (PDE) with convex analysis to reformulate, discretize, and optimize transportation problems. The end result is a set of theoretically-justified models suitable for domains with thousands or millions of vertices. Since numerical optimal transport is a relatively new discipline, special emphasis is placed on identifying and explaining open problems in need of mathematical insight and additional research.
We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point cloud are irregular and unordered, thus a direct convolving of kernels against the features associated with the points will result in deserting the shape information while being variant to the orders. To address these problems, we propose to learn a X-transformation from the input points, and then use it to simultaneously weight the input features associated with the points and permute them into latent potentially canonical order, before the element-wise product and sum operations are applied. The proposed method is a generalization of typical CNNs into learning features from point cloud, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.
The evaluation of interactive machine learning systems remains a difficult task. These systems learn from and adapt to the human, but at the same time, the human receives feedback and adapts to the system. Getting a clear understanding of these subtle mechanisms of co-operation and co-adaptation is challenging. In this chapter, we report on our experience in designing and evaluating various interactive machine learning applications from different domains. We argue for coupling two types of validation: algorithm-centered analysis, to study the computational behaviour of the system; and human-centered evaluation, to observe the utility and effectiveness of the application for end-users. We use a visual analytics application for guided search, built using an interactive evolutionary approach, as an exemplar of our work. Our observation is that human-centered design and evaluation complement algorithmic analysis, and can play an important role in addressing the "black-box" effect of machine learning. Finally, we discuss research opportunities that require human-computer interaction methodologies, in order to support both the visible and hidden roles that humans play in interactive machine learning.
Geometric analysis is a very capable theory to understand the influence of the high dimensionality of the input data in machine learning (ML) and knowledge discovery (KD). With our approach we can assess how far the application of a specific KD/ML-algorithm to a concrete data set is prone to the curse of dimensionality. To this end we extend V.~Pestov's axiomatic approach to the instrinsic dimension of data sets, based on the seminal work by M.~Gromov on concentration phenomena, and provide an adaptable and computationally feasible model for studying observable geometric invariants associated to features that are natural to both the data and the learning procedure. In detail, we investigate data represented by formal contexts and give first theoretical as well as experimental insights into the intrinsic dimension of a concept lattice. Because of the correspondence between formal concepts and maximal cliques in graphs, applications to social network analysis are at hand.
Although Recurrent Neural Network (RNN) has been a powerful tool for modeling sequential data, its performance is inadequate when processing sequences with multiple patterns. In this paper, we address this challenge by introducing an external memory and constructing a novel persistent memory augmented RNN (termed as PRNN). The PRNN captures the principle patterns in training sequences and stores them in an external memory. By leveraging the persistent memory, the proposed method can adaptively update states according to the similarities between encoded inputs and memory slots, leading to a stronger capacity in assimilating sequences with multiple patterns. Content-based addressing is suggested in memory accessing, and gradient descent is utilized for implicitly updating the memory. Our approach can be further extended by combining the prior knowledge of data. Experiments on several datasets demonstrate the effectiveness of the proposed method.
In this paper, we address referring expression comprehension: localizing an image region described by a natural language expression. While most recent work treats expressions as a single unit, we propose to decompose them into three modular components related to subject appearance, location, and relationship to other objects. This allows us to flexibly adapt to expressions containing different types of information in an end-to-end framework. In our model, which we call the Modular Attention Network (MAttNet), two types of attention are utilized: language-based attention that learns the module weights as well as the word/phrase attention that each module should focus on; and visual attention that allows the subject and relationship modules to focus on relevant image components. Module weights combine scores from all three modules dynamically to output an overall score. Experiments show that MAttNet outperforms previous state-of-art methods by a large margin on both bounding-box-level and pixel-level comprehension tasks. Demo and code are provided.
In this paper, we study the problem of discovering the Markov blanket (MB) of a target variable from multiple interventional datasets. Datasets attained from interventional experiments contain richer causal information than passively observed data (observational data) for MB discovery. However, almost all existing MB discovery methods are designed for finding MBs from a single observational dataset. To identify MBs from multiple interventional datasets, we face two challenges: (1) unknown intervention variables; (2) nonidentical data distributions. To tackle the challenges, we theoretically analyze (a) under what conditions we can find the correct MB of a target variable, and (b) under what conditions we can identify the causes of the target variable via discovering its MB. Based on the theoretical analysis, we propose a new algorithm for discovering MBs from multiple interventional datasets, and present the conditions/assumptions which assure the correctness of the algorithm. To our knowledge, this work is the first to present the theoretical analyses about the conditions for MB discovery in multiple interventional datasets and the algorithm to find the MBs in relation to the conditions. Using benchmark Bayesian networks and real-world datasets, the experiments have validated the effectiveness and efficiency of the proposed algorithm in the paper.
To solve the text-based question and answering task that requires relational reasoning, it is necessary to memorize a large amount of information and find out the question relevant information from the memory. Most approaches were based on external memory and four components proposed by Memory Network. The distinctive component among them was the way of finding the necessary information and it contributes to the performance. Recently, a simple but powerful neural network module for reasoning called Relation Network (RN) has been introduced. We analyzed RN from the view of Memory Network, and realized that its MLP component is able to reveal the complicate relation between question and object pair. Motivated from it, we introduce which uses MLP to find out relevant information on Memory Network architecture. It shows new state-of-the-art results in jointly trained bAbI-10k story-based question answering tasks and bAbI dialog-based question answering tasks.
This paper presents a novel hybrid algorithm named Since Cosine Crow Search Algorithm. To propose the SCCSA, two novel algorithms are considered including Crow Search Algorithm (CSA) and Since Cosine Algorithm (SCA). The advantages of the two algorithms are considered and utilize to design an efficient hybrid algorithm which can perform significantly better in various benchmark functions. The combination of concept and operators of the two algorithms enable the SCCSA to make an appropriate trade-off between exploration and exploitation abilities of the algorithm. To evaluate the performance of the proposed SCCSA, seven well-known benchmark functions are utilized. The results indicated that the proposed hybrid algorithm is able to provide very competitive solution comparing to other state-of-the-art meta heuristics.
This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: (1) identification of novel regulatory variants located in noncoding domains and their function as applied to pharmacoepigenomics; (2) patient stratification from medical records; and (3) prediction of drugs, targets, and their interactions. Deep learning encapsulates a family of machine learning algorithms that over the last decade has transformed many important subfields of artificial intelligence (AI) and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical, and demographic datasets.
Knowledge graphs contain rich relational structures of the world, and thus complement data-driven machine learning in heterogeneous data. One of the most effective methods in representing knowledge graphs is to embed symbolic relations and entities into continuous spaces, where relations are approximately linear translation between projected images of entities in the relation space. However, state-of-the-art relation projection methods such as TransR, TransD or TransSparse do not model the correlation between relations, and thus are not scalable to complex knowledge graphs with thousands of relations, both in computational demand and in statistical robustness. To this end we introduce TransF, a novel translation-based method which mitigates the burden of relation projection by explicitly modeling the basis subspaces of projection matrices. As a result, TransF is far more light weight than the existing projection methods, and is robust when facing a high number of relations. Experimental results on the canonical link prediction task show that our proposed model outperforms competing rivals by a large margin and achieves state-of-the-art performance. Especially, TransF improves by 9%/5% in the head/tail entity prediction task for N-to-1/1-to-N relations over the best performing translation-based method.
We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be violated. We show how to exploit the typically smooth dynamics of these systems and enable RL algorithms to never violate constraints during learning. Our technique is to directly add to the policy a safety layer that analytically solves an action correction formulation per each state. The novelty of obtaining an elegant closed-form solution is attained due to a linearized model, learned on past trajectories consisting of arbitrary actions. This is to mimic the real-world circumstances where data logs were generated with a behavior policy that is implausible to describe mathematically; such cases render the known safety-aware off-policy methods inapplicable. We demonstrate the efficacy of our approach on new representative physics-based environments, and prevail where reward shaping fails by maintaining zero constraint violations.
Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential in among others successfully playing computer games. However, there only exists a few game platforms that provide diversity in tasks and state-space needed to advance RL algorithms. The existing platforms offer RL access to Atari- and a few web-based games, but no platform fully expose access to Flash games. This is unfortunate because applying RL to Flash games have potential to push the research of RL algorithms. This paper introduces the Flash Reinforcement Learning platform (FlashRL) which attempts to fill this gap by providing an environment for thousands of Flash games on a novel platform for Flash automation. It opens up easy experimentation with RL algorithms for Flash games, which has previously been challenging. The platform shows excellent performance with as little as 5% CPU utilization on consumer hardware. It shows promising results for novel reinforcement learning algorithms.
We introduce a new formal model -- based on the mathematical construct of sheaves -- for representing contradictory information in textual sources. This model has the advantage of letting us (a) identify the causes of the inconsistency; (b) measure how strong it is; (c) and do something about it, e.g. suggest ways to reconcile inconsistent advice. This model naturally represents the distinction between contradictions and disagreements. It is based on the idea of representing natural language sentences as formulas with parameters sitting on lattices, creating partial orders based on predicates shared by theories, and building sheaves on these partial orders with products of lattices as stalks. Degrees of disagreement are measured by the existence of global and local sections. Limitations of the sheaf approach and connections to recent work in natural language processing, as well as the topics of contextuality in physics, data fusion, topological data analysis and epistemology are also discussed.
This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexity in heterogeneous disease progression and treatment choices, with the goal of providing doctor and patients the data-driven personalized decision recommendations. The proposed DRL framework comprises (i) a supervised learning step to predict the most possible expert actions, and (ii) a deep reinforcement learning step to estimate the long-term value function of Dynamic Treatment Regimes. Both steps depend on deep neural networks. As a key motivational example, we have implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease after transplantation. In the experimental results, we have demonstrated promising accuracy in predicting human experts' decisions, as well as the high expected reward function in the DRL-based dynamic treatment regimes.
Proportional representation (PR) is a fundamental principle of many democracies world-wide which employ PR-based voting rules to elect their representatives. The normative properties of these voting rules however, are often only understood in the context of sincere voting. In this paper we consider PR in the presence of strategic voters. We construct a voting rule such that for every preference profile there exists at least one costly voting equilibrium satisfying PR with respect to voters' private and unrevealed preferences - such a voting rule is said to be strategically robust. In contrast, a commonly applied voting rule is shown not be strategically robust. Furthermore, we prove a limit on `how strategically robust' a PR-based voting rule can be; we show that there is no PR-based voting rule which ensures that every equilibrium satisfies PR. Collectively, our results highlight the possibility and limit of achieving PR in the presence of strategic voters and a positive role for mechanisms, such as pre-election polls, which coordinate voter behaviour towards equilibria which satisfy PR.
We propose two general and falsifiable hypotheses about expectations on generalization error when learning in the context of concept drift. One posits that as drift rate increases, the forgetting rate that minimizes generalization error will also increase and vice versa. The other posits that as a learner's forgetting rate increases, the bias/variance profile that minimizes generalization error will have lower variance and vice versa. These hypotheses lead to the concept of the sweet path, a path through the 3-d space of alternative drift rates, forgetting rates and bias/variance profiles on which generalization error will be minimized, such that slow drift is coupled with low forgetting and low bias, while rapid drift is coupled with fast forgetting and low variance. We present experiments that support the existence of such a sweet path. We also demonstrate that simple learners that select appropriate forgetting rates and bias/variance profiles are highly competitive with the state-of-the-art in incremental learners for concept drift on real-world drift problems.
Systematic reviews are essential to summarizing the results of different clinical and social science studies. The first step in a systematic review task is to identify all the studies relevant to the review. The task of identifying relevant studies for a given systematic review is usually performed manually, and as a result, involves substantial amounts of expensive human resource. Lately, there have been some attempts to reduce this manual effort using active learning. In this work, we build upon some such existing techniques, and validate by experimenting on a larger and comprehensive dataset than has been attempted until now. Our experiments provide insights on the use of different feature extraction models for different disciplines. More importantly, we identify that a naive active learning based screening process is biased in favour of selecting similar documents. We aimed to improve the performance of the screening process using a novel active learning algorithm with success. Additionally, we propose a mechanism to choose the best feature extraction method for a given review.
Multi-vehicle routing has become increasingly important with the rapid development of autonomous vehicle technology. Dial-a-ride problem, a variant of vehicle routing problem (VRP), deals with the allocation of customer requests to vehicles, scheduling the pick-up and drop-off times and the sequence of serving those requests by ensuring high customer satisfaction with minimized travel cost. In this paper, we propose an improved tabu search (ITS) heuristic for static dial-a-ride problem (DARP) with the objective of obtaining high-quality solutions in short time. Two new techniques, initialization heuristic, and time window adjustment are proposed to achieve faster convergence to the global optimum. Various numerical experiments are conducted for the proposed solution methodology using DARP test instances from the literature and the convergence speed up is validated.
Relational data sources are still one of the most popular ways to store enterprise or Web data, however, the issue with relational schema is the lack of a well-defined semantic description. A common ontology provides a way to represent the meaning of a relational schema and can facilitate the integration of heterogeneous data sources within a domain. Semantic labeling is achieved by mapping attributes from the data sources to the classes and properties in the ontology. We formulate this problem as a multi-class classification problem where previously labeled data sources are used to learn rules for labeling new data sources. The majority of existing approaches for semantic labeling have focused on data integration challenges such as naming conflicts and semantic heterogeneity. In addition, machine learning approaches typically have issues around class imbalance, lack of labeled instances and relative importance of attributes. To address these issues, we develop a new machine learning model with engineered features as well as two deep learning models which do not require extensive feature engineering. We evaluate our new approaches with the state-of-the-art.
A flashover occurs when a fire spreads very rapidly through crevices due to intense heat. Flashovers present one of the most frightening and challenging fire phenomena to those who regularly encounter them: firefighters. Firefighters' safety and lives often depend on their ability to predict flashovers before they occur. Typical pre-flashover fire characteristics include dark smoke, high heat, and rollover ("angel fingers") and can be quantified by color, size, and shape. Using a color video stream from a firefighter's body camera, we applied generative adversarial neural networks for image enhancement. The neural networks were trained to enhance very dark fire and smoke patterns in videos and monitor dynamic changes in smoke and fire areas. Preliminary tests with limited flashover training videos showed that we predicted a flashover as early as 55 seconds before it occurred.
Accurate and transparent prediction of cancer survival times on the level of individual patients can inform and improve patient care and treatment practices. In this paper, we design a model that concurrently learns to accurately predict patient-specific survival distributions and to explain its predictions in terms of patient attributes such as clinical tests or assessments. Our model is flexible and based on a recurrent network, can handle various modalities of data including temporal measurements, and yet constructs and uses simple explanations in the form of patient- and time-specific linear regression. For analysis, we use two publicly available datasets and show that our networks outperform a number of baselines in prediction while providing a way to inspect the reasons behind each prediction.
Effective collaboration between humans and AI-based systems requires effective modeling of the human in the loop, both in terms of the mental state as well as the physical capabilities of the latter. However, these models can also open up pathways for manipulating and exploiting the human in the hopes of achieving some greater good, especially when the intent or values of the AI and the human are not aligned or when they have an asymmetrical relationship with respect to knowledge or computation power. In fact, such behavior does not necessarily require any malicious intent but can rather be borne out of cooperative scenarios. It is also beyond simple misinterpretation of intents, as in the case of value alignment problems, and thus can be effectively engineered if desired. Such techniques already exist and pose several unresolved ethical and moral questions with regards to the design of autonomy. In this paper, we illustrate some of these issues in a teaming scenario and investigate how they are perceived by participants in a thought experiment.
Clustering is inherently ill-posed: there often exist multiple valid clusterings of a single dataset, and without any additional information a clustering system has no way of knowing which clustering it should produce. This motivates the use of constraints in clustering, as they allow users to communicate their interests to the clustering system. Active constraint-based clustering algorithms select the most useful constraints to query, aiming to produce a good clustering using as few constraints as possible. We propose COBRA, an active method that first over-clusters the data by running K-means with a $K$ that is intended to be too large, and subsequently merges the resulting small clusters into larger ones based on pairwise constraints. In its merging step, COBRA is able to keep the number of pairwise queries low by maximally exploiting constraint transitivity and entailment. We experimentally show that COBRA outperforms the state of the art in terms of clustering quality and runtime, without requiring the number of clusters in advance.
Generalized planning is concerned with the characterization and computation of plans that solve many instances at once. In the standard formulation, a generalized plan is a mapping from feature or observation histories into actions, assuming that the instances share a common pool of features and actions. This assumption, however, excludes the standard relational planning domains where actions and objects change across instances. In this work, we extend the formulation of generalized planning to such domains. This is achieved by projecting the actions over the features, resulting in a common set of abstract actions which can be tested for soundness and completeness, and which can be used for generating general policies such as "if the gripper is empty, pick the clear block above x and place it on the table" that achieve the goal clear(x) in any Blocksworld instance. In this policy, "pick the clear block above x" is an abstract action that may represent the action Unstack(a, b) in one situation and the action Unstack(b, c) in another. Transformations are also introduced for computing such policies by means of fully observable non-deterministic (FOND) planners. The value of generalized representations for learning general policies is also discussed.
It is inconceivable how chaotic the world would look to humans, faced with innumerable decisions a day to be made under uncertainty, had they been lacking the capacity to distinguish the relevant from the irrelevant---a capacity which computationally amounts to handling probabilistic independence relations. The highly parallel and distributed computational machinery of the brain suggests that a satisfying process-level account of human independence judgment should also mimic these features. In this work, we present the first rational, distributed, message-passing, process-level account of independence judgment, called $\mathcal{D}^\ast$. Interestingly, $\mathcal{D}^\ast$ shows a curious, but normatively-justified tendency for quick detection of dependencies, whenever they hold. Furthermore, $\mathcal{D}^\ast$ outperforms all the previously proposed algorithms in the AI literature in terms of worst-case running time, and a salient aspect of it is supported by recent work in neuroscience investigating possible implementations of Bayes nets at the neural level. $\mathcal{D}^\ast$ nicely exemplifies how the pursuit of cognitive plausibility can lead to the discovery of state-of-the-art algorithms with appealing properties, and its simplicity makes $\mathcal{D}^\ast$ potentially a good candidate for pedagogical purposes.
The cross entropy (CE) method is a model based search method to solve optimization problems where the objective function has minimal structure. The Monte-Carlo version of the CE method employs the naive sample averaging technique which is inefficient, both computationally and space wise. We provide a novel stochastic approximation version of the CE method, where the sample averaging is replaced with incremental geometric averaging. This approach can save considerable computational and storage costs. Our algorithm is incremental in nature and possesses additional attractive features such as accuracy, stability, robustness and convergence to the global optimum for a particular class of objective functions. We evaluate the algorithm on a variety of global optimization benchmark problems and the results obtained corroborate our theoretical findings.
Deep learning relies on a very specific kind of neural networks: those superposing several neural layers. In the last few years, deep learning achieved major breakthroughs in many tasks such as image analysis, speech recognition, natural language processing, and so on. Yet, there is no theoretical explanation of this success. In particular, it is not clear why the deeper the network, the better it actually performs. We argue that the explanation is intimately connected to a key feature of the data collected from our surrounding universe to feed the machine learning algorithms: large non-parallelizable logical depth. Roughly speaking, we conjecture that the shortest computational descriptions of the universe are algorithms with inherently large computation times, even when a large number of computers are available for parallelization. Interestingly, this conjecture, combined with the folklore conjecture in theoretical computer science that $ P \neq NC$, explains the success of deep learning.
Pretraining with expert demonstrations have been found useful in speeding up the training process of deep reinforcement learning algorithms since less online simulation data is required. Some people use supervised learning to speed up the process of feature learning, others pretrain the policies by imitating expert demonstrations. However, these methods are unstable and not suitable for actor-critic reinforcement learning algorithms. Also, some existing methods rely on the global optimum assumption, which is not true in most scenarios. In this paper, we employ expert demonstrations in a actor-critic reinforcement learning framework, and meanwhile ensure that the performance is not affected by the fact that expert demonstrations are not global optimal. We theoretically derive a method for computing policy gradients and value estimators with only expert demonstrations. Our method is theoretically plausible for actor-critic reinforcement learning algorithms that pretrains both policy and value functions. We apply our method to two of the typical actor-critic reinforcement learning algorithms, DDPG and ACER, and demonstrate with experiments that our method not only outperforms the RL algorithms without pretraining process, but also is more simulation efficient.
Novice programmers often struggle with the formal syntax of programming languages. To assist them, we design a novel programming language correction framework amenable to reinforcement learning. The framework allows an agent to mimic human actions for text navigation and editing. We demonstrate that the agent can be trained through self-exploration directly from the raw input, that is, program text itself, without any knowledge of the formal syntax of the programming language. We leverage expert demonstrations for one tenth of the training data to accelerate training. The proposed technique is evaluated on 6975 erroneous C programs with typographic errors, written by students during an introductory programming course. Our technique fixes 14% more programs and 29% more compiler error messages relative to those fixed by a state-of-the-art tool, DeepFix, which uses a fully supervised neural machine translation approach.
Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning can allow data-driven models with a long memory of past states. We advocate for predictive musical interaction, where a predictive model is embedded in a musical interface, assisting users by predicting unknown states of musical processes. We propose a framework for incorporating such predictive models into the sensing, processing, and result architecture that is often used in musical interface design. We show that our framework accommodates deep generative models, as well as models for predicting gestural states, or other high-level musical information. We motivate the framework with two examples from our recent work, as well as systems from the literature, and suggest musical use-cases where prediction is a necessary component.
We present a model for recursive Bayesian filtering based on lifted multiset states. Combining multisets with lifting makes it possible to simultaneously exploit multiple strategies for reducing inference complexity when compared to list-based grounded state representations. The core idea is to borrow the concept of Maximally Parallel Multiset Rewriting Systems and to enhance it by concepts from Rao-Blackwellisation and Lifted Inference, giving a representation of state distributions that enables efficient inference. In worlds where the random variables that define the system state are exchangeable - where the identity of entities does not matter - it automatically uses a representation that abstracts from ordering (achieving an exponential reduction in complexity) and it automatically adapts when observations or system dynamics destroy exchangeability by breaking symmetry.
For 50 years, research in the area of inductive inference aims at investigating the learning of formal languages and is influenced by computability theory, complexity theory, cognitive science, machine learning, and more generally artificial intelligence. Being one of the pioneers, Gold investigated the most common formalization, learning in the limit both from solely positive examples as well as from positive and negative information. The first mode of presentation has been studied extensively, including insights in how different additional requirements on the hypothesis sequence of the learner or requested properties of the latter itself, restrict what collections of languages are learnable. We focus on the second paradigm, learning from informants, and study how imposing different restrictions on the learning process effects learnability. For example, we show that learners can be assumed to only change their hypothesis in case it is inconsistent with the data (such learners are called conservative). Further, we give a picture of how the most important learning restrictions relate. Our investigations underpin the claim for delayability being the right structural property to gain a deeper understanding concerning the nature of learning restrictions.
Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives.
When humans perform inductive learning, they often enhance the process with background knowledge. With the increasing availability of well-formed collaborative knowledge bases, the performance of learning algorithms could be significantly enhanced if a way were found to exploit these knowledge bases. In this work, we present a novel algorithm for injecting external knowledge into induction algorithms using feature generation. Given a feature, the algorithm defines a new learning task over its set of values, and uses the knowledge base to solve the constructed learning task. The resulting classifier is then used as a new feature for the original problem. We have applied our algorithm to the domain of text classification using large semantic knowledge bases. We have shown that the generated features significantly improve the performance of existing learning algorithms.
Though deep neural networks (DNNs) achieve remarkable performances in many artificial intelligence tasks, the lack of training instances remains a notorious challenge. As the network goes deeper, the generalization accuracy decays rapidly in the situation of lacking massive amounts of training data. In this paper, we propose novel deep neural network structures that can be inherited from all existing DNNs with almost the same level of complexity, and develop simple training algorithms. We show our paradigm successfully resolves the lack of data issue. Tests on the CIFAR10 and CIFAR100 image recognition datasets show that the new paradigm leads to 20$\%$ to $30\%$ relative error rate reduction compared to their base DNNs. The intuition of our algorithms for deep residual network stems from theories of the partial differential equation (PDE) control problems. Code will be made available.
We propose an architecture for VQA which utilizes recurrent layers to generate visual and textual attention. The memory characteristic of the proposed recurrent attention units offers a rich joint embedding of visual and textual features and enables the model to reason relations between several parts of the image and question. Our single model outperforms the first place winner on the VQA 1.0 dataset, performs within margin to the current state-of-the-art ensemble model. We also experiment with replacing attention mechanisms in other state-of-the-art models with our implementation and show increased accuracy. In both cases, our recurrent attention mechanism improves performance in tasks requiring sequential or relational reasoning on the VQA dataset.
In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 256^3 by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.
We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimization-based attacks, we find defenses relying on this effect can be circumvented. For each of the three types of obfuscated gradients we discover, we describe characteristic behaviors of defenses exhibiting this effect and develop attack techniques to overcome it. In a case study, examining non-certified white-box-secure defenses at ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 8 defenses relying on obfuscated gradients. Our new attacks successfully circumvent 6 completely and 1 partially.
We present Adaptive Memory Networks (AMN) that processes input-question pairs to dynamically construct a network architecture optimized for lower inference times for Question Answering (QA) tasks. AMN processes the input story to extract entities and stores them in memory banks. Starting from a single bank, as the number of input entities increases, AMN learns to create new banks as the entropy in a single bank becomes too high. Hence, after processing an input-question(s) pair, the resulting network represents a hierarchical structure where entities are stored in different banks, distanced by question relevance. At inference, one or few banks are used, creating a tradeoff between accuracy and performance. AMN is enabled by dynamic networks that allow input dependent network creation and efficiency in dynamic mini-batching as well as our novel bank controller that allows learning discrete decision making with high accuracy. In our results, we demonstrate that AMN learns to create variable depth networks depending on task complexity and reduces inference times for QA tasks.
Recently, feature selection has become an increasingly important area of research due to the surge in high-dimensional datasets in all areas of modern life. A plethora of feature selection algorithms have been proposed, but it is difficult to truly analyse the quality of a given algorithm. Ideally, an algorithm would be evaluated by measuring how well it removes known bad features. Acquiring datasets with such features is inherently difficult, and so a common technique is to add synthetic bad features to an existing dataset. While adding noisy features is an easy task, it is very difficult to automatically add complex, redundant features. This work proposes one of the first approaches to generating redundant features, using a novel genetic programming approach. Initial experiments show that our proposed method can automatically create difficult, redundant features which have the potential to be used for creating high-quality feature selection benchmark datasets.
Model interpretability is a requirement in many applications in which crucial decisions are made by users relying on a model's outputs. The recent movement for "algorithmic fairness" also stipulates explainability, and therefore interpretability of learning models. And yet the most successful contemporary Machine Learning approaches, the Deep Neural Networks, produce models that are highly non-interpretable. We attempt to address this challenge by proposing a technique called CNN-INTE to interpret deep Convolutional Neural Networks (CNN) via meta-learning. In this work, we interpret a specific hidden layer of the deep CNN model on the MNIST image dataset. We use a clustering algorithm in a two-level structure to find the meta-level training data and Random Forest as base learning algorithms to generate the meta-level test data. The interpretation results are displayed visually via diagrams, which clearly indicates how a specific test instance is classified. Our method achieves global interpretation for all the test instances without sacrificing the accuracy obtained by the original deep CNN model. This means our model is faithful to the deep CNN model, which leads to reliable interpretations.
This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, the interpretability is always the Achilles' heel of deep neural networks. At present, deep neural networks obtain high discrimination power at the cost of low interpretability of their black-box representations. We believe that high model interpretability may help people to break several bottlenecks of deep learning, e.g., learning from very few annotations, learning via human-computer communications at the semantic level, and semantically debugging network representations. We focus on convolutional neural networks (CNNs), and we revisit the visualization of CNN representations, methods of diagnosing representations of pre-trained CNNs, approaches for disentangling pre-trained CNN representations, learning of CNNs with disentangled representations, and middle-to-end learning based on model interpretability. Finally, we discuss prospective trends in explainable artificial intelligence.
Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable in the specific context of verification. Suppose we have a machine learning system that predicts X, and we provide rationale for this prediction X. Given an input, an explanation, and an output, is the output consistent with the input and the supposed rationale? Via a series of user-studies, we identify what kinds of increases in complexity have the greatest effect on the time it takes for humans to verify the rationale, and which seem relatively insensitive.
Training deep neural networks results in strong learned representations that show good generalization capabilities. In most cases, training involves iterative modification of all weights inside the network via back-propagation. In Extreme Learning Machines, it has been suggested to set the first layer of a network to fixed random values instead of learning it. In this paper, we propose to take this approach a step further and fix almost all layers of a deep convolutional neural network, allowing only a small portion of the weights to be learned. As our experiments show, fixing even the majority of the parameters of the network often results in performance which is on par with the performance of learning all of them. The implications of this intriguing property of deep neural networks are discussed and we suggest ways to harness it to create more robust representations.
Multi-view sequential learning is a fundamental problem in machine learning dealing with multi-view sequences. In a multi-view sequence, there exists two forms of interactions between different views: view-specific interactions and cross-view interactions. In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time. The first component of the MFN is called the System of LSTMs, where view-specific interactions are learned in isolation through assigning an LSTM function to each view. The cross-view interactions are then identified using a special attention mechanism called the Delta-memory Attention Network (DMAN) and summarized through time with a Multi-view Gated Memory. Through extensive experimentation, MFN is compared to various proposed approaches for multi-view sequential learning on multiple publicly available benchmark datasets. MFN outperforms all the existing multi-view approaches. Furthermore, MFN outperforms all current state-of-the-art models, setting new state-of-the-art results for these multi-view datasets.
Knowledge graphs, on top of entities and their relationships, contain another important element: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entities alone. Most of the existing work on embedding (or latent feature) based knowledge graph modeling focuses mainly on the relations between entities. In this work, we study the effect of incorporating literal information into existing knowledge graph models. Our approach, which we name LiteralE, is an extension that can be plugged into existing latent feature methods. LiteralE merges entity embeddings with their literal information using a learnable, parametrized function, such as a simple linear or nonlinear transformation, or a multilayer neural network. We extend several popular embedding models using LiteralE and evaluate the performance on the task of link prediction. Despite its simplicity, LiteralE proves to be an effective way to incorporate literal information into existing embedding based models, improving their performance on different standard datasets, which we augmented with their literals and provide as testbed for further research.
Multi-person articulated pose tracking in complex unconstrained videos is an important and challenging problem. In this paper, going along the road of top-down approaches, we propose a decent and efficient pose tracker based on pose flows. First, we design an online optimization framework to build association of cross-frame poses and form pose flows. Second, a novel pose flow non maximum suppression (NMS) is designed to robustly reduce redundant pose flows and re-link temporal disjoint pose flows. Extensive experiments show our method significantly outperforms best reported results on two standard Pose Tracking datasets (PoseTrack dataset and PoseTrack Challenge dataset) by 13 mAP 25 MOTA and 6 mAP 3 MOTA respectively. Moreover, in the case of working on detected poses in individual frames, the extra computation of proposed pose tracker is very minor, requiring 0.01 second per frame only.
Recent work in explanation generation for decision making agents has looked at how unexplained behavior of autonomous systems can be understood in terms of differences in the model of the system and the human's understanding of the same, and how the explanation process as a result of this mismatch can be then seen as a process of reconciliation of these models. Existing algorithms in such settings, while having been built on contrastive, selective and social properties of explanations as studied extensively in the psychology literature, have not, to the best of our knowledge, been evaluated in settings with actual humans in the loop. As such, the applicability of such explanations to human-AI and human-robot interactions remains suspect. In this paper, we set out to evaluate these explanation generation algorithms in a series of studies in a mock search and rescue scenario with an internal semi-autonomous robot and an external human commander. We demonstrate to what extent the properties of these algorithms hold as they are evaluated by humans, and how the dynamics of trust between the human and the robot evolve during the process of these interactions.
Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced features like quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized to demonstrate the NIFW MAV model, which has points of interest over first principle based modelling since it does not depend on the system dynamics, rather based on data and can incorporate various uncertainties like sensor error. The same clustering strategy is used to develop an adaptive fuzzy controller. The controller is then utilized to control the altitude of the NIFW MAV, that can adapt with environmental disturbances by tuning the antecedent and consequent parameters of the fuzzy system.
In recent years, neural network approaches have been widely adopted for machine learning tasks, with applications in computer vision. More recently, unsupervised generative models based on neural networks have been successfully applied to model data distributions via low-dimensional latent spaces. In this paper, we use Generative Adversarial Networks (GANs) to impose structure in compressed sensing problems, replacing the usual sparsity constraint. We propose to train the GANs in a task-aware fashion, specifically for reconstruction tasks. We also show that it is possible to train our model without using any (or much) non-compressed data. Finally, we show that the latent space of the GAN carries discriminative information and can further be regularized to generate input features for general inference tasks. We demonstrate the effectiveness of our method on a variety of reconstruction and classification problems.
We build a virtual agent for learning language in a 2D maze-like world. The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards. It interactively learns the teacher's language from scratch based on two language use cases: sentence-directed navigation and question answering. It learns simultaneously the visual representations of the world, the language, and the action control. By disentangling language grounding from other computational routines and sharing a concept detection function between language grounding and prediction, the agent reliably interpolates and extrapolates to interpret sentences that contain new word combinations or new words missing from training sentences. The new words are transferred from the answers of language prediction. Such a language ability is trained and evaluated on a population of over 1.6 million distinct sentences consisting of 119 object words, 8 color words, 9 spatial-relation words, and 50 grammatical words. The proposed model significantly outperforms five comparison methods for interpreting zero-shot sentences. In addition, we demonstrate human-interpretable intermediate outputs of the model in the appendix.
This paper describes a problem arising in sea exploration, where the aim is to schedule the expedition of a ship for collecting information about the resources on the seafloor. The aim is to collect data by probing on a set of carefully chosen locations, so that the information available is optimally enriched. This problem has similarities with the orienteering problem, where the aim is to plan a time-limited trip for visiting a set of vertices, collecting a prize at each of them, in such a way that the total value collected is maximum. In our problem, the score at each vertex is associated with an estimation of the level of the resource on the given surface, which is done by regression using Gaussian processes. Hence, there is a correlation among scores on the selected vertices; this is a first difference with respect to the standard orienteering problem. The second difference is the location of each vertex, which in our problem is a freely chosen point on a given surface.
Reinforcement learning in environments with many action-state pairs is challenging. At issue is the number of episodes needed to thoroughly search the policy space. Most conventional heuristics address this search problem in a stochastic manner. This can leave large portions of the policy space unvisited during the early training stages. In this paper, we propose an uncertainty-based, information-theoretic approach for performing guided stochastic searches that more effectively cover the policy space. Our approach is based on the value of information, a criterion that provides the optimal trade-off between expected costs and the granularity of the search process. The value of information yields a stochastic routine for choosing actions during learning that can explore the policy space in a coarse to fine manner. We augment this criterion with a state-transition uncertainty factor, which guides the search process into previously unexplored regions of the policy space.
Cycles of attacking arguments pose non-trivial issues in Dung style argumentation theory, apparent behavioural difference between odd and even length cycles being a notable one. While a few methods were proposed for treating them, to - in particular - enable selection of acceptable arguments in an odd-length cycle when Dung semantics could select none, so far the issues have been observed from a purely argument-graph-theoretic perspective. Per contra, we consider argument graphs together with a certain lattice like semantic structure over arguments e.g. ontology. As we show, the semantic-argumentgraphic hybrid theory allows us to apply abstract interpretation, a widely known methodology in static program analysis, to formal argumentation. With this, even where no arguments in a cycle could be selected sensibly, we could say more about arguments acceptability of an argument framework that contains it. In a certain sense, we can verify Dung extensions with respect to a semantic structure in this hybrid theory, to consolidate our confidence in their suitability. By defining the theory, and by making comparisons to existing approaches, we ultimately discover that whether Dung semantics, or an alternative semantics such as cf2, is adequate or problematic depends not just on an argument graph but also on the semantic relation among the arguments in the graph.
We focus on learning the desired objective function for a robot. Although trajectory demonstrations can be very informative of the desired objective, they can also be difficult for users to provide. Answers to comparison queries, asking which of two trajectories is preferable, are much easier for users, and have emerged as an effective alternative. Unfortunately, comparisons are far less informative. We propose that there is much richer information that users can easily provide and that robots ought to leverage. We focus on augmenting comparisons with feature queries, and introduce a unified formalism for treating all answers as observations about the true desired reward. We derive an active query selection algorithm, and test these queries in simulation and on real users. We find that richer, feature-augmented queries can extract more information faster, leading to robots that better match user preferences in their behavior.
Decomposition methods have been proposed in the past to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility decomposition can be used where each individual entity is considered independently. The individual utility functions are then combined in real time to solve the global problem. Although these techniques can perform well empirically, they sacrifice optimality. This paper proposes an approach inspired from multi-fidelity optimization to learn a correction term with a neural network representation. Learning this correction can significantly improve performance. We demonstrate this approach on a pedestrian avoidance problem for autonomous driving. By leveraging strategies to avoid a single pedestrian, the decomposition method can scale to avoid multiple pedestrians. We verify empirically that the proposed correction method leads to a significant improvement over the decomposition method alone and outperforms a policy trained on the full scale problem without utility decomposition.
Attention-based sequence-to-sequence model has proved successful in Neural Machine Translation (NMT). However, the attention without consideration of decoding history, which includes the past information in the decoder and the attention mechanism, often causes much repetition. To address this problem, we propose the decoding-history-based Adaptive Control of Attention (ACA) for the NMT model. ACA learns to control the attention by keeping track of the decoding history and the current information with a memory vector, so that the model can take the translated contents and the current information into consideration. Experiments on Chinese-English translation and the English-Vietnamese translation have demonstrated that our model significantly outperforms the strong baselines. The analysis shows that our model is capable of generating translation with less repetition and higher accuracy. The code will be available at https://github.com/lancopku
In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness sometimes at the cost of scarifying accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation. The aim of this paper is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.
Variational encoder-decoders (VEDs) have shown promising results in dialogue generation. However, the latent variable distributions are usually approximated by a much simpler model than the powerful RNN structure used for encoding and decoding, yielding the KL-vanishing problem and inconsistent training objective. In this paper, we separate the training step into two phases: The first phase learns to autoencode discrete texts into continuous embeddings, from which the second phase learns to generalize latent representations by reconstructing the encoded embedding. In this case, latent variables are sampled by transforming Gaussian noise through multi-layer perceptrons and are trained with a separate VED model, which has the potential of realizing a much more flexible distribution. We compare our model with current popular models and the experiment demonstrates substantial improvement in both metric-based and human evaluations.
Inception and the Resnet family of Convolutional Neural Network archi-tectures have broken records in the past few years, but recent state of the art models have also incurred very high computational cost in terms of training, inference and model size. Making the deployment of these models on Edge devices, impractical. In light of this, we present a new novel architecture that is designed for high computational efficiency on both GPUs and CPUs, and is highly suited for deployment on Mobile Applications, Smart Cameras, Iot devices and controllers as well as low cost drones. Our architecture boasts competitive accuracies on standard Datasets even out-performing the original Resnet. We present below the motivation for this research, the architecture of the network, single test accuracies on CIFAR 10 and CIFAR 100 , a detailed comparison with other well-known architectures and link to an implementation in Keras.
Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications. However, low-cost inertial sensors, as commonly found in smartphones, are plagued by bias and noise, which leads to unbounded growth in error when accelerations are double integrated to obtain displacement. Small errors in state estimation propagate to make odometry virtually unusable in a matter of seconds. We propose to break the cycle of continuous integration, and instead segment inertial data into independent windows. The challenge becomes estimating the latent states of each window, such as velocity and orientation, as these are not directly observable from sensor data. We demonstrate how to formulate this as an optimization problem, and show how deep recurrent neural networks can yield highly accurate trajectories, outperforming state-of-the-art shallow techniques, on a wide range of tests and attachments. In particular, we demonstrate that IONet can generalize to estimate odometry for non-periodic motion, such as a shopping trolley or baby-stroller, an extremely challenging task for existing techniques.
Bayesian optimization has become a standard technique for hyperparameter optimization, including data-intensive models such as deep neural networks that may take days or weeks to train. We consider the setting where previous optimization runs are available, and we wish to use their results to warm-start a new optimization run. We develop an ensemble model that can incorporate the results of past optimization runs, while avoiding the poor scaling that comes with putting all results into a single Gaussian process model. The ensemble combines models from past runs according to estimates of their generalization performance on the current optimization. Results from a large collection of hyperparameter optimization benchmark problems and from optimization of a production computer vision platform at Facebook show that the ensemble can substantially reduce the time it takes to obtain near-optimal configurations, and is useful for warm-starting expensive searches or running quick re-optimizations.
This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms. We apply it to a new bi-criteria formulation of the travelling thief problem, which is known to the Evolutionary Computation community as a benchmark multi-component optimisation problem that interconnects two classical NP-hard problems: the travelling salesman problem and the 0-1 knapsack problem. Our approach employs the exact dynamic programming algorithm for the underlying Packing-While-Travelling (PWT) problem as a subroutine within a bi-objective evolutionary algorithm. This design takes advantage of the data extracted from Pareto fronts generated by the dynamic program to achieve better solutions. Furthermore, we develop a number of novel indicators and selection mechanisms to strengthen synergy of the two algorithmic components of our approach. The results of computational experiments show that the approach is capable to outperform the state-of-the-art results for the single-objective case of the problem.
Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences. We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables. Through extensive experiments we show that it consistently yields higher-scoring structures than its competitors on complete data sets. We then consider the problem of structure learning from incomplete data sets. This can be addressed by structural EM, which however is computationally very demanding. We thus adopt the novel k-MAX algorithm in the maximization step of structural EM, obtaining an efficient computation of the expected sufficient statistics. We test the resulting structural EM method on the task of imputing missing data, comparing it against the state-of-the-art approach based on random forests. Our approach achieves the same imputation accuracy of the competitors, but in about one tenth of the time. Furthermore we show that it has worst-case complexity linear in the input size, and that it is easily parallelizable.
We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep apnea (0.8298). We compare two semi-supervised train- ing methods, semi-supervised sequence learning and heuristic pretraining, and show they outperform hand-engineered biomarkers from the medical literature. We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear.
We present PPFNet - Point Pair Feature NETwork for deeply learning a globally informed 3D local feature descriptor to find correspondences in unorganized point clouds. PPFNet learns local descriptors on pure geometry and is highly aware of the global context, an important cue in deep learning. Our 3D representation is computed as a collection of point-pair-features combined with the points and normals within a local vicinity. Our permutation invariant network design is inspired by PointNet and sets PPFNet to be ordering-free. As opposed to voxelization, our method is able to consume raw point clouds to exploit the full sparsity. PPFNet uses a novel $\textit{N-tuple}$ loss and architecture injecting the global information naturally into the local descriptor. It shows that context awareness also boosts the local feature representation. Qualitative and quantitative evaluations of our network suggest increased recall, improved robustness and invariance as well as a vital step in the 3D descriptor extraction performance.
Fish in schooling formations navigate complex flow-fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behaviour has been associated with evolutionary advantages including collective energy savings. How fish harvest energy from their complex fluid environment and the underlying physical mechanisms governing energy-extraction during collective swimming, is still unknown. Here we show that fish can improve their sustained propulsive efficiency by actively following, and judiciously intercepting, vortices in the wake of other swimmers. This swimming strategy leads to collective energy-savings and is revealed through the first ever combination of deep reinforcement learning with high-fidelity flow simulations. We find that a `smart-swimmer' can adapt its position and body deformation to synchronise with the momentum of the oncoming vortices, improving its average swimming-efficiency at no cost to the leader. The results show that fish may harvest energy deposited in vortices produced by their peers, and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep reinforcement learning can produce navigation algorithms for complex flow-fields, with promising implications for energy savings in autonomous robotic swarms.
While the incipient internet was largely text-based, the modern digital world is becoming increasingly multi-modal. Here, we examine multi-modal classification where one modality is discrete, e.g. text, and the other is continuous, e.g. visual representations transferred from a convolutional neural network. In particular, we focus on scenarios where we have to be able to classify large quantities of data quickly. We investigate various methods for performing multi-modal fusion and analyze their trade-offs in terms of classification accuracy and computational efficiency. Our findings indicate that the inclusion of continuous information improves performance over text-only on a range of multi-modal classification tasks, even with simple fusion methods. In addition, we experiment with discretizing the continuous features in order to speed up and simplify the fusion process even further. Our results show that fusion with discretized features outperforms text-only classification, at a fraction of the computational cost of full multi-modal fusion, with the additional benefit of improved interpretability.
Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable to transfer to new nodes and graphs as they are tied to vertex identity. In this work, we introduce the Role2Vec framework which uses the flexible notion of attributed random walks, and serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many others that leverage random walks. Our proposed framework enables these methods to be more widely applicable for both transductive and inductive learning as well as for use on graphs with attributes (if available). This is achieved by learning functions that generalize to new nodes and graphs. We show that our proposed framework is effective with an average AUC improvement of 16:55% while requiring on average 853x less space than existing methods on a variety of graphs.
In the era of Big Data and Internet-of-Things (IoT), all real-world environments are gradually becoming cyber-physical (e.g., emergency management, healthcare, smart manufacturing, etc.), with the presence of connected devices and embedded ICT systems (e.g., smartphones, sensors, actuators) producing huge amounts of data and events that influence the enactment of the Cyber Physical Processes (CPPs) enacted in such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention at run-time. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS that combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on well-established action-based formalisms in Artificial Intelligence, which allow to interpret the ever-changing knowledge of cyber-physical environments and to adapt CPPs by preserving their base structure.
The world is connected through the Internet. As the abundance of Internet users connected into the Web and the popularity of cloud computing research, the need of Artificial Intelligence (AI) is demanding. In this research, Genetic Algorithm (GA) as AI optimization method through natural selection and genetic evolution is utilized. There are many applications of GA such as web mining, load balancing, routing, and scheduling or web service selection. Hence, it is a challenging task to discover whether the code mainly server side and web based language technology affects the performance of GA. Travelling Salesperson Problem (TSP) as Non Polynomial-hard (NP-hard) problem is provided to be a problem domain to be solved by GA. While many scientists prefer Python in GA implementation, another popular high-level interpreter programming language such as PHP (PHP Hypertext Preprocessor) and Ruby were benchmarked. Line of codes, file sizes, and performances based on GA implementation and runtime were found varies among these programming languages. Based on the result, the use of Ruby in GA implementation is recommended.
Within the context of video games the notion of perfectly rational agents can be undesirable as it leads to uninteresting situations, where humans face tough adversarial decision makers. Current frameworks for stochastic games and reinforcement learning prohibit tuneable strategies as they seek optimal performance. In this paper, we enable such tuneable behaviour by generalising soft Q-learning to stochastic games, where more than one agent interact strategically. We contribute both theoretically and empirically. On the theory side, we show that games with soft Q-learning exhibit a unique value and generalise team games and zero-sum games far beyond these two extremes to cover a continuous spectrum of gaming behaviour. Experimentally, we show how tuning agents' constraints affect performance and demonstrate, through a neural network architecture, how to reliably balance games with high-dimensional representations.
Robust reinforcement learning aims to produce policies that have strong guarantees even in the face of environments/transition models whose parameters have strong uncertainty. Existing work uses value-based methods and the usual primitive action setting. In this paper, we propose robust methods for learning temporally abstract actions, in the framework of options. We present a Robust Options Policy Iteration (ROPI) algorithm with convergence guarantees, which learns options that are robust to model uncertainty. We utilize ROPI to learn robust options with the Robust Options Deep Q Network (RO-DQN) that solves multiple tasks and mitigates model misspecification due to model uncertainty. We present experimental results which suggest that policy iteration with linear features may have an inherent form of robustness when using coarse feature representations. In addition, we present experimental results which demonstrate that robustness helps policy iteration implemented on top of deep neural networks to generalize over a much broader range of dynamics than non-robust policy iteration.
Inference in continuous label Markov random fields is a challenging task. We use particle belief propagation (PBP) for solving the inference problem in continuous label space. Sampling particles from the belief distribution is typically done by using Metropolis-Hastings Markov chain Monte Carlo methods which involves sampling from a proposal distribution. This proposal distribution has to be carefully designed depending on the particular model and input data to achieve fast convergence. We propose to avoid dependence on a proposal distribution by introducing a slice sampling based PBP algorithm. The proposed approach shows superior convergence performance on an image denoising toy example. Our findings are validated on a challenging relational 2D feature tracking application.
ATPboost is a system for solving sets of large-theory problems by interleaving ATP runs with state-of-the-art machine learning of premise selection from the proofs. Unlike many previous approaches that use multi-label setting, the learning is implemented as binary classification that estimates the pairwise-relevance of (theorem, premise) pairs. ATPboost uses for this the XGBoost gradient boosting algorithm, which is fast and has state-of-the-art performance on many tasks. Learning in the binary setting however requires negative examples, which is nontrivial due to many alternative proofs. We discuss and implement several solutions in the context of the ATP/ML feedback loop, and show that ATPboost with such methods significantly outperforms the k-nearest neighbors multilabel classifier.
The robust and efficient recognition of visual relations in images is a hallmark of biological vision. Here, we argue that, despite recent progress in visual recognition, modern machine vision algorithms are severely limited in their ability to learn visual relations. Through controlled experiments, we demonstrate that visual-relation problems strain convolutional neural networks (CNNs). The networks eventually break altogether when rote memorization becomes impossible such as when the intra-class variability exceeds their capacity. We further show that another type of feedforward network, called a relational network (RN), which was shown to successfully solve seemingly difficult visual question answering (VQA) problems on the CLEVR datasets, suffers similar limitations. Motivated by the comparable success of biological vision, we argue that feedback mechanisms including working memory and attention are the key computational components underlying abstract visual reasoning.
An important issue in neural network research is how to choose the number of nodes and layers such as to solve a classification problem. We provide new intuitions based on earlier results by An et al. (2015) by deriving an upper bound on the number of nodes in networks with two hidden layers such that linear separability can be achieved. Concretely, we show that if the data can be described in terms of N finite sets and the used activation function f is non-constant, increasing and has a left asymptote, we can derive how many nodes are needed to linearly separate these sets. This will be an upper bound that depends on the structure of the data. This structure can be analyzed using an algorithm. For the leaky rectified linear activation function, we prove separately that under some conditions on the slope, the same number of layers and nodes as for the aforementioned activation functions is sufficient. We empirically validate our claims.
High spectral dimensionality and the shortage of annotations make hyperspectral image (HSI) classification a challenging problem. Recent studies suggest that convolutional neural networks can learn discriminative spatial features, which play a paramount role in HSI interpretation. However, most of these methods ignore the distinctive spectral-spatial characteristic of hyperspectral data. In addition, a large amount of unlabeled data remains an unexploited gold mine for efficient data use. Therefore, we proposed an integration of generative adversarial networks (GANs) and probabilistic graphical models for HSI classification. Specifically, we used a spectral-spatial generator and a discriminator to identify land cover categories of hyperspectral cubes. Moreover, to take advantage of a large amount of unlabeled data, we adopted a conditional random field to refine the preliminary classification results generated by GANs. Experimental results obtained using two commonly studied datasets demonstrate that the proposed framework achieved encouraging classification accuracy using a small number of data for training.
Learning a deep model from small data is yet an opening and challenging problem. We focus on one-shot classification by deep learning approach based on a small quantity of training samples. We proposed a novel deep learning approach named Local Contrast Learning (LCL) based on the key insight about a human cognitive behavior that human recognizes the objects in a specific context by contrasting the objects in the context or in her/his memory. LCL is used to train a deep model that can contrast the recognizing sample with a couple of contrastive samples randomly drawn and shuffled. On one-shot classification task on Omniglot, the deep model based LCL with 122 layers and 1.94 millions of parameters, which was trained on a tiny dataset with only 60 classes and 20 samples per class, achieved the accuracy 97.99% that outperforms human and state-of-the-art established by Bayesian Program Learning (BPL) trained on 964 classes. LCL is a fundamental idea which can be applied to alleviate parametric model's overfitting resulted by lack of training samples.
The given work describes methodological principles of design instrumental complex of ontological purpose. Instrumental complex intends for the implementation of the integrated information technologies automated build of domain ontologies. Results focus on enhancing the effectiveness of the automatic analysis and understanding of natural-language texts, building of knowledge description of subject areas (primarily in the area of science and technology) and for interdisciplinary research in conjunction with the solution of complex problems.
Graph representations of large knowledge bases may comprise billions of edges. Usually built upon human-generated ontologies, several knowledge bases do not feature declared ontological rules and are far from being complete. Current rule mining approaches rely on schemata or store the graph in-memory, which can be unfeasible for large graphs. In this paper, we introduce HornConcerto, an algorithm to discover Horn clauses in large graphs without the need of a schema. Using a standard fact-based confidence score, we can mine close Horn rules having an arbitrary body size. We show that our method can outperform existing approaches in terms of runtime and memory consumption and mine high-quality rules for the link prediction task, achieving state-of-the-art results on a widely-used benchmark. Moreover, we find that rules alone can perform inference significantly faster than embedding-based methods and achieve accuracies on link prediction comparable to resource-demanding approaches such as Markov Logic Networks.
Graph planning gives rise to fundamental algorithmic questions such as shortest path, traveling salesman problem, etc. A classical problem in discrete planning is to consider a weighted graph and construct a path that maximizes the sum of weights for a given time horizon $T$. However, in many scenarios, the time horizon is not fixed, but the stopping time is chosen according to some distribution such that the expected stopping time is $T$. If the stopping time distribution is not known, then to ensure robustness, the distribution is chosen by an adversary, to represent the worst-case scenario. A stationary plan for every vertex always chooses the same outgoing edge. For fixed horizon or fixed stopping-time distribution, stationary plans are not sufficient for optimality. Quite surprisingly we show that when an adversary chooses the stopping-time distribution with expected stopping time $T$, then stationary plans are sufficient. While computing optimal stationary plans for fixed horizon is NP-complete, we show that computing optimal stationary plans under adversarial stopping-time distribution can be achieved in polynomial time. Consequently, our polynomial-time algorithm for adversarial stopping time also computes an optimal plan among all possible plans.
The famous Policy Iteration algorithm alternates between policy improvement and policy evaluation. Implementations of this algorithm with several variants of the latter evaluation stage, e.g, $n$-step and trace-based returns, have been analyzed in previous works. However, the case of multiple-step lookahead policy improvement, despite the recent increase in empirical evidence of its strength, has to our knowledge not been carefully analyzed yet. In this work, we introduce the first such analysis. Namely, we formulate variants of multiple-step policy improvement, derive new algorithms using these definitions and prove their convergence. Moreover, we show that recent prominent Reinforcement Learning algorithms are, in fact, instances of our framework. We thus shed light on their empirical success and give a recipe for deriving new algorithms for future study.
We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability. Although it is not competitive with state-of-the-art SAT solvers, NeuroSAT can solve problems that are substantially larger and more difficult than it ever saw during training by simply running for more iterations. Moreover, NeuroSAT generalizes to novel distributions; after training only on random SAT problems, at test time it can solve SAT problems encoding graph coloring, clique detection, dominating set, and vertex cover problems, all on a range of distributions over small random graphs.
Ontology learning (OL) is the process of automatically generating an ontological knowledge base from a plain text document. In this paper, we propose a new ontology learning approach and tool, called DLOL, which generates a knowledge base in the description logic (DL) SHOQ(D) from a collection of factual non-negative IS-A sentences in English. We provide extensive experimental results on the accuracy of DLOL, giving experimental comparisons to three state-of-the-art existing OL tools, namely Text2Onto, FRED, and LExO. Here, we use the standard OL accuracy measure, called lexical accuracy, and a novel OL accuracy measure, called instance-based inference model. In our experimental results, DLOL turns out to be about 21% and 46%, respectively, better than the best of the other three approaches.
AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applications already using mobile hardware, and even browsers for computationally demanding AI applications, we are already witnessing the emergence of client-side (federated) machine learning algorithms, driven by the interests of large corporations and startups alike. Apart from mathematical and algorithmic concerns, this trend especially demands new levels of computational efficiency from client environments. Consequently, this paper deals with the question of state-of-the-art performance by presenting a comparison study between native code and different browser-based implementations: JavaScript, ASM.js as well as WebAssembly on a representative mix of algorithms. Our results show that current efforts in runtime optimization push the boundaries well towards (and even beyond) native binary performance. We analyze the results obtained and speculate on the reasons behind some surprises, rounding the paper off by outlining future possibilities as well as some of our own research efforts.
We propose a connectionist-inspired kernel machine model with three key advantages over traditional kernel machines. First, it is capable of learning distributed and hierarchical representations. Second, its performance is highly robust to the choice of kernel function. Third, the solution space is not limited to the span of images of training data in reproducing kernel Hilbert space (RKHS). Together with the architecture, we propose a greedy learning algorithm that allows the proposed multilayer network to be trained layer-wise without backpropagation by optimizing the geometric properties of images in RKHS. With a single fixed generic kernel for each layer and two layers in total, our model compares favorably with state-of-the-art multiple kernel learning algorithms using significantly more kernels and popular deep architectures on widely used classification benchmarks.
We study the problem of explaining a rich class of behavioral properties of deep neural networks. Distinctively, our influence-directed explanations approach this problem by peering inside the net- work to identify neurons with high influence on the property and distribution of interest using an axiomatically justified influence measure, and then providing an interpretation for the concepts these neurons represent. We evaluate our approach by training convolutional neural net- works on MNIST, ImageNet, Pubfig, and Diabetic Retinopathy datasets. Our evaluation demonstrates that influence-directed explanations (1) identify influential concepts that generalize across instances, (2) help extract the essence of what the network learned about a class, (3) isolate individual features the network uses to make decisions and distinguish related instances, and (4) assist in understanding misclassifications.
In general, neural networks are not currently capable of learning tasks in a sequential fashion. When a novel, unrelated task is learnt by a neural network, it substantially forgets how to solve previously learnt tasks. One of the original solutions to this problem is pseudo-rehearsal, which involves learning the new task while rehearsing generated items representative of the previous task/s. This is very effective for simple tasks. However, pseudo-rehearsal has not yet been successfully applied to very complex tasks because in these tasks it is difficult to generate representative items. We accomplish pseudo-rehearsal by using a Generative Adversarial Network to generate items so that our deep network can learn to sequentially classify the CIFAR-10, SVHN and MNIST datasets. After training on all tasks, our network loses only 1.67% absolute accuracy on CIFAR-10 and gains 0.24% absolute accuracy on SVHN. Our model's performance is a substantial improvement compared to the current state of the art solution.
Faced with distribution shift between training and test set, we wish to detect and quantify the shift, and to correct our classifiers without test set labels. Motivated by medical diagnosis, where diseases (targets), cause symptoms (observations), we focus on label shift, where the label marginal $p(y)$ changes but the conditional $p(x|y)$ does not. We propose Black Box Shift Estimation (BBSE) to estimate the test distribution $p(y)$. BBSE exploits arbitrary black box predictors to reduce dimensionality prior to shift correction. While better predictors give tighter estimates, BBSE works even when predictors are biased, inaccurate, or uncalibrated, so long as their confusion matrices are invertible. We prove BBSE's consistency, bound its error, and introduce a statistical test that uses BBSE to detect shift. We also leverage BBSE to correct classifiers. Experiments demonstrate accurate estimates and improved prediction, even on high-dimensional datasets of natural images.
In this note we describe an application of Wasserstein distance to Reinforcement Learning. The Wasserstein distance in question is between the distribution of mappings of trajectories of a policy into some metric space, and some other fixed distribution (which may, for example, come from another policy). Different policies induce different distributions, so given an underlying metric, the Wasserstein distance quantifies how different policies are. This can be used to learn multiple polices which are different in terms of such Wasserstein distances by using a Wasserstein regulariser. Changing the sign of the regularisation parameter, one can learn a policy for which its trajectory mapping distribution is attracted to a given fixed distribution.
Watchlist (also hint list) is a mechanism that allows related proofs to guide a proof search for a new conjecture. This mechanism has been used with the Otter and Prover9 theorem provers, both for interactive formalizations and for human-assisted proving of open conjectures in small theories. In this work we explore the use of watchlists in large theories coming from first-order translations of large ITP libraries, aiming at improving hammer-style automation by smarter internal guidance of the ATP systems. In particular, we (i) design watchlist-based clause evaluation heuristics inside the E ATP system, and (ii) develop new proof guiding algorithms that load many previous proofs inside the ATP and focus the proof search using a dynamically updated notion of proof matching. The methods are evaluated on a large set of problems coming from the Mizar library, showing significant improvement of E's standard portfolio of strategies, and also of the previous best set of strategies invented for Mizar by evolutionary methods.
In this paper, we deal with a calculus system SLCD (Syllogistic Logic with Carroll Diagrams), which gives a formal approach to logical reasoning with diagrams, for representations of the fundamental Aristotelian categorical propositions and show that they are closed under the syllogistic criterion of inference which is the deletion of middle term. Therefore, it is implemented to let the formalism comprise synchronically bilateral and trilateral diagrammatical appearance and a naive algorithmic nature. And also, there is no need specific knowledge or exclusive ability to understand as well as to use it. Consequently, we give an effective algorithm used to determine whether a syllogistic reasoning valid or not by using SLCD.
Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. As the representation learned captures the variation in the environment generated by agents, this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning. This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment, their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research.
Noisy observations coupled with nonlinear dynamics pose one of the biggest challenges in robot motion planning. By decomposing the nonlinear dynamics into a discrete set of local dynamics models, hybrid dynamics provide a natural way to model nonlinear dynamics, especially in systems with sudden "jumps" in the dynamics, due to factors such as contacts. We propose a hierarchical POMDP planner that develops locally optimal motion plans for hybrid dynamics models. The hierarchical planner first develops a high-level motion plan to sequence the local dynamics models to be visited. The high-level plan is then converted into a detailed cost-optimized continuous state plan. This hierarchical planning approach results in a decomposition of the POMDP planning problem into smaller sub-parts that can be solved with significantly lower computational costs. The ability to sequence the visitation of local dynamics models also provides a powerful way to leverage the hybrid dynamics to reduce state uncertainty. We evaluate the proposed planner for two navigation and localization tasks in simulated domains, as well as an assembly task with a real robotic manipulator.
We present an end-to-end framework for solving Vehicle Routing Problem (VRP) using deep reinforcement learning. In this approach, we train a single model that finds near-optimal solutions for problem instances sampled from a given distribution, only by observing the reward signals and following feasibility rules. Our model represents a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance. Our method is faster in both training and inference than a recent method that solves the Traveling Salesman Problem (TSP), with nearly identical solution quality. On the more general VRP, our approach outperforms classical heuristics on medium-sized instances in both solution quality and computation time (after training). Our proposed framework can be applied to variants of the VRP such as the stochastic VRP, and has the potential to be applied more generally to combinatorial optimization problems.
In this work, we propose a simple but effective method to interpret black-box machine learning models globally. That is, we use a compact binary tree, the interpretation tree, to explicitly represent the most important decision rules that are implicitly contained in the black-box machine learning models. This tree is learned from the contribution matrix which consists of the contributions of input variables to predicted scores for each single prediction. To generate the interpretation tree, a unified process recursively partitions the input variable space by maximizing the difference in the average contribution of the split variable between the divided spaces. We demonstrate the effectiveness of our method in diagnosing machine learning models on multiple tasks. Also, it is useful for new knowledge discovery as such insights are not easily identifiable when only looking at single predictions. In general, our work makes it easier and more efficient for human beings to understand machine learning models.
Modern reinforcement learning algorithms reach super-human performance in many board and video games, but they are sample inefficient, i.e. they typically require significantly more playing experience than humans to reach an equal performance level. To improve sample efficiency, an agent may build a model of the environment and use planning methods to update its policy. In this article we introduce VaST (Variational State Tabulation), which maps an environment with a high-dimensional state space (e.g. the space of visual inputs) to an abstract tabular environment. Prioritized sweeping with small backups, a highly efficient planning method, can then be used to update state-action values. We show how VaST can rapidly learn to maximize reward in tasks like 3D navigation and efficiently adapt to sudden changes in rewards or transition probabilities.
Motivated by problems in data clustering, we establish general conditions under which families of nonparametric mixture models are identifiable by introducing a novel framework for clustering overfitted \emph{parametric} (i.e. misspecified) mixture models. These conditions generalize existing conditions in the literature, and are flexible enough to include for example mixtures of Gaussian mixtures. In contrast to the recent literature on estimating nonparametric mixtures, we allow for general nonparametric mixture components, and instead impose regularity assumptions on the underlying mixing measure. As our primary application, we apply these results to partition-based clustering, generalizing the well-known notion of a Bayes optimal partition from classical model-based clustering to nonparametric settings. Furthermore, this framework is constructive in that it yields a practical algorithm for learning identified mixtures, which is illustrated through several examples. The key conceptual device in the analysis is the convex, metric geometry of probability distributions on metric spaces and its connection to optimal transport and the Wasserstein convergence of mixing measures. The result is a flexible framework for nonparametric clustering with formal consistency guarantees.
Training large neural networks requires distributing learning across multiple workers, where the cost of communicating gradients can be a significant bottleneck. signSGD alleviates this problem by transmitting just the sign of each minibatch stochastic gradient. We prove that it can get the best of both worlds: compressed gradients and SGD-level convergence rate. signSGD can exploit mismatches between L1 and L2 geometry: when noise and curvature are much sparser than the gradients, signSGD is expected to converge at the same rate or faster than full-precision SGD. Measurements of the L1 versus L2 geometry of real networks support our theoretical claims, and we find that the momentum counterpart of signSGD is able to match the accuracy and convergence speed of Adam on deep Imagenet models. We extend our theory to the distributed setting, where the parameter server uses majority vote to aggregate gradient signs from each worker enabling 1-bit compression of worker-server communication in both directions. Using a theorem by Gauss, we prove that the non-convex convergence rate of majority vote matches that of distributed SGD. Thus, there is great promise for sign-based optimisation schemes to achieve both communication efficiency and high accuracy.
We use model-free reinforcement learning, extensive simulation, and transfer learning to develop a continuous control algorithm that has good zero-shot performance in a real physical environment. We train a simulated agent to act optimally across a set of similar environments, each with dynamics drawn from a prior distribution. We propose that the agent is able to adjust its actions almost immediately, based on small set of observations. This robust and adaptive behavior is enabled by using a policy gradient algorithm with an Long Short Term Memory (LSTM) function approximation. Finally, we train an agent to navigate a two-dimensional environment with uncertain dynamics and noisy observations. We demonstrate that this agent has good zero-shot performance in a real physical environment. Our preliminary results indicate that the agent is able to infer the environmental dynamics after only a few timesteps, and adjust its actions accordingly.
Sentential decision diagrams (SDDs) introduced by Darwiche in 2011 are a promising representation type used in knowledge compilation. The relative succinctness of representation types is an important subject in this area. The aim of the paper is to identify which kind of Boolean functions can be represented by SDDs of small size with respect to the number of variables the functions are defined on. For this reason the sets of Boolean functions representable by different representation types in polynomial size are investigated and SDDs are compared with representation types from the classical knowledge compilation map of Darwiche and Marquis. Ordered binary decision diagrams (OBDDs) which are a popular data structure for Boolean functions are one of these representation types. SDDs are more general than OBDDs by definition but only recently, a Boolean function was presented with polynomial SDD size but exponential OBDD size. This result is strengthened in several ways. The main result is a quasipolynomial simulation of SDDs by equivalent unambiguous nondeterministic OBDDs, a nondeterministic variant where there exists exactly one accepting computation for each satisfying input. As a side effect an open problem about the relative succinctness between SDDs and free binary decision diagrams (FBDDs) which are more general than OBDDs is answered.
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or sparse rewards. To tackle this problem, we present a diversity-driven approach for exploration, which can be easily combined with both off- and on-policy reinforcement learning algorithms. We show that by simply adding a distance measure to the loss function, the proposed methodology significantly enhances an agent's exploratory behaviors, and thus preventing the policy from being trapped in local optima. We further propose an adaptive scaling method for stabilizing the learning process. Our experimental results in Atari 2600 show that our method outperforms baseline approaches in several tasks in terms of mean scores and exploration efficiency.
In recent years, the importance of deep learning has significantly increased in pattern recognition, computer vision, and artificial intelligence research, as well as in industry. However, despite the existence of multiple deep learning frameworks, there is a lack of comprehensible and easy-to-use high-level tools for the design, training, and testing of deep neural networks (DNNs). In this paper, we introduce Barista, an open-source graphical high-level interface for the Caffe deep learning framework. While Caffe is one of the most popular frameworks for training DNNs, editing prototext files in order to specify the net architecture and hyper parameters can become a cumbersome and error-prone task. Instead, Barista offers a fully graphical user interface with a graph-based net topology editor and provides an end-to-end training facility for DNNs, which allows researchers to focus on solving their problems without having to write code, edit text files, or manually parse logged data.
In this work, we present a weakly supervised sentence extraction technique for identifying important sentences in scientific papers that are worthy of inclusion in the abstract. We propose a new attention based deep learning architecture that jointly learns to identify important content, as well as the cue phrases that are indicative of summary worthy sentences. We propose a new context embedding technique for determining the focus of a given paper using topic models and use it jointly with an LSTM based sequence encoder to learn attention weights across the sentence words. We use a collection of articles publicly available through ACL anthology for our experiments. Our system achieves a performance that is better, in terms of several ROUGE metrics, as compared to several state of art extractive techniques. It also generates more coherent summaries and preserves the overall structure of the document.
Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine expert policies, as evaluated in the domain of simulated bipedal locomotion across different classes of terrain. We also introduce an input injection method for augmenting an existing policy network to exploit new input features. Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills. The combination of these methods allows a policy to be incrementally augmented with new skills. We compare our progressive learning and integration via distillation (PLAID) method against three alternative baselines.
We propose a meta-learning approach for learning gradient-based reinforcement learning (RL) algorithms. The idea is to evolve a differentiable loss function, such that an agent, which optimizes its policy to minimize this loss, will achieve high rewards. The loss is parametrized via temporal convolutions over the agent's experience. Because this loss is highly flexible in its ability to take into account the agent's history, it enables fast task learning and eliminates the need for reward shaping at test time. Empirical results show that our evolved policy gradient algorithm achieves faster learning on several randomized environments compared to an off-the-shelf policy gradient method. Moreover, at test time, our learner optimizes only its learned loss function, and requires no explicit reward signal. In effect, the agent internalizes the reward structure, suggesting a direction toward agents that learn to solve new tasks simply from intrinsic motivation.
There is no denying the tremendous leap in the performance of machine learning methods in the past half-decade. Some might even say that specific sub-fields in pattern recognition, such as machine-vision, are as good as solved, reaching human and super-human levels. Arguably, lack of training data and computation power are all that stand between us and solving the remaining ones. In this position paper we underline cases in vision which are challenging to machines and even to human observers. This is to show limitations of contemporary models that are hard to ameliorate by following the current trend to increase training data, network capacity or computational power. Moreover, we claim that attempting to do so is in principle a suboptimal approach. We provide a taster of such examples in hope to encourage and challenge the machine learning community to develop new directions to solve the said difficulties.
In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards. This paper argues that such research has overlooked an important and useful intrinsic motivator: social interaction. We posit that making an AI agent aware of implicit social feedback from humans can allow for faster learning of more generalizable and useful representations, and could potentially impact AI safety. We collect social feedback in the form of facial expression reactions to samples from Sketch RNN, an LSTM-based variational autoencoder (VAE) designed to produce sketch drawings. We use a Latent Constraints GAN (LC-GAN) to learn from the facial feedback of a small group of viewers, and then show in an independent evaluation with 76 users that this model produced sketches that lead to significantly more positive facial expressions. Thus, we establish that implicit social feedback can improve the output of a deep learning model.
Human behavior understanding is arguably one of the most important mid-level components in artificial intelligence. In order to efficiently make use of data, multi-task learning has been studied in diverse computer vision tasks including human behavior understanding. However, multi-task learning relies on task specific datasets and constructing such datasets can be cumbersome. It requires huge amounts of data, labeling efforts, statistical consideration etc. In this paper, we leverage existing single-task datasets for human action classification and captioning data for efficient human behavior learning. Since the data in each dataset has respective heterogeneous annotations, traditional multi-task learning is not effective in this scenario. To this end, we propose a novel alternating directional optimization method to efficiently learn from the heterogeneous data. We demonstrate the effectiveness of our model and show performance improvements on both classification and sentence retrieval tasks in comparison to the models trained on each of the single-task datasets.
The problem of rating the performance of soccer players is attracting the interest of many companies, websites, and the scientific community, thanks to the availability of massive data capturing all the events generated during a game (e.g., tackles, passes, shots, etc.). Existing approaches fail to fully exploit the richness of the available data and lack of a proper validation. In this paper, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We validate the framework through an experimental analysis advised by soccer experts, based on a massive dataset of millions of events pertaining four seasons of the five prominent European leagues. Experiments show that PlayeRank is robust in agreeing with the experts' evaluation of players, significantly improving the state of the art. We also explore an application of PlayeRank --- i.e. searching players --- by introducing a special form of spatial query on the soccer field. This shows its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.
Patients in the intensive care unit (ICU) require constant and close supervision. To assist clinical staff in this task, hospitals use monitoring systems that trigger audiovisual alarms if their algorithms indicate that a patient's condition may be worsening. However, current monitoring systems are extremely sensitive to movement artefacts and technical errors. As a result, they typically trigger hundreds to thousands of false alarms per patient per day - drowning the important alarms in noise and adding to the exhaustion of clinical staff. In this setting, data is abundantly available, but obtaining trustworthy annotations by experts is laborious and expensive. We frame the problem of false alarm reduction from multivariate time series as a machine-learning task and address it with a novel multitask network architecture that utilises distant supervision through multiple related auxiliary tasks in order to reduce the number of expensive labels required for training. We show that our approach leads to significant improvements over several state-of-the-art baselines on real-world ICU data and provide new insights on the importance of task selection and architectural choices in distantly supervised multitask learning.
The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties in input parameters. Additionally, due to changes in the system in time and in order to increase the accuracy and robustness of the prediction, the Ensemble Kalman Filter (EnKF) is used to update the flow rate predictions based on new observations. The developed approach was tested on the data from two mature gas production wells in which their production is highly dynamic and suffering from salt deposition. The results show that the flow predictions using the EnKF updated model leads to better Jeffreys' J-divergences than the predictions without the EnKF model updating scheme.
This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap and images from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves around the murder of a person with a Wikipedia article and populates the game with suspects who must be arrested by the player if guilty of the murder or absolved if innocent. Starting from only one person as the victim, an extensive generative pipeline finds suspects, their alibis, and paths connecting them from open data, transforms open data into cities, buildings, non-player characters, locks and keys and dialog options. The paper describes in detail each generative step, provides a specific playthrough of one WikiMystery where Albert Einstein is murdered, and evaluates the outcomes of games generated for the 100 most influential people of the 20th century.
Deep-embedding methods aim to discover representations of a domain that make explicit the domain's class structure. Disentangling methods aim to make explicit compositional or factorial structure. We combine these two active but independent lines of research and propose a new paradigm for discovering disentangled representations of class structure; these representations reveal the underlying factors that jointly determine class. We propose and evaluate a novel loss function based on the $F$ statistic, which describes the separation of two or more distributions. By ensuring that distinct classes are well separated on a subset of embedding dimensions, we obtain embeddings that are useful for few-shot learning. By not requiring separation on all dimensions, we encourage the discovery of disentangled representations. Our embedding procedure matches or beats state-of-the-art procedures on deep embeddings, as evaluated by performance on recall@$k$ and few-shot learning tasks. To evaluate alternative approaches on disentangling, we formalize two key properties of a disentangled representation: modularity and explicitness. By these criteria, our procedure yields disentangled representations, whereas traditional procedures fail. The goal of our work is to obtain more interpretable, manipulable, and generalizable deep representations of concepts and categories.
Robust real-world learning should benefit from both demonstrations and interactions with the environment. Current approaches to learning from demonstration and reward perform supervised learning on expert demonstration data and use reinforcement learning to further improve performance based on the reward received from the environment. These tasks have divergent losses which are difficult to jointly optimize and such methods can be very sensitive to noisy demonstrations. We propose a unified reinforcement learning algorithm, Normalized Actor-Critic (NAC), that effectively normalizes the Q-function, reducing the Q-values of actions unseen in the demonstration data. NAC learns an initial policy network from demonstrations and refines the policy in the environment, surpassing the demonstrator's performance. Crucially, both learning from demonstration and interactive refinement use the same objective, unlike prior approaches that combine distinct supervised and reinforcement losses. This makes NAC robust to suboptimal demonstration data since the method is not forced to mimic all of the examples in the dataset. We show that our unified reinforcement learning algorithm can learn robustly and outperform existing baselines when evaluated on several realistic driving games.
Despite the recent successes of deep neural networks in various fields such as image and speech recognition, natural language processing, and reinforcement learning, we still face big challenges in bringing the power of numeric optimization to symbolic reasoning. Researchers have proposed different avenues such as neural machine translation for proof synthesis, vectorization of symbols and expressions for representing symbolic patterns, and coupling of neural back-ends for dimensionality reduction with symbolic front-ends for decision making. However, these initial explorations are still only point solutions, and bear other shortcomings such as lack of correctness guarantees. In this paper, we present our approach of casting symbolic reasoning as games, and directly harnessing the power of deep reinforcement learning in the style of Alpha(Go) Zero on symbolic problems. Using the Boolean Satisfiability (SAT) problem as showcase, we demonstrate the feasibility of our method, and the advantages of modularity, efficiency, and correctness guarantees.
Multi-channel speech enhancement with ad-hoc sensors has been a challenging task. Speech model guided beamforming algorithms are able to recover natural sounding speech, but the speech models tend to be oversimplified or the inference would otherwise be too complicated. On the other hand, deep learning based enhancement approaches are able to learn complicated speech distributions and perform efficient inference, but they are unable to deal with variable number of input channels. Also, deep learning approaches introduce a lot of errors, particularly in the presence of unseen noise types and settings. We have therefore proposed an enhancement framework called DEEPBEAM, which combines the two complementary classes of algorithms. DEEPBEAM introduces a beamforming filter to produce natural sounding speech, but the filter coefficients are determined with the help of a monaural speech enhancement neural network. Experiments on synthetic and real-world data show that DEEPBEAM is able to produce clean, dry and natural sounding speech, and is robust against unseen noise.
Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Existing methods depend either on limited active variables or the additive form of the objective function. We propose a new method for high-dimensional Bayesian optimization, that uses a dropout strategy to optimize only a subset of variables at each iteration. We derive theoretical bounds for the regret and show how it can inform the derivation of our algorithm. We demonstrate the efficacy of our algorithms for optimization on two benchmark functions and two real-world applications- training cascade classifiers and optimizing alloy composition.
Existing multi-agent reinforcement learning methods are limited typically to a small number of agents. When the agent number increases largely, the learning becomes intractable due to the curse of the dimensionality and the exponential growth of user interactions. In this paper, we present Mean Field Reinforcement Learning where the interactions within the population of agents are approximated by those between a single agent and the average effect from the overall population or neighboring agents; the interplay between the two entities is mutually reinforced: the learning of the individual agent's optimal policy depends on the dynamics of the population, while the dynamics of the population change according to the collective patterns of the individual policies. We develop practical mean field Q-learning and mean field Actor-Critic algorithms and analyze the convergence of the solution. Experiments on resource allocation, Ising model estimation, and battle game tasks verify the learning effectiveness of our mean field approaches in handling many-agent interactions in population.
The discovery of time series motifs has emerged as one of the most useful primitives in time series data mining. Researchers have shown its utility for exploratory data mining, summarization, visualization, segmentation, classification, clustering, and rule discovery. Although there has been more than a decade of extensive research, there is still no technique to allow the discovery of time series motifs in the presence of missing data, despite the well-documented ubiquity of missing data in scientific, industrial, and medical datasets. In this work, we introduce a technique for motif discovery in the presence of missing data. We formally prove that our method is admissible, producing no false negatives. We also show that our method can piggy-back off the fastest known motif discovery method with a small constant factor time/space overhead. We will demonstrate our approach on diverse datasets with varying amounts of missing data
During sleep and awake rest, the hippocampus replays sequences of place cells that have been activated during prior experiences. These have been interpreted as a memory consolidation process, but recent results suggest a possible interpretation in terms of reinforcement learning. The Dyna reinforcement learning algorithms use off-line replays to improve learning. Under limited replay budget, a prioritized sweeping approach, which requires a model of the transitions to the predecessors, can be used to improve performance. We investigate whether such algorithms can explain the experimentally observed replays. We propose a neural network version of prioritized sweeping Q-learning, for which we developed a growing multiple expert algorithm, able to cope with multiple predecessors. The resulting architecture is able to improve the learning of simulated agents confronted to a navigation task. We predict that, in animals, learning the world model should occur during rest periods, and that the corresponding replays should be shuffled.
In the modern era, abundant information is easily accessible from various sources, however only a few of these sources are reliable as they mostly contain unverified contents. We develop a system to validate the truthfulness of a given statement together with underlying evidence. The proposed system provides supporting evidence when the statement is tagged as false. Our work relies on an inference method on a knowledge graph (KG) to identify the truthfulness of statements. In order to extract the evidence of falseness, the proposed algorithm takes into account combined knowledge from KG and ontologies. The system shows very good results as it provides valid and concise evidence. The quality of KG plays a role in the performance of the inference method which explicitly affects the performance of our evidence-extracting algorithm.
Given a target name, which can be a product aspect or entity, identifying its aspect words and opinion words in a given corpus is a fine-grained task in target-based sentiment analysis (TSA). This task is challenging, especially when we have no labeled data and we want to perform it for any given domain. To address it, we propose a general two-stage approach. Stage one extracts/groups the target-related words (call t-words) for a given target. This is relatively easy as we can apply an existing semantics-based learning technique. Stage two separates the aspect and opinion words from the grouped t-words, which is challenging because we often do not have enough word-level aspect and opinion labels. In this work, we formulate this problem in a PU learning setting and incorporate the idea of lifelong learning to solve it. Experimental results show the effectiveness of our approach.
Integrated task and motion planning has emerged as a challenging problem in sequential decision making, where a robot needs to compute high-level strategy and low-level motion plans for solving complex tasks. While high-level strategies require decision making over longer time-horizons and scales, their feasibility depends on low-level constraints based upon the geometries and continuous dynamics of the environment. The hybrid nature of this problem makes it difficult to scale; most existing approaches focus on deterministic, fully observable scenarios. We present a new approach where the high-level decision problem occurs in a stochastic setting and can be modeled as a Markov decision process. In contrast to prior efforts, we show that complete MDP policies, or contingent behaviors, can be computed effectively in an anytime fashion. Our algorithm continuously improves the quality of the solution and is guaranteed to be probabilistically complete. We evaluate the performance of our approach on a challenging, realistic test problem: autonomous aircraft inspection. Our results show that we can effectively compute consistent task and motion policies for the most likely execution-time outcomes using only a fraction of the computation required to develop the complete task and motion policy.
In this paper, we unify causal and non-causal feature selection methods based on the Bayesian network framework. We first show that the objectives of causal and non-causal feature selection methods are equal and are to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We demonstrate that causal and non-causal feature selection take different assumptions of dependency among features to find Markov blanket, and their algorithms are shown different level of approximation for finding Markov blanket. In this framework, we are able to analyze the sample and error bounds of casual and non-causal methods. We conducted extensive experiments to show the correctness of our theoretical analysis.
This work exploits translation data as a source of semantically relevant learning signal for models of word representation. In particular, we exploit equivalence through translation as a form of distributed context and jointly learn how to embed and align with a deep generative model. Our EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. Besides, it embeds words as posterior probability densities, rather than point estimates, which allows us to compare words in context using a measure of overlap between distributions (e.g. KL divergence). We investigate our model's performance on a range of lexical semantics tasks achieving competitive results on several standard benchmarks including natural language inference, paraphrasing, and text similarity.
We present a technique for estimating the similarity between objects such as movies or foods whose proper representation depends on human perception. Our technique combines a modest number of human similarity assessments to infer a pairwise similarity function between the objects. This similarity function captures some human notion of similarity which may be difficult or impossible to automatically extract, such as which movie from a collection would be a better substitute when the desired one is unavailable. In contrast to prior techniques, our method does not assume that all similarity questions on the collection can be answered or that all users perceive similarity in the same way. When combined with a user model, we find how each assessor's tastes vary, affecting their perception of similarity.
Recently, the interest in reinforcement learning in game playing has been renewed. This is evidenced by the groundbreaking results achieved by AlphaGo. General Game Playing (GGP) provides a good testbed for reinforcement learning, currently one of the hottest fields of AI. In GGP, a specification of games rules is given. The description specifies a reinforcement learning problem, leaving programs to find strategies for playing well. Q-learning is one of the canonical reinforcement learning methods, which is used as baseline on some previous work (Banerjee & Stone, IJCAI 2007). We implement Q-learning in GGP for three small board games (Tic-Tac-Toe, Connect-Four, Hex). We find that Q-learning converges, and thus that this general reinforcement learning method is indeed applicable to General Game Playing. However, convergence is slow, in comparison to MCTS (a reinforcement learning method reported to achieve good results). We enhance Q-learning with Monte Carlo Search. This enhancement improves performance of pure Q-learning, although it does not yet out-perform MCTS. Future work is needed into the relation between MCTS and Q-learning, and on larger problem instances.
Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown objects. In this work we improve on one of the most promising approaches, the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We propose a new architecture for the GQ-CNN and describe practical improvements that increase the model validation accuracy from 92.2% to 95.8% and from 85.9% to 88.0% on respectively image-wise and object-wise training and validation splits.
The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we present a procedure to statistically test hypotheses about model performance which goes beyond the state-of-the-practice in the community to analyze both algorithms and feature extraction methods from raw data. We apply this method to a series of algorithms and feature sets derived from a large sample of Massive Open Online Courses (MOOCs). While a complete comparison of all potential modeling approaches is beyond the scope of this paper, we show that this approach reveals a large gap in dropout prediction performance between forum-, assignment-, and clickstream-based feature extraction methods, where the latter is significantly better than the former two, which are in turn indistinguishable from one another. This work has methodological implications for evaluating predictive or AI-based models of student success, and practical implications for the design and targeting of at-risk student models and interventions.
Although chatbots have been very popular in recent years, they still have some serious weaknesses which limit the scope of their applications. One major weakness is that they cannot learn new knowledge during the conversation process, i.e., their knowledge is fixed beforehand and cannot be expanded or updated during conversation. In this paper, we propose to build a general knowledge learning engine for chatbots to enable them to continuously and interactively learn new knowledge during conversations. As time goes by, they become more and more knowledgeable and better and better at learning and conversation. We model the task as an open-world knowledge base completion problem and propose a novel technique called lifelong interactive learning and inference (LiLi) to solve it. LiLi works by imitating how humans acquire knowledge and perform inference during an interactive conversation. Our experimental results show LiLi is highly promising.
In this paper, we consider an online optimization process, where the objective functions are not convex (nor concave) but instead belong to a broad class of continuous submodular functions. We first propose a variant of the Frank-Wolfe algorithm that has access to the full gradient of the objective functions. We show that it achieves a regret bound of $O(\sqrt{T})$ (where $T$ is the horizon of the online optimization problem) against a $(1-1/e)$-approximation to the best feasible solution in hindsight. However, in many scenarios, only an unbiased estimate of the gradients are available. For such settings, we then propose an online stochastic gradient ascent algorithm that also achieves a regret bound of $O(\sqrt{T})$ regret, albeit against a weaker $1/2$-approximation to the best feasible solution in hindsight. We also generalize our results to $\gamma$-weakly submodular functions and prove the same sublinear regret bounds. Finally, we demonstrate the efficiency of our algorithms on a few problem instances, including non-convex/non-concave quadratic programs, multilinear extensions of submodular set functions, and D-optimal design.
Users of AI systems may rely upon them to produce plans for achieving desired objectives. Such AI systems should be able to compute obfuscated plans whose execution in adversarial situations protects privacy as well as legible plans which are easy for team-members to understand in collaborative situations. We develop a unified framework that addresses these dual problems by computing plans with a desired level of comprehensibility from the point of view of a partially informed observer. Our approach produces obfuscated plans with observations that are consistent with at least 'k' goals from a given set of decoy goals. In addition, when the goal is known to the observer, our approach generates obfuscated plans with observations that are diverse with at least 'l' candidate plans. Our approach for plan legibility produces plans that achieve a goal while being consistent with at most 'j' goals in a given set of confounding goals. We provide an empirical evaluation to show the feasibility and usefulness of our approaches.
Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational efficiency of planning under uncertainty and made it a valuable tool for robotics in practice. This work takes one step further by leveraging both CPU and GPU parallelization in order to achieve near real-time online planning performance for complex tasks with large state, action, and observation spaces. Specifically, we propose Hybrid Parallel DESPOT (HyP-DESPOT), a massively parallel online planning algorithm that integrates CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT tree search by simultaneously traversing multiple independent paths using multi-core CPUs and performs parallel Monte-Carlo simulations at the leaf nodes of the search tree using GPUs. Experimental results show that HyP-DESPOT speeds up online planning by up to several hundred times, compared with the original DESPOT algorithm, in several challenging robotic tasks in simulation.
Effective optimization is essential for interactive systems to provide a satisfactory user experience. However, it is often challenging to find an objective to optimize for. Generally, such objectives are manually crafted and rarely capture complex user needs accurately. Conversely, we propose an approach that infers the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. Then we introduce: Interactive System Optimizer (ISO), a novel algorithm that uses these inferred objectives for optimization. Our main contribution is a new general principled approach to optimizing interactive systems using data-driven objectives. We demonstrate the high effectiveness of ISO over several GridWorld simulations.
A common challenge in estimating parameters of probability density functions is the intractability of the normalizing constant. While in such cases maximum likelihood estimation may be implemented using numerical integration, the approach becomes computationally intensive. In contrast, the score matching method of Hyv\"arinen (2005) avoids direct calculation of the normalizing constant and yields closed-form estimates for exponential families of continuous distributions over $\mathbb{R}^m$. Hyv\"arinen (2007) extended the approach to distributions supported on the non-negative orthant $\mathbb{R}_+^m$. In this paper, we give a generalized form of score matching for non-negative data that improves estimation efficiency. We also generalize the regularized score matching method of Lin et al. (2016) for non-negative Gaussian graphical models, with improved theoretical guarantees.
In this paper we consider online mirror descent (OMD) algorithms, a class of scalable online learning algorithms exploiting data geometric structures through mirror maps. Necessary and sufficient conditions are presented in terms of the step size sequence $\{\eta_t\}_{t}$ for the convergence of an OMD algorithm with respect to the expected Bregman distance induced by the mirror map. The condition is $\lim_{t\to\infty}\eta_t=0, \sum_{t=1}^{\infty}\eta_t=\infty$ in the case of positive variances. It is reduced to $\sum_{t=1}^{\infty}\eta_t=\infty$ in the case of zero variances for which the linear convergence may be achieved by taking a constant step size sequence. A sufficient condition on the almost sure convergence is also given. We establish tight error bounds under mild conditions on the mirror map, the loss function, and the regularizer. Our results are achieved by some novel analysis on the one-step progress of the OMD algorithm using smoothness and strong convexity of the mirror map and the loss function.
String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVMs in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We present the first approximation named ESP+SFM for alignment kernels by leveraging a metric embedding named edit-sensitive parsing (ESP) and space-efficient feature maps (SFM) for random Fourier features (RFF) for large-scale string analyses. Input strings are projected into vectors of RFF by leveraging ESP and SFM. Then, SVMs are trained on the projected vectors, which enables to significantly improve the scalability of alignment kernels while preserving their prediction accuracies. We experimentally test ESP+ SFM on its ability to learn SVMs for large-scale string classifications with various massive string data, and we demonstrate the superior performance of ESP+SFM with respect to prediction accuracy, scalability and computation efficiency.
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Critically, the learner cannot in general know a priori the relevant time scale over which meaningful relationships will be observed. Widely used reinforcement learning algorithms discretize continuous time and use the Bellman equation to estimate exponentially-discounted future reward. However, exponential discounting introduces a time scale to the computation of value. Scaling is a serious problem in continuous time: efficient learning with scaled algorithms requires prior knowledge of the relevant scale. That is, with scaled algorithms one must know at least part of the solution to a problem prior to attempting a solution. We present a computational mechanism, developed based on work in psychology and neuroscience, for computing a scale-invariant timeline of future events. This mechanism efficiently computes a model for future time on a logarithmically-compressed scale, and can be used to generate a scale-invariant power-law-discounted estimate of expected future reward. Moreover, the representation of future time retains information about what will happen when, enabling flexible decision making based on future events. The entire timeline can be constructed in a single parallel operation.
Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportation efficiency. An efficient fleet management strategy not only can significantly improve the utilization of transportation resources but also increase the revenue and customer satisfaction. It is a challenging task to design an effective fleet management strategy that can adapt to an environment involving complex dynamics between demand and supply. Existing studies usually work on a simplified problem setting that can hardly capture the complicated stochastic demand-supply variations in high-dimensional space. In this paper we propose to tackle the large-scale fleet management problem using reinforcement learning, and propose a contextual multi-agent reinforcement learning framework including two concrete algorithms, namely contextual deep Q-learning and contextual multi-agent actor-critic, to achieve explicit coordination among a large number of agents adaptive to different contexts. We show significant improvements of the proposed framework over state-of-the-art approaches through extensive empirical studies.
Neural networks are very powerful learning systems, but they do not readily generalize from one task to the other. This is partly due to the fact that they do not learn in a compositional way, that is, by discovering skills that are shared by different tasks, and recombining them to solve new problems. In this paper, we explore the compositional generalization capabilities of recurrent neural networks (RNNs). We first propose the lookup table composition domain as a simple setup to test compositional behaviour and show that it is theoretically possible for a standard RNN to learn to behave compositionally in this domain when trained with standard gradient descent and provided with additional supervision. We then remove this additional supervision and perform a search over a large number of model initializations to investigate the proportion of RNNs that can still converge to a compositional solution. We discover that a small but non-negligible proportion of RNNs do reach partial compositional solutions even without special architectural constraints. This suggests that a combination of gradient descent and evolutionary strategies directly favouring the minority models that developed more compositional approaches might suffice to lead standard RNNs towards compositional solutions.
Constrained Markov Decision Process (CMDP) is a natural framework for reinforcement learning tasks with safety constraints, where agents learn a policy that maximizes the long-term reward while satisfying the constraints on the long-term cost. A canonical approach for solving CMDPs is the primal-dual method which updates parameters in primal and dual spaces in turn. Existing methods for CMDPs only use on-policy data for dual updates, which results in sample inefficiency and slow convergence. In this paper, we propose a policy search method for CMDPs called Accelerated Primal-Dual Optimization (APDO), which incorporates an off-policy trained dual variable in the dual update procedure while updating the policy in primal space with on-policy likelihood ratio gradient. Experimental results on a simulated robot locomotion task show that APDO achieves better sample efficiency and faster convergence than state-of-the-art approaches for CMDPs.
We provide a new computationally-efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models: in the classical Huber epsilon-contamination model and in heavy-tailed settings. Our workhorse is a novel robust variant of gradient descent, and we provide conditions under which our gradient descent variant provides accurate estimators in a general convex risk minimization problem. We provide specific consequences of our theory for linear regression, logistic regression and for estimation of the canonical parameters in an exponential family. These results provide some of the first computationally tractable and provably robust estimators for these canonical statistical models. Finally, we study the empirical performance of our proposed methods on synthetic and real datasets, and find that our methods convincingly outperform a variety of baselines.
Hierarchical learning (HL) is key to solving complex sequential decision problems with long horizons and sparse rewards. It allows learning agents to break-up large problems into smaller, more manageable subtasks. A common approach to HL, is to provide the agent with a number of high-level skills that solve small parts of the overall problem. A major open question, however, is how to identify a suitable set of reusable skills. We propose a principled approach that uses human demonstrations to infer a set of subgoals based on changes in the demonstration dynamics. Using these subgoals, we decompose the learning problem into an abstract high-level representation and a set of low-level subtasks. The abstract description captures the overall problem structure, while subtasks capture desired skills. We demonstrate that we can jointly optimize over both levels of learning. We show that the resulting method significantly outperforms previous baselines on two challenging problems: the Atari 2600 game Montezuma's Revenge, and a simulated robotics problem moving the ant robot through a maze.
In this paper, we introduce a novel approach for diagnosis of Parkinson's Disease (PD) based on deep Echo State Networks (ESNs). The identification of PD is performed by analyzing the whole time-series collected from a tablet device during the sketching of spiral tests, without the need for feature extraction and data preprocessing. We evaluated the proposed approach on a public dataset of spiral tests. The results of experimental analysis show that DeepESNs perform significantly better than shallow ESN model. Overall, the proposed approach obtains state-of-the-art results in the identification of PD on this kind of temporal data.
Hierarchical classification is supervised multi-class classification problem over the set of class labels organized according to a hierarchy. In this report, we study the work by Ramaswamy et. al. on hierarchical classification over symmetric tree distance loss. We extend the consistency of hierarchical classification algorithm over asymmetric tree distance loss. We design a $\mathcal{O}(nk\log{}n)$ algorithm to find Bayes optimal classification for a k-ary tree as a hierarchy. We show that under reasonable assumptions over asymmetric loss function, the Bayes optimal classification over this asymmetric loss can be found in $\mathcal{O}(k\log{}n)$. We exploit this insight and attempt to extend the Ova-Cascade algorithm \citet{ramaswamy2015convex} for hierarchical classification over the asymmetric loss.
Deep neural networks, although shown to be a successful class of machine learning algorithms, are known to be extremely unstable to adversarial perturbations. Improving the robustness of neural networks against these attacks is important, especially for security-critical applications. To defend against such attacks, we propose dividing the input image into multiple patches, denoising each patch independently, and reconstructing the image, without losing significant image content. This proposed defense mechanism is non-differentiable which makes it non-trivial for an adversary to apply gradient-based attacks. Moreover, we do not fine-tune the network with adversarial examples, making it more robust against unknown attacks. We present a thorough analysis of the tradeoff between accuracy and robustness against adversarial attacks. We evaluate our method under black-box, grey-box, and white-box settings. The proposed method outperforms the state-of-the-art by a significant margin on the ImageNet dataset under grey-box attacks while maintaining good accuracy on clean images. We also establish a strong baseline for a novel white-box attack.
We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach to this problem in both theory and practice involves performing source coding to first compress the text and then channel coding to add robustness for the transmission across the channel. This approach is optimal in terms of minimizing end-to-end distortion with arbitrarily large block lengths of both the source and channel codes when transmission is over discrete memoryless channels. However, the optimality of this approach is no longer ensured for documents of finite length and limitations on the length of the encoding. We will show in this scenario that we can achieve lower word error rates by developing a deep learning based encoder and decoder. While the approach of separate source and channel coding would minimize bit error rates, our approach preserves semantic information of sentences by first embedding sentences in a semantic space where sentences closer in meaning are located closer together, and then performing joint source and channel coding on these embeddings.
We propose a new way of deriving policy gradient updates for reinforcement learning. Our technique, based on Fourier analysis, recasts integrals that arise with expected policy gradients as convolutions and turns them into multiplications. The obtained analytical solutions allow us to capture the low variance benefits of EPG in a broad range of settings. For the critic, we treat trigonometric and radial basis functions, two function families with the universal approximation property. The choice of policy can be almost arbitrary, including mixtures or hybrid continuous-discrete probability distributions. Moreover, we derive a general family of sample-based estimators for stochastic policy gradients, which unifies existing results on sample-based approximation. We believe that this technique has the potential to shape the next generation of policy gradient approaches, powered by analytical results.
There is a growing interest within the AI research community to develop autonomous systems capable of explaining their behavior to users. One aspect of the explanation generation problem that has yet to receive much attention is the task of explaining plans to users whose level of expertise differ from that of the explainer. We propose an approach for addressing this problem by representing the user's model as an abstraction of the domain model that the planner uses. We present algorithms for generating minimal explanations in cases where this abstract human model is not known. We reduce the problem of generating explanation to a search over the space of abstract models and investigate possible greedy approximations for minimal explanations. We also empirically show that our approach can efficiently compute explanations for a variety of problems.
In this paper we construct preimage attack on the truncated variant of the MD4 hash function. Specifically, we study the MD4-39 function defined by the first 39 steps of the MD4 algorithm. We suggest a new attack on MD4-39, which develops the ideas proposed by H. Dobbertin in 1998. Namely, the special relaxation constraints are introduced in order to simplify the equations corresponding to the problem of finding a preimage for an arbitrary MD4-39 hash value. The equations supplemented with the relaxation constraints are then reduced to the Boolean Satisfiability Problem (SAT) and solved using the state-of-the-art SAT solvers. We show that the effectiveness of a set of relaxation constraints can be evaluated using the black-box function of a special kind. Thus, we suggest automatic method of relaxation constraints generation by applying the black-box optimization to this function. The proposed method made it possible to find new relaxation constraints that contribute to a SAT-based preimage attack on MD4-39 which significantly outperforms the competition.
Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc. Important though the problem is, we are unaware of any benchmark document level data that correctly addresses the evaluation of automatic novelty detection techniques in a classification framework. To bridge this gap, we present here a resource for benchmarking the techniques for document level novelty detection. We create the resource via event-specific crawling of news documents across several domains in a periodic manner. We release the annotated corpus with necessary statistics and show its use with a developed system for the problem in concern.
The problem of identifying end-use electrical appliances from their individual consumption profiles, known as the appliance identification problem, is a primary stage in both Non-Intrusive Load Monitoring (NILM) and automated plug-wise metering. Therefore, appliance identification has received dedicated studies with various electric appliance signatures, classification models, and evaluation datasets. In this paper, we propose a neural network ensembles approach to address this problem using high resolution measurements. The models are trained on the raw current and voltage waveforms, and thus, eliminating the need for well engineered appliance signatures. We evaluate the proposed model on a publicly available appliance dataset from 55 residential buildings, 11 appliance categories, and over 1000 measurements. We further study the stability of the trained models with respect to training dataset, sampling frequency, and variations in the steady-state operation of appliances.
We study the problem of maximizing a monotone set function subject to a cardinality constraint $k$ in the setting where some number of elements $\tau$ is deleted from the returned set. The focus of this work is on the worst-case adversarial setting. While there exist constant-factor guarantees when the function is submodular, there are no guarantees for non-submodular objectives. In this work, we present a new algorithm Oblivious-Greedy and prove the first constant-factor approximation guarantees for a wider class of non-submodular objectives. The obtained theoretical bounds are the first constant-factor bounds that also hold in the linear regime, i.e. when the number of deletions $\tau$ is linear in $k$. Our bounds depend on established parameters such as the submodularity ratio and some novel ones such as the inverse curvature. We bound these parameters for two important objectives including support selection and variance reduction. Finally, we numerically demonstrate the robust performance of Oblivious-Greedy for these two objectives on various datasets.
Chatbots, taking advantage of the success of the messaging apps and recent advances in Artificial Intelligence, have become very popular, from helping business to improve customer services to chatting to users for the sake of conversation and engagement (celebrity or personal bots). However, developing and improving a chatbot requires understanding their data generated by its users. Dialog data has a different nature of a simple question and answering interaction, in which context and temporal properties (turn order) creates a different understanding of such data. In this paper, we propose a novelty metric to compute dialogs' similarity based not only on the text content but also on the information related to the dialog structure. Our experimental results performed over the Switchboard dataset show that using evidence from both textual content and the dialog structure leads to more accurate results than using each measure in isolation.
Exploration is a fundamental challenge in reinforcement learning (RL). Many of the current exploration methods for deep RL use task-agnostic objectives, such as information gain or bonuses based on state visitation. However, many practical applications of RL involve learning more than a single task, and prior tasks can be used to inform how exploration should be performed in new tasks. In this work, we explore how prior tasks can inform an agent about how to explore effectively in new situations. We introduce a novel gradient-based fast adaptation algorithm -- model agnostic exploration with structured noise (MAESN) -- to learn exploration strategies from prior experience. The prior experience is used both to initialize a policy and to acquire a latent exploration space that can inject structured stochasticity into a policy, producing exploration strategies that are informed by prior knowledge and are more effective than random action-space noise. We show that MAESN is more effective at learning exploration strategies when compared to prior meta-RL methods, RL without learned exploration strategies, and task-agnostic exploration methods. We evaluate our method on a variety of simulated tasks: locomotion with a wheeled robot, locomotion with a quadrupedal walker, and object manipulation.
Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to mathematically formalize these abilities using a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically naturalistic simulated environment in which an agent can move and interact with objects it sees, we propose a "world-model" network that learns to predict the dynamic consequences of the agent's actions. Simultaneously, we train a separate explicit "self-model" that allows the agent to track the error map of its own world-model, and then uses the self-model to adversarially challenge the developing world-model. We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering. Moreover, the world-model that the agent learns supports improved performance on object dynamics prediction, detection, localization and recognition tasks. Taken together, our results are initial steps toward creating flexible autonomous agents that self-supervise in complex novel physical environments.
Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to replicate some of these abilities with a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically naturalistic simulated environment in which the agent can move and interact with objects it sees, the agent learns a world model predicting the dynamic consequences of its actions. Simultaneously, the agent learns to take actions that adversarially challenge the developing world model, pushing the agent to explore novel and informative interactions with its environment. We demonstrate that this policy leads to the self-supervised emergence of a spectrum of complex behaviors, including ego motion prediction, object attention, and object gathering. Moreover, the world model that the agent learns supports improved performance on object dynamics prediction and localization tasks. Our results are a proof-of-principle that computational models of intrinsic motivation might account for key features of developmental visuomotor learning in infants.
This paper introduces epistemic graphs as a generalization of the epistemic approach to probabilistic argumentation. In these graphs, an argument can be believed or disbelieved up to a given degree, thus providing a more fine--grained alternative to the standard Dung's approaches when it comes to determining the status of a given argument. Furthermore, the flexibility of the epistemic approach allows us to both model the rationale behind the existing semantics as well as completely deviate from them when required. Epistemic graphs can model both attack and support as well as relations that are neither support nor attack. The way other arguments influence a given argument is expressed by the epistemic constraints that can restrict the belief we have in an argument with a varying degree of specificity. The fact that we can specify the rules under which arguments should be evaluated and we can include constraints between unrelated arguments permits the framework to be more context--sensitive. It also allows for better modelling of imperfect agents, which can be important in multi--agent applications.
In this paper we propose a novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network. Given an input we find what should be minimally and sufficiently present (viz. important object pixels in an image) to justify its classification and analogously what should be minimally and necessarily \emph{absent} (viz. certain background pixels). We argue that such explanations are natural for humans and are used commonly in domains such as health care and criminology. What is minimally but critically \emph{absent} is an important part of an explanation, which to the best of our knowledge, has not been touched upon by current explanation methods that attempt to explain predictions of neural networks. We validate our approach on three real datasets obtained from diverse domains; namely, a handwritten digits dataset MNIST, a large procurement fraud dataset and an fMRI brain imaging dataset. In all three cases, we witness the power of our approach in generating precise explanations that are also easy for human experts to understand and evaluate.
Despite a growing body of research focused on creating interpretable machine learning methods, there have been few empirical studies verifying whether interpretable methods achieve their intended effects on end users. We present a framework for assessing the effects of model interpretability on users via pre-registered experiments in which participants are shown functionally identical models that vary in factors thought to influence interpretability. Using this framework, we ran a sequence of large-scale randomized experiments, varying two putative drivers of interpretability: the number of features and the model transparency (clear or black-box). We measured how these factors impact trust in model predictions, the ability to simulate a model, and the ability to detect a model's mistakes. We found that participants who were shown a clear model with a small number of features were better able to simulate the model's predictions. However, we found no difference in multiple measures of trust and found that clear models did not improve the ability to correct mistakes. These findings suggest that interpretability research could benefit from more emphasis on empirically verifying that interpretable models achieve all their intended effects.
We present the first class of policy-gradient algorithms that work with both state-value and policy function-approximation, and are guaranteed to converge under off-policy training. Our solution targets problems in reinforcement learning where the action representation adds to the-curse-of-dimensionality; that is, with continuous or large action sets, thus making it infeasible to estimate state-action value functions (Q functions). Using state-value functions helps to lift the curse and as a result naturally turn our policy-gradient solution into classical Actor-Critic architecture whose Actor uses state-value function for the update. Our algorithms, Gradient Actor-Critic and Emphatic Actor-Critic, are derived based on the exact gradient of averaged state-value function objective and thus are guaranteed to converge to its optimal solution, while maintaining all the desirable properties of classical Actor-Critic methods with no additional hyper-parameters. To our knowledge, this is the first time that convergent off-policy learning methods have been extended to classical Actor-Critic methods with function approximation.
In ensemble methods, the outputs of a collection of diverse classifiers are combined in the expectation that the global prediction be more accurate than the individual ones. Heterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, the combination of their decisions is beneficial. In this work, a family of heterogeneous ensembles is built by pooling classifiers from M homogeneous ensembles of different types of size T. Depending on the fraction of base classifiers of each type, a particular heterogeneous combination in this family is represented by a point in a regular simplex in M dimensions. The M vertices of this simplex represent the different homogeneous ensembles. A displacement away from one of these vertices effects a smooth transformation of the corresponding homogeneous ensemble into a heterogeneous one. The optimal composition of such heterogeneous ensemble can be determined using cross-validation or, if bootstrap samples are used to build the individual classifiers, out-of-bag data. An empirical analysis of such combinations of bootstraped ensembles composed of neural networks, SVMs, and random trees (i.e. from a standard random forest) illustrates the gains that can be achieved by this heterogeneous ensemble creation method.
This paper proposes a class of well-conditioned neural networks in which a unit amount of change in the inputs causes at most a unit amount of change in the outputs or any of the internal layers. We develop the known methodology of controlling Lipschitz constants to realize its full potential in maximizing robustness: our linear and convolution layers subsume those in the previous Parseval networks as a special case and allow greater degrees of freedom; aggregation, pooling, splitting and other operators are adapted in new ways, and a new loss function is proposed, all for the purpose of improving robustness. With MNIST and CIFAR-10 classifiers, we demonstrate a number of advantages. Without needing any adversarial training, the proposed classifiers exceed the state of the art in robustness against white-box L2-bounded adversarial attacks. Their outputs are quantitatively more meaningful than ordinary networks and indicate levels of confidence. They are also free of exploding gradients, among other desirable properties.
We study the robustness of classifiers to various kinds of random noise models. In particular, we consider noise drawn uniformly from the $\ell\_p$ ball for $p \in [1, \infty]$ and Gaussian noise with an arbitrary covariance matrix. We characterize this robustness to random noise in terms of the distance to the decision boundary of the classifier. This analysis applies to linear classifiers as well as classifiers with locally approximately flat decision boundaries, a condition which is satisfied by state-of-the-art deep neural networks. The predicted robustness is verified experimentally.
We address the task of generating query suggestions for task-based search. The current state of the art relies heavily on suggestions provided by a major search engine. In this paper, we solve the task without reliance on search engines. Specifically, we focus on the first step of a two-stage pipeline approach, which is dedicated to the generation of query suggestion candidates. We present three methods for generating candidate suggestions and apply them on multiple information sources. Using a purpose-built test collection, we find that these methods are able to generate high-quality suggestion candidates.
Entity-oriented search deals with a wide variety of information needs, from displaying direct answers to interacting with services. In this work, we aim to understand what are prominent entity-oriented search intents and how they can be fulfilled. We develop a scheme of entity intent categories, and use them to annotate a sample of queries. Specifically, we annotate unique query refiners on the level of entity types. We observe that, on average, over half of those refiners seek to interact with a service, while over a quarter of the refiners search for information that may be looked up in a knowledge base.
Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signal cognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given via points.
Algorithmic collusion is an emerging concept in current artificial intelligence age. Whether algorithmic collusion is a creditable threat remains as an argument. In this paper, we propose an algorithm which can extort its human rival to collude in a Cournot duopoly competing market. In experiments, we show that, the algorithm can successfully extorted its human rival and gets higher profit in long run, meanwhile the human rival will fully collude with the algorithm. As a result, the social welfare declines rapidly and stably. Both in theory and in experiment, our work confirms that, algorithmic collusion can be a creditable threat. In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.
Deep models that are both effective and explainable are desirable in many settings; prior explainable models have been unimodal, offering either image-based visualization of attention weights or text-based generation of post-hoc justifications. We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths. We collect two new datasets to define and evaluate this task, and propose a novel model which can provide joint textual rationale generation and attention visualization. Our datasets define visual and textual justifications of a classification decision for activity recognition tasks (ACT-X) and for visual question answering tasks (VQA-X). We quantitatively show that training with the textual explanations not only yields better textual justification models, but also better localizes the evidence that supports the decision. We also qualitatively show cases where visual explanation is more insightful than textual explanation, and vice versa, supporting our thesis that multimodal explanation models offer significant benefits over unimodal approaches.
We propose a reliable intersection control mechanism for strategic autonomous and connected vehicles (agents) in non-cooperative environments. Each agent has access to his/her earliest possible and desired passing times, and reports a passing time to the intersection manager, who allocates the intersection temporally to the agents in a First-Come-First-Serve basis. However, the agents might have conflicting interests and can take actions strategically. To this end, we analyze the strategic behaviors of the agents and formulate Nash equilibria for all possible scenarios. Furthermore, among all Nash equilibria we identify a socially optimal equilibrium that leads to a fair intersection allocation, and correspondingly we describe a strategy-proof intersection mechanism, which achieves reliable intersection control such that the strategic agents do not have any incentive to misreport their passing times strategically.
Description Logics (DLs) under Rational Closure (RC) is a well-known framework for non-monotonic reasoning in DLs. In this paper, we address the concept subsumption decision problem under RC for nominal safe $\mathcal{ELO}_\bot$, a notable and practically important DL representative of the OWL 2 profile OWL 2 EL. Our contribution here is to define a polynomial time subsumption procedure for nominal safe $\mathcal{ELO}_\bot$ under RC that relies entirely on a series of classical, monotonic $\mathcal{EL}_\bot$ subsumption tests. Therefore, any existing classical monotonic $\mathcal{EL}_\bot$ reasoner can be used as a black box to implement our method. We then also adapt the method to one of the known extensions of RC for DLs, namely Defeasible Inheritance-based DLs without losing the computational tractability.
We introduce tensor field networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer. 3D rotation equivariance removes the need for data augmentation to identify features in arbitrary orientations. Our network uses filters built from spherical harmonics; due to the mathematical consequences of this filter choice, each layer accepts as input (and guarantees as output) scalars, vectors, and higher-order tensors, in the geometric sense of these terms. We demonstrate how tensor field networks learn to model simple physics (Newtonian gravitation and moment of inertia), classify simple 3D shapes (trained on one orientation and tested on shapes in arbitrary orientations), and, given a small organic molecule with an atom removed, replace the correct element at the correct location in space.
Vanishing long-term gradients are a major issue in training standard recurrent neural networks (RNNs), which can be alleviated by long short-term memory (LSTM) models with memory cells. However, the extra parameters associated with the memory cells mean an LSTM layer has four times as many parameters as an RNN with the same hidden vector size. This paper addresses the vanishing gradient problem using a high order RNN (HORNN) which has additional connections from multiple previous time steps. Speech recognition experiments using British English multi-genre broadcast (MGB3) data showed that the proposed HORNN architectures for rectified linear unit and sigmoid activation functions reduced word error rates (WER) by 4.2% and 6.3% over the corresponding RNNs, and gave similar WERs to a (projected) LSTM while using only 20%--50% of the recurrent layer parameters and computation.
We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a densely connected autoencoder architecture capable of learning a joint representation of both local graph structure and available external node features for the multi-task learning of link prediction and node classification. To the best of our knowledge, this is the first architecture that can be efficiently trained end-to-end in a single learning stage to simultaneously perform link prediction and node classification. We provide comprehensive empirical evaluation of our models on a range of challenging benchmark graph-structured datasets, and demonstrate significant improvement in accuracy over related methods for graph representation learning. Code implementation is available at https://github.com/vuptran/graph-representation-learning
Real-time bidding (RTB) is almost the most important mechanism in online display advertising, where proper bid for each page view plays a vital and essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB mechanism where the advertisers hope to maximize total value of winning impressions under a pre-set budget constraint. However, the optimal strategy is hard to be derived due to complexity and volatility of the auction environment. To address the challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process. Quite different from prior model-based work, we propose a novel framework based on model-free reinforcement learning which sequentially regulates the bidding parameter rather than directly producing bid. Along this line, we further innovate a reward function which deploys a deep neural network to learn appropriate reward and thus leads the agent to deliver the optimal policy effectively; we also design an adaptive $\epsilon$-greedy strategy which adjusts the exploration behaviour dynamically and further improves the performance. Experimental results on real dataset demonstrate the effectiveness of our framework.
Neural networks are commonly regarded as black boxes performing incomprehensible functions. For classification problems networks provide maps from high dimensional feature space to K-dimensional image space. Images of training vector are projected on polygon vertices, providing visualization of network function. Such visualization may show the dynamics of learning, allow for comparison of different networks, display training vectors around which potential problems may arise, show differences due to regularization and optimization procedures, investigate stability of network classification under perturbation of original vectors, and place new data sample in relation to training data, allowing for estimation of confidence in classification of a given sample. An illustrative example for the three-class Wine data and five-class Satimage data is described. The visualization method proposed here is applicable to any black box system that provides continuous outputs.
Despite single agent deep reinforcement learning has achieved significant success due to the experience replay mechanism, Concerns should be reconsidered in multiagent environments. This work focus on the stochastic cooperative environment. We apply a specific adaptation to one recently proposed weighted double estimator and propose a multiagent deep reinforcement learning framework, named Weighted Double Deep Q-Network (WDDQN). To achieve efficient cooperation, \textit{Lenient Reward Network} and \textit{Mixture Replay Strategy} are introduced. By utilizing the deep neural network and the weighted double estimator, WDDQN can not only reduce the bias effectively but also be extended to many deep RL scenarios with only raw pixel images as input. Empirically, the WDDQN outperforms the existing DRL algorithm (double DQN) and multiagent RL algorithm (lenient Q-learning) in terms of performance and convergence within stochastic cooperative environments.
With an increasing demand from emerging logistics businesses, Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been introduced to manage package delivery services from a supplier to customers. However, almost all of existing studies focus on the deterministic problem that assumes all parameters are known perfectly at the time when the planning and routing decisions are made. In reality, some parameters are random and unknown. Therefore, in this paper, we consider VRPPC with hard time windows and random demand, called Optimal Delivery Planning (ODP). The proposed ODP aims to minimize the total package delivery cost while meeting the customer time window constraints. We use stochastic integer programming to formulate the optimization problem incorporating the customer demand uncertainty. Moreover, we evaluate the performance of the ODP using test data from benchmark dataset and from actual Singapore road map.
There has been recent interest in applying cognitively or empirically motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, gram- mars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.
Understanding and visualizing human discourse has long being a challenging task. Although recent work on argument mining have shown success in classifying the role of various sentences, the task of recognizing concepts and understanding the ways in which they are discussed remains challenging. Given an email thread or a transcript of a group discussion, our task is to extract the relevant concepts and understand how they are referenced and re-referenced throughout the discussion. In the present work, we present a preliminary approach for extracting and visualizing group discourse by adapting Wikipedia's category hierarchy to be an external concept ontology. From a user study, we found that our method achieved better results than 4 strong alternative approaches, and we illustrate our visualization method based on the extracted discourse flows.
Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others. One approach to this problem is through controlled experiments/trials - but controlled experiments are expensive. Hence it is important to choose the best policies on the basis of observational data. This presents two difficult challenges: (i) missing counterfactuals, and (ii) selection bias. This paper presents theoretical bounds on estimation errors of counterfactuals from observational data by making connections to domain adaptation theory. It also presents a principled way of choosing optimal policies using domain adversarial neural networks. We illustrate the effectiveness of domain adversarial training together with various features of our algorithm on a semi-synthetic breast cancer dataset and a supervised UCI dataset (Statlog).
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mostly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms. We also introduce basic concepts of deep learning including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review the broad range of utilized deep learning algorithms. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future.
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the non-unique, high-dimensional nature of graphs and the complex, non-local dependencies that exist between edges in a given graph. Here we propose GraphRNN, a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure. GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated so far. In order to quantitatively evaluate the performance of GraphRNN, we introduce a benchmark suite of datasets, baselines and novel evaluation metrics based on Maximum Mean Discrepancy, which measure distances between sets of graphs. Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models.
Combinatorial optimization is a common theme in computer science which underlies a considerable variety of problems. In contrast to the continuous setting, combinatorial problems require special solution strategies, and it's hard to come by generic schemes like gradient methods for continuous domains. We follow a standard construction of a parametric sampling distribution that transforms the problem to the continuous domain, allowing us to optimize the expectation of a given objective using estimates of the gradient. In spite of the apparent generality, such constructions are known to suffer from highly variable gradient estimates, and thus require careful tuning that is done in a problem specific manner. We show that a simple trick of converting the objective values to their cumulative probabilities fixes the distribution of the objective, allowing us to derive an online optimization algorithm that can be applied in a generic fashion. As an experimental benchmark we use the task of finding cliques in undirected graphs, and we show that our method, even when blindly applied, consistently outperforms related methods. Notably, on the DIMACS clique benchmark, our method approaches the performance of the best clique finding algorithms without access to the graph structure, and only through objective function evaluations, thus providing significant evidence to the generality and effectivity of our method.
Designing conversational user interface experience is complicated because conversation comes with many expectations. When these expectations are met, we feel the interface is natural, but once violated, we feel something is amiss. The last decade witnessed human language technologies and behaviours to enable humans converse with software using spoken dialogue to access, create and process information. Less is known about the practicalities of designing chatbot interactions. In this paper, we introduce the nature of conversational user interfaces (CUIs) and describe the underlying technologies they are based on. Moreover, we define guidelines for designing conversational interfaces in various domains. This paper particularly focuses on classifying the elements and techniques used in CUI design patterns. After concluding certain challenges with CUI, we discuss important features and chatbot states to be considered in CUI design for specific domain. We envisage this study to support CUI researchers to design tailored chatbots applicable into certain domain and improve the current state of research challenges in the field of Artificial Intelligence and conversational agents.
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports. To mine these data properly, attributable to their innate ambiguity, a Word Sense Disambiguation (WSD) algorithm can avoid numbers of difficulties in Natural Language Processing (NLP) pipeline. However, considering a large number of ambiguous words in one language or technical domain, we may encounter limiting constraints for proper deployment of existing WSD models. This paper attempts to address the problem of one-classifier-per-one-word WSD algorithms by proposing a single Bidirectional Long Short-Term Memory (BLSTM) network which by considering senses and context sequences works on all ambiguous words collectively. Evaluated on SensEval-3 benchmark, we show the result of our model is comparable with top-performing WSD algorithms. We also discuss how applying additional modifications alleviates the model fault and the need for more training data.
Category, or property generalization is a central function in the human cognition. It plays a crucial role in a variety of domains, such as learning, everyday reasoning, specialized reasoning, and decision making. Judging the content of a dish as edible, a hormone level as healthy, a building as belonging to the same architectural style as previously seen buildings, are examples of category generalization. In this paper, we propose self-organizing maps as candidates to explain the psychological mechanisms underlying category generalization. Self-organizing maps are psychologically and biologically plausible neural network models that learn after limited exposure to positive category examples, without any need of contrastive information. Just like humans. They reproduce human behavior in category generalization, in particular for what concerns the well-known Numerosity and Variability effects, which are usually explained with Bayesian tools. Where category generalization is concerned, self-organizing maps are good candidates to bridge the gap between the computational level of analysis in Marr's hierarchy (where Bayesian models are situated) and the algorithmic level of aanalysis in Marr's hierarchy (where Bayesian models are situated) and the algorithmic level of analysis in which plausible mechanisms are described.
The purpose of this technical report is two-fold. First of all, it introduces a suite of challenging continuous control tasks (integrated with OpenAI Gym) based on currently existing robotics hardware. The tasks include pushing, sliding and pick & place with a Fetch robotic arm as well as in-hand object manipulation with a Shadow Dexterous Hand. All tasks have sparse binary rewards and follow a Multi-Goal Reinforcement Learning (RL) framework in which an agent is told what to do using an additional input. The second part of the paper presents a set of concrete research ideas for improving RL algorithms, most of which are related to Multi-Goal RL and Hindsight Experience Replay.
We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent. We apply this approach to robotic manipulation tasks and train end-to-end visuomotor policies that map directly from RGB camera inputs to joint velocities. We demonstrate that our approach can solve a wide variety of visuomotor tasks, for which engineering a scripted controller would be laborious. Our experiments indicate that our reinforcement and imitation agent achieves significantly better performances than agents trained with reinforcement learning or imitation learning alone. We also illustrate that these policies, trained with large visual and dynamics variations, can achieve preliminary successes in zero-shot sim2real transfer. A brief visual description of this work can be viewed in https://youtu.be/EDl8SQUNjj0
We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility. The reward function depends on the hidden state (or goal) of both agents, so the agents must infer the other players' hidden goals from their observed behavior in order to solve the tasks. We propose a new approach for learning in these domains: Self Other-Modeling (SOM), in which an agent uses its own policy to predict the other agent's actions and update its belief of their hidden state in an online manner. We evaluate this approach on three different tasks and show that the agents are able to learn better policies using their estimate of the other players' hidden states, in both cooperative and adversarial settings.
This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.
Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS.
In sequential hypothesis testing, Generalized Binary Search (GBS) greedily chooses the test with the highest information gain at each step. It is known that GBS obtains the gold standard query cost of $O(\log n)$ for problems satisfying the $k$-neighborly condition, which requires any two tests to be connected by a sequence of tests where neighboring tests disagree on at most $k$ hypotheses. In this paper, we introduce a weaker condition, split-neighborly, which requires that for the set of hypotheses two neighbors disagree on, any subset is splittable by some test. For four problems that are not $k$-neighborly for any constant $k$, we prove that they are split-neighborly, which allows us to obtain the optimal $O(\log n)$ worst-case query cost.
Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize a specific goal such as maximizing the revenue led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a strategic response with respect to other advertisers bidding in the market. In this paper, we formulate bidding optimization with multi-agent reinforcement learning. To deal with a large number of advertisers, we propose a clustering method and assign each cluster with a strategic bidding agent. A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. The empirical study on our industry-scaled real-world data has demonstrated the effectiveness of our modeling methods. Our results show that a cluster based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than the purely self-interested bidding agents.
Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic.
We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformation.We show their performance in terms of quality and speed through various tasks of remote sensing multimodal image alignment. In particular, we are able to register correctly cadastral maps of buildings as well as road polylines onto RGB images, and outperform current keypoint matching methods.
Complex data is usually produced by interacting sources with different mechanisms. Here we introduce a parameter-free model-based approach, based upon the seminal concept of Algorithmic Probability, that decomposes an observation and signal into its most likely algorithmic generative sources. Our methods use a causal calculus to infer model representations. We demonstrate the method ability to distinguish interacting mechanisms and deconvolve them, regardless of whether the objects produce strings, space-time evolution diagrams, images or networks. We numerically test and evaluate our causal separation methods and find that it can disentangle examples of observations from discrete dynamical systems, and complex networks. We think that these causal separating techniques can contribute to tackle the challenge of causation for estimations of better rooted probability distributions thereby complementing more limited statistical-oriented techniques that otherwise would lack model inference capabilities.
Recent work has shown that tight concentration of the entire spectrum of singular values of a deep network's input-output Jacobian around one at initialization can speed up learning by orders of magnitude. Therefore, to guide important design choices, it is important to build a full theoretical understanding of the spectra of Jacobians at initialization. To this end, we leverage powerful tools from free probability theory to provide a detailed analytic understanding of how a deep network's Jacobian spectrum depends on various hyperparameters including the nonlinearity, the weight and bias distributions, and the depth. For a variety of nonlinearities, our work reveals the emergence of new universal limiting spectral distributions that remain concentrated around one even as the depth goes to infinity.
The loss functions of deep neural networks are complex and their geometric properties are not well understood. We show that the optima of these complex loss functions are in fact connected by simple curves, such as a polygonal chain with only one bend, over which training and test accuracy are nearly constant. We introduce a training procedure to discover these high-accuracy pathways between modes. Inspired by this new geometric insight, we also propose a new ensembling method entitled Fast Geometric Ensembling (FGE). Using FGE we can train high-performing ensembles in the time required to train a single model. We achieve improved performance compared to the recent state-of-the-art Snapshot Ensembles, on CIFAR-10 and CIFAR-100, using state-of-the-art deep residual networks. On ImageNet we improve the top-1 error-rate of a pre-trained ResNet by 0.56% by running FGE for just 5 epochs.
What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors on human performance. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors. We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, e.g. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play. Videos and the game manipulations are available at https://rach0012.github.io/humanRL_website/
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require manual, time-consuming, and error-prone calculations that are further hindered by the use of static variable thresholds derived from aggregate patient populations. These coarse frameworks do not capture time-sensitive individual physiological patterns and are not suitable for instantaneous assessment of patients' acuity trajectories, a critical task for the ICU where conditions often change rapidly. Furthermore, they are ill-suited to capitalize on the emerging availability of streaming electronic health record data. We propose a novel acuity score framework (DeepSOFA) that leverages temporal patient measurements in conjunction with deep learning models to make accurate assessments of a patient's illness severity at any point during their ICU stay. We compare DeepSOFA with SOFA baseline models using the same predictors and find that at any point during an ICU admission, DeepSOFA yields more accurate predictions of in-hospital mortality.
Deep neural networks have achieved remarkable accuracy in many artificial intelligence applications, e.g. computer vision, at the cost of a large number of parameters and high computational complexity. Weight pruning can compress DNN models by removing redundant parameters in the networks, but it brings sparsity in the weight matrix, and therefore makes the computation inefficient on GPUs. Although pruning can remove more than 80% of the weights, it actually hurts inference performance (speed) when running models on GPUs. Two major problems cause this unsatisfactory performance on GPUs. First, lowering convolution onto matrix multiplication reduces data reuse opportunities and wastes memory bandwidth. Second, the sparsity brought by pruning makes the computation irregular, which leads to inefficiency when running on massively parallel GPUs. To overcome these two limitations, we propose Escort, an efficient sparse convolutional neural networks on GPUs. Instead of using the lowering method, we choose to compute the sparse convolutions directly. We then orchestrate the parallelism and locality for the direct sparse convolution kernel, and apply customized optimization techniques to further improve performance. Evaluation on NVIDIA GPUs show that Escort can improve sparse convolution speed by 2.63x and 3.07x, and inference speed by 1.38x and 1.60x, compared to CUBLAS and CUSPARSE respectively.
General Video Game Playing (GVGP) aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL). The framework has been expanded into several tracks during the last few years to meet the demand of different research directions. The agents are required to either play multiples unknown games with or without access to game simulations, or to design new game levels or rules. This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVGAI framework in research, education and competitions five years after its birth. A future plan of framework improvements is also described.
In systems of multiple agents, identifying the cause of observed agent dynamics is challenging. Often, these agents operate in diverse, non-stationary environments, where models rely on hand-crafted environment-specific features to infer influential regions in the system's surroundings. To overcome the limitations of these inflexible models, we present GP-LAPLACE, a technique for locating sources and sinks from trajectories in time-varying fields. Using Gaussian processes, we jointly infer a spatio-temporal vector field, as well as canonical vector calculus operations on that field. Notably, we do this from only agent trajectories without requiring knowledge of the environment, and also obtain a metric for denoting the significance of inferred causal features in the environment by exploiting our probabilistic method. To evaluate our approach, we apply it to both synthetic and real-world GPS data, demonstrating the applicability of our technique in the presence of multiple agents, as well as its superiority over existing methods.
In psychological measurements, two levels should be distinguished: the 'individual level', relative to the different participants in a given cognitive situation, and the 'collective level', relative to the overall statistics of their outcomes, which we propose to associate with a notion of 'collective participant'. When the distinction between these two levels is properly formalized, it reveals why the modeling of the collective participant generally requires beyond-quantum - non-Bornian - probabilistic models, when sequential measurements at the individual level are considered, and this though a pure quantum description remains valid for single measurement situations.
Most reinforcement learning algorithms are inefficient for learning multiple tasks in complex robotic systems, where different tasks share a set of actions. In such environments a compound policy may be learnt with shared neural network parameters, which performs multiple tasks concurrently. However such compound policy may get biased towards a task or the gradients from different tasks negate each other, making the learning unstable and sometimes less data efficient. In this paper, we propose a new approach for simultaneous training of multiple tasks sharing a set of common actions in continuous action spaces, which we call as DiGrad (Differential Policy Gradient). The proposed framework is based on differential policy gradients and can accommodate multi-task learning in a single actor-critic network. We also propose a simple heuristic in the differential policy gradient update to further improve the learning. The proposed architecture was tested on 8 link planar manipulator and 27 degrees of freedom(DoF) Humanoid for learning multi-goal reachability tasks for 3 and 2 end effectors respectively. We show that our approach supports efficient multi-task learning in complex robotic systems, outperforming related methods in continuous action spaces.
Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, and understand human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by introducing an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on a action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance.
Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample complexity, which hinders their use in real-world domains. Alternatively, model-based reinforcement learning promises to reduce sample complexity, but tends to require careful tuning and to date have succeeded mainly in restrictive domains where simple models are sufficient for learning. In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training. To overcome this issue, we propose to use an ensemble of models to maintain the model uncertainty and regularize the learning process. We further show that the use of likelihood ratio derivatives yields much more stable learning than backpropagation through time. Altogether, our approach Model-Ensemble Trust-Region Policy Optimization (ME-TRPO) significantly reduces the sample complexity compared to model-free deep RL methods on challenging continuous control benchmark tasks.
In the recent literature the important role of depth in deep learning has been emphasized. In this paper we argue that sufficient width of a feedforward network is equally important by answering the simple question under which conditions the decision regions of a neural network are connected. It turns out that for a class of activation functions including leaky ReLU, neural networks having a pyramidal structure, that is no layer has more hidden units than the input dimension, produce necessarily connected decision regions. This implies that a sufficiently wide layer is necessary to produce disconnected decision regions. We discuss the implications of this result for the construction of neural networks, in particular the relation to the problem of adversarial manipulation of classifiers.
Recent model-free reinforcement learning algorithms have proposed incorporating learned dynamics models as a source of additional data with the intention of reducing sample complexity. Such methods hold the promise of incorporating imagined data coupled with a notion of model uncertainty to accelerate the learning of continuous control tasks. Unfortunately, they rely on heuristics that limit usage of the dynamics model. We present model-based value expansion, which controls for uncertainty in the model by only allowing imagination to fixed depth. By enabling wider use of learned dynamics models within a model-free reinforcement learning algorithm, we improve value estimation, which, in turn, reduces the sample complexity of learning.
In partially observed environments, it can be useful for a human to provide the robot with declarative information that augments its direct sensory observations. For instance, given a robot on a search-and-rescue mission, a human operator might suggest locations of interest. We provide a representation for the robot's internal knowledge that supports efficient combination of raw sensory information with high-level declarative information presented in a formal language. Computational efficiency is achieved by dynamically selecting an appropriate factoring of the belief state, combining aspects of the belief when they are correlated through information and separating them when they are not. This strategy works in open domains, in which the set of possible objects is not known in advance, and provides significant improvements in inference time, leading to more efficient planning for complex partially observable tasks. We validate our approach experimentally in two open-domain planning problems: a 2D discrete gridworld task and a 3D continuous cooking task.
Despite recent advances in training recurrent neural networks (RNNs), capturing long-term dependencies in sequences remains a fundamental challenge. Most approaches use backpropagation through time (BPTT), which is difficult to scale to very long sequences. This paper proposes a simple method that improves the ability to capture long term dependencies in RNNs by adding an unsupervised auxiliary loss to the original objective. This auxiliary loss forces RNNs to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel image classification with sequence lengths up to 16\,000, and a real document classification benchmark. Our results highlight good performance and resource efficiency of this approach over competitive baselines, including other recurrent models and a comparable sized Transformer. Further analyses reveal beneficial effects of the auxiliary loss on optimization and regularization, as well as extreme cases where there is little to no backpropagation.
Human inertial thinking schemes can be formed through learning, which are then applied to quickly solve similar problems later. However, when problems are significantly different, inertial thinking generally presents the solutions that are definitely imperfect. In such cases, people will apply creative thinking, such as reverse thinking, to solve problems. Similarly, machine learning methods also form inertial thinking schemes through learning the knowledge from a large amount of data. However, when the testing data are vastly difference, the formed inertial thinking schemes will inevitably generate errors. This kind of inertial thinking is called illusion inertial thinking. Because all machine learning methods do not consider illusion inertial thinking, in this paper we propose a new method that uses reverse thinking to correct illusion inertial thinking, which increases the generalization ability of machine learning methods. Experimental results on benchmark datasets are used to validate the proposed method.
The Iterated Prisoner's Dilemma has guided research on social dilemmas for decades. However, it distinguishes between only two atomic actions: cooperate and defect. In real-world prisoner's dilemmas, these choices are temporally extended and different strategies may correspond to sequences of actions, reflecting grades of cooperation. We introduce a Sequential Prisoner's Dilemma (SPD) game to better capture the aforementioned characteristics. In this work, we propose a deep multiagent reinforcement learning approach that investigates the evolution of mutual cooperation in SPD games. Our approach consists of two phases. The first phase is offline: it synthesizes policies with different cooperation degrees and then trains a cooperation degree detection network. The second phase is online: an agent adaptively selects its policy based on the detected degree of opponent cooperation. The effectiveness of our approach is demonstrated in two representative SPD 2D games: the Apple-Pear game and the Fruit Gathering game. Experimental results show that our strategy can avoid being exploited by exploitative opponents and achieve cooperation with cooperative opponents.
Facial expression recognition (FER) has always been a challenging issue in computer vision. The different expressions of emotion and uncontrolled environmental factors lead to inconsistencies in the complexity of FER and variability of between expression categories, which is often overlooked in most facial expression recognition systems. In order to solve this problem effectively, we presented a simple and efficient CNN model to extract facial features, and proposed a complexity perception classification (CPC) algorithm for FER. The CPC algorithm divided the dataset into an easy classification sample subspace and a complex classification sample subspace by evaluating the complexity of facial features that are suitable for classification. The experimental results of our proposed algorithm on Fer2013 and CK-plus datasets demonstrated the algorithm's effectiveness and superiority over other state-of-the-art approaches.
The design of gaits for robot locomotion can be a daunting process which requires significant expert knowledge and engineering. This process is even more challenging for robots that do not have an accurate physical model, such as compliant or micro-scale robots. Data-driven gait optimization provides an automated alternative to analytical gait design. In this paper, we propose a novel approach to efficiently learn a wide range of locomotion tasks with walking robots. This approach formalizes locomotion as a contextual policy search task to collect data, and subsequently uses that data to learn multi-objective locomotion primitives that can be used for planning. As a proof-of-concept we consider a simulated hexapod modeled after a recently developed microrobot, and we thoroughly evaluate the performance of this microrobot on different tasks and gaits. Our results validate the proposed controller and learning scheme on single and multi-objective locomotion tasks. Moreover, the experimental simulations show that without any prior knowledge about the robot used (e.g., dynamics model), our approach is capable of learning locomotion primitives within 250 trials and subsequently using them to successfully navigate through a maze.
In order to explore and act autonomously in an environment, an agent needs to learn from the sensorimotor information that is captured while acting. By extracting the regularities in this sensorimotor stream, it can learn a model of the world, which in turn can be used as a basis for action and exploration. This requires the acquisition of compact representations from a possibly high dimensional raw observation, which is noisy and ambiguous. In this paper, we learn sensory representations from sensorimotor prediction. We propose a model which integrates sensorimotor information over time, and project it in a sensory representation which is useful for prediction. We emphasize on a simple example the role of motor and memory for learning sensory representations.
Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance.
Multilayer graphs encode different kind of interactions between the same set of entities. When one wants to cluster such a multilayer graph, the natural question arises how one should merge the information different layers. We introduce in this paper a one-parameter family of matrix power means for merging the Laplacians from different layers and analyze it in expectation in the stochastic block model. We show that this family allows to recover ground truth clusters under different settings and verify this in real world data. While computing the matrix power mean can be very expensive for large graphs, we introduce a numerical scheme to efficiently compute its eigenvectors for the case of large sparse graphs.
The tasks that an agent will need to solve often are not known during training. However, if the agent knows which properties of the environment are important then, after learning how its actions affect those properties, it may be able to use this knowledge to solve complex tasks without training specifically for them. Towards this end, we consider a setup in which an environment is augmented with a set of user defined attributes that parameterize the features of interest. We propose a method that learns a policy for transitioning between "nearby" sets of attributes, and maintains a graph of possible transitions. Given a task at test time that can be expressed in terms of a target set of attributes, and a current state, our model infers the attributes of the current state and searches over paths through attribute space to get a high level plan, and then uses its low level policy to execute the plan. We show in 3D block stacking, grid-world games, and StarCraft that our model is able to generalize to longer, more complex tasks at test time by composing simpler learned policies.
Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system.
We study the problem of learning policies over long time horizons. We present a framework that leverages and integrates two key concepts. First, we utilize hierarchical policy classes that enable planning over different time scales, i.e., the high level planner proposes a sequence of subgoals for the low level planner to achieve. Second, we utilize expert demonstrations within the hierarchical action space to dramatically reduce cost of exploration. Our framework is flexible and can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels of the hierarchy. Using long-horizon benchmarks, including Montezuma's Revenge, we empirically demonstrate that our approach can learn significantly faster compared to hierarchical RL, and can be significantly more label- and sample-efficient compared to flat IL. We also provide theoretical analysis of the labeling cost for certain instantiations of our framework.
We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. The proposed semi-parametric topological memory (SPTM) consists of a (non-parametric) graph with nodes corresponding to locations in the environment and a (parametric) deep network capable of retrieving nodes from the graph based on observations. The graph stores no metric information, only connectivity of locations corresponding to the nodes. We use SPTM as a planning module in a navigation system. Given only 5 minutes of footage of a previously unseen maze, an SPTM-based navigation agent can build a topological map of the environment and use it to confidently navigate towards goals. The average success rate of the SPTM agent in goal-directed navigation across test environments is higher than the best-performing baseline by a factor of three. A video of the agent is available at https://youtu.be/vRF7f4lhswo
Aerial robots are becoming popular among general public, and with the development of artificial intelligence (AI), there is a trend to equip aerial robots with a natural user interface (NUI). Hand/arm gestures are an intuitive way to communicate for humans, and various research works have focused on controlling an aerial robot with natural gestures. However, the techniques in this area are still far from mature. Many issues in this area have been poorly addressed, such as the principles of choosing gestures from the design point of view, hardware requirements from an economic point of view, considerations of data availability, and algorithm complexity from a practical perspective. Our work focuses on building an economical monocular system particularly designed for gesture-based piloting of an aerial robot. Natural arm gestures are mapped to rich target directions and convenient fine adjustment is achieved. Practical piloting scenarios, hardware cost and algorithm applicability are jointly considered in our system design. The entire system is successfully implemented in an aerial robot and various properties of the system are tested.
Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces. However, they have so far assumed that self-generated goals are sampled in a specifically engineered feature space, limiting their autonomy. In this work, we propose to use deep representation learning algorithms to learn an adequate goal space. This is a developmental 2-stage approach: first, in a perceptual learning stage, deep learning algorithms use passive raw sensor observations of world changes to learn a corresponding latent space; then goal exploration happens in a second stage by sampling goals in this latent space. We present experiments where a simulated robot arm interacts with an object, and we show that exploration algorithms using such learned representations can match the performance obtained using engineered representations.
Automatic chess problem or puzzle composition typically involves generating and testing various different positions, sometimes using particular piece sets. Once a position has been generated, it is then usually tested for positional legality based on the game rules. However, it is useful to be able to estimate what the search space size for particular piece combinations is to begin with. So if a desirable chess problem was successfully generated by examining 'merely' 100,000 or so positions in a theoretical search space of about 100 billion, this would imply the composing approach used was quite viable and perhaps even impressive. In this article, I explain a method of calculating the size of this search space using a combinatorics and permutations approach. While the mathematics itself may already be established, a precise method and justification of applying it with regard to the chessboard and chess pieces has not been documented, to the best of our knowledge. Additionally, the method could serve as a useful starting point for further estimations of search space size which filter out positions for legality and rotation, depending on how the automatic composer is allowed to place pieces on the board (because this affects its total search space size).
Training neural networks involves finding minima of a high-dimensional non-convex loss function. Knowledge of the structure of this energy landscape is sparse. Relaxing from linear interpolations, we construct continuous paths between minima of recent neural network architectures on CIFAR10 and CIFAR100. Surprisingly, the paths are essentially flat in both the training and test landscapes. This implies that neural networks have enough capacity for structural changes, or that these changes are small between minima. Also, each minimum has at least one vanishing Hessian eigenvalue in addition to those resulting from trivial invariance.
The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year's US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems.
We propose a Multi-Instance-Learning (MIL) approach for weakly-supervised learning problems, where a training set is formed by bags (sets of feature vectors or instances) and only labels at bag-level are provided. Specifically, we consider the Multi-Instance Dynamic-Ordinal-Regression (MI-DOR) setting, where the instance labels are naturally represented as ordinal variables and bags are structured as temporal sequences. To this end, we propose Multi-Instance Dynamic Ordinal Random Fields (MI-DORF). In this framework, we treat instance-labels as temporally-dependent latent variables in an Undirected Graphical Model. Different MIL assumptions are modelled via newly introduced high-order potentials relating bag and instance-labels within the energy function of the model. We also extend our framework to address the Partially-Observed MI-DOR problems, where a subset of instance labels are available during training. We show on the tasks of weakly-supervised facial behavior analysis, Facial Action Unit (DISFA dataset) and Pain (UNBC dataset) Intensity estimation, that the proposed framework outperforms alternative learning approaches. Furthermore, we show that MIDORF can be employed to reduce the data annotation efforts in this context by large-scale.
It is widely conjectured that the reason that training algorithms for neural networks are successful because all local minima lead to similar performance, for example, see (LeCun et al., 2015, Choromanska et al., 2015, Dauphin et al., 2014). Performance is typically measured in terms of two metrics: training performance and generalization performance. Here we focus on the training performance of single-layered neural networks for binary classification, and provide conditions under which the training error is zero at all local minima of a smooth hinge loss function. Our conditions are roughly in the following form: the neurons have to be strictly convex and the surrogate loss function should be a smooth version of hinge loss. We also provide counterexamples to show that when the loss function is replaced with quadratic loss or logistic loss, the result may not hold.
Decision trees effectively represent the sparse, high dimensional and noisy nature of chemical data from experiments. Having learned a function from this data, we may want to thereafter optimize the function, e.g., picking the best chemical process catalyst. In this way, we may repurpose legacy predictive models. This work studies a large-scale, industrially-relevant mixed-integer quadratic optimization problem involving: (i) gradient-boosted pre-trained regression trees modeling catalyst behavior, (ii) penalty functions mitigating risk, and (iii) penalties enforcing composition constraints. We develop heuristic methods and an exact, branch-and-bound algorithm leveraging structural properties of gradient-boosted trees and penalty functions. We numerically test our methods on an industrial instance.
Simulation is an appealing option for validating the safety of autonomous vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative human driver models. These human driver models were learned through training in single-agent environments, but they have difficulty in generalizing to multi-agent driving scenarios. We argue these difficulties arise because observations at training and test time are sampled from different distributions. This difference makes such models unsuitable for the simulation of driving scenes, where multiple agents must interact realistically over long time horizons. We extend GAIL to address these shortcomings through a parameter-sharing approach grounded in curriculum learning. Compared with single-agent GAIL policies, policies generated by our PS-GAIL method prove superior at interacting stably in a multi-agent setting and capturing the emergent behavior of human drivers.
Employing voice-based emotion recognition function in artificial intelligence (AI) product will improve the user experience. Most of researches that have been done only focus on the speech collected under controlled conditions. The scenarios evaluated in these research were well controlled. The conventional approach may fail when background noise or nonspeech filler exist. In this paper, we propose an ensemble framework combining several aspects of features from audio. The framework incorporates gender and speaker information relying on multi-task learning. Therefore it is able to dig and capture emotional information as much as possible. This framework is evaluated on multimodal emotion challenge (MEC) 2017 corpus which is close to real world. The proposed framework outperformed the best baseline system by 29.5% (relative improvement).
We provide new theoretical insights on why over-parametrization is effective in learning neural networks. For a $k$ hidden node shallow network with quadratic activation and $n$ training data points, we show as long as $ k \ge \sqrt{2n}$, over-parametrization enables local search algorithms to find a \emph{globally} optimal solution for general smooth and convex loss functions. Further, despite that the number of parameters may exceed the sample size, using theory of Rademacher complexity, we show with weight decay, the solution also generalizes well if the data is sampled from a regular distribution such as Gaussian. To prove when $k\ge \sqrt{2n}$, the loss function has benign landscape properties, we adopt an idea from smoothed analysis, which may have other applications in studying loss surfaces of neural networks.
The considered problem is how to optimally allocate a set of jobs to technicians of different skills such that the number of technicians of each skill does not exceed the number of persons with that skill designation. The key motivation is the quick sensitivity analysis in terms of the workforce size which is quite necessary in many industries in the presence of unexpected work orders. A time-indexed mathematical model is proposed to minimize the total weighted completion time of the jobs. The proposed model is decomposed into a number of single-skill sub-problems so that each one is a combination of a series of nested binary Knapsack problems. A heuristic procedure is proposed to solve the problem. Our experimental results, based on a real-world case study, reveal that the proposed method quickly produces a schedule statistically close to the optimal one while the classical optimal procedure is very time-consuming.
For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks.
It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption, and recapitulate evidence for and against this postulate. We also report the results of an evaluation in a crowd-sourcing study, which does not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then continue to review criteria for interpretability from the psychological literature, evaluate some of them, and briefly discuss their potential use in machine learning.
In its simplest form, the traffic flow prediction problem is restricted to predicting a single time-step into the future. Multi-step traffic flow prediction extends this set-up to the case where predicting multiple time-steps into the future based on some finite history is of interest. This problem is significantly more difficult than its single-step variant and is known to suffer from degradation in predictions as the time step increases. In this paper, two approaches to improve multi-step traffic flow prediction performance in recursive and multi-output settings are introduced. In particular, a model that allows recursive prediction approaches to take into account the temporal context in term of time-step index when making predictions is introduced. In addition, a conditional generative adversarial network-based data augmentation method is proposed to improve prediction performance in the multi-output setting. The experiments on a real-world traffic flow dataset show that the two methods improve on multi-step traffic flow prediction in recursive and multi-output settings, respectively.
Autonomous vehicles (AVs) require accurate metric and topological location estimates for safe, effective navigation and decision-making. Although many high-definition (HD) roadmaps exist, they are not always accurate since public roads are dynamic, shaped unpredictably by both human activity and nature. Thus, AVs must be able to handle situations in which the topology specified by the map does not agree with reality. We present the Variable Structure Multiple Hidden Markov Model (VSM-HMM) as a framework for localizing in the presence of topological uncertainty, and demonstrate its effectiveness on an AV where lane membership is modeled as a topological localization process. VSM-HMMs use a dynamic set of HMMs to simultaneously reason about location within a set of most likely current topologies and therefore may also be applied to topological structure estimation as well as AV lane estimation. In addition, we present an extension to the Earth Mover's Distance which allows uncertainty to be taken into account when computing the distance between belief distributions on simplices of arbitrary relative sizes.
With the growing integration of smartphones into our daily lives, and their increased ease of use, mobile games have become highly popular across all demographics. People listen to music, play games or read the news while in transit or bridging gap times. While mobile gaming is gaining popularity, mobile expression of creativity is still in its early stages. We present here a new type of mobile app -- fluidic games -- and illustrate our iterative approach to their design. This new type of app seamlessly integrates exploration of the design space into the actual user experience of playing the game, and aims to enrich the user experience. To better illustrate the game domain and our approach, we discuss one specific fluidic game, which is available as a commercial product. We also briefly discuss open challenges such as player support and how generative techniques can aid the exploration of the game space further.
To real-time management of the bridges under dynamic conditions, this paper develops a rule-based decision support framework to extract the necessary rules from simulation results made by Aimsun. In this rule-based system, the supervised and the unsupervised learning algorithms are applied to generalize the rules where the initial set of rules are provided by the aid of fuzzy rule generation algorithms on the results of Aimsun traffic micro-simulation software. As a pilot case study, Nasr Bridge in Tehran is simulated in Aimsun7 and WEKA data mining software is used to execute the learning algorithms. Based on this experiment, the accuracy of the supervised algorithms to generalize the rules is greater than 80%. In addition, CART decision tree and sequential minimal optimization (SMO) provides 100% accuracy for normal data and so these algorithms are so reliable for crisis management on bridge. This means that, it is possible to use such machine learning methods to manage bridges in the real-time conditions.
Estimating the structure of directed acyclic graphs (DAGs, also known as Bayesian networks) is a challenging problem since the search space of DAGs is combinatorial and scales superexponentially with the number of nodes. Existing approaches rely on various local heuristics for enforcing the acyclicity constraint and are not well-suited to general purpose optimization packages for their solution. In this paper, we introduce a fundamentally different strategy: We formulate the structure learning problem as a smooth, constrained optimization problem over real matrices that avoids this combinatorial constraint entirely. This is achieved by a novel characterization of acyclicity that is not only smooth but also exact. The resulting nonconvex, constrained program involves smooth functions whose gradients are easy to compute and only involve elementary matrix operations. By using existing black-box optimization routines, our method uses global search to find an optimal DAG and can be implemented in about 50 lines of Python and outperforms existing methods without imposing any structural constraints.
We describe N-body networks, a neural network architecture for learning the behavior and properties of complex many body physical systems. Our specific application is to learn atomic potential energy surfaces for use in molecular dynamics simulations. Our architecture is novel in that (a) it is based on a hierarchical decomposition of the many body system into subsytems, (b) the activations of the network correspond to the internal state of each subsystem, (c) the "neurons" in the network are constructed explicitly so as to guarantee that each of the activations is covariant to rotations, (d) the neurons operate entirely in Fourier space, and the nonlinearities are realized by tensor products followed by Clebsch-Gordan decompositions. As part of the description of our network, we give a characterization of what way the weights of the network may interact with the activations so as to ensure that the covariance property is maintained.
Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection using training data with only benign users. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator with a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art one-class classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.
Machine learning (ML) is the fastest growing field in computer science and healthcare, providing future benefits in improved medical diagnoses, disease analyses and prevention. In this paper, we introduce an application of interactive machine learning (iML) in a telemedicine system, to enable automatic and personalised interventions for lifestyle promotion. We first present the high level architecture of the system and the components forming the overall architecture. We then illustrate the interactive machine learning process design. Prediction models are expected to be trained through the participants' profiles, activity performance, and feedback from the caregiver. Finally, we show some preliminary results during the system implementation and discuss future directions. We envisage the proposed system to be digitally implemented, and behaviourally designed to promote healthy lifestyle and activities, and hence prevent users from the risk of chronic diseases.
Evaluation of summarization tasks is extremely crucial to determining the quality of machine generated summaries. Over the last decade, ROUGE has become the standard automatic evaluation measure for evaluating summarization tasks. While ROUGE has been shown to be effective in capturing n-gram overlap between system and human composed summaries, there are several limitations with the existing ROUGE measures in terms of capturing synonymous concepts and coverage of topics. Thus, often times ROUGE scores do not reflect the true quality of summaries and prevents multi-faceted evaluation of summaries (i.e. by topics, by overall content coverage and etc). In this paper, we introduce ROUGE 2.0, which has several updated measures of ROUGE: ROUGE-N+Synonyms, ROUGE-Topic, ROUGE-Topic+Synonyms, ROUGE-TopicUniq and ROUGE-TopicUniq+Synonyms; all of which are improvements over the core ROUGE measures.
Autonomous systems in remote locations have a high degree of autonomy and there is a need to explain what they are doing and why in order to increase transparency and maintain trust. Here, we describe a natural language chat interface that enables vehicle behaviour to be queried by the user. We obtain an interpretable model of autonomy through having an expert 'speak out-loud' and provide explanations during a mission. This approach is agnostic to the type of autonomy model and as expert and operator are from the same user-group, we predict that these explanations will align well with the operator's mental model, increase transparency and assist with operator training.
Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been proposed to help a supplier manage package delivery services from a single depot to multiple customers. Most of the existing VRPPC works consider deterministic parameters which may not be practical and uncertainty has to be taken into account. In this paper, we propose the Optimal Stochastic Delivery Planning with Deadline (ODPD) to help a supplier plan and optimize the package delivery. The aim of ODPD is to service all customers within a given deadline while considering the randomness in customer demands and traveling time. We formulate the ODPD as a stochastic integer programming, and use the cardinality minimization approach for calculating the deadline violation probability. To accelerate computation, the L-shaped decomposition method is adopted. We conduct extensive performance evaluation based on real customer locations and traveling time from Google Map.
We present an algorithm for rapidly learning controllers for robotics systems. The algorithm follows the model-based reinforcement learning paradigm, and improves upon existing algorithms; namely Probabilistic learning in Control (PILCO) and a sample-based version of PILCO with neural network dynamics (Deep-PILCO). We propose training a neural network dynamics model using variational dropout with truncated Log-Normal noise. This allows us to obtain a dynamics model with calibrated uncertainty, which can be used to simulate controller executions via rollouts. We also describe set of techniques, inspired by viewing PILCO as a recurrent neural network model, that are crucial to improve the convergence of the method. We test our method on a variety of benchmark tasks, demonstrating data-efficiency that is competitive with PILCO, while being able to optimize complex neural network controllers. Finally, we assess the performance of the algorithm for learning motor controllers for a six legged autonomous underwater vehicle. This demonstrates the potential of the algorithm for scaling up the dimensionality and dataset sizes, in more complex control tasks.
Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise. Specifically, we show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI (Bowman et. al, 2015) and 53% of MultiNLI (Williams et. al, 2017). Our analysis reveals that specific linguistic phenomena such as negation and vagueness are highly correlated with certain inference classes. Our findings suggest that the success of natural language inference models to date has been overestimated, and that the task remains a hard open problem.
State-action value functions (i.e., Q-values) are ubiquitous in reinforcement learning (RL), giving rise to popular algorithms such as SARSA and Q-learning. We propose a new notion of action value defined by a Gaussian smoothed version of the expected Q-value. We show that such smoothed Q-values still satisfy a Bellman equation, making them learnable from experience sampled from an environment. Moreover, the gradients of expected reward with respect to the mean and covariance of a parameterized Gaussian policy can be recovered from the gradient and Hessian of the smoothed Q-value function. Based on these relationships, we develop new algorithms for training a Gaussian policy directly from a learned smoothed Q-value approximator. The approach is additionally amenable to proximal optimization by augmenting the objective with a penalty on KL-divergence from a previous policy. We find that the ability to learn both a mean and covariance during training leads to significantly improved results on standard continuous control benchmarks.
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis. Comparative analysis show that the sensitivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and approaches based on visualization of activations are constrained by the resolution of the convolutional layer. The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives.
Object cosegmentation addresses the problem of discovering similar objects from multiple images and segmenting them as foreground simultaneously. In this paper, we propose a novel end-to-end pipeline to segment the similar objects simultaneously from relevant set of images using supervised learning via deep-learning framework. We experiment with multiple set of object proposal generation techniques and perform extensive numerical evaluations by training the Siamese network with generated object proposals. Similar objects proposals for the test images are retrieved using the ANNOY (Approximate Nearest Neighbor) library and deep semantic segmentation is performed on them. Finally, we form a collage from the segmented similar objects based on the relative importance of the objects.
In this work, we present a Multi-Channel deep convolutional Pyramid Person Matching Network (MC-PPMN) based on the combination of the semantic-components and the color-texture distributions to address the problem of person re-identification. In particular, we learn separate deep representations for semantic-components and color-texture distributions from two person images and then employ pyramid person matching network (PPMN) to obtain correspondence representations. These correspondence representations are fused to perform the re-identification task. Further, the proposed framework is optimized via a unified end-to-end deep learning scheme. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art literature, especially on the rank-1 recognition rate.
In this paper, we propose GPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering.
Extracting action sequences from texts in natural language is challenging, which requires commonsense inferences based on world knowledge. Although there has been work on extracting action scripts, instructions, navigation actions, etc., they require either the set of candidate actions is provided in advance, or action descriptions are restricted in a specific form, e.g., description templates. In this paper, we aim to extract action sequences from texts in free natural language, i.e., without any restricted templates, provided the candidate set of actions is unknown. We propose to extract action sequences from texts based on the deep reinforcement learning framework. Specifically, we view "selecting" or "eliminating" words from texts as "actions", and texts associated with actions as "states". We then build Q-networks to learn the policy of extracting actions and extract plans from the labelled texts. We exhibit the effectiveness of our approach in several datasets with comparison to state-of-the-art approaches, including online experiments interacting with humans.
Learning distributed sentence representations remains an interesting problem in the field of Natural Language Processing (NLP). We want to learn a model that approximates the conditional latent space over the representations of a logical antecedent of the given statement. In our paper, we propose an approach to generating sentences, conditioned on an input sentence and a logical inference label. We do this by modeling the different possibilities for the output sentence as a distribution over the latent representation, which we train using an adversarial objective. We evaluate the model using two state-of-the-art models for the Recognizing Textual Entailment (RTE) task, and measure the BLEU scores against the actual sentences as a probe for the diversity of sentences produced by our model. The experiment results show that, given our framework, we have clear ways to improve the quality and diversity of generated sentences.
Ontologies are critical sources of semantic information for many application domains. Hence, there are ontologies proposed and utilized for domains such as medicine, chemical engineering, and electrical energy. In this paper, we present an improved and extended version of a wind energy ontology previously proposed. First, the ontology is restructured to increase its understandability and coverage. Secondly, it is enriched with new concepts, crisp/fuzzy attributes, and instances to increase its usability in semantic applications regarding wind energy. The ultimate ontology is utilized within a Web-based semantic portal application for wind energy, in order to showcase its contribution in a genuine application. Hence, the current study is a significant to wind and thereby renewable energy informatics, with the presented publicly-available wind energy ontology and the implemented proof-of-concept system.
Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-around time remains a key bottleneck in research and in practice. We investigate how to optimize existing deep RL algorithms for modern computers, specifically for a combination of CPUs and GPUs. We confirm that both policy gradient and Q-value learning algorithms can be adapted to learn using many parallel simulator instances. We further find it possible to train using batch sizes considerably larger than are standard, without negatively affecting sample complexity or final performance. We leverage these facts to build a unified framework for parallelization that dramatically hastens experiments in both classes of algorithm. All neural network computations use GPUs, accelerating both data collection and training. Our results include using an entire NVIDIA DGX-1 to learn successful strategies in Atari games in single-digit minutes, using both synchronous and asynchronous algorithms.
In high dimensions, most machine learning methods are brittle to even a small fraction of structured outliers. To address this, we introduce a new meta-algorithm that can take in a base learner such as least squares or stochastic gradient descent, and harden the learner to be resistant to outliers. Our method, Sever, possesses strong theoretical guarantees yet is also highly scalable -- beyond running the base learner itself, it only requires computing the top singular vector of a certain $n \times d$ matrix. We apply Sever on a drug design dataset and a spam classification dataset, and find that in both cases it has substantially greater robustness than several baselines. On the spam dataset, with $1\%$ corruptions, we achieved $7.4\%$ test error, compared to $13.4\%-20.5\%$ for the baselines, and $3\%$ error on the uncorrupted dataset. Similarly, on the drug design dataset, with $10\%$ corruptions, we achieved $1.42$ mean-squared error test error, compared to $1.51$-$2.33$ for the baselines, and $1.23$ error on the uncorrupted dataset.
Much of the recent literature on bandit learning focuses on algorithms that aim to converge on an optimal action. One shortcoming is that this orientation does not account for time sensitivity, which can play a crucial role when learning an optimal action requires much more information than near-optimal ones. Indeed, popular approaches such as upper-confidence-bound methods and Thompson sampling can fare poorly in such situations. We consider instead learning a satisficing action, which is near-optimal while requiring less information, and propose satisficing Thompson sampling, an algorithm that serves this purpose. We establish a general bound on expected discounted regret and study the application of satisficing Thompson sampling to linear and infinite-armed bandits, demonstrating arbitrarily large benefits over Thompson sampling. We also discuss the relation between the notion of satisficing and the theory of rate distortion, which offers guidance on the selection of satisficing actions.
In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and recent work on learning sentence representations, we reformulate the problem of predicting the context in which a sentence appears as a classification problem. Given a sentence and its context, a classifier distinguishes context sentences from other contrastive sentences based on their vector representations. This allows us to efficiently learn different types of encoding functions, and we show that the model learns high-quality sentence representations. We demonstrate that our sentence representations outperform state-of-the-art unsupervised and supervised representation learning methods on several downstream NLP tasks that involve understanding sentence semantics while achieving an order of magnitude speedup in training time.
We propose novel techniques for task allocation and planning in multi-robot systems operating in uncertain environments. Task allocation is performed simultaneously with planning, which provides more detailed information about individual robot behaviour, but also exploits the independence between tasks to do so efficiently. We use Markov decision processes to model robot behaviour and linear temporal logic to specify tasks and safety constraints. Building upon techniques and tools from formal verification, we show how to generate a sequence of multi-robot policies, iteratively refining them to reallocate tasks if individual robots fail, and providing probabilistic guarantees on the performance (and safe operation) of the team of robots under the resulting policy. We implement our approach and evaluate it on a benchmark multi-robot example.
In multiagent environments, the capability of learning is important for an agent to behave appropriately in face of unknown opponents and dynamic environment. From the system designer's perspective, it is desirable if the agents can learn to coordinate towards socially optimal outcomes, while also avoiding being exploited by selfish opponents. To this end, we propose a novel gradient ascent based algorithm (SA-IGA) which augments the basic gradient-ascent algorithm by incorporating social awareness into the policy update process. We theoretically analyze the learning dynamics of SA-IGA using dynamical system theory and SA-IGA is shown to have linear dynamics for a wide range of games including symmetric games. The learning dynamics of two representative games (the prisoner's dilemma game and the coordination game) are analyzed in details. Based on the idea of SA-IGA, we further propose a practical multiagent learning algorithm, called SA-PGA, based on Q-learning update rule. Simulation results show that SA-PGA agent can achieve higher social welfare than previous social-optimality oriented Conditional Joint Action Learner (CJAL) and also is robust against individually rational opponents by reaching Nash equilibrium solutions.
We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. Drawing inspiration from first principles of computer organization, MAC moves away from monolithic black-box neural architectures towards a design that encourages both transparency and versatility. The model approaches problems by decomposing them into a series of attention-based reasoning steps, each performed by a novel recurrent Memory, Attention, and Composition (MAC) cell that maintains a separation between control and memory. By stringing the cells together and imposing structural constraints that regulate their interaction, MAC effectively learns to perform iterative reasoning processes that are directly inferred from the data in an end-to-end approach. We demonstrate the model's strength, robustness and interpretability on the challenging CLEVR dataset for visual reasoning, achieving a new state-of-the-art 98.9% accuracy, halving the error rate of the previous best model. More importantly, we show that the model is computationally-efficient and data-efficient, in particular requiring 5x less data than existing models to achieve strong results.
The enormous amount of data to be represented using large graphs exceeds in some cases the resources of a conventional computer. Edges in particular can take up a considerable amount of memory as compared to the number of nodes. However, rigorous edge storage might not always be essential to be able to draw the needed conclusions. A similar problem takes records with many variables and attempts to extract the most discernible features. It is said that the "dimension" of this data is reduced. Following an approach with the same objective in mind, we can map a graph representation to a k-dimensional space and answer queries of neighboring nodes by measuring Euclidean distances. The accuracy of our answers would decrease but would be compensated for by fuzzy logic which gives an idea about the likelihood of error. This method allows for reasonable representation in memory while maintaining a fair amount of useful information. Promising preliminary results are obtained and reported by testing the proposed approach on a number of Facebook graphs.
Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation. Traditional RL algorithms, however, fail to scale to large domains due to the curse of dimensionality. We propose a novel Dialogue Management architecture, based on Feudal RL, which decomposes the decision into two steps; a first step where a master policy selects a subset of primitive actions, and a second step where a primitive action is chosen from the selected subset. The structural information included in the domain ontology is used to abstract the dialogue state space, taking the decisions at each step using different parts of the abstracted state. This, combined with an information sharing mechanism between slots, increases the scalability to large domains. We show that an implementation of this approach, based on Deep-Q Networks, significantly outperforms previous state of the art in several dialogue domains and environments, without the need of any additional reward signal.
Network quantization is an effective solution to compress deep neural networks for practical usage. Existing network quantization methods cannot sufficiently exploit the depth information to generate low-bit compressed network. In this paper, we propose two novel network quantization approaches, single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ) for extremely low-bit quantization (ternary).We are the first to consider the network quantization from both width and depth level. In the width level, parameters are divided into two parts: one for quantization and the other for re-training to eliminate the quantization loss. SLQ leverages the distribution of the parameters to improve the width level. In the depth level, we introduce incremental layer compensation to quantize layers iteratively which decreases the quantization loss in each iteration. The proposed approaches are validated with extensive experiments based on the state-of-the-art neural networks including AlexNet, VGG-16, GoogleNet and ResNet-18. Both SLQ and MLQ achieve impressive results.
Increasing energy efficiency in buildings can reduce costs and emissions substantially. Historically, this has been treated as a local, or single-agent, optimization problem. However, many buildings utilize the same types of thermal equipment e.g. electric heaters and hot water vessels. During operation, occupants in these buildings interact with the equipment differently thereby driving them to diverse regions in the state-space. Reinforcement learning agents can learn from these interactions, recorded as sensor data, to optimize the overall energy efficiency. However, if these agents operate individually at a household level, they can not exploit the replicated structure in the problem. In this paper, we demonstrate that this problem can indeed benefit from multi-agent collaboration by making use of targeted exploration of the state-space allowing for better generalization. We also investigate trade-offs between integrating human knowledge and additional sensors. Results show that savings of over 40% are possible with collaborative multi-agent systems making use of either expert knowledge or additional sensors with no loss of occupant comfort. We find that such multi-agent systems comfortably outperform comparable single agent systems.
Classical anomaly detection (AD) is principally concerned with point-based anomalies, anomalies that occur at a single point in time. While point-based anomalies are useful, many real-world anomalies are range-based, meaning they occur over a period of time. Therefore, applying classical point-based accuracy measures to range-based AD systems can be misleading. In this paper, we present a new mathematical model that more accurately gauges the classification correctness of AD systems for range-based anomalies. Unlike prior work, our mathematical definitions are a superset of the classical AD definitions, enabling our system to also subsume point-based anomalies. Moreover, our system is broadly generalizable and provides a number of specialization functions that can control the application's bias along a multi-dimensional axis.
Multitask learning, i.e. learning several tasks at once with the same neural network, can improve performance in each of the tasks. Designing deep neural network architectures for multitask learning is a challenge: There are many ways to tie the tasks together, and the design choices matter. The size and complexity of this problem exceeds human design ability, making it a compelling domain for evolutionary optimization. Using the existing state of the art soft ordering architecture as the starting point, methods for evolving the modules of this architecture and for evolving the overall topology or routing between modules are evaluated in this paper. A synergetic approach of evolving custom routings with evolved, shared modules for each task is found to be very powerful, significantly improving the state of the art in the Omniglot multitask, multialphabet character recognition domain. This result demonstrates how evolution can be instrumental in advancing deep neural network and complex system design in general.
Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The challenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. In this paper, we propose a Neural Network architecture called AR-MDN, that simultaneously models associative factors, time-series trends and the variance in the demand. We first identify several causal features and use a combination of feature embeddings, MLP and LSTM to represent them. We then model the output density as a learned mixture of Gaussian distributions. The AR-MDN can be trained end-to-end without the need for additional supervision. We experiment on a dataset of an year's worth of data over tens-of-thousands of products from Flipkart. The proposed architecture yields a significant improvement in forecasting accuracy when compared with existing alternatives.
We present a semantically rich graph representation for indoor robotic navigation. Our graph representation encodes: semantic locations such as offices or corridors as nodes, and navigational behaviors such as enter office or cross a corridor as edges. In particular, our navigational behaviors operate directly from visual inputs to produce motor controls and are implemented with deep learning architectures. This enables the robot to avoid explicit computation of its precise location or the geometry of the environment, and enables navigation at a higher level of semantic abstraction. We evaluate the effectiveness of our representation by simulating navigation tasks in a large number of virtual environments. Our results show that using a simple sets of perceptual and navigational behaviors, the proposed approach can successfully guide the way of the robot as it completes navigational missions such as going to a specific office. Furthermore, our implementation shows to be effective to control the selection and switching of behaviors.
Although there is an emerging trend towards generating embeddings for primarily unstructured data, and recently for structured data, there is not yet any systematic suite for measuring the quality of embeddings. This deficiency is further sensed with respect to embeddings generated for structured data because there are no concrete evaluation metrics measuring the quality of encoded structure as well as semantic patterns in the embedding space. In this paper, we introduce a framework containing three distinct tasks concerned with the individual aspects of ontological concepts: (i) the categorization aspect, (ii) the hierarchical aspect, and (iii) the relational aspect. Then, in the scope of each task, a number of intrinsic metrics are proposed for evaluating the quality of the embeddings. Furthermore, w.r.t. this framework multiple experimental studies were run to compare the quality of the available embedding models. Employing this framework in future research can reduce misjudgment and provide greater insight about quality comparisons of embeddings for ontological concepts.
In a multi-source environment, each source has its own credibility. If there is no external knowledge about credibility then we can use the information provided by the sources to assess their credibility. In this paper, we propose a way to measure conflict in a multi-source environment as a normal measure. We examine our algorithm using three simulated examples of increasing conflict and one experimental example. The results demonstrate that the proposed measure can represent conflict in a meaningful way similar to what a human might expect and from it we can identify conflict within our sources.
Propositional satisfiability (SAT) is at the nucleus of state-of-the-art approaches to a variety of computationally hard problems, one of which is cryptanalysis. Moreover, a number of practical applications of SAT can only be tackled efficiently by identifying and exploiting a subset of formula's variables called backdoor set (or simply backdoors). This paper proposes a new class of backdoor sets for SAT used in the context of cryptographic attacks, namely guess-and-determine attacks. The idea is to identify the best set of backdoor variables subject to a statistically estimated hardness of the guess-and-determine attack using a SAT solver. Experimental results on weakened variants of the renowned encryption algorithms exhibit advantage of the proposed approach compared to the state of the art in terms of the estimated hardness of the resulting guess-and-determine attacks.
In this work, we provide theoretical guarantees for reward decomposition in deterministic MDPs. Reward decomposition is a special case of Hierarchical Reinforcement Learning, that allows one to learn many policies in parallel and combine them into a composite solution. Our approach builds on mapping this problem into a Reward Discounted Traveling Salesman Problem, and then deriving approximate solutions for it. In particular, we focus on approximate solutions that are local, i.e., solutions that only observe information about the current state. Local policies are easy to implement and do not require substantial computational resources as they do not perform planning. While local deterministic policies, like Nearest Neighbor, are being used in practice for hierarchical reinforcement learning, we propose three stochastic policies that guarantee better performance than any deterministic policy.
Residential location choice modeling is one of the substantial components of land use and transportation models. While numerous aggregated mathematical and statistical approaches have been developed to model the residence choice behavior of households, disaggregated approaches such as the agent-based modeling have shown interesting capabilities. In this article, a novel agent-based approach is developed to simulate the residential location choice of tenants in Tehran, the capital of Iran. Tenants are considered as agents who select their desired residential alternatives according to their characteristics and preferences for various criteria such as the rent, accessibility to different services and facilities, environmental pollution, and distance from their workplace and former residence. The choice set of agents is limited to their desired residential alternatives by applying a constrained NSGA-II algorithm. Then, agents compete with each other to select their final residence among their alternatives. Results of the proposed approach are validated by comparing simulated and actual residences of a sample of tenants. Results show that the proposed approach is able to accurately simulate the residence of 59.3% of tenants at the traffic analysis zone level.
Because of improving accessibility, transport developments play an important role in residence choice of renter households. In this paper, an agent-based model is developed to investigate impacts of different transport developments on residence choice of renter households in Tehran, the capital of Iran. In the proposed model, renter households are considered as agents who make a multi-objective decision and compete with each other to rent a preferred residential zone. Then, three transport development scenarios including construction a new highway, subway and bus rapid transit (BRT) line are simulated and resulting changes in residence choice of agents are evaluated. Results show that transport development scenarios significantly affect residence choice behavior of different socio-economic categories of renter households and lead to considerable changes in the residential demand, composition of residents, mean income level and mean car ownership in their vicinities.
The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy. Existing exploration methods are mostly based on adding noise to the on-going actor policy and can only explore \emph{local} regions close to what the actor policy dictates. In this work, we develop a simple meta-policy gradient algorithm that allows us to adaptively learn the exploration policy in DDPG. Our algorithm allows us to train flexible exploration behaviors that are independent of the actor policy, yielding a \emph{global exploration} that significantly speeds up the learning process. With an extensive study, we show that our method significantly improves the sample-efficiency of DDPG on a variety of reinforcement learning tasks.
Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates sparsity, hierarchy and modularity resembling the working principles of neocortex. Feature encoding is an important step to create sparse binary patterns. This sparsity is introduced by the binary weights and random weight assignment in the initialization stage of the HTM. We propose the alternative deterministic method for the HTM initialization stage, which connects the HTM weights to the input data and preserves natural sparsity of the input information. Further, we introduce the hardware implementation of the deterministic approach and compare it to the traditional HTM and existing hardware implementation. We test the proposed approach on the face recognition problem and show that it outperforms the conventional HTM approach.
Deep reinforcement learning (DRL) has proven to be an effective tool for creating general video-game AI. However most current DRL video-game agents learn end-to-end from the video-output of the game, which is superfluous for many applications and creates a number of additional problems. More importantly, directly working on pixel-based raw video data is substantially distinct from what a human player does.In this paper, we present a novel method which enables DRL agents to learn directly from object information. This is obtained via use of an object embedding network (OEN) that compresses a set of object feature vectors of different lengths into a single fixed-length unified feature vector representing the current game-state and fulfills the DRL simultaneously. We evaluate our OEN-based DRL agent by comparing to several state-of-the-art approaches on a selection of games from the GVG-AI Competition. Experimental results suggest that our object-based DRL agent yields performance comparable to that of those approaches used in our comparative study.
Accurate Traffic Sign Detection (TSD) can help drivers make better decision according to the traffic regulations. TSD, regarded as a typical small object detection problem in some way, is fundamental in the field of self-driving and advanced driver assistance systems. However, small object detection is still an open question. In this paper, we proposed a human brain inspired network to handle this problem. Attention mechanism is an essential function of our brain, we used a novel recurrent attentive neural network to improve the detection accuracy in a fine-grained manner. Further, as we human can combine domain specific knowledge and intuitive knowledge to solve tricky tasks, we proposed an assumption that the location of the traffic signs obeys the reverse gaussian distribution, which means the location is around the central bias of every picture. Experimental result shows that our methods achieved better performance than several popular methods used in object detection.
Short-term synaptic plasticity (STSP) affects the efficiency of synaptic transmission for persistent presynaptic activities. We consider attractor neural networks, for which the attractors are given, in the absence of STSP, by cell assemblies of excitatory cliques. We show that STSP may transform these attracting states into attractor relics, inducing ongoing transient-state dynamics in terms of sequences of transiently activated cell assemblies, the former attractors. Subsequent cell assemblies may be both disjoint or partially overlapping. It may hence be possible to use the resulting dynamics for the generation of motor control sequences.
In this work we describe a novel deep reinforcement learning neural network architecture that allows multiple actions to be selected at every time-step. Multi-action policies allows complex behaviors to be learnt that are otherwise hard to achieve when using single action selection techniques. This work describes an algorithm that uses both imitation learning (IL) and temporal difference (TD) reinforcement learning (RL) to provide a 4x improvement in training time and 2.5x improvement in performance over single action selection TD RL. We demonstrate the capabilities of this network using a complex in-house 3D game. Mimicking the behavior of the expert teacher significantly improves world state exploration and allows the agents vision system to be trained more rapidly than TD RL alone. This initial training technique kick-starts TD learning and the agent quickly learns to surpass the capabilities of the expert.
Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence. We show that simple averaging of multiple points along the trajectory of SGD, with a cyclical or constant learning rate, leads to better generalization than conventional training. We also show that this Stochastic Weight Averaging (SWA) procedure finds much broader optima than SGD, and approximates the recent Fast Geometric Ensembling (FGE) approach with a single model. Using SWA we achieve notable improvement in test accuracy over conventional SGD training on a range of state-of-the-art residual networks, PyramidNets, DenseNets, and Shake-Shake networks on CIFAR-10, CIFAR-100, and ImageNet. In short, SWA is extremely easy to implement, improves generalization, and has almost no computational overhead.
We present a new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering. Together, these constitute the AI2 Reasoning Challenge (ARC), which requires far more powerful knowledge and reasoning than previous challenges such as SQuAD or SNLI. The ARC question set is partitioned into a Challenge Set and an Easy Set, where the Challenge Set contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurence algorithm. The dataset contains only natural, grade-school science questions (authored for human tests), and is the largest public-domain set of this kind (7,787 questions). We test several baselines on the Challenge Set, including leading neural models from the SQuAD and SNLI tasks, and find that none are able to significantly outperform a random baseline, reflecting the difficult nature of this task. We are also releasing the ARC Corpus, a corpus of 14M science sentences relevant to the task, and implementations of the three neural baseline models tested. Can your model perform better? We pose ARC as a challenge to the community.
In this study we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes n-grams, skip-grams and clustering-based word representations. We apply approaches based on single classifiers as well as more advanced ensemble classifiers and stacked generalization, achieving the best result of 80% accuracy for this 3-class classification task. Analysis of the results reveals that discriminating hate speech and profanity is not a simple task, which may require features that capture a deeper understanding of the text not always possible with surface n-grams. The variability of gold labels in the annotated data, due to differences in the subjective adjudications of the annotators, is also an issue. Other directions for future work are discussed.
Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance.
Deep Neural Networks (DNNs) have achieved remarkable performance in a myriad of realistic applications. However, recent studies show that well-trained DNNs can be easily misled by adversarial examples (AE) -- the maliciously crafted inputs by introducing small and imperceptible input perturbations. Existing mitigation solutions, such as adversarial training and defensive distillation, suffer from expensive retraining cost and demonstrate marginal robustness improvement against the state-of-the-art attacks like CW family adversarial examples. In this work, we propose a novel low-cost "feature distillation" strategy to purify the adversarial input perturbations of AEs by redesigning the popular image compression framework "JPEG". The proposed "feature distillation" wisely maximizes the malicious feature loss of AE perturbations during image compression while suppressing the distortions of benign features essential for high accurate DNN classification. Experimental results show that our method can drastically reduce the success rate of various state-of-the-art AE attacks by ~60% on average for both CIFAR-10 and ImageNet benchmarks without harming the testing accuracy, outperforming existing solutions like default JPEG compression and "feature squeezing".
In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.
Go gaming is a struggle between adversaries, black and white simple stones, and aim to control the most Go board territory for success. Rules are simple but Go game fighting is highly intricate. Stones placement and interaction on board is random-appearance, likewise interaction phenomena among basic elements in physics thermodynamics, chemistry, biology, or social issues. We model the Go game dynamic employing an Ising model energy function, whose interaction coefficients reflect the application of rules and tactics to build long-term strategies. At any step of the game, the energy function of the model assesses the control and strength of a player over the board. A close fit between predictions of the model with actual game's scores is obtained. AlphaGo computer is the current top Go player, but its behavior does not wholly reveal the Go gaming nature. The Ising function allows for precisely model the stochastic evolutions of Go gaming patterns, so, to advance the understanding on Go own-dynamic -beyond the player`s abilities. The analysis of the frequency and combination of tactics shows the formation of patterns in the groups of stones during a game, regarding the turn of each player, or if human or computer adversaries are confronted.
There is a need for systems to dynamically interact with ageing populations to gather information, monitor health condition and provide support, especially after hospital discharge or at-home settings. Several smart devices have been delivered by digital health, bundled with telemedicine systems, smartphone and other digital services. While such solutions offer personalised data and suggestions, the real disruptive step comes from the interaction of new digital ecosystem, represented by chatbots. Chatbots will play a leading role by embodying the function of a virtual assistant and bridging the gap between patients and clinicians. Powered by AI and machine learning algorithms, chatbots are forecasted to save healthcare costs when used in place of a human or assist them as a preliminary step of helping to assess a condition and providing self-care recommendations. This paper describes integrating chatbots into telemedicine systems intended for elderly patient after their hospital discharge. The paper discusses possible ways to utilise chatbots to assist healthcare providers and support patients with their condition.
We introduce MeSys, a meaning-based approach to solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach more understands the meaning of each quantity in the text.
The Renormalisation Group (RG) provides a framework in which it is possible to assess whether a deep-learning network is sensitive to small changes in the input data and hence prone to error, or susceptible to adversarial attack. Distinct classification outputs are associated with different RG fixed points and sensitivity to small changes in the input data is due to the presence of relevant operators at a fixed point. A numerical scheme, based on Monte Carlo RG ideas, is proposed for identifying the existence of relevant operators and the corresponding directions of greatest sensitivity in the input data. Thus, a trained deep-learning network may be tested for its robustness and, if it is vulnerable to attack, dangerous perturbations of the input data identified.
In this short paper, we consider the roles of HCI in enabling the better governance of consequential machine learning systems using the rights and obligations laid out in the recent 2016 EU General Data Protection Regulation (GDPR)---a law which involves heavy interaction with people and systems. Focussing on those areas that relate to algorithmic systems in society, we propose roles for HCI in legal contexts in relation to fairness, bias and discrimination; data protection by design; data protection impact assessments; transparency and explanations; the mitigation and understanding of automation bias; and the communication of envisaged consequences of processing.
We describe an efficient, scalable machine learning library that enables very fast training of generalized linear models. We demonstrate that our library can remove the training time as a bottleneck for machine learning workloads, opening the door to a range of new applications. For instance, it allows more agile development, faster and more fine-grained exploration of the hyper-parameter space, enables scaling to massive datasets and makes frequent re-training of models possible in order to adapt to events as they occur. Our library, named Snap Machine Learning (Snap ML), combines recent advances in machine learning systems and algorithms in a nested manner to reflect the hierarchical architecture of modern distributed systems. This allows us to effectively leverage available network, memory and heterogeneous compute resources. On a terabyte-scale publicly available dataset for click-through-rate prediction in computational advertising, we demonstrate the training of a logistic regression classifier in 1.53 minutes, a 46x improvement over the fastest reported performance.
This work proposed a novel learning objective to train a deep neural network to perform end-to-end image pixel clustering. We applied the approach to instance segmentation, which is at the intersection of image semantic segmentation and object detection. We utilize the most fundamental property of instance labeling -- the pairwise relationship between pixels -- as the supervision to formulate the learning objective, then apply it to train a fully convolutional network (FCN) for learning to perform pixel-wise clustering. The resulting clusters can be used as the instance labeling directly. To support labeling of an unlimited number of instance, we further formulate ideas from graph coloring theory into the proposed learning objective. The evaluation on the Cityscapes dataset demonstrates strong performance and therefore proof of the concept. Moreover, our approach won the second place in the lane detection competition of 2017 CVPR Autonomous Driving Challenge, and was the top performer without using external data.
To adequately model mathematical arguments the analyst must be able to represent the mathematical objects under discussion and the relationships between them, as well as inferences drawn about these objects and relationships as the discourse unfolds. We introduce a framework with these properties, which has been applied to both mathematical dialogues and expository texts. The framework can recover salient elements of discourse at, and within, the sentence level, as well as the way mathematical content connects to form larger argumentative structures. We show how the framework might be used to support computational reasoning, and argue that it provides a more natural way to examine the process of proving theorems than do Lamport's structured proofs.
Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular spiking activity of a balanced network, and reproduce the heterogeneous spiking rate patterns of cortical neurons engaged in motor planning and movement. We identify sufficient conditions for successful learning, characterize two types of learning errors, and assess the network capacity. Our findings show that synaptically-coupled recurrent spiking networks possess a vast computational capability that can support the diverse activity patterns in the brain.
Answering complex questions is a time-consuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. Conversely, semantic parsers have been successful at handling compositionality, but only when the information resides in a target knowledge-base. In this paper, we present a novel framework for answering broad and complex questions, assuming answering simple questions is possible using a search engine and a reading comprehension model. We propose to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers. To illustrate the viability of our approach, we create a new dataset of complex questions, ComplexWebQuestions, and present a model that decomposes questions and interacts with the web to compute an answer. We empirically demonstrate that question decomposition improves performance from 20.8 precision@1 to 27.5 precision@1 on this new dataset.
Selecting a set of alternatives based on the preferences of agents is an important problem in committee selection and beyond. Among the various criteria put forth for the desirability of a committee, Pareto optimality is a minimal and important requirement. As asking agents to specify their preferences over exponentially many subsets of alternatives is practically infeasible, we assume that each agent specifies a weak order on single alternatives, from which a preference relation over subsets is derived using some preference extension. We consider five prominent extensions (responsive, downward lexicographic, upward lexicographic, best, and worst). For each of them, we consider the corresponding Pareto optimality notion, and we study the complexity of computing and verifying Pareto optimal outcomes. We also consider strategic issues: for four of the set extensions, we present a linear-time, Pareto optimal and strategyproof algorithm that even works for weak preferences.
Machine learning is a crucial aspect of artificial intelligence. This paper details an approach for quantum Hebbian learning through a batched version of quantum state exponentiation. Here, batches of quantum data are interacted with learning and processing quantum bits (qubits) by a series of elementary controlled partial swap operations, resulting in a Hamiltonian simulation of the statistical ensemble of the data. We decompose this elementary operation into one and two qubit quantum gates from the Clifford+$T$ set and use the decomposition to perform an efficiency analysis. Our construction of quantum Hebbian learning is motivated by extension from the established classical approach, and it can be used to find details about the data such as eigenvalues through phase estimation. This work contributes to the near-term development and implementation of quantum machine learning techniques.
A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions. We dispel such beliefs by introducing a random search method for training static, linear policies for continuous control problems, matching state-of-the-art sample efficiency on the benchmark MuJoCo locomotion tasks. Our method also finds a nearly optimal controller for a challenging instance of the Linear Quadratic Regulator, a classical problem in control theory, when the dynamics are not known. Computationally, our random search algorithm is at least 15 times more efficient than the fastest competing model-free methods on these benchmarks. We take advantage of this computational efficiency to evaluate the performance of our method over hundreds of random seeds and many different hyperparameter configurations for each benchmark task. Our simulations highlight a high variability in performance in these benchmark tasks, suggesting that commonly used estimations of sample efficiency do not adequately evaluate the performance of RL algorithms.
Reward shaping allows reinforcement learning (RL) agents to accelerate learning by receiving additional reward signals. However, these signals can be difficult to design manually, especially for complex RL tasks. We propose a simple and general approach that determines the reward of pre-defined events by their rarity alone. Here events become less rewarding as they are experienced more often, which encourages the agent to continually explore new types of events as it learns. The adaptiveness of this reward function results in a form of automated curriculum learning that does not have to be specified by the experimenter. We demonstrate that this Rarity of Events (RoE) approach enables the agent to succeed in challenging VizDoom scenarios without access to the extrinsic reward from the environment. Furthermore, the results demonstrate that RoE learns a more versatile policy that adapts well to critical changes in the environment. Rewarding events based on their rarity could help in many unsolved RL environments that are characterized by sparse extrinsic rewards but a plethora of known event types.
This paper presents a systematic survey on recent development of neural text generation models. Specifically, we start from recurrent neural network language models with the traditional maximum likelihood estimation training scheme and point out its shortcoming for text generation. We thus introduce the recently proposed methods for text generation based on reinforcement learning, re-parametrization tricks and generative adversarial nets (GAN) techniques. We compare different properties of these models and the corresponding techniques to handle their common problems such as gradient vanishing and generation diversity. Finally, we conduct a benchmarking experiment with different types of neural text generation models on two well-known datasets and discuss the empirical results along with the aforementioned model properties.
This paper describes the methodology followed to build a neural machine translation system in the biomedical domain for the English-Catalan language pair. This task can be considered a low-resourced task from the point of view of the domain and the language pair. To face this task, this paper reports experiments on a cascade pivot strategy through Spanish for the neural machine translation using the English-Spanish SCIELO and Spanish-Catalan El Peri\'odico database. To test the final performance of the system, we have created a new test data set for English-Catalan in the biomedical domain which is freely available on request.
Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs). However, effective and efficient methods for incorporation of temporal information into CNNs are still being actively explored in the recent literature. Motivated by the popular recurrent attention models in the research area of natural language processing, we propose the Attention-based Temporal Weighted CNN (ATW), which embeds a visual attention model into a temporal weighted multi-stream CNN. This attention model is simply implemented as temporal weighting yet it effectively boosts the recognition performance of video representations. Besides, each stream in the proposed ATW framework is capable of end-to-end training, with both network parameters and temporal weights optimized by stochastic gradient descent (SGD) with backpropagation. Our experiments show that the proposed attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments.
It has been challenging for the technical and regulatory communities to formulate requirements for trustworthiness of the cyber-physical systems (CPS) due to the complexity of the issues associated with their design, deployment, and operations. The US National Institute of Standards and Technology (NIST), through a public working group, has released a CPS Framework that adopts a broad and integrated view of CPS and positions trustworthiness among other aspects of CPS. This paper takes the model created by the CPS Framework and its further developments one step further, by applying ontological approaches and reasoning techniques in order to achieve greater understanding of CPS. The example analyzed in the paper demonstrates the enrichment of the original CPS model obtained through ontology and reasoning and its ability to deliver additional insights to the developers and operators of CPS.
As concerns about unfairness and discrimination in "black box" machine learning systems rise, a legal "right to an explanation" has emerged as a compellingly attractive approach for challenge and redress. We outline recent debates on the limited provisions in European data protection law, and introduce and analyze newer explanation rights in French administrative law and the draft modernized Council of Europe Convention 108. While individual rights can be useful, in privacy law they have historically unreasonably burdened the average data subject. "Meaningful information" about algorithmic logics is more technically possible than commonly thought, but this exacerbates a new "transparency fallacy"---an illusion of remedy rather than anything substantively helpful. While rights-based approaches deserve a firm place in the toolbox, other forms of governance, such as impact assessments, "soft law," judicial review, and model repositories deserve more attention, alongside catalyzing agencies acting for users to control algorithmic system design.
A useful computation when acting in a complex environment is to infer the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficult. Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops. Here we use Graph Neural Networks (GNNs) to learn a message-passing algorithm that solves these inference tasks. We first show that the architecture of GNNs is well-matched to inference tasks. We then demonstrate the efficacy of this inference approach by training GNNs on an ensemble of graphical models and showing that they substantially outperform belief propagation on loopy graphs. Our message-passing algorithms generalize out of the training set to larger graphs and graphs with different structure.
Existing research studies on vision and language grounding for robot navigation focus on improving model-free deep reinforcement learning (DRL) models in synthetic environments. However, model-free DRL models do not consider the dynamics in the real-world environments, and they often fail to generalize to new scenes. In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices---We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task. Our look-ahead module tightly integrates a look-ahead policy model with an environment model that predicts the next state and the reward. Experimental results suggest that our proposed method significantly outperforms the baselines and achieves the best on the real-world Room-to-Room dataset. Moreover, our scalable method is more generalizable when transferring to unseen environments, and the relative success rate is increased by 15.5% on the unseen test set.
Recently, increasing attention has been directed to the study of the speech emotion recognition, in which global acoustic features of an utterance are mostly used to eliminate the content differences. However, the expression of speech emotion is a dynamic process, which is reflected through dynamic durations, energies, and some other prosodic information when one speaks. In this paper, a novel local dynamic pitch probability distribution feature, which is obtained by drawing the histogram, is proposed to improve the accuracy of speech emotion recognition. Compared with most of the previous works using global features, the proposed method takes advantage of the local dynamic information conveyed by the emotional speech. Several experiments on Berlin Database of Emotional Speech are conducted to verify the effectiveness of the proposed method. The experimental results demonstrate that the local dynamic information obtained with the proposed method is more effective for speech emotion recognition than the traditional global features.
Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and functional significance of these spatial representations remain largely mysterious. As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. All these different functional types of neurons have been observed experimentally. The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies. Together, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits.
Automatic human action recognition is indispensable for almost artificial intelligent systems such as video surveillance, human-computer interfaces, video retrieval, etc. Despite a lot of progress, recognizing actions in an unknown video is still a challenging task in computer vision. Recently, deep learning algorithms have proved its great potential in many vision-related recognition tasks. In this paper, we propose the use of Deep Residual Neural Networks (ResNets) to learn and recognize human action from skeleton data provided by Kinect sensor. Firstly, the body joint coordinates are transformed into 3D-arrays and saved in RGB images space. Five different deep learning models based on ResNet have been designed to extract image features and classify them into classes. Experiments are conducted on two public video datasets for human action recognition containing various challenges. The results show that our method achieves the state-of-the-art performance comparing with existing approaches.
We consider the problem of metric learning for multi-view data and present a novel method for learning within-view as well as between-view metrics in vector-valued kernel spaces, as a way to capture multi-modal structure of the data. We formulate two convex optimization problems to jointly learn the metric and the classifier or regressor in kernel feature spaces. An iterative three-step multi-view metric learning algorithm is derived from the optimization problems. In order to scale the computation to large training sets, a block-wise Nystr{\"o}m approximation of the multi-view kernel matrix is introduced. We justify our approach theoretically and experimentally, and show its performance on real-world datasets against relevant state-of-the-art methods.
Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddings have shown promising results on tasks such as link prediction, entity recommendation, question answering, and triplet classification. However, only a few methods can compute low-dimensional embeddings of very large knowledge bases. In this paper, we propose KG2Vec, a novel approach to Knowledge Graph Embedding based on the skip-gram model. Instead of using a predefined scoring function, we learn it relying on Long Short-Term Memories. We evaluated the goodness of our embeddings on knowledge graph completion and show that KG2Vec is comparable to the quality of the scalable state-of-the-art approaches and can process large graphs by parsing more than a hundred million triples in less than 6 hours on common hardware.
Formal Concept Analysis and its associated conceptual structures have been used to support exploratory search through conceptual navigation. Relational Concept Analysis (RCA) is an extension of Formal Concept Analysis to process relational datasets. RCA and its multiple interconnected structures represent good candidates to support exploratory search in relational datasets, as they are enabling navigation within a structure as well as between the connected structures. However, building the entire structures does not present an efficient solution to explore a small localised area of the dataset, for instance to retrieve the closest alternatives to a given query. In these cases, generating only a concept and its neighbour concepts at each navigation step appears as a less costly alternative. In this paper, we propose an algorithm to compute a concept and its neighbourhood in extended concept lattices. The concepts are generated directly from the relational context family, and possess both formal and relational attributes. The algorithm takes into account two RCA scaling operators. We illustrate it on an example.
Dynamic topic models (DTMs) model the evolution of prevalent themes in literature, online media, and other forms of text over time. DTMs assume that word co-occurrence statistics change continuously and therefore impose continuous stochastic process priors on their model parameters. These dynamical priors make inference much harder than in regular topic models, and also limit scalability. In this paper, we present several new results around DTMs. First, we extend the class of tractable priors from Wiener processes to the generic class of Gaussian processes (GPs). This allows us to explore topics that develop smoothly over time, that have a long-term memory or are temporally concentrated (for event detection). Second, we show how to perform scalable approximate inference in these models based on ideas around stochastic variational inference and sparse Gaussian processes. This way we can train a rich family of DTMs to massive data. Our experiments on several large-scale datasets show that our generalized model allows us to find interesting patterns that were not accessible by previous approaches.
Selfridge, along with Sutherland and Marr provided some of the earliest proposals for how to program computers to recognize shapes. Their emphasis on filtering for contour features, especially the orientation of boundary segments, was reinforced by the Nobel Prize winning work of Hubel & Wiesel who discovered that neurons in primary visual cortex selectively respond as a function of contour orientation. Countless investigators and theorists have continued to build on this approach. These models are often described as neuromorphic, which implies that the computational methods are based on biologically plausible principles. Recent work from the present lab has challenged the emphasis on orientation selectivity and the use of neural network principles. The goal of the present report is not to relitigate those issues, but to provide an alternative concept for encoding of shape information that may be useful to neuromorphic modelers.
In this paper, we study the problem of image-text matching. Inferring the latent semantic alignment between objects or other salient stuffs (e.g. snow, sky, lawn) and the corresponding words in sentences allows to capture fine-grained interplay between vision and language, and makes image-text matching more interpretable. Prior works either simply aggregate the similarity of all possible pairs of regions and words without attending differentially to more and less important words or regions, or use a multi-step attentional process to capture limited number of semantic alignments which is less interpretable. In this paper, we present Stacked Cross Attention to discover the full latent alignments using both image regions and words in sentence as context and infer the image-text similarity. Our approach achieves the state-of-the-art results on the MS-COCO and Flickr30K datasets. On Flickr30K, our approach outperforms the current best methods by 22.1% in text retrieval from image query, and 18.2% in image retrieval with text query (based on Recall@1). On MS-COCO, our approach improves sentence retrieval by 17.8% and image retrieval by 16.6% (based on Recall@1 using the 5K test set).
We revisit the Blind Deconvolution problem with a focus on understanding its robustness and convergence properties. Provable robustness to noise and other perturbations is receiving recent interest in vision, from obtaining immunity to adversarial attacks to assessing and describing failure modes of algorithms in mission critical applications. Further, many blind deconvolution methods based on deep architectures internally make use of or optimize the basic formulation, so a clearer understanding of how this sub-module behaves, when it can be solved, and what noise injection it can tolerate is a first order requirement. We derive new insights into the theoretical underpinnings of blind deconvolution. The algorithm that emerges has nice convergence guarantees and is provably robust in a sense we formalize in the paper. Interestingly, these technical results play out very well in practice, where on standard datasets our algorithm yields results competitive with or superior to the state of the art. Keywords: blind deconvolution, robust continuous optimization
Considered as a data-driven approach, Fingerprinting Localization Solutions (FPSs) enjoy huge popularity due to their good performance and minimal environment information requirement. This papers addresses applications of artificial intelligence to solve two problems in Received Signal Strength Indicator (RSSI) based FPS, first the cumbersome training database construction and second the extrapolation of fingerprinting algorithm for similar buildings with slight environmental changes. After a concise overview of deep learning design techniques, two main techniques widely used in deep learning are exploited for the above mentioned issues namely data augmentation and transfer learning. We train a multi-layer neural network that learns the mapping from the observations to the locations. A data augmentation method is proposed to increase the training database size based on the structure of RSSI measurements and hence reducing effectively the amount of training data. Then it is shown experimentally how a model trained for a particular building can be transferred to a similar one by fine tuning with significantly smaller training numbers. The paper implicitly discusses the new guidelines to consider about deep learning designs when they are employed in a new application context.
Learning-based methods have been successful in solving complex control tasks without significant prior knowledge about the system. However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications. In this paper, we present a learning-based model predictive control scheme that provides provable high-probability safety guarantees. To this end, we exploit regularity assumptions on the dynamics in terms of a Gaussian process prior to construct provably accurate confidence intervals on predicted trajectories. Unlike previous approaches, we do not assume that model uncertainties are independent. Based on these predictions, we guarantee that trajectories satisfy safety constraints. Moreover, we use a terminal set constraint to recursively guarantee the existence of safe control actions at every iteration. In our experiments, we show that the resulting algorithm can be used to safely and efficiently explore and learn about dynamic systems.
Cultural adaptation, i.e., the matching of a robot's behaviours to the cultural norms and preferences of its user, is a well known key requirement for the success of any assistive application. However, culture-dependent robot behaviours are often implicitly set by designers, thus not allowing for an easy and automatic adaptation to different cultures. This paper presents a method for the design of culture-aware robots, that can automatically adapt their behaviour to conform to a given culture. We propose a mapping from cultural factors to related parameters of robot behaviours which relies on linguistic variables to encode heterogeneous cultural factors in a uniform formalism, and on fuzzy rules to encode qualitative relations among multiple variables. We illustrate the approach in two practical case studies.
Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA). The structure prediction model is able to abstain from predicting some labels in the structured output at a cost chosen by the user in a flexible way. For that purpose, we decompose the problem into the learning of a pair of predictors, one devoted to structured abstention and the other, to structured output prediction. To compare fully labeled training data with predictions potentially containing abstentions, we define a wide class of asymmetric abstention-aware losses. Learning is achieved by surrogate regression in an appropriate feature space while prediction with abstention is performed by solving a new pre-image problem. Thus, SOLA extends recent ideas about Structured Output Prediction via surrogate problems and calibration theory and enjoys statistical guarantees on the resulting excess risk. Instantiated on a hierarchical abstention-aware loss, SOLA is shown to be relevant for fine-grained opinion mining and gives state-of-the-art results on this task. Moreover, the abstention-aware representations can be used to competitively predict user-review ratings based on a sentence-level opinion predictor.
Conversational agents have become ubiquitous, ranging from goal-oriented systems for helping with reservations to chit-chat models found in modern virtual assistants. In this survey paper, we explore this fascinating field. We look at some of the pioneering work that defined the field and gradually move to the current state-of-the-art models. We look at statistical, neural, generative adversarial network based and reinforcement learning based approaches and how they evolved. Along the way we discuss various challenges that the field faces, lack of context in utterances, not having a good quantitative metric to compare models, lack of trust in agents because they do not have a consistent persona etc. We structure this paper in a way that answers these pertinent questions and discusses competing approaches to solve them.
Deep reinforcement learning has been successfully applied to several visual-input tasks using model-free methods. In this paper, we propose a model-based approach that combines learning a DNN-based transition model with Monte Carlo tree search to solve a block-placing task in Minecraft. Our learned transition model predicts the next frame and the rewards one step ahead given the last four frames of the agent's first-person-view image and the current action. Then a Monte Carlo tree search algorithm uses this model to plan the best sequence of actions for the agent to perform. On the proposed task in Minecraft, our model-based approach reaches the performance comparable to the Deep Q-Network's, but learns faster and, thus, is more training sample efficient.
Unseen Action Recognition (UAR) aims to recognise novel action categories without training examples. While previous methods focus on inner-dataset seen/unseen splits, this paper proposes a pipeline using a large-scale training source to achieve a Universal Representation (UR) that can generalise to a more realistic Cross-Dataset UAR (CD-UAR) scenario. We first address UAR as a Generalised Multiple-Instance Learning (GMIL) problem and discover 'building-blocks' from the large-scale ActivityNet dataset using distribution kernels. Essential visual and semantic components are preserved in a shared space to achieve the UR that can efficiently generalise to new datasets. Predicted UR exemplars can be improved by a simple semantic adaptation, and then an unseen action can be directly recognised using UR during the test. Without further training, extensive experiments manifest significant improvements over the UCF101 and HMDB51 benchmarks.
We present a method for generating colored 3D shapes from natural language. To this end, we first learn joint embeddings of freeform text descriptions and colored 3D shapes. Our model combines and extends learning by association and metric learning approaches to learn implicit cross-modal connections, and produces a joint representation that captures the many-to-many relations between language and physical properties of 3D shapes such as color and shape. To evaluate our approach, we collect a large dataset of natural language descriptions for physical 3D objects in the ShapeNet dataset. With this learned joint embedding we demonstrate text-to-shape retrieval that outperforms baseline approaches. Using our embeddings with a novel conditional Wasserstein GAN framework, we generate colored 3D shapes from text. Our method is the first to connect natural language text with realistic 3D objects exhibiting rich variations in color, texture, and shape detail. See video at https://youtu.be/zraPvRdl13Q
We propose an effective way to create interpretable control agents, by re-purposing the function of a biological neural circuit model, to govern simulated and real world reinforcement learning (RL) test-beds. We model the tap-withdrawal (TW) neural circuit of the nematode, C. elegans, a circuit responsible for the worm's reflexive response to external mechanical touch stimulations, and learn its synaptic and neuronal parameters as a policy for controlling basic RL tasks. We also autonomously park a real rover robot on a pre-defined trajectory, by deploying such neuronal circuit policies learned in a simulated environment. For reconfiguration of the purpose of the TW neural circuit, we adopt a search-based RL algorithm. We show that our neuronal policies perform as good as deep neural network policies with the advantage of realizing interpretable dynamics at the cell level.
Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof. Existing techniques in this field are able to predict, at each step of a case, the likelihood that the case will end up in an undesired outcome. These techniques, however, do not take into account what process workers may do with the generated predictions in order to decrease the likelihood of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive monitoring approaches with concepts of alarms, interventions, compensations, and mitigation effects. The framework incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptive process monitoring in a given setting. The paper also outlines an approach to optimize the generation of alarms given a dataset and a set of cost model parameters. The proposed approach is empirically evaluated using a range of real-life event logs.
Random Differential Equations provide a natural extension of Ordinary Differential Equations to the stochastic setting. We show how, and under which conditions, every equilibrium state of a Random Differential Equation (RDE) can be described by a Structural Causal Model (SCM), while pertaining the causal semantics. This provides an SCM that captures the stochastic and causal behavior of the RDE, which can model both cycles and confounders. This enables the study of the equilibrium states of the RDE by applying the theory and statistical tools available for SCMs, for example, marginalizations and Markov properties, as we illustrate by means of an example. Our work thus provides a direct connection between two fields that so far have been developing in isolation.
This paper introduces an innovative approach for handling 2D compound hypotheses within the Belief Function Theory framework. We propose a polygon-based generic rep- resentation which relies on polygon clipping operators. This approach allows us to account in the computational cost for the precision of the representation independently of the cardinality of the discernment frame. For the BBA combination and decision making, we propose efficient algorithms which rely on hashes for fast lookup, and on a topological ordering of the focal elements within a directed acyclic graph encoding their interconnections. Additionally, an implementation of the functionalities proposed in this paper is provided as an open source library. Experimental results on a pedestrian localization problem are reported. The experiments show that the solution is accurate and that it fully benefits from the scalability of the 2D search space granularity provided by our representation.
Chu Spaces and Channel Theory are well established areas of investigation in the general context of category theory. We review a range of examples and applications of these methods in logic and computer science, including Formal Concept Analysis, distributed systems and ontology development. We then employ these methods to describe human object perception, beginning with the construction of uncategorized object files and proceeding through categorization, individual object identification and the tracking of object identity through time. We investigate the relationship between abstraction and mereological categorization, particularly as these affect object identity tracking. This we accomplish in terms of information flow that is semantically structured in terms of local logics, while at the same time this framework also provides an inferential mechanism towards identification and perception. We show how a mereotopology naturally emerges from the representation of classifications by simplicial complexes, and briefly explore the emergence of geometric relations and interactions between objects.
The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives. A pilot study to explore the possible connections between bipolar affective disorder and mobile phone usage was conducted. In this study, participants were provided a mobile phone to use as their primary phone. This phone was loaded with a custom keyboard that collected metadata consisting of keypress entry time and accelerometer movement. Individual character data with the exceptions of the backspace key and space bar were not collected due to privacy concerns. We propose an end-to-end deep architecture based on late fusion, named DeepMood, to model the multi-view metadata for the prediction of mood scores. Experimental results show that 90.31% prediction accuracy on the depression score can be achieved based on session-level mobile phone typing dynamics which is typically less than one minute. It demonstrates the feasibility of using mobile phone metadata to infer mood disturbance and severity.
We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e.g. L-shape room). Our method operates directly on the panoramic image, rather than decomposing into perspective images as do recent works. Our network architecture is similar to that of RoomNet, but we show improvements due to aligning the image based on vanishing points, predicting multiple layout elements (corners, boundaries, size and translation), and fitting a constrained Manhattan layout to the resulting predictions. Our method compares well in speed and accuracy to other existing work on panoramas, achieves among the best accuracy for perspective images, and can handle both cuboid-shaped and more general Manhattan layouts.
Deep Convolutional Neural Networks have become a Swiss knife in solving critical artificial intelligence tasks. However, deploying deep CNN models for latency-critical tasks remains to be challenging because of the complex nature of CNNs. Recently, FPGA has become a favorable device to accelerate deep CNNs thanks to its high parallel processing capability and energy efficiency. In this work, we explore different fast convolution algorithms including Winograd and Fast Fourier Transform (FFT), and find an optimal strategy to apply them together on different types of convolutions. We also propose an optimization scheme to exploit parallelism on novel CNN architectures such as Inception modules in GoogLeNet. We implement a configurable IP-based face recognition acceleration system based on FaceNet using High-Level Synthesis. Our implementation on a Xilinx Ultrascale device achieves 3.75x latency speedup compared to a high-end NVIDIA GPU and surpasses previous FPGA results significantly.
Currently there is no standard way to identify how a dataset was created, and what characteristics, motivations, and potential skews it represents. To begin to address this issue, we propose the concept of a datasheet for datasets, a short document to accompany public datasets, commercial APIs, and pretrained models. The goal of this proposal is to enable better communication between dataset creators and users, and help the AI community move toward greater transparency and accountability. By analogy, in computer hardware, it has become industry standard to accompany everything from the simplest components (e.g., resistors), to the most complex microprocessor chips, with datasheets detailing standard operating characteristics, test results, recommended usage, and other information. We outline some of the questions a datasheet for datasets should answer. These questions focus on when, where, and how the training data was gathered, its recommended use cases, and, in the case of human-centric datasets, information regarding the subjects' demographics and consent as applicable. We develop prototypes of datasheets for two well-known datasets: Labeled Faces in The Wild~\cite{lfw} and the Pang \& Lee Polarity Dataset~\cite{polarity}.
We propose MRU (Multi-Range Reasoning Units), a new fast compositional encoder for machine comprehension (MC). Our proposed MRU encoders are characterized by multi-ranged gating, executing a series of parameterized contract-and-expand layers for learning gating vectors that benefit from long and short-term dependencies. The aims of our approach are as follows: (1) learning representations that are concurrently aware of long and short-term context, (2) modeling relationships between intra-document blocks and (3) fast and efficient sequence encoding. We show that our proposed encoder demonstrates promising results both as a standalone encoder and as well as a complementary building block. We conduct extensive experiments on three challenging MC datasets, namely RACE, SearchQA and NarrativeQA, achieving highly competitive performance on all. On the RACE benchmark, our model outperforms DFN (Dynamic Fusion Networks) by 1.5%-6% without using any recurrent or convolution layers. Similarly, we achieve competitive performance relative to AMANDA on the SearchQA benchmark and BiDAF on the NarrativeQA benchmark without using any LSTM/GRU layers. Finally, incorporating MRU encoders with standard BiLSTM architectures further improves performance, achieving state-of-the-art results.
Designers of autonomous agents, whether in physical or virtual environments, need to express nondeterminisim, failure, and parallelism in behaviors, as well as accounting for synchronous coordination between agents. Behavior Trees are a semi-formalism deployed widely for this purpose in the games industry, but with challenges to scalability, reasoning, and reuse of common sub-behaviors. We present an alternative formulation of behavior trees through a language design perspective, giving a formal operational semantics, type system, and corresponding implementation. We express specifications for atomic behaviors as linear logic formulas describing how they transform the environment, and our type system uses linear sequent calculus to derive a compositional type assignment to behavior tree expressions. These types expose the conditions required for behaviors to succeed and allow abstraction over parameters to behaviors, enabling the development of behavior "building blocks" amenable to compositional reasoning and reuse.
We present a neural model for representing snippets of code as continuous distributed vectors. The main idea is to represent code as a collection of paths in its abstract syntax tree, and aggregate these paths, in a smart and scalable way, into a single fixed-length $\textit{code vector}$, which can be used to predict semantic properties of the snippet. We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body. We evaluate our approach by training a model on a dataset of $14$M methods. We show that code vectors trained on this dataset can predict method names from files that were completely unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies. Comparing previous techniques over the same data set, our approach obtains a relative improvement of over $75\%$, being the first to successfully predict method names based on a large, cross-project, corpus.
The aggregate behaviors of users can collectively encode deep semantic information about the objects with which they interact. In this paper, we demonstrate novel ways in which the synthesis of these data can illuminate the terrain of users' environment and support them in their decision making and wayfinding. A novel application of Recurrent Neural Networks and skip-gram models, approaches popularized by their application to modeling language, are brought to bear on student university enrollment sequences to create vector representations of courses and map out traversals across them. We present demonstrations of how scrutability from these neural networks can be gained and how the combination of these techniques can be seen as an evolution of content tagging and a means for a recommender to balance user preferences inferred from data with those explicitly specified. From validation of the models to the development of a UI, we discuss additional requisite functionality informed by the results of a field study leading to the ultimate deployment of the system at a university.
Models of intrinsic motivation present an important means to produce sensible behaviour in the absence of extrinsic rewards. Applications in video games are varied, and range from intrinsically motivated general game-playing agents to non-player characters such as companions and enemies. The information-theoretic quantity of Empowerment is a particularly promising candidate motivation to produce believable, generic and robust behaviour. However, while it can be used in the absence of external reward functions that would need to be crafted and learned, empowerment is computationally expensive. In this paper, we propose a modified UCT tree search method to mitigate empowerment's computational complexity in discrete and deterministic scenarios. We demonstrate how to modify a Monte-Carlo Search Tree with UCT to realise empowerment maximisation, and discuss three additional modifications that facilitate better sampling. We evaluate the approach both quantitatively, by analysing how close our approach gets to the baseline of exhaustive empowerment computation with varying amounts of computational resources, and qualitatively, by analysing the resulting behaviour in a Minecraft-like scenario.
Due to the intractable partition function, the exact likelihood function for a Markov random field (MRF), in many situations, can only be approximated. Major approximation approaches include pseudolikelihood and Laplace approximation. In this paper, we propose a novel way of approximating the likelihood function through first approximating the marginal likelihood functions of individual parameters and then reconstructing the joint likelihood function from these marginal likelihood functions. For approximating the marginal likelihood functions, we derive a particular likelihood function from a modified scenario of coin tossing which is useful for capturing how one parameter interacts with the remaining parameters in the likelihood function. For reconstructing the joint likelihood function, we use an appropriate copula to link up these marginal likelihood functions. Numerical investigation suggests the superior performance of our approach. Especially as the size of the MRF increases, both the numerical performance and the computational cost of our approach remain consistently satisfactory, whereas Laplace approximation deteriorates and pseudolikelihood becomes computationally unbearable.
We propose Image-Semantic-Transformation-Reconstruction-Circle(ISTRC) model, a novel and powerful method using facenet's Euclidean latent space to understand the images. As the name suggests, ISTRC construct the circle, able to perfectly reconstruct images. One powerful Euclidean latent space embedded in ISTRC is FaceNet's last layer with the power of distinguishing and understanding images. Our model will reconstruct the images and manipulate Euclidean latent vectors to achieve semantic transformations and semantic images arthimetic calculations. In this paper, we show that ISTRC performs 10 high-level semantic transformations like "Male and female","add smile","open mouth", "deduct beard or add mustache", "bigger/smaller nose", "make older and younger", "bigger lips", "bigger eyes", "bigger/smaller mouths" and "more attractive". It just takes 3 hours(GTX 1080) to train the models of 10 semantic transformations.
Unfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid unfair price differences between groups of customers. This paper shows how to solve dynamic pricing by using Reinforcement Learning (RL) techniques so that prices are maximized while keeping a balance between revenue and fairness. We demonstrate that RL provides two main features to support fairness in dynamic pricing: on the one hand, RL is able to learn from recent experience, adapting the pricing policy to complex market environments; on the other hand, it provides a trade-off between short and long-term objectives, hence integrating fairness into the model's core. Considering these two features, we propose the application of RL for revenue optimization, with the additional integration of fairness as part of the learning procedure by using Jain's index as a metric. Results in a simulated environment show a significant improvement in fairness while at the same time maintaining optimisation of revenue.
This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced.
In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e.g. same classes), but also different latent data structures (e.g. different acquisition conditions). In such a situation, the model will perform poorly on the new data, since the classifier is specialized to recognize visual cues specific to the source domain. In this work we explore a solution, named DeepJDOT, to tackle this problem: through a measure of discrepancy on joint deep representations/labels based on optimal transport, we not only learn new data representations aligned between the source and target domain, but also simultaneously preserve the discriminative information used by the classifier. We applied DeepJDOT to a series of visual recognition tasks, where it compares favorably against state-of-the-art deep domain adaptation methods.
Speech recognition has received a less attention in Bengali literature due to the lack of a comprehensive dataset. In this paper, we describe the development process of the first comprehensive Bengali speech dataset on real numbers. It comprehends all the possible words that may arise in uttering any Bengali real number. The corpus has ten speakers from the different regions of Bengali native people. It comprises of more than two thousands of speech samples in a total duration of closed to four hours. We also provide a deep analysis of our corpus, highlight some of the notable features of it, and finally evaluate the performances of two of the notable Bengali speech recognizers on it.
Goals for reinforcement learning problems are typically defined through hand-specified rewards. To design such problems, developers of learning algorithms must inherently be aware of what the task goals are, yet we often require agents to discover them on their own without any supervision beyond these sparse rewards. While much of the power of reinforcement learning derives from the concept that agents can learn with little guidance, this requirement greatly burdens the training process. If we relax this one restriction and endow the agent with knowledge of the reward function, and in particular of the goal, we can leverage backwards induction to accelerate training. To achieve this, we propose training a model to learn to take imagined reversal steps from known goal states. Rather than training an agent exclusively to determine how to reach a goal while moving forwards in time, our approach travels backwards to jointly predict how we got there. We evaluate our work in Gridworld and Towers of Hanoi and empirically demonstrate that it yields better performance than standard DDQN.
This paper uses neuroevolution of augmenting topologies to evolve control tactics for groups of units in real-time strategy games. In such games, players build economies to generate armies composed of multiple types of units with different attack and movement characteristics to combat each other. This paper evolves neural networks to control movement and attack commands, also called micro, for a group of ranged units skirmishing with a group of melee units. Our results show that neuroevolution of augmenting topologies can effectively generate neural networks capable of good micro for our ranged units against a group of hand-coded melee units. The evolved neural networks lead to kiting behavior for the ranged units which is a common tactic used by professional players in ranged versus melee skirmishes in popular real-time strategy games like Starcraft. The evolved neural networks also generalized well to other starting positions and numbers of units. We believe these results indicate the potential of neuroevolution for generating effective micro in real-time strategy games.
Can deep learning (DL) guide our understanding of computations happening in biological brain? We will first briefly consider how DL has contributed to the research on visual object recognition. In the main part we will assess whether DL could also help us to clarify the computations underlying higher cognitive functions such as Theory of Mind. In addition, we will compare the objectives and learning signals of brains and machines, leading us to conclude that simply scaling up the current DL algorithms will not lead to human level mindreading skills. We then provide some insights about how to fairly compare human and DL performance. In the end we find that DL can contribute to our understanding of biological computations by providing an example of an end-to-end algorithm that solves the same problems the biological agents face.
Quantified modal logic provides a natural logical language for reasoning about modal attitudes even while retaining the richness of quantification for referring to predicates over domains. But then most fragments of the logic are undecidable, over many model classes. Over the years, only a few fragments (such as the monodic) have been shown to be decidable. In this paper, we study fragments that bundle quantifiers and modalities together, inspired by earlier work on epistemic logics of know-how/why/what. As always with quantified modal logics, it makes a significant difference whether the domain stays the same across worlds, or not. In particular, we show that the bundle $\forall \Box$ is undecidable over constant domain interpretations, even with only monadic predicates, whereas $\exists \Box$ bundle is decidable. On the other hand, over increasing domain interpretations, we get decidability with both $\forall \Box$ and $\exists \Box$ bundles with unrestricted predicates. In these cases, we also obtain tableau based procedures that run in \PSPACE. We further show that the $\exists \Box$ bundle cannot distinguish between constant domain and increasing domain interpretations.
The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing , and machine learning. This paper introduces the 5th CHiME Challenge, which considers the task of distant multi-microphone conversational ASR in real home environments. Speech material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech and recorded by 6 Kinect microphone arrays and 4 binaural microphone pairs. The challenge features a single-array track and a multiple-array track and, for each track, distinct rankings will be produced for systems focusing on robustness with respect to distant-microphone capture vs. systems attempting to address all aspects of the task including conversational language modeling. We discuss the rationale for the challenge and provide a detailed description of the data collection procedure, the task, and the baseline systems for array synchronization, speech enhancement, and conventional and end-to-end ASR.
With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often difficult for non-expert users. In this work, we tackle the problem of learning the quality function of super- resolution images from scores provided by experts. More specifically, we are proposing a system based on a deep neural network that can provide a quantitative quality measure of a STED image of neuronal structures given as input. We conduct a user study in order to evaluate the quality of the predictions of the neural network against those of a human expert. Results show the potential while highlighting some of the limits of the proposed approach.
We propose a generalization of the best arm identification problem in stochastic multi-armed bandits (MAB) to the setting where every pull of an arm is associated with delayed feedback. The delay in feedback increases the effective sample complexity of standard algorithms, but can be offset if we have access to partial feedback received before a pull is completed. We propose a general framework to model the relationship between partial and delayed feedback, and as a special case we introduce efficient algorithms for settings where the partial feedback are biased or unbiased estimators of the delayed feedback. Additionally, we propose a novel extension of the algorithms to the parallel MAB setting where an agent can control a batch of arms. Our experiments in real-world settings, involving policy search and hyperparameter optimization in computational sustainability domains for fast charging of batteries and wildlife corridor construction, demonstrate that exploiting the structure of partial feedback can lead to significant improvements over baselines in both sequential and parallel MAB.
As multicore computing is now standard, it seems irresponsible for constraints researchers to ignore the implications of it. Researchers need to address a number of issues to exploit parallelism, such as: investigating which constraint algorithms are amenable to parallelisation; whether to use shared memory or distributed computation; whether to use static or dynamic decomposition; and how to best exploit portfolios and cooperating search. We review the literature, and see that we can sometimes do quite well, some of the time, on some instances, but we are far from a general solution. Yet there seems to be little overall guidance that can be given on how best to exploit multicore computers to speed up constraint solving. We hope at least that this survey will provide useful pointers to future researchers wishing to correct this situation. Under consideration in Theory and Practice of Logic Programming (TPLP).
Rear-end collision warning system has a great role to enhance the driving safety. In this system some measures are used to estimate the dangers and the system warns drivers to be more cautious. The real-time processes should be executed in such system, to remain enough time and distance to avoid collision with the front vehicle. To this end, in this paper a new system is developed by using random forest classifier. To evaluate the performance of the proposed system, vehicles trajectory data of 100 car's database from Virginia tech transportation institute are used and the methods are compared based on their accuracy and their processing time. By using TOPSIS multi-criteria selection method, we show that the results of the implemented classifier is better than the results of different classifiers including Bayesian network, naive Bayes, MLP neural network, support vector machine, nearest neighbor, rule-based methods and decision tree. The presented experiments reveals that the random forest is an acceptable algorithm for the proposed driver assistant system with 88.4% accuracy for detecting warning situations and 94.7% for detecting safe situations.
SMOTE is one of the oversampling techniques for balancing the datasets and it is considered as a pre-processing step in learning algorithms. In this paper, four new enhanced SMOTE are proposed that include an improved version of KNN in which the attribute weights are defined by mutual information firstly and then they are replaced by maximum entropy, Renyi entropy and Tsallis entropy. These four pre-processing methods are combined with 1NN and J48 classifiers and their performance are compared with the previous methods on 11 imbalanced datasets from KEEL repository. The results show that these pre-processing methods improves the accuracy compared with the previous stablished works. In addition, as a case study, the first pre-processing method is applied on transportation data of Tehran-Bazargan Highway in Iran with IR equal to 36.
We present a training framework for neural abstractive summarization based on actor-critic approaches from reinforcement learning. In the traditional neural network based methods, the objective is only to maximize the likelihood of the predicted summaries, no other assessment constraints are considered, which may generate low-quality summaries or even incorrect sentences. To alleviate this problem, we employ an actor-critic framework to enhance the training procedure. For the actor, we employ the typical attention based sequence-to-sequence (seq2seq) framework as the policy network for summary generation. For the critic, we combine the maximum likelihood estimator with a well designed global summary quality estimator which is a neural network based binary classifier aiming to make the generated summaries indistinguishable from the human-written ones. Policy gradient method is used to conduct the parameter learning. An alternating training strategy is proposed to conduct the joint training of the actor and critic models. Extensive experiments on some benchmark datasets in different languages show that our framework achieves improvements over the state-of-the-art methods.
We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes. The segmentation is consistent in 3D across the slices such that it can be directly used for mesh generation. Two specific neural networks with multi-scale coarse-to-fine prediction structure are proposed to cope with the small training dataset and trained using an original loss function. The former segments a slice in the middle of the volume. Then the latter iteratively propagates the slice segmentations towards the base and the apex, in a spatially consistent way. We perform 5-fold cross-validation on the 15 cases from STACOM to validate the method. For training, we use real cases and their synthetic variants generated by combining motion simulation and image synthesis. Accurate and consistent testing results are obtained.
Human conversation is a complex mechanism with subtle nuances. It is hence an ambitious goal to develop artificial intelligence agents that can participate fluently in a conversation. While we are still far from achieving this goal, recent progress in visual question answering, image captioning, and visual question generation shows that dialog systems may be realizable in the not too distant future. To this end, a novel dataset was introduced recently and encouraging results were demonstrated, particularly for question answering. In this paper, we demonstrate a simple symmetric discriminative baseline, that can be applied to both predicting an answer as well as predicting a question. We show that this method performs on par with the state of the art, even memory net based methods. In addition, for the first time on the visual dialog dataset, we assess the performance of a system asking questions, and demonstrate how visual dialog can be generated from discriminative question generation and question answering.
In the face of shifting means of production from manual human labor to labor automation, one solution that stands out is the advancement of a Universal Basic Income, UBI to every citizen from the government with no strings attached. The proposal, however, has encountered sharp criticism from different quarters questioning the morality behind sourcing of funds, largely through taxation, to uphold an institution designed to provide social support. Others also perceive the idea as a form of socialism, or a capitalist road to communism. The current discussion, however, seeks to demonstrate that the provision of such stipend can occur through the utilization of revenues realized from production driven by Artificial Intelligence (AI), and to a small extent, philanthropic contributions from the top 1 percent of the population.
Recently, caption generation with an encoder-decoder framework has been extensively studied and applied in different domains, such as image captioning, code captioning, and so on. In this paper, we propose a novel architecture, namely Auto-Reconstructor Network (ARNet), which, coupling with the conventional encoder-decoder framework, works in an end-to-end fashion to generate captions. ARNet aims at reconstructing the previous hidden state with the present one, besides behaving as the input-dependent transition operator. Therefore, ARNet encourages the current hidden state to embed more information from the previous one, which can help regularize the transition dynamics of recurrent neural networks (RNNs). Extensive experimental results show that our proposed ARNet boosts the performance over the existing encoder-decoder models on both image captioning and source code captioning tasks. Additionally, ARNet remarkably reduces the discrepancy between training and inference processes for caption generation. Furthermore, the performance on permuted sequential MNIST demonstrates that ARNet can effectively regularize RNN, especially on modeling long-term dependencies. Our code is available at: https://github.com/chenxinpeng/ARNet
We propose a scalable, efficient and accurate approach to retrieve 3D models for objects in the wild. Our contribution is twofold. We first present a 3D pose estimation approach for object categories which significantly outperforms the state-of-the-art on Pascal3D+. Second, we use the estimated pose as a prior to retrieve 3D models which accurately represent the geometry of objects in RGB images. For this purpose, we render depth images from 3D models under our predicted pose and match learned image descriptors of RGB images against those of rendered depth images using a CNN-based multi-view metric learning approach. In this way, we are the first to report quantitative results for 3D model retrieval on Pascal3D+, where our method chooses the same models as human annotators for 50% of the validation images on average. In addition, we show that our method, which was trained purely on Pascal3D+, retrieves rich and accurate 3D models from ShapeNet given RGB images of objects in the wild.
There is an increasing concern in computer vision devices invading the privacy of their users by recording unwanted videos. On one hand, we want the camera systems/robots to recognize important events and assist human daily life by understanding its videos, but on the other hand we also want to ensure that they do not intrude people's privacy. In this paper, we propose a new principled approach for learning a video face anonymizer. We use an adversarial training setting in which two competing systems fight: (1) a video anonymizer that modifies the original video to remove privacy-sensitive information (i.e., human face) while still trying to maximize spatial action detection performance, and (2) a discriminator that tries to extract privacy-sensitive information from such anonymized videos. The end result is a video anonymizer that performs a pixel-level modification to anonymize each person's face, with minimal effect on action detection performance. We experimentally confirm the benefit of our approach compared to conventional hand-crafted video/face anonymization methods including masking, blurring, and noise adding. See the project page https://jason718.github.io/project/privacy/main.html for a demo video and more results.
Prospection, the act of predicting the consequences of many possible futures, is intrinsic to human planning and action, and may even be at the root of consciousness. Surprisingly, this idea has been explored comparatively little in robotics. In this work, we propose a neural network architecture and associated planning algorithm that (1) learns a representation of the world useful for generating prospective futures after the application of high-level actions, (2) uses this generative model to simulate the result of sequences of high-level actions in a variety of environments, and (3) uses this same representation to evaluate these actions and perform tree search to find a sequence of high-level actions in a new environment. Models are trained via imitation learning on a variety of domains, including navigation, pick-and-place, and a surgical robotics task. Our approach allows us to visualize intermediate motion goals and learn to plan complex activity from visual information.
In the encoding of many real-world problems to propositional satisfiability, the cardinality constraint is a recurrent constraint that needs to be managed effectively. Several efficient encodings have been proposed while missing that such a constraint can be involved in a more general propositional formulation. To avoid combinatorial explosion, Tseitin principle usually used to translate such general propositional formula to Conjunctive Normal Form (CNF), introduces fresh propositional variables to represent sub-formulas and/or complex contraints. Thanks to Plaisted and Greenbaum improvement, the polarity of the sub-formula $\Phi$ is taken into account leading to conditional constraints of the form $y\rightarrow \Phi$, or $\Phi\rightarrow y$, where $y$ is a fresh propositional variable. In the case where $\Phi$ represents a cardinality constraint, such translation leads to conditional cardinality constraints subject of the present paper. We first show that when all the clauses encoding the cardinality constraint are augmented with an additional new variable, most of the well-known encodings cease to maintain the generalized arc consistency property. Then, we consider some of these encodings and show how they can be extended to recover such important property. An experimental validation is conducted on a SAT-based pattern mining application, where such conditional cardinality constraints is a cornerstone, showing the relevance of our proposed approach.
Player modeling is an important concept that has gained much attention in game research due to its utility in developing adaptive techniques to target better designs for engagement and retention. Previous work has explored modeling individual differences using machine learning algorithms per- formed on aggregated game actions. However, players' individual differences may be better manifested through sequential patterns of the in-game player's actions. While few works have explored sequential analysis of player data, none have explored the use of Hidden Markov Models (HMM) to model individual differences, which is the topic of this paper. In par- ticular, we developed a modeling approach using data col- lected from players playing a Role-Playing Game (RPG). Our proposed approach is two fold: 1. We present a Hidden Markov Model (HMM) of player in-game behaviors to model individual differences, and 2. using the output of the HMM, we generate behavioral features used to classify real world players' characteristics, including game expertise and the big five personality traits. Our results show predictive power for some of personality traits, such as game expertise and conscientiousness, but the most influential factor was game expertise.
We address a largely open problem of multilabel classification over graphs. Unlike traditional vector input, a graph has rich variable-size substructures which are related to the labels in some ways. We believe that uncovering these relations might hold the key to classification performance and explainability. We introduce GAML (Graph Attentional Multi-Label learning), a novel graph neural network that can handle this problem effectively. GAML regards labels as auxiliary nodes and models them in conjunction with the input graph. By applying message passing and attention mechanisms to both the label nodes and the input nodes iteratively, GAML can capture the relations between the labels and the input subgraphs at various resolution scales. Moreover, our model can take advantage of explicit label dependencies. It also scales linearly with the number of labels and graph size thanks to our proposed hierarchical attention. We evaluate GAML on an extensive set of experiments with both graph-structured inputs and classical unstructured inputs. The results show that GAML significantly outperforms other competing methods. Importantly, GAML enables intuitive visualizations for better understanding of the label-substructure relations and explanation of the model behaviors.
Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed in low curvature directions. Its performance depends crucially on a damping coefficient $\beta$. Large $\beta$ values can potentially deliver much larger speedups, but are prone to oscillations and instability; hence one typically resorts to small values such as 0.5 or 0.9. We propose Aggregated Momentum (AggMo), a variant of momentum which combines multiple velocity vectors with different $\beta$ parameters. AggMo is trivial to implement, but significantly dampens oscillations, enabling it to remain stable even for aggressive $\beta$ values such as 0.999. We reinterpret Nesterov's accelerated gradient descent as a special case of AggMo and provide theoretical convergence bounds for online convex optimization. Empirically, we find that AggMo is a suitable drop-in replacement for other momentum methods, and frequently delivers faster convergence.
This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal external reward for training DRL algorithms with intrinsic reward signals measured by curiosity. We test our approach in a mapless navigation setting, where the autonomous agent is required to navigate without the occupancy map of the environment, to targets whose relative locations can be easily acquired through low-cost solutions (e.g., visible light localization, Wi-Fi signal localization). We validate that the intrinsic motivation is crucial for improving DRL performance in tasks with challenging exploration requirements. Our experimental results show that our proposed method is able to more effectively learn navigation policies, and has better generalization capabilities in previously unseen environments. A video of our experimental results can be found at https://goo.gl/pWbpcF.
In the recent times, autoencoders, besides being used for compression, have been proven quite useful even for regenerating similar images or help in image denoising. They have also been explored for anomaly detection in a few cases. However, due to location invariance property of convolutional neural network, autoencoders tend to learn from or search for learned features in the complete image. This creates issues when all the items in the image are not equally important and their location matters. For such cases, a semi supervised solution - regional priority based autoencoder (RPAE) has been proposed. In this model, similar to object detection models, a region proposal network identifies the relevant areas in the images as belonging to one of the predefined categories and then those bounding boxes are fed into appropriate decoder based on the category they belong to. Finally, the error scores from all the decoders are combined based on their importance to provide total reconstruction error.
While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretability, which is essential in many real-world applications such as health informatics. Existing attempts based on local interpretations aim to identify relevant features contributing the most to the prediction of DNN by monitoring the neighborhood of a given input. They usually simply ignore the intermediate layers of the DNN that might contain rich information for interpretation. To bridge the gap, in this paper, we propose to investigate a guided feature inversion framework for taking advantage of the deep architectures towards effective interpretation. The proposed framework not only determines the contribution of each feature in the input but also provides insights into the decision-making process of DNN models. By further interacting with the neuron of the target category at the output layer of the DNN, we enforce the interpretation result to be class-discriminative. We apply the proposed interpretation model to different CNN architectures to provide explanations for image data and conduct extensive experiments on ImageNet and PASCAL VOC07 datasets. The interpretation results demonstrate the effectiveness of our proposed framework in providing class-discriminative interpretation for DNN-based prediction.
Predictions of driver's intentions and their behaviors using the road is of great importance for planning and decision making processes of autonomous driving vehicles. In particular, relatively short-term driving intentions are the fundamental units that constitute more sophisticated driving goals, behaviors, such as overtaking the slow vehicle in front, exit or merge onto a high way, etc. While it is not uncommon that most of the time human driver can rationalize, in advance, various on-road behaviors, intentions, as well as the associated risks, aggressiveness, reciprocity characteristics, etc., such reasoning skills can be challenging and difficult for an autonomous driving system to learn. In this article, we demonstrate a disciplined methodology that can be used to build and train a predictive drive system, therefore to learn the on-road characteristics aforementioned.
In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain "background" information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over strong baseline methods. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling.
Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Thus, different from previous studies, our approach enables us to deal with various kinds of prediction tasks based on the given specification. A prototype implementing our approach has been developed and experiments using a real-life event log have been conducted.
A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via image-based goals. We were able to achieve successful transfer of visuomotor planning strategies across robots with significantly different morphologies and actuation capabilities.
Traditional machine learning models have problems with handling sequence data, because the lengths of sequences may vary between samples. In this paper, we present an unsupervised learning model for sequence data, called the Integrated Sequence Autoencoder (ISA), to learn a fixed-length vectorial representation by minimizing the reconstruction error. Specifically, we propose to integrate two classical mechanisms for sequence reconstruction which takes into account both the global silhouette information and the local temporal dependencies. Furthermore, we propose a stop feature that serves as a temporal stamp to guide the reconstruction process, and which results in a higher-quality representation. Extensive validation on real-world datasets shows that the learned representation is able to effectively summarize not only the apparent features, but also the underlying and high-level style information. Take for example a speech sequence sample: our ISA model can not only recognize the spoken text (apparent feature), but can also discriminate the speaker who utters the audio (more high-level style).
We present an approach where two different models (Deep and Shallow) are trained separately on the data and a weighted average of the outputs is taken as the final result. For the Deep approach, we use different combinations of models like Convolution Neural Network, pretrained word2vec embeddings and LSTMs to get representations which are then used to train a Deep Neural Network. For Clarity prediction, we also use an Attentive Pooling approach for the pooling operation so as to be aware of the Title-Category pair. For the shallow approach, we use boosting technique LightGBM on features generated using title and categories. We find that an ensemble of these approaches does a better job than using them alone suggesting that the results of the deep and shallow approach are highly complementary
We propose the idea of transferring common-sense knowledge from source categories to target categories for scalable object detection. In our setting, the training data for the source categories have bounding box annotations, while those for the target categories only have image-level annotations. Current state-of-the-art approaches focus on image-level visual or semantic similarity to adapt a detector trained on the source categories to the new target categories. In contrast, our key idea is to (i) use similarity not at image-level, but rather at region-level, as well as (ii) leverage richer common-sense (based on attribute, spatial, etc.,) to guide the algorithm towards learning the correct detections. We acquire such common-sense cues automatically from readily-available knowledge bases without any extra human effort. On the challenging MS COCO dataset, we find that using common-sense knowledge substantially improves detection performance over existing transfer-learning baselines.
Modern 3D human pose estimation techniques rely on deep networks, which require large amounts of training data. While weakly-supervised methods require less supervision, by utilizing 2D poses or multi-view imagery without annotations, they still need a sufficiently large set of samples with 3D annotations for learning to succeed. In this paper, we propose to overcome this problem by learning a geometry-aware body representation from multi-view images without annotations. To this end, we use an encoder-decoder that predicts an image from one viewpoint given an image from another viewpoint. Because this representation encodes 3D geometry, using it in a semi-supervised setting makes it easier to learn a mapping from it to 3D human pose. As evidenced by our experiments, our approach significantly outperforms fully-supervised methods given the same amount of labeled data, and improves over other semi-supervised methods while using as little as 1% of the labeled data.
Synthesizing user-intended programs from a small number of input-output examples is a challenging problem with several important applications like spreadsheet manipulation, data wrangling and code refactoring. Existing synthesis systems either completely rely on deductive logic techniques that are extensively hand-engineered or on purely statistical models that need massive amounts of data, and in general fail to provide real-time synthesis on challenging benchmarks. In this work, we propose Neural Guided Deductive Search (NGDS), a hybrid synthesis technique that combines the best of both symbolic logic techniques and statistical models. Thus, it produces programs that satisfy the provided specifications by construction and generalize well on unseen examples, similar to data-driven systems. Our technique effectively utilizes the deductive search framework to reduce the learning problem of the neural component to a simple supervised learning setup. Further, this allows us to both train on sparingly available real-world data and still leverage powerful recurrent neural network encoders. We demonstrate the effectiveness of our method by evaluating on real-world customer scenarios by synthesizing accurate programs with up to 12x speed-up compared to state-of-the-art systems.
Mining frequent sequential patterns consists in extracting recurrent behaviors, modeled as patterns, in a big sequence dataset. Such patterns inform about which events are frequently observed in sequences, i.e. what does really happen. Sometimes, knowing that some specific event does not happen is more informative than extracting a lot of observed events. Negative sequential patterns (NSP) formulate recurrent behaviors by patterns containing both observed events and absent events. Few approaches have been proposed to mine such NSPs. In addition, the syntax and semantics of NSPs differ in the different methods which makes it difficult to compare them. This article provides a unified framework for the formulation of the syntax and the semantics of NSPs. Then, we introduce a new algorithm, NegPSpan, that extracts NSPs using a PrefixSpan depth-first scheme and enabling maxgap constraints that other approaches do not take into account. The formal framework allows for highlighting the differences between the proposed approach wrt to the methods from the literature, especially wrt the state of the art approach eNSP. Intensive experiments on synthetic and real datasets show that NegPSpan can extract meaningful NSPs and that it can process bigger datasets than eNSP thanks to significantly lower memory requirements and better computation times.
Word embeddings have emerged as a popular approach to unsupervised learning of word relationships in machine learning and natural language processing. In this article, we benchmark two of the most popular algorithms, GloVe and word2vec, to assess their suitability for capturing medical relationships in large sources of biomedical data. Leaning on recent theoretical insights, we provide a unified view of these algorithms and demonstrate how different sources of data can be combined to construct the largest ever set of embeddings for 108,477 medical concepts using an insurance claims database of 60 million members, 20 million clinical notes, and 1.7 million full text biomedical journal articles. We evaluate our approach, called cui2vec, on a set of clinically relevant benchmarks and in many instances demonstrate state of the art performance relative to previous results. Finally, we provide a downloadable set of pre-trained embeddings for other researchers to use, as well as an online tool for interactive exploration of the cui2vec embeddings.
This paper describes an abstractive summarization method for tabular data which employs a knowledge base semantic embedding to generate the summary. Assuming the dataset contains descriptive text in headers, columns and/or some augmenting metadata, the system employs the embedding to recommend a subject/type for each text segment. Recommendations are aggregated into a small collection of super types considered to be descriptive of the dataset by exploiting the hierarchy of types in a pre-specified ontology. Using February 2015 Wikipedia as the knowledge base, and a corresponding DBpedia ontology as types, we present experimental results on open data taken from several sources--OpenML, CKAN and data.world--to illustrate the effectiveness of the approach.
Being able to predict what may happen in the future requires an in-depth understanding of the physical and causal rules that govern the world. A model that is able to do so has a number of appealing applications, from robotic planning to representation learning. However, learning to predict raw future observations, such as frames in a video, is exceedingly challenging -- the ambiguous nature of the problem can cause a naively designed model to average together possible futures into a single, blurry prediction. Recently, this has been addressed by two distinct approaches: (a) latent variational variable models that explicitly model underlying stochasticity and (b) adversarially-trained models that aim to produce naturalistic images. However, a standard latent variable model can struggle to produce realistic results, and a standard adversarially-trained model underutilizes latent variables and fails to produce diverse predictions. We show that these distinct methods are in fact complementary. Combining the two produces predictions that look more realistic to human raters and better cover the range of possible futures. Our method outperforms prior and concurrent work in these aspects.
We revisit the classical problem of exact inference on probabilistic graphical models (PGMs). Our algorithm is based on recent worst-case optimal database join algorithms, which can be asymptotically faster than traditional data processing methods. We present the first empirical evaluation of these new algorithms via JoinInfer, a new exact inference engine. We empirically explore the properties of the data for which our engine can be expected to outperform traditional inference engines refining current theoretical notions. Further, JoinInfer outperforms existing state-of-the-art inference engines (ACE, IJGP and libDAI) on some standard benchmark datasets by up to a factor of 630x. Finally, we propose a promising data-driven heuristic that extends JoinInfer to automatically tailor its parameters and/or switch to the traditional inference algorithms.
Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the gradient estimates. We propose a novel rejection sampling step that discards samples from the variational posterior which are assigned low likelihoods by the model. Our approach provides an arbitrarily accurate approximation of the true posterior at the expense of extra computation. Using a new gradient estimator for the resulting unnormalized proposal distribution, we achieve average improvements of 3.71 nats and 0.21 nats over state-of-the-art single-sample and multi-sample alternatives respectively for estimating marginal log-likelihoods using sigmoid belief networks on the MNIST dataset.
We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust distributed reading and writing mechanism. The memory is analytically tractable, which enables optimal on-line compression via a Bayesian update-rule. We formulate it as a hierarchical conditional generative model, where memory provides a rich data-dependent prior distribution. Consequently, the top-down memory and bottom-up perception are combined to produce the code representing an observation. Empirically, we demonstrate that the adaptive memory significantly improves generative models trained on both the Omniglot and CIFAR datasets. Compared with the Differentiable Neural Computer (DNC) and its variants, our memory model has greater capacity and is significantly easier to train.
Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection. Data-driven methods for training saliency models using eye-fixation data are increasingly popular, particularly with the introduction of large-scale datasets and deep architectures. However, current methods in this latter paradigm use loss functions designed for classification or regression tasks whereas saliency estimation is evaluated on topographical maps. In this work, we introduce a new saliency map model which formulates a map as a generalized Bernoulli distribution. We then train a deep architecture to predict such maps using novel loss functions which pair the softmax activation function with measures designed to compute distances between probability distributions. We show in extensive experiments the effectiveness of such loss functions over standard ones on four public benchmark datasets, and demonstrate improved performance over state-of-the-art saliency methods.
In 2015, Google's DeepMind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games. However, the current manifestation of DRL is still immature, and has significant drawbacks. One of DRL's imperfections is its lack of "exploration" during the training process, especially when working with high-dimensional problems. In this paper, we propose a mixed strategy approach that mimics behaviors of human when interacting with environment, and create a "thinking" agent that allows for more efficient exploration in the DRL training process. The simulation results based on the Breakout game show that our scheme achieves a higher probability of obtaining a maximum score than does the baseline DRL algorithm, i.e., the asynchronous advantage actor-critic method. The proposed scheme therefore can be applied effectively to solving a complicated task in a real-world application.
Miss-ratio curve (MRC), or equivalently hit-ratio curve (HRC), construction techniques have recently gathered the attention of many researchers. Recent advancements have allowed for approximating these curves in constant time, allowing for online working-set-size (WSS) measurement. Techniques span the algorithmic design paradigm from classic dynamic programming to artificial intelligence inspired techniques. Our survey produces broad classification of the current techniques primarily based on \emph{what} locality metric is being recorded and \emph{how} that metric is stored for processing. Applications of theses curves span from dynamic cache partitioning in the processor, to improving block allocation at the operating system level. Our survey will give an overview of the historical, exact MRC construction methods, and compare them with the state-of-the-art methods present in today's literature. In addition, we will show where there are still open areas of research and remain excited to see what this domain can produce with a strong theoretical background.
State-of-the-art pedestrian detection models have achieved great success in many benchmarks. However, these models require lots of annotation information and the labeling process usually takes much time and efforts. In this paper, we propose a method to generate labeled pedestrian data and adapt them to support the training of pedestrian detectors. The proposed framework is built on the Generative Adversarial Network (GAN) with multiple discriminators, trying to synthesize realistic pedestrians and learn the background context simultaneously. To handle the pedestrians of different sizes, we adopt the Spatial Pyramid Pooling (SPP) layer in the discriminator. We conduct experiments on two benchmarks. The results show that our framework can smoothly synthesize pedestrians on background images of variations and different levels of details. To quantitatively evaluate our approach, we add the generated samples into training data of the baseline pedestrian detectors and show the synthetic images are able to improve the detectors' performance.
Object retrieval and classification in point cloud data is challenged by noise, irregular sampling density and occlusion. To address this issue, we propose a point pair descriptor that is robust to noise and occlusion and achieves high retrieval accuracy. We further show how the proposed descriptor can be used in a 4D convolutional neural network for the task of object classification. We propose a novel 4D convolutional layer that is able to learn class-specific clusters in the descriptor histograms. Finally, we provide experimental validation on 3 benchmark datasets, which confirms the superiority of the proposed approach.
With the ever growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged QoE that satisfies end-user's functional and QoS requirements is necessary. SDN (Software-Defined Networking) and NFV (Network Function Virtualization) are considered key enabling technologies for 5G core networks. In this regard, this paper proposes a reinforcement learning based QoS/QoE-aware Service Function Chaining (SFC) in SDN/NFV-enabled 5G slices. First, it implements a lightweight QoS information collector based on LLDP, which works in a piggyback fashion on the southbound interface of the SDN controller, to enable QoS-awareness. Then, a DQN (Deep Q Network) based agent framework is designed to support SFC in the context of NFV. The agent takes into account the QoE and QoS as key aspects to formulate the reward so that it is expected to maximize QoE while respecting QoS constraints. The experiment results show that this framework exhibits good performance in QoE provisioning and QoS requirements maintenance for SFC in dynamic network environments.
The idea of end-to-end learning of communications systems through neural network -based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm iterates between supervised training of the receiver and reinforcement learning -based training of the transmitter. We demonstrate that this approach works as well as fully supervised methods on additive white Gaussian noise (AWGN) and Rayleigh block-fading (RBF) channels. Surprisingly, while our method converges slower on AWGN channels than supervised training, it converges faster on RBF channels. Our results are a first step towards learning of communications systems over any type of channel without prior assumptions.
The problem of comparing concepts of dependence in general rough sets with those in probability theory had been initiated by the present author in some of her recent papers. This problem relates to the identification of the limitations of translating between the methodologies and possibilities in the identification of concepts. Comparison of ideas of dependence in the approaches had been attempted from a set-valuation based minimalist perspective by the present author. The deviant probability framework has been the result of such an approach. Other Bayesian reasoning perspectives (involving numeric valuations) and frequentist approaches are also known. In this research, duality results are adapted to demonstrate the possibility of improved comparisons across implications between ontologically distinct concepts in a common logic-based framework by the present author. Both positive and negative results are proved that delimit possible comparisons in a clearer way by her.
One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary. Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.g. hand- engineered features). Humans, however, do not learn to communicate based on well-summarized features. In this work, we train neural agents to simultaneously develop visual perception from raw image pixels, and learn to communicate with a sequence of discrete symbols. The agents play an image description game where the image contains factors such as colors and shapes. We train the agents using the obverter technique where an agent introspects to generate messages that maximize its own understanding. Through qualitative analysis, visualization and a zero-shot test, we show that the agents can develop, out of raw image pixels, a language with compositional properties, given a proper pressure from the environment.
Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques.
We study the problem of generating interpretable and verifiable policies through reinforcement learning. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, in which the policy is represented by a neural network, the aim in Programmatically Interpretable Reinforcement Learning is to find a policy that can be represented in a high-level programming language. Such programmatic policies have the benefits of being more easily interpreted than neural networks, and being amenable to verification by symbolic methods. We propose a new method, called Neurally Directed Program Search (NDPS), for solving the challenging nonsmooth optimization problem of finding a programmatic policy with maxima reward. NDPS works by first learning a neural policy network using DRL, and then performing a local search over programmatic policies that seeks to minimize a distance from this neural "oracle". We evaluate NDPS on the task of learning to drive a simulated car in the TORCS car-racing environment. We demonstrate that NDPS is able to discover human-readable policies that pass some significant performance bars. We also find that a well-designed policy language can serve as a regularizer, and result in the discovery of policies that lead to smoother trajectories and are more easily transferred to environments not encountered during training.
We present a novel algorithm for reciprocal collision avoidance between heterogeneous agents of different shapes and sizes. We present a novel CTMAT representation based on medial axis transform to compute a tight fitting bounding shape for each agent. Each CTMAT is represented using tuples, which are composed of circular arcs and line segments. Based on the reciprocal velocity obstacle formulation, we reduce the problem to solving a low-dimensional linear programming between each pair of tuples belonging to adjacent agents. We precompute the Minkowski Sums of tuples to accelerate the runtime performance. Finally, we provide an efficient method to update the orientation of each agent in a local manner. We have implemented the algorithm and highlight its performance on benchmarks corresponding to road traffic scenarios and different vehicles. The overall runtime performance is comparable to prior multi-agent collision avoidance algorithms that use circular or elliptical agents. Our approach is less conservative and results in fewer false collisions.
Partially Observable Markov Decision Processes (POMDPs) offer an elegant framework to model sequential decision making in uncertain environments. Solving POMDPs online is an active area of research and given the size of real-world problems approximate solvers are used. Recently, a few approaches have been suggested for solving POMDPs by using MDP solvers in conjunction with imitation learning. MDP based POMDP solvers work well for some cases, while catastrophically failing for others. The main failure point of such solvers is the lack of motivation for MDP solvers to gain information, since under their assumption the environment is either already known as much as it can be or the uncertainty will disappear after the next step. However for solving POMDP problems gaining information can lead to efficient solutions. In this paper we derive a set of conditions where MDP based POMDP solvers are provably sub-optimal. We then use the well-known tiger problem to demonstrate such sub-optimality. We show that multi-resolution, budgeted information gathering cannot be addressed using MDP based POMDP solvers. The contribution of the paper helps identify the properties of a POMDP problem for which the use of MDP based POMDP solvers is inappropriate, enabling better design choices.
Self Organizing Map is trained using unsupervised learning to produce a two-dimensional discretized representation of input space of the training cases. Growing Hierarchical SOM is an architecture which grows both in a hierarchical way representing the structure of data distribution and in a horizontal way representation the size of each individual maps. The control method of the growing degree of GHSOM by pruning off the redundant branch of hierarchy in SOM is proposed in this paper. Moreover, the interface tool for the proposed method called interactive GHSOM is developed. We discuss the computation results of Iris data by using the developed tool.
We present and evaluate the Fast (conditional) Independence Test (FIT) -- a nonparametric conditional independence test. The test is based on the idea that when $P(X \mid Y, Z) = P(X \mid Y)$, $Z$ is not useful as a feature to predict $X$, as long as $Y$ is also a regressor. On the contrary, if $P(X \mid Y, Z) \neq P(X \mid Y)$, $Z$ might improve prediction results. FIT applies to thousand-dimensional random variables with a hundred thousand samples in a fraction of the time required by alternative methods. We provide an extensive evaluation that compares FIT to six extant nonparametric independence tests. The evaluation shows that FIT has low probability of making both Type I and Type II errors compared to other tests, especially as the number of available samples grows. Our implementation of FIT is publicly available.
The convergence speed of stochastic gradient descent (SGD) can be improved by actively selecting mini-batches. We explore sampling schemes where similar data points are less likely to be selected in the same mini-batch. In particular, we prove that such repulsive sampling schemes lowers the variance of the gradient estimator. This generalizes recent work on using Determinantal Point Processes (DPPs) for mini-batch diversification (Zhang et al., 2017) to the broader class of repulsive point processes. We first show that the phenomenon of variance reduction by diversified sampling generalizes in particular to non-stationary point processes. We then show that other point processes may be computationally much more efficient than DPPs. In particular, we propose and investigate Poisson Disk sampling---frequently encountered in the computer graphics community---for this task. We show empirically that our approach improves over standard SGD both in terms of convergence speed as well as final model performance.
Recently, a high technique of image processing is required to extract the image features in real time. In our research, the tourist subject data are collected from the Mobile Phone based Participatory Sensing (MPPS) system. Each record consists of image files with GPS, geographic location name, user's numerical evaluation, and comments written in natural language at sightseeing spots where a user really visits. In our previous research, the famous landmarks in sightseeing spot can be detected by Clonal Selection Algorithm with Immunological Memory Cell (CSAIM). However, some landmarks was not detected correctly by the previous method because they didn't have enough amount of information for the feature extraction. In order to improve the weakness, we propose the generation method of immunological memory by Restricted Boltzmann Machines. To verify the effectiveness of the method, some experiments for classification of the subjective data are executed by using machine learning tools for Deep Learning.
Decentralized (PO)MDPs provide an expressive framework for sequential decision making in a multiagent system. Given their computational complexity, recent research has focused on tractable yet practical subclasses of Dec-POMDPs. We address such a subclass called CDEC-POMDP where the collective behavior of a population of agents affects the joint-reward and environment dynamics. Our main contribution is an actor-critic (AC) reinforcement learning method for optimizing CDEC-POMDP policies. Vanilla AC has slow convergence for larger problems. To address this, we show how a particular decomposition of the approximate action-value function over agents leads to effective updates, and also derive a new way to train the critic based on local reward signals. Comparisons on a synthetic benchmark and a real-world taxi fleet optimization problem show that our new AC approach provides better quality solutions than previous best approaches.
The most enigmatic aspect of consciousness is the fact that it is felt, as a subjective sensation. This particular aspect is explained by the theory proposed here. The theory encompasses both the computation that is presumably involved and the way in which that computation may be realized in the brain's neurobiology. It is assumed that the brain makes an internal estimate of an individual's own evolutionary fitness, which can be shown to produce an irreducible, distinct cause. Communicating components of the fitness estimate (either for external or internal use) requires inverting them. Such inversion can be performed by the thalamocortical feedback loop in the mammalian brain, if that loop is operating in a switched, dual-stage mode. A first (nonconscious) stage produces forward estimates, whereas the second (conscious) stage inverts those estimates. It is argued that inversion produces irreducible, distinct, and spatially localized causes, which are plausibly sensed as the feeling of consciousness.
This paper investigates to what extent do cognitive biases affect human understanding of interpretable machine learning models, in particular of rules discovered from data. Twenty cognitive biases (illusions, effects) are covered, as are possibly effective debiasing techniques that can be adopted by designers of machine learning algorithms and software. While there seems no universal approach for eliminating all the identified cognitive biases, it follows from our analysis that the effect of most biases can be ameliorated by making rule-based models more concise. Due to lack of previous research, our review transfers general results obtained in cognitive psychology to the domain of machine learning. It needs to be succeeded by empirical studies specifically aimed at the machine learning domain.
Hate speech detection is a critical, yet challenging problem in Natural Language Processing (NLP). Despite the existence of numerous studies dedicated to the development of NLP hate speech detection approaches, the accuracy is still poor. The central problem is that social media posts are short and noisy, and most existing hate speech detection solutions take each post as an isolated input instance, which is likely to yield high false positive and negative rates. In this paper, we radically improve automated hate speech detection by presenting a novel model that leverages intra-user and inter-user representation learning for robust hate speech detection on Twitter. In addition to the target Tweet, we collect and analyze the user's historical posts to model intra-user Tweet representations. To suppress the noise in a single Tweet, we also model the similar Tweets posted by all other users with reinforced inter-user representation learning techniques. Experimentally, we show that leveraging these two representations can significantly improve the f-score of a strong bidirectional LSTM baseline model by 10.1%.
Rapidly creating effective visualizations using expressive grammars is challenging for users who have limited time and limited skills in statistics and data visualization. Even high-level, dedicated visualization tools often require users to manually select among data attributes, decide which transformations to apply, and specify mappings between visual encoding variables and raw or transformed attributes. In this paper, we introduce Data2Vis, a neural translation model, for automatically generating visualizations from given datasets. We formulate visualization generation as a sequence to sequence translation problem where data specification is mapped to a visualization specification in a declarative language (Vega-Lite). To this end, we train a multilayered Long Short-Term Memory (LSTM) model with attention on a corpus of visualization specifications. Qualitative results show that our model learns the vocabulary and syntax for a valid visualization specification, appropriate transformations (count, bins, mean) and how to use common data selection patterns that occur within data visualizations. Our model generates visualizations that are comparable to manually-created visualizations in a fraction of the time, with potential to learn more complex visualization strategies at scale.
Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.
Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence tuples, and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation tuples). In order to overcome these weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering (QA) approaches to produce high quality relation triples across sentences. Based on the framework, we develop a large IE benchmark with high quality human evaluation. This benchmark contains 293K documents, 2M golden relation triples, and 636 relation types. We compare our system with some IE baselines on our benchmark and the results show that our system achieves great improvements.
Variational autoencoders (VAE) combined with hierarchical RNNs have emerged as a powerful framework for conversation modeling. However, they suffer from the notorious degeneration problem, where the decoders learn to ignore latent variables and reduce to vanilla RNNs. We empirically show that this degeneracy occurs mostly due to two reasons. First, the expressive power of hierarchical RNN decoders is often high enough to model the data using only its decoding distributions without relying on the latent variables. Second, the conditional VAE structure whose generation process is conditioned on a context, makes the range of training targets very sparse; that is, the RNN decoders can easily overfit to the training data ignoring the latent variables. To solve the degeneration problem, we propose a novel model named Variational Hierarchical Conversation RNNs (VHCR), involving two key ideas of (1) using a hierarchical structure of latent variables, and (2) exploiting an utterance drop regularization. With evaluations on two datasets of Cornell Movie Dialog and Ubuntu Dialog Corpus, we show that our VHCR successfully utilizes latent variables and outperforms state-of-the-art models for conversation generation. Moreover, it can perform several new utterance control tasks, thanks to its hierarchical latent structure.
This paper proposes learning disentangled but complementary face features with minimal supervision by face identification. Specifically, we construct an identity Distilling and Dispelling Autoencoder (D2AE) framework that adversarially learns the identity-distilled features for identity verification and the identity-dispelled features to fool the verification system. Thanks to the design of two-stream cues, the learned disentangled features represent not only the identity or attribute but the complete input image. Comprehensive evaluations further demonstrate that the proposed features not only maintain state-of-the-art identity verification performance on LFW, but also acquire competitive discriminative power for face attribute recognition on CelebA and LFWA. Moreover, the proposed system is ready to semantically control the face generation/editing based on various identities and attributes in an unsupervised manner.
Probabilistic topic models are popular unsupervised learning methods, including probabilistic latent semantic indexing (pLSI) and latent Dirichlet allocation (LDA). By now, their training is implemented on general purpose computers (GPCs), which are flexible in programming but energy-consuming. Towards low-energy implementations, this paper investigates their training on an emerging hardware technology called the neuromorphic multi-chip systems (NMSs). NMSs are very effective for a family of algorithms called spiking neural networks (SNNs). We present three SNNs to train topic models. The first SNN is a batch algorithm combining the conventional collapsed Gibbs sampling (CGS) algorithm and an inference SNN to train LDA. The other two SNNs are online algorithms targeting at both energy- and storage-limited environments. The two online algorithms are equivalent with training LDA by using maximum-a-posterior estimation and maximizing the semi-collapsed likelihood, respectively. They use novel, tailored ordinary differential equations for stochastic optimization. We simulate the new algorithms and show that they are comparable with the GPC algorithms, while being suitable for NMS implementation. We also propose an extension to train pLSI and a method to prune the network to obey the limited fan-in of some NMSs.
Logic is a foundation for many modern areas of computer science. In artificial intelligence, as a basis of database query languages, as well as in formal software and hardware verification --- modelling scenarios using logical formalisms and inferring new knowledge are important skills for going-to-be computer scientists. The Iltis project aims at providing a web-based, interactive system that supports teaching logical methods. In particular the system shall (a) support to learn to model knowledge and to infer new knowledge using propositional logic, modal logic and first-order logic, and (b) provide immediate feedback and support to students. This article presents a prototypical system that currently supports the above tasks for propositional logic. First impressions on its use in a second year logic course for computer science students are reported.
Research has shown that personalization of health interventions can contribute to an improved effectiveness. Reinforcement learning algorithms can be used to perform such tailoring using data that is collected about users. Learning is however very fragile for health interventions as only limited time is available to learn from the user before disengagement takes place, or before the opportunity to intervene passes. In this paper, we present a cluster-based reinforcement learning approach which learns across groups of users. Such an approach can speed up the learning process while still giving a level of personalization. The clustering algorithm uses a distance metric over traces of states and rewards. We apply both online and batch learning to learn policies over the clusters and introduce a publicly available simulator which we have developed to evaluate the approach. The results show batch learning clearly outperforms online learning. Furthermore, clustering can be beneficial provided that a proper clustering is found.
We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we show the circumstances under which representations aligned with the underlying generative factors of variation of data emerge when optimising the modified ELBO bound in $\beta$-VAE, as training progresses. From these insights, we propose a modification to the training regime of $\beta$-VAE, that progressively increases the information capacity of the latent code during training. This modification facilitates the robust learning of disentangled representations in $\beta$-VAE, without the previous trade-off in reconstruction accuracy.
The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. In this work, we focus on the transfer scenario where the dynamics among tasks are the same, but their goals differ. Although general value function (Sutton et al., 2011) has been shown to be useful for knowledge transfer, learning a universal value function can be challenging in practice. To attack this, we propose (1) to use universal successor representations (USR) to represent the transferable knowledge and (2) a USR approximator (USRA) that can be trained by interacting with the environment. Our experiments show that USR can be effectively applied to new tasks, and the agent initialized by the trained USRA can achieve the goal considerably faster than random initialization.
We propose Cooperative Training (CoT) for training generative models that measure a tractable density function for target data. CoT coordinately trains a generator $G$ and an auxiliary predictive mediator $M$. The training target of $M$ is to estimate a mixture density of the learned distribution $G$ and the target distribution $P$, and that of $G$ is to minimize the Jensen-Shannon divergence estimated through $M$. CoT achieves independent success without the necessity of pre-training via Maximum Likelihood Estimation or involving high-variance algorithms like REINFORCE. This low-variance algorithm is theoretically proved to be unbiased for both generative and predictive tasks. We also theoretically and empirically show the superiority of CoT over most previous algorithms, in terms of generative quality and diversity, predictive generalization ability and computational cost.
An innovative model of parcel distribution is emerging from the accelerated evolution of drones and the effort of logistic companies to proceed faster deliveries at a reduced cost. This new modality originated the Flying Sidekick Traveling Salesman Problem (FSTSP) in which customers are served either by a truck or a drone. Additionally, this variant of the Traveling Salesman Problem (TSP) presents several new restrictions concerning the drone such as endurance and payload capacity. This work proposes a hybrid heuristic that the initial solution is created from the optimal TSP solution reached by a Mixed-Integer Programming (MIP) solver. Next, an implementation of the General Variable Neighborhood Search is used to obtain the delivery routes of truck and drone. Computational experiments show the potential of the algorithm to improve the total delivery time up to 67.79%. New best-known solutions (BKS) are established for all FSTSP instances that results are reported in the literature. Furthermore, a new set of instances based on well-known TSPLIB instances is provided.
A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make predictive process monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviors over time. As a solution to this problem, we propose the use of algorithms that allow the incremental construction of the predictive model. These incremental learning algorithms update the model whenever new cases become available so that the predictive model evolves over time to fit the current circumstances. The algorithms have been implemented using different case encoding strategies and evaluated on a number of real and synthetic datasets. The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting.
We present a simulation-based approach for generating barrier certificate functions for safety verification of cyber-physical systems (CPS) that contain neural network-based controllers. A linear programming solver is utilized to find a candidate generator function from a set of simulation traces obtained by randomly selecting initial states for the CPS model. A level set of the generator function is then selected to act as a barrier certificate for the system, meaning it demonstrates that no unsafe system states are reachable from a given set of initial states. The barrier certificate properties are verified with an SMT solver. This approach is demonstrated on a case study in which a Dubins car model of an autonomous vehicle is controlled by a neural network to follow a given path.
Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction. We introduce two communication protocols -- one grounded in the semantics of the game, and one which is \textit{a priori} ungrounded and is a form of cheap talk. We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded channel. However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge. We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.
The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks. Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neural network agents on referential communication games. We extend previous work, in which agents were trained in symbolic environments, by developing agents which are able to learn from raw pixel data, a more challenging and realistic input representation. We find that the degree of structure found in the input data affects the nature of the emerged protocols, and thereby corroborate the hypothesis that structured compositional language is most likely to emerge when agents perceive the world as being structured.
Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a major limitation of counters is their locality. While there are a few model-based solutions to this shortcoming, a model-free approach is still missing. We propose $E$-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories. We compare our approach to commonly used RL techniques, and show that using $E$-values improves learning and performance over traditional counters. We also show how our method can be implemented with function approximation to efficiently learn continuous MDPs. We demonstrate this by showing that our approach surpasses state of the art performance in the Freeway Atari 2600 game.
Inferring socioeconomic attributes of social media users such as occupation and income is an important problem in computational social science. Automated inference of such characteristics has applications in personalised recommender systems, targeted computational advertising and online political campaigning. While previous work has shown that language features can reliably predict socioeconomic attributes on Twitter, employing information coming from users' social networks has not yet been explored for such complex user characteristics. In this paper, we describe a method for predicting the occupational class and the income of Twitter users given information extracted from their extended networks by learning a low-dimensional vector representation of users, i.e. graph embeddings. We use this representation to train predictive models for occupational class and income. Results on two publicly available datasets show that our method consistently outperforms the state-of-the-art methods in both tasks. We also obtain further significant improvements when we combine graph embeddings with textual features, demonstrating that social network and language information are complementary.
Market making is a fundamental trading problem in which an agent provides liquidity by continually offering to buy and sell a security. The problem is challenging due to inventory risk, the risk of accumulating an unfavourable position and ultimately losing money. In this paper, we develop a high-fidelity simulation of limit order book markets, and use it to design a market making agent using temporal-difference reinforcement learning. We use a linear combination of tile codings as a value function approximator, and design a custom reward function that controls inventory risk. We demonstrate the effectiveness of our approach by showing that our agent outperforms both simple benchmark strategies and a recent online learning approach from the literature.
In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts. This paper proposes a novel and effective approach -- exploiting task-oriented resources to learn word embeddings for expanding abbreviations in clinical notes. We achieved 82.27\% accuracy, close to expert human performance.
In several recently proposed stochastic optimization methods (e.g. RMSProp, Adam, Adadelta), parameter updates are scaled by the inverse square roots of exponential moving averages of squared past gradients. Maintaining these per-parameter second-moment estimators requires memory equal to the number of parameters. For the case of neural network weight matrices, we propose maintaining only the per-row and per-column sums of these moving averages, and estimating the per-parameter second moments based on these sums. We demonstrate empirically that this method produces similar results to the baseline. Secondly, we show that adaptive methods can produce larger-than-desired updates when the decay rate of the second moment accumulator is too slow. We propose update clipping and a gradually increasing decay rate scheme as remedies. Combining these methods and dropping momentum, we achieve comparable results to the published Adam regime in training the Transformer model on the WMT 2014 English-German machine translation task, while using very little auxiliary storage in the optimizer. Finally, we propose scaling the parameter updates based on the scale of the parameters themselves.
We suggest that the analysis of incomplete contracting developed by law and economics researchers can provide a useful framework for understanding the AI alignment problem and help to generate a systematic approach to finding solutions. We first provide an overview of the incomplete contracting literature and explore parallels between this work and the problem of AI alignment. As we emphasize, misalignment between principal and agent is a core focus of economic analysis. We highlight some technical results from the economics literature on incomplete contracts that may provide insights for AI alignment researchers. Our core contribution, however, is to bring to bear an insight that economists have been urged to absorb from legal scholars and other behavioral scientists: the fact that human contracting is supported by substantial amounts of external structure, such as generally available institutions (culture, law) that can supply implied terms to fill the gaps in incomplete contracts. We propose a research agenda for AI alignment work that focuses on the problem of how to build AI that can replicate the human cognitive processes that connect individual incomplete contracts with this supporting external structure.
A new large-scale video dataset for human action recognition, called STAIR Actions is introduced. STAIR Actions contains 100 categories of action labels representing fine-grained everyday home actions so that it can be applied to research in various home tasks such as nursing, caring, and security. In STAIR Actions, each video has a single action label. Moreover, for each action category, there are around 1,000 videos that were obtained from YouTube or produced by crowdsource workers. The duration of each video is mostly five to six seconds. The total number of videos is 102,462. We explain how we constructed STAIR Actions and show the characteristics of STAIR Actions compared to existing datasets for human action recognition. Experiments with three major models for action recognition show that STAIR Actions can train large models and achieve good performance. STAIR Actions can be downloaded from http://actions.stair.center.
This paper demonstrates two novel methods to estimate the global SNR of speech signals. In both methods, Deep Neural Network-Hidden Markov Model (DNN-HMM) acoustic model used in speech recognition systems is leveraged for the additional task of SNR estimation. In the first method, the entropy of the DNN-HMM output is computed. Recent work on bayesian deep learning has shown that a DNN-HMM trained with dropout can be used to estimate model uncertainty by approximating it as a deep Gaussian process. In the second method, this approximation is used to obtain model uncertainty estimates. Noise specific regressors are used to predict the SNR from the entropy and model uncertainty. The DNN-HMM is trained on GRID corpus and tested on different noise profiles from the DEMAND noise database at SNR levels ranging from -10 dB to 30 dB.
The Column Subset Selection Problem provides a natural framework for unsupervised feature selection. Despite being a hard combinatorial optimization problem, there exist efficient algorithms that provide good approximations. The drawback of the problem formulation is that it incorporates no form of regularization, and is therefore very sensitive to noise when presented with scarce data. In this paper we propose a regularized formulation of this problem, and derive a correct greedy algorithm that is similar in efficiency to existing greedy methods for the unregularized problem. We study its adequacy for feature selection and propose suitable formulations. Additionally, we derive a lower bound for the error of the proposed problems. Through various numerical experiments on real and synthetic data, we demonstrate the significantly increased robustness and stability of our method, as well as the improved conditioning of its output, all while remaining efficient for practical use.
3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs. An equivariant representation vector has two components: the invariant identity part, and a discernable encoding of the transformation. Models that can't explain pose-differences risk "diluting" the representation, in pursuit of optimizing a classification or regression loss function. We introduce a Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions. We call this network CubeNet, reflecting its cube-like symmetry. By construction, this network helps preserve a 3D shape's global and local signature, as it is transformed through successive layers. We apply this network to a variety of 3D inference problems, achieving state-of-the-art on the ModelNet10 classification challenge, and comparable performance on the ISBI 2012 Connectome Segmentation Benchmark. To the best of our knowledge, this is the first 3D rotation equivariant CNN for voxel representations.
Image segmentation needs both local boundary position information and global object context information. The performance of the recent state-of-the-art method, fully convolutional networks, reaches a bottleneck due to the neural network limit after balancing between the two types of information simultaneously in an end-to-end training style. To overcome this problem, we divide the semantic image segmentation into temporal subtasks. First, we find a possible pixel position of some object boundary; then trace the boundary at steps within a limited length until the whole object is outlined. We present the first deep reinforcement learning approach to semantic image segmentation, called DeepOutline, which outperforms other algorithms in Coco detection leaderboard in the middle and large size person category in Coco val2017 dataset. Meanwhile, it provides an insight into a divide and conquer way by reinforcement learning on computer vision problems.
Joint visual attention is characterized by two or more individuals looking at a common target at the same time. The ability to identify joint attention in scenes, the people involved, and their common target, is fundamental to the understanding of social interactions, including others' intentions and goals. In this work we deal with the extraction of joint attention events, and the use of such events for image descriptions. The work makes two novel contributions. First, our extraction algorithm is the first which identifies joint visual attention in single static images. It computes 3D gaze direction, identifies the gaze target by combining gaze direction with a 3D depth map computed for the image, and identifies the common gaze target. Second, we use a human study to demonstrate the sensitivity of humans to joint attention, suggesting that the detection of such a configuration in an image can be useful for understanding the image, including the goals of the agents and their joint activity, and therefore can contribute to image captioning and related tasks.
The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically-generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a web page and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.
Unintentional falls can cause severe injuries and even death, especially if no immediate assistance is given. The aim of Fall Detection Systems (FDSs) is to detect an occurring fall. This information can be used to trigger the necessary assistance in case of injury. This can be done by using either ambient-based sensors, e.g. cameras, or wearable devices. The aim of this work is to study the technical aspects of FDSs based on wearable devices and artificial intelligence techniques, in particular Deep Learning (DL), to implement an effective algorithm for on-line fall detection. The proposed classifier is based on a Recurrent Neural Network (RNN) model with underlying Long Short-Term Memory (LSTM) blocks. The method is tested on the publicly available SisFall dataset, with extended annotation, and compared with the results obtained by the SisFall authors.
The W3C's Web of Things working group is aimed at addressing the interoperability problem on the Internet of Things using Linked Data as uniform interface. While Linked Data paves the way towards combining such devices into integrated applications, traditional solutions for specifying the control flow of applications do not work seamlessly with Linked Data. We therefore tackle the problem of the specification, execution, and monitoring of applications in the context of Linked Data. We present a novel approach that combines workflows, semantic reasoning, and RESTful interaction into one integrated solution. We contribute to the state of the art by (1) defining an ontology for describing workflow models and instances, (2) providing operational semantics for the ontology that allows for the execution and monitoring of workflow instances, (3) presenting a benchmark to evaluate our solution. Moreover, we showcase how we used the ontology and the operational semantics to monitor pilots executing workflows in virtual aircraft cockpits.
The analysis of practical probabilistic models on the computer demands a convenient representation for the available knowledge and an efficient algorithm to perform inference. An appealing representation is the influence diagram, a network that makes explicit the random variables in a model and their probabilistic dependencies. Recent advances have developed solution procedures based on the influence diagram. In this paper, we examine the fundamental properties that underlie those techniques, and the information about the probabilistic structure that is available in the influence diagram representation. The influence diagram is a convenient representation for computer processing while also being clear and non-mathematical. It displays probabilistic dependence precisely, in a way that is intuitive for decision makers and experts to understand and communicate. As a result, the same influence diagram can be used to build, assess and analyze a model, facilitating changes in the formulation and feedback from sensitivity analysis. The goal in this paper is to determine arbitrary conditional probability distributions from a given probabilistic model. Given qualitative information about the dependence of the random variables in the model we can, for a specific conditional expression, specify precisely what quantitative information we need to be able to determine the desired conditional probability distribution. It is also shown how we can find that probability distribution by performing operations locally, that is, over subspaces of the joint distribution. In this way, we can exploit the conditional independence present in the model to avoid having to construct or manipulate the full joint distribution. These results are extended to include maximal processing when the information available is incomplete, and optimal decision making in an uncertain environment. Influence diagrams as a computer-aided modeling tool were developed by Miller, Merkofer, and Howard [5] and extended by Howard and Matheson [2]. Good descriptions of how to use them in modeling are in Owen [7] and Howard and Matheson [2]. The notion of solving a decision problem through influence diagrams was examined by Olmsted [6] and such an algorithm was developed by Shachter [8]. The latter paper also shows how influence diagrams can be used to perform a variety of sensitivity analyses. This paper extends those results by developing a theory of the properties of the diagram that are used by the algorithm, and the information needed to solve arbitrary probability inference problems. Section 2 develops the notation and the framework for the paper and the relationship between influence diagrams and joint probability distributions. The general probabilistic inference problem is posed in Section 3. In Section 4 the transformations on the diagram are developed and then put together into a solution procedure in Section 5. In Section 6, this procedure is used to calculate the information requirement to solve an inference problem and the maximal processing that can be performed with incomplete information. Section 7 contains a summary of results.
This paper surveys the emerging science of how to design a ``COllective INtelligence'' (COIN). A COIN is a large multi-agent system where: (i) There is little to no centralized communication or control; and (ii) There is a provided world utility function that rates the possible histories of the full system. In particular, we are interested in COINs in which each agent runs a reinforcement learning (RL) algorithm. Rather than use a conventional modeling approach (e.g., model the system dynamics, and hand-tune agents to cooperate), we aim to solve the COIN design problem implicitly, via the ``adaptive'' character of the RL algorithms of each of the agents. This approach introduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we do not have phenomena like the tragedy of the commons, Braess's paradox, or the liquidity trap? Although still very young, research specifically concentrating on the COIN design problem has already resulted in successes in artificial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur's El Farol bar problem. It is expected that as it matures and draws upon other disciplines related to COINs, this research will greatly expand the range of tasks addressable by human engineers. Moreover, in addition to drawing on them, such a fully developed scie nce of COIN design may provide much insight into other already established scientific fields, such as economics, game theory, and population biology.
This paper shows how a machine, which observes stimuli through an uncharacterized, uncalibrated channel and sensor, can glean machine-independent information (i.e., channel- and sensor-independent information) about the stimuli. First, we demonstrate that a machine defines a specific coordinate system on the stimulus state space, with the nature of that coordinate system depending on the device's channel and sensor. Thus, machines with different channels and sensors "see" the same stimulus trajectory through state space, but in different machine-specific coordinate systems. For a large variety of physical stimuli, statistical properties of that trajectory endow the stimulus configuration space with differential geometric structure (a metric and parallel transfer procedure), which can then be used to represent relative stimulus configurations in a coordinate-system-independent manner (and, therefore, in a channel- and sensor-independent manner). The resulting description is an "inner" property of the stimulus time series in the sense that it does not depend on extrinsic factors like the observer's choice of a coordinate system in which the stimulus is viewed (i.e., the observer's choice of channel and sensor). This methodology is illustrated with analytic examples and with a numerically simulated experiment. In an intelligent sensory device, this kind of representation "engine" could function as a "front-end" that passes channel/sensor-independent stimulus representations to a pattern recognition module. After a pattern recognizer has been trained in one of these devices, it could be used without change in other devices having different channels and sensors.
We present results from the first geological field tests of the `Cyborg Astrobiologist', which is a wearable computer and video camcorder system that we are using to test and train a computer-vision system towards having some of the autonomous decision-making capabilities of a field-geologist and field-astrobiologist. The Cyborg Astrobiologist platform has thus far been used for testing and development of these algorithms and systems: robotic acquisition of quasi-mosaics of images, real-time image segmentation, and real-time determination of interesting points in the image mosaics. The hardware and software systems function reliably, and the computer-vision algorithms are adequate for the first field tests. In addition to the proof-of-concept aspect of these field tests, the main result of these field tests is the enumeration of those issues that we can improve in the future, including: first, detection and accounting for shadows caused by 3D jagged edges in the outcrop; second, reincorporation of more sophisticated texture-analysis algorithms into the system; third, creation of hardware and software capabilities to control the camera's zoom lens in an intelligent manner; and fourth, development of algorithms for interpretation of complex geological scenery. Nonetheless, despite these technical inadequacies, this Cyborg Astrobiologist system, consisting of a camera-equipped wearable-computer and its computer-vision algorithms, has demonstrated its ability of finding genuinely interesting points in real-time in the geological scenery, and then gathering more information about these interest points in an automated manner.
We present results from the first geological field tests of the `Cyborg Astrobiologist', which is a wearable computer and video camcorder system that we are using to test and train a computer-vision system towards having some of the autonomous decision-making capabilities of a field-geologist. The Cyborg Astrobiologist platform has thus far been used for testing and development of these algorithms and systems: robotic acquisition of quasi-mosaics of images, real-time image segmentation, and real-time determination of interesting points in the image mosaics. This work is more of a test of the whole system, rather than of any one part of the system. However, beyond the concept of the system itself, the uncommon map (despite its simplicity) is the main innovative part of the system. The uncommon map helps to determine interest-points in a context-free manner. Overall, the hardware and software systems function reliably, and the computer-vision algorithms are adequate for the first field tests. In addition to the proof-of-concept aspect of these field tests, the main result of these field tests is the enumeration of those issues that we can improve in the future, including: dealing with structural shadow and microtexture, and also, controlling the camera's zoom lens in an intelligent manner. Nonetheless, despite these and other technical inadequacies, this Cyborg Astrobiologist system, consisting of a camera-equipped wearable-computer and its computer-vision algorithms, has demonstrated its ability of finding genuinely interesting points in real-time in the geological scenery, and then gathering more information about these interest points in an automated manner. We use these capabilities for autonomous guidance towards geological points-of-interest.
In recent years genetic algorithms have emerged as a useful tool for the heuristic solution of complex discrete optimisation problems. In particular there has been considerable interest in their use in tackling problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle constraints and successful implementations usually require some sort of modification to enable the search to exploit problem specific knowledge in order to overcome this shortcoming. This paper is concerned with the development of a family of genetic algorithms for the solution of a nurse rostering problem at a major UK hospital. The hospital is made up of wards of up to 30 nurses. Each ward has its own group of nurses whose shifts have to be scheduled on a weekly basis. In addition to fulfilling the minimum demand for staff over three daily shifts, nurses' wishes and qualifications have to be taken into account. The schedules must also be seen to be fair, in that unpopular shifts have to be spread evenly amongst all nurses, and other restrictions, such as team nursing and special conditions for senior staff, have to be satisfied. The basis of the family of genetic algorithms is a classical genetic algorithm consisting of n-point crossover, single-bit mutation and a rank-based selection. The solution space consists of all schedules in which each nurse works the required number of shifts, but the remaining constraints, both hard and soft, are relaxed and penalised in the fitness function. The talk will start with a detailed description of the problem and the initial implementation and will go on to highlight the shortcomings of such an approach, in terms of the key element of balancing feasibility, i.e. covering the demand and work regulations, and quality, as measured by the nurses' preferences. A series of experiments involving parameter adaptation, niching, intelligent weights, delta coding, local hill climbing, migration and special selection rules will then be outlined and it will be shown how a series of these enhancements were able to eradicate these difficulties. Results based on several months' real data will be used to measure the impact of each modification, and to show that the final algorithm is able to compete with a tabu search approach currently employed at the hospital. The talk will conclude with some observations as to the overall quality of this approach to this and similar problems.
Gesture recognition is mainly apprehensive on analyzing the functionality of human wits. The main goal of gesture recognition is to create a system which can recognize specific human gestures and use them to convey information or for device control. Hand gestures provide a separate complementary modality to speech for expressing ones ideas. Information associated with hand gestures in a conversation is degree,discourse structure, spatial and temporal structure. The approaches present can be mainly divided into Data-Glove Based and Vision Based approaches. An important face feature point is the nose tip. Since nose is the highest protruding point from the face. Besides that, it is not affected by facial expressions.Another important function of the nose is that it is able to indicate the head pose. Knowledge of the nose location will enable us to align an unknown 3D face with those in a face database. Eye detection is divided into eye position detection and eye contour detection. Existing works in eye detection can be classified into two major categories: traditional image-based passive approaches and the active IR based approaches. The former uses intensity and shape of eyes for detection and the latter works on the assumption that eyes have a reflection under near IR illumination and produce bright/dark pupil effect. The traditional methods can be broadly classified into three categories: template based methods,appearance based methods and feature based methods. The purpose of this paper is to compare various human Gesture recognition systems for interfacing machines directly to human wits without any corporeal media in an ambient environment.
Summary of results in last project period (1. 10. 2009 - 30. 9. 2010) of SNFS Project "From locomotion to cognition" The research that we have been involved in, and will continue to do, starts from the insight that in order to understand and design intelligent behavior, we must adopt an embodied perspective, i.e. we must take the entire agent, including its shape or morphology, the materials out of which it is built, and its interaction with the environment into account, in addition to the neural control. A lot of our research in the past has been on relatively low-level sensory-motor tasks such as locomotion (e.g. walking, running, jumping), navigation, and grasping. While this research is of interest in itself, in the context of artificial intelligence and cognitive science, this leads to the question of what these kinds of tasks have to do with higher levels of cognition, or to put it more provocatively, "What does walking have to do with thinking?" This question is of course reminiscent of the notorious "symbol grounding problem". In contrast to most of the research on symbol grounding, we propose to exploit the dynamic interaction between the embodied agent and the environment as the basis for grounding. We use the term "morphological computation" to designate the fact that some of the control or computation can be taken over by the dynamic interaction derived from morphological properties (e.g. the passive forward swing of the leg in walking, the spring-like properties of the muscles, and the weight distribution). By taking morphological computation into account, an agent will be able to achieve not only faster, more robust, and more energy-efficient behavior, but also more situated exploration by the agent for the comprehensive understanding of the environment.
Summary of results (project period 1. 10. 2008 - 30. 9. 2009) of SNFS Project "From locomotion to cognition" The research that we have been involved in, and will continue to do, starts from the insight that in order to understand and design intelligent behavior, we must adopt an embodied perspective, i.e. we must take the entire agent, including its shape or morphology, the materials out of which it is built, and its interaction with the environment into account, in addition to the neural control. A lot of our research in the past has been on relatively low-level sensory-motor tasks such as locomotion (e.g. walking, running, jumping), navigation, and grasping. While this research is of interest in itself, in the context of artificial intelligence and cognitive science, this leads to the question of what these kinds of tasks have to do with higher levels of cognition, or to put it more provocatively, "What does walking have to do with thinking?" This question is of course reminiscent of the notorious "symbol grounding problem". In contrast to most of the research on symbol grounding, we propose to exploit the dynamic interaction between the embodied agent and the environment as the basis for grounding. We use the term "morphological computation" to designate the fact that some of the control or computation can be taken over by the dynamic interaction derived from morphological properties (e.g. the passive forward swing of the leg in walking, the spring-like properties of the muscles, and the weight distribution). By taking morphological computation into account, an agent will be able to achieve not only faster, more robust, and more energy-efficient behavior, but also more situated exploration by the agent for the comprehensive understanding of the environment.
In India financial markets have existed for many years. A functionally accented, diverse, efficient and flexible financial system is vital to the national objective of creating a market driven, productive and competitive economy. Today markets of varying maturity exist in equity, debt, commodities and foreign exchange. In this work we attempt to generate prediction rules scheme for stock price movement at Bombay Stock Exchange using an important Soft Computing paradigm viz., Rough Fuzzy Multi Layer Perception. The use of Computational Intelligence Systems such as Neural Networks, Fuzzy Sets, Genetic Algorithms, etc. for Stock Market Predictions has been widely established. The process is to extract knowledge in the form of rules from daily stock movements. These rules can then be used to guide investors. To increase the efficiency of the prediction process, Rough Sets is used to discretize the data. The methodology uses a Genetic Algorithm to obtain a structured network suitable for both classification and rule extraction. The modular concept, based on divide and conquer strategy, provides accelerated training and a compact network suitable for generating a minimum number of rules with high certainty values. The concept of variable mutation operator is introduced for preserving the localized structure of the constituting Knowledge Based sub-networks, while they are integrated and evolved. Rough Set Dependency Rules are generated directly from the real valued attribute table containing Fuzzy membership values. The paradigm is thus used to develop a rule extraction algorithm. The extracted rules are compared with some of the related rule extraction techniques on the basis of some quantitative performance indices. The proposed methodology extracts rules which are less in number, are accurate, have high certainty factor and have low confusion with less computation time.
If we consider Big History as simply 'our' example of the process of cosmic evolution playing out, then we can seek to broaden our view of our possible fate as a species by asking questions about what paths or trajectories other species' own versions of Big History might take or have taken. This paper explores the broad outlines of possible scenarios for the evolution of long-lived intelligent engineering species---scenarios which might have been part of another species' own Big History story, or which may yet lie ahead in our own distant future. A sufficiently long-lived engineering-oriented species may decide to undertake a program of macro-engineering projects that might eventually lead to a re-engineered galaxy so altered that its artificiality may be detectable from Earth. We consider activities that lead ultimately to a galactic structure consisting of a central inner core surrounded by a more distant ring of stars separated by a relatively sparser 'gap', where star systems and stellar materials may have been removed, 'lifted' or turned into Dyson Spheres. When one looks to the sky, one finds that such galaxies do indeed exist---including the beautiful ringed galaxy known as 'Hoag's Object' (PGC 54559) in the constellation Serpens. This leads us to pose the question: Is Hoag's Object an example of galaxy-scale macro-engineering? And this suggests a program of possible observational activities and theoretical explorations, several of which are presented here, that could be carried out in order to begin to investigate this beguiling question.
Every day, billions of mobile network events (i.e. CDRs) are generated by cellular phone operator companies. Latent in this data are inspiring insights about human actions and behaviors, the discovery of which is important because context-aware applications and services hold the key to user-driven, intelligent services, which can enhance our everyday lives such as social and economic development, urban planning, and health prevention. The major challenge in this area is that interpreting such a big stream of data requires a deep understanding of mobile network events' context through available background knowledge. This article addresses the issues in context awareness given heterogeneous and uncertain data of mobile network events missing reliable information on the context of this activity. The contribution of this research is a model from a combination of logical and statistical reasoning standpoints for enabling human activity inference in qualitative terms from open geographical data that aimed at improving the quality of human behaviors recognition tasks from CDRs. We use open geographical data, Openstreetmap (OSM), as a proxy for predicting the content of human activity in the area. The user study performed in Trento shows that predicted human activities (top level) match the survey data with around 93% overall accuracy. The extensive validation for predicting a more specific economic type of human activity performed in Barcelona, by employing credit card transaction data. The analysis identifies that appropriately normalized data on points of interest (POI) is a good proxy for predicting human economical activities, with 84% accuracy on average. So the model is proven to be efficient for predicting the context of human activity, when its total level could be efficiently observed from cell phone data records, missing contextual information however.
We propose a high level network architecture for an economic system that integrates money, governance and reputation. We introduce a method for issuing, and redeeming a digital coin using a mechanism to create a sustainable global economy and a free market. To maintain a currency's value over time, and therefore be money proper, we claim it must be issued by the buyer and backed for value by the seller, exchanging the products of labour, in a free market. We also claim that a free market and sustainable economy cannot be maintained using economically arbitrary creation and allocation of money. Nakamoto, with Bitcoin, introduced a new technology called the cryptographic blockchain to operate a decentralised and distributed accounts ledger without the need for an untrusted third party. This blockchain technology creates and allocates new digital currency as a reward for "proof-of-work", to secure the network. However, no currency, digital or otherwise, has solved how to create and allocate money in an economically non-arbitrary way, or how to govern and trust a world-scale free enterprise money system. We propose an "Ontologically Networked Exchange" (ONE), with purpose as its highest order domain. Each purpose is defined in a contract, and the entire economy of contracts is structured in a unified ontology. We claim to secure the ONE network using economically non-arbitrary methodologies and economically incented human behaviour. Decisions influenced by reputation help to secure the network without an untrusted third party. The stack of contracts, organised in a unified ontology, functions as a super recursive algorithm, with individual use programming the algorithm, acting as the "oracle". The state of the algorithm becomes the "memory" of a scalable and trustable artificial intelligence (AI). This AI offers a new platform for what we call the "Autonomy-of-Things" (AoT).
Route choice in multimodal networks shows a considerable variation between different individuals as well as the current situational context. Personalization of recommendation algorithms are already common in many areas, e.g., online retail. However, most online routing applications still provide shortest distance or shortest travel-time routes only, neglecting individual preferences as well as the current situation. Both aspects are of particular importance in a multimodal setting as attractivity of some transportation modes such as biking crucially depends on personal characteristics and exogenous factors like the weather. This paper introduces the FAVourite rOUte Recommendation (FAVOUR) approach to provide personalized, situation-aware route proposals based on three steps: first, at the initialization stage, the user provides limited information (home location, work place, mobility options, sociodemographics) used to select one out of a small number of initial profiles. Second, based on this information, a stated preference survey is designed in order to sharpen the profile. In this step a mass preference prior is used to encode the prior knowledge on preferences from the class identified in step one. And third, subsequently the profile is continuously updated during usage of the routing services. The last two steps use Bayesian learning techniques in order to incorporate information from all contributing individuals. The FAVOUR approach is presented in detail and tested on a small number of survey participants. The experimental results on this real-world dataset show that FAVOUR generates better-quality recommendations w.r.t. alternative learning algorithms from the literature. In particular the definition of the mass preference prior for initialization of step two is shown to provide better predictions than a number of alternatives from the literature.
Nowadays large amounts of GPS trajectory data is being continuously collected by GPS-enabled devices such as vehicles navigation systems and mobile phones. GPS trajectory data is useful for applications such as traffic management, location forecasting, and itinerary planning. Such applications often need to extract the time-stamped Sequence of Visited Locations (SVLs) of the mobile objects. The nearest neighbor query (NNQ) is the most applied method for labeling the visited locations based on the IDs of the POIs in the process of SVL generation. NNQ in some scenarios is not accurate enough. To improve the quality of the extracted SVLs, instead of using NNQ, we label the visited locations as the IDs of the POIs which geometrically intersect with the GPS observations. Intersection operator requires the accurate geometry of the points of interest which we refer to them as the Geometries of Interest (GOIs). In some application domains (e.g. movement trajectories of animals), adequate information about the POIs and their GOIs may not be available a priori, or they may not be publicly accessible and, therefore, they need to be derived from GPS trajectory data. In this paper we propose a novel method for estimating the POIs and their GOIs, which consists of three phases: (i) extracting the geometries of the stay regions; (ii) constructing the geometry of destination regions based on the extracted stay regions; and (iii) constructing the GOIs based on the geometries of the destination regions. Using the geometric similarity to known GOIs as the major evaluation criterion, the experiments we performed using long-term GPS trajectory data show that our method outperforms the existing approaches.
We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being grounded in vision enough to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial v0.9 has been released and contains 1 dialog with 10 question-answer pairs on ~120k images from COCO, with a total of ~1.2M dialog question-answer pairs. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders -- Late Fusion, Hierarchical Recurrent Encoder and Memory Network -- and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank of human response. We quantify gap between machine and human performance on the Visual Dialog task via human studies. Putting it all together, we demonstrate the first 'visual chatbot'! Our dataset, code, trained models and visual chatbot are available on https://visualdialog.org
In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accuracy, FNT parameters were optimized by using differential evolution algorithm. The performance of the FNT-based CI model was evaluated and compared with other CI techniques: multilayer perceptron, Gaussian process regression, and reduced error pruning tree. The accuracy of the CI model was evaluated experimentally using die filling as a case study. The die filling experiments were performed using a model shoe system and three different grades of microcrystalline cellulose (MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were roll-compacted and milled into granules. The granules were then sieved into samples of various size classes. The mass of granules deposited into the die at different shoe speeds was measured. From these experiments, a dataset consisting true density, mean diameter (d50), granule size, and shoe speed as the inputs and the deposited mass as the output was generated. Cross-validation (CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate the predictive models. It was found that the FNT-based CI model (for both CV methods) performed much better than other CI models. Additionally, it was observed that process variables such as the granule size and the shoe speed had a higher impact on the predictability than that of the powder property such as d50. Furthermore, validation of model prediction with experimental data showed that the die filling behavior of coarse granules could be better predicted than that of fine granules.
Object detection is considered one of the most challenging problems in this field of computer vision, as it involves the combination of object classification and object localization within a scene. Recently, deep neural networks (DNNs) have been demonstrated to achieve superior object detection performance compared to other approaches, with YOLOv2 (an improved You Only Look Once model) being one of the state-of-the-art in DNN-based object detection methods in terms of both speed and accuracy. Although YOLOv2 can achieve real-time performance on a powerful GPU, it still remains very challenging for leveraging this approach for real-time object detection in video on embedded computing devices with limited computational power and limited memory. In this paper, we propose a new framework called Fast YOLO, a fast You Only Look Once framework which accelerates YOLOv2 to be able to perform object detection in video on embedded devices in a real-time manner. First, we leverage the evolutionary deep intelligence framework to evolve the YOLOv2 network architecture and produce an optimized architecture (referred to as O-YOLOv2 here) that has 2.8X fewer parameters with just a ~2% IOU drop. To further reduce power consumption on embedded devices while maintaining performance, a motion-adaptive inference method is introduced into the proposed Fast YOLO framework to reduce the frequency of deep inference with O-YOLOv2 based on temporal motion characteristics. Experimental results show that the proposed Fast YOLO framework can reduce the number of deep inferences by an average of 38.13%, and an average speedup of ~3.3X for objection detection in video compared to the original YOLOv2, leading Fast YOLO to run an average of ~18FPS on a Nvidia Jetson TX1 embedded system.
Mobile network that millions of people use every day is one of the most complex systems in real world. Optimization of mobile network to meet exploding customer demand and reduce CAPEX/OPEX poses greater challenges than in prior works. Learning to solve complex problems in real world to benefit everyone and make the world better has long been ultimate goal of AI. However, it still remains an unsolved problem for deep reinforcement learning (DRL), given imperfect information in real world, huge state/action space, lots of data needed for training, associated time/cost, multi-agent interactions, potential negative impact to real world, etc. To bridge this reality gap, we proposed a DRL framework to direct transfer optimal policy learned from multi-tasks in source domain to unseen similar tasks in target domain without any further training in both domains. First, we distilled temporal-spatial relationships between cells and mobile users to scalable 3D image-like tensor to best characterize partially observed mobile network. Second, inspired by AlphaGo, we used a novel self-play mechanism to empower DRL agent to gradually improve its intelligence by competing for best record on multiple tasks. Third, a decentralized DRL method is proposed to coordinate multi-agents to compete and cooperate as a team to maximize global reward and minimize potential negative impact. Using 7693 unseen test tasks over 160 unseen simulated mobile networks and 6 field trials over 4 commercial mobile networks in real world, we demonstrated the capability of our approach to direct transfer the learning from one simulator to another simulator, and from simulation to real world. This is the first time that a DRL agent successfully transfers its learning directly from simulation to very complex real world problems with incomplete and imperfect information, huge state/action space and multi-agent interactions.
Recent breakthroughs in machine learning especially artificial intelligence shift the paradigm of wireless communication towards intelligence radios. One of their core operations is automatic modulation recognition (AMR). Existing research focuses on coherent modulation schemes such as QAM, PSK and FSK. The AMR of (non-coherent) space-time modulation remains an uncharted area despite its wide deployment in modern multiple-input-multiple-output (MIMO) systems. The scheme using a so called Grassmann constellation enables rate-enhancement using multi-antennas and blind detection. In this work, we propose an AMR approach for Grassmann constellation based on data clustering, which differs from traditional AMR based on classification using a modulation database. The approach allows algorithms for clustering on the Grassmann manifold, such as Grassmann K-means and depth-first search, originally developed for computer vision to be applied to AMR. We further develop an analytical framework for studying and designing these algorithms in the context of AMR. First, the maximum-likelihood Grassmann constellation detection is proved to be equivalent to clustering on the Grassmannian. Thereby, a well-known machine-learning result that was originally established only for the Euclidean space is rediscovered for the Grassmannian. Next, despite a rich literature on algorithmic design, theoretical analysis of data clustering is largely overlooked due to the lack of tractable techniques. We tackle the challenge by introducing probabilistic metrics for measuring the inter-cluster separability and intra-cluster connectivity of received space-time symbols and deriving them using tools from differential geometry and Grassmannian packing. The results provide useful insights into the effects of various parameters ranging from the signal-to-noise ratio to constellation size, facilitating algorithmic design.
Neoteny, also spelled Paedomorphosis, can be defined in biological terms as the retention by an organism of juvenile or even larval traits into later life. In some species, all morphological development is retarded; the organism is juvenilized but sexually mature. Such shifts of reproductive capability would appear to have adaptive significance to organisms that exhibit it. In terms of evolutionary theory, the process of paedomorphosis suggests that larval stages and developmental phases of existing organisms may give rise, under certain circumstances, to wholly new organisms. Although the present work does not pretend to model or simulate the biological details of such a concept in any way, these ideas were incorporated by a rather simple abstract computational strategy, in order to allow (if possible) for faster convergence into simple non-memetic Genetic Algorithms, i.e. without using local improvement procedures (e.g. via Baldwin or Lamarckian learning). As a case-study, the Genetic Algorithm was used for colour image segmentation purposes by using K-mean unsupervised clustering methods, namely for guiding the evolutionary algorithm in his search for finding the optimal or sub-optimal data partition. Average results suggest that the use of neotonic strategies by employing juvenile genotypes into the later generations and the use of linear-dynamic mutation rates instead of constant, can increase fitness values by 58% comparing to classical Genetic Algorithms, independently from the starting population characteristics on the search space. KEYWORDS: Genetic Algorithms, Artificial Neoteny, Dynamic Mutation Rates, Faster Convergence, Colour Image Segmentation, Classification, Clustering.
Barring swarm robotics, a substantial share of current machine-human and machine-machine learning and interaction mechanisms are being developed and fed by results of agent-based computer simulations, game-theoretic models, or robotic experiments based on a dyadic communication pattern. Yet, in real life, humans no less frequently communicate in groups, and gain knowledge and take decisions basing on information cumulatively gleaned from more than one single source. These properties should be taken into consideration in the design of autonomous artificial cognitive systems construed to interact with learn from more than one contact or 'neighbour'. To this end, significant practical import can be gleaned from research applying strict science methodology to human and social phenomena, e.g. to discovery of realistic creativity potential spans, or the 'exposure thresholds' after which new information could be accepted by a cognitive agent. The results will be presented of a project analysing the social propagation of neologisms in a microblogging service. From local, low-level interactions and information flows between agents inventing and imitating discrete lexemes we aim to describe the processes of the emergence of more global systemic order and dynamics, using the latest methods of complexity science. Whether in order to mimic them, or to 'enhance' them, parameters gleaned from complexity science approaches to humans' social and humanistic behaviour should subsequently be incorporated as points of reference in the field of robotics and human-machine interaction.
It has been repeatedly proposed to expand the scope for SETI, and one of the suggested alternatives to radio is the biological media. Genomic DNA is already used on Earth to store non-biological information. Though smaller in capacity, but stronger in noise immunity is the genetic code. The code is a flexible mapping between codons and amino acids, and this flexibility allows modifying the code artificially. But once fixed, the code might stay unchanged over cosmological timescales. Thus, it represents a reliable storage for an intelligent signature, if that conforms to biological and thermodynamic requirements. As the actual scenario for the origin of terrestrial life is far from being settled, the proposal that it might have been seeded intentionally cannot be ruled out. A statistically strong signal in the genetic code is then a testable consequence of such scenario. Here we show that the terrestrial code displays a thorough precision orderliness matching the criteria to be considered an informational signal. Simple arrangements of the code reveal an ensemble of arithmetical and ideographical patterns of the same symbolic language. Accurate and systematic, these underlying patterns appear as a product of precision logic and nontrivial computing rather than of stochastic processes. The patterns are profound to the extent that the code mapping itself is uniquely deduced from their algebraic representation. The signal displays readily recognizable hallmarks of artificiality. Besides, extraction of the signal involves logically straightforward but abstract operations, making the patterns essentially irreducible to any natural origin. Plausible way of embedding the signal into the code and possible interpretation of its content are discussed. Overall, while the code is nearly optimized biologically, its limited capacity is used extremely efficiently to store non-biological information.
We propose an artificial immune model for intrusion detection in distributed systems based on a relatively recent theory in immunology called Danger theory. Based on Danger theory, immune response in natural systems is a result of sensing corruption as well as sensing unknown substances. In contrast, traditional self-nonself discrimination theory states that immune response is only initiated by sensing nonself (unknown) patterns. Danger theory solves many problems that could only be partially explained by the traditional model. Although the traditional model is simpler, such problems result in high false positive rates in immune-inspired intrusion detection systems. We believe using danger theory in a multi-agent environment that computationally emulates the behavior of natural immune systems is effective in reducing false positive rates. We first describe a simplified scenario of immune response in natural systems based on danger theory and then, convert it to a computational model as a network protocol. In our protocol, we define several immune signals and model cell signaling via message passing between agents that emulate cells. Most messages include application-specific patterns that must be meaningfully extracted from various system properties. We show how to model these messages in practice by performing a case study on the problem of detecting distributed denial-of-service attacks in wireless sensor networks. We conduct a set of systematic experiments to find a set of performance metrics that can accurately distinguish malicious patterns. The results indicate that the system can be efficiently used to detect malicious patterns with a high level of accuracy.
The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: Additive Noise Methods (ANM) and Information Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs that consists of data for 100 different cause-effect pairs selected from 37 datasets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the additive-noise method originally proposed by Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of 0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.
By drawing on ideas from optimisation theory, artificial neural networks (ANN), graph embeddings and sparse representations, I develop a novel technique, termed SENNS (Sparse Extraction Neural NetworkS), aimed at addressing the feature extraction problem. The proposed method uses (preferably deep) ANNs for projecting input attribute vectors to an output space wherein pairwise distances are maximized for vectors belonging to different classes, but minimized for those belonging to the same class, while simultaneously enforcing sparsity on the ANN outputs. The vectors that result from the projection can then be used as features in any classifier of choice. Mathematically, I formulate the proposed method as the minimisation of an objective function which can be interpreted, in the ANN output space, as a negative factor of the sum of the squares of the pair-wise distances between output vectors belonging to different classes, added to a positive factor of the sum of squares of the pair-wise distances between output vectors belonging to the same classes, plus sparsity and weight decay terms. To derive an algorithm for minimizing the objective function via gradient descent, I use the multi-variate version of the chain rule to obtain the partial derivatives of the function with respect to ANN weights and biases, and find that each of the required partial derivatives can be expressed as a sum of six terms. As it turns out, four of those six terms can be computed using the standard back propagation algorithm; the fifth can be computed via a slight modification of the standard backpropagation algorithm; while the sixth one can be computed via simple arithmetic. Finally, I propose experiments on the ARABASE Arabic corpora of digits and letters, the CMU PIE database of faces, the MNIST digits database, and other standard machine learning databases.
We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no change in the model architecture from our base system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. The rest of the model, which includes encoder, decoder and attention, remains unchanged and is shared across all languages. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model without any increase in parameters, which is significantly simpler than previous proposals for Multilingual NMT. Our method often improves the translation quality of all involved language pairs, even while keeping the total number of model parameters constant. On the WMT'14 benchmarks, a single multilingual model achieves comparable performance for English$\rightarrow$French and surpasses state-of-the-art results for English$\rightarrow$German. Similarly, a single multilingual model surpasses state-of-the-art results for French$\rightarrow$English and German$\rightarrow$English on WMT'14 and WMT'15 benchmarks respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and show some interesting examples when mixing languages.
The foundations of all methodologies for the measurement and verification (M&V) of energy savings are based on the same five key principles: accuracy, completeness, conservatism, consistency and transparency. The most widely accepted methodologies tend to generalise M&V so as to ensure applicability across the spectrum of energy conservation measures (ECM's). These do not provide a rigid calculation procedure to follow. This paper aims to bridge the gap between high-level methodologies and the practical application of modelling algorithms, with a focus on the industrial buildings sector. This is achieved with the development of a novel, machine learning supported methodology for M&V 2.0 which enables accurate quantification of savings. A novel and computationally efficient feature selection algorithm and powerful machine learning regression algorithms are employed to maximise the effectiveness of available data. The baseline period energy consumption is modelled using artificial neural networks, support vector machines, k-nearest neighbours and multiple ordinary least squares regression. Improved knowledge discovery and an expanded boundary of analysis allow more complex energy systems be analysed, thus increasing the applicability of M&V. A case study in a large biomedical manufacturing facility is used to demonstrate the methodology's ability to accurately quantify the savings under real-world conditions. The ECM was found to result in 604,527 kWh of energy savings with 57% uncertainty at a confidence interval of 68%. 20 baseline energy models are developed using an exhaustive approach with the optimal model being used to quantify savings. The range of savings estimated with each model are presented and the acceptability of uncertainty is reviewed. The case study demonstrates the ability of the methodology to perform M&V to an acceptable standard in challenging circumstances.
Although simple individually, artificial neurons provide state-of-the-art performance when interconnected in deep networks. Unknown to many, there exists an arguably even simpler and more versatile learning mechanism, namely, the Tsetlin Automaton. Merely by means of a single integer as memory, it learns the optimal action in stochastic environments. In this paper, we introduce the Tsetlin Machine, which solves complex pattern recognition problems with easy-to-interpret propositional formulas, composed by a collective of Tsetlin Automata. To eliminate the longstanding problem of vanishing signal-to-noise ratio, the Tsetlin Machine orchestrates the automata using a novel game. Our theoretical analysis establishes that the Nash equilibria of the game are aligned with the propositional formulas that provide optimal pattern recognition accuracy. This translates to learning without local optima, only global ones. We argue that the Tsetlin Machine finds the propositional formula that provides optimal accuracy, with probability arbitrarily close to unity. In four distinct benchmarks, the Tsetlin Machine outperforms both Neural Networks, SVMs, Random Forests, the Naive Bayes Classifier and Logistic Regression. It further turns out that the accuracy advantage of the Tsetlin Machine increases with lack of data. The Tsetlin Machine has a significant computational performance advantage since both inputs, patterns, and outputs are expressed as bits, while recognition of patterns relies on bit manipulation. The combination of accuracy, interpretability, and computational simplicity makes the Tsetlin Machine a promising tool for a wide range of domains, including safety-critical medicine. Being the first of its kind, we believe the Tsetlin Machine will kick-start completely new paths of research, with a potentially significant impact on the AI field and the applications of AI.
Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NP-hard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be cast as a classical path finding problem, it is attracting a growing number of AI researchers interested in heuristic search algorithms as a challenge with actual practical relevance. In this paper, we first review two previous, complementary lines of research. Based on Hirschbergs algorithm, Dynamic Programming needs O(kN^(k-1)) space to store both the search frontier and the nodes needed to reconstruct the solution path, for k sequences of length N. Best first search, on the other hand, has the advantage of bounding the search space that has to be explored using a heuristic. However, it is necessary to maintain all explored nodes up to the final solution in order to prevent the search from re-expanding them at higher cost. Earlier approaches to reduce the Closed list are either incompatible with pruning methods for the Open list, or must retain at least the boundary of the Closed list. In this article, we present an algorithm that attempts at combining the respective advantages; like A* it uses a heuristic for pruning the search space, but reduces both the maximum Open and Closed size to O(kN^(k-1)), as in Dynamic Programming. The underlying idea is to conduct a series of searches with successively increasing upper bounds, but using the DP ordering as the key for the Open priority queue. With a suitable choice of thresholds, in practice, a running time below four times that of A* can be expected. In our experiments we show that our algorithm outperforms one of the currently most successful algorithms for optimal multiple sequence alignments, Partial Expansion A*, both in time and memory. Moreover, we apply a refined heuristic based on optimal alignments not only of pairs of sequences, but of larger subsets. This idea is not new; however, to make it practically relevant we show that it is equally important to bound the heuristic computation appropriately, or the overhead can obliterate any possible gain. Furthermore, we discuss a number of improvements in time and space efficiency with regard to practical implementations. Our algorithm, used in conjunction with higher-dimensional heuristics, is able to calculate for the first time the optimal alignment for almost all of the problems in Reference 1 of the benchmark database BAliBASE.
Given a universe of discourse X-a domain of possible outcomes-an experiment may consist of selecting one of its elements, subject to the operation of chance, or of observing the elements, subject to imprecision. A priori uncertainty about the actual result of the experiment may be quantified, representing either the likelihood of the choice of :r_X or the degree to which any such X would be suitable as a description of the outcome. The former case corresponds to a probability distribution, while the latter gives a possibility assignment on X. The study of such assignments and their properties falls within the purview of possibility theory [DP88, Y80, Z783. It, like probability theory, assigns values between 0 and 1 to express likelihoods of outcomes. Here, however, the similarity ends. Possibility theory uses the maximum and minimum functions to combine uncertainties, whereas probability theory uses the plus and times operations. This leads to very dissimilar theories in terms of analytical framework, even though they share several semantic concepts. One of the shared concepts consists of expressing quantitatively the uncertainty associated with a given distribution. In probability theory its value corresponds to the gain of information that would result from conducting an experiment and ascertaining an actual result. This gain of information can equally well be viewed as a decrease in uncertainty about the outcome of an experiment. In this case the standard measure of information, and thus uncertainty, is Shannon entropy [AD75, G77]. It enjoys several advantages-it is characterized uniquely by a few, very natural properties, and it can be conveniently used in decision processes. This application is based on the principle of maximum entropy; it has become a popular method of relating decisions to uncertainty. This paper demonstrates that an equally integrated theory can be built on the foundation of possibility theory. We first show how to define measures of in formation and uncertainty for possibility assignments. Next we construct an information-based metric on the space of all possibility distributions defined on a given domain. It allows us to capture the notion of proximity in information content among the distributions. Lastly, we show that all the above constructions can be carried out for continuous distributions-possibility assignments on arbitrary measurable domains. We consider this step very significant-finite domains of discourse are but approximations of the real-life infinite domains. If possibility theory is to represent real world situations, it must handle continuous distributions both directly and through finite approximations. In the last section we discuss a principle of maximum uncertainty for possibility distributions. We show how such a principle could be formalized as an inference rule. We also suggest it could be derived as a consequence of simple assumptions about combining information. We would like to mention that possibility assignments can be viewed as fuzzy sets and that every fuzzy set gives rise to an assignment of possibilities. This correspondence has far reaching consequences in logic and in control theory. Our treatment here is independent of any special interpretation; in particular we speak of possibility distributions and possibility measures, defining them as measurable mappings into the interval [0, 1]. Our presentation is intended as a self-contained, albeit terse summary. Topics discussed were selected with care, to demonstrate both the completeness and a certain elegance of the theory. Proofs are not included; we only offer illustrative examples.
Coalition formation is a fundamental type of interaction that involves the creation of coherent groupings of distinct, autonomous, agents in order to efficiently achieve their individual or collective goals. Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal. This usually requires calculating a value for every possible coalition, known as the coalition value, which indicates how beneficial that coalition would be if it was formed. Once these values are calculated, the agents usually need to find a combination of coalitions, in which every agent belongs to exactly one coalition, and by which the overall outcome of the system is maximized. However, this coalition structure generation problem is extremely challenging due to the number of possible solutions that need to be examined, which grows exponentially with the number of agents involved. To date, therefore, many algorithms have been proposed to solve this problem using different techniques ranging from dynamic programming, to integer programming, to stochastic search all of which suffer from major limitations relating to execution time, solution quality, and memory requirements. With this in mind, we develop an anytime algorithm to solve the coalition structure generation problem. Specifically, the algorithm uses a novel representation of the search space, which partitions the space of possible solutions into sub-spaces such that it is possible to compute upper and lower bounds on the values of the best coalition structures in them. These bounds are then used to identify the sub-spaces that have no potential of containing the optimal solution so that they can be pruned. The algorithm, then, searches through the remaining sub-spaces very efficiently using a branch-and-bound technique to avoid examining all the solutions within the searched subspace(s). In this setting, we prove that our algorithm enumerates all coalition structures efficiently by avoiding redundant and invalid solutions automatically. Moreover, in order to effectively test our algorithm we develop a new type of input distribution which allows us to generate more reliable benchmarks compared to the input distributions previously used in the field. Given this new distribution, we show that for 27 agents our algorithm is able to find solutions that are optimal in 0.175% of the time required by the fastest available algorithm in the literature. The algorithm is anytime, and if interrupted before it would have normally terminated, it can still provide a solution that is guaranteed to be within a bound from the optimal one. Moreover, the guarantees we provide on the quality of the solution are significantly better than those provided by the previous state of the art algorithms designed for this purpose. For example, for the worst case distribution given 25 agents, our algorithm is able to find a 90% efficient solution in around 10% of time it takes to find the optimal solution.
(l) I have enough evidence to render the sentence S probable. (la) So, relative to what I know, it is rational of me to believe S. (2) Now that I have more evidence, S may no longer be probable. (2a) So now, relative to what I know, it is not rational of me to believe S. These seem a perfectly ordinary, common sense, pair of situations. Generally and vaguely, I take them to embody what I shall call probabilistic inference. This form of inference is clearly non-monotonic. Relatively few people have taken this form of inference, based on high probability, to serve as a foundation for non-monotonic logic or for a logical or defeasible inference. There are exceptions: Jane Nutter [16] thinks that sometimes probability has something to do with non-monotonic reasoning. Judea Pearl [ 17] has recently been exploring the possibility. There are any number of people whom one might call probability enthusiasts who feel that probability provides all the answers by itself, with no need of help from logic. Cheeseman [1], Henrion [5] and others think it useful to look at a distribution of probabilities over a whole algebra of statements, to update that distribution in the light of new evidence, and to use the latest updated distribution of probability over the algebra as a basis for planning and decision making. A slightly weaker form of this approach is captured by Nilsson [15], where one assumes certain probabilities for certain statements, and infers the probabilities, or constraints on the probabilities of other statement. None of this corresponds to what I call probabilistic inference. All of the inference that is taking place, either in Bayesian updating, or in probabilistic logic, is strictly deductive. Deductive inference, particularly that concerned with the distribution of classical probabilities or chances, is of great importance. But this is not to say that there is no important role for what earlier logicians have called "ampliative" or "inductive" or "scientific" inference, in which the conclusion goes beyond the premises, asserts more than do the premises. This depends on what David Israel [6] has called "real rules of inference". It is characteristic of any such logic or inference procedure that it can go wrong: that statements accepted at one point may be rejected at a later point. Research underlying the results reported here has been partially supported by the Signals Warfare Center of the United States Army.
Probability theory, epistemically interpreted, provides an excellent, if not the best available account of inductive reasoning. This is so because there are general and definite rules for the change of subjective probabilities through information or experience; induction and belief change are one and same topic, after all. The most basic of these rules is simply to conditionalize with respect to the information received; and there are similar and more general rules. 1 Hence, a fundamental reason for the epistemological success of probability theory is that there at all exists a well-behaved concept of conditional probability. Still, people have, and have reasons for, various concerns over probability theory. One of these is my starting point: Intuitively, we have the notion of plain belief; we believe propositions2 to be true (or to be false or neither). Probability theory, however, offers no formal counterpart to this notion. Believing A is not the same as having probability 1 for A, because probability 1 is incorrigible3; but plain belief is clearly corrigible. And believing A is not the same as giving A a probability larger than some 1 - c, because believing A and believing B is usually taken to be equivalent to believing A & B.4 Thus, it seems that the formal representation of plain belief has to take a non-probabilistic route. Indeed, representing plain belief seems easy enough: simply represent an epistemic state by the set of all propositions believed true in it or, since I make the common assumption that plain belief is deductively closed, by the conjunction of all propositions believed true in it. But this does not yet provide a theory of induction, i.e. an answer to the question how epistemic states so represented are changed tbrough information or experience. There is a convincing partial answer: if the new information is compatible with the old epistemic state, then the new epistemic state is simply represented by the conjunction of the new information and the old beliefs. This answer is partial because it does not cover the quite common case where the new information is incompatible with the old beliefs. It is, however, important to complete the answer and to cover this case, too; otherwise, we would not represent plain belief as conigible. The crucial problem is that there is no good completion. When epistemic states are represented simply by the conjunction of all propositions believed true in it, the answer cannot be completed; and though there is a lot of fruitful work, no other representation of epistemic states has been proposed, as far as I know, which provides a complete solution to this problem. In this paper, I want to suggest such a solution. In [4], I have more fully argued that this is the only solution, if certain plausible desiderata are to be satisfied. Here, in section 2, I will be content with formally defining and intuitively explaining my proposal. I will compare my proposal with probability theory in section 3. It will turn out that the theory I am proposing is structurally homomorphic to probability theory in important respects and that it is thus equally easily implementable, but moreover computationally simpler. Section 4 contains a very brief comparison with various kinds of logics, in particular conditional logic, with Shackle's functions of potential surprise and related theories, and with the Dempster - Shafer theory of belief functions.
Graphs are commonly used to characterise interactions between objects of interest. Because they are based on a straightforward formalism, they are used in many scientific fields from computer science to historical sciences. In this paper, we give an introduction to some methods relying on graphs for learning. This includes both unsupervised and supervised methods. Unsupervised learning algorithms usually aim at visualising graphs in latent spaces and/or clustering the nodes. Both focus on extracting knowledge from graph topologies. While most existing techniques are only applicable to static graphs, where edges do not evolve through time, recent developments have shown that they could be extended to deal with evolving networks. In a supervised context, one generally aims at inferring labels or numerical values attached to nodes using both the graph and, when they are available, node characteristics. Balancing the two sources of information can be challenging, especially as they can disagree locally or globally. In both contexts, supervised and un-supervised, data can be relational (augmented with one or several global graphs) as described above, or graph valued. In this latter case, each object of interest is given as a full graph (possibly completed by other characteristics). In this context, natural tasks include graph clustering (as in producing clusters of graphs rather than clusters of nodes in a single graph), graph classification, etc. 1 Real networks One of the first practical studies on graphs can be dated back to the original work of Moreno [51] in the 30s. Since then, there has been a growing interest in graph analysis associated with strong developments in the modelling and the processing of these data. Graphs are now used in many scientific fields. In Biology [54, 2, 7], for instance, metabolic networks can describe pathways of biochemical reactions [41], while in social sciences networks are used to represent relation ties between actors [66, 56, 36, 34]. Other examples include powergrids [71] and the web [75]. Recently, networks have also been considered in other areas such as geography [22] and history [59, 39]. In machine learning, networks are seen as powerful tools to model problems in order to extract information from data and for prediction purposes. This is the object of this paper. For more complete surveys, we refer to [28, 62, 49, 45]. In this section, we introduce notations and highlight properties shared by most real networks. In Section 2, we then consider methods aiming at extracting information from a unique network. We will particularly focus on clustering methods where the goal is to find clusters of vertices. Finally, in Section 3, techniques that take a series of networks into account, where each network is
A spacially extended model of the collective behavior of a large number of locally acting organisms is proposed in which organisms move probabilistically between local cells in space, but with weights dependent on local morphogenetic substances, or morphogens. The morphogens are in turn are effected by the passage of an organism. The evolution of the morphogens, and the corresponding flow of the organisms constitutes the collective behavior of the group. Such models have various types of phase transitions and self-organizing properties controlled both by the level of the noise, and other parameters. The model is then applied to the specific case of ants moving on a lattice. The local behavior of the ants is inspired by the actual behavior observed in the laboratory, and analytic results for the collective behavior are compared to the corresponding laboratory results. It is hoped that the present model might serve as a paradigmatic example of a complex cooperative system in nature. In particular swarm models can be used to explore the relation of nonequilibrium phase transitions to at least three important issues encountered in artificial life. Firstly, that of emergence as complex adaptive behavior. Secondly, as an exploration of continuous phase transitions in biological systems. Lastly, to derive behavioral criteria for the evolution of collective behavior in social organisms.
This paper shows that a new type of artificial neural network (ANN) -- the Simultaneous Recurrent Network (SRN) -- can, if properly trained, solve a difficult function approximation problem which conventional ANNs -- either feedforward or Hebbian -- cannot. This problem, the problem of generalized maze navigation, is typical of problems which arise in building true intelligent control systems using neural networks. (Such systems are discussed in the chapter by Werbos in K.Pribram, Brain and Values, Erlbaum 1998.) The paper provides a general review of other types of recurrent networks and alternative training techniques, including a flowchart of the Error Critic training design, arguable the only plausible approach to explain how the brain adapts time-lagged recurrent systems in real-time. The C code of the test is appended. As in the first tests of backprop, the training here was slow, but there are ways to do better after more experience using this type of network.
We introduce constraints necessary for type checking a higher-order concurrent constraint language, and solve them with an incremental algorithm. Our constraint system extends rational unification by constraints x$\subseteq$ y saying that ``$x$ has at least the structure of $y$'', modelled by a weak instance relation between trees. This notion of instance has been carefully chosen to be weaker than the usual one which renders semi-unification undecidable. Semi-unification has more than once served to link unification problems arising from type inference and those considered in computational linguistics. Just as polymorphic recursion corresponds to subsumption through the semi-unification problem, our type constraint problem corresponds to weak subsumption of feature graphs in linguistics. The decidability problem for \WhatsIt for feature graphs has been settled by D\"orre~\cite{Doerre:WeakSubsumption:94}. \nocite{RuppRosnerJohnson:94} In contrast to D\"orre's, our algorithm is fully incremental and does not refer to finite state automata. Our algorithm also is a lot more flexible. It allows a number of extensions (records, sorts, disjunctive types, type declarations, and others) which make it suitable for type inference of a full-fledged programming language.
This paper shows how agents' choice in communicative action can be designed to mitigate the effect of their resource limits in the context of particular features of a collaborative planning task. I first motivate a number of hypotheses about effective language behavior based on a statistical analysis of a corpus of natural collaborative planning dialogues. These hypotheses are then tested in a dialogue testbed whose design is motivated by the corpus analysis. Experiments in the testbed examine the interaction between (1) agents' resource limits in attentional capacity and inferential capacity; (2) agents' choice in communication; and (3) features of communicative tasks that affect task difficulty such as inferential complexity, degree of belief coordination required, and tolerance for errors. The results show that good algorithms for communication must be defined relative to the agents' resource limits and the features of the task. Algorithms that are inefficient for inferentially simple, low coordination or fault-tolerant tasks are effective when tasks require coordination or complex inferences, or are fault-intolerant. The results provide an explanation for the occurrence of utterances in human dialogues that, prima facie, appear inefficient, and provide the basis for the design of effective algorithms for communicative choice for resource limited agents.
Over the past thirty years, there has been considerable progress in the design of natural language interfaces to databases. Most of this work has concerned snapshot databases, in which there are only limited facilities for manipulating time-varying information. The database community is becoming increasingly interested in temporal databases, databases with special support for time-dependent entries. We have developed a framework for constructing natural language interfaces to temporal databases, drawing on research on temporal phenomena within logic and linguistics. The central part of our framework is a logic-like formal language, called TOP, which can capture the semantics of a wide range of English sentences. We have implemented an HPSG-based sentence analyser that converts a large set of English queries involving time into TOP formulae, and have formulated a provably correct procedure for translating TOP expressions into queries in the TSQL2 temporal database language. In this way we have established a sound route from English to a general-purpose temporal database language.
FASTUS is a system for extracting information from natural language text for entry into a database and for other applications. It works essentially as a cascaded, nondeterministic finite-state automaton. There are five stages in the operation of FASTUS. In Stage 1, names and other fixed form expressions are recognized. In Stage 2, basic noun groups, verb groups, and prepositions and some other particles are recognized. In Stage 3, certain complex noun groups and verb groups are constructed. Patterns for events of interest are identified in Stage 4 and corresponding ``event structures'' are built. In Stage 5, distinct event structures that describe the same event are identified and merged, and these are used in generating database entries. This decomposition of language processing enables the system to do exactly the right amount of domain-independent syntax, so that domain-dependent semantic and pragmatic processing can be applied to the right larger-scale structures. FASTUS is very efficient and effective, and has been used successfully in a number of applications.
Most existing natural language database interfaces (NLDBs) were designed to be used with database systems that provide very limited facilities for manipulating time-dependent data, and they do not support adequately temporal linguistic mechanisms (verb tenses, temporal adverbials, temporal subordinate clauses, etc.). The database community is becoming increasingly interested in temporal database systems, that are intended to store and manipulate in a principled manner information not only about the present, but also about the past and future. When interfacing to temporal databases, supporting temporal linguistic mechanisms becomes crucial. We present a framework for constructing natural language interfaces for temporal databases (NLTDBs), that draws on research in tense and aspect theories, temporal logics, and temporal databases. The framework consists of a temporal intermediate representation language, called TOP, an HPSG grammar that maps a wide range of questions involving temporal mechanisms to appropriate TOP expressions, and a provably correct method for translating from TOP to TSQL2, TSQL2 being a recently proposed temporal extension of the SQL database language. This framework was employed to implement a prototype NLTDB using ALE and Prolog.
This work has been motivated by two long term goals: to understand how humans learn language and to build programs that can understand language. Using a representation that makes the relevant features explicit is a prerequisite for successful learning and understanding. Therefore, I chose to represent relations between individual words explicitly in my model. Lexical attraction is defined as the likelihood of such relations. I introduce a new class of probabilistic language models named lexical attraction models which can represent long distance relations between words and I formalize this new class of models using information theory. Within the framework of lexical attraction, I developed an unsupervised language acquisition program that learns to identify linguistic relations in a given sentence. The only explicitly represented linguistic knowledge in the program is lexical attraction. There is no initial grammar or lexicon built in and the only input is raw text. Learning and processing are interdigitated. The processor uses the regularities detected by the learner to impose structure on the input. This structure enables the learner to detect higher level regularities. Using this bootstrapping procedure, the program was trained on 100 million words of Associated Press material and was able to achieve 60% precision and 50% recall in finding relations between content-words. Using knowledge of lexical attraction, the program can identify the correct relations in syntactically ambiguous sentences such as ``I saw the Statue of Liberty flying over New York.''
We introduce a highly structured family of hard satisfiable 3-SAT formulas corresponding to an ordered spin-glass model from statistical physics. This model has provably "glassy" behavior; that is, it has many local optima with large energy barriers between them, so that local search algorithms get stuck and have difficulty finding the true ground state, i.e., the unique satisfying assignment. We test the hardness of our formulas with two Davis-Putnam solvers, Satz and zChaff, the recently introduced Survey Propagation (SP), and two local search algorithms, Walksat and Record-to-Record Travel (RRT). We compare our formulas to random 3-XOR-SAT formulas and to two other generators of hard satisfiable instances, the minimum disagreement parity formulas of Crawford et al., and Hirsch's hgen. For the complete solvers the running time of our formulas grows exponentially in sqrt(n), and exceeds that of random 3-XOR-SAT formulas for small problem sizes. SP is unable to solve our formulas with as few as 25 variables. For Walksat, our formulas appear to be harder than any other known generator of satisfiable instances. Finally, our formulas can be solved efficiently by RRT but only if the parameter d is tuned to the height of the barriers between local minima, and we use this parameter to measure the barrier heights in random 3-XOR-SAT formulas as well.
Conditional logics play an important role in recent attempts to formulate theories of default reasoning. This paper investigates first-order conditional logic. We show that, as for first-order probabilistic logic, it is important not to confound statistical conditionals over the domain (such as ``most birds fly''), and subjective conditionals over possible worlds (such as ``I believe that Tweety is unlikely to fly''). We then address the issue of ascribing semantics to first-order conditional logic. As in the propositional case, there are many possible semantics. To study the problem in a coherent way, we use plausibility structures. These provide us with a general framework in which many of the standard approaches can be embedded. We show that while these standard approaches are all the same at the propositional level, they are significantly different in the context of a first-order language. Furthermore, we show that plausibilities provide the most natural extension of conditional logic to the first-order case: We provide a sound and complete axiomatization that contains only the KLM properties and standard axioms of first-order modal logic. We show that most of the other approaches have additional properties, which result in an inappropriate treatment of an infinitary version of the lottery paradox.
In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations ``eat a peach'' and ``eat a beach'' is more likely. Statistical NLP methods determine the likelihood of a word combination from its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in any given corpus. In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on ``most similar'' words. We describe probabilistic word association models based on distributional word similarity, and apply them to two tasks, language modeling and pseudo-word disambiguation. In the language modeling task, a similarity-based model is used to improve probability estimates for unseen bigrams in a back-off language model. The similarity-based method yields a 20% perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speech-recognition error. We also compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency to avoid giving too much weight to easy-to-disambiguate high-frequency configurations. The similarity-based methods perform up to 40% better on this particular task.
Several important decision problems on conjunctive queries (CQs) are NP-complete in general but become tractable, and actually highly parallelizable, if restricted to acyclic or nearly acyclic queries. Examples are the evaluation of Boolean CQs and query containment. These problems were shown tractable for conjunctive queries of bounded treewidth and of bounded degree of cyclicity. The so far most general concept of nearly acyclic queries was the notion of queries of bounded query-width introduced by Chekuri and Rajaraman (1997). While CQs of bounded query width are tractable, it remained unclear whether such queries are efficiently recognizable. Chekuri and Rajaraman stated as an open problem whether for each constant k it can be determined in polynomial time if a query has query width less than or equal to k. We give a negative answer by proving this problem NP-complete (specifically, for k=4). In order to circumvent this difficulty, we introduce the new concept of hypertree decomposition of a query and the corresponding notion of hypertree width. We prove: (a) for each k, the class of queries with query width bounded by k is properly contained in the class of queries whose hypertree width is bounded by k; (b) unlike query width, constant hypertree-width is efficiently recognizable; (c) Boolean queries of constant hypertree width can be efficiently evaluated.
Scheduling dialogs, during which people negotiate the times of appointments, are common in everyday life. This paper reports the results of an in-depth empirical investigation of resolving explicit temporal references in scheduling dialogs. There are four phases of this work: data annotation and evaluation, model development, system implementation and evaluation, and model evaluation and analysis. The system and model were developed primarily on one set of data, and then applied later to a much more complex data set, to assess the generalizability of the model for the task being performed. Many different types of empirical methods are applied to pinpoint the strengths and weaknesses of the approach. Detailed annotation instructions were developed and an intercoder reliability study was performed, showing that naive annotators can reliably perform the targeted annotations. A fully automatic system has been developed and evaluated on unseen test data, with good results on both data sets. We adopt a pure realization of a recency-based focus model to identify precisely when it is and is not adequate for the task being addressed. In addition to system results, an in-depth evaluation of the model itself is presented, based on detailed manual annotations. The results are that few errors occur specifically due to the model of focus being used, and the set of anaphoric relations defined in the model are low in ambiguity for both data sets.
The relationship between the Bayesian approach and the minimum description length approach is established. We sharpen and clarify the general modeling principles MDL and MML, abstracted as the ideal MDL principle and defined from Bayes's rule by means of Kolmogorov complexity. The basic condition under which the ideal principle should be applied is encapsulated as the Fundamental Inequality, which in broad terms states that the principle is valid when the data are random, relative to every contemplated hypothesis and also these hypotheses are random relative to the (universal) prior. Basically, the ideal principle states that the prior probability associated with the hypothesis should be given by the algorithmic universal probability, and the sum of the log universal probability of the model plus the log of the probability of the data given the model should be minimized. If we restrict the model class to the finite sets then application of the ideal principle turns into Kolmogorov's minimal sufficient statistic. In general we show that data compression is almost always the best strategy, both in hypothesis identification and prediction.
The paper argues that Fodor and Lepore are misguided in their attack on Pustejovsky's Generative Lexicon, largely because their argument rests on a traditional, but implausible and discredited, view of the lexicon on which it is effectively empty of content, a view that stands in the long line of explaining word meaning (a) by ostension and then (b) explaining it by means of a vacuous symbol in a lexicon, often the word itself after typographic transmogrification. (a) and (b) both share the wrong belief that to a word must correspond a simple entity that is its meaning. I then turn to the semantic rules that Pustejovsky uses and argue first that, although they have novel features, they are in a well-established Artificial Intelligence tradition of explaining meaning by reference to structures that mention other structures assigned to words that may occur in close proximity to the first. It is argued that Fodor and Lepore's view that there cannot be such rules is without foundation, and indeed systems using such rules have proved their practical worth in computational systems. Their justification descends from line of argument, whose high points were probably Wittgenstein and Quine that meaning is not to be understood by simple links to the world, ostensive or otherwise, but by the relationship of whole cultural representational structures to each other and to the world as a whole.
The abstract mathematical theory of partial differential equations (PDEs) is formulated in terms of manifolds, scalar fields, tensors, and the like, but these algebraic structures are hardly recognizable in actual PDE solvers. The general aim of the Sophus programming style is to bridge the gap between theory and practice in the domain of PDE solvers. Its main ingredients are a library of abstract datatypes corresponding to the algebraic structures used in the mathematical theory and an algebraic expression style similar to the expression style used in the mathematical theory. Because of its emphasis on abstract datatypes, Sophus is most naturally combined with object-oriented languages or other languages supporting abstract datatypes. The resulting source code patterns are beyond the scope of current compiler optimizations, but are sufficiently specific for a dedicated source-to-source optimizer. The limited, domain-specific, character of Sophus is the key to success here. This kind of optimization has been tested on computationally intensive Sophus style code with promising results. The general approach may be useful for other styles and in other application domains as well.
We consider the problem of how to design large decentralized multi-agent systems (MAS's) in an automated fashion, with little or no hand-tuning. Our approach has each agent run a reinforcement learning algorithm. This converts the problem into one of how to automatically set/update the reward functions for each of the agents so that the global goal is achieved. In particular we do not want the agents to ``work at cross-purposes'' as far as the global goal is concerned. We use the term artificial COllective INtelligence (COIN) to refer to systems that embody solutions to this problem. In this paper we present a summary of a mathematical framework for COINs. We then investigate the real-world applicability of the core concepts of that framework via two computer experiments: we show that our COINs perform near optimally in a difficult variant of Arthur's bar problem (and in particular avoid the tragedy of the commons for that problem), and we also illustrate optimal performance for our COINs in the leader-follower problem.
We study here constraint satisfaction problems that are based on predefined, explicitly given finite constraints. To solve them we propose a notion of rule consistency that can be expressed in terms of rules derived from the explicit representation of the initial constraints. This notion of local consistency is weaker than arc consistency for constraints of arbitrary arity but coincides with it when all domains are unary or binary. For Boolean constraints rule consistency coincides with the closure under the well-known propagation rules for Boolean constraints. By generalizing the format of the rules we obtain a characterization of arc consistency in terms of so-called inclusion rules. The advantage of rule consistency and this rule based characterization of the arc consistency is that the algorithms that enforce both notions can be automatically generated, as CHR rules. So these algorithms could be integrated into constraint logic programming systems such as Eclipse. We illustrate the usefulness of this approach to constraint propagation by discussing the implementations of both algorithms and their use on various examples, including Boolean constraints, three valued logic of Kleene, constraints dealing with Waltz's language for describing polyhedreal scenes, and Allen's qualitative approach to temporal logic.
The semantic framework for the modal logic of knowledge due to Halpern and Moses provides a way to ascribe knowledge to agents in distributed and multi-agent systems. In this paper we study two special cases of this framework: full systems and hypercubes. Both model static situations in which no agent has any information about another agent's state. Full systems and hypercubes are an appropriate model for the initial configurations of many systems of interest. We establish a correspondence between full systems and hypercube systems and certain classes of Kripke frames. We show that these classes of systems correspond to the same logic. Moreover, this logic is also the same as that generated by the larger class of weakly directed frames. We provide a sound and complete axiomatization, S5WDn, of this logic. Finally, we show that under certain natural assumptions, in a model where knowledge evolves over time, S5WDn characterizes the properties of knowledge not just at the initial configuration, but also at all later configurations. In particular, this holds for homogeneous broadcast systems, which capture settings in which agents are initially ignorant of each others local states, operate synchronously, have perfect recall and can communicate only by broadcasting.
In this paper, we focus on the problem of existence and computing of small and large stable models. We show that for every fixed integer k, there is a linear-time algorithm to decide the problem LSM (large stable models problem): does a logic program P have a stable model of size at least |P|-k. In contrast, we show that the problem SSM (small stable models problem) to decide whether a logic program P has a stable model of size at most k is much harder. We present two algorithms for this problem but their running time is given by polynomials of order depending on k. We show that the problem SSM is fixed-parameter intractable by demonstrating that it is W[2]-hard. This result implies that it is unlikely, an algorithm exists to compute stable models of size at most k that would run in time O(n^c), where c is a constant independent of k. We also provide an upper bound on the fixed-parameter complexity of the problem SSM by showing that it belongs to the class W[3].
Global SLS-resolution and SLG-resolution are two representative mechanisms for top-down evaluation of the well-founded semantics of general logic programs. Global SLS-resolution is linear for query evaluation but suffers from infinite loops and redundant computations. In contrast, SLG-resolution resolves infinite loops and redundant computations by means of tabling, but it is not linear. The principal disadvantage of a non-linear approach is that it cannot be implemented using a simple, efficient stack-based memory structure nor can it be easily extended to handle some strictly sequential operators such as cuts in Prolog. In this paper, we present a linear tabling method, called SLT-resolution, for top-down evaluation of the well-founded semantics. SLT-resolution is a substantial extension of SLDNF-resolution with tabling. Its main features include: (1) It resolves infinite loops and redundant computations while preserving the linearity. (2) It is terminating, and sound and complete w.r.t. the well-founded semantics for programs with the bounded-term-size property with non-floundering queries. Its time complexity is comparable with SLG-resolution and polynomial for function-free logic programs. (3) Because of its linearity for query evaluation, SLT-resolution bridges the gap between the well-founded semantics and standard Prolog implementation techniques. It can be implemented by an extension to any existing Prolog abstract machines such as WAM or ATOAM.
We introduced decomposable negation normal form (DNNF) recently as a tractable form of propositional theories, and provided a number of powerful logical operations that can be performed on it in polynomial time. We also presented an algorithm for compiling any conjunctive normal form (CNF) into DNNF and provided a structure-based guarantee on its space and time complexity. We present in this paper a linear-time algorithm for converting an ordered binary decision diagram (OBDD) representation of a propositional theory into an equivalent DNNF, showing that DNNFs scale as well as OBDDs. We also identify a subclass of DNNF which we call deterministic DNNF, d-DNNF, and show that the previous complexity guarantees on compiling DNNF continue to hold for this stricter subclass, which has stronger properties. In particular, we present a new operation on d-DNNF which allows us to count its models under the assertion, retraction and flipping of every literal by traversing the d-DNNF twice. That is, after such traversal, we can test in constant-time: the entailment of any literal by the d-DNNF, and the consistency of the d-DNNF under the retraction or flipping of any literal. We demonstrate the significance of these new operations by showing how they allow us to implement linear-time, complete truth maintenance systems and linear-time, complete belief revision systems for two important classes of propositional theories.
Infinite loops and redundant computations are long recognized open problems in Prolog. Two ways have been explored to resolve these problems: loop checking and tabling. Loop checking can cut infinite loops, but it cannot be both sound and complete even for function-free logic programs. Tabling seems to be an effective way to resolve infinite loops and redundant computations. However, existing tabulated resolutions, such as OLDT-resolution, SLG- resolution, and Tabulated SLS-resolution, are non-linear because they rely on the solution-lookup mode in formulating tabling. The principal disadvantage of non-linear resolutions is that they cannot be implemented using a simple stack-based memory structure like that in Prolog. Moreover, some strictly sequential operators such as cuts may not be handled as easily as in Prolog. In this paper, we propose a hybrid method to resolve infinite loops and redundant computations. We combine the ideas of loop checking and tabling to establish a linear tabulated resolution called TP-resolution. TP-resolution has two distinctive features: (1) It makes linear tabulated derivations in the same way as Prolog except that infinite loops are broken and redundant computations are reduced. It handles cuts as effectively as Prolog. (2) It is sound and complete for positive logic programs with the bounded-term-size property. The underlying algorithm can be implemented by an extension to any existing Prolog abstract machines such as WAM or ATOAM.
We present a general, consistency-based framework for belief change. Informally, in revising K by A, we begin with A and incorporate as much of K as consistently possible. Formally, a knowledge base K and sentence A are expressed, via renaming propositions in K, in separate languages. Using a maximization process, we assume the languages are the same insofar as consistently possible. Lastly, we express the resultant knowledge base in a single language. There may be more than one way in which A can be so extended by K: in choice revision, one such ``extension'' represents the revised state; alternately revision consists of the intersection of all such extensions. The most general formulation of our approach is flexible enough to express other approaches to revision and update, the merging of knowledge bases, and the incorporation of static and dynamic integrity constraints. Our framework differs from work based on ordinal conditional functions, notably with respect to iterated revision. We argue that the approach is well-suited for implementation: the choice revision operator gives better complexity results than general revision; the approach can be expressed in terms of a finite knowledge base; and the scope of a revision can be restricted to just those propositions mentioned in the sentence for revision A.
In solving a query, the SLD proof procedure for definite programs sometimes searches an infinite space for a non existing solution. For example, querying a planner for an unreachable goal state. Such programs motivate the development of methods to prove the absence of a solution. Considering the definite program and the query ``<- Q'' as clauses of a first order theory, one can apply model generators which search for a finite interpretation in which the program clauses as well as the clause ``false <- Q'' are true. This paper develops a new approach which exploits the fact that all clauses are definite. It is based on a goal directed abductive search in the space of finite pre-interpretations for a pre-interpretation such that ``Q'' is false in the least model of the program based on it. Several methods for efficiently searching the space of pre-interpretations are presented. Experimental results confirm that our approach find solutions with less search than with the use of a first order model generator.
We study here a natural situation when constraint programming can be entirely reduced to rule-based programming. To this end we explain first how one can compute on constraint satisfaction problems using rules represented by simple first-order formulas. Then we consider constraint satisfaction problems that are based on predefined, explicitly given constraints. To solve them we first derive rules from these explicitly given constraints and limit the computation process to a repeated application of these rules, combined with labeling.We consider here two types of rules. The first type, that we call equality rules, leads to a new notion of local consistency, called {\em rule consistency} that turns out to be weaker than arc consistency for constraints of arbitrary arity (called hyper-arc consistency in \cite{MS98b}). For Boolean constraints rule consistency coincides with the closure under the well-known propagation rules for Boolean constraints. The second type of rules, that we call membership rules, yields a rule-based characterization of arc consistency. To show feasibility of this rule-based approach to constraint programming we show how both types of rules can be automatically generated, as {\tt CHR} rules of \cite{fruhwirth-constraint-95}. This yields an implementation of this approach to programming by means of constraint logic programming. We illustrate the usefulness of this approach to constraint programming by discussing various examples, including Boolean constraints, two typical examples of many valued logics, constraints dealing with Waltz's language for describing polyhedral scenes, and Allen's qualitative approach to temporal logic.
Answer-set programming (ASP) has emerged recently as a viable programming paradigm well attuned to search problems in AI, constraint satisfaction and combinatorics. Propositional logic is, arguably, the simplest ASP system with an intuitive semantics supporting direct modeling of problem constraints. However, for some applications, especially those requiring that transitive closure be computed, it requires additional variables and results in large theories. Consequently, it may not be a practical computational tool for such problems. On the other hand, ASP systems based on nonmonotonic logics, such as stable logic programming, can handle transitive closure computation efficiently and, in general, yield very concise theories as problem representations. Their semantics is, however, more complex. Searching for the middle ground, in this paper we introduce a new nonmonotonic logic, DATALOG with constraints or DC. Informally, DC theories consist of propositional clauses (constraints) and of Horn rules. The semantics is a simple and natural extension of the semantics of the propositional logic. However, thanks to the presence of Horn rules in the system, modeling of transitive closure becomes straightforward. We describe the syntax and semantics of DC, and study its properties. We discuss an implementation of DC and present results of experimental study of the effectiveness of DC, comparing it with CSAT, a satisfiability checker and SMODELS implementation of stable logic programming. Our results show that DC is competitive with the other two approaches, in case of many search problems, often yielding much more efficient solutions.
The implicit theory that a simulation represents is precisely not in the individual choices but rather in the 'envelope' of possible trajectories - what is important is the shape of the whole envelope. Typically a huge amount of computation is required when experimenting with factors bearing on the dynamics of a simulation to tease out what affects the shape of this envelope. In this paper we present a methodology aimed at systematically exploring this envelope. We propose a method for searching for tendencies and proving their necessity relative to a range of parameterisations of the model and agents' choices, and to the logic of the simulation language. The exploration consists of a forward chaining generation of the trajectories associated to and constrained by such a range of parameterisations and choices. Additionally, we propose a computational procedure that helps implement this exploration by translating a Multi Agent System simulation into a constraint-based search over possible trajectories by 'compiling' the simulation rules into a more specific form, namely by partitioning the simulation rules using appropriate modularity in the simulation. An example of this procedure is exhibited. Keywords: Constraint Search, Constraint Logic Programming, Proof, Emergence, Tendencies
For years, Caisse des Depots et Consignations has produced information filtering applications. To be operational, these applications require high filtering performances which are achieved by using rule-based filters. With this technique, an administrator has to tune a set of rules for each topic. However, filters become obsolescent over time. The decrease of their performances is due to diachronic polysemy of terms that involves a loss of precision and to diachronic polymorphism of concepts that involves a loss of recall. To help the administrator to maintain his filters, we have developed a method which automatically detects filtering obsolescence. It consists in making a learning-based control filter using a set of documents which have already been categorised as relevant or not relevant by the rule-based filter. The idea is to supervise this filter by processing a differential comparison of its outcomes with those of the control one. This method has many advantages. It is simple to implement since the training set used by the learning is supplied by the rule-based filter. Thus, both the making and the use of the control filter are fully automatic. With automatic detection of obsolescence, learning-based filtering finds a rich application which offers interesting prospects.
For the TREC-8 routing, one specific filter is built for each topic. Each filter is a classifier trained to recognize the documents that are relevant to the topic. When presented with a document, each classifier estimates the probability for the document to be relevant to the topic for which it has been trained. Since the procedure for building a filter is topic-independent, the system is fully automatic. By making use of a sample of documents that have previously been evaluated as relevant or not relevant to a particular topic, a term selection is performed, and a neural network is trained. Each document is represented by a vector of frequencies of a list of selected terms. This list depends on the topic to be filtered; it is constructed in two steps. The first step defines the characteristic words used in the relevant documents of the corpus; the second one chooses, among the previous list, the most discriminant ones. The length of the vector is optimized automatically for each topic. At the end of the term selection, a vector of typically 25 words is defined for the topic, so that each document which has to be processed is represented by a vector of term frequencies. This vector is subsequently input to a classifier that is trained from the same sample. After training, the classifier estimates for each document of a test set its probability of being relevant; for submission to TREC, the top 1000 documents are ranked in order of decreasing relevance.
We describe a slightly sub-exponential time algorithm for learning parity functions in the presence of random classification noise. This results in a polynomial-time algorithm for the case of parity functions that depend on only the first O(log n log log n) bits of input. This is the first known instance of an efficient noise-tolerant algorithm for a concept class that is provably not learnable in the Statistical Query model of Kearns. Thus, we demonstrate that the set of problems learnable in the statistical query model is a strict subset of those problems learnable in the presence of noise in the PAC model. In coding-theory terms, what we give is a poly(n)-time algorithm for decoding linear k by n codes in the presence of random noise for the case of k = c log n loglog n for some c > 0. (The case of k = O(log n) is trivial since one can just individually check each of the 2^k possible messages and choose the one that yields the closest codeword.) A natural extension of the statistical query model is to allow queries about statistical properties that involve t-tuples of examples (as opposed to single examples). The second result of this paper is to show that any class of functions learnable (strongly or weakly) with t-wise queries for t = O(log n) is also weakly learnable with standard unary queries. Hence this natural extension to the statistical query model does not increase the set of weakly learnable functions.
Thinking is one of the most interesting mental processes. Its complexity is sometimes simplified and its different manifestations are classified into normal and abnormal, like the delusional and disorganized thought or the creative one. The boundaries between these facets of thinking are fuzzy causing difficulties in medical, academic, and philosophical discussions. Considering the dopaminergic signal-to-noise neuronal modulation in the central nervous system, and the existence of semantic maps in human brain, a self-organizing neural network model was developed to unify the different thought processes into a single neurocomputational substrate. Simulations were performed varying the dopaminergic modulation and observing the different patterns that emerged at the semantic map. Assuming that the thought process is the total pattern elicited at the output layer of the neural network, the model shows how the normal and abnormal thinking are generated and that there are no borders between their different manifestations. Actually, a continuum of different qualitative reasoning, ranging from delusion to disorganization of thought, and passing through the normal and the creative thinking, seems to be more plausible. The model is far from explaining the complexities of human thinking but, at least, it seems to be a good metaphorical and unifying view of the many facets of this phenomenon usually studied in separated settings.
We examine carefully the rationale underlying the approaches to belief change taken in the literature, and highlight what we view as methodological problems. We argue that to study belief change carefully, we must be quite explicit about the ``ontology'' or scenario underlying the belief change process. This is something that has been missing in previous work, with its focus on postulates. Our analysis shows that we must pay particular attention to two issues that have often been taken for granted: The first is how we model the agent's epistemic state. (Do we use a set of beliefs, or a richer structure, such as an ordering on worlds? And if we use a set of beliefs, in what language are these beliefs are expressed?) We show that even postulates that have been called ``beyond controversy'' are unreasonable when the agent's beliefs include beliefs about her own epistemic state as well as the external world. The second is the status of observations. (Are observations known to be true, or just believed? In the latter case, how firm is the belief?) Issues regarding the status of observations arise particularly when we consider iterated belief revision, and we must confront the possibility of revising by p and then by not-p.
We consider an approach to update nonmonotonic knowledge bases represented as extended logic programs under answer set semantics. New information is incorporated into the current knowledge base subject to a causal rejection principle enforcing that, in case of conflicts, more recent rules are preferred and older rules are overridden. Such a rejection principle is also exploited in other approaches to update logic programs, e.g., in dynamic logic programming by Alferes et al. We give a thorough analysis of properties of our approach, to get a better understanding of the causal rejection principle. We review postulates for update and revision operators from the area of theory change and nonmonotonic reasoning, and some new properties are considered as well. We then consider refinements of our semantics which incorporate a notion of minimality of change. As well, we investigate the relationship to other approaches, showing that our approach is semantically equivalent to inheritance programs by Buccafurri et al. and that it coincides with certain classes of dynamic logic programs, for which we provide characterizations in terms of graph conditions. Therefore, most of our results about properties of causal rejection principle apply to these approaches as well. Finally, we deal with computational complexity of our approach, and outline how the update semantics and its refinements can be implemented on top of existing logic programming engines.
The notion of arc consistency plays a central role in constraint satisfaction. It is known that the notion of local consistency can be extended to constraint optimisation problems defined by soft constraint frameworks based on an idempotent cost combination operator. This excludes non idempotent operators such as + which define problems which are very important in practical applications such as Max-CSP, where the aim is to minimize the number of violated constraints. In this paper, we show that using a weak additional axiom satisfied by most existing soft constraints proposals, it is possible to define a notion of soft arc consistency that extends the classical notion of arc consistency and this even in the case of non idempotent cost combination operators. A polynomial time algorithm for enforcing this soft arc consistency exists and its space and time complexities are identical to that of enforcing arc consistency in CSPs when the cost combination operator is strictly monotonic (for example Max-CSP). A directional version of arc consistency is potentially even stronger than the non-directional version, since it allows non local propagation of penalties. We demonstrate the utility of directional arc consistency by showing that it not only solves soft constraint problems on trees, but that it also implies a form of local optimality, which we call arc irreducibility.
Most recently, Answer Set Programming (ASP) is attracting interest as a new paradigm for problem solving. An important aspect which needs to be supported is the handling of preferences between rules, for which several approaches have been presented. In this paper, we consider the problem of implementing preference handling approaches by means of meta-interpreters in Answer Set Programming. In particular, we consider the preferred answer set approaches by Brewka and Eiter, by Delgrande, Schaub and Tompits, and by Wang, Zhou and Lin. We present suitable meta-interpreters for these semantics using DLV, which is an efficient engine for ASP. Moreover, we also present a meta-interpreter for the weakly preferred answer set approach by Brewka and Eiter, which uses the weak constraint feature of DLV as a tool for expressing and solving an underlying optimization problem. We also consider advanced meta-interpreters, which make use of graph-based characterizations and often allow for more efficient computations. Our approach shows the suitability of ASP in general and of DLV in particular for fast prototyping. This can be fruitfully exploited for experimenting with new languages and knowledge-representation formalisms.
We consider the question of whether collusion among bidders (a "bidding ring") can be supported in equilibrium of unrepeated first-price auctions. Unlike previous work on the topic such as that by McAfee and McMillan [1992] and Marshall and Marx [2007], we do not assume that non-colluding agents have perfect knowledge about the number of colluding agents whose bids are suppressed by the bidding ring, and indeed even allow for the existence of multiple cartels. Furthermore, while we treat the association of bidders with bidding rings as exogenous, we allow bidders to make strategic decisions about whether to join bidding rings when invited. We identify a bidding ring protocol that results in an efficient allocation in Bayes{Nash equilibrium, under which non-colluding agents bid straightforwardly, and colluding agents join bidding rings when invited and truthfully declare their valuations to the ring center. We show that bidding rings benefit ring centers and all agents, both members and non-members of bidding rings, at the auctioneer's expense. The techniques we introduce in this paper may also be useful for reasoning about other problems in which agents have asymmetric information about a setting.
This paper is aimed at providing a uniform framework for reasoning about beliefs of multiple agents and their fusion. In the first part of the paper, we develop logics for reasoning about cautiously merged beliefs of agents with different degrees of reliability. The logics are obtained by combining the multi-agent epistemic logic and multi-sources reasoning systems. Every ordering for the reliability of the agents is represented by a modal operator, so we can reason with the merged results under different situations. The fusion is cautious in the sense that if an agent's belief is in conflict with those of higher priorities, then his belief is completely discarded from the merged result. We consider two strategies for the cautious merging of beliefs. In the first one, if inconsistency occurs at some level, then all beliefs at the lower levels are discarded simultaneously, so it is called level cutting strategy. For the second one, only the level at which the inconsistency occurs is skipped, so it is called level skipping strategy. The formal semantics and axiomatic systems for these two strategies are presented. In the second part, we extend the logics both syntactically and semantically to cover some more sophisticated belief fusion and revision operators. While most existing approaches treat belief fusion operators as meta-level constructs, these operators are directly incorporated into our object logic language. Thus it is possible to reason not only with the merged results but also about the fusion process in our logics. The relationship of our extended logics with the conditional logics of belief revision is also discussed.
Previous works suggested the use of Branch and Bound techniques for finding the optimal allocation in (multi-unit) combinatorial auctions. They remarked that Linear Programming could provide a good upper-bound to the optimal allocation, but they went on using lighter and less tight upper-bound heuristics, on the ground that LP was too time-consuming to be used repetitively to solve large combinatorial auctions. We present the results of extensive experiments solving large (multi-unit) combinatorial auctions generated according to distributions proposed by different researchers. Our surprising conclusion is that Linear Programming is worth using. Investing almost all of one's computing time in using LP to bound from above the value of the optimal solution in order to prune aggressively pays off. We present a way to save on the number of calls to the LP routine and experimental results comparing different heuristics for choosing the bid to be considered next. Those results show that the ordering based on the square root of the size of the bids that was shown to be theoretically optimal in a previous paper by the authors performs surprisingly better than others in practice. Choosing to deal first with the bid with largest coefficient (typically 1) in the optimal solution of the relaxed LP problem, is also a good choice. The gap between the lower bound provided by greedy heuristics and the upper bound provided by LP is typically small and pruning is therefore extensive. For most distributions, auctions of a few hundred goods among a few thousand bids can be solved in practice. All experiments were run on a PC under Matlab.
Tarski gave a general semantics for deductive reasoning: a formula a may be deduced from a set A of formulas iff a holds in all models in which each of the elements of A holds. A more liberal semantics has been considered: a formula a may be deduced from a set A of formulas iff a holds in all of the "preferred" models in which all the elements of A hold. Shoham proposed that the notion of "preferred" models be defined by a partial ordering on the models of the underlying language. A more general semantics is described in this paper, based on a set of natural properties of choice functions. This semantics is here shown to be equivalent to a semantics based on comparing the relative "importance" of sets of models, by what amounts to a qualitative probability measure. The consequence operations defined by the equivalent semantics are then characterized by a weakening of Tarski's properties in which the monotonicity requirement is replaced by three weaker conditions. Classical propositional connectives are characterized by natural introduction-elimination rules in a nonmonotonic setting. Even in the nonmonotonic setting, one obtains classical propositional logic, thus showing that monotonicity is not required to justify classical propositional connectives.
We introduce a methodology and framework for expressing general preference information in logic programming under the answer set semantics. An ordered logic program is an extended logic program in which rules are named by unique terms, and in which preferences among rules are given by a set of atoms of form s < t where s and t are names. An ordered logic program is transformed into a second, regular, extended logic program wherein the preferences are respected, in that the answer sets obtained in the transformed program correspond with the preferred answer sets of the original program. Our approach allows the specification of dynamic orderings, in which preferences can appear arbitrarily within a program. Static orderings (in which preferences are external to a logic program) are a trivial restriction of the general dynamic case. First, we develop a specific approach to reasoning with preferences, wherein the preference ordering specifies the order in which rules are to be applied. We then demonstrate the wide range of applicability of our framework by showing how other approaches, among them that of Brewka and Eiter, can be captured within our framework. Since the result of each of these transformations is an extended logic program, we can make use of existing implementations, such as dlv and smodels. To this end, we have developed a publicly available compiler as a front-end for these programming systems.
In multiagent settings where the agents have different preferences, preference aggregation is a central issue. Voting is a general method for preference aggregation, but seminal results have shown that all general voting protocols are manipulable. One could try to avoid manipulation by using voting protocols where determining a beneficial manipulation is hard. Especially among computational agents, it is reasonable to measure this hardness by computational complexity. Some earlier work has been done in this area, but it was assumed that the number of voters and candidates is unbounded. We derive hardness results for practical multiagent settings where the number of candidates is small but the number of voters can be large. We show that with complete information about the others' votes, individual manipulation is easy, and coalitional manipulation is easy with unweighted voters. However, constructive coalitional manipulation with weighted voters is intractable for all of the voting protocols under study, except for the nonrandomized Cup. Destructive manipulation tends to be easier. Randomizing over instantiations of the protocols (such as schedules of the Cup protocol) can be used to make manipulation hard. Finally, we show that under weak assumptions, if weighted coalitional manipulation with complete information about the others' votes is hard in some voting protocol, then individual and unweighted manipulation is hard when there is uncertainty about the others' votes.
This paper studies the problem of modeling complex domains of actions and change within high-level action description languages. We investigate two main issues of concern: (a) can we represent complex domains that capture together different problems such as ramifications, non-determinism and concurrency of actions, at a high-level, close to the given natural ontology of the problem domain and (b) what features of such a representation can affect, and how, its computational behaviour. The paper describes the main problems faced in this representation task and presents the results of an empirical study, carried out through a series of controlled experiments, to analyze the computational performance of reasoning in these representations. The experiments compare different representations obtained, for example, by changing the basic ontology of the domain or by varying the degree of use of indirect effect laws through domain constraints. This study has helped to expose the main sources of computational difficulty in the reasoning and suggest some methodological guidelines for representing complex domains. Although our work has been carried out within one particular high-level description language, we believe that the results, especially those that relate to the problems of representation, are independent of the specific modeling language.
Nested logic programs have recently been introduced in order to allow for arbitrarily nested formulas in the heads and the bodies of logic program rules under the answer sets semantics. Nested expressions can be formed using conjunction, disjunction, as well as the negation as failure operator in an unrestricted fashion. This provides a very flexible and compact framework for knowledge representation and reasoning. Previous results show that nested logic programs can be transformed into standard (unnested) disjunctive logic programs in an elementary way, applying the negation as failure operator to body literals only. This is of great practical relevance since it allows us to evaluate nested logic programs by means of off-the-shelf disjunctive logic programming systems, like DLV. However, it turns out that this straightforward transformation results in an exponential blow-up in the worst-case, despite the fact that complexity results indicate that there is a polynomial translation among both formalisms. In this paper, we take up this challenge and provide a polynomial translation of logic programs with nested expressions into disjunctive logic programs. Moreover, we show that this translation is modular and (strongly) faithful. We have implemented both the straightforward as well as our advanced transformation; the resulting compiler serves as a front-end to DLV and is publicly available on the Web.
Groenendijk and Stokhof (1984, 1996; Groenendijk 1999) provide a logically attractive theory of the semantics of natural language questions, commonly referred to as the partition theory. Two central notions in this theory are entailment between questions and answerhood. For example, the question "Who is going to the party?" entails the question "Is John going to the party?", and "John is going to the party" counts as an answer to both. Groenendijk and Stokhof define these two notions in terms of partitions of a set of possible worlds. We provide a syntactic characterization of entailment between questions and answerhood . We show that answers are, in some sense, exactly those formulas that are built up from instances of the question. This result lets us compare the partition theory with other approaches to interrogation -- both linguistic analyses, such as Hamblin's and Karttunen's semantics, and computational systems, such as Prolog. Our comparison separates a notion of answerhood into three aspects: equivalence (when two questions or answers are interchangeable), atomic answers (what instances of a question count as answers), and compound answers (how answers compose).
A recently introduced general-purpose heuristic for finding high-quality solutions for many hard optimization problems is reviewed. The method is inspired by recent progress in understanding far-from-equilibrium phenomena in terms of {\em self-organized criticality,} a concept introduced to describe emergent complexity in physical systems. This method, called {\em extremal optimization,} successively replaces the value of extremely undesirable variables in a sub-optimal solution with new, random ones. Large, avalanche-like fluctuations in the cost function self-organize from this dynamics, effectively scaling barriers to explore local optima in distant neighborhoods of the configuration space while eliminating the need to tune parameters. Drawing upon models used to simulate the dynamics of granular media, evolution, or geology, extremal optimization complements approximation methods inspired by equilibrium statistical physics, such as {\em simulated annealing}. It may be but one example of applying new insights into {\em non-equilibrium phenomena} systematically to hard optimization problems. This method is widely applicable and so far has proved competitive with -- and even superior to -- more elaborate general-purpose heuristics on testbeds of constrained optimization problems with up to $10^5$ variables, such as bipartitioning, coloring, and satisfiability. Analysis of a suitable model predicts the only free parameter of the method in accordance with all experimental results.
This paper presents the DLV system, which is widely considered the state-of-the-art implementation of disjunctive logic programming, and addresses several aspects. As for problem solving, we provide a formal definition of its kernel language, function-free disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems. We then illustrate the usage of DLV as a tool for knowledge representation and reasoning, describing a new declarative programming methodology which allows one to encode complex problems (up to $\Delta^P_3$-complete problems) in a declarative fashion. On the foundational side, we provide a detailed analysis of the computational complexity of the language of DLV, and by deriving new complexity results we chart a complete picture of the complexity of this language and important fragments thereof. Furthermore, we illustrate the general architecture of the DLV system which has been influenced by these results. As for applications, we overview application front-ends which have been developed on top of DLV to solve specific knowledge representation tasks, and we briefly describe the main international projects investigating the potential of the system for industrial exploitation. Finally, we report about thorough experimentation and benchmarking, which has been carried out to assess the efficiency of the system. The experimental results confirm the solidity of DLV and highlight its potential for emerging application areas like knowledge management and information integration.
With the inclusion of an effective methodology, this article answers in detail a question that, for a quarter of a century, remained open despite intense study by various researchers. Is the formula XCB = e(x,e(e(e(x,y),e(z,y)),z)) a single axiom for the classical equivalential calculus when the rules of inference consist of detachment (modus ponens) and substitution? Where the function e represents equivalence, this calculus can be axiomatized quite naturally with the formulas e(x,x), e(e(x,y),e(y,x)), and e(e(x,y),e(e(y,z),e(x,z))), which correspond to reflexivity, symmetry, and transitivity, respectively. (We note that e(x,x) is dependent on the other two axioms.) Heretofore, thirteen shortest single axioms for classical equivalence of length eleven had been discovered, and XCB was the only remaining formula of that length whose status was undetermined. To show that XCB is indeed such a single axiom, we focus on the rule of condensed detachment, a rule that captures detachment together with an appropriately general, but restricted, form of substitution. The proof we present in this paper consists of twenty-five applications of condensed detachment, completing with the deduction of transitivity followed by a deduction of symmetry. We also discuss some factors that may explain in part why XCB resisted relinquishing its treasure for so long. Our approach relied on diverse strategies applied by the automated reasoning program OTTER. Thus ends the search for shortest single axioms for the equivalential calculus.
Answer-set programming (ASP) paradigm is a way of using logic to solve search problems. Given a search problem, to solve it one designs a theory in the logic so that models of this theory represent problem solutions. To compute a solution to a problem one needs to compute a model of the corresponding theory. Several answer-set programming formalisms have been developed on the basis of logic programming with the semantics of stable models. In this paper we show that also the logic of predicate calculus gives rise to effective implementations of the ASP paradigm, similar in spirit to logic programming with stable model semantics and with a similar scope of applicability. Specifically, we propose two logics based on predicate calculus as formalisms for encoding search problems. We show that the expressive power of these logics is given by the class NP-search. We demonstrate how to use them in programming and develop computational tools for model finding. In the case of one of the logics our techniques reduce the problem to that of propositional satisfiability and allow one to use off-the-shelf satisfiability solvers. The language of the other logic has more complex syntax and provides explicit means to model some high-level constraints. For theories in this logic, we designed our own solver that takes advantage of the expanded syntax. We present experimental results demonstrating computational effectiveness of the overall approach.
This paper begins with a general theory of error in cross-validation testing of algorithms for supervised learning from examples. It is assumed that the examples are described by attribute-value pairs, where the values are symbolic. Cross-validation requires a set of training examples and a set of testing examples. The value of the attribute that is to be predicted is known to the learner in the training set, but unknown in the testing set. The theory demonstrates that cross-validation error has two components: error on the training set (inaccuracy) and sensitivity to noise (instability). This general theory is then applied to voting in instance-based learning. Given an example in the testing set, a typical instance-based learning algorithm predicts the designated attribute by voting among the k nearest neighbors (the k most similar examples) to the testing example in the training set. Voting is intended to increase the stability (resistance to noise) of instance-based learning, but a theoretical analysis shows that there are circumstances in which voting can be destabilizing. The theory suggests ways to minimize cross-validation error, by insuring that voting is stable and does not adversely affect accuracy.
This paper first analyzes the resolution complexity of two random CSP models (i.e. Model RB/RD) for which we can establish the existence of phase transitions and identify the threshold points exactly. By encoding CSPs into CNF formulas, it is proved that almost all instances of Model RB/RD have no tree-like resolution proofs of less than exponential size. Thus, we not only introduce new families of CNF formulas hard for resolution, which is a central task of Proof-Complexity theory, but also propose models with both many hard instances and exact phase transitions. Then, the implications of such models are addressed. It is shown both theoretically and experimentally that an application of Model RB/RD might be in the generation of hard satisfiable instances, which is not only of practical importance but also related to some open problems in cryptography such as generating one-way functions. Subsequently, a further theoretical support for the generation method is shown by establishing exponential lower bounds on the complexity of solving random satisfiable and forced satisfiable instances of RB/RD near the threshold. Finally, conclusions are presented, as well as a detailed comparison of Model RB/RD with the Hamiltonian cycle problem and random 3-SAT, which, respectively, exhibit three different kinds of phase transition behavior in NP-complete problems.
The paper studies an implementation methodology for partial and disjunctive stable models where partiality and disjunctions are unfolded from a logic program so that an implementation of stable models for normal (disjunction-free) programs can be used as the core inference engine. The unfolding is done in two separate steps. Firstly, it is shown that partial stable models can be captured by total stable models using a simple linear and modular program transformation. Hence, reasoning tasks concerning partial stable models can be solved using an implementation of total stable models. Disjunctive partial stable models have been lacking implementations which now become available as the translation handles also the disjunctive case. Secondly, it is shown how total stable models of disjunctive programs can be determined by computing stable models for normal programs. Hence, an implementation of stable models of normal programs can be used as a core engine for implementing disjunctive programs. The feasibility of the approach is demonstrated by constructing a system for computing stable models of disjunctive programs using the smodels system as the core engine. The performance of the resulting system is compared to that of dlv which is a state-of-the-art special purpose system for disjunctive programs.
It has been shown that a neural network model recently proposed to describe basic memory performance is based on a ternary/binary coding/decoding algorithm which leads to a new neural network assembly memory model (NNAMM) providing maximum-likelihood recall/recognition properties and implying a new memory unit architecture with Hopfield two-layer network, N-channel time gate, auxiliary reference memory, and two nested feedback loops. For the data coding used, conditions are found under which a version of Hopfied network implements maximum-likelihood convolutional decoding algorithm and, simultaneously, linear statistical classifier of arbitrary binary vectors with respect to Hamming distance between vector analyzed and reference vector given. In addition to basic memory performance and etc, the model explicitly describes the dependence on time of memory trace retrieval, gives a possibility of one-trial learning, metamemory simulation, generalized knowledge representation, and distinct description of conscious and unconscious mental processes. It has been shown that an assembly memory unit may be viewed as a model of a smallest inseparable part or an 'atom' of consciousness. Some nontraditional neurobiological backgrounds (dynamic spatiotemporal synchrony, properties of time dependent and error detector neurons, early precise spike firing, etc) and the model's application to solve some interdisciplinary problems from different scientific fields are discussed.
The study of Complex Systems is considered by many to be a new scientific field, and is distinguished by being a discipline that has applications within many separate areas of scientific study. The study of Neural Networks, Traffic Patterns, Artificial Intelligence, Social Systems, and many other scientific areas can all be considered to fall within the realm of Complex Systems, and can be studied from this new perspective. The advent of more capable computer systems has allowed these systems to be simulated and modeled with far greater ease, and new understanding of computer modeling approaches has allowed the fledgling science to be studied as never before. The preliminary focus of this paper will be to provide a general overview of the science of Complex Systems, including terminology, definitions, history, and examples. I will attempt to look at some of the most important trends in different areas of research, and give a general overview of research methods that have been used in parallel with computer modeling. Also, I will further define the areas of the science that concern themselves with computer modeling and simulation, and I will attempt to make it clear why the science only came into its own when the proper modeling and simulation tools were finally available. In addition, although there seems to be general agreement between different authors and institutes regarding the generalities of the study, there are some differences in terminology and methodology. I have attempted in this paper to bring as many elements together as possible, as far as the scope of the subject is concerned, without losing focus by studying Complex System techniques that are bound to one particular area of scientific study, unless that area is that of computer modeling.
When reasoning with uncertainty there are many situations where evidences are not only uncertain but their propositions may also be weakly specified in the sense that it may not be certain to which event a proposition is referring. It is then crucial not to combine such evidences in the mistaken belief that they are referring to the same event. This situation would become manageable if the evidences could be clustered into subsets representing events that should be handled separately. In an earlier article we established within Dempster-Shafer theory a criterion function called the metaconflict function. With this criterion we can partition a set of evidences into subsets. Each subset representing a separate event. In this article we will not only find the most plausible subset for each piece of evidence, we will also find the plausibility for every subset that the evidence belongs to the subset. Also, when the number of subsets are uncertain we aim to find a posterior probability distribution regarding the number of subsets.
Thomas M. Strat has developed a decision-theoretic apparatus for Dempster-Shafer theory (Decision analysis using belief functions, Intern. J. Approx. Reason. 4(5/6), 391-417, 1990). In this apparatus, expected utility intervals are constructed for different choices. The choice with the highest expected utility is preferable to others. However, to find the preferred choice when the expected utility interval of one choice is included in that of another, it is necessary to interpolate a discerning point in the intervals. This is done by the parameter rho, defined as the probability that the ambiguity about the utility of every nonsingleton focal element will turn out as favorable as possible. If there are several different decision makers, we might sometimes be more interested in having the highest expected utility among the decision makers rather than only trying to maximize our own expected utility regardless of choices made by other decision makers. The preference of each choice is then determined by the probability of yielding the highest expected utility. This probability is equal to the maximal interval length of rho under which an alternative is preferred. We must here take into account not only the choices already made by other decision makers but also the rational choices we can assume to be made by later decision makers. In Strats apparatus, an assumption, unwarranted by the evidence at hand, has to be made about the value of rho. We demonstrate that no such assumption is necessary. It is sufficient to assume a uniform probability distribution for rho to be able to discern the most preferable choice. We discuss when this approach is justifiable.
Currently, there is renewed interest in the problem, raised by Shafer in 1985, of updating probabilities when observations are incomplete. This is a fundamental problem in general, and of particular interest for Bayesian networks. Recently, Grunwald and Halpern have shown that commonly used updating strategies fail in this case, except under very special assumptions. In this paper we propose a new method for updating probabilities with incomplete observations. Our approach is deliberately conservative: we make no assumptions about the so-called incompleteness mechanism that associates complete with incomplete observations. We model our ignorance about this mechanism by a vacuous lower prevision, a tool from the theory of imprecise probabilities, and we use only coherence arguments to turn prior into posterior probabilities. In general, this new approach to updating produces lower and upper posterior probabilities and expectations, as well as partially determinate decisions. This is a logical consequence of the existing ignorance about the incompleteness mechanism. We apply the new approach to the problem of classification of new evidence in probabilistic expert systems, where it leads to a new, so-called conservative updating rule. In the special case of Bayesian networks constructed using expert knowledge, we provide an exact algorithm for classification based on our updating rule, which has linear-time complexity for a class of networks wider than polytrees. This result is then extended to the more general framework of credal networks, where computations are often much harder than with Bayesian nets. Using an example, we show that our rule appears to provide a solid basis for reliable updating with incomplete observations, when no strong assumptions about the incompleteness mechanism are justified.
Solomonoff unified Occam's razor and Epicurus' principle of multiple explanations to one elegant, formal, universal theory of inductive inference, which initiated the field of algorithmic information theory. His central result is that the posterior of his universal semimeasure M converges rapidly to the true sequence generating posterior mu, if the latter is computable. Hence, M is eligible as a universal predictor in case of unknown mu. We investigate the existence and convergence of computable universal (semi)measures for a hierarchy of computability classes: finitely computable, estimable, enumerable, and approximable. For instance, M is known to be enumerable, but not finitely computable, and to dominate all enumerable semimeasures. We define seven classes of (semi)measures based on these four computability concepts. Each class may or may not contain a (semi)measure which dominates all elements of another class. The analysis of these 49 cases can be reduced to four basic cases, two of them being new. The results hold for discrete and continuous semimeasures. We also investigate more closely the types of convergence, possibly implied by universality: in difference and in ratio, with probability 1, in mean sum, and for Martin-Loef random sequences. We introduce a generalized concept of randomness for individual sequences and use it to exhibit difficulties regarding these issues.
We give a purely model-theoretic characterization of the semantics of logic programs with negation-as-failure allowed in clause bodies. In our semantics the meaning of a program is, as in the classical case, the unique minimum model in a program-independent ordering. We use an expanded truth domain that has an uncountable linearly ordered set of truth values between False (the minimum element) and True (the maximum), with a Zero element in the middle. The truth values below Zero are ordered like the countable ordinals. The values above Zero have exactly the reverse order. Negation is interpreted as reflection about Zero followed by a step towards Zero; the only truth value that remains unaffected by negation is Zero. We show that every program has a unique minimum model M_P, and that this model can be constructed with a T_P iteration which proceeds through the countable ordinals. Furthermore, we demonstrate that M_P can also be obtained through a model intersection construction which generalizes the well-known model intersection theorem for classical logic programming. Finally, we show that by collapsing the true and false values of the infinite-valued model M_P to (the classical) True and False, we obtain a three-valued model identical to the well-founded one.
Coalition formation is a key problem in automated negotiation among self-interested agents, and other multiagent applications. A coalition of agents can sometimes accomplish things that the individual agents cannot, or can do things more efficiently. However, motivating the agents to abide to a solution requires careful analysis: only some of the solutions are stable in the sense that no group of agents is motivated to break off and form a new coalition. This constraint has been studied extensively in cooperative game theory. However, the computational questions around this constraint have received less attention. When it comes to coalition formation among software agents (that represent real-world parties), these questions become increasingly explicit. In this paper we define a concise general representation for games in characteristic form that relies on superadditivity, and show that it allows for efficient checking of whether a given outcome is in the core. We then show that determining whether the core is nonempty is $\mathcal{NP}$-complete both with and without transferable utility. We demonstrate that what makes the problem hard in both cases is determining the collaborative possibilities (the set of outcomes possible for the grand coalition), by showing that if these are given, the problem becomes tractable in both cases. However, we then demonstrate that for a hybrid version of the problem, where utility transfer is possible only within the grand coalition, the problem remains $\mathcal{NP}$-complete even when the collaborative possibilities are given.
Voting is a general method for preference aggregation in multiagent settings, but seminal results have shown that all (nondictatorial) voting protocols are manipulable. One could try to avoid manipulation by using voting protocols where determining a beneficial manipulation is hard computationally. A number of recent papers study the complexity of manipulating existing protocols. This paper is the first work to take the next step of designing new protocols that are especially hard to manipulate. Rather than designing these new protocols from scratch, we instead show how to tweak existing protocols to make manipulation hard, while leaving much of the original nature of the protocol intact. The tweak studied consists of adding one elimination preround to the election. Surprisingly, this extremely simple and universal tweak makes typical protocols hard to manipulate! The protocols become NP-hard, #P-hard, or PSPACE-hard to manipulate, depending on whether the schedule of the preround is determined before the votes are collected, after the votes are collected, or the scheduling and the vote collecting are interleaved, respectively. We prove general sufficient conditions on the protocols for this tweak to introduce the hardness, and show that the most common voting protocols satisfy those conditions. These are the first results in voting settings where manipulation is in a higher complexity class than NP (presuming PSPACE $\neq$ NP).
The aim of this work is to provide a family of qualitative theories for spatial change in general, and for motion of spatial scenes in particular. To achieve this, we consider a spatio-temporalisation MTALC(D_x), of the well-known ALC(D) family of Description Logics (DLs) with a concrete domainan. In particular, the concrete domain D_x is generated by a qualitative spatial Relation Algebra (RA) x. We show the important result that satisfiability of an MTALC(D_x) concept with respect to a weakly cyclic TBox is decidable in nondeterministic exponential time, by reducing it to the emptiness problem of a weak alternating automaton augmented with spatial constraints, which we show to remain decidable, although the accepting condition of a run involves, additionally to the standard case, consistency of a CSP (Constraint Satisfaction Problem) potentially infinite. The result provides an effective tableaux-like satisfiability procedure which is discussed.
We define a ternary Relation Algebra (RA) of relative position relations on two-dimensional directed lines (d-lines for short). A d-line has two degrees of freedom (DFs): a rotational DF (RDF), and a translational DF (TDF). The representation of the RDF of a d-line will be handled by an RA of 2D orientations, CYC_t, known in the literature. A second algebra, TA_t, which will handle the TDF of a d-line, will be defined. The two algebras, CYC_t and TA_t, will constitute, respectively, the translational and the rotational components of the RA, PA_t, of relative position relations on d-lines: the PA_t atoms will consist of those pairs of a TA_t atom and a CYC_t atom that are compatible. We present in detail the RA PA_t, with its converse table, its rotation table and its composition tables. We show that a (polynomial) constraint propagation algorithm, known in the literature, is complete for a subset of PA_t relations including almost all of the atomic relations. We will discuss the application scope of the RA, which includes incidence geometry, GIS (Geographic Information Systems), shape representation, localisation in (multi-)robot navigation, and the representation of motion prepositions in NLP (Natural Language Processing). We then compare the RA to existing ones, such as an algebra for reasoning about rectangles parallel to the axes of an (orthogonal) coordinate system, a ``spatial Odyssey'' of Allen's interval algebra, and an algebra for reasoning about 2D segments.
This paper describes how the elements of the SP theory (Wolff, 2003a) may be realised with neural structures and processes. To the extent that this is successful, the insights that have been achieved in the SP theory - the integration and simplification of a range of phenomena in perception and cognition - may be incorporated in a neural view of brain function. These proposals may be seen as a development of Hebb's (1949) concept of a 'cell assembly'. By contrast with that concept and variants of it, the version described in this paper proposes that any one neuron can belong in one assembly and only one assembly. A distinctive feature of the present proposals is that any neuron or cluster of neurons within a cell assembly may serve as a proxy or reference for another cell assembly or class of cell assemblies. This device provides solutions to many of the problems associated with cell assemblies, it allows information to be stored in a compressed form, and it provides a robust mechanism by which assemblies may be connected to form hierarchies, grammars and other kinds of knowledge structure. Drawing on insights derived from the SP theory, the paper also describes how unsupervised learning may be achieved with neural structures and processes. This theory of learning overcomes weaknesses in the Hebbian concept of learning and it is, at the same time, compatible with the observations that Hebb's theory was designed to explain.
Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. A limitation of previous keyphrase extraction algorithms is that the selected keyphrases are occasionally incoherent. That is, the majority of the output keyphrases may fit together well, but there may be a minority that appear to be outliers, with no clear semantic relation to the majority or to each other. This paper presents enhancements to the Kea keyphrase extraction algorithm that are designed to increase the coherence of the extracted keyphrases. The approach is to use the degree of statistical association among candidate keyphrases as evidence that they may be semantically related. The statistical association is measured using web mining. Experiments demonstrate that the enhancements improve the quality of the extracted keyphrases. Furthermore, the enhancements are not domain-specific: the algorithm generalizes well when it is trained on one domain (computer science documents) and tested on another (physics documents).
A ternary/binary data coding algorithm and conditions under which Hopfield networks implement optimal convolutional or Hamming decoding algorithms has been described. Using the coding/decoding approach (an optimal Binary Signal Detection Theory, BSDT) introduced a Neural Network Assembly Memory Model (NNAMM) is built. The model provides optimal (the best) basic memory performance and demands the use of a new memory unit architecture with two-layer Hopfield network, N-channel time gate, auxiliary reference memory, and two nested feedback loops. NNAMM explicitly describes the dependence on time of a memory trace retrieval, gives a possibility of metamemory simulation, generalized knowledge representation, and distinct description of conscious and unconscious mental processes. A model of smallest inseparable part or an "atom" of consciousness is also defined. The NNAMM's neurobiological backgrounds and its applications to solving some interdisciplinary problems are shortly discussed. BSDT could implement the "best neural code" used in nervous tissues of animals and humans.
We present a new method for clustering based on compression. The method doesn't use subject-specific features or background knowledge, and works as follows: First, we determine a universal similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is universal in that it is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, co-developed by one of the authors, is provably optimal but uses the non-computable notion of Kolmogorov complexity. We propose precise notions of similarity metric, normal compressor, and show that the NCD based on a normal compressor is a similarity metric that approximates universality. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (binary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors. In genomics we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis.
We discuss the computational complexity of random 2D Ising spin glasses, which represent an interesting class of constraint satisfaction problems for black box optimization. Two extremal cases are considered: (1) the +/- J spin glass, and (2) the Gaussian spin glass. We also study a smooth transition between these two extremal cases. The computational complexity of all studied spin glass systems is found to be dominated by rare events of extremely hard spin glass samples. We show that complexity of all studied spin glass systems is closely related to Frechet extremal value distribution. In a hybrid algorithm that combines the hierarchical Bayesian optimization algorithm (hBOA) with a deterministic bit-flip hill climber, the number of steps performed by both the global searcher (hBOA) and the local searcher follow Frechet distributions. Nonetheless, unlike in methods based purely on local search, the parameters of these distributions confirm good scalability of hBOA with local search. We further argue that standard performance measures for optimization algorithms--such as the average number of evaluations until convergence--can be misleading. Finally, our results indicate that for highly multimodal constraint satisfaction problems, such as Ising spin glasses, recombination-based search can provide qualitatively better results than mutation-based search.
Mutual information is widely used, in a descriptive way, to measure the stochastic dependence of categorical random variables. In order to address questions such as the reliability of the descriptive value, one must consider sample-to-population inferential approaches. This paper deals with the posterior distribution of mutual information, as obtained in a Bayesian framework by a second-order Dirichlet prior distribution. The exact analytical expression for the mean, and analytical approximations for the variance, skewness and kurtosis are derived. These approximations have a guaranteed accuracy level of the order O(1/n^3), where n is the sample size. Leading order approximations for the mean and the variance are derived in the case of incomplete samples. The derived analytical expressions allow the distribution of mutual information to be approximated reliably and quickly. In fact, the derived expressions can be computed with the same order of complexity needed for descriptive mutual information. This makes the distribution of mutual information become a concrete alternative to descriptive mutual information in many applications which would benefit from moving to the inductive side. Some of these prospective applications are discussed, and one of them, namely feature selection, is shown to perform significantly better when inductive mutual information is used.
One proposes a first alternative rule of combination to WAO (Weighted Average Operator) proposed recently by Josang, Daniel and Vannoorenberghe, called Proportional Conflict Redistribution rule (denoted PCR1). PCR1 and WAO are particular cases of WO (the Weighted Operator) because the conflicting mass is redistributed with respect to some weighting factors. In this first PCR rule, the proportionalization is done for each non-empty set with respect to the non-zero sum of its corresponding mass matrix - instead of its mass column average as in WAO, but the results are the same as Ph. Smets has pointed out. Also, we extend WAO (which herein gives no solution) for the degenerate case when all column sums of all non-empty sets are zero, and then the conflicting mass is transferred to the non-empty disjunctive form of all non-empty sets together; but if this disjunctive form happens to be empty, then one considers an open world (i.e. the frame of discernment might contain new hypotheses) and thus all conflicting mass is transferred to the empty set. In addition to WAO, we propose a general formula for PCR1 (WAO for non-degenerate cases).
The development of effective knowledge discovery techniques has become in the recent few years a very active research area due to the important impact it has in several relevant application areas. One interesting task thereof is that of singling out anomalous individuals from a given population, e.g., to detect rare events in time-series analysis settings, or to identify objects whose behavior is deviant w.r.t. a codified standard set of "social" rules. Such exceptional individuals are usually referred to as outliers in the literature. Recently, outlier detection has also emerged as a relevant KR&R problem. In this paper, we formally state the concept of outliers by generalizing in several respects an approach recently proposed in the context of default logic, for instance, by having outliers not being restricted to single individuals but, rather, in the more general case, to correspond to entire (sub)theories. We do that within the context of logic programming and, mainly through examples, we discuss its potential practical impact in applications. The formalization we propose is a novel one and helps in shedding some light on the real nature of outliers. Moreover, as a major contribution of this work, we illustrate the exploitation of minimality criteria in outlier detection. The computational complexity of outlier detection problems arising in this novel setting is thoroughly investigated and accounted for in the paper as well. Finally, we also propose a rewriting algorithm that transforms any outlier detection problem into an equivalent inference problem under the stable model semantics, thereby making outlier computation effective and realizable on top of any stable model solver.
Normal forms for logic programs under stable/answer set semantics are introduced. We argue that these forms can simplify the study of program properties, mainly consistency. The first normal form, called the {\em kernel} of the program, is useful for studying existence and number of answer sets. A kernel program is composed of the atoms which are undefined in the Well-founded semantics, which are those that directly affect the existence of answer sets. The body of rules is composed of negative literals only. Thus, the kernel form tends to be significantly more compact than other formulations. Also, it is possible to check consistency of kernel programs in terms of colorings of the Extended Dependency Graph program representation which we previously developed. The second normal form is called {\em 3-kernel.} A 3-kernel program is composed of the atoms which are undefined in the Well-founded semantics. Rules in 3-kernel programs have at most two conditions, and each rule either belongs to a cycle, or defines a connection between cycles. 3-kernel programs may have positive conditions. The 3-kernel normal form is very useful for the static analysis of program consistency, i.e., the syntactic characterization of existence of answer sets. This result can be obtained thanks to a novel graph-like representation of programs, called Cycle Graph which presented in the companion article \cite{Cos04b}.
Consider the problem of tracking a set of moving targets. Apart from the tracking result, it is often important to know where the tracking fails, either to steer sensors to that part of the state-space, or to inform a human operator about the status and quality of the obtained information. An intuitive quality measure is the correlation between two tracking results based on uncorrelated observations. In the case of Bayesian trackers such a correlation measure could be the Kullback-Leibler difference. We focus on a scenario with a large number of military units moving in some terrain. The units are observed by several types of sensors and "meta-sensors" with force aggregation capabilities. The sensors register units of different size. Two separate multi-target probability hypothesis density (PHD) particle filters are used to track some type of units (e.g., companies) and their sub-units (e.g., platoons), respectively, based on observations of units of those sizes. Each observation is used in one filter only. Although the state-space may well be the same in both filters, the posterior PHD distributions are not directly comparable -- one unit might correspond to three or four spatially distributed sub-units. Therefore, we introduce a mapping function between distributions for different unit size, based on doctrine knowledge of unit configuration. The mapped distributions can now be compared -- locally or globally -- using some measure, which gives the correlation between two PHD distributions in a bounded volume of the state-space. To locate areas where the tracking fails, a discretized quality map of the state-space can be generated by applying the measure locally to different parts of the space.
Segmentation of a colour image composed of different kinds of texture regions can be a hard problem, namely to compute for an exact texture fields and a decision of the optimum number of segmentation areas in an image when it contains similar and/or unstationary texture fields. In this work, a method is described for evolving adaptive procedures for these problems. In many real world applications data clustering constitutes a fundamental issue whenever behavioural or feature domains can be mapped into topological domains. We formulate the segmentation problem upon such images as an optimisation problem and adopt evolutionary strategy of Genetic Algorithms for the clustering of small regions in colour feature space. The present approach uses k-Means unsupervised clustering methods into Genetic Algorithms, namely for guiding this last Evolutionary Algorithm in his search for finding the optimal or sub-optimal data partition, task that as we know, requires a non-trivial search because of its intrinsic NP-complete nature. To solve this task, the appropriate genetic coding is also discussed, since this is a key aspect in the implementation. Our purpose is to demonstrate the efficiency of Genetic Algorithms to automatic and unsupervised texture segmentation. Some examples in Colour Maps, Ornamental Stones and in Human Skin Mark segmentation are presented and overall results discussed. KEYWORDS: Genetic Algorithms, Colour Image Segmentation, Classification, Clustering.
In the absence of a pure noise-free image it is hard to define what noise is, in any original noisy image, and as a consequence also where it is, and in what amount. In fact, the definition of noise depends largely on our own aim in the whole image analysis process, and (perhaps more important) in our self-perception of noise. For instance, when we perceive noise as disconnected and small it is normal to use MM-ASF filters to treat it. There is two evidences of this. First, in many instances there is no ideal and pure noise-free image to compare our filtering process (nothing but our self-perception of its pure image); second, and related with this first point, MM transformations that we chose are only based on our self - and perhaps - fuzzy notion. The present proposal combines the results of two MM filtering transformations (FT1, FT2) and makes use of some measures and quantitative relations on their Size/Intensity Diagrams to find the most appropriate noise removal process. Results can also be used for finding the most appropriate stop criteria, and the right sequence of MM operators combination on Alternating Sequential Filters (ASF), if these measures are applied, for instance, on a Genetic Algorithm's target function.
This paper introduces a fundamental result, which is relevant for Answer Set programming, and planning. For the first time since the definition of the stable model semantics, the class of logic programs for which a stable model exists is given a syntactic characterization. This condition may have a practical importance both for defining new algorithms for checking consistency and computing answer sets, and for improving the existing systems. The approach of this paper is to introduce a new canonical form (to which any logic program can be reduced to), to focus the attention on cyclic dependencies. The technical result is then given in terms of programs in canonical form (canonical programs), without loss of generality. The result is based on identifying the cycles contained in the program, showing that stable models of the overall program are composed of stable models of suitable sub-programs, corresponding to the cycles, and on defining the Cycle Graph. Each vertex of this graph corresponds to one cycle, and each edge corresponds to onehandle, which is a literal containing an atom that, occurring in both cycles, actually determines a connection between them. In fact, the truth value of the handle in the cycle where it appears as the head of a rule, influences the truth value of the atoms of the cycle(s) where it occurs in the body. We can therefore introduce the concept of a handle path, connecting different cycles. If for every odd cycle we can find a handle path with certain properties, then the existence of stable model is guaranteed.
Computability logic is a formal theory of computational tasks and resources. Formulas in it represent interactive computational problems, and "truth" is understood as algorithmic solvability. Interactive computational problems, in turn, are defined as a certain sort games between a machine and its environment, with logical operators standing for operations on such games. Within the ambitious program of finding axiomatizations for incrementally rich fragments of this semantically introduced logic, the earlier article "From truth to computability I" proved soundness and completeness for system CL3, whose language has the so called parallel connectives (including negation), choice connectives, choice quantifiers, and blind quantifiers. The present paper extends that result to the significantly more expressive system CL4 with the same collection of logical operators. What makes CL4 expressive is the presence of two sorts of atoms in its language: elementary atoms, representing elementary computational problems (i.e. predicates, i.e. problems of zero degree of interactivity), and general atoms, representing arbitrary computational problems. CL4 conservatively extends CL3, with the latter being nothing but the general-atom-free fragment of the former. Removing the blind (classical) group of quantifiers from the language of CL4 is shown to yield a decidable logic despite the fact that the latter is still first-order. A comprehensive online source on computability logic can be found at http://www.cis.upenn.edu/~giorgi/cl.html
Answer set programming (ASP) with disjunction offers a powerful tool for declaratively representing and solving hard problems. Many NP-complete problems can be encoded in the answer set semantics of logic programs in a very concise and intuitive way, where the encoding reflects the typical "guess and check" nature of NP problems: The property is encoded in a way such that polynomial size certificates for it correspond to stable models of a program. However, the problem-solving capacity of full disjunctive logic programs (DLPs) is beyond NP, and captures a class of problems at the second level of the polynomial hierarchy. While these problems also have a clear "guess and check" structure, finding an encoding in a DLP reflecting this structure may sometimes be a non-obvious task, in particular if the "check" itself is a coNP-complete problem; usually, such problems are solved by interleaving separate guess and check programs, where the check is expressed by inconsistency of the check program. In this paper, we present general transformations of head-cycle free (extended) disjunctive logic programs into stratified and positive (extended) disjunctive logic programs based on meta-interpretation techniques. The answer sets of the original and the transformed program are in simple correspondence, and, moreover, inconsistency of the original program is indicated by a designated answer set of the transformed program. Our transformations facilitate the integration of separate "guess" and "check" programs, which are often easy to obtain, automatically into a single disjunctive logic program. Our results complement recent results on meta-interpretation in ASP, and extend methods and techniques for a declarative "guess and check" problem solving paradigm through ASP.
To test incomplete search algorithms for constraint satisfaction problems such as 3-SAT, we need a source of hard, but satisfiable, benchmark instances. A simple way to do this is to choose a random truth assignment A, and then choose clauses randomly from among those satisfied by A. However, this method tends to produce easy problems, since the majority of literals point toward the ``hidden'' assignment A. Last year, Achlioptas, Jia and Moore proposed a problem generator that cancels this effect by hiding both A and its complement. While the resulting formulas appear to be just as hard for DPLL algorithms as random 3-SAT formulas with no hidden assignment, they can be solved by WalkSAT in only polynomial time. Here we propose a new method to cancel the attraction to A, by choosing a clause with t > 0 literals satisfied by A with probability proportional to q^t for some q < 1. By varying q, we can generate formulas whose variables have no bias, i.e., which are equally likely to be true or false; we can even cause the formula to ``deceptively'' point away from A. We present theoretical and experimental results suggesting that these formulas are exponentially hard both for DPLL algorithms and for incomplete algorithms such as WalkSAT.
The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. Decision Tree (DT) classification models used within such a technique gives experts additional information by making this classification scheme observable. The use of the Markov Chain Monte Carlo (MCMC) methodology of stochastic sampling makes the Bayesian DT technique feasible to perform. However, in practice, the MCMC technique may become stuck in a particular DT which is far away from a region with a maximal posterior. Sampling such DTs causes bias in the posterior estimates, and as a result the evaluation of classification uncertainty may be incorrect. In a particular case, the negative effect of such sampling may be reduced by giving additional prior information on the shape of DTs. In this paper we describe a new approach based on sweeping the DTs without additional priors on the favorite shape of DTs. The performances of Bayesian DT techniques with the standard and sweeping strategies are compared on a synthetic data as well as on real datasets. Quantitatively evaluating the uncertainty in terms of entropy of class posterior probabilities, we found that the sweeping strategy is superior to the standard strategy.
Objective: The aim of this paper is to survey the recent work in medical documents summarization. Background: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology: This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and conclusions: The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications
Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in applications such as financial in which risks have to be estimated. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the diversity of a classifier ensemble and the required performance. The interpretability of classification models can also give useful information for experts responsible for making reliable classifications. For this reason Decision Trees (DTs) seem to be attractive classification models. The required diversity of the DT ensemble can be achieved by using the Bayesian model averaging all possible DTs. In practice, the Bayesian approach can be implemented on the base of a Markov Chain Monte Carlo (MCMC) technique of random sampling from the posterior distribution. For sampling large DTs, the MCMC method is extended by Reversible Jump technique which allows inducing DTs under given priors. For the case when the prior information on the DT size is unavailable, the sweeping technique defining the prior implicitly reveals a better performance. Within this Chapter we explore the classification uncertainty of the Bayesian MCMC techniques on some datasets from the StatLog Repository and real financial data. The classification uncertainty is compared within an Uncertainty Envelope technique dealing with the class posterior distribution and a given confidence probability. This technique provides realistic estimates of the classification uncertainty which can be easily interpreted in statistical terms with the aim of risk evaluation.
Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the classifier diversity and the required performance. The interpretability of MCSs can also give useful information for experts responsible for making reliable classifications. For this reason Decision Trees (DTs) seem to be attractive classification models for experts. The required diversity of MCSs exploiting such classification models can be achieved by using two techniques, the Bayesian model averaging and the randomised DT ensemble. Both techniques have revealed promising results when applied to real-world problems. In this paper we experimentally compare the classification uncertainty of the Bayesian model averaging with a restarting strategy and the randomised DT ensemble on a synthetic dataset and some domain problems commonly used in the machine learning community. To make the Bayesian DT averaging feasible, we use a Markov Chain Monte Carlo technique. The classification uncertainty is evaluated within an Uncertainty Envelope technique dealing with the class posterior distribution and a given confidence probability. Exploring a full posterior distribution, this technique produces realistic estimates which can be easily interpreted in statistical terms. In our experiments we found out that the Bayesian DTs are superior to the randomised DT ensembles within the Uncertainty Envelope technique.
An important task for Homeland Security is the prediction of threat vulnerabilities, such as through the detection of relationships between seemingly disjoint entities. A structure used for this task is a "semantic graph", also known as a "relational data graph" or an "attributed relational graph". These graphs encode relationships as "typed" links between a pair of "typed" nodes. Indeed, semantic graphs are very similar to semantic networks used in AI. The node and link types are related through an ontology graph (also known as a schema). Furthermore, each node has a set of attributes associated with it (e.g., "age" may be an attribute of a node of type "person"). Unfortunately, the selection of types and attributes for both nodes and links depends on human expertise and is somewhat subjective and even arbitrary. This subjectiveness introduces biases into any algorithm that operates on semantic graphs. Here, we raise some knowledge representation issues for semantic graphs and provide some possible solutions using recently developed ideas in the field of complex networks. In particular, we use the concept of transitivity to evaluate the relevance of individual links in the semantic graph for detecting relationships. We also propose new statistical measures for semantic graphs and illustrate these semantic measures on graphs constructed from movies and terrorism data.
In the frame of designing a knowledge discovery system, we have developed stochastic models based on high-order hidden Markov models. These models are capable to map sequences of data into a Markov chain in which the transitions between the states depend on the \texttt{n} previous states according to the order of the model. We study the process of achieving information extraction fromspatial and temporal data by means of an unsupervised classification. We use therefore a French national database related to the land use of a region, named Teruti, which describes the land use both in the spatial and temporal domain. Land-use categories (wheat, corn, forest, ...) are logged every year on each site regularly spaced in the region. They constitute a temporal sequence of images in which we look for spatial and temporal dependencies. The temporal segmentation of the data is done by means of a second-order Hidden Markov Model (\hmmd) that appears to have very good capabilities to locate stationary segments, as shown in our previous work in speech recognition. Thespatial classification is performed by defining a fractal scanning ofthe images with the help of a Hilbert-Peano curve that introduces atotal order on the sites, preserving the relation ofneighborhood between the sites. We show that the \hmmd performs aclassification that is meaningful for the agronomists.Spatial and temporal classification may be achieved simultaneously by means of a 2 levels \hmmd that measures the \aposteriori probability to map a temporal sequence of images onto a set of hidden classes.
The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Detection of such outliers is important for many applications such as fraud detection and customer migration. Most such applications are high dimensional domains in which the data may contain hundreds of dimensions. However, the outlier detection problem itself is not well defined and none of the existing definitions are widely accepted, especially in high dimensional space. In this paper, our first contribution is to propose a unified framework for outlier detection in high dimensional spaces from an ensemble-learning viewpoint. In our new framework, the outlying-ness of each data object is measured by fusing outlier factors in different subspaces using a combination function. Accordingly, we show that all existing researches on outlier detection can be regarded as special cases in the unified framework with respect to the set of subspaces considered and the type of combination function used. In addition, to demonstrate the usefulness of the ensemble-learning based outlier detection framework, we developed a very simple and fast algorithm, namely SOE1 (Subspace Outlier Ensemble using 1-dimensional Subspaces) in which only subspaces with one dimension is used for mining outliers from large categorical datasets. The SOE1 algorithm needs only two scans over the dataset and hence is very appealing in real data mining applications. Experimental results on real datasets and large synthetic datasets show that: (1) SOE1 has comparable performance with respect to those state-of-art outlier detection algorithms on identifying true outliers and (2) SOE1 can be an order of magnitude faster than one of the fastest outlier detection algorithms known so far.
The present paper investigates consequence relations that are both non-monotonic and paraconsistent. More precisely, we put the focus on preferential consequence relations, i.e. those relations that can be defined by a binary preference relation on states labelled by valuations. We worked with a general notion of valuation that covers e.g. the classical valuations as well as certain kinds of many-valued valuations. In the many-valued cases, preferential consequence relations are paraconsistant (in addition to be non-monotonic), i.e. they are capable of drawing reasonable conclusions which contain contradictions. The first purpose of this paper is to provide in our general framework syntactic characterizations of several families of preferential relations. The second and main purpose is to provide, again in our general framework, characterizations of several families of preferential discriminative consequence relations. They are defined exactly as the plain version, but any conclusion such that its negation is also a conclusion is rejected (these relations bring something new essentially in the many-valued cases).
We consider the problem of sequential decision making under uncertainty in which the loss caused by a decision depends on the following binary observation. In competitive on-line learning, the goal is to design decision algorithms that are almost as good as the best decision rules in a wide benchmark class, without making any assumptions about the way the observations are generated. However, standard algorithms in this area can only deal with finite-dimensional (often countable) benchmark classes. In this paper we give similar results for decision rules ranging over an arbitrary reproducing kernel Hilbert space. For example, it is shown that for a wide class of loss functions (including the standard square, absolute, and log loss functions) the average loss of the master algorithm, over the first $N$ observations, does not exceed the average loss of the best decision rule with a bounded norm plus $O(N^{-1/2})$. Our proof technique is very different from the standard ones and is based on recent results about defensive forecasting. Given the probabilities produced by a defensive forecasting algorithm, which are known to be well calibrated and to have good resolution in the long run, we use the expected loss minimization principle to find a suitable decision.
Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they generate cyclic influences, which are disallowed in Bayesian networks. Therefore, in existing PLP approaches logic programs with recursive loops are considered to be problematic and thus are excluded. In this paper, we propose an approach that makes use of recursive loops to build a stationary dynamic Bayesian network. Our work stems from an observation that recursive loops in a logic program imply a time sequence and thus can be used to model a stationary dynamic Bayesian network without using explicit time parameters. We introduce a Bayesian knowledge base with logic clauses of the form $A \leftarrow A_1,...,A_l, true, Context, Types$, which naturally represents the knowledge that the $A_i$s have direct influences on $A$ in the context $Context$ under the type constraints $Types$. We then use the well-founded model of a logic program to define the direct influence relation and apply SLG-resolution to compute the space of random variables together with their parental connections. We introduce a novel notion of influence clauses, based on which a declarative semantics for a Bayesian knowledge base is established and algorithms for building a two-slice dynamic Bayesian network from a logic program are developed.
Query containment and query answering are two important computational tasks in databases. While query answering amounts to compute the result of a query over a database, query containment is the problem of checking whether for every database, the result of one query is a subset of the result of another query. In this paper, we deal with unions of conjunctive queries, and we address query containment and query answering under Description Logic constraints. Every such constraint is essentially an inclusion dependencies between concepts and relations, and their expressive power is due to the possibility of using complex expressions, e.g., intersection and difference of relations, special forms of quantification, regular expressions over binary relations, in the specification of the dependencies. These types of constraints capture a great variety of data models, including the relational, the entity-relationship, and the object-oriented model, all extended with various forms of constraints, and also the basic features of the ontology languages used in the context of the Semantic Web. We present the following results on both query containment and query answering. We provide a method for query containment under Description Logic constraints, thus showing that the problem is decidable, and analyze its computational complexity. We prove that query containment is undecidable in the case where we allow inequalities in the right-hand side query, even for very simple constraints and queries. We show that query answering under Description Logic constraints can be reduced to query containment, and illustrate how such a reduction provides upper bound results with respect to both combined and data complexity.
This paper introduces Latent Relational Analysis (LRA), a method for measuring semantic similarity. LRA measures similarity in the semantic relations between two pairs of words. When two pairs have a high degree of relational similarity, they are analogous. For example, the pair cat:meow is analogous to the pair dog:bark. There is evidence from cognitive science that relational similarity is fundamental to many cognitive and linguistic tasks (e.g., analogical reasoning). In the Vector Space Model (VSM) approach to measuring relational similarity, the similarity between two pairs is calculated by the cosine of the angle between the vectors that represent the two pairs. The elements in the vectors are based on the frequencies of manually constructed patterns in a large corpus. LRA extends the VSM approach in three ways: (1) patterns are derived automatically from the corpus, (2) Singular Value Decomposition is used to smooth the frequency data, and (3) synonyms are used to reformulate word pairs. This paper describes the LRA algorithm and experimentally compares LRA to VSM on two tasks, answering college-level multiple-choice word analogy questions and classifying semantic relations in noun-modifier expressions. LRA achieves state-of-the-art results, reaching human-level performance on the analogy questions and significantly exceeding VSM performance on both tasks.
We develop and analyze methods for computing provably optimal {\em maximum a posteriori} (MAP) configurations for a subclass of Markov random fields defined on graphs with cycles. By decomposing the original distribution into a convex combination of tree-structured distributions, we obtain an upper bound on the optimal value of the original problem (i.e., the log probability of the MAP assignment) in terms of the combined optimal values of the tree problems. We prove that this upper bound is tight if and only if all the tree distributions share an optimal configuration in common. An important implication is that any such shared configuration must also be a MAP configuration for the original distribution. Next we develop two approaches to attempting to obtain tight upper bounds: (a) a {\em tree-relaxed linear program} (LP), which is derived from the Lagrangian dual of the upper bounds; and (b) a {\em tree-reweighted max-product message-passing algorithm} that is related to but distinct from the max-product algorithm. In this way, we establish a connection between a certain LP relaxation of the mode-finding problem, and a reweighted form of the max-product (min-sum) message-passing algorithm.
We posit a new paradigm for image information processing. For the last 25 years, this task was usually approached in the frame of Treisman's two-stage paradigm [1]. The latter supposes an unsupervised, bottom-up directed process of preliminary information pieces gathering at the lower processing stages and a supervised, top-down directed process of information pieces binding and grouping at the higher stages. It is acknowledged that these sub-processes interact and intervene between them in a tricky and a complicated manner. Notwithstanding the prevalence of this paradigm in biological and computer vision, we nevertheless propose to replace it with a new one, which we would like to designate as a two-part paradigm. In it, information contained in an image is initially extracted in an independent top-down manner by one part of the system, and then it is examined and interpreted by another, separate system part. We argue that the new paradigm seems to be more plausible than its forerunner. We provide evidence from human attention vision studies and insights of Kolmogorov's complexity theory to support our arguments. We also provide some reasons in favor of separate image interpretation issues.
Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the protein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-1 and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies.
When solving numerical constraints such as nonlinear equations and inequalities, solvers often exploit pruning techniques, which remove redundant value combinations from the domains of variables, at pruning steps. To find the complete solution set, most of these solvers alternate the pruning steps with branching steps, which split each problem into subproblems. This forms the so-called branch-and-prune framework, well known among the approaches for solving numerical constraints. The basic branch-and-prune search strategy that uses domain bisections in place of the branching steps is called the bisection search. In general, the bisection search works well in case (i) the solutions are isolated, but it can be improved further in case (ii) there are continuums of solutions (this often occurs when inequalities are involved). In this paper, we propose a new branch-and-prune search strategy along with several variants, which not only allow yielding better branching decisions in the latter case, but also work as well as the bisection search does in the former case. These new search algorithms enable us to employ various pruning techniques in the construction of inner and outer approximations of the solution set. Our experiments show that these algorithms speed up the solving process often by one order of magnitude or more when solving problems with continuums of solutions, while keeping the same performance as the bisection search when the solutions are isolated.
For graphs $G$ and $H$, a mapping $f: V(G)\dom V(H)$ is a homomorphism of $G$ to $H$ if $uv\in E(G)$ implies $f(u)f(v)\in E(H).$ If, moreover, each vertex $u \in V(G)$ is associated with costs $c_i(u), i \in V(H)$, then the cost of the homomorphism $f$ is $\sum_{u\in V(G)}c_{f(u)}(u)$. For each fixed graph $H$, we have the {\em minimum cost homomorphism problem}, written as MinHOM($H)$. The problem is to decide, for an input graph $G$ with costs $c_i(u),$ $u \in V(G), i\in V(H)$, whether there exists a homomorphism of $G$ to $H$ and, if one exists, to find one of minimum cost. Minimum cost homomorphism problems encompass (or are related to) many well studied optimization problems. We describe a dichotomy of the minimum cost homomorphism problems for graphs $H$, with loops allowed. When each connected component of $H$ is either a reflexive proper interval graph or an irreflexive proper interval bigraph, the problem MinHOM($H)$ is polynomial time solvable. In all other cases the problem MinHOM($H)$ is NP-hard. This solves an open problem from an earlier paper. Along the way, we prove a new characterization of the class of proper interval bigraphs.
Open answer set programming (OASP) is an extension of answer set programming where one may ground a program with an arbitrary superset of the program's constants. We define a fixed point logic (FPL) extension of Clark's completion such that open answer sets correspond to models of FPL formulas and identify a syntactic subclass of programs, called (loosely) guarded programs. Whereas reasoning with general programs in OASP is undecidable, the FPL translation of (loosely) guarded programs falls in the decidable (loosely) guarded fixed point logic (mu(L)GF). Moreover, we reduce normal closed ASP to loosely guarded OASP, enabling for the first time, a characterization of an answer set semantics by muLGF formulas. We further extend the open answer set semantics for programs with generalized literals. Such generalized programs (gPs) have interesting properties, e.g., the ability to express infinity axioms. We restrict the syntax of gPs such that both rules and generalized literals are guarded. Via a translation to guarded fixed point logic, we deduce 2-exptime-completeness of satisfiability checking in such guarded gPs (GgPs). Bound GgPs are restricted GgPs with exptime-complete satisfiability checking, but still sufficiently expressive to optimally simulate computation tree logic (CTL). We translate Datalog lite programs to GgPs, establishing equivalence of GgPs under an open answer set semantics, alternation-free muGF, and Datalog lite.
Program analysis and verification require decision procedures to reason on theories of data structures. Many problems can be reduced to the satisfiability of sets of ground literals in theory T. If a sound and complete inference system for first-order logic is guaranteed to terminate on T-satisfiability problems, any theorem-proving strategy with that system and a fair search plan is a T-satisfiability procedure. We prove termination of a rewrite-based first-order engine on the theories of records, integer offsets, integer offsets modulo and lists. We give a modularity theorem stating sufficient conditions for termination on a combinations of theories, given termination on each. The above theories, as well as others, satisfy these conditions. We introduce several sets of benchmarks on these theories and their combinations, including both parametric synthetic benchmarks to test scalability, and real-world problems to test performances on huge sets of literals. We compare the rewrite-based theorem prover E with the validity checkers CVC and CVC Lite. Contrary to the folklore that a general-purpose prover cannot compete with reasoners with built-in theories, the experiments are overall favorable to the theorem prover, showing that not only the rewriting approach is elegant and conceptually simple, but has important practical implications.
Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.
Through the Internet and the World-Wide Web, a vast number of information sources has become available, which offer information on various subjects by different providers, often in heterogeneous formats. This calls for tools and methods for building an advanced information-processing infrastructure. One issue in this area is the selection of suitable information sources in query answering. In this paper, we present a knowledge-based approach to this problem, in the setting where one among a set of information sources (prototypically, data repositories) should be selected for evaluating a user query. We use extended logic programs (ELPs) to represent rich descriptions of the information sources, an underlying domain theory, and user queries in a formal query language (here, XML-QL, but other languages can be handled as well). Moreover, we use ELPs for declarative query analysis and generation of a query description. Central to our approach are declarative source-selection programs, for which we define syntax and semantics. Due to the structured nature of the considered data items, the semantics of such programs must carefully respect implicit context information in source-selection rules, and furthermore combine it with possible user preferences. A prototype implementation of our approach has been realized exploiting the DLV KR system and its plp front-end for prioritized ELPs. We describe a representative example involving specific movie databases, and report about experimental results.
Logical formalisms for reasoning about relations between spatial regions play a fundamental role in geographical information systems, spatial and constraint databases, and spatial reasoning in AI. In analogy with Halpern and Shoham's modal logic of time intervals based on the Allen relations, we introduce a family of modal logics equipped with eight modal operators that are interpreted by the Egenhofer-Franzosa (or RCC8) relations between regions in topological spaces such as the real plane. We investigate the expressive power and computational complexity of logics obtained in this way. It turns out that our modal logics have the same expressive power as the two-variable fragment of first-order logic, but are exponentially less succinct. The complexity ranges from (undecidable and) recursively enumerable to highly undecidable, where the recursively enumerable logics are obtained by considering substructures of structures induced by topological spaces. As our undecidability results also capture logics based on the real line, they improve upon undecidability results for interval temporal logics by Halpern and Shoham. We also analyze modal logics based on the five RCC5 relations, with similar results regarding the expressive power, but weaker results regarding the complexity.
Fuzzy {\it discrete event systems} (DESs) were proposed recently by Lin and Ying [19], which may better cope with the real-world problems with fuzziness, impreciseness, and subjectivity such as those in biomedicine. As a continuation of [19], in this paper we further develop fuzzy DESs by dealing with supervisory control of fuzzy DESs. More specifically, (i) we reformulate the parallel composition of crisp DESs, and then define the parallel composition of fuzzy DESs that is equivalent to that in [19]; {\it max-product} and {\it max-min} automata for modeling fuzzy DESs are considered; (ii) we deal with a number of fundamental problems regarding supervisory control of fuzzy DESs, particularly demonstrate controllability theorem and nonblocking controllability theorem of fuzzy DESs, and thus present the conditions for the existence of supervisors in fuzzy DESs; (iii) we analyze the complexity for presenting a uniform criterion to test the fuzzy controllability condition of fuzzy DESs modeled by max-product automata; in particular, we present in detail a general computing method for checking whether or not the fuzzy controllability condition holds, if max-min automata are used to model fuzzy DESs, and by means of this method we can search for all possible fuzzy states reachable from initial fuzzy state in max-min automata; also, we introduce the fuzzy $n$-controllability condition for some practical problems; (iv) a number of examples serving to illustrate the applications of the derived results and methods are described; some basic properties related to supervisory control of fuzzy DESs are investigated. To conclude, some related issues are raised for further consideration.
This paper presents results of an ongoing interdisciplinary study to develop a computational theory of creativity for engineering design. Human design activities are surveyed, and popular computer-aided design methodologies are examined. It is argued that semiotics has the potential to merge and unite various design approaches into one fundamental theory that is naturally interpretable and so comprehensible in terms of computer use. Reviewing related work in philosophy, psychology, and cognitive science provides a general and encompassing vision of the creativity phenomenon. Basic notions of algebraic semiotics are given and explained in terms of design. This is to define a model of the design creative process, which is seen as a process of semiosis, where concepts and their attributes represented as signs organized into systems are evolved, blended, and analyzed, resulting in the development of new concepts. The model allows us to formally describe and investigate essential properties of the design process, namely its dynamics and non-determinism inherent in creative thinking. A stable pattern of creative thought - analogical and metaphorical reasoning - is specified to demonstrate the expressive power of the modeling approach; illustrative examples are given. The developed theory is applied to clarify the nature of emergence in design: it is shown that while emergent properties of a product may influence its creative value, emergence can simply be seen as a by-product of the creative process. Concluding remarks summarize the research, point to some unresolved issues, and outline directions for future work.
We present an unsupervised learning algorithm that mines large text corpora for patterns that express implicit semantic relations. For a given input word pair X:Y with some unspecified semantic relations, the corresponding output list of patterns is ranked according to how well each pattern Pi expresses the relations between X and Y. For example, given X=ostrich and Y=bird, the two highest ranking output patterns are "X is the largest Y" and "Y such as the X". The output patterns are intended to be useful for finding further pairs with the same relations, to support the construction of lexicons, ontologies, and semantic networks. The patterns are sorted by pertinence, where the pertinence of a pattern Pi for a word pair X:Y is the expected relational similarity between the given pair and typical pairs for Pi. The algorithm is empirically evaluated on two tasks, solving multiple-choice SAT word analogy questions and classifying semantic relations in noun-modifier pairs. On both tasks, the algorithm achieves state-of-the-art results, performing significantly better than several alternative pattern ranking algorithms, based on tf-idf.
We develop a Genetic Programming-based methodology that enables discovery of novel functional forms for classical inter-atomic force-fields, used in molecular dynamics simulations. Unlike previous efforts in the field, that fit only the parameters to the fixed functional forms, we instead use a novel algorithm to search the space of many possible functional forms. While a follow-on practical procedure will use experimental and {\it ab inito} data to find an optimal functional form for a forcefield, we first validate the approach using a manufactured solution. This validation has the advantage of a well-defined metric of success. We manufactured a training set of atomic coordinate data with an associated set of global energies using the well-known Lennard-Jones inter-atomic potential. We performed an automatic functional form fitting procedure starting with a population of random functions, using a genetic programming functional formulation, and a parallel tempering Metropolis-based optimization algorithm. Our massively-parallel method independently discovered the Lennard-Jones function after searching for several hours on 100 processors and covering a miniscule portion of the configuration space. We find that the method is suitable for unsupervised discovery of functional forms for inter-atomic potentials/force-fields. We also find that our parallel tempering Metropolis-based approach significantly improves the optimization convergence time, and takes good advantage of the parallel cluster architecture.
In answer set programming (ASP), a problem at hand is solved by (i) writing a logic program whose answer sets correspond to the solutions of the problem, and by (ii) computing the answer sets of the program using an answer set solver as a search engine. Typically, a programmer creates a series of gradually improving logic programs for a particular problem when optimizing program length and execution time on a particular solver. This leads the programmer to a meta-level problem of ensuring that the programs are equivalent, i.e., they give rise to the same answer sets. To ease answer set programming at methodological level, we propose a translation-based method for verifying the equivalence of logic programs. The basic idea is to translate logic programs P and Q under consideration into a single logic program EQT(P,Q) whose answer sets (if such exist) yield counter-examples to the equivalence of P and Q. The method is developed here in a slightly more general setting by taking the visibility of atoms properly into account when comparing answer sets. The translation-based approach presented in the paper has been implemented as a translator called lpeq that enables the verification of weak equivalence within the smodels system using the same search engine as for the search of models. Our experiments with lpeq and smodels suggest that establishing the equivalence of logic programs in this way is in certain cases much faster than naive cross-checking of answer sets.
Termination is a major question in both logic and computer science. In logic, termination is at the heart of proof theory where it is usually called strong normalization (of cut elimination). In computer science, termination has always been an important issue for showing programs correct. In the early days of logic, strong normalization was usually shown by assigning ordinals to expressions in such a way that eliminating a cut would yield an expression with a smaller ordinal. In the early days of verification, computer scientists used similar ideas, interpreting the arguments of a program call by a natural number, such as their size. Showing the size of the arguments to decrease for each recursive call gives a termination proof of the program, which is however rather weak since it can only yield quite small ordinals. In the sixties, Tait invented a new method for showing cut elimination of natural deduction, based on a predicate over the set of terms, such that the membership of an expression to the predicate implied the strong normalization property for that expression. The predicate being defined by induction on types, or even as a fixpoint, this method could yield much larger ordinals. Later generalized by Girard under the name of reducibility or computability candidates, it showed very effective in proving the strong normalization property of typed lambda-calculi...
We consider sensor scheduling as the optimal observability problem for partially observable Markov decision processes (POMDP). This model fits to the cases where a Markov process is observed by a single sensor which needs to be dynamically adjusted or by a set of sensors which are selected one at a time in a way that maximizes the information acquisition from the process. Similar to conventional POMDP problems, in this model the control action is based on all past measurements; however here this action is not for the control of state process, which is autonomous, but it is for influencing the measurement of that process. This POMDP is a controlled version of the hidden Markov process, and we show that its optimal observability problem can be formulated as an average cost Markov decision process (MDP) scheduling problem. In this problem, a policy is a rule for selecting sensors or adjusting the measuring device based on the measurement history. Given a policy, we can evaluate the estimation entropy for the joint state-measurement processes which inversely measures the observability of state process for that policy. Considering estimation entropy as the cost of a policy, we show that the problem of finding optimal policy is equivalent to an average cost MDP scheduling problem where the cost function is the entropy function over the belief space. This allows the application of the policy iteration algorithm for finding the policy achieving minimum estimation entropy, thus optimum observability.
We give some semantic results for an epistemic logic incorporating dynamic operators to describe information changing events. Such events include epistemic changes, where agents become more informed about the non-changing state of the world, and ontic changes, wherein the world changes. The events are executed in information states that are modeled as pointed Kripke models. Our contribution consists of three semantic results. (i) Given two information states, there is an event transforming one into the other. The linguistic correspondent to this is that every consistent formula can be made true in every information state by the execution of an event. (ii) A more technical result is that: every event corresponds to an event in which the postconditions formalizing ontic change are assignments to `true' and `false' only (instead of assignments to arbitrary formulas in the logical language). `Corresponds' means that execution of either event in a given information state results in bisimilar information states. (iii) The third, also technical, result is that every event corresponds to a sequence of events wherein all postconditions are assignments of a single atom only (instead of simultaneous assignments of more than one atom).
Recently, the diagnosability of {\it stochastic discrete event systems} (SDESs) was investigated in the literature, and, the failure diagnosis considered was {\it centralized}. In this paper, we propose an approach to {\it decentralized} failure diagnosis of SDESs, where the stochastic system uses multiple local diagnosers to detect failures and each local diagnoser possesses its own information. In a way, the centralized failure diagnosis of SDESs can be viewed as a special case of the decentralized failure diagnosis presented in this paper with only one projection. The main contributions are as follows: (1) We formalize the notion of codiagnosability for stochastic automata, which means that a failure can be detected by at least one local stochastic diagnoser within a finite delay. (2) We construct a codiagnoser from a given stochastic automaton with multiple projections, and the codiagnoser associated with the local diagnosers is used to test codiagnosability condition of SDESs. (3) We deal with a number of basic properties of the codiagnoser. In particular, a necessary and sufficient condition for the codiagnosability of SDESs is presented. (4) We give a computing method in detail to check whether codiagnosability is violated. And (5) some examples are described to illustrate the applications of the codiagnosability and its computing method.
Weighted Max-SAT is the optimization version of SAT and many important problems can be naturally encoded as such. Solving weighted Max-SAT is an important problem from both a theoretical and a practical point of view. In recent years, there has been considerable interest in finding efficient solving techniques. Most of this work focus on the computation of good quality lower bounds to be used within a branch and bound DPLL-like algorithm. Most often, these lower bounds are described in a procedural way. Because of that, it is difficult to realize the {\em logic} that is behind. In this paper we introduce an original framework for Max-SAT that stresses the parallelism with classical SAT. Then, we extend the two basic SAT solving techniques: {\em search} and {\em inference}. We show that many algorithmic {\em tricks} used in state-of-the-art Max-SAT solvers are easily expressable in {\em logic} terms with our framework in a unified manner. Besides, we introduce an original search algorithm that performs a restricted amount of {\em weighted resolution} at each visited node. We empirically compare our algorithm with a variety of solving alternatives on several benchmarks. Our experiments, which constitute to the best of our knowledge the most comprehensive Max-sat evaluation ever reported, show that our algorithm is generally orders of magnitude faster than any competitor.
A fuzzy logic based classification engine has been developed for classifying mass spectra obtained with an imaging internal source Fourier transform mass spectrometer (I^2LD-FTMS). Traditionally, an operator uses the relative abundance of ions with specific mass-to-charge (m/z) ratios to categorize spectra. An operator does this by comparing the spectrum of m/z versus abundance of an unknown sample against a library of spectra from known samples. Automated positioning and acquisition allow I^2LD-FTMS to acquire data from very large grids, this would require classification of up to 3600 spectrum per hour to keep pace with the acquisition. The tedious job of classifying numerous spectra generated in an I^2LD-FTMS imaging application can be replaced by a fuzzy rule base if the cues an operator uses can be encapsulated. We present the translation of linguistic rules to a fuzzy classifier for mineral phases in basalt. This paper also describes a method for gathering statistics on ions, which are not currently used in the rule base, but which may be candidates for making the rule base more accurate and complete or to form new rule bases based on data obtained from known samples. A spatial method for classifying spectra with low membership values, based on neighboring sample classifications, is also presented.
Description Logics (DLs) are appropriate, widely used, logics for managing structured knowledge. They allow reasoning about individuals and concepts, i.e. set of individuals with common properties. Typically, DLs are limited to dealing with crisp, well defined concepts. That is, concepts for which the problem whether an individual is an instance of it is yes/no question. More often than not, the concepts encountered in the real world do not have a precisely defined criteria of membership: we may say that an individual is an instance of a concept only to a certain degree, depending on the individual's properties. The DLs that deal with such fuzzy concepts are called fuzzy DLs. In order to deal with fuzzy, incomplete, indeterminate and inconsistent concepts, we need to extend the fuzzy DLs, combining the neutrosophic logic with a classical DL. In particular, concepts become neutrosophic (here neutrosophic means fuzzy, incomplete, indeterminate, and inconsistent), thus reasoning about neutrosophic concepts is supported. We'll define its syntax, its semantics, and describe its properties.
The local reconstruction of a railway schedule following a small perturbation of the traffic, seeking minimization of the total accumulated delay, is a very difficult and tightly constrained combinatorial problem. Notoriously enough, the railway company's public image degrades proportionally to the amount of daily delays, and the same goes for its profit! This paper describes an inoculation procedure which greatly enhances an evolutionary algorithm for train re-scheduling. The procedure consists in building the initial population around a pre-computed solution based on problem-related information available beforehand. The optimization is performed by adapting times of departure and arrival, as well as allocation of tracks, for each train at each station. This is achieved by a permutation-based evolutionary algorithm that relies on a semi-greedy heuristic scheduler to gradually reconstruct the schedule by inserting trains one after another. Experimental results are presented on various instances of a large real-world case involving around 500 trains and more than 1 million constraints. In terms of competition with commercial math ematical programming tool ILOG CPLEX, it appears that within a large class of instances, excluding trivial instances as well as too difficult ones, and with very few exceptions, a clever initialization turns an encouraging failure into a clear-cut success auguring of substantial financial savings.
A product configurator which is complete, backtrack free and able to compute the valid domains at any state of the configuration can be constructed by building a Binary Decision Diagram (BDD). Despite the fact that the size of the BDD is exponential in the number of variables in the worst case, BDDs have proved to work very well in practice. Current BDD-based techniques can only handle interactive configuration with small finite domains. In this paper we extend the approach to handle string variables constrained by regular expressions. The user is allowed to change the strings by adding letters at the end of the string. We show how to make a data structure that can perform fast valid domain computations given some assignment on the set of string variables. We first show how to do this by using one large DFA. Since this approach is too space consuming to be of practical use, we construct a data structure that simulates the large DFA and in most practical cases are much more space efficient. As an example a configuration problem on $n$ string variables with only one solution in which each string variable is assigned to a value of length of $k$ the former structure will use $\Omega(k^n)$ space whereas the latter only need $O(kn)$. We also show how this framework easily can be combined with the recent BDD techniques to allow both boolean, integer and string variables in the configuration problem.
Recently, M. Chertkov and V.Y. Chernyak derived an exact expression for the partition sum (normalization constant) corresponding to a graphical model, which is an expansion around the Belief Propagation solution. By adding correction terms to the BP free energy, one for each "generalized loop" in the factor graph, the exact partition sum is obtained. However, the usually enormous number of generalized loops generally prohibits summation over all correction terms. In this article we introduce Truncated Loop Series BP (TLSBP), a particular way of truncating the loop series of M. Chertkov and V.Y. Chernyak by considering generalized loops as compositions of simple loops. We analyze the performance of TLSBP in different scenarios, including the Ising model, regular random graphs and on Promedas, a large probabilistic medical diagnostic system. We show that TLSBP often improves upon the accuracy of the BP solution, at the expense of increased computation time. We also show that the performance of TLSBP strongly depends on the degree of interaction between the variables. For weak interactions, truncating the series leads to significant improvements, whereas for strong interactions it can be ineffective, even if a high number of terms is considered.
Constraint Programming (CP) has been successfully applied to both constraint satisfaction and constraint optimization problems. A wide variety of specialized global constraints provide critical assistance in achieving a good model that can take advantage of the structure of the problem in the search for a solution. However, a key outstanding issue is the representation of 'ad-hoc' constraints that do not have an inherent combinatorial nature, and hence are not modeled well using narrowly specialized global constraints. We attempt to address this issue by considering a hybrid of search and compilation. Specifically we suggest the use of Reduced Ordered Multi-Valued Decision Diagrams (ROMDDs) as the supporting data structure for a generic global constraint. We give an algorithm for maintaining generalized arc consistency (GAC) on this constraint that amortizes the cost of the GAC computation over a root-to-leaf path in the search tree without requiring asymptotically more space than used for the MDD. Furthermore we present an approach for incrementally maintaining the reduced property of the MDD during the search, and show how this can be used for providing domain entailment detection. Finally we discuss how to apply our approach to other similar data structures such as AOMDDs and Case DAGs. The technique used can be seen as an extension of the GAC algorithm for the regular language constraint on finite length input.
Axiomatic approach has demonstrated its power in mathematics. The main goal of this preprint is to show that axiomatic methods are also very efficient for computer science. It is possible to apply these methods to many problems in computer science. Here the main modes of computer functioning and program execution are described, formalized, and studied in an axiomatic context. The emphasis is on three principal modes: computation, decision, and acceptation. Now the prevalent mode for computers is computation. Problems of artificial intelligence involve decision mode, while communication functions of computer demand accepting mode. The main goal of this preprint is to study properties of these modes and relations between them. These problems are closely related to such fundamental concepts of computer science and technology as computability, decidability, and acceptability. In other words, we are concerned with the question what computers and software systems can do working in this or that mode. Consequently, results of this preprint allow one to achieve higher understanding of computations and in such a way, to find some basic properties of computers and their applications. Classes of algorithms, which model different kinds of computers and software, are compared with respect to their computing, accepting or deciding power. Operations with algorithms and machines are introduced. Examples show how to apply axiomatic results to different classes of algorithms and machines in order to enhance their performance.
In this article, we study directed graphs (digraphs) with a coloring constraint due to Von Neumann and related to Nim-type games. This is equivalent to the notion of kernels of digraphs, which appears in numerous fields of research such as game theory, complexity theory, artificial intelligence (default logic, argumentation in multi-agent systems), 0-1 laws in monadic second order logic, combinatorics (perfect graphs)... Kernels of digraphs lead to numerous difficult questions (in the sense of NP-completeness, #P-completeness). However, we show here that it is possible to use a generating function approach to get new informations: we use technique of symbolic and analytic combinatorics (generating functions and their singularities) in order to get exact and asymptotic results, e.g. for the existence of a kernel in a circuit or in a unicircuit digraph. This is a first step toward a generatingfunctionology treatment of kernels, while using, e.g., an approach "a la Wright". Our method could be applied to more general "local coloring constraints" in decomposable combinatorial structures.
Discrete temporal transitions occur in a variety of domains, but this work is mainly motivated by applications in molecular biology: explaining and analyzing observed transcriptome and proteome time series by literature and database knowledge. The starting point of a formal concept analysis model is presented. The objects of a formal context are states of the interesting entities, and the attributes are the variable properties defining the current state (e.g. observed presence or absence of proteins). Temporal transitions assign a relation to the objects, defined by deterministic or non-deterministic transition rules between sets of pre- and postconditions. This relation can be generalized to its transitive closure, i.e. states are related if one results from the other by a transition sequence of arbitrary length. The focus of the work is the adaptation of the attribute exploration algorithm to such a relational context, so that questions concerning temporal dependencies can be asked during the exploration process and be answered from the computed stem base. Results are given for the abstract example of a game and a small gene regulatory network relevant to a biomedical question.
We propose a new class of quantum computing algorithms which generalize many standard ones. The goal of our algorithms is to estimate probability distributions. Such estimates are useful in, for example, applications of Decision Theory and Artificial Intelligence, where inferences are made based on uncertain knowledge. The class of algorithms that we propose is based on a construction method that generalizes a Fredkin-Toffoli (F-T) construction method used in the field of classical reversible computing. F-T showed how, given any binary deterministic circuit, one can construct another binary deterministic circuit which does the same calculations in a reversible manner. We show how, given any classical stochastic network (classical Bayesian net), one can construct a quantum network (quantum Bayesian net). By running this quantum Bayesian net on a quantum computer, one can calculate any conditional probability that one would be interested in calculating for the original classical Bayesian net. Thus, we generalize the F-T construction method so that it can be applied to any classical stochastic circuit, not just binary deterministic ones. We also show that, in certain situations, our class of algorithms can be combined with Grover's algorithm to great advantage.
We present some results from simulation of a network of nodes connected by c-NOT gates with nearest neighbors. Though initially we begin with pure states of varying boundary conditions, the updating with time quickly involves a complicated entanglement involving all or most nodes. As a normal c-NOT gate, though unitary for a single pair of nodes, seems to be not so when used in a network in a naive way, we use a manifestly unitary form of the transition matrix with c?-NOT gates, which invert the phase as well as flipping the qubit. This leads to complete entanglement of the net, but with variable coefficients for the different components of the superposition. It is interesting to note that by a simple logical back projection the original input state can be recovered in most cases. We also prove that it is not possible for a sequence of unitary operators working on a net to make it move from an aperiodic regime to a periodic one, unlike some classical cases where phase-locking happens in course of evolution. However, we show that it is possible to introduce by hand periodic orbits to sets of initial states, which may be useful in forming dynamic pattern recognition systems.
Graphical models of probabilistic dependencies have been extensively investigated in the context of classical uncertainty. However, in some domains (most notably, in computational physics and quantum computing) the nature of the relevant uncertainty is non-classical, and the laws of classical probability theory are superseded by those of quantum mechanics. In this paper we introduce Markovian Entanglement Networks (MEN), a novel class of graphical representations of quantum-mechanical dependencies in the context of such non-classical systems. MEN are the quantum-mechanical analogue of Markovian Networks, a family of undirected graphical representations which, in the classical domain, exploit a notion of conditional independence among subsystems. After defining a notion of conditional independence appropriate to our domain (conditional separability), we prove that the conditional separabilities induced by a quantum-mechanical wave function are effectively reflected in the graphical structure of MEN. Specifically, we show that for any wave function there exists a MEN which is a perfect map of its conditional separabilities. Next, we show how the graphical structure of MEN can be used to effectively classify the pure states of three-qubit systems. We also demonstrate that, in large systems, exploiting conditional independencies may dramatically reduce the computational burden of various inference tasks. In principle, the graph-theoretic representation of conditional independencies afforded by MEN may not only facilitate the classical simulation of quantum systems, but also provide a guide to the efficient design and complexity analysis of quantum algorithms and circuits.
Motivation: Profile hidden Markov Models (pHMMs) are a popular and very useful tool in the detection of the remote homologue protein families. Unfortunately, their performance is not always satisfactory when proteins are in the 'twilight zone'. We present HMMER-STRUCT, a model construction algorithm and tool that tries to improve pHMM performance by using structural information while training pHMMs. As a first step, HMMER-STRUCT constructs a set of pHMMs. Each pHMM is constructed by weighting each residue in an aligned protein according to a specific structural property of the residue. Properties used were primary, secondary and tertiary structures, accessibility and packing. HMMER-STRUCT then prioritizes the results by voting. Results: We used the SCOP database to perform our experiments. Throughout, we apply leave-one-family-out cross-validation over protein superfamilies. First, we used the MAMMOTH-mult structural aligner to align the training set proteins. Then, we performed two sets of experiments. In a first experiment, we compared structure weighted models against standard pHMMs and against each other. In a second experiment, we compared the voting model against individual pHMMs. We compare method performance through ROC curves and through Precision/Recall curves, and assess significance through the paired two tailed t-test. Our results show significant performance improvements of all structurally weighted models over default HMMER, and a significant improvement in sensitivity of the combined models over both the original model and the structurally weighted models.
One way of getting a better view of data is using frequent patterns. In this paper frequent patterns are subsets that occur a minimal number of times in a stream of itemsets. However, the discovery of frequent patterns in streams has always been problematic. Because streams are potentially endless it is in principle impossible to say if a pattern is often occurring or not. Furthermore the number of patterns can be huge and a good overview of the structure of the stream is lost quickly. The proposed approach will use clustering to facilitate the analysis of the structure of the stream. A clustering on the co-occurrence of patterns will give the user an improved view on the structure of the stream. Some patterns might occur so much together that they should form a combined pattern. In this way the patterns in the clustering will be the largest frequent patterns: maximal frequent patterns. Our approach to decide if patterns occur often together will be based on a method of clustering when only the distance between pairs is known. The number of maximal frequent patterns is much smaller and combined with clustering methods these patterns provide a good view on the structure of the stream.
Mining frequent subgraphs is an area of research where we have a given set of graphs (each graph can be seen as a transaction), and we search for (connected) subgraphs contained in many of these graphs. In this work we will discuss techniques used in our framework Lattice2SAR for mining and analysing frequent subgraph data and their corresponding lattice information. Lattice information is provided by the graph mining algorithm gSpan; it contains all supergraph-subgraph relations of the frequent subgraph patterns -- and their supports. Lattice2SAR is in particular used in the analysis of frequent graph patterns where the graphs are molecules and the frequent subgraphs are fragments. In the analysis of fragments one is interested in the molecules where patterns occur. This data can be very extensive and in this paper we focus on a technique of making it better available by using the lattice information in our clustering. Now we can reduce the number of times the highly compressed occurrence data needs to be accessed by the user. The user does not have to browse all the occurrence data in search of patterns occurring in the same molecules. Instead one can directly see which frequent subgraphs are of interest.
The SEMATECH sponsored J-88-E project teaming Texas Instruments with NeuroDyne (et al.) focused on Fault Detection and Classification (FDC) on a Lam 9600 aluminum plasma etch reactor, used in the process of semiconductor fabrication. Fault classification was accomplished by implementing a series of virtual sensor models which used data from real sensors (Lam Station sensors, Optical Emission Spectroscopy, and RF Monitoring) to predict recipe setpoints and wafer state characteristics. Fault detection and classification were performed by comparing predicted recipe and wafer state values with expected values. Models utilized include linear PLS, Polynomial PLS, and Neural Network PLS. Prediction of recipe setpoints based upon sensor data provides a capability for cross-checking that the machine is maintaining the desired setpoints. Wafer state characteristics such as Line Width Reduction and Remaining Oxide were estimated on-line using these same process sensors (Lam, OES, RFM). Wafer-to-wafer measurement of these characteristics in a production setting (where typically this information may be only sparsely available, if at all, after batch processing runs with numerous wafers have been completed) would provide important information to the operator that the process is or is not producing wafers within acceptable bounds of product quality. Production yield is increased, and correspondingly per unit cost is reduced, by providing the operator with the opportunity to adjust the process or machine before etching more wafers.
Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. The main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods (Integer Linear Programming, Semidefinite Programming, SAT encoding) that, at present, are adequate only for moderate size instances. We believe that metaheuristic and hybrid approaches could provide a better scalability. Moreover, metaheuristics can be very easily combined with problem specific heuristics and they can also be integrated with tree-based search techniques, thus providing a promising framework for hybrid systems in which a good trade-off between effectiveness and efficiency can be reached. In this paper we illustrate a feasibility study of the approach and discuss some relevant design issues, such as modeling and design of approximate solvers that combine constructive heuristics, local search-based improvement strategies and learning mechanisms. Besides the relevance of the Haplotype Inference problem itself, this preliminary analysis is also an interesting case study because the formulation of the problem poses some challenges in modeling and hybrid metaheuristic solver design that can be generalized to other problems.
Many systems can be described in terms of networks of discrete elements and their various relationships to one another. A semantic network, or multi-relational network, is a directed labeled graph consisting of a heterogeneous set of entities connected by a heterogeneous set of relationships. Semantic networks serve as a promising general-purpose modeling substrate for complex systems. Various standardized formats and tools are now available to support practical, large-scale semantic network models. First, the Resource Description Framework (RDF) offers a standardized semantic network data model that can be further formalized by ontology modeling languages such as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent introduction of highly performant triple-stores (i.e. semantic network databases) allows semantic network models on the order of $10^9$ edges to be efficiently stored and manipulated. RDF and its related technologies are currently used extensively in the domains of computer science, digital library science, and the biological sciences. This article will provide an introduction to RDF/RDFS/OWL and an examination of its suitability to model discrete element complex systems.
In this paper we present efficient evaluation algorithms for the Horn Transaction Logic (a generalization of the regular Horn logic programs with state updates). We present two complementary methods for optimizing the implementation of Transaction Logic. The first method is based on tabling and we modified the proof theory to table calls and answers on states (practically, equivalent to dynamic programming). The call-answer table is indexed on the call and a signature of the state in which the call was made. The answer columns contain the answer unification and a signature of the state after the call was executed. The states are signed efficiently using a technique based on tries and counting. The second method is based on incremental evaluation and it applies when the data oracle contains derived relations. The deletions and insertions (executed in the transaction oracle) change the state of the database. Using the heuristic of inertia (only a part of the state changes in response to elementary updates), most of the time it is cheaper to compute only the changes in the state than to recompute the entire state from scratch. The two methods are complementary by the fact that the first method optimizes the evaluation when a call is repeated in the same state, and the second method optimizes the evaluation of a new state when a call-state pair is not found by the tabling mechanism (i.e. the first method). The proof theory of Transaction Logic with the application of tabling and incremental evaluation is sound and complete with respect to its model theory.
A growing number of indicators are now being used with some confidence to measure the metallicity(Z) of photoionisation regions in planetary nebulae, galactic HII regions(GHIIRs), extra-galactic HII regions(EGHIIRs) and HII galaxies(HIIGs). However, a universal indicator valid also at high metallicities has yet to be found. Here, we report on a new artificial intelligence-based approach to determine metallicity indicators that shows promise for the provision of improved empirical fits. The method hinges on the application of an evolutionary neural network to observational emission line data. The network's DNA, encoded in its architecture, weights and neuron transfer functions, is evolved using a genetic algorithm. Furthermore, selection, operating on a set of 10 distinct neuron transfer functions, means that the empirical relation encoded in the network solution architecture is in functional rather than numerical form. Thus the network solutions provide an equation for the metallicity in terms of line ratios without a priori assumptions. Tapping into the mathematical power offered by this approach, we applied the network to detailed observations of both nebula and auroral emission lines in the optical for a sample of 96 HII-type regions and we were able to obtain an empirical relation between Z and S23 with a dispersion of only 0.16 dex. We show how the method can be used to identify new diagnostics as well as the nonlinear relationship supposed to exist between the metallicity Z, ionisation parameter U and effective (or equivalent) temperature T*.
In this paper we study cellular automata (CAs) that perform the computational Majority task. This task is a good example of what the phenomenon of emergence in complex systems is. We take an interest in the reasons that make this particular fitness landscape a difficult one. The first goal is to study the landscape as such, and thus it is ideally independent from the actual heuristics used to search the space. However, a second goal is to understand the features a good search technique for this particular problem space should possess. We statistically quantify in various ways the degree of difficulty of searching this landscape. Due to neutrality, investigations based on sampling techniques on the whole landscape are difficult to conduct. So, we go exploring the landscape from the top. Although it has been proved that no CA can perform the task perfectly, several efficient CAs for this task have been found. Exploiting similarities between these CAs and symmetries in the landscape, we define the Olympus landscape which is regarded as the ''heavenly home'' of the best local optima known (blok). Then we measure several properties of this subspace. Although it is easier to find relevant CAs in this subspace than in the overall landscape, there are structural reasons that prevent a searcher from finding overfitted CAs in the Olympus. Finally, we study dynamics and performance of genetic algorithms on the Olympus in order to confirm our analysis and to find efficient CAs for the Majority problem with low computational cost.
We develop a general framework for MAP estimation in discrete and Gaussian graphical models using Lagrangian relaxation techniques. The key idea is to reformulate an intractable estimation problem as one defined on a more tractable graph, but subject to additional constraints. Relaxing these constraints gives a tractable dual problem, one defined by a thin graph, which is then optimized by an iterative procedure. When this iterative optimization leads to a consistent estimate, one which also satisfies the constraints, then it corresponds to an optimal MAP estimate of the original model. Otherwise there is a ``duality gap'', and we obtain a bound on the optimal solution. Thus, our approach combines convex optimization with dynamic programming techniques applicable for thin graphs. The popular tree-reweighted max-product (TRMP) method may be seen as solving a particular class of such relaxations, where the intractable graph is relaxed to a set of spanning trees. We also consider relaxations to a set of small induced subgraphs, thin subgraphs (e.g. loops), and a connected tree obtained by ``unwinding'' cycles. In addition, we propose a new class of multiscale relaxations that introduce ``summary'' variables. The potential benefits of such generalizations include: reducing or eliminating the ``duality gap'' in hard problems, reducing the number or Lagrange multipliers in the dual problem, and accelerating convergence of the iterative optimization procedure.
Electrical Impedance Tomography (EIT) is a functional imaging method that is being developed for bedside use in critical care medicine. Aiming at improving the chest anatomical resolution of EIT images we developed a fuzzy model based on EIT high temporal resolution and the functional information contained in the pulmonary perfusion and ventilation signals. EIT data from an experimental animal model were collected during normal ventilation and apnea while an injection of hypertonic saline was used as a reference . The fuzzy model was elaborated in three parts: a modeling of the heart, a pulmonary map from ventilation images and, a pulmonary map from perfusion images. Image segmentation was performed using a threshold method and a ventilation/perfusion map was generated. EIT images treated by the fuzzy model were compared with the hypertonic saline injection method and CT-scan images, presenting good results in both qualitative (the image obtained by the model was very similar to that of the CT-scan) and quantitative (the ROC curve provided an area equal to 0.93) point of view. Undoubtedly, these results represent an important step in the EIT images area, since they open the possibility of developing EIT-based bedside clinical methods, which are not available nowadays. These achievements could serve as the base to develop EIT diagnosis system for some life-threatening diseases commonly found in critical care medicine.
Near optimal decoding of good error control codes is generally a difficult task. However, for a certain type of (sufficiently) good codes an efficient decoding algorithm with near optimal performance exists. These codes are defined via a combination of constituent codes with low complexity trellis representations. Their decoding algorithm is an instance of (loopy) belief propagation and is based on an iterative transfer of constituent beliefs. The beliefs are thereby given by the symbol probabilities computed in the constituent trellises. Even though weak constituent codes are employed close to optimal performance is obtained, i.e., the encoder/decoder pair (almost) achieves the information theoretic capacity. However, (loopy) belief propagation only performs well for a rather specific set of codes, which limits its applicability. In this paper a generalisation of iterative decoding is presented. It is proposed to transfer more values than just the constituent beliefs. This is achieved by the transfer of beliefs obtained by independently investigating parts of the code space. This leads to the concept of discriminators, which are used to improve the decoder resolution within certain areas and defines discriminated symbol beliefs. It is shown that these beliefs approximate the overall symbol probabilities. This leads to an iteration rule that (below channel capacity) typically only admits the solution of the overall decoding problem. Via a Gauss approximation a low complexity version of this algorithm is derived. Moreover, the approach may then be applied to a wide range of channel maps without significant complexity increase.
We consider the discrete-time infinite-horizon optimal control problem formalized by Markov Decision Processes. We revisit the work of Bertsekas and Ioffe, that introduced $\lambda$ Policy Iteration, a family of algorithms parameterized by $\lambda$ that generalizes the standard algorithms Value Iteration and Policy Iteration, and has some deep connections with the Temporal Differences algorithm TD($\lambda$) described by Sutton and Barto. We deepen the original theory developped by the authors by providing convergence rate bounds which generalize standard bounds for Value Iteration described for instance by Puterman. Then, the main contribution of this paper is to develop the theory of this algorithm when it is used in an approximate form and show that this is sound. Doing so, we extend and unify the separate analyses developped by Munos for Approximate Value Iteration and Approximate Policy Iteration. Eventually, we revisit the use of this algorithm in the training of a Tetris playing controller as originally done by Bertsekas and Ioffe. We provide an original performance bound that can be applied to such an undiscounted control problem. Our empirical results are different from those of Bertsekas and Ioffe (which were originally qualified as "paradoxical" and "intriguing"), and much more conform to what one would expect from a learning experiment. We discuss the possible reason for such a difference.
The pace of progress in the fields of Evolutionary Computation and Machine Learning is currently limited -- in the former field, by the improbability of making advantageous extensions to evolutionary algorithms when their capacity for adaptation is poorly understood, and in the latter by the difficulty of finding effective semi-principled reductions of hard real-world problems to relatively simple optimization problems. In this paper we explain why a theory which can accurately explain the simple genetic algorithm's remarkable capacity for adaptation has the potential to address both these limitations. We describe what we believe to be the impediments -- historic and analytic -- to the discovery of such a theory and highlight the negative role that the building block hypothesis (BBH) has played. We argue based on experimental results that a fundamental limitation which is widely believed to constrain the SGA's adaptive ability (and is strongly implied by the BBH) is in fact illusionary and does not exist. The SGA therefore turns out to be more powerful than it is currently thought to be. We give conditions under which it becomes feasible to numerically approximate and study the multivariate marginals of the search distribution of an infinite population SGA over multiple generations even when its genomes are long, and explain why this analysis is relevant to the riddle of the SGA's remarkable adaptive abilities.
In this paper, we present a Mirroring Neural Network architecture to perform non-linear dimensionality reduction and Object Recognition using a reduced lowdimensional characteristic vector. In addition to dimensionality reduction, the network also reconstructs (mirrors) the original high-dimensional input vector from the reduced low-dimensional data. The Mirroring Neural Network architecture has more number of processing elements (adalines) in the outer layers and the least number of elements in the central layer to form a converging-diverging shape in its configuration. Since this network is able to reconstruct the original image from the output of the innermost layer (which contains all the information about the input pattern), these outputs can be used as object signature to classify patterns. The network is trained to minimize the discrepancy between actual output and the input by back propagating the mean squared error from the output layer to the input layer. After successfully training the network, it can reduce the dimension of input vectors and mirror the patterns fed to it. The Mirroring Neural Network architecture gave very good results on various test patterns.
This paper proposes an unsupervised learning technique by using Multi-layer Mirroring Neural Network and Forgy's clustering algorithm. Multi-layer Mirroring Neural Network is a neural network that can be trained with generalized data inputs (different categories of image patterns) to perform non-linear dimensionality reduction and the resultant low-dimensional code is used for unsupervised pattern classification using Forgy's algorithm. By adapting the non-linear activation function (modified sigmoidal function) and initializing the weights and bias terms to small random values, mirroring of the input pattern is initiated. In training, the weights and bias terms are changed in such a way that the input presented is reproduced at the output by back propagating the error. The mirroring neural network is capable of reducing the input vector to a great degree (approximately 1/30th the original size) and also able to reconstruct the input pattern at the output layer from this reduced code units. The feature set (output of central hidden layer) extracted from this network is fed to Forgy's algorithm, which classify input data patterns into distinguishable classes. In the implementation of Forgy's algorithm, initial seed points are selected in such a way that they are distant enough to be perfectly grouped into different categories. Thus a new method of unsupervised learning is formulated and demonstrated in this paper. This method gave impressive results when applied to classification of different image patterns.
Logic programming under the answer-set semantics nowadays deals with numerous different notions of program equivalence. This is due to the fact that equivalence for substitution (known as strong equivalence) and ordinary equivalence are different concepts. The former holds, given programs P and Q, iff P can be faithfully replaced by Q within any context R, while the latter holds iff P and Q provide the same output, that is, they have the same answer sets. Notions in between strong and ordinary equivalence have been introduced as theoretical tools to compare incomplete programs and are defined by either restricting the syntactic structure of the considered context programs R or by bounding the set A of atoms allowed to occur in R (relativized equivalence).For the latter approach, different A yield properly different equivalence notions, in general. For the former approach, however, it turned out that any ``reasonable'' syntactic restriction to R coincides with either ordinary, strong, or uniform equivalence. In this paper, we propose a parameterization for equivalence notions which takes care of both such kinds of restrictions simultaneously by bounding, on the one hand, the atoms which are allowed to occur in the rule heads of the context and, on the other hand, the atoms which are allowed to occur in the rule bodies of the context. We introduce a general semantical characterization which includes known ones as SE-models (for strong equivalence) or UE-models (for uniform equivalence) as special cases. Moreover,we provide complexity bounds for the problem in question and sketch a possible implementation method. To appear in Theory and Practice of Logic Programming (TPLP).
Computability logic (CL) (see http://www.cis.upenn.edu/~giorgi/cl.html) is a semantical platform and research program for redeveloping logic as a formal theory of computability, as opposed to the formal theory of truth which it has more traditionally been. Formulas in CL stand for (interactive) computational problems, understood as games between a machine and its environment; logical operators represent operations on such entities; and "truth" is understood as existence of an effective solution, i.e., of an algorithmic winning strategy. The formalism of CL is open-ended, and may undergo series of extensions as the study of the subject advances. The main groups of operators on which CL has been focused so far are the parallel, choice, branching, and blind operators. The present paper introduces a new important group of operators, called sequential. The latter come in the form of sequential conjunction and disjunction, sequential quantifiers, and sequential recurrences. As the name may suggest, the algorithmic intuitions associated with this group are those of sequential computations, as opposed to the intuitions of parallel computations associated with the parallel group of operations: playing a sequential combination of games means playing its components in a sequential fashion, one after one. The main technical result of the present paper is a sound and complete axiomatization of the propositional fragment of computability logic whose vocabulary, together with negation, includes all three -- parallel, choice and sequential -- sorts of conjunction and disjunction. An extension of this result to the first-order level is also outlined.
Many problems that arise in machine learning domain deal with nonlinearity and quite often demand users to obtain global optimal solutions rather than local optimal ones. Optimization problems are inherent in machine learning algorithms and hence many methods in machine learning were inherited from the optimization literature. Popularly known as the initialization problem, the ideal set of parameters required will significantly depend on the given initialization values. The recently developed TRUST-TECH (TRansformation Under STability-reTaining Equilibria CHaracterization) methodology systematically explores the subspace of the parameters to obtain a complete set of local optimal solutions. In this thesis work, we propose TRUST-TECH based methods for solving several optimization and machine learning problems. Two stages namely, the local stage and the neighborhood-search stage, are repeated alternatively in the solution space to achieve improvements in the quality of the solutions. Our methods were tested on both synthetic and real datasets and the advantages of using this novel framework are clearly manifested. This framework not only reduces the sensitivity to initialization, but also allows the flexibility for the practitioners to use various global and local methods that work well for a particular problem of interest. Other hierarchical stochastic algorithms like evolutionary algorithms and smoothing algorithms are also studied and frameworks for combining these methods with TRUST-TECH have been proposed and evaluated on several test systems.
Most definitions of ontology, viewed as a "specification of a conceptualization", agree on the fact that if an ontology can take different forms, it necessarily includes a vocabulary of terms and some specification of their meaning in relation to the domain's conceptualization. And as domain knowledge is mainly conveyed through scientific and technical texts, we can hope to extract some useful information from them for building ontology. But is it as simple as this? In this article we shall see that the lexical structure, i.e. the network of words linked by linguistic relationships, does not necessarily match the domain conceptualization. We have to bear in mind that writing documents is the concern of textual linguistics, of which one of the principles is the incompleteness of text, whereas building ontology - viewed as task-independent knowledge - is concerned with conceptualization based on formal and not natural languages. Nevertheless, the famous Sapir and Whorf hypothesis, concerning the interdependence of thought and language, is also applicable to formal languages. This means that the way an ontology is built and a concept is defined depends directly on the formal language which is used; and the results will not be the same. The introduction of the notion of ontoterminology allows to take into account epistemological principles for formal ontology building.
Disjunctive Logic Programming (DLP) is a very expressive formalism: it allows for expressing every property of finite structures that is decidable in the complexity class SigmaP2 (= NP^NP). Despite this high expressiveness, there are some simple properties, often arising in real-world applications, which cannot be encoded in a simple and natural manner. Especially properties that require the use of arithmetic operators (like sum, times, or count) on a set or multiset of elements, which satisfy some conditions, cannot be naturally expressed in classic DLP. To overcome this deficiency, we extend DLP by aggregate functions in a conservative way. In particular, we avoid the introduction of constructs with disputed semantics, by requiring aggregates to be stratified. We formally define the semantics of the extended language (called DLP^A), and illustrate how it can be profitably used for representing knowledge. Furthermore, we analyze the computational complexity of DLP^A, showing that the addition of aggregates does not bring a higher cost in that respect. Finally, we provide an implementation of DLP^A in DLV -- a state-of-the-art DLP system -- and report on experiments which confirm the usefulness of the proposed extension also for the efficiency of computation.
There are two kinds of approaches for termination analysis of logic programs: "transformational" and "direct" ones. Direct approaches prove termination directly on the basis of the logic program. Transformational approaches transform a logic program into a term rewrite system (TRS) and then analyze termination of the resulting TRS instead. Thus, transformational approaches make all methods previously developed for TRSs available for logic programs as well. However, the applicability of most existing transformations is quite restricted, as they can only be used for certain subclasses of logic programs. (Most of them are restricted to well-moded programs.) In this paper we improve these transformations such that they become applicable for any definite logic program. To simulate the behavior of logic programs by TRSs, we slightly modify the notion of rewriting by permitting infinite terms. We show that our transformation results in TRSs which are indeed suitable for automated termination analysis. In contrast to most other methods for termination of logic programs, our technique is also sound for logic programming without occur check, which is typically used in practice. We implemented our approach in the termination prover AProVE and successfully evaluated it on a large collection of examples.
We provide deterministic, polynomial-time computable voting rules that approximate Dodgson's and (the ``minimization version'' of) Young's scoring rules to within a logarithmic factor. Our approximation of Dodgson's rule is tight up to a constant factor, as Dodgson's rule is $\NP$-hard to approximate to within some logarithmic factor. The ``maximization version'' of Young's rule is known to be $\NP$-hard to approximate by any constant factor. Both approximations are simple, and natural as rules in their own right: Given a candidate we wish to score, we can regard either its Dodgson or Young score as the edit distance between a given set of voter preferences and one in which the candidate to be scored is the Condorcet winner. (The difference between the two scoring rules is the type of edits allowed.) We regard the marginal cost of a sequence of edits to be the number of edits divided by the number of reductions (in the candidate's deficit against any of its opponents in the pairwise race against that opponent) that the edits yield. Over a series of rounds, our scoring rules greedily choose a sequence of edits that modify exactly one voter's preferences and whose marginal cost is no greater than any other such single-vote-modifying sequence.
This paper focuses on the problem of kernelizing an existing supervised Mahalanobis distance learner. The following features are included in the paper. Firstly, three popular learners, namely, "neighborhood component analysis", "large margin nearest neighbors" and "discriminant neighborhood embedding", which do not have kernel versions are kernelized in order to improve their classification performances. Secondly, an alternative kernelization framework called "KPCA trick" is presented. Implementing a learner in the new framework gains several advantages over the standard framework, e.g. no mathematical formulas and no reprogramming are required for a kernel implementation, the framework avoids troublesome problems such as singularity, etc. Thirdly, while the truths of representer theorems are just assumptions in previous papers related to ours, here, representer theorems are formally proven. The proofs validate both the kernel trick and the KPCA trick in the context of Mahalanobis distance learning. Fourthly, unlike previous works which always apply brute force methods to select a kernel, we investigate two approaches which can be efficiently adopted to construct an appropriate kernel for a given dataset. Finally, numerical results on various real-world datasets are presented.
This paper explores the links between Knowledge Management and new community-based models of the organization from both a theoretical and an empirical perspective. From a theoretical standpoint, we look at Communities of Practice (CoPs) and Knowledge Management (KM) and explore the links between the two as they relate to the use of information systems to manage knowledge. We begin by reviewing technologically supported approaches to KM and introduce the idea of "Systemes d'Aide a la Gestion des Connaissances" SAGC (Systems to aid the Management of Knowledge). Following this we examine the contribution that communal structures such as CoPs can make to intraorganizational KM and highlight some of 'success factors' for this approach to KM that are found in the literature. From an empirical standpoint, we present the results of a survey involving the Chief Knowledge Officers (CKOs) of twelve large French businesses; the objective of this study was to identify the factors that might influence the success of such approaches. The survey was analysed using thematic content analysis and the results are presented here with some short illustrative quotes from the CKOs. Finally, the paper concludes with some brief reflections on what can be learnt from looking at this problem from these two perspectives.
Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Data mining methods are important in the management of complex systems. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. Neural networks have been successfully applied in wide range of supervised and unsupervised learning applications. Neural network methods are not commonly used for data mining tasks, because they often produce incomprehensible models, and require long training times. One way in which the collective properties of a neural network may be used to implement a computational task is by way of the concept of energy minimization. The Hopfield network is well-known example of such an approach. The Hopfield network is useful as content addressable memory or an analog computer for solving combinatorial-type optimization problems. Wan Abdullah [1] proposed a method of doing logic programming on a Hopfield neural network. Optimization of logical inconsistency is carried out by the network after the connection strengths are defined from the logic program; the network relaxes to neural states corresponding to a valid interpretation. In this article, we describe how Hopfield network is able to induce logical rules from large database by using reverse analysis method: given the values of the connections of a network, we can hope to know what logical rules are entrenched in the database.
Computability logic (CL) (see http://www.cis.upenn.edu/~giorgi/cl.html) is a recently launched program for redeveloping logic as a formal theory of computability, as opposed to the formal theory of truth that logic has more traditionally been. Formulas in it represent computational problems, "truth" means existence of an algorithmic solution, and proofs encode such solutions. Within the line of research devoted to finding axiomatizations for ever more expressive fragments of CL, the present paper introduces a new deductive system CL12 and proves its soundness and completeness with respect to the semantics of CL. Conservatively extending classical predicate calculus and offering considerable additional expressive and deductive power, CL12 presents a reasonable, computationally meaningful, constructive alternative to classical logic as a basis for applied theories. To obtain a model example of such theories, this paper rebuilds the traditional, classical-logic-based Peano arithmetic into a computability-logic-based counterpart. Among the purposes of the present contribution is to provide a starting point for what, as the author wishes to hope, might become a new line of research with a potential of interesting findings -- an exploration of the presumably quite unusual metatheory of CL-based arithmetic and other CL-based applied systems.
Many social Web sites allow users to publish content and annotate with descriptive metadata. In addition to flat tags, some social Web sites have recently began to allow users to organize their content and metadata hierarchically. The social photosharing site Flickr, for example, allows users to group related photos in sets, and related sets in collections. The social bookmarking site Del.icio.us similarly lets users group related tags into bundles. Although the sites themselves don't impose any constraints on how these hierarchies are used, individuals generally use them to capture relationships between concepts, most commonly the broader/narrower relations. Collective annotation of content with hierarchical relations may lead to an emergent classification system, called a folksonomy. While some researchers have explored using tags as evidence for learning folksonomies, we believe that hierarchical relations described above offer a high-quality source of evidence for this task. We propose a simple approach to aggregate shallow hierarchies created by many distinct Flickr users into a common folksonomy. Our approach uses statistics to determine if a particular relation should be retained or discarded. The relations are then woven together into larger hierarchies. Although we have not carried out a detailed quantitative evaluation of the approach, it looks very promising since it generates very reasonable, non-trivial hierarchies.
In the last year more than 70,000 people have been brought to the UK hospitals with serious injuries. Each time a clinician has to urgently take a patient through a screening procedure to make a reliable decision on the trauma treatment. Typically, such procedure comprises around 20 tests; however the condition of a trauma patient remains very difficult to be tested properly. What happens if these tests are ambiguously interpreted, and information about the severity of the injury will come misleading? The mistake in a decision can be fatal: using a mild treatment can put a patient at risk of dying from posttraumatic shock, while using an overtreatment can also cause death. How can we reduce the risk of the death caused by unreliable decisions? It has been shown that probabilistic reasoning, based on the Bayesian methodology of averaging over decision models, allows clinicians to evaluate the uncertainty in decision making. Based on this methodology, in this paper we aim at selecting the most important screening tests, keeping a high performance. We assume that the probabilistic reasoning within the Bayesian methodology allows us to discover new relationships between the screening tests and uncertainty in decisions. In practice, selection of the most informative tests can also reduce the cost of a screening procedure in trauma care centers. In our experiments we use the UK Trauma data to compare the efficiency of the proposed technique in terms of the performance. We also compare the uncertainty in decisions in terms of entropy.
The way a rational agent changes her belief in certain propositions/hypotheses in the light of new evidence lies at the heart of Bayesian inference. The basic natural assumption, as summarized in van Fraassen's Reflection Principle ([1984]), would be that in the absence of new evidence the belief should not change. Yet, there are examples that are claimed to violate this assumption. The apparent paradox presented by such examples, if not settled, would demonstrate the inconsistency and/or incompleteness of the Bayesian approach and without eliminating this inconsistency, the approach cannot be regarded as scientific. The Sleeping Beauty Problem is just such an example. The existing attempts to solve the problem fall into three categories. The first two share the view that new evidence is absent, but differ about the conclusion of whether Sleeping Beauty should change her belief or not, and why. The third category is characterized by the view that, after all, new evidence (although hidden from the initial view) is involved. My solution is radically different and does not fall in either of these categories. I deflate the paradox by arguing that the two different degrees of belief presented in the Sleeping Beauty Problem are in fact beliefs in two different propositions, i.e. there is no need to explain the (un)change of belief.
It is generally accepted that human vision is an extremely powerful information processing system that facilitates our interaction with the surrounding world. However, despite extended and extensive research efforts, which encompass many exploration fields, the underlying fundamentals and operational principles of visual information processing in human brain remain unknown. We still are unable to figure out where and how along the path from eyes to the cortex the sensory input perceived by the retina is converted into a meaningful object representation, which can be consciously manipulated by the brain. Studying the vast literature considering the various aspects of brain information processing, I was surprised to learn that the respected scholarly discussion is totally indifferent to the basic keynote question: "What is information?" in general or "What is visual information?" in particular. In the old days, it was assumed that any scientific research approach has first to define its basic departure points. Why was it overlooked in brain information processing research remains a conundrum. In this paper, I am trying to find a remedy for this bizarre situation. I propose an uncommon definition of "information", which can be derived from Kolmogorov's Complexity Theory and Chaitin's notion of Algorithmic Information. Embracing this new definition leads to an inevitable revision of traditional dogmas that shape the state of the art of brain information processing research. I hope this revision would better serve the challenging goal of human visual information processing modeling.
We investigate the use of message-passing algorithms for the problem of finding the max-weight independent set (MWIS) in a graph. First, we study the performance of the classical loopy max-product belief propagation. We show that each fixed point estimate of max-product can be mapped in a natural way to an extreme point of the LP polytope associated with the MWIS problem. However, this extreme point may not be the one that maximizes the value of node weights; the particular extreme point at final convergence depends on the initialization of max-product. We then show that if max-product is started from the natural initialization of uninformative messages, it always solves the correct LP -- if it converges. This result is obtained via a direct analysis of the iterative algorithm, and cannot be obtained by looking only at fixed points. The tightness of the LP relaxation is thus necessary for max-product optimality, but it is not sufficient. Motivated by this observation, we show that a simple modification of max-product becomes gradient descent on (a convexified version of) the dual of the LP, and converges to the dual optimum. We also develop a message-passing algorithm that recovers the primal MWIS solution from the output of the descent algorithm. We show that the MWIS estimate obtained using these two algorithms in conjunction is correct when the graph is bipartite and the MWIS is unique. Finally, we show that any problem of MAP estimation for probability distributions over finite domains can be reduced to an MWIS problem. We believe this reduction will yield new insights and algorithms for MAP estimation.
This project describes the electricity demand and energy consumption management system and its application to Southern Peru smelter. It is composed of an hourly demand-forecasting module and of a simulation component for a plant electrical system. The first module was done using dynamic neural networks with backpropagation training algorithm; it is used to predict the electric power demanded every hour, with an error percentage below of 1%. This information allows efficient management of energy peak demands before this happen, distributing the raise of electric load to other hours or improving those equipments that increase the demand. The simulation module is based in advanced estimation techniques, such as: parametric estimation, neural network modeling, statistic regression and previously developed models, which simulates the electric behavior of the smelter plant. These modules facilitate electricity demand and consumption proper planning, because they allow knowing the behavior of the hourly demand and the consumption patterns of the plant, including the bill components, but also energy deficiencies and opportunities for improvement, based on analysis of information about equipments, processes and production plans, as well as maintenance programs. Finally the results of its application in Southern Peru smelter are presented.
The notions of hypertree width and generalized hypertree width were introduced by Gottlob, Leone, and Scarcello in order to extend the concept of hypergraph acyclicity. These notions were further generalized by Grohe and Marx, who introduced the fractional hypertree width of a hypergraph. All these width parameters on hypergraphs are useful for extending tractability of many problems in database theory and artificial intelligence. In this paper, we study the approximability of (generalized, fractional) hyper treewidth of sparse hypergraphs where the criterion of sparsity reflects the sparsity of their incidence graphs. Our first step is to prove that the (generalized, fractional) hypertree width of a hypergraph H is constant-factor sandwiched by the treewidth of its incidence graph, when the incidence graph belongs to some apex-minor-free graph class. This determines the combinatorial borderline above which the notion of (generalized, fractional) hypertree width becomes essentially more general than treewidth, justifying that way its functionality as a hypergraph acyclicity measure. While for more general sparse families of hypergraphs treewidth of incidence graphs and all hypertree width parameters may differ arbitrarily, there are sparse families where a constant factor approximation algorithm is possible. In particular, we give a constant factor approximation polynomial time algorithm for (generalized, fractional) hypertree width on hypergraphs whose incidence graphs belong to some H-minor-free graph class.
Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.
In this paper, a Gaifman-Shapiro-style module architecture is tailored to the case of Smodels programs under the stable model semantics. The composition of Smodels program modules is suitably limited by module conditions which ensure the compatibility of the module system with stable models. Hence the semantics of an entire Smodels program depends directly on stable models assigned to its modules. This result is formalized as a module theorem which truly strengthens Lifschitz and Turner's splitting-set theorem for the class of Smodels programs. To streamline generalizations in the future, the module theorem is first proved for normal programs and then extended to cover Smodels programs using a translation from the latter class of programs to the former class. Moreover, the respective notion of module-level equivalence, namely modular equivalence, is shown to be a proper congruence relation: it is preserved under substitutions of modules that are modularly equivalent. Principles for program decomposition are also addressed. The strongly connected components of the respective dependency graph can be exploited in order to extract a module structure when there is no explicit a priori knowledge about the modules of a program. The paper includes a practical demonstration of tools that have been developed for automated (de)composition of Smodels programs. To appear in Theory and Practice of Logic Programming.
Multi-relational networks are used extensively to structure knowledge. Perhaps the most popular instance, due to the widespread adoption of the Semantic Web, is the Resource Description Framework (RDF). One of the primary purposes of a knowledge network is to reason; that is, to alter the topology of the network according to an algorithm that uses the existing topological structure as its input. There exist many such reasoning algorithms. With respect to the Semantic Web, the bivalent, monotonic reasoners of the RDF Schema (RDFS) and the Web Ontology Language (OWL) are the most prevalent. However, nothing prevents other forms of reasoning from existing in the Semantic Web. This article presents a non-bivalent, non-monotonic, evidential logic and reasoner that is an algebraic ring over a multi-relational network equipped with two binary operations that can be composed to execute various forms of inference. Given its multi-relational grounding, it is possible to use the presented evidential framework as another method for structuring knowledge and reasoning in the Semantic Web. The benefits of this framework are that it works with arbitrary, partial, and contradictory knowledge while, at the same time, it supports a tractable approximate reasoning process.
We proof a theorem that shows that a collection of experimental data of membership weights of items with respect to a pair of concepts and its conjunction cannot be modeled within a classical measure theoretic weight structure in case the experimental data contain the effect called overextension. Since the effect of overextension, analogue to the well-known guppy effect for concept combinations, is abundant in all experiments testing weights of items with respect to pairs of concepts and their conjunctions, our theorem constitutes a no-go theorem for classical measure structure for common data of membership weights of items with respect to concepts and their combinations. We put forward a simple geometric criterion that reveals the non classicality of the membership weight structure and use experimentally measured membership weights estimated by subjects in experiments to illustrate our geometrical criterion. The violation of the classical weight structure is similar to the violation of the well-known Bell inequalities studied in quantum mechanics, and hence suggests that the quantum formalism and hence the modeling by quantum membership weights can accomplish what classical membership weights cannot do.
Inspired by a quantum mechanical formalism to model concepts and their disjunctions and conjunctions, we put forward in this paper a specific hypothesis. Namely that within human thought two superposed layers can be distinguished: (i) a layer given form by an underlying classical deterministic process, incorporating essentially logical thought and its indeterministic version modeled by classical probability theory; (ii) a layer given form under influence of the totality of the surrounding conceptual landscape, where the different concepts figure as individual entities rather than (logical) combinations of others, with measurable quantities such as 'typicality', 'membership', 'representativeness', 'similarity', 'applicability', 'preference' or 'utility' carrying the influences. We call the process in this second layer 'quantum conceptual thought', which is indeterministic in essence, and contains holistic aspects, but is equally well, although very differently, organized than logical thought. A substantial part of the 'quantum conceptual thought process' can be modeled by quantum mechanical probabilistic and mathematical structures. We consider examples of three specific domains of research where the effects of the presence of quantum conceptual thought and its deviations from classical logical thought have been noticed and studied, i.e. economics, decision theory, and concept theories and which provide experimental evidence for our hypothesis.
In the context of the Semantic Web, several approaches to the combination of ontologies, given in terms of theories of classical first-order logic and rule bases, have been proposed. They either cast rules into classical logic or limit the interaction between rules and ontologies. Autoepistemic logic (AEL) is an attractive formalism which allows to overcome these limitations, by serving as a uniform host language to embed ontologies and nonmonotonic logic programs into it. For the latter, so far only the propositional setting has been considered. In this paper, we present three embeddings of normal and three embeddings of disjunctive non-ground logic programs under the stable model semantics into first-order AEL. While the embeddings all correspond with respect to objective ground atoms, differences arise when considering non-atomic formulas and combinations with first-order theories. We compare the embeddings with respect to stable expansions and autoepistemic consequences, considering the embeddings by themselves, as well as combinations with classical theories. Our results reveal differences and correspondences of the embeddings and provide useful guidance in the choice of a particular embedding for knowledge combination.
Collaborative tagging systems, such as Delicious, CiteULike, and others, allow users to annotate resources, e.g., Web pages or scientific papers, with descriptive labels called tags. The social annotations contributed by thousands of users, can potentially be used to infer categorical knowledge, classify documents or recommend new relevant information. Traditional text inference methods do not make best use of social annotation, since they do not take into account variations in individual users' perspectives and vocabulary. In a previous work, we introduced a simple probabilistic model that takes interests of individual annotators into account in order to find hidden topics of annotated resources. Unfortunately, that approach had one major shortcoming: the number of topics and interests must be specified a priori. To address this drawback, we extend the model to a fully Bayesian framework, which offers a way to automatically estimate these numbers. In particular, the model allows the number of interests and topics to change as suggested by the structure of the data. We evaluate the proposed model in detail on the synthetic and real-world data by comparing its performance to Latent Dirichlet Allocation on the topic extraction task. For the latter evaluation, we apply the model to infer topics of Web resources from social annotations obtained from Delicious in order to discover new resources similar to a specified one. Our empirical results demonstrate that the proposed model is a promising method for exploiting social knowledge contained in user-generated annotations.
Many databases store data in relational format, with different types of entities and information about links between the entities. The field of statistical-relational learning (SRL) has developed a number of new statistical models for such data. In this paper we focus on learning class-level or first-order dependencies, which model the general database statistics over attributes of linked objects and links (e.g., the percentage of A grades given in computer science classes). Class-level statistical relationships are important in themselves, and they support applications like policy making, strategic planning, and query optimization. Most current SRL methods find class-level dependencies, but their main task is to support instance-level predictions about the attributes or links of specific entities. We focus only on class-level prediction, and describe algorithms for learning class-level models that are orders of magnitude faster for this task. Our algorithms learn Bayes nets with relational structure, leveraging the efficiency of single-table nonrelational Bayes net learners. An evaluation of our methods on three data sets shows that they are computationally feasible for realistic table sizes, and that the learned structures represent the statistical information in the databases well. After learning compiles the database statistics into a Bayes net, querying these statistics via Bayes net inference is faster than with SQL queries, and does not depend on the size of the database.
The Quantum Decision Theory, developed recently by the authors, is applied to clarify the role of risk and uncertainty in decision making and in particular in relation to the phenomenon of dynamic inconsistency. By formulating this notion in precise mathematical terms, we distinguish three types of inconsistency: time inconsistency, planning paradox, and inconsistency occurring in some discounting effects. While time inconsistency is well accounted for in classical decision theory, the planning paradox is in contradiction with classical utility theory. It finds a natural explanation in the frame of the Quantum Decision Theory. Different types of discounting effects are analyzed and shown to enjoy a straightforward explanation within the suggested theory. We also introduce a general methodology based on self-similar approximation theory for deriving the evolution equations for the probabilities of future prospects. This provides a novel classification of possible discount factors, which include the previously known cases (exponential or hyperbolic discounting), but also predicts a novel class of discount factors that decay to a strictly positive constant for very large future time horizons. This class may be useful to deal with very long-term discounting situations associated with intergenerational public policy choices, encompassing issues such as global warming and nuclear waste disposal.
We have proposed a model based upon flocking on a complex network, and then developed two clustering algorithms on the basis of it. In the algorithms, firstly a \textit{k}-nearest neighbor (knn) graph as a weighted and directed graph is produced among all data points in a dataset each of which is regarded as an agent who can move in space, and then a time-varying complex network is created by adding long-range links for each data point. Furthermore, each data point is not only acted by its \textit{k} nearest neighbors but also \textit{r} long-range neighbors through fields established in space by them together, so it will take a step along the direction of the vector sum of all fields. It is more important that these long-range links provides some hidden information for each data point when it moves and at the same time accelerate its speed converging to a center. As they move in space according to the proposed model, data points that belong to the same class are located at a same position gradually, whereas those that belong to different classes are away from one another. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the rates of convergence of clustering algorithms are fast enough. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.
We introduce novel results for approximate inference on planar graphical models using the loop calculus framework. The loop calculus (Chertkov and Chernyak, 2006) allows to express the exact partition function of a graphical model as a finite sum of terms that can be evaluated once the belief propagation (BP) solution is known. In general, full summation over all correction terms is intractable. We develop an algorithm for the approach presented in (Certkov et al., 2008) which represents an efficient truncation scheme on planar graphs and a new representation of the series in terms of Pfaffians of matrices. We analyze the performance of the algorithm for the partition function approximation for models with binary variables and pairwise interactions on grids and other planar graphs. We study in detail both the loop series and the equivalent Pfaffian series and show that the first term of the Pfaffian series for the general, intractable planar model, can provide very accurate approximations. The algorithm outperforms previous truncation schemes of the loop series and is competitive with other state-of-the-art methods for approximate inference.
This paper presents several types of evolutionary algorithms (EAs) used for global optimization on real domains. The interest has been focused on multimodal problems, where the difficulties of a premature convergence usually occurs. First the standard genetic algorithm (SGA) using binary encoding of real values and its unsatisfactory behavior with multimodal problems is briefly reviewed together with some improvements of fighting premature convergence. Two types of real encoded methods based on differential operators are examined in detail: the differential evolution (DE), a very modern and effective method firstly published by R. Storn and K. Price, and the simplified real-coded differential genetic algorithm SADE proposed by the authors. In addition, an improvement of the SADE method, called CERAF technology, enabling the population of solutions to escape from local extremes, is examined. All methods are tested on an identical set of objective functions and a systematic comparison based on a reliable methodology is presented. It is confirmed that real coded methods generally exhibit better behavior on real domains than the binary algorithms, even when extended by several improvements. Furthermore, the positive influence of the differential operators due to their possibility of self-adaptation is demonstrated. From the reliability point of view, it seems that the real encoded differential algorithm, improved by the technology described in this paper, is a universal and reliable method capable of solving all proposed test problems.
We study the combination of the following already known ideas for showing confluence of unconditional or conditional term rewriting systems into practically more useful confluence criteria for conditional systems: Our syntactical separation into constructor and non-constructor symbols, Huet's introduction and Toyama's generalization of parallel closedness for non-noetherian unconditional systems, the use of shallow confluence for proving confluence of noetherian and non-noetherian conditional systems, the idea that certain kinds of limited confluence can be assumed for checking the fulfilledness or infeasibility of the conditions of conditional critical pairs, and the idea that (when termination is given) only prime superpositions have to be considered and certain normalization restrictions can be applied for the substitutions fulfilling the conditions of conditional critical pairs. Besides combining and improving already known methods, we present the following new ideas and results: We strengthen the criterion for overlay joinable noetherian systems, and, by using the expressiveness of our syntactical separation into constructor and non-constructor symbols, we are able to present criteria for level confluence that are not criteria for shallow confluence actually and also able to weaken the severe requirement of normality (stiffened with left-linearity) in the criteria for shallow confluence of noetherian and non-noetherian conditional systems to the easily satisfied requirement of quasi-normality. Finally, the whole paper may also give a practically useful overview of the syntactical means for showing confluence of conditional term rewriting systems.
We present the only proof of Pierre Fermat by descente infinie that is known to exist today. As the text of its Latin original requires active mathematical interpretation, it is more a proof sketch than a proper mathematical proof. We discuss descente infinie from the mathematical, logical, historical, linguistic, and refined logic-historical points of view. We provide the required preliminaries from number theory and develop a self-contained proof in a modern form, which nevertheless is intended to follow Fermat's ideas closely. We then annotate an English translation of Fermat's original proof with terms from the modern proof. Including all important facts, we present a concise and self-contained discussion of Fermat's proof sketch, which is easily accessible to laymen in number theory as well as to laymen in the history of mathematics, and which provides new clarification of the Method of Descente Infinie to the experts in these fields. Last but not least, this paper fills a gap regarding the easy accessibility of the subject.
1-Nearest Neighbor with the Dynamic Time Warping (DTW) distance is one of the most effective classifiers on time series domain. Since the global constraint has been introduced in speech community, many global constraint models have been proposed including Sakoe-Chiba (S-C) band, Itakura Parallelogram, and Ratanamahatana-Keogh (R-K) band. The R-K band is a general global constraint model that can represent any global constraints with arbitrary shape and size effectively. However, we need a good learning algorithm to discover the most suitable set of R-K bands, and the current R-K band learning algorithm still suffers from an 'overfitting' phenomenon. In this paper, we propose two new learning algorithms, i.e., band boundary extraction algorithm and iterative learning algorithm. The band boundary extraction is calculated from the bound of all possible warping paths in each class, and the iterative learning is adjusted from the original R-K band learning. We also use a Silhouette index, a well-known clustering validation technique, as a heuristic function, and the lower bound function, LB_Keogh, to enhance the prediction speed. Twenty datasets, from the Workshop and Challenge on Time Series Classification, held in conjunction of the SIGKDD 2007, are used to evaluate our approach.
Affective computing has proven to be a viable field of research comprised of a large number of multidisciplinary researchers resulting in work that is widely published. The majority of this work consists of computational models of emotion recognition, computational modeling of causal factors of emotion and emotion expression through rendered and robotic faces. A smaller part is concerned with modeling the effects of emotion, formal modeling of cognitive appraisal theory and models of emergent emotions. Part of the motivation for affective computing as a field is to better understand emotional processes through computational modeling. One of the four major topics in affective computing is computers that have emotions (the others are recognizing, expressing and understanding emotions). A critical and neglected aspect of having emotions is the experience of emotion (Barrett, Mesquita, Ochsner, and Gross, 2007): what does the content of an emotional episode look like, how does this content change over time and when do we call the episode emotional. Few modeling efforts have these topics as primary focus. The launch of a journal on synthetic emotions should motivate research initiatives in this direction, and this research should have a measurable impact on emotion research in psychology. I show that a good way to do so is to investigate the psychological core of what an emotion is: an experience. I present ideas on how the experience of emotion could be modeled and provide evidence that several computational models of emotion are already addressing the issue.
In this work, we deal with the question of modeling programming exercises for novices pointing to an e-learning scenario. Our purpose is to identify basic requirements, raise some key questions and propose potential answers from a conceptual perspective. Presented as a general picture, we hypothetically situate our work in a general context where e-learning instructional material needs to be adapted to form part of an introductory Computer Science (CS) e-learning course at the CS1-level. Meant is a potential course which aims at improving novices skills and knowledge on the essentials of programming by using e-learning based approaches in connection (at least conceptually) with a general host framework like Activemath (www.activemath.org). Our elaboration covers contextual and, particularly, cognitive elements preparing the terrain for eventual research stages in a derived project, as indicated. We concentrate our main efforts on reasoning mechanisms about exercise complexity that can eventually offer tool support for the task of exercise authoring. We base our requirements analysis on our own perception of the exercise subsystem provided by Activemath especially within the domain reasoner area. We enrich the analysis by bringing to the discussion several relevant contextual elements from the CS1 courses, its definition and implementation. Concerning cognitive models and exercises, we build upon the principles of Bloom's Taxonomy as a relatively standardized basis and use them as a framework for study and analysis of complexity in basic programming exercises. Our analysis includes requirements for the domain reasoner which are necessary for the exercise analysis. We propose for such a purpose a three-layered conceptual model considering exercise evaluation, programming and metaprogramming.
Complexity theory is a useful tool to study computational issues surrounding the elicitation of preferences, as well as the strategic manipulation of elections aggregating together preferences of multiple agents. We study here the complexity of determining when we can terminate eliciting preferences, and prove that the complexity depends on the elicitation strategy. We show, for instance, that it may be better from a computational perspective to elicit all preferences from one agent at a time than to elicit individual preferences from multiple agents. We also study the connection between the strategic manipulation of an election and preference elicitation. We show that what we can manipulate affects the computational complexity of manipulation. In particular, we prove that there are voting rules which are easy to manipulate if we can change all of an agent's vote, but computationally intractable if we can change only some of their preferences. This suggests that, as with preference elicitation, a fine-grained view of manipulation may be informative. Finally, we study the connection between predicting the winner of an election and preference elicitation. Based on this connection, we identify a voting rule where it is computationally difficult to decide the probability of a candidate winning given a probability distribution over the votes.
Semantic memory is the subsystem of human memory that stores knowledge of concepts or meanings, as opposed to life specific experiences. The organization of concepts within semantic memory can be understood as a semantic network, where the concepts (nodes) are associated (linked) to others depending on perceptions, similarities, etc. Lexical access is the complementary part of this system and allows the retrieval of such organized knowledge. While conceptual information is stored under certain underlying organization (and thus gives rise to a specific topology), it is crucial to have an accurate access to any of the information units, e.g. the concepts, for efficiently retrieving semantic information for real-time needings. An example of an information retrieval process occurs in verbal fluency tasks, and it is known to involve two different mechanisms: -clustering-, or generating words within a subcategory, and, when a subcategory is exhausted, -switching- to a new subcategory. We extended this approach to random-walking on a network (clustering) in combination to jumping (switching) to any node with certain probability and derived its analytical expression based on Markov chains. Results show that this dual mechanism contributes to optimize the exploration of different network models in terms of the mean first passage time. Additionally, this cognitive inspired dual mechanism opens a new framework to better understand and evaluate exploration, propagation and transport phenomena in other complex systems where switching-like phenomena are feasible.
Observational astronomy has changed drastically in the last decade: manually driven target-by-target instruments have been replaced by fully automated robotic telescopes. Data acquisition methods have advanced to the point that terabytes of data are flowing in and being stored on a daily basis. At the same time, the vast majority of analysis tools in stellar astrophysics still rely on manual expert interaction. To bridge this gap, we foresee that the next decade will witness a fundamental shift in the approaches to data analysis: case-by-case methods will be replaced by fully automated pipelines that will process the data from their reduction stage, through analysis, to storage. While major effort has been invested in data reduction automation, automated data analysis has mostly been neglected despite the urgent need. Scientific data mining will face serious challenges to identify, understand and eliminate the sources of systematic errors that will arise from this automation. As a special case, we present an artificial intelligence (AI) driven pipeline that is prototyped in the domain of stellar astrophysics (eclipsing binaries in particular), current results and the challenges still ahead.
This papers develops a logical language for representing probabilistic causal laws. Our interest in such a language is twofold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In our formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way. In this way, our work extends Shafer's by offering a convenient logical representation for his semantical objects. Second, this language also has relevance for the area of probabilistic logic programming. In particular, we prove that the formal semantics of a theory in our language can be equivalently defined as a probability distribution over the well-founded models of certain logic programs, rendering it formally quite similar to existing languages such as ICL or PRISM. Because we can motivate and explain our language in a completely self-contained way as a representation of probabilistic causal laws, this provides a new way of explaining the intuitions behind such probabilistic logic programs: we can say precisely which knowledge such a program expresses, in terms that are equally understandable by a non-logician. Moreover, we also obtain an additional piece of knowledge representation methodology for probabilistic logic programs, by showing how they can express probabilistic causal laws.
Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a pattern-based subspace cluster, is a subset of rows and columns in a data matrix for which all the rows induce the same linear ordering of columns. Existing OPSM discovery methods do not scale well to increasingly large expression datasets. In particular, twig clusters having few genes and many experiments incur explosive computational costs and are completely pruned off by existing methods. However, it is of particular interest to determine small groups of genes that are tightly coregulated across many conditions. In this paper, we present KiWi, an OPSM subspace clustering algorithm that is scalable to massive datasets, capable of discovering twig clusters and identifying negative as well as positive correlations. We extensively validate KiWi using relevant biological datasets and show that KiWi correctly assigns redundant probes to the same cluster, groups experiments with common clinical annotations, differentiates real promoter sequences from negative control sequences, and shows good association with cis-regulatory motif predictions.
Real world datasets are sparse, dirty and contain hundreds of items. In such situations, discovering interesting rules (results) using traditional frequent itemset mining approach by specifying a user defined input support threshold is not appropriate. Since without any domain knowledge, setting support threshold small or large can output nothing or a large number of redundant uninteresting results. Recently a novel approach of mining only N-most/Top-K interesting frequent itemsets has been proposed, which discovers the top N interesting results without specifying any user defined support threshold. However, mining interesting frequent itemsets without minimum support threshold are more costly in terms of itemset search space exploration and processing cost. Thereby, the efficiency of their mining highly depends upon three main factors (1) Database representation approach used for itemset frequency counting, (2) Projection of relevant transactions to lower level nodes of search space and (3) Algorithm implementation technique. Therefore, to improve the efficiency of mining process, in this paper we present two novel algorithms called (N-MostMiner and Top-K-Miner) using the bit-vector representation approach which is very efficient in terms of itemset frequency counting and transactions projection. In addition to this, several efficient implementation techniques of N-MostMiner and Top-K-Miner are also present which we experienced in our implementation. Our experimental results on benchmark datasets suggest that the NMostMiner and Top-K-Miner are very efficient in terms of processing time as compared to current best algorithms BOMO and TFP.
Computability logic (CL) (see http://www.cis.upenn.edu/~giorgi/cl.html ) is a research program for redeveloping logic as a formal theory of computability, as opposed to the formal theory of truth which it has more traditionally been. Formulas in CL stand for interactive computational problems, seen as games between a machine and its environment; logical operators represent operations on such entities; and "truth" is understood as existence of an effective solution. The formalism of CL is open-ended, and may undergo series of extensions as the studies of the subject advance. So far three -- parallel, sequential and choice -- sorts of conjunction and disjunction have been studied. The present paper adds one more natural kind to this collection, termed toggling. The toggling operations can be characterized as lenient versions of choice operations where choices are retractable, being allowed to be reconsidered any finite number of times. This way, they model trial-and-error style decision steps in interactive computation. The main technical result of this paper is constructing a sound and complete axiomatization for the propositional fragment of computability logic whose vocabulary, together with negation, includes all four -- parallel, toggling, sequential and choice -- kinds of conjunction and disjunction. Along with toggling conjunction and disjunction, the paper also introduces the toggling versions of quantifiers and recurrence operations.
Approximately over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain long-term EEG data with seizure activities, especially in areas short of medical resources. We demonstrate in this paper that we can use the interictal scalp EEG data, which is much easier to collect than the ictal data, to automatically diagnose whether a person is epileptic. In our automated EEG recognition system, we extract three classes of features from the EEG data and build Probabilistic Neural Networks (PNNs) fed with these features. We optimize the feature extraction parameters and combine these PNNs through a voting mechanism. As a result, our system achieves an impressive 94.07% accuracy, which is very close to reported human recognition accuracy by experienced medical professionals.
This paper studies the stable model semantics of logic programs with (abstract) constraint atoms and their properties. We introduce a succinct abstract representation of these constraint atoms in which a constraint atom is represented compactly. We show two applications. First, under this representation of constraint atoms, we generalize the Gelfond-Lifschitz transformation and apply it to define stable models (also called answer sets) for logic programs with arbitrary constraint atoms. The resulting semantics turns out to coincide with the one defined by Son et al., which is based on a fixpoint approach. One advantage of our approach is that it can be applied, in a natural way, to define stable models for disjunctive logic programs with constraint atoms, which may appear in the disjunctive head as well as in the body of a rule. As a result, our approach to the stable model semantics for logic programs with constraint atoms generalizes a number of previous approaches. Second, we show that our abstract representation of constraint atoms provides a means to characterize dependencies of atoms in a program with constraint atoms, so that some standard characterizations and properties relying on these dependencies in the past for logic programs with ordinary atoms can be extended to logic programs with constraint atoms.
Our aim in this paper is to analyse the phenotypic effects (evolvability) of diverse coding conversion operators in an instance of the states based evolutionary algorithm (SEA). Since the representation of solutions or the selection of the best encoding during the optimization process has been proved to be very important for the efficiency of evolutionary algorithms (EAs), we will discuss a strategy of coupling more than one representation and different procedures of conversion from one coding to another during the search. Elsewhere, some EAs try to use multiple representations (SM-GA, SEA, etc.) in intention to benefit from the characteristics of each of them. In spite of those results, this paper shows that the change of the representation is also a crucial approach to take into consideration while attempting to increase the performances of such EAs. As a demonstrative example, we use a two states SEA (2-SEA) which has two identical search spaces but different coding conversion operators. The results show that the way of changing from one coding to another and not only the choice of the best representation nor the representation itself is very advantageous and must be taken into account in order to well-desing and improve EAs execution.
Many regression problems involve not one but several response variables (y's). Often the responses are suspected to share a common underlying structure, in which case it may be advantageous to share information across them; this is known as multitask learning. As a special case, we can use multiple responses to better identify shared predictive features -- a project we might call multitask feature selection. This thesis is organized as follows. Section 1 introduces feature selection for regression, focusing on ell_0 regularization methods and their interpretation within a Minimum Description Length (MDL) framework. Section 2 proposes a novel extension of MDL feature selection to the multitask setting. The approach, called the "Multiple Inclusion Criterion" (MIC), is designed to borrow information across regression tasks by more easily selecting features that are associated with multiple responses. We show in experiments on synthetic and real biological data sets that MIC can reduce prediction error in settings where features are at least partially shared across responses. Section 3 surveys hypothesis testing by regression with a single response, focusing on the parallel between the standard Bonferroni correction and an MDL approach. Mirroring the ideas in Section 2, Section 4 proposes a novel MIC approach to hypothesis testing with multiple responses and shows that on synthetic data with significant sharing of features across responses, MIC sometimes outperforms standard FDR-controlling methods in terms of finding true positives for a given level of false positives. Section 5 concludes.
Mining frequent sequential patterns from sequence databases has been a central research topic in data mining and various efficient mining sequential patterns algorithms have been proposed and studied. Recently, in many problem domains (e.g, program execution traces), a novel sequential pattern mining research, called mining repetitive gapped sequential patterns, has attracted the attention of many researchers, considering not only the repetition of sequential pattern in different sequences but also the repetition within a sequence is more meaningful than the general sequential pattern mining which only captures occurrences in different sequences. However, the number of repetitive gapped sequential patterns generated by even these closed mining algorithms may be too large to understand for users, especially when support threshold is low. In this paper, we propose and study the problem of compressing repetitive gapped sequential patterns. Inspired by the ideas of summarizing frequent itemsets, RPglobal, we develop an algorithm, CRGSgrow (Compressing Repetitive Gapped Sequential pattern grow), including an efficient pruning strategy, SyncScan, and an efficient representative pattern checking scheme, -dominate sequential pattern checking. The CRGSgrow is a two-step approach: in the first step, we obtain all closed repetitive sequential patterns as the candidate set of representative repetitive sequential patterns, and at the same time get the most of representative repetitive sequential patterns; in the second step, we only spend a little time in finding the remaining the representative patterns from the candidate set. An empirical study with both real and synthetic data sets clearly shows that the CRGSgrow has good performance.
Among many existing distance measures for time series data, Dynamic Time Warping (DTW) distance has been recognized as one of the most accurate and suitable distance measures due to its flexibility in sequence alignment. However, DTW distance calculation is computationally intensive. Especially in very large time series databases, sequential scan through the entire database is definitely impractical, even with random access that exploits some index structures since high dimensionality of time series data incurs extremely high I/O cost. More specifically, a sequential structure consumes high CPU but low I/O costs, while an index structure requires low CPU but high I/O costs. In this work, we therefore propose a novel indexed sequential structure called TWIST (Time Warping in Indexed Sequential sTructure) which benefits from both sequential access and index structure. When a query sequence is issued, TWIST calculates lower bounding distances between a group of candidate sequences and the query sequence, and then identifies the data access order in advance, hence reducing a great number of both sequential and random accesses. Impressively, our indexed sequential structure achieves significant speedup in a querying process by a few orders of magnitude. In addition, our method shows superiority over existing rival methods in terms of query processing time, number of page accesses, and storage requirement with no false dismissal guaranteed.
Conference paper assignment, i.e., the task of assigning paper submissions to reviewers, presents multi-faceted issues for recommender systems research. Besides the traditional goal of predicting `who likes what?', a conference management system must take into account aspects such as: reviewer capacity constraints, adequate numbers of reviews for papers, expertise modeling, conflicts of interest, and an overall distribution of assignments that balances reviewer preferences with conference objectives. Among these, issues of modeling preferences and tastes in reviewing have traditionally been studied separately from the optimization of paper-reviewer assignment. In this paper, we present an integrated study of both these aspects. First, due to the paucity of data per reviewer or per paper (relative to other recommender systems applications) we show how we can integrate multiple sources of information to learn paper-reviewer preference models. Second, our models are evaluated not just in terms of prediction accuracy but in terms of the end-assignment quality. Using a linear programming-based assignment optimization formulation, we show how our approach better explores the space of unsupplied assignments to maximize the overall affinities of papers assigned to reviewers. We demonstrate our results on real reviewer preference data from the IEEE ICDM 2007 conference.
We study the termination problem of the chase algorithm, a central tool in various database problems such as the constraint implication problem, Conjunctive Query optimization, rewriting queries using views, data exchange, and data integration. The basic idea of the chase is, given a database instance and a set of constraints as input, to fix constraint violations in the database instance. It is well-known that, for an arbitrary set of constraints, the chase does not necessarily terminate (in general, it is even undecidable if it does or not). Addressing this issue, we review the limitations of existing sufficient termination conditions for the chase and develop new techniques that allow us to establish weaker sufficient conditions. In particular, we introduce two novel termination conditions called safety and inductive restriction, and use them to define the so-called T-hierarchy of termination conditions. We then study the interrelations of our termination conditions with previous conditions and the complexity of checking our conditions. This analysis leads to an algorithm that checks membership in a level of the T-hierarchy and accounts for the complexity of termination conditions. As another contribution, we study the problem of data-dependent chase termination and present sufficient termination conditions w.r.t. fixed instances. They might guarantee termination although the chase does not terminate in the general case. As an application of our techniques beyond those already mentioned, we transfer our results into the field of query answering over knowledge bases where the chase on the underlying database may not terminate, making existing algorithms applicable to broader classes of constraints.
In most contemporary approaches to decision making, a decision problem is described by a sets of states and set of outcomes, and a rich set of acts, which are functions from states to outcomes over which the decision maker (DM) has preferences. Most interesting decision problems, however, do not come with a state space and an outcome space. Indeed, in complex problems it is often far from clear what the state and outcome spaces would be. We present an alternative foundation for decision making, in which the primitive objects of choice are syntactic programs. A representation theorem is proved in the spirit of standard representation theorems, showing that if the DM's preference relation on objects of choice satisfies appropriate axioms, then there exist a set S of states, a set O of outcomes, a way of interpreting the objects of choice as functions from S to O, a probability on S, and a utility function on O, such that the DM prefers choice a to choice b if and only if the expected utility of a is higher than that of b. Thus, the state space and outcome space are subjective, just like the probability and utility; they are not part of the description of the problem. In principle, a modeler can test for SEU behavior without having access to states or outcomes. We illustrate the power of our approach by showing that it can capture decision makers who are subject to framing effects.
Although researchers often comment on the rising popularity of nature-inspired meta-heuristics (NIM), there has been a paucity of data to directly support the claim that NIM are growing in prominence compared to other optimization techniques. This study presents evidence that the use of NIM is not only growing, but indeed appears to have surpassed mathematical optimization techniques (MOT) in several important metrics related to academic research activity (publication frequency) and commercial activity (patenting frequency). Motivated by these findings, this article discusses some of the possible origins of this growing popularity. I review different explanations for NIM popularity and discuss why some of these arguments remain unsatisfying. I argue that a compelling and comprehensive explanation should directly account for the manner in which most NIM success has actually been achieved, e.g. through hybridization and customization to different problem environments. By taking a problem lifecycle perspective, this paper offers a fresh look at the hypothesis that nature-inspired meta-heuristics derive much of their utility from being flexible. I discuss global trends within the business environments where optimization algorithms are applied and I speculate that highly flexible algorithm frameworks could become increasingly popular within our diverse and rapidly changing world.
Capability planning problems are pervasive throughout many areas of human interest with prominent examples found in defense and security. Planning provides a unique context for optimization that has not been explored in great detail and involves a number of interesting challenges which are distinct from traditional optimization research. Planning problems demand solutions that can satisfy a number of competing objectives on multiple scales related to robustness, adaptiveness, risk, etc. The scenario method is a key approach for planning. Scenarios can be defined for long-term as well as short-term plans. This paper introduces computational scenario-based planning problems and proposes ways to accommodate strategic positioning within the tactical planning domain. We demonstrate the methodology in a resource planning problem that is solved with a multi-objective evolutionary algorithm. Our discussion and results highlight the fact that scenario-based planning is naturally framed within a multi-objective setting. However, the conflicting objectives occur on different system levels rather than within a single system alone. This paper also contends that planning problems are of vital interest in many human endeavors and that Evolutionary Computation may be well positioned for this problem domain.
Collective graphical models exploit inter-instance associative dependence to output more accurate labelings. However existing models support very limited kind of associativity which restricts accuracy gains. This paper makes two major contributions. First, we propose a general collective inference framework that biases data instances to agree on a set of {\em properties} of their labelings. Agreement is encouraged through symmetric clique potentials. We show that rich properties leads to bigger gains, and present a systematic inference procedure for a large class of such properties. The procedure performs message passing on the cluster graph, where property-aware messages are computed with cluster specific algorithms. This provides an inference-only solution for domain adaptation. Our experiments on bibliographic information extraction illustrate significant test error reduction over unseen domains. Our second major contribution consists of algorithms for computing outgoing messages from clique clusters with symmetric clique potentials. Our algorithms are exact for arbitrary symmetric potentials on binary labels and for max-like and majority-like potentials on multiple labels. For majority potentials, we also provide an efficient Lagrangian Relaxation based algorithm that compares favorably with the exact algorithm. We present a 13/15-approximation algorithm for the NP-hard Potts potential, with runtime sub-quadratic in the clique size. In contrast, the best known previous guarantee for graphs with Potts potentials is only 1/2. We empirically show that our method for Potts potentials is an order of magnitude faster than the best alternatives, and our Lagrangian Relaxation based algorithm for majority potentials beats the best applicable heuristic -- ICM.
Making decisions about the structure of a future military fleet is a challenging task. Several issues need to be considered such as the existence of multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future might hold. It is uncertain what future events might be encountered; how fleet design decisions will influence and shape the future; and how present and future decision makers will act based on available information, their personal biases regarding the importance of different objectives, and their economic preferences. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different classes of uncertainty are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present clear risks to the fleet. We call this the analysis of a fleet's strategic positioning. This paper introduces how strategic positioning can be evaluated using computer simulations. Our main aim is to introduce a framework for capturing information that can be useful to a decision maker and for defining the concepts of robustness and adaptiveness in the context of future fleet design. We demonstrate our conceptual framework using simulation studies of an air transportation fleet. We capture uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions, different model assumptions, and different economic preferences. Proposed changes to a fleet are then analysed based on their influence on the fleet's robustness, adaptiveness, and risk to different scenarios.
Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typical application is to solve Traveling Salesman Problem (TSP), where t, m, and N denotes the iteration number, number of ants, number of cities respectively. Cutting down running time is one of study focuses, and one way is to decrease parameter t and N, especially N. For this focus, the following method is presented in this paper. Firstly, design a novel clustering algorithm named Special Local Clustering algorithm (SLC), then apply it to classify all cities into compact classes, where compact class is the class that all cities in this class cluster tightly in a small region. Secondly, let ACO act on every class to get a local TSP route. Thirdly, all local TSP routes are jointed to form solution. Fourthly, the inaccuracy of solution caused by clustering is eliminated. Simulation shows that the presented method improves the running speed of ACO by 200 factors at least. And this high speed is benefit from two factors. One is that class has small size and parameter N is cut down. The route length at every iteration step is convergent when ACO acts on compact class. The other factor is that, using the convergence of route length as termination criterion of ACO and parameter t is cut down.
A key question in cooperative game theory is that of coalitional stability, usually captured by the notion of the \emph{core}--the set of outcomes such that no subgroup of players has an incentive to deviate. However, some coalitional games have empty cores, and any outcome in such a game is unstable. In this paper, we investigate the possibility of stabilizing a coalitional game by using external payments. We consider a scenario where an external party, which is interested in having the players work together, offers a supplemental payment to the grand coalition (or, more generally, a particular coalition structure). This payment is conditional on players not deviating from their coalition(s). The sum of this payment plus the actual gains of the coalition(s) may then be divided among the agents so as to promote stability. We define the \emph{cost of stability (CoS)} as the minimal external payment that stabilizes the game. We provide general bounds on the cost of stability in several classes of games, and explore its algorithmic properties. To develop a better intuition for the concepts we introduce, we provide a detailed algorithmic study of the cost of stability in weighted voting games, a simple but expressive class of games which can model decision-making in political bodies, and cooperation in multiagent settings. Finally, we extend our model and results to games with coalition structures.
The study of topological information of spatial objects has for a long time been a focus of research in disciplines like computational geometry, spatial reasoning, cognitive science, and robotics. While the majority of these researches emphasised the topological relations between spatial objects, this work studies the internal topological structure of bounded plane regions, which could consist of multiple pieces and/or have holes and islands to any finite level. The insufficiency of simple regions (regions homeomorphic to closed disks) to cope with the variety and complexity of spatial entities and phenomena has been widely acknowledged. Another significant drawback of simple regions is that they are not closed under set operations union, intersection, and difference. This paper considers bounded semi-algebraic regions, which are closed under set operations and can closely approximate most plane regions arising in practice.
Grid environment is a service oriented infrastructure in which many heterogeneous resources participate to provide the high performance computation. One of the bug issues in the grid environment is the vagueness and uncertainty between advertised resources and requested resources. Furthermore, in an environment such as grid dynamicity is considered as a crucial issue which must be dealt with. Classical rough set have been used to deal with the uncertainty and vagueness. But it can just be used on the static systems and can not support dynamicity in a system. In this work we propose a solution, called Dynamic Rough Set Resource Discovery (DRSRD), for dealing with cases of vagueness and uncertainty problems based on Dynamic rough set theory which considers dynamic features in this environment. In this way, requested resource properties have a weight as priority according to which resource matchmaking and ranking process is done. We also report the result of the solution obtained from the simulation in GridSim simulator. The comparison has been made between DRSRD, classical rough set theory based algorithm, and UDDI and OWL S combined algorithm. DRSRD shows much better precision for the cases with vagueness and uncertainty in a dynamic system such as the grid rather than the classical rough set theory based algorithm, and UDDI and OWL S combined algorithm.
In many scenarios, such as emergency response or ad hoc collaboration, it is critical to reduce the overhead in integrating data. Ideally, one could perform the entire process interactively under one unified interface: defining extractors and wrappers for sources, creating a mediated schema, and adding schema mappings ? while seeing how these impact the integrated view of the data, and refining the design accordingly. We propose a novel smart copy and paste (SCP) model and architecture for seamlessly combining the design-time and run-time aspects of data integration, and we describe an initial prototype, the CopyCat system. In CopyCat, the user does not need special tools for the different stages of integration: instead, the system watches as the user copies data from applications (including the Web browser) and pastes them into CopyCat?s spreadsheet-like workspace. CopyCat generalizes these actions and presents proposed auto-completions, each with an explanation in the form of provenance. The user provides feedback on these suggestions ? through either direct interactions or further copy-and-paste operations ? and the system learns from this feedback. This paper provides an overview of our prototype system, and identifies key research challenges in achieving SCP in its full generality.
This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.
Actor-Critic based approaches were among the first to address reinforcement learning in a general setting. Recently, these algorithms have gained renewed interest due to their generality, good convergence properties, and possible biological relevance. In this paper, we introduce an online temporal difference based actor-critic algorithm which is proved to converge to a neighborhood of a local maximum of the average reward. Linear function approximation is used by the critic in order estimate the value function, and the temporal difference signal, which is passed from the critic to the actor. The main distinguishing feature of the present convergence proof is that both the actor and the critic operate on a similar time scale, while in most current convergence proofs they are required to have very different time scales in order to converge. Moreover, the same temporal difference signal is used to update the parameters of both the actor and the critic. A limitation of the proposed approach, compared to results available for two time scale convergence, is that convergence is guaranteed only to a neighborhood of an optimal value, rather to an optimal value itself. The single time scale and identical temporal difference signal used by the actor and the critic, may provide a step towards constructing more biologically realistic models of reinforcement learning in the brain.
We consider multi-agent systems where agents' preferences are aggregated via sequential majority voting: each decision is taken by performing a sequence of pairwise comparisons where each comparison is a weighted majority vote among the agents. Incompleteness in the agents' preferences is common in many real-life settings due to privacy issues or an ongoing elicitation process. In addition, there may be uncertainty about how the preferences are aggregated. For example, the agenda (a tree whose leaves are labelled with the decisions being compared) may not yet be known or fixed. We therefore study how to determine collectively optimal decisions (also called winners) when preferences may be incomplete, and when the agenda may be uncertain. We show that it is computationally easy to determine if a candidate decision always wins, or may win, whatever the agenda. On the other hand, it is computationally hard to know wheth er a candidate decision wins in at least one agenda for at least one completion of the agents' preferences. These results hold even if the agenda must be balanced so that each candidate decision faces the same number of majority votes. Such results are useful for reasoning about preference elicitation. They help understand the complexity of tasks such as determining if a decision can be taken collectively, as well as knowing if the winner can be manipulated by appropriately ordering the agenda.
We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization of HMMs that can model smooth state evolution as in Linear Dynamical Systems (LDSs) as well as non-log-concave predictive distributions as in continuous-observation HMMs. RR-HMMs assume an m-dimensional latent state and n discrete observations, with a transition matrix of rank k <= m. This implies the dynamics evolve in a k-dimensional subspace, while the shape of the set of predictive distributions is determined by m. Latent state belief is represented with a k-dimensional state vector and inference is carried out entirely in R^k, making RR-HMMs as computationally efficient as k-state HMMs yet more expressive. To learn RR-HMMs, we relax the assumptions of a recently proposed spectral learning algorithm for HMMs (Hsu, Kakade and Zhang 2009) and apply it to learn k-dimensional observable representations of rank-k RR-HMMs. The algorithm is consistent and free of local optima, and we extend its performance guarantees to cover the RR-HMM case. We show how this algorithm can be used in conjunction with a kernel density estimator to efficiently model high-dimensional multivariate continuous data. We also relax the assumption that single observations are sufficient to disambiguate state, and extend the algorithm accordingly. Experiments on synthetic data and a toy video, as well as on a difficult robot vision modeling problem, yield accurate models that compare favorably with standard alternatives in simulation quality and prediction capability.
We analyze and exploit some scaling properties of the Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007). First we observe that a divide and conquer strategy, used on a large data set hierarchically reduces the complexity ${\cal O}(N^2)$ to ${\cal O}(N^{(h+2)/(h+1)})$, for a data-set of size $N$ and a depth $h$ of the hierarchical strategy. For a data-set embedded in a $d$-dimensional space, we show that this is obtained without notably damaging the precision except in dimension $d=2$. In fact, for $d$ larger than 2 the relative loss in precision scales like $N^{(2-d)/(h+1)d}$. Finally, under some conditions we observe that there is a value $s^*$ of the penalty coefficient, a free parameter used to fix the number of clusters, which separates a fragmentation phase (for $ss^*$) of the underlying hidden cluster structure. At this precise point holds a self-similarity property which can be exploited by the hierarchical strategy to actually locate its position. From this observation, a strategy based on \AP can be defined to find out how many clusters are present in a given dataset.
Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.
The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyse a technique called Evolutionary Squeaky Wheel Optimisation and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimisation improves the original Squeaky Wheel Optimisation's effectiveness and execution speed by incorporating two extra steps (Selection and Mutation) for added evolution. In the Evolutionary Squeaky Wheel Optimisation, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The aim of the Analysis step is to identify below average solution components by calculating a fitness value for all components. The Selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The Mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repairs are carried out by using the Prioritization to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, improvement in the Evolutionary Squeaky Wheel Optimisation is achieved by selective solution disruption mixed with interative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.
Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.
We consider directed graphs over a set of n agents, where an edge (i,j) is taken to mean that agent i supports or trusts agent j. Given such a graph and an integer k\leq n, we wish to select a subset of k agents that maximizes the sum of indegrees, i.e., a subset of k most popular or most trusted agents. At the same time we assume that each individual agent is only interested in being selected, and may misreport its outgoing edges to this end. This problem formulation captures realistic scenarios where agents choose among themselves, which can be found in the context of Internet search, social networks like Twitter, or reputation systems like Epinions. Our goal is to design mechanisms without payments that map each graph to a k-subset of agents to be selected and satisfy the following two constraints: strategyproofness, i.e., agents cannot benefit from misreporting their outgoing edges, and approximate optimality, i.e., the sum of indegrees of the selected subset of agents is always close to optimal. Our first main result is a surprising impossibility: for k \in {1,...,n-1}, no deterministic strategyproof mechanism can provide a finite approximation ratio. Our second main result is a randomized strategyproof mechanism with an approximation ratio that is bounded from above by four for any value of k, and approaches one as k grows.
Boolean Satisfiability (SAT) solvers are now routinely used in the verification of large industrial problems. However, their application in safety-critical domains such as the railways, avionics, and automotive industries requires some form of assurance for the results, as the solvers can (and sometimes do) have bugs. Unfortunately, the complexity of modern, highly optimized SAT solvers renders impractical the development of direct formal proofs of their correctness. This paper presents an alternative approach where an untrusted, industrial-strength, SAT solver is plugged into a trusted, formally certified, SAT proof checker to provide industrial-strength certified SAT solving. The key novelties and characteristics of our approach are (i) that the checker is automatically extracted from the formal development, (ii), that the combined system can be used as a standalone executable program independent of any supporting theorem prover, and (iii) that the checker certifies any SAT solver respecting the agreed format for satisfiability and unsatisfiability claims. The core of the system is a certified checker for unsatisfiability claims that is formally designed and verified in Coq. We present its formal design and outline the correctness proofs. The actual standalone checker is automatically extracted from the the Coq development. An evaluation of the certified checker on a representative set of industrial benchmarks from the SAT Race Competition shows that, albeit it is slower than uncertified SAT checkers, it is significantly faster than certified checkers implemented on top of an interactive theorem prover.
Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation. Machine learning has been applied to the affinity classifier to produce affinity graphs that are good in the sense of minimizing edge misclassification rates. However, this error measure is only indirectly related to the quality of segmentations produced by ultimately partitioning the affinity graph. We present the first machine learning algorithm for training a classifier to produce affinity graphs that are good in the sense of producing segmentations that directly minimize the Rand index, a well known segmentation performance measure. The Rand index measures segmentation performance by quantifying the classification of the connectivity of image pixel pairs after segmentation. By using the simple graph partitioning algorithm of finding the connected components of the thresholded affinity graph, we are able to train an affinity classifier to directly minimize the Rand index of segmentations resulting from the graph partitioning. Our learning algorithm corresponds to the learning of maximin affinities between image pixel pairs, which are predictive of the pixel-pair connectivity.
Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the $\epsilon$-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance.
The problem is sequence prediction in the following setting. A sequence $x_1,...,x_n,...$ of discrete-valued observations is generated according to some unknown probabilistic law (measure) $\mu$. After observing each outcome, it is required to give the conditional probabilities of the next observation. The measure $\mu$ belongs to an arbitrary but known class $C$ of stochastic process measures. We are interested in predictors $\rho$ whose conditional probabilities converge (in some sense) to the "true" $\mu$-conditional probabilities if any $\mu\in C$ is chosen to generate the sequence. The contribution of this work is in characterizing the families $C$ for which such predictors exist, and in providing a specific and simple form in which to look for a solution. We show that if any predictor works, then there exists a Bayesian predictor, whose prior is discrete, and which works too. We also find several sufficient and necessary conditions for the existence of a predictor, in terms of topological characterizations of the family $C$, as well as in terms of local behaviour of the measures in $C$, which in some cases lead to procedures for constructing such predictors. It should be emphasized that the framework is completely general: the stochastic processes considered are not required to be i.i.d., stationary, or to belong to any parametric or countable family.
We propose a database model that allows users to annotate data with belief statements. Our motivation comes from scientific database applications where a community of users is working together to assemble, revise, and curate a shared data repository. As the community accumulates knowledge and the database content evolves over time, it may contain conflicting information and members can disagree on the information it should store. For example, Alice may believe that a tuple should be in the database, whereas Bob disagrees. He may also insert the reason why he thinks Alice believes the tuple should be in the database, and explain what he thinks the correct tuple should be instead. We propose a formal model for Belief Databases that interprets users' annotations as belief statements. These annotations can refer both to the base data and to other annotations. We give a formal semantics based on a fragment of multi-agent epistemic logic and define a query language over belief databases. We then prove a key technical result, stating that every belief database can be encoded as a canonical Kripke structure. We use this structure to describe a relational representation of belief databases, and give an algorithm for translating queries over the belief database into standard relational queries. Finally, we report early experimental results with our prototype implementation on synthetic data.
In this paper, we propose causality as a unified framework to explain query answers and non-answers, thus generalizing and extending several previously proposed approaches of provenance and missing query result explanations. We develop our framework starting from the well-studied definition of actual causes by Halpern and Pearl. After identifying some undesirable characteristics of the original definition, we propose functional causes as a refined definition of causality with several desirable properties. These properties allow us to apply our notion of causality in a database context and apply it uniformly to define the causes of query results and their individual contributions in several ways: (i) we can model both provenance as well as non-answers, (ii) we can define explanations as either data in the input relations or relational operations in a query plan, and (iii) we can give graded degrees of responsibility to individual causes, thus allowing us to rank causes. In particular, our approach allows us to explain contributions to relational aggregate functions and to rank causes according to their respective responsibilities. We give complexity results and describe polynomial algorithms for evaluating causality in tractable cases. Throughout the paper, we illustrate the applicability of our framework with several examples. Overall, we develop in this paper the theoretical foundations of causality theory in a database context.
Internet and expert systems have offered new ways of sharing and distributing knowledge, but there is a lack of researches in the area of web based expert systems. This paper introduces a development of a web-based expert system for the regulations of civil service in the Kingdom of Saudi Arabia named as RCSES. It is the first time to develop such system (application of civil service regulations) as well the development of it using web based approach. The proposed system considers 17 regulations of the civil service system. The different phases of developing the RCSES system are presented, as knowledge acquiring and selection, ontology and knowledge representations using XML format. XML Rule-based knowledge sources and the inference mechanisms were implemented using ASP.net technique. An interactive tool for entering the ontology and knowledge base, and the inferencing was built. It gives the ability to use, modify, update, and extend the existing knowledge base in an easy way. The knowledge was validated by experts in the domain of civil service regulations, and the proposed RCSES was tested, verified, and validated by different technical users and the developers staff. The RCSES system is compared with other related web based expert systems, that comparison proved the goodness, usability, and high performance of RCSES.
In our research we investigate the output accuracy of discrete event simulation models and agent based simulation models when studying human centric complex systems. In this paper we focus on human reactive behaviour as it is possible in both modelling approaches to implement human reactive behaviour in the model by using standard methods. As a case study we have chosen the retail sector, and here in particular the operations of the fitting room in the women wear department of a large UK department store. In our case study we looked at ways of determining the efficiency of implementing new management policies for the fitting room operation through modelling the reactive behaviour of staff and customers of the department. First, we have carried out a validation experiment in which we compared the results from our models to the performance of the real system. This experiment also allowed us to establish differences in output accuracy between the two modelling methids. In a second step a multi-scenario experiment was carried out to study the behaviour of the models when they are used for the purpose of operational improvement. Overall we have found that for our case study example both discrete event simulation and agent based simulation have the same potential to support the investigation into the efficiency of implementing new management policies.
Many problems can be specified by patterns of propositional formulae depending on a parameter, e.g. the specification of a circuit usually depends on the number of bits of its input. We define a logic whose formulae, called "iterated schemata", allow to express such patterns. Schemata extend propositional logic with indexed propositions, e.g. P_i, P_i+1, P_1, and with generalized connectives, e.g. /\i=1..n or i=1..n (called "iterations") where n is an (unbound) integer variable called a "parameter". The expressive power of iterated schemata is strictly greater than propositional logic: it is even out of the scope of first-order logic. We define a proof procedure, called DPLL*, that can prove that a schema is satisfiable for at least one value of its parameter, in the spirit of the DPLL procedure. However the converse problem, i.e. proving that a schema is unsatisfiable for every value of the parameter, is undecidable so DPLL* does not terminate in general. Still, we prove that it terminates for schemata of a syntactic subclass called "regularly nested". This is the first non trivial class for which DPLL* is proved to terminate. Furthermore the class of regularly nested schemata is the first decidable class to allow nesting of iterations, i.e. to allow schemata of the form /\i=1..n (/\j=1..n ...).
Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore decoupling the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. This paper presents a robust face recognition technique based on the extraction and matching of SIFT features related to independent face areas. Both a global and local (as recognition from parts) matching strategy is proposed. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. In order to reduce the identification errors, the Dempster-Shafer decision theory is applied to fuse the two matching techniques. The proposed algorithms are evaluated with the ORL and the IITK face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition techniques also in the case of partially occluded faces or with missing information.
Ear biometric is considered as one of the most reliable and invariant biometrics characteristics in line with iris and fingerprint characteristics. In many cases, ear biometrics can be compared with face biometrics regarding many physiological and texture characteristics. In this paper, a robust and efficient ear recognition system is presented, which uses Scale Invariant Feature Transform (SIFT) as feature descriptor for structural representation of ear images. In order to make it more robust to user authentication, only the regions having color probabilities in a certain ranges are considered for invariant SIFT feature extraction, where the K-L divergence is used for keeping color consistency. Ear skin color model is formed by Gaussian mixture model and clustering the ear color pattern using vector quantization. Finally, K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference model and probe ear images. After segmentation of ear images in some color slice regions, SIFT keypoints are extracted and an augmented vector of extracted SIFT features are created for matching, which is accomplished between a pair of reference model and probe ear images. The proposed technique has been tested on the IITK Ear database and the experimental results show improvements in recognition accuracy while invariant features are extracted from color slice regions to maintain the robustness of the system.
The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify unknown motifs which repeat within time series data. The power of the algorithm is derived from its use of a small number of parameters with minimal assumptions. The algorithm searches from a completely neutral perspective that is independent of the data being analysed, and the underlying motifs. In this paper the motif tracking algorithm is applied to the search for patterns within sequences of low level system calls between the Linux kernel and the operating system's user space. The MTA is able to compress data found in large system call data sets to a limited number of motifs which summarise that data. The motifs provide a resource from which a profile of executed processes can be built. The potential for these profiles and new implications for security research are highlighted. A higher level call system language for measuring similarity between patterns of such calls is also suggested.
In this paper, research on AI based modeling technique to optimize development of new alloys with necessitated improvements in properties and chemical mixture over existing alloys as per functional requirements of product is done. The current research work novels AI in lieu of predictions to establish association between material and product customary. Advanced computational simulation techniques like CFD, FEA interrogations are made viable to authenticate product dynamics in context to experimental investigations. Accordingly, the current research is focused towards binding relationships between material design and product design domains. The input to feed forward back propagation prediction network model constitutes of material design features. Parameters relevant to product design strategies are furnished as target outputs. The outcomes of ANN shows good sign of correlation between material and product design domains. The study enriches a new path to illustrate material factors at the time of new product development.
The Application of Bio Inspired Algorithms to complicated Power System Stability Problems has recently attracted the researchers in the field of Artificial Intelligence. Low frequency oscillations after a disturbance in a Power system, if not sufficiently damped, can drive the system unstable. This paper provides a systematic procedure to damp the low frequency oscillations based on Bio Inspired Genetic (GA) and Particle Swarm Optimization (PSO) algorithms. The proposed controller design is based on formulating a System Damping ratio enhancement based Optimization criterion to compute the optimal controller parameters for better stability. The Novel and contrasting feature of this work is the mathematical modeling and simulation of the Synchronous generator model including the Steam Governor Turbine (GT) dynamics. To show the robustness of the proposed controller, Non linear Time domain simulations have been carried out under various system operating conditions. Also, a detailed Comparative study has been done to show the superiority of the Bio inspired algorithm based controllers over the Conventional Lead lag controller.
The max-product algorithm, a local message-passing scheme that attempts to compute the most probable assignment (MAP) of a given probability distribution, has been successfully employed as a method of approximate inference for applications arising in coding theory, computer vision, and machine learning. However, the max-product algorithm is not guaranteed to converge to the MAP assignment, and if it does, is not guaranteed to recover the MAP assignment. Alternative convergent message-passing schemes have been proposed to overcome these difficulties. This work provides a systematic study of such message-passing algorithms that extends the known results by exhibiting new sufficient conditions for convergence to local and/or global optima, providing a combinatorial characterization of these optima based on graph covers, and describing a new convergent and correct message-passing algorithm whose derivation unifies many of the known convergent message-passing algorithms. While convergent and correct message-passing algorithms represent a step forward in the analysis of max-product style message-passing algorithms, the conditions needed to guarantee convergence to a global optimum can be too restrictive in both theory and practice. This limitation of convergent and correct message-passing schemes is characterized by graph covers and illustrated by example.
Association rules are among the most widely employed data analysis methods in the field of Data Mining. An association rule is a form of partial implication between two sets of binary variables. In the most common approach, association rules are parameterized by a lower bound on their confidence, which is the empirical conditional probability of their consequent given the antecedent, and/or by some other parameter bounds such as "support" or deviation from independence. We study here notions of redundancy among association rules from a fundamental perspective. We see each transaction in a dataset as an interpretation (or model) in the propositional logic sense, and consider existing notions of redundancy, that is, of logical entailment, among association rules, of the form "any dataset in which this first rule holds must obey also that second rule, therefore the second is redundant". We discuss several existing alternative definitions of redundancy between association rules and provide new characterizations and relationships among them. We show that the main alternatives we discuss correspond actually to just two variants, which differ in the treatment of full-confidence implications. For each of these two notions of redundancy, we provide a sound and complete deduction calculus, and we show how to construct complete bases (that is, axiomatizations) of absolutely minimum size in terms of the number of rules. We explore finally an approach to redundancy with respect to several association rules, and fully characterize its simplest case of two partial premises.
Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.
Recent advancement in web services plays an important role in business to business and business to consumer interaction. Discovery mechanism is not only used to find a suitable service but also provides collaboration between service providers and consumers by using standard protocols. A static web service discovery mechanism is not only time consuming but requires continuous human interaction. This paper proposed an efficient dynamic web services discovery mechanism that can locate relevant and updated web services from service registries and repositories with timestamp based on indexing value and categorization for faster and efficient discovery of service. The proposed prototype focuses on quality of service issues and introduces concept of local cache, categorization of services, indexing mechanism, CSP (Constraint Satisfaction Problem) solver, aging and usage of translator. Performance of proposed framework is evaluated by implementing the algorithm and correctness of our method is shown. The results of proposed framework shows greater performance and accuracy in dynamic discovery mechanism of web services resolving the existing issues of flexibility, scalability, based on quality of service, and discovers updated and most relevant services with ease of usage.
Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence. Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters. Considering all these, the problem of handwritten numeral recognition is addressed under the present work in respect to handwritten Arabic numerals. Arabic is spoken throughout the Arab World and the fifth most popular language in the world slightly before Portuguese and Bengali. For the present work, we have developed a feature set of 88 features is designed to represent samples of handwritten Arabic numerals for this work. It includes 72 shadow and 16 octant features. A Multi Layer Perceptron (MLP) based classifier is used here for recognition handwritten Arabic digits represented with the said feature set. On experimentation with a database of 3000 samples, the technique yields an average recognition rate of 94.93% evaluated after three-fold cross validation of results. It is useful for applications related to OCR of handwritten Arabic Digit and can also be extended to include OCR of handwritten characters of Arabic alphabet.
We propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular we consider the setting of using a language that combines description logics with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation of the semantics of the representation formalism to the task of frequent pattern discovery, and for the core of our method, we propose an algorithm that exploits the semantics of the combined knowledge base. We have developed a proof-of-concept data mining implementation of this. Using this we have empirically shown that using the combined knowledge base to perform semantic tests can make data mining faster by pruning useless candidate patterns before their evaluation. We have also shown that the quality of the set of patterns produced may be improved: the patterns are more compact, and there are fewer patterns. We conclude that exploiting the semantics of a chosen representation formalism is key to the design and application of (onto-)relational frequent pattern discovery methods. Note: To appear in Theory and Practice of Logic Programming (TPLP)
This paper is concern about developing a semantic agreement maintenance method based on semantic distance by calculating the change of local schema or ontology. This approach is important in dynamic and autonomous environment, in which the current approach assumed that agreement or mapping in static environment. The contribution of this research is to develop a framework based on semantic agreement maintenance approach for P2P environment. This framework based on two level hybrid P2P model architecture, which consist of two peer type: (1) super peer that use to register and manage the other peers, and (2) simple peer, as a simple peer, it exports and shares its contents with others. This research develop a model to maintain the semantic agreement in P2P environment, so the current approach which does not have the mechanism to know the change, since it assumed that ontology and local schema are in the static condition, and it is different in dynamic condition. The main issues are how to calculate the change of local schema or common ontology and the calculation result is used to determine which algorithm in maintaining the agreement. The experiment on the job matching domain in Indonesia have been done to show how far the performance of the approach. From the experiment, the main result are (i) the more change so the F-measure value tend to be decreased, (ii) there is no significant different in F-measure value for various modification type (add, delete, rename), and (iii) the correct choice of algorithm would improve the F-measure value.
Multi-agent systems offer a new and exciting way of understanding the world of work. We apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between people management practices on the shop-floor and retail performance. Despite the fact we are working within a relatively novel and complex domain, it is clear that using an agent-based approach offers great potential for improving organizational capabilities in the future. Our multi-disciplinary research team has worked closely with one of the UK's top ten retailers to collect data and build an understanding of shop-floor operations and the key actors in a department (customers, staff, and managers). Based on this case study we have built and tested our first version of a retail branch agent-based simulation model where we have focused on how we can simulate the effects of people management practices on customer satisfaction and sales. In our experiments we have looked at employee development and cashier empowerment as two examples of shop floor management practices. In this paper we describe the underlying conceptual ideas and the features of our simulation model. We present a selection of experiments we have conducted in order to validate our simulation model and to show its potential for answering "what-if" questions in a retail context. We also introduce a novel performance measure which we have created to quantify customers' satisfaction with service, based on their individual shopping experiences.
This paper reports on continuing research into the modelling of an order picking process within a Crossdocking distribution centre using Simulation Optimisation. The aim of this project is to optimise a discrete event simulation model and to understand factors that affect finding its optimal performance. Our initial investigation revealed that the precision of the selected simulation output performance measure and the number of replications required for the evaluation of the optimisation objective function through simulation influences the ability of the optimisation technique. We experimented with Common Random Numbers, in order to improve the precision of our simulation output performance measure, and intended to use the number of replications utilised for this purpose as the initial number of replications for the optimisation of our Crossdocking distribution centre simulation model. Our results demonstrate that we can improve the precision of our selected simulation output performance measure value using Common Random Numbers at various levels of replications. Furthermore, after optimising our Crossdocking distribution centre simulation model, we are able to achieve optimal performance using fewer simulations runs for the simulation model which uses Common Random Numbers as compared to the simulation model which does not use Common Random Numbers.
We consider a living organism as an observer of the evolution of its environment recording sensory information about the state space X of the environment in real time. Sensory information is sampled and then processed on two levels. On the biological level, the organism serves as an evaluation mechanism of the subjective relevance of the incoming data to the observer: the observer assigns excitation values to events in X it could recognize using its sensory equipment. On the algorithmic level, sensory input is used for updating a database, the memory of the observer whose purpose is to serve as a geometric/combinatorial model of X, whose nodes are weighted by the excitation values produced by the evaluation mechanism. These values serve as a guidance system for deciding how the database should transform as observation data mounts. We define a searching problem for the proposed model and discuss the model's flexibility and its computational efficiency, as well as the possibility of implementing it as a dynamic network of neuron-like units. We show how various easily observable properties of the human memory and thought process can be explained within the framework of this model. These include: reasoning (with efficiency bounds), errors, temporary and permanent loss of information. We are also able to define general learning problems in terms of the new model, such as the language acquisition problem.
Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, generalizing submodular set functions to adaptive policies. We prove that if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. In addition to providing performance guarantees for both stochastic maximization and coverage, adaptive submodularity can be exploited to drastically speed up the greedy algorithm by using lazy evaluations. We illustrate the usefulness of the concept by giving several examples of adaptive submodular objectives arising in diverse applications including sensor placement, viral marketing and active learning. Proving adaptive submodularity for these problems allows us to recover existing results in these applications as special cases, improve approximation guarantees and handle natural generalizations.
As an immune inspired algorithm, the Dendritic Cell Algorithm (DCA) has been applied to a range of problems, particularly in the area of intrusion detection. Ideally, the intrusion detection should be performed in real-time, to continuously detect misuses as soon as they occur. Consequently, the analysis process performed by an intrusion detection system must operate in real-time or near-to real-time. The analysis process of the DCA is currently performed offline, therefore to improve the algorithm's performance we suggest the development of a real-time analysis component. The initial step of the development is to apply segmentation to the DCA. This involves segmenting the current output of the DCA into slices and performing the analysis in various ways. Two segmentation approaches are introduced and tested in this paper, namely antigen based segmentation (ABS) and time based segmentation (TBS). The results of the corresponding experiments suggest that applying segmentation produces different and significantly better results in some cases, when compared to the standard DCA without segmentation. Therefore, we conclude that the segmentation is applicable to the DCA for the purpose of real-time analysis.
In density estimation task, maximum entropy model (Maxent) can effectively use reliable prior information via certain constraints, i.e., linear constraints without empirical parameters. However, reliable prior information is often insufficient, and the selection of uncertain constraints becomes necessary but poses considerable implementation complexity. Improper setting of uncertain constraints can result in overfitting or underfitting. To solve this problem, a generalization of Maxent, under Tsallis entropy framework, is proposed. The proposed method introduces a convex quadratic constraint for the correction of (expected) Tsallis entropy bias (TEB). Specifically, we demonstrate that the expected Tsallis entropy of sampling distributions is smaller than the Tsallis entropy of the underlying real distribution. This expected entropy reduction is exactly the (expected) TEB, which can be expressed by a closed-form formula and act as a consistent and unbiased correction. TEB indicates that the entropy of a specific sampling distribution should be increased accordingly. This entails a quantitative re-interpretation of the Maxent principle. By compensating TEB and meanwhile forcing the resulting distribution to be close to the sampling distribution, our generalized TEBC Maxent can be expected to alleviate the overfitting and underfitting. We also present a connection between TEB and Lidstone estimator. As a result, TEB-Lidstone estimator is developed by analytically identifying the rate of probability correction in Lidstone. Extensive empirical evaluation shows promising performance of both TEBC Maxent and TEB-Lidstone in comparison with various state-of-the-art density estimation methods.
Message passing type algorithms such as the so-called Belief Propagation algorithm have recently gained a lot of attention in the statistics, signal processing and machine learning communities as attractive algorithms for solving a variety of optimization and inference problems. As a decentralized, easy to implement and empirically successful algorithm, BP deserves attention from the theoretical standpoint, and here not much is known at the present stage. In order to fill this gap we consider the performance of the BP algorithm in the context of the capacitated minimum-cost network flow problem - the classical problem in the operations research field. We prove that BP converges to the optimal solution in the pseudo-polynomial time, provided that the optimal solution of the underlying problem is unique and the problem input is integral. Moreover, we present a simple modification of the BP algorithm which gives a fully polynomial-time randomized approximation scheme (FPRAS) for the same problem, which no longer requires the uniqueness of the optimal solution. This is the first instance where BP is proved to have fully-polynomial running time. Our results thus provide a theoretical justification for the viability of BP as an attractive method to solve an important class of optimization problems.
Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluation is the comparison between unstructured and structured events using the same FIS setting. The second comparison is the model settings between FIS and ANFIS for classifying structured events. The data set consists of news articles related to terrorism events in three southern provinces of Thailand. The experimental results show that the classification performance of the FIS resulting from structured events achieves satisfactory accuracy and is better than the unstructured events. In addition, the classification of structured events using ANFIS gives higher performance than the events using only FIS in the prediction of terrorism events.
This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.
The successful execution of a construction project is heavily impacted by making the right decision during tendering processes. Managing tender procedures is very complex and uncertain involving coordination of many tasks and individuals with different priorities and objectives. Bias and inconsistent decision are inevitable if the decision-making process is totally depends on intuition, subjective judgement or emotion. In making transparent decision and healthy competition tendering, there exists a need for flexible guidance tool for decision support. Aim of this paper is to give a review on current practices of Decision Support Systems (DSS) technology in construction tendering processes. Current practices of general tendering processes as applied to the most countries in different regions such as United States, Europe, Middle East and Asia are comprehensively discussed. Applications of Web-based tendering processes is also summarised in terms of its properties. Besides that, a summary of Decision Support System (DSS) components is included in the next section. Furthermore, prior researches on implementation of DSS approaches in tendering processes are discussed in details. Current issues arise from both of paper-based and Web-based tendering processes are outlined. Finally, conclusion is included at the end of this paper.
Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM)and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.
Biometric authentication techniques are more consistent and efficient than conventional authentication techniques and can be used in monitoring, transaction authentication, information retrieval, access control, forensics, etc. In this paper, we have presented a detailed comparative analysis between Principle Component Analysis (PCA) and Independent Component Analysis (ICA) which are used for feature extraction on the basis of different Artificial Neural Network (ANN) such as Back Propagation (BP), Radial Basis Function (RBF) and Learning Vector Quantization (LVQ). In this paper, we have chosen "TULIPS1 database, (Movellan, 1995)" which is a small audiovisual database of 12 subjects saying the first 4 digits in English for the incorporation of above methods. The six geometric lip features i.e. height of the outer corners of the mouth, width of the outer corners of the mouth, height of the inner corners of the mouth, width of the inner corners of the mouth, height of the upper lip, and height of the lower lip which extracts the identity relevant information are considered for the research work. After the comprehensive analysis and evaluation a maximum of 91.07% accuracy in speaker recognition is achieved using PCA and RBF and 87.36% accuracy is achieved using ICA and RBF. Speaker identification has a wide scope of applications such as access control, monitoring, transaction authentication, information retrieval, forensics, etc.
We often encounter probability distributions given as unnormalized products of non-negative functions. The factorization structures are represented by hypergraphs called factor graphs. Such distributions appear in various fields, including statistics, artificial intelligence, statistical physics, error correcting codes, etc. Given such a distribution, computations of marginal distributions and the normalization constant are often required. However, they are computationally intractable because of their computational costs. One successful approximation method is Loopy Belief Propagation (LBP) algorithm. The focus of this thesis is an analysis of the LBP algorithm. If the factor graph is a tree, i.e. having no cycle, the algorithm gives the exact quantities. If the factor graph has cycles, however, the LBP algorithm does not give exact results and possibly exhibits oscillatory and non-convergent behaviors. The thematic question of this thesis is "How the behaviors of the LBP algorithm are affected by the discrete geometry of the factor graph?" The primary contribution of this thesis is the discovery of a formula that establishes the relation between the LBP, the Bethe free energy and the graph zeta function. This formula provides new techniques for analysis of the LBP algorithm, connecting properties of the graph and of the LBP and the Bethe free energy. We demonstrate applications of the techniques to several problems including (non) convexity of the Bethe free energy, the uniqueness and stability of the LBP fixed point. We also discuss the loop series initiated by Chertkov and Chernyak. The loop series is a subgraph expansion of the normalization constant, or partition function, and reflects the graph geometry. We investigate theoretical natures of the series. Moreover, we show a partial connection between the loop series and the graph zeta function.
One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance guarantees. There are few, if any, approaches that successfully address the two goals simultaneously. Performance guarantees become crucial for tasks such as microarray data analysis due to very small sample sizes resulting in limited empirical evaluation. To the best of our knowledge, such algorithms that give theoretical bounds on the future performance have not been proposed so far in the context of the classification of gene expression data. In this work, we investigate the premise of learning a conjunction (or disjunction) of decision stumps in Occam's Razor, Sample Compression, and PAC-Bayes learning settings for identifying a small subset of attributes that can be used to perform reliable classification tasks. We apply the proposed approaches for gene identification from DNA microarray data and compare our results to those of well known successful approaches proposed for the task. We show that our algorithm not only finds hypotheses with much smaller number of genes while giving competitive classification accuracy but also have tight risk guarantees on future performance unlike other approaches. The proposed approaches are general and extensible in terms of both designing novel algorithms and application to other domains.
Probabilistic databases play a crucial role in the management and understanding of uncertain data. However, incorporating probabilities into the semantics of incomplete databases has posed many challenges, forcing systems to sacrifice modeling power, scalability, or restrict the class of relational algebra formula under which they are closed. We propose an alternative approach where the underlying relational database always represents a single world, and an external factor graph encodes a distribution over possible worlds; Markov chain Monte Carlo (MCMC) inference is then used to recover this uncertainty to a desired level of fidelity. Our approach allows the efficient evaluation of arbitrary queries over probabilistic databases with arbitrary dependencies expressed by graphical models with structure that changes during inference. MCMC sampling provides efficiency by hypothesizing {\em modifications} to possible worlds rather than generating entire worlds from scratch. Queries are then run over the portions of the world that change, avoiding the onerous cost of running full queries over each sampled world. A significant innovation of this work is the connection between MCMC sampling and materialized view maintenance techniques: we find empirically that using view maintenance techniques is several orders of magnitude faster than naively querying each sampled world. We also demonstrate our system's ability to answer relational queries with aggregation, and demonstrate additional scalability through the use of parallelization.
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
The email is used daily by millions of people to communicate around the globe and it is a mission-critical application for many businesses. Over the last decade, unsolicited bulk email has become a major problem for email users. An overwhelming amount of spam is flowing into users' mailboxes daily. In 2004, an estimated 62% of all email was attributed to spam. Spam is not only frustrating for most email users, it strains the IT infrastructure of organizations and costs businesses billions of dollars in lost productivity. In recent years, spam has evolved from an annoyance into a serious security threat, and is now a prime medium for phishing of sensitive information, as well the spread of malicious software. This work presents a first approach to attack the spam problem. We propose an algorithm that will improve a classifier's results by adjusting its training set data. It improves the document's vocabulary representation by detecting good topic descriptors and discriminators.
Representing distributions over permutations can be a daunting task due to the fact that the number of permutations of $n$ objects scales factorially in $n$. One recent way that has been used to reduce storage complexity has been to exploit probabilistic independence, but as we argue, full independence assumptions impose strong sparsity constraints on distributions and are unsuitable for modeling rankings. We identify a novel class of independence structures, called \emph{riffled independence}, encompassing a more expressive family of distributions while retaining many of the properties necessary for performing efficient inference and reducing sample complexity. In riffled independence, one draws two permutations independently, then performs the \emph{riffle shuffle}, common in card games, to combine the two permutations to form a single permutation. Within the context of ranking, riffled independence corresponds to ranking disjoint sets of objects independently, then interleaving those rankings. In this paper, we provide a formal introduction to riffled independence and present algorithms for using riffled independence within Fourier-theoretic frameworks which have been explored by a number of recent papers. Additionally, we propose an automated method for discovering sets of items which are riffle independent from a training set of rankings. We show that our clustering-like algorithms can be used to discover meaningful latent coalitions from real preference ranking datasets and to learn the structure of hierarchically decomposable models based on riffled independence.
Interval temporal logics (ITLs) are logics for reasoning about temporal statements expressed over intervals, i.e., periods of time. The most famous ITL studied so far is Halpern and Shoham's HS, which is the logic of the thirteen Allen's interval relations. Unfortunately, HS and most of its fragments have an undecidable satisfiability problem. This discouraged the research in this area until recently, when a number non-trivial decidable ITLs have been discovered. This paper is a contribution towards the complete classification of all different fragments of HS. We consider different combinations of the interval relations Begins, After, Later and their inverses Abar, Bbar, and Lbar. We know from previous works that the combination ABBbarAbar is decidable only when finite domains are considered (and undecidable elsewhere), and that ABBbar is decidable over the natural numbers. We extend these results by showing that decidability of ABBar can be further extended to capture the language ABBbarLbar, which lays in between ABBar and ABBbarAbar, and that turns out to be maximal w.r.t decidability over strongly discrete linear orders (e.g. finite orders, the naturals, the integers). We also prove that the proposed decision procedure is optimal with respect to the complexity class.
This research investigated the simulation model behaviour of a traditional and combined discrete event as well as agent based simulation models when modelling human reactive and proactive behaviour in human centric complex systems. A departmental store was chosen as human centric complex case study where the operation system of a fitting room in WomensWear department was investigated. We have looked at ways to determine the efficiency of new management policies for the fitting room operation through simulating the reactive and proactive behaviour of staff towards customers. Once development of the simulation models and their verification had been done, we carried out a validation experiment in the form of a sensitivity analysis. Subsequently, we executed a statistical analysis where the mixed reactive and proactive behaviour experimental results were compared with some reactive experimental results from previously published works. Generally, this case study discovered that simple proactive individual behaviour could be modelled in both simulation models. In addition, we found the traditional discrete event model performed similar in the simulation model output compared to the combined discrete event and agent based simulation when modelling similar human behaviour.
In response to a 1997 problem of M. Vidyasagar, we state a necessary and sufficient condition for distribution-free PAC learnability of a concept class $\mathscr C$ under the family of all non-atomic (diffuse) measures on the domain $\Omega$. Clearly, finiteness of the classical Vapnik-Chervonenkis dimension of $\mathscr C$ is a sufficient, but no longer necessary, condition. Besides, learnability of $\mathscr C$ under non-atomic measures does not imply the uniform Glivenko-Cantelli property with regard to non-atomic measures. Our learnability criterion is stated in terms of a combinatorial parameter $\VC({\mathscr C}\,{\mathrm{mod}}\,\omega_1)$ which we call the VC dimension of $\mathscr C$ modulo countable sets. The new parameter is obtained by ``thickening up'' single points in the definition of VC dimension to uncountable ``clusters''. Equivalently, $\VC(\mathscr C\modd\omega_1)\leq d$ if and only if every countable subclass of $\mathscr C$ has VC dimension $\leq d$ outside a countable subset of $\Omega$. The new parameter can be also expressed as the classical VC dimension of $\mathscr C$ calculated on a suitable subset of a compactification of $\Omega$. We do not make any measurability assumptions on $\mathscr C$, assuming instead the validity of Martin's Axiom (MA).
Notions of core, support and inversion of a soft set have been defined and studied. Soft approximations are soft sets developed through core and support, and are used for granulating the soft space. Membership structure of a soft set has been probed in and many interesting properties presented. The mathematical apparatus developed so far in this paper yields a detailed analysis of two works viz. [N. Cagman, S. Enginoglu, Soft set theory and uni-int decision making, European Jr. of Operational Research (article in press, available online 12 May 2010)] and [N. Cagman, S. Enginoglu, Soft matrix theory and its decision making, Computers and Mathematics with Applications 59 (2010) 3308 - 3314.]. We prove (Theorem 8.1) that uni-int method of Cagman is equivalent to a core-support expression which is computationally far less expansive than uni-int. This also highlights some shortcomings in Cagman's uni-int method and thus motivates us to improve the method. We first suggest an improvement in uni-int method and then present a new conjecture to solve the optimum choice problem given by Cagman and Enginoglu. Our Example 8.6 presents a case where the optimum choice is intuitively clear yet both uni-int methods (Cagman's and our improved one) give wrong answer but the new conjecture solves the problem correctly.
Constraint satisfaction problems (or CSPs) have been extensively studied in, for instance, artificial intelligence, database theory, graph theory, and statistical physics. From a practical viewpoint, it is beneficial to approximately solve those CSPs. When one tries to approximate the total number of truth assignments that satisfy all Boolean-valued constraints for (unweighted) Boolean CSPs, there is a known trichotomy theorem by which all such counting problems are neatly classified into exactly three categories under polynomial-time (randomized) approximation-preserving reductions. In contrast, we obtain a dichotomy theorem of approximate counting for complex-weighted Boolean CSPs, provided that all complex-valued unary constraints are freely available to use. It is the expressive power of free unary constraints that enables us to prove such a stronger, complete classification theorem. This discovery makes a step forward in the quest for the approximation-complexity classification of all counting CSPs. To deal with complex weights, we employ proof techniques of factorization and arity reduction along the line of solving Holant problems. Moreover, we introduce a novel notion of T-constructibility that naturally induces approximation-preserving reducibility. Our result also gives an approximation analogue of the dichotomy theorem on the complexity of exact counting for complex-weighted Boolean CSPs.
We consider a common type of symmetry where we have a matrix of decision variables with interchangeable rows and columns. A simple and efficient method to deal with such row and column symmetry is to post symmetry breaking constraints like DOUBLELEX and SNAKELEX. We provide a number of positive and negative results on posting such symmetry breaking constraints. On the positive side, we prove that we can compute in polynomial time a unique representative of an equivalence class in a matrix model with row and column symmetry if the number of rows (or of columns) is bounded and in a number of other special cases. On the negative side, we show that whilst DOUBLELEX and SNAKELEX are often effective in practice, they can leave a large number of symmetric solutions in the worst case. In addition, we prove that propagating DOUBLELEX completely is NP-hard. Finally we consider how to break row, column and value symmetry, correcting a result in the literature about the safeness of combining different symmetry breaking constraints. We end with the first experimental study on how much symmetry is left by DOUBLELEX and SNAKELEX on some benchmark problems.
Classic decision-theory is based on the maximum expected utility (MEU) principle, but crucially ignores the resource costs incurred when determining optimal decisions. Here we propose an axiomatic framework for bounded decision-making that considers resource costs. Agents are formalized as probability measures over input-output streams. We postulate that any such probability measure can be assigned a corresponding conjugate utility function based on three axioms: utilities should be real-valued, additive and monotonic mappings of probabilities. We show that these axioms enforce a unique conversion law between utility and probability (and thereby, information). Moreover, we show that this relation can be characterized as a variational principle: given a utility function, its conjugate probability measure maximizes a free utility functional. Transformations of probability measures can then be formalized as a change in free utility due to the addition of new constraints expressed by a target utility function. Accordingly, one obtains a criterion to choose a probability measure that trades off the maximization of a target utility function and the cost of the deviation from a reference distribution. We show that optimal control, adaptive estimation and adaptive control problems can be solved this way in a resource-efficient way. When resource costs are ignored, the MEU principle is recovered. Our formalization might thus provide a principled approach to bounded rationality that establishes a close link to information theory.
From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classifiers. In this paper, a paradigm shift is proposed whereby a committee of classifiers is used to determine the final classification output. Two of the key components of an ensemble system are that there should be diversity among the classifiers and that there should be a mechanism through which the results are combined. In this paper, the members of the ensemble system include: Linear SVM, Gaussian SVM and Quadratic SVM. The final output was determined through a simple majority vote of the individual classifiers. From the results obtained it was observed that the final derived map generated by an ensemble system can potentially improve on the results derived from the individual classifiers making up the ensemble system. The ensemble system classification accuracy was, in this case, better than the linear and quadratic SVM result. It was however less than that of the RBF SVM. Areas for further research could focus on improving the diversity of the ensemble system used in this research.
Strategic Environmental Assessment is a procedure aimed at introducing systematic assessment of the environmental effects of plans and programs. This procedure is based on the so-called coaxial matrices that define dependencies between plan activities (infrastructures, plants, resource extractions, buildings, etc.) and positive and negative environmental impacts, and dependencies between these impacts and environmental receptors. Up to now, this procedure is manually implemented by environmental experts for checking the environmental effects of a given plan or program, but it is never applied during the plan/program construction. A decision support system, based on a clear logic semantics, would be an invaluable tool not only in assessing a single, already defined plan, but also during the planning process in order to produce an optimized, environmentally assessed plan and to study possible alternative scenarios. We propose two logic-based approaches to the problem, one based on Constraint Logic Programming and one on Probabilistic Logic Programming that could be, in the future, conveniently merged to exploit the advantages of both. We test the proposed approaches on a real energy plan and we discuss their limitations and advantages.
This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian networks, which is the problem of querying the most probable state configuration of some of the network variables given evidence. First, it is demonstrated that the problem remains hard even in networks with very simple topology, such as binary polytrees and simple trees (including the Naive Bayes structure). Such proofs extend previous complexity results for the problem. Inapproximability results are also derived in the case of trees if the number of states per variable is not bounded. Although the problem is shown to be hard and inapproximable even in very simple scenarios, a new exact algorithm is described that is empirically fast in networks of bounded treewidth and bounded number of states per variable. The same algorithm is used as basis of a Fully Polynomial Time Approximation Scheme for MAP under such assumptions. Approximation schemes were generally thought to be impossible for this problem, but we show otherwise for classes of networks that are important in practice. The algorithms are extensively tested using some well-known networks as well as random generated cases to show their effectiveness.
An approach to the revision of logic programs under the answer set semantics is presented. For programs P and Q, the goal is to determine the answer sets that correspond to the revision of P by Q, denoted P * Q. A fundamental principle of classical (AGM) revision, and the one that guides the approach here, is the success postulate. In AGM revision, this stipulates that A is in K * A. By analogy with the success postulate, for programs P and Q, this means that the answer sets of Q will in some sense be contained in those of P * Q. The essential idea is that for P * Q, a three-valued answer set for Q, consisting of positive and negative literals, is first determined. The positive literals constitute a regular answer set, while the negated literals make up a minimal set of naf literals required to produce the answer set from Q. These literals are propagated to the program P, along with those rules of Q that are not decided by these literals. The approach differs from work in update logic programs in two main respects. First, we ensure that the revising logic program has higher priority, and so we satisfy the success postulate; second, for the preference implicit in a revision P * Q, the program Q as a whole takes precedence over P, unlike update logic programs, since answer sets of Q are propagated to P. We show that a core group of the AGM postulates are satisfied, as are the postulates that have been proposed for update logic programs.
The stable marriage problem is a well-known problem of matching men to women so that no man and woman, who are not married to each other, both prefer each other. Such a problem has a wide variety of practical applications, ranging from matching resident doctors to hospitals, to matching students to schools or more generally to any two-sided market. In the classical stable marriage problem, both men and women express a strict preference order over the members of the other sex, in a qualitative way. Here we consider stable marriage problems with quantitative preferences: each man (resp., woman) provides a score for each woman (resp., man). Such problems are more expressive than the classical stable marriage problems. Moreover, in some real-life situations it is more natural to express scores (to model, for example, profits or costs) rather than a qualitative preference ordering. In this context, we define new notions of stability and optimality, and we provide algorithms to find marriages which are stable and/or optimal according to these notions. While expressivity greatly increases by adopting quantitative preferences, we show that in most cases the desired solutions can be found by adapting existing algorithms for the classical stable marriage problem.
In this work we have compared two indexing algorithms that have been used to index and retrieve Carnatic music songs. We have compared a modified algorithm of the Dual ternary indexing algorithm for music indexing and retrieval with the multi-key hashing indexing algorithm proposed by us. The modification in the dual ternary algorithm was essential to handle variable length query phrase and to accommodate features specific to Carnatic music. The dual ternary indexing algorithm is adapted for Carnatic music by segmenting using the segmentation technique for Carnatic music. The dual ternary algorithm is compared with the multi-key hashing algorithm designed by us for indexing and retrieval in which features like MFCC, spectral flux, melody string and spectral centroid are used as features for indexing data into a hash table. The way in which collision resolution was handled by this hash table is different than the normal hash table approaches. It was observed that multi-key hashing based retrieval had a lesser time complexity than dual-ternary based indexing The algorithms were also compared for their precision and recall in which multi-key hashing had a better recall than modified dual ternary indexing for the sample data considered.
Event-driven automation of reactive functionalities for complex event processing is an urgent need in today's distributed service-oriented architectures and Web-based event-driven environments. An important problem to be addressed is how to correctly and efficiently capture and process the event-based behavioral, reactive logic embodied in reaction rules, and combining this with other conditional decision logic embodied, e.g., in derivation rules. This paper elaborates a homogeneous integration approach that combines derivation rules, reaction rules and other rule types such as integrity constraints into the general framework of logic programming, the industrial-strength version of declarative programming. We describe syntax and semantics of the language, implement a distributed web-based middleware using enterprise service technologies and illustrate its adequacy in terms of expressiveness, efficiency and scalability through examples extracted from industrial use cases. The developed reaction rule language provides expressive features such as modular ID-based updates with support for external imports and self-updates of the intensional and extensional knowledge bases, transactions including integrity testing and roll-backs of update transition paths. It also supports distributed complex event processing, event messaging and event querying via efficient and scalable enterprise middleware technologies and event/action reasoning based on an event/action algebra implemented by an interval-based event calculus variant as a logic inference formalism.
In this paper we analyze judgement aggregation problems in which a group of agents independently votes on a set of complex propositions that has some interdependency constraint between them(e.g., transitivity when describing preferences). We consider the issue of judgement aggregation from the perspective of approximation. That is, we generalize the previous results by studying approximate judgement aggregation. We relax the main two constraints assumed in the current literature, Consistency and Independence and consider mechanisms that only approximately satisfy these constraints, that is, satisfy them up to a small portion of the inputs. The main question we raise is whether the relaxation of these notions significantly alters the class of satisfying aggregation mechanisms. The recent works for preference aggregation of Kalai, Mossel, and Keller fit into this framework. The main result of this paper is that, as in the case of preference aggregation, in the case of a subclass of a natural class of aggregation problems termed `truth-functional agendas', the set of satisfying aggregation mechanisms does not extend non-trivially when relaxing the constraints. Our proof techniques involve Boolean Fourier transform and analysis of voter influences for voting protocols. The question we raise for Approximate Aggregation can be stated in terms of Property Testing. For instance, as a corollary from our result we get a generalization of the classic result for property testing of linearity of Boolean functions. An updated version (RePEc:huj:dispap:dp574R) is available at http://www.ratio.huji.ac.il/dp_files/dp574R.pdf
Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this paper, thus, we present NESVM, a fast gradient SVM solver that can optimize various SVM models, e.g., classical SVM, linear programming SVM and least square SVM. Compared against SVM-Perf \cite{SVM_Perf}\cite{PerfML} (its convergence rate in solving the dual SVM is upper bounded by $\mathcal O(1/\sqrt{k})$, wherein $k$ is the number of iterations.) and Pegasos \cite{Pegasos} (online SVM that converges at rate $\mathcal O(1/k)$ for the primal SVM), NESVM achieves the optimal convergence rate at $\mathcal O(1/k^{2})$ and a linear time complexity. In particular, NESVM smoothes the non-differentiable hinge loss and $\ell_1$-norm in the primal SVM. Then the optimal gradient method without any line search is adopted to solve the optimization. In each iteration round, the current gradient and historical gradients are combined to determine the descent direction, while the Lipschitz constant determines the step size. Only two matrix-vector multiplications are required in each iteration round. Therefore, NESVM is more efficient than existing SVM solvers. In addition, NESVM is available for both linear and nonlinear kernels. We also propose "homotopy NESVM" to accelerate NESVM by dynamically decreasing the smooth parameter and using the continuation method. Our experiments on census income categorization, indoor/outdoor scene classification, event recognition and scene recognition suggest the efficiency and the effectiveness of NESVM. The MATLAB code of NESVM will be available on our website for further assessment.
The search strategy of a CP solver is determined by the variable and value ordering heuristics it employs and by the branching scheme it follows. Although the effects of variable and value ordering heuristics on search effort have been widely studied, the effects of different branching schemes have received less attention. In this paper we study this effect through an experimental evaluation that includes standard branching schemes such as 2-way, d-way, and dichotomic domain splitting, as well as variations of set branching where branching is performed on sets of values. We also propose and evaluate a generic approach to set branching where the partition of a domain into sets is created using the scores assigned to values by a value ordering heuristic, and a clustering algorithm from machine learning. Experimental results demonstrate that although exponential differences between branching schemes, as predicted in theory between 2-way and d-way branching, are not very common, still the choice of branching scheme can make quite a difference on certain classes of problems. Set branching methods are very competitive with 2-way branching and outperform it on some problem classes. A statistical analysis of the results reveals that our generic clustering-based set branching method is the best among the methods compared.
We motivate and analyse a new Tree Search algorithm, GPTS, based on recent theoretical advances in the use of Gaussian Processes for Bandit problems. We consider tree paths as arms and we assume the target/reward function is drawn from a GP distribution. The posterior mean and variance, after observing data, are used to define confidence intervals for the function values, and we sequentially play arms with highest upper confidence bounds. We give an efficient implementation of GPTS and we adapt previous regret bounds by determining the decay rate of the eigenvalues of the kernel matrix on the whole set of tree paths. We consider two kernels in the feature space of binary vectors indexed by the nodes of the tree: linear and Gaussian. The regret grows in square root of the number of iterations T, up to a logarithmic factor, with a constant that improves with bigger Gaussian kernel widths. We focus on practical values of T, smaller than the number of arms. Finally, we apply GPTS to Open Loop Planning in discounted Markov Decision Processes by modelling the reward as a discounted sum of independent Gaussian Processes. We report similar regret bounds to those of the OLOP algorithm.
An answer to a query has a well-defined lineage expression (alternatively called how-provenance) that explains how the answer was derived. Recent work has also shown how to compute the lineage of a non-answer to a query. However, the cause of an answer or non-answer is a more subtle notion and consists, in general, of only a fragment of the lineage. In this paper, we adapt Halpern, Pearl, and Chockler's recent definitions of causality and responsibility to define the causes of answers and non-answers to queries, and their degree of responsibility. Responsibility captures the notion of degree of causality and serves to rank potentially many causes by their relative contributions to the effect. Then, we study the complexity of computing causes and responsibilities for conjunctive queries. It is known that computing causes is NP-complete in general. Our first main result shows that all causes to conjunctive queries can be computed by a relational query which may involve negation. Thus, causality can be computed in PTIME, and very efficiently so. Next, we study computing responsibility. Here, we prove that the complexity depends on the conjunctive query and demonstrate a dichotomy between PTIME and NP-complete cases. For the PTIME cases, we give a non-trivial algorithm, consisting of a reduction to the max-flow computation problem. Finally, we prove that, even when it is in PTIME, responsibility is complete for LOGSPACE, implying that, unlike causality, it cannot be computed by a relational query.
Complex network theory aims to model and analyze complex systems that consist of multiple and interdependent components. Among all studies on complex networks, topological structure analysis is of the most fundamental importance, as it represents a natural route to understand the dynamics, as well as to synthesize or optimize the functions, of networks. A broad spectrum of network structural patterns have been respectively reported in the past decade, such as communities, multipartites, hubs, authorities, outliers, bow ties, and others. Here, we show that most individual real-world networks demonstrate multiplex structures. That is, a multitude of known or even unknown (hidden) patterns can simultaneously situate in the same network, and moreover they may be overlapped and nested with each other to collaboratively form a heterogeneous, nested or hierarchical organization, in which different connective phenomena can be observed at different granular levels. In addition, we show that the multiplex structures hidden in exploratory networks can be well defined as well as effectively recognized within an unified framework consisting of a set of proposed concepts, models, and algorithms. Our findings provide a strong evidence that most real-world complex systems are driven by a combination of heterogeneous mechanisms that may collaboratively shape their ubiquitous multiplex structures as we observe currently. This work also contributes a mathematical tool for analyzing different sources of networks from a new perspective of unveiling multiplex structures, which will be beneficial to multiple disciplines including sociology, economics and computer science.
Margin theory provides one of the most popular explanations to the success of \texttt{AdaBoost}, where the central point lies in the recognition that \textit{margin} is the key for characterizing the performance of \texttt{AdaBoost}. This theory has been very influential, e.g., it has been used to argue that \texttt{AdaBoost} usually does not overfit since it tends to enlarge the margin even after the training error reaches zero. Previously the \textit{minimum margin bound} was established for \texttt{AdaBoost}, however, \cite{Breiman1999} pointed out that maximizing the minimum margin does not necessarily lead to a better generalization. Later, \cite{Reyzin:Schapire2006} emphasized that the margin distribution rather than minimum margin is crucial to the performance of \texttt{AdaBoost}. In this paper, we first present the \textit{$k$th margin bound} and further study on its relationship to previous work such as the minimum margin bound and Emargin bound. Then, we improve the previous empirical Bernstein bounds \citep{Maurer:Pontil2009,Audibert:Munos:Szepesvari2009}, and based on such findings, we defend the margin-based explanation against Breiman's doubts by proving a new generalization error bound that considers exactly the same factors as \cite{Schapire:Freund:Bartlett:Lee1998} but is sharper than \cite{Breiman1999}'s minimum margin bound. By incorporating factors such as average margin and variance, we present a generalization error bound that is heavily related to the whole margin distribution. We also provide margin distribution bounds for generalization error of voting classifiers in finite VC-dimension space.
Pervasive user-centric applications are systems which are meant to sense the presence, mood, and intentions of users in order to optimize user comfort and performance. Building such applications requires not only state-of-the art techniques from artificial intelligence but also sound software engineering methods for facilitating modular design, runtime adaptation and verification of critical system requirements. In this paper we focus on high-level design and analysis, and use the algebraic rewriting language Real-Time Maude for specifying applications in a real-time setting. We propose a generic component-based approach for modeling pervasive user-centric systems and we show how to analyze and prove crucial properties of the system architecture through model checking and simulation. For proving time-dependent properties we use Metric Temporal Logic (MTL) and present analysis algorithms for model checking two subclasses of MTL formulas: time-bounded response and time-bounded safety MTL formulas. The underlying idea is to extend the Real-Time Maude model with suitable clocks, to transform the MTL formulas into LTL formulas over the extended specification, and then to use the LTL model checker of Maude. It is shown that these analyses are sound and complete for maximal time sampling. The approach is illustrated by a simple adaptive advertising scenario in which an adaptive advertisement display can react to actions of the users in front of the display.
The aim of this work is to develop a study from the perspective of Abstract Algebraic Logic of some bilattice-based logical systems introduced in the nineties by Ofer Arieli and Arnon Avron. The motivation for such an investigation has two main roots. On the one hand there is an interest in bilattices as an elegant formalism that gave rise in the last two decades to a variety of applications, especially in the field of Theoretical Computer Science and Artificial Intelligence. In this respect, the present study aims to be a contribution to a better understanding of the mathematical and logical framework that underlie these applications. On the other hand, our interest in bilattice-based logics comes from Abstract Algebraic Logic. In very general terms, algebraic logic can be described as the study of the connections between algebra and logic. One of the main reasons that motivate this study is the possibility to treat logical problems with algebraic methods and viceversa: this is accomplished by associating to a logical system a class of algebraic models that can be regarded as the algebraic counterpart of that logic. Starting from the work of Tarski and his collaborators, the method of algebraizing logics has been increasingly developed and generalized. In the last two decades, algebraic logicians have focused their attention on the process of algebraization itself: this kind of investigation forms now a subfield of algebraic logic known as Abstract Algebraic Logic (which we abbreviate AAL).
Modern scientific data mainly consist of huge datasets gathered by a very large number of techniques and stored in very diversified and often incompatible data repositories. More in general, in the e-science environment, it is considered as a critical and urgent requirement to integrate services across distributed, heterogeneous, dynamic "virtual organizations" formed by different resources within a single enterprise. In the last decade, Astronomy has become an immensely data rich field due to the evolution of detectors (plates to digital to mosaics), telescopes and space instruments. The Virtual Observatory approach consists into the federation under common standards of all astronomical archives available worldwide, as well as data analysis, data mining and data exploration applications. The main drive behind such effort being that once the infrastructure will be completed, it will allow a new type of multi-wavelength, multi-epoch science which can only be barely imagined. Data Mining, or Knowledge Discovery in Databases, while being the main methodology to extract the scientific information contained in such MDS (Massive Data Sets), poses crucial problems since it has to orchestrate complex problems posed by transparent access to different computing environments, scalability of algorithms, reusability of resources, etc. In the present paper we summarize the present status of the MDS in the Virtual Observatory and what is currently done and planned to bring advanced Data Mining methodologies in the case of the DAME (DAta Mining & Exploration) project.
We describe a mathematical models of grounded symbols in the brain. It also serves as a computational foundations for Perceptual Symbol System (PSS). This development requires new mathematical methods of dynamic logic (DL), which have overcome limitations of classical artificial intelligence and connectionist approaches. The paper discusses these past limitations, relates them to combinatorial complexity (exponential explosion) of algorithms in the past, and further to the static nature of classical logic. The new mathematical theory, DL, is a process-logic. A salient property of this process is evolution of vague representations into crisp. The paper first applies it to one aspect of PSS: situation learning from object perceptions. Then we relate DL to the essential PSS mechanisms of concepts, simulators, grounding, productivity, binding, recursion, and to the mechanisms relating grounded and amodal symbols. We discuss DL as a general theory describing the process of cognition on multiple levels of abstraction. We also discuss the implications of this theory for interactions between cognition and language, mechanisms of language grounding, and possible role of language in grounding abstract cognition. The developed theory makes experimental predictions, and will impact future theoretical developments in cognitive science, including knowledge representation, and perception-cognition interaction. Experimental neuroimaging evidence for DL and PSS in brain imaging is discussed as well as future research directions.
This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a relational representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.
We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications, reinforcement learning (RL) is complicated by the fact that state is either high-dimensional or partially observable. Therefore, RL methods are designed to work with features of state rather than state itself, and the success or failure of learning is often determined by the suitability of the selected features. By comparison, subspace identification (SSID) methods are designed to select a feature set which preserves as much information as possible about state. In this paper we connect the two approaches, looking at the problem of reinforcement learning with a large set of features, each of which may only be marginally useful for value function approximation. We introduce a new algorithm for this situation, called Predictive State Temporal Difference (PSTD) learning. As in SSID for predictive state representations, PSTD finds a linear compression operator that projects a large set of features down to a small set that preserves the maximum amount of predictive information. As in RL, PSTD then uses a Bellman recursion to estimate a value function. We discuss the connection between PSTD and prior approaches in RL and SSID. We prove that PSTD is statistically consistent, perform several experiments that illustrate its properties, and demonstrate its potential on a difficult optimal stopping problem.
Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open. We develop an algorithm based on Supervised Random Walks that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function. Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction.
Distributed search problems are ubiquitous in Artificial Life (ALife). Many distributed search problems require identifying a rare and previously unseen event and producing a rapid response. This challenge amounts to finding and removing an unknown needle in a very large haystack. Traditional computational search models are unlikely to find, nonetheless, appropriately respond to, novel events, particularly given data distributed across multiple platforms in a variety of formats and sources with variable and unknown reliability. Biological systems have evolved solutions to distributed search and response under uncertainty. Immune systems and ant colonies efficiently scale up massively parallel search with automated response in highly dynamic environments, and both do so using distributed coordination without centralized control. These properties are relevant to ALife, where distributed, autonomous, robust and adaptive control is needed to design robot swarms, mobile computing networks, computer security systems and other distributed intelligent systems. They are also relevant for searching, tracking the spread of ideas, and understanding the impact of innovations in online social networks. We review design principles for Scalable Robust, Adaptive, Decentralized search with Automated Response (Scalable RADAR) in biology. We discuss how biological RADAR scales up efficiently, and then discuss in detail how modular search in the immune system can be mimicked or built upon in ALife. Such search mechanisms are particularly useful when components have limited capacity to communicate and social or physical distance makes long distance communication more costly.
Both deterministic and indeterministic physical laws are incompatible with control by genuine (non-illusory) free will. We propose that an indeterministic dynamics can be $weakly$ compatible with free will (FW), whereby the latter acts by altering the probability distribution over allowed outcomes. In the quantum physical world, such a FW can collapse the wave function, introducing deviations from the Born rule. In principle, this deviation would stand in conflict with both special relativity and (a variant of) the Strong Church-Turing thesis, implying that the brain may be an arena of exotic, non-standard physics. However, in practice, these deviations would not be directly or easily observable, because they occur in sub-neuronal superpositions in the brain, where they would be shrouded in random measurement errors, noise and statistical fluctuations. Our result elucidates the difference between the FW of human observers and that of observed particles in the Free Will Theorem. This difference is a basic reason for why FW (and, in general, consciousness) cannot be recreated by standard artificial intelligence (AI) technology. We propose various neurobiological experiments to test our proposed theory. We speculate that for observers to be aware of a physical theory such as quantum mechanics, FW is necessary and that the theory must therefore not be universal. We suggest that FW may be regarded as a primitive principle in Nature for explaining quantum indeterminism.
Graph coloring, also known as vertex coloring, considers the problem of assigning colors to the nodes of a graph such that adjacent nodes do not share the same color. The optimization version of the problem concerns the minimization of the number of used colors. In this paper we deal with the problem of finding valid colorings of graphs in a distributed way, that is, by means of an algorithm that only uses local information for deciding the color of the nodes. Such algorithms prescind from any central control. Due to the fact that quite a few practical applications require to find colorings in a distributed way, the interest in distributed algorithms for graph coloring has been growing during the last decade. As an example consider wireless ad-hoc and sensor networks, where tasks such as the assignment of frequencies or the assignment of TDMA slots are strongly related to graph coloring. The algorithm proposed in this paper is inspired by the calling behavior of Japanese tree frogs. Male frogs use their calls to attract females. Interestingly, groups of males that are located nearby each other desynchronize their calls. This is because female frogs are only able to correctly localize the male frogs when their calls are not too close in time. We experimentally show that our algorithm is very competitive with the current state of the art, using different sets of problem instances and comparing to one of the most competitive algorithms from the literature.
Bayesian networks are basic graphical models, used widely both in statistics and artificial intelligence. These statistical models of conditional independence structure are described by acyclic directed graphs whose nodes correspond to (random) variables in consideration. A quite important topic is the learning of Bayesian network structures, which is determining the best fitting statistical model on the basis of given data. Although there are learning methods based on statistical conditional independence tests, contemporary methods are mainly based on maximization of a suitable quality criterion that evaluates how good the graph explains the occurrence of the observed data. This leads to a nonlinear combinatorial optimization problem that is in general NP-hard to solve. In this paper we deal with the complexity of learning restricted Bayesian network structures, that is, we wish to find network structures of highest score within a given subset of all possible network structures. For this, we introduce a new unique algebraic representative for these structures, called the characteristic imset. We show that these imsets are always 0-1-vectors and that they have many nice properties that allow us to simplify long proofs for some known results and to easily establish new complexity results for learning restricted Bayes network structures.
There are a huge number of problems, from various areas, being solved by reducing them to SAT. However, for many applications, translation into SAT is performed by specialized, problem-specific tools. In this paper we describe a new system for uniform solving of a wide class of problems by reducing them to SAT. The system uses a new specification language URSA that combines imperative and declarative programming paradigms. The reduction to SAT is defined precisely by the semantics of the specification language. The domain of the approach is wide (e.g., many NP-complete problems can be simply specified and then solved by the system) and there are problems easily solvable by the proposed system, while they can be hardly solved by using other programming languages or constraint programming systems. So, the system can be seen not only as a tool for solving problems by reducing them to SAT, but also as a general-purpose constraint solving system (for finite domains). In this paper, we also describe an open-source implementation of the described approach. The performed experiments suggest that the system is competitive to state-of-the-art related modelling systems.
The previous decade has brought a remarkable increase of the interest in applications that deal with querying and mining of time series data. Many of the research efforts in this context have focused on introducing new representation methods for dimensionality reduction or novel similarity measures for the underlying data. In the vast majority of cases, each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive experimental study re-implementing eight different time series representations and nine similarity measures and their variants, and testing their effectiveness on thirty-eight time series data sets from a wide variety of application domains. In this paper, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. In addition to providing a unified validation of some of the existing achievements, our experiments also indicate that, in some cases, certain claims in the literature may be unduly optimistic.
Temporal Logic Model Checking is a verification method in which we describe a system, the model, and then we verify whether some properties, expressed in a temporal logic formula, hold in the system. It has many industrial applications. In order to improve performance, some tools allow preprocessing of the model, verifying on-line a set of properties reusing the same compiled model; we prove that the complexity of the Model Checking problem, without any preprocessing or preprocessing the model or the formula in a polynomial data structure, is the same. As a result preprocessing does not always exponentially improve performance. Symbolic Model Checking algorithms work by manipulating sets of states, and these sets are often represented by BDDs. It has been observed that the size of BDDs may grow exponentially as the model and formula increase in size. As a side result, we formally prove that a superpolynomial increase of the size of these BDDs is unavoidable in the worst case. While this exponential growth has been empirically observed, to the best of our knowledge it has never been proved so far in general terms. This result not only holds for all types of BDDs regardless of the variable ordering, but also for more powerful data structures, such as BEDs, RBCs, MTBDDs, and ADDs.
In massively collaborative projects such as scientific or community databases, users often need to agree or disagree on the content of individual data items. On the other hand, trust relationships often exist between users, allowing them to accept or reject other users' beliefs by default. As those trust relationships become complex, however, it becomes difficult to define and compute a consistent snapshot of the conflicting information. Previous solutions to a related problem, the update reconciliation problem, are dependent on the order in which the updates are processed and, therefore, do not guarantee a globally consistent snapshot. This paper proposes the first principled solution to the automatic conflict resolution problem in a community database. Our semantics is based on the certain tuples of all stable models of a logic program. While evaluating stable models in general is well known to be hard, even for very simple logic programs, we show that the conflict resolution problem admits a PTIME solution. To the best of our knowledge, ours is the first PTIME algorithm that allows conflict resolution in a principled way. We further discuss extensions to negative beliefs and prove that some of these extensions are hard. This work is done in the context of the BeliefDB project at the University of Washington, which focuses on the efficient management of conflicts in community databases.
This article deals with Part family formation problem which is believed to be moderately complicated to be solved in polynomial time in the vicinity of Group Technology (GT). In the past literature researchers investigated that the part family formation techniques are principally based on production flow analysis (PFA) which usually considers operational requirements, sequences and time. Part Coding Analysis (PCA) is merely considered in GT which is believed to be the proficient method to identify the part families. PCA classifies parts by allotting them to different families based on their resemblances in: (1) design characteristics such as shape and size, and/or (2) manufacturing characteristics (machining requirements). A novel approach based on simulated annealing namely SAPFOCS is adopted in this study to develop effective part families exploiting the PCA technique. Thereafter Taguchi's orthogonal design method is employed to solve the critical issues on the subject of parameters selection for the proposed metaheuristic algorithm. The adopted technique is therefore tested on 5 different datasets of size 5 {\times} 9 to 27 {\times} 9 and the obtained results are compared with C-Linkage clustering technique. The experimental results reported that the proposed metaheuristic algorithm is extremely effective in terms of the quality of the solution obtained and has outperformed C-Linkage algorithm in most instances.
In a Role-Playing Game, finding optimal trajectories is one of the most important tasks. In fact, the strategy decision system becomes a key component of a game engine. Determining the way in which decisions are taken (online, batch or simulated) and the consumed resources in decision making (e.g. execution time, memory) will influence, in mayor degree, the game performance. When classical search algorithms such as A* can be used, they are the very first option. Nevertheless, such methods rely on precise and complete models of the search space, and there are many interesting scenarios where their application is not possible. Then, model free methods for sequential decision making under uncertainty are the best choice. In this paper, we propose a heuristic planning strategy to incorporate the ability of heuristic-search in path-finding into a Dyna agent. The proposed Dyna-H algorithm, as A* does, selects branches more likely to produce outcomes than other branches. Besides, it has the advantages of being a model-free online reinforcement learning algorithm. The proposal was evaluated against the one-step Q-Learning and Dyna-Q algorithms obtaining excellent experimental results: Dyna-H significantly overcomes both methods in all experiments. We suggest also, a functional analogy between the proposed sampling from worst trajectories heuristic and the role of dreams (e.g. nightmares) in human behavior.
Engineered systems are designed to deftly operate under predetermined conditions yet are notoriously fragile when unexpected perturbations arise. In contrast, biological systems operate in a highly flexible manner; learn quickly adequate responses to novel conditions, and evolve new routines/traits to remain competitive under persistent environmental change. A recent theory on the origins of biological flexibility has proposed that degeneracy - the existence of multi-functional components with partially overlapping functions - is a primary determinant of the robustness and adaptability found in evolved systems. While degeneracy's contribution to biological flexibility is well documented, there has been little investigation of degeneracy design principles for achieving flexibility in systems engineering. Actually, the conditions that can lead to degeneracy are routinely eliminated in engineering design. With the planning of transportation vehicle fleets taken as a case study, this paper reports evidence that degeneracy improves robustness and adaptability of a simulated fleet without incurring costs to efficiency. We find degeneracy dramatically increases robustness of a fleet to unpredicted changes in the environment while it also facilitates robustness to anticipated variations. When we allow a fleet's architecture to be adapted in response to environmental change, we find degeneracy can be selectively acquired, leading to faster rates of design adaptation and ultimately to better designs. Given the range of conditions where favorable short-term and long-term performance outcomes are observed, we propose that degeneracy design principles fundamentally alter the propensity for adaptation and may be useful within several engineering and planning contexts.
Techniques in which words are represented as vectors have proved useful in many applications in computational linguistics, however there is currently no general semantic formalism for representing meaning in terms of vectors. We present a framework for natural language semantics in which words, phrases and sentences are all represented as vectors, based on a theoretical analysis which assumes that meaning is determined by context. In the theoretical analysis, we define a corpus model as a mathematical abstraction of a text corpus. The meaning of a string of words is assumed to be a vector representing the contexts in which it occurs in the corpus model. Based on this assumption, we can show that the vector representations of words can be considered as elements of an algebra over a field. We note that in applications of vector spaces to representing meanings of words there is an underlying lattice structure; we interpret the partial ordering of the lattice as describing entailment between meanings. We also define the context-theoretic probability of a string, and, based on this and the lattice structure, a degree of entailment between strings. We relate the framework to existing methods of composing vector-based representations of meaning, and show that our approach generalises many of these, including vector addition, component-wise multiplication, and the tensor product.
External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.
The emerging need for qualitative approaches in context-aware information processing calls for proper modeling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modeling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendability remains limited. In this paper, we present f-Context as a service-based context-awareness framework, based on language-action perspective (LAP) theory for modeling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer architecture is proposed for implementing f-Context that uses computing with words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed using a realistic scenario involving a group of mobile users. We believe that the proposed approach can open the door to future research on context-awareness by offering a theoretical foundation based on human communication, and a service-based layered architecture which exploits CWW for context-aware, group-based and platform-independent access to information systems.
Recent research in multi-robot exploration and mapping has focused on sampling environmental fields, which are typically modeled using the Gaussian process (GP). Existing information-theoretic exploration strategies for learning GP-based environmental field maps adopt the non-Markovian problem structure and consequently scale poorly with the length of history of observations. Hence, it becomes computationally impractical to use these strategies for in situ, real-time active sampling. To ease this computational burden, this paper presents a Markov-based approach to efficient information-theoretic path planning for active sampling of GP-based fields. We analyze the time complexity of solving the Markov-based path planning problem, and demonstrate analytically that it scales better than that of deriving the non-Markovian strategies with increasing length of planning horizon. For a class of exploration tasks called the transect sampling task, we provide theoretical guarantees on the active sampling performance of our Markov-based policy, from which ideal environmental field conditions and sampling task settings can be established to limit its performance degradation due to violation of the Markov assumption. Empirical evaluation on real-world temperature and plankton density field data shows that our Markov-based policy can generally achieve active sampling performance comparable to that of the widely-used non-Markovian greedy policies under less favorable realistic field conditions and task settings while enjoying significant computational gain over them.
Boolean Satisfiability solvers have gone through dramatic improvements in their performances and scalability over the last few years by considering symmetries. It has been shown that by using graph symmetries and generating symmetry breaking predicates (SBPs) it is possible to break symmetries in Conjunctive Normal Form (CNF). The SBPs cut down the search space to the nonsymmetric regions of the space without affecting the satisfiability of the CNF formula. The symmetry breaking predicates are created by representing the formula as a graph, finding the graph symmetries and using some symmetry extraction mechanism (Crawford et al.). Here in this paper we take one non-trivial CNF and explore its symmetries. Finally, we generate the SBPs and adding it to CNF we show how it helps to prune the search tree, so that SAT solver would take short time. Here we present the pruning procedure of the search tree from scratch, starting from the CNF and its graph representation. As we explore the whole mechanism by a non-trivial example, it would be easily comprehendible. Also we have given a new idea of generating symmetry breaking predicates for breaking symmetry in CNF, not derived from Crawford's conditions. At last we propose a backtrack SAT solver with inbuilt SBP generator.
One of the most interesting scientific challenges nowadays deals with the analysis and the understanding of complex networks' dynamics and how their processes lead to emergence according to the interactions among their components. In this paper we approach the definition of new methodologies for the visualization and the exploration of the dynamics at play in real dynamic social networks. We present a recently introduced formalism called TVG (for time-varying graphs), which was initially developed to model and analyze highly-dynamic and infrastructure-less communication networks such as mobile ad-hoc networks, wireless sensor networks, or vehicular networks. We discuss its applicability to complex networks in general, and social networks in particular, by showing how it enables the specification and analysis of complex dynamic phenomena in terms of temporal interactions, and allows to easily switch the perspective between local and global dynamics. As an example, we chose the case of scientific communities by analyzing portion of the ArXiv repository (ten years of publications in physics) focusing on the social determinants (e.g. goals and potential interactions among individuals) behind the emergence and the resilience of scientific communities. We consider that scientific communities are at the same time communities of practice (through co-authorship) and that they exist also as representations in the scientists' mind, since references to other scientists' works is not merely an objective link to a relevant work, but it reveals social objects that one manipulates, select and refers to. In the paper we show the emergence/selection of a community as a goal-driven preferential attachment toward a set of authors among which there are some key scientists (Nobel prizes).
We give the first analysis of the computational complexity of {\it coalition structure generation over graphs}. Given an undirected graph $G=(N,E)$ and a valuation function $v:2^N\rightarrow\RR$ over the subsets of nodes, the problem is to find a partition of $N$ into connected subsets, that maximises the sum of the components' values. This problem is generally NP--complete; in particular, it is hard for a defined class of valuation functions which are {\it independent of disconnected members}---that is, two nodes have no effect on each other's marginal contribution to their vertex separator. Nonetheless, for all such functions we provide bounds on the complexity of coalition structure generation over general and minor free graphs. Our proof is constructive and yields algorithms for solving corresponding instances of the problem. Furthermore, we derive polynomial time bounds for acyclic, $K_{2,3}$ and $K_4$ minor free graphs. However, as we show, the problem remains NP--complete for planar graphs, and hence, for any $K_k$ minor free graphs where $k\geq 5$. Moreover, our hardness result holds for a particular subclass of valuation functions, termed {\it edge sum}, where the value of each subset of nodes is simply determined by the sum of given weights of the edges in the induced subgraph.
In this paper we introduce the olog, or ontology log, a category-theoretic model for knowledge representation (KR). Grounded in formal mathematics, ologs can be rigorously formulated and cross-compared in ways that other KR models (such as semantic networks) cannot. An olog is similar to a relational database schema; in fact an olog can serve as a data repository if desired. Unlike database schemas, which are generally difficult to create or modify, ologs are designed to be user-friendly enough that authoring or reconfiguring an olog is a matter of course rather than a difficult chore. It is hoped that learning to author ologs is much simpler than learning a database definition language, despite their similarity. We describe ologs carefully and illustrate with many examples. As an application we show that any primitive recursive function can be described by an olog. We also show that ologs can be aligned or connected together into a larger network using functors. The various methods of information flow and institutions can then be used to integrate local and global world-views. We finish by providing several different avenues for future research.
We present a novel variant of decision making based on the mathematical theory of separable Hilbert spaces. This mathematical structure captures the effect of superposition of composite prospects, including many incorporated intentions, which allows us to describe a variety of interesting fallacies and anomalies that have been reported to particularize the decision making of real human beings. The theory characterizes entangled decision making, non-commutativity of subsequent decisions, and intention interference. We demonstrate how the violation of the Savage's sure-thing principle, known as the disjunction effect, can be explained quantitatively as a result of the interference of intentions, when making decisions under uncertainty. The disjunction effects, observed in experiments, are accurately predicted using a theorem on interference alternation that we derive, which connects aversion-to-uncertainty to the appearance of negative interference terms suppressing the probability of actions. The conjunction fallacy is also explained by the presence of the interference terms. A series of experiments are analysed and shown to be in excellent agreement with a priori evaluation of interference effects. The conjunction fallacy is also shown to be a sufficient condition for the disjunction effect and novel experiments testing the combined interplay between the two effects are suggested.
The problem of consciousness faced several challenges for a few reasons: (a) a lack of necessary and sufficient conditions, without which we would not know how close we are to the solution, (b) a lack of a synthesis framework to build conscious systems and (c) a lack of mechanisms explaining the transition between the lower-level chemical dynamics and the higher-level abstractions. In this paper, I address these issues using a new framework. The central result is that a person is 'minimally' conscious if and only if he knows at least one truth. This lets us move away from the vagueness surrounding consciousness and instead focus equivalently on: (i) what truths are and how our brain represents/relates them to each other and (ii) how we attain a feeling of knowing for a truth. For the former problem, since truths are things that do not change, I replace the abstract notion with a dynamical one called fixed sets. These sets are guaranteed to exist for our brain and other stable parallel looped systems. The relationships between everyday events are now built using relationships between fixed sets, until our brain creates a unique dynamical state called the self-sustaining threshold 'membrane' of fixed sets. For the latter problem, I present necessary and sufficient conditions for attaining a feeling of knowing using a definition of continuity applied to abstractions. Combining these results, I now say that a person is minimally conscious if and only if his brain has a self-sustaining dynamical membrane with abstract continuous paths. A synthetic system built to satisfy this equivalent self-sustaining membrane condition appears indistinguishable from human consciousness.
Bisimulations have been widely used in many areas of computer science to model equivalence between various systems, and to reduce the number of states of these systems, whereas uniform fuzzy relations have recently been introduced as a means to model the fuzzy equivalence between elements of two possible different sets. Here we use the conjunction of these two concepts as a powerful tool in the study of equivalence between fuzzy automata. We prove that a uniform fuzzy relation between fuzzy automata $\cal A$ and $\cal B$ is a forward bisimulation if and only if its kernel and co-kernel are forward bisimulation fuzzy equivalences on $\cal A$ and $\cal B$ and there is a special isomorphism between factor fuzzy automata with respect to these fuzzy equivalences. As a consequence we get that fuzzy automata $\cal A$ and $\cal B$ are UFB-equivalent, i.e., there is a uniform forward bisimulation between them, if and only if there is a special isomorphism between the factor fuzzy automata of $\cal A$ and $\cal B$ with respect to their greatest forward bisimulation fuzzy equivalences. This result reduces the problem of testing UFB-equivalence to the problem of testing isomorphism of fuzzy automata, which is closely related to the well-known graph isomorphism problem. We prove some similar results for backward-forward bisimulations, and we point to fundamental differences. Because of the duality with the studied concepts, backward and forward-backward bisimulations are not considered separately. Finally, we give a comprehensive overview of various concepts on deterministic, nondeterministic, fuzzy, and weighted automata, which are related to bisimulations.
The problem of business-IT alignment is of widespread economic concern. As one way of addressing the problem, this paper describes an online system that functions as a kind of Wiki -- one that supports the collaborative writing and running of business and scientific applications, as rules in open vocabulary, executable English, using a browser. Since the rules are in English, they are indexed by Google and other search engines. This is useful when looking for rules for a task that one has in mind. The design of the system integrates the semantics of data, with a semantics of an inference method, and also with the meanings of English sentences. As such, the system has functionality that may be useful for the Rules, Logic, Proof and Trust requirements of the Semantic Web. The system accepts rules, and small numbers of facts, typed or copy-pasted directly into a browser. One can then run the rules, again using a browser. For larger amounts of data, the system uses information in the rules to automatically generate and run SQL over networked databases. From a few highly declarative rules, the system typically generates SQL that would be too complicated to write reliably by hand. However, the system can explain its results in step-by-step hypertexted English, at the business or scientific level As befits a Wiki, shared use of the system is free.
The Elo system for rating chess players, also used in other games and sports, was adopted by the World Chess Federation over four decades ago. Although not without controversy, it is accepted as generally reliable and provides a method for assessing players' strengths and ranking them in official tournaments. It is generally accepted that the distribution of players' rating data is approximately normal but, to date, no stochastic model of how the distribution might have arisen has been proposed. We propose such an evolutionary stochastic model, which models the arrival of players into the rating pool, the games they play against each other, and how the results of these games affect their ratings. Using a continuous approximation to the discrete model, we derive the distribution for players' ratings at time $t$ as a normal distribution, where the variance increases in time as a logarithmic function of $t$. We validate the model using published rating data from 2007 to 2010, showing that the parameters obtained from the data can be recovered through simulations of the stochastic model. The distribution of players' ratings is only approximately normal and has been shown to have a small negative skew. We show how to modify our evolutionary stochastic model to take this skewness into account, and we validate the modified model using the published official rating data.
An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences. Conceptual contents of languages and cultures to significant extent are determined by words and their semantic differences; these could be borrowed among languages and exchanged among cultures. Emotional differences, as suggested in the paper, are related to grammar and mostly cannot be borrowed. Conceptual and emotional mechanisms of languages are considered here along with their functions in the mind and cultural evolution. A fundamental contradiction in human mind is considered: language evolution requires reduced emotionality, but "too low" emotionality makes language "irrelevant to life," disconnected from sensory-motor experience. Neural mechanisms of these processes are suggested as well as their mathematical models: the knowledge instinct, the language instinct, the dual model connecting language and cognition, dynamic logic, neural modeling fields. Mathematical results are related to cognitive science, linguistics, and psychology. Experimental evidence and theoretical arguments are discussed. Approximate equations for evolution of human minds and cultures are obtained. Their solutions identify three types of cultures: "conceptual"-pragmatic cultures, in which emotionality of language is reduced and differentiation overtakes synthesis resulting in fast evolution at the price of uncertainty of values, self doubts, and internal crises; "traditional-emotional" cultures where differentiation lags behind synthesis, resulting in cultural stability at the price of stagnation; and "multi-cultural" societies combining fast cultural evolution and stability. Unsolved problems and future theoretical and experimental directions are discussed.
Knowledge compilation is an approach to tackle the computational intractability of general reasoning problems. According to this approach, knowledge bases are converted off-line into a target compilation language which is tractable for on-line querying. Reduced ordered binary decision diagram (ROBDD) is one of the most influential target languages. We generalize ROBDD by associating some implied literals in each node and the new language is called reduced ordered binary decision diagram with implied literals (ROBDD-L). Then we discuss a kind of subsets of ROBDD-L called ROBDD-i with precisely i implied literals (0 \leq i \leq \infty). In particular, ROBDD-0 is isomorphic to ROBDD; ROBDD-\infty requires that each node should be associated by the implied literals as many as possible. We show that ROBDD-i has uniqueness over some specific variables order, and ROBDD-\infty is the most succinct subset in ROBDD-L and can meet most of the querying requirements involved in the knowledge compilation map. Finally, we propose an ROBDD-i compilation algorithm for any i and a ROBDD-\infty compilation algorithm. Based on them, we implement a ROBDD-L package called BDDjLu and then get some conclusions from preliminary experimental results: ROBDD-\infty is obviously smaller than ROBDD for all benchmarks; ROBDD-\infty is smaller than the d-DNNF the benchmarks whose compilation results are relatively small; it seems that it is better to transform ROBDDs-\infty into FBDDs and ROBDDs rather than straight compile the benchmarks.
We show that several important resource allocation problems in wireless networks fit within the common framework of Constraint Satisfaction Problems (CSPs). Inspired by the requirements of these applications, where variables are located at distinct network devices that may not be able to communicate but may interfere, we define natural criteria that a CSP solver must possess in order to be practical. We term these algorithms decentralized CSP solvers. The best known CSP solvers were designed for centralized problems and do not meet these criteria. We introduce a stochastic decentralized CSP solver and prove that it will find a solution in almost surely finite time, should one exist, also showing it has many practically desirable properties. We benchmark the algorithm's performance on a well-studied class of CSPs, random k-SAT, illustrating that the time the algorithm takes to find a satisfying assignment is competitive with stochastic centralized solvers on problems with order a thousand variables despite its decentralized nature. We demonstrate the solver's practical utility for the problems that motivated its introduction by using it to find a non-interfering channel allocation for a network formed from data from downtown Manhattan.
We study decision making in environments where the reward is only partially observed, but can be modeled as a function of an action and an observed context. This setting, known as contextual bandits, encompasses a wide variety of applications including health-care policy and Internet advertising. A central task is evaluation of a new policy given historic data consisting of contexts, actions and received rewards. The key challenge is that the past data typically does not faithfully represent proportions of actions taken by a new policy. Previous approaches rely either on models of rewards or models of the past policy. The former are plagued by a large bias whereas the latter have a large variance. In this work, we leverage the strength and overcome the weaknesses of the two approaches by applying the doubly robust technique to the problems of policy evaluation and optimization. We prove that this approach yields accurate value estimates when we have either a good (but not necessarily consistent) model of rewards or a good (but not necessarily consistent) model of past policy. Extensive empirical comparison demonstrates that the doubly robust approach uniformly improves over existing techniques, achieving both lower variance in value estimation and better policies. As such, we expect the doubly robust approach to become common practice.
Some existing notions of redundancy among association rules allow for a logical-style characterization and lead to irredundant bases of absolutely minimum size. One can push the intuition of redundancy further and find an intuitive notion of interest of an association rule, in terms of its "novelty" with respect to other rules. Namely: an irredundant rule is so because its confidence is higher than what the rest of the rules would suggest; then, one can ask: how much higher? We propose to measure such a sort of "novelty" through the confidence boost of a rule, which encompasses two previous similar notions (confidence width and rule blocking, of which the latter is closely related to the earlier measure "improvement"). Acting as a complement to confidence and support, the confidence boost helps to obtain small and crisp sets of mined association rules, and solves the well-known problem that, in certain cases, rules of negative correlation may pass the confidence bound. We analyze the properties of two versions of the notion of confidence boost, one of them a natural generalization of the other. We develop efficient algorithmics to filter rules according to their confidence boost, compare the concept to some similar notions in the bibliography, and describe the results of some experimentation employing the new notions on standard benchmark datasets. We describe an open-source association mining tool that embodies one of our variants of confidence boost in such a way that the data mining process does not require the user to select any value for any parameter.
Social computation, whether in the form of searches performed by swarms of agents or collective predictions of markets, often supplies remarkably good solutions to complex problems. In many examples, individuals trying to solve a problem locally can aggregate their information and work together to arrive at a superior global solution. This suggests that there may be general principles of information aggregation and coordination that can transcend particular applications. Here we show that the general structure of this problem can be cast in terms of information theory and derive mathematical conditions that lead to optimal multi-agent searches. Specifically, we illustrate the problem in terms of local search algorithms for autonomous agents looking for the spatial location of a stochastic source. We explore the types of search problems, defined in terms of the statistical properties of the source and the nature of measurements at each agent, for which coordination among multiple searchers yields an advantage beyond that gained by having the same number of independent searchers. We show that effective coordination corresponds to synergy and that ineffective coordination corresponds to independence as defined using information theory. We classify explicit types of sources in terms of their potential for synergy. We show that sources that emit uncorrelated signals provide no opportunity for synergetic coordination while sources that emit signals that are correlated in some way, do allow for strong synergy between searchers. These general considerations are crucial for designing optimal algorithms for particular search problems in real world settings.
Homomorphisms between relational structures are not only fundamental mathematical objects, but are also of great importance in an applied computational context. Indeed, constraint satisfaction problems (CSPs), a wide class of algorithmic problems that occur in many different areas of computer science such as artificial intelligence or database theory, may be viewed as asking for homomorphisms between two relational structures [FedVar98]. In a logical setting, homomorphisms may be viewed as witnesses for positive primitive formulas in a relational language. As we shall see, homomorphisms, or more precisely the numbers of homomorphisms between two structures, are also related to a fundamental computational problem of statistical physics. In this article, we are concerned with the complexity of counting homomorphisms from a given structure A to a fixed structure B. Actually, we are mainly interested in a generalization of this problem to weighted homomorphisms (or partition functions). We almost exclusively focus on graphs. The first part of the article is a short survey of what is known about the problem. In the second part, we give a proof of a theorem due to Bulatov and the first author of this paper [BulGro05], which classifies the complexity of partition functions described by matrices with non-negative entries. The proof we give here is essentially the same as the original one, with a few shortcuts due to [Thu09], but it is phrased in a different, more graph theoretical language that may make it more accessible to most readers.
Free variables occur frequently in mathematics and computer science with ad hoc and altering semantics. We present the most recent version of our free-variable framework for two-valued logics with properly improved functionality, but only two kinds of free variables left (instead of three): implicitly universally and implicitly existentially quantified ones, now simply called "free atoms" and "free variables", respectively. The quantificational expressiveness and the problem-solving facilities of our framework exceed standard first-order and even higher-order modal logics, and directly support Fermat's descente infinie. With the improved version of our framework, we can now model also Henkin quantification, neither using quantifiers (binders) nor raising (Skolemization). We propose a new semantics for Hilbert's epsilon as a choice operator with the following features: We avoid overspecification (such as right-uniqueness), but admit indefinite choice, committed choice, and classical logics. Moreover, our semantics for the epsilon supports reductive proof search optimally.
Answer Set Programming (ASP) is an increasingly popular framework for declarative programming that admits the description of problems by means of rules and constraints that form a disjunctive logic program. In particular, many AI problems such as reasoning in a nonmonotonic setting can be directly formulated in ASP. Although the main problems of ASP are of high computational complexity, located at the second level of the Polynomial Hierarchy, several restrictions of ASP have been identified in the literature, under which ASP problems become tractable. In this paper we use the concept of backdoors to identify new restrictions that make ASP problems tractable. Small backdoors are sets of atoms that represent "clever reasoning shortcuts" through the search space and represent a hidden structure in the problem input. The concept of backdoors is widely used in the areas of propositional satisfiability and constraint satisfaction. We show that it can be fruitfully adapted to ASP. We demonstrate how backdoors can serve as a unifying framework that accommodates several tractable restrictions of ASP known from the literature. Furthermore, we show how backdoors allow us to deploy recent algorithmic results from parameterized complexity theory to the domain of answer set programming.
We present in this paper a novel approach for training deterministic auto-encoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function, we can achieve results that equal or surpass those attained by other regularized auto-encoders as well as denoising auto-encoders on a range of datasets. This penalty term corresponds to the Frobenius norm of the Jacobian matrix of the encoder activations with respect to the input. We show that this penalty term results in a localized space contraction which in turn yields robust features on the activation layer. Furthermore, we show how this penalty term is related to both regularized auto-encoders and denoising encoders and how it can be seen as a link between deterministic and non-deterministic auto-encoders. We find empirically that this penalty helps to carve a representation that better captures the local directions of variation dictated by the data, corresponding to a lower-dimensional non-linear manifold, while being more invariant to the vast majority of directions orthogonal to the manifold. Finally, we show that by using the learned features to initialize a MLP, we achieve state of the art classification error on a range of datasets, surpassing other methods of pre-training.
One of the key challenges in electronic government (e-government) is the development of systems that can be easily integrated and interoperated to provide seamless services delivery to citizens. In recent years, Semantic Web technologies based on ontology have emerged as promising solutions to the above engineering problems. However, current research practicing semantic development in e-government does not focus on the application of available methodologies and platforms for developing government domain ontologies. Furthermore, only a few of these researches provide detailed guidelines for developing semantic ontology models from a government service domain. This research presents a case study combining an ontology building methodology and two state-of-the-art Semantic Web platforms namely Protege and Java Jena ontology API for semantic ontology development in e-government. Firstly, a framework adopted from the Uschold and King ontology building methodology is employed to build a domain ontology describing the semantic content of a government service domain. Thereafter, UML is used to semi-formally represent the domain ontology. Finally, Protege and Jena API are employed to create the Web Ontology Language (OWL) and Resource Description Framework (RDF) representations of the domain ontology respectively to enable its computer processing. The study aims at: (1) providing e-government developers, particularly those from the developing world with detailed guidelines for practicing semantic content development in their e-government projects and (2), strengthening the adoption of semantic technologies in e-government. The study would also be of interest to novice Semantic Web developers who might used it as a starting point for further investigations.
Mutation has traditionally been regarded as an important operator in evolutionary algorithms. In particular, there have been many experimental studies which showed the effectiveness of adapting mutation rates for various static optimization problems. Given the perceived effectiveness of adaptive and self-adaptive mutation for static optimization problems, there have been speculations that adaptive and self-adaptive mutation can benefit dynamic optimization problems even more since adaptation and self-adaptation are capable of following a dynamic environment. However, few theoretical results are available in analyzing rigorously evolutionary algorithms for dynamic optimization problems. It is unclear when adaptive and self-adaptive mutation rates are likely to be useful for evolutionary algorithms in solving dynamic optimization problems. This paper provides the first rigorous analysis of adaptive mutation and its impact on the computation times of evolutionary algorithms in solving certain dynamic optimization problems. More specifically, for both individual-based and population-based EAs, we have shown that any time-variable mutation rate scheme will not significantly outperform a fixed mutation rate on some dynamic optimization problem instances. The proofs also offer some insights into conditions under which any time-variable mutation scheme is unlikely to be useful and into the relationships between the problem characteristics and algorithmic features (e.g., different mutation schemes).
The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of L1 methods, in this paper we find that L1 regularisation often dramatically underperforms in terms of predictive performance when compared with other methods for inferring sparsity. We focus on unsupervised latent variable models, and develop L1 minimising factor models, Bayesian variants of "L1", and Bayesian models with a stronger L0-like sparsity induced through spike-and-slab distributions. These spike-and-slab Bayesian factor models encourage sparsity while accounting for uncertainty in a principled manner and avoiding unnecessary shrinkage of non-zero values. We demonstrate on a number of data sets that in practice spike-and-slab Bayesian methods outperform L1 minimisation, even on a computational budget. We thus highlight the need to re-assess the wide use of L1 methods in sparsity-reliant applications, particularly when we care about generalising to previously unseen data, and provide an alternative that, over many varying conditions, provides improved generalisation performance.
Using the Hilbert-Bernays account as a spring-board, we first define four ways in which two objects can be discerned from one another, using the non-logical vocabulary of the language concerned. (These definitions are based on definitions made by Quine and Saunders.) Because of our use of the Hilbert-Bernays account, these definitions are in terms of the syntax of the language. But we also relate our definitions to the idea of permutations on the domain of quantification, and their being symmetries. These relations turn out to be subtle---some natural conjectures about them are false. We will see in particular that the idea of symmetry meshes with a species of indiscernibility that we will call `absolute indiscernibility'. We then report all the logical implications between our four kinds of discernibility. We use these four kinds as a resource for stating four metaphysical theses about identity. Three of these theses articulate two traditional philosophical themes: viz. the principle of the identity of indiscernibles (which will come in two versions), and haecceitism. The fourth is recent. Its most notable feature is that it makes diversity (i.e. non-identity) weaker than what we will call individuality (being an individual): two objects can be distinct but not individuals. For this reason, it has been advocated both for quantum particles and for spacetime points. Finally, we locate this fourth metaphysical thesis in a broader position, which we call structuralism. We conclude with a discussion of the semantics suitable for a structuralist, with particular reference to physical theories as well as elementary model theory.
We generalize the belief-propagation algorithm to sparse random networks with arbitrary distributions of motifs (triangles, loops, etc.). Each vertex in these networks belongs to a given set of motifs (generalization of the configuration model). These networks can be treated as sparse uncorrelated hypergraphs in which hyperedges represent motifs. Here a hypergraph is a generalization of a graph, where a hyperedge can connect any number of vertices. These uncorrelated hypergraphs are tree-like (hypertrees), which crucially simplify the problem and allow us to apply the belief-propagation algorithm to these loopy networks with arbitrary motifs. As natural examples, we consider motifs in the form of finite loops and cliques. We apply the belief-propagation algorithm to the ferromagnetic Ising model on the resulting random networks. We obtain an exact solution of this model on networks with finite loops or cliques as motifs. We find an exact critical temperature of the ferromagnetic phase transition and demonstrate that with increasing the clustering coefficient and the loop size, the critical temperature increases compared to ordinary tree-like complex networks. Our solution also gives the birth point of the giant connected component in these loopy networks.
Natural language generation (NLG) systems are computer software systems that produce texts in English and other human languages, often from non-linguistic input data. NLG systems, like most AI systems, need substantial amounts of knowledge. However, our experience in two NLG projects suggests that it is difficult to acquire correct knowledge for NLG systems; indeed, every knowledge acquisition (KA) technique we tried had significant problems. In general terms, these problems were due to the complexity, novelty, and poorly understood nature of the tasks our systems attempted, and were worsened by the fact that people write so differently. This meant in particular that corpus-based KA approaches suffered because it was impossible to assemble a sizable corpus of high-quality consistent manually written texts in our domains; and structured expert-oriented KA techniques suffered because experts disagreed and because we could not get enough information about special and unusual cases to build robust systems. We believe that such problems are likely to affect many other NLG systems as well. In the long term, we hope that new KA techniques may emerge to help NLG system builders. In the shorter term, we believe that understanding how individual KA techniques can fail, and using a mixture of different KA techniques with different strengths and weaknesses, can help developers acquire NLG knowledge that is mostly correct.
Search is a major technique for planning. It amounts to exploring a state space of planning domains typically modeled as a directed graph. However, prohibitively large sizes of the search space make search expensive. Developing better heuristic functions has been the main technique for improving search efficiency. Nevertheless, recent studies have shown that improving heuristics alone has certain fundamental limits on improving search efficiency. Recently, a new direction of research called partial order based reduction (POR) has been proposed as an alternative to improving heuristics. POR has shown promise in speeding up searches. POR has been extensively studied in model checking research and is a key enabling technique for scalability of model checking systems. Although the POR theory has been extensively studied in model checking, it has never been developed systematically for planning before. In addition, the conditions for POR in the model checking theory are abstract and not directly applicable in planning. Previous works on POR algorithms for planning did not establish the connection between these algorithms and existing theory in model checking. In this paper, we develop a theory for POR in planning. The new theory we develop connects the stubborn set theory in model checking and POR methods in planning. We show that previous POR algorithms in planning can be explained by the new theory. Based on the new theory, we propose a new, stronger POR algorithm. Experimental results on various planning domains show further search cost reduction using the new algorithm.
The AdaBoost algorithm was designed to combine many "weak" hypotheses that perform slightly better than random guessing into a "strong" hypothesis that has very low error. We study the rate at which AdaBoost iteratively converges to the minimum of the "exponential loss." Unlike previous work, our proofs do not require a weak-learning assumption, nor do they require that minimizers of the exponential loss are finite. Our first result shows that at iteration $t$, the exponential loss of AdaBoost's computed parameter vector will be at most $\epsilon$ more than that of any parameter vector of $\ell_1$-norm bounded by $B$ in a number of rounds that is at most a polynomial in $B$ and $1/\epsilon$. We also provide lower bounds showing that a polynomial dependence on these parameters is necessary. Our second result is that within $C/\epsilon$ iterations, AdaBoost achieves a value of the exponential loss that is at most $\epsilon$ more than the best possible value, where $C$ depends on the dataset. We show that this dependence of the rate on $\epsilon$ is optimal up to constant factors, i.e., at least $\Omega(1/\epsilon)$ rounds are necessary to achieve within $\epsilon$ of the optimal exponential loss.
With the recent technological feasibility of electronic commerce over the Internet, much attention has been given to the design of electronic markets for various types of electronically-tradable goods. Such markets, however, will normally need to function in some relationship with markets for other related goods, usually those downstream or upstream in the supply chain. Thus, for example, an electronic market for rubber tires for trucks will likely need to be strongly influenced by the rubber market as well as by the truck market. In this paper we design protocols for exchange of information between a sequence of markets along a single supply chain. These protocols allow each of these markets to function separately, while the information exchanged ensures efficient global behavior across the supply chain. Each market that forms a link in the supply chain operates as a double auction, where the bids on one side of the double auction come from bidders in the corresponding segment of the industry, and the bids on the other side are synthetically generated by the protocol to express the combined information from all other links in the chain. The double auctions in each of the markets can be of several types, and we study several variants of incentive compatible double auctions, comparing them in terms of their efficiency and of the market revenue.
Multiagent learning is a necessary yet challenging problem as multiagent systems become more prevalent and environments become more dynamic. Much of the groundbreaking work in this area draws on notable results from game theory, in particular, the concept of Nash equilibria. Learners that directly learn an equilibrium obviously rely on their existence. Learners that instead seek to play optimally with respect to the other players also depend upon equilibria since equilibria are fixed points for learning. From another perspective, agents with limitations are real and common. These may be undesired physical limitations as well as self-imposed rational limitations, such as abstraction and approximation techniques, used to make learning tractable. This article explores the interactions of these two important concepts: equilibria and limitations in learning. We introduce the question of whether equilibria continue to exist when agents have limitations. We look at the general effects limitations can have on agent behavior, and define a natural extension of equilibria that accounts for these limitations. Using this formalization, we make three major contributions: (i) a counterexample for the general existence of equilibria with limitations, (ii) sufficient conditions on limitations that preserve their existence, (iii) three general classes of games and limitations that satisfy these conditions. We then present empirical results from a specific multiagent learning algorithm applied to a specific instance of limited agents. These results demonstrate that learning with limitations is feasible, when the conditions outlined by our theoretical analysis hold.
A time series consists of a series of values or events obtained over repeated measurements in time. Analysis of time series represents and important tool in many application areas, such as stock market analysis, process and quality control, observation of natural phenomena, medical treatments, etc. A vital component in many types of time-series analysis is the choice of an appropriate distance/similarity measure. Numerous measures have been proposed to date, with the most successful ones based on dynamic programming. Being of quadratic time complexity, however, global constraints are often employed to limit the search space in the matrix during the dynamic programming procedure, in order to speed up computation. Furthermore, it has been reported that such constrained measures can also achieve better accuracy. In this paper, we investigate two representative time-series distance/similarity measures based on dynamic programming, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS), and the effects of global constraints on them. Through extensive experiments on a large number of time-series data sets, we demonstrate how global constrains can significantly reduce the computation time of DTW and LCS. We also show that, if the constraint parameter is tight enough (less than 10-15% of time-series length), the constrained measure becomes significantly different from its unconstrained counterpart, in the sense of producing qualitatively different 1-nearest neighbor graphs. This observation explains the potential for accuracy gains when using constrained measures, highlighting the need for careful tuning of constraint parameters in order to achieve a good trade-off between speed and accuracy.
In previous work we have introduced a network-based model that abstracts many details of the underlying landscape and compresses the landscape information into a weighted, oriented graph which we call the local optima network. The vertices of this graph are the local optima of the given fitness landscape, while the arcs are transition probabilities between local optima basins. Here we extend this formalism to neutral fitness landscapes, which are common in difficult combinatorial search spaces. By using two known neutral variants of the NK family (i.e. NKp and NKq) in which the amount of neutrality can be tuned by a parameter, we show that our new definitions of the optima networks and the associated basins are consistent with the previous definitions for the non-neutral case. Moreover, our empirical study and statistical analysis show that the features of neutral landscapes interpolate smoothly between landscapes with maximum neutrality and non-neutral ones. We found some unknown structural differences between the two studied families of neutral landscapes. But overall, the network features studied confirmed that neutrality, in landscapes with percolating neutral networks, may enhance heuristic search. Our current methodology requires the exhaustive enumeration of the underlying search space. Therefore, sampling techniques should be developed before this analysis can have practical implications. We argue, however, that the proposed model offers a new perspective into the problem difficulty of combinatorial optimization problems and may inspire the design of more effective search heuristics.
Knowledge mining is the process of deriving new and useful knowledge from vast volumes of data and background knowledge. Modern healthcare organizations regularly generate huge amount of electronic data stored in the databases. These data are a valuable resource for mining useful knowledge to help medical practitioners making appropriate and accurate decision on the diagnosis and treatment of diseases. In this paper, we propose the design of a novel medical expert system based on a logic-programming framework. The proposed system includes a knowledge-mining component as a repertoire of tools for discovering useful knowledge. The implementation of classification and association mining tools based on the higher order and meta-level programming schemes using Prolog has been presented to express the power of logic-based language. Such language also provides a pattern matching facility, which is an essential function for the development of knowledge-intensive tasks. Besides the major goal of medical decision support, the knowledge discovered by our logic-based knowledge-mining component can also be deployed as background knowledge to pre-treatment data from other sources as well as to guard the data repositories against constraint violation. A framework for knowledge deployment is also presented.
Many real world domains require the representation of a measure of uncertainty. The most common such representation is probability, and the combination of probability with logic programs has given rise to the field of Probabilistic Logic Programming (PLP), leading to languages such as the Independent Choice Logic, Logic Programs with Annotated Disjunctions (LPADs), Problog, PRISM and others. These languages share a similar distribution semantics, and methods have been devised to translate programs between these languages. The complexity of computing the probability of queries to these general PLP programs is very high due to the need to combine the probabilities of explanations that may not be exclusive. As one alternative, the PRISM system reduces the complexity of query answering by restricting the form of programs it can evaluate. As an entirely different alternative, Possibilistic Logic Programs adopt a simpler metric of uncertainty than probability. Each of these approaches -- general PLP, restricted PLP, and Possibilistic Logic Programming -- can be useful in different domains depending on the form of uncertainty to be represented, on the form of programs needed to model problems, and on the scale of the problems to be solved. In this paper, we show how the PITA system, which originally supported the general PLP language of LPADs, can also efficiently support restricted PLP and Possibilistic Logic Programs. PITA relies on tabling with answer subsumption and consists of a transformation along with an API for library functions that interface with answer subsumption.
An important problem in bioinformatics is the inference of gene regulatory networks (GRN) from temporal expression profiles. In general, the main limitations faced by GRN inference methods is the small number of samples with huge dimensionalities and the noisy nature of the expression measurements. In face of these limitations, alternatives are needed to get better accuracy on the GRNs inference problem. This work addresses this problem by presenting an alternative feature selection method that applies prior knowledge on its search strategy, called SFFS-BA. The proposed search strategy is based on the Sequential Floating Forward Selection (SFFS) algorithm, with the inclusion of a scale-free (Barab\'asi-Albert) topology information in order to guide the search process to improve inference. The proposed algorithm explores the scale-free property by pruning the search space and using a power law as a weight for reducing it. In this way, the search space traversed by the SFFS-BA method combines a breadth-first search when the number of combinations is small ( <= 2) with a depth-first search when the number of combinations becomes explosive ( >= 3), being guided by the scale-free prior information. Experimental results show that the SFFS-BA provides a better inference similarities than SFS and SFFS, keeping the robustness of the SFS and SFFS methods, thus presenting very good results.
Learning to operate a vehicle is generally accomplished by forming a new cognitive map between the body motions and extrapersonal space. Here, we consider the challenge of remapping movement-to-space representations in survivors of spinal cord injury, for the control of powered wheelchairs. Our goal is to facilitate this remapping by developing interfaces between residual body motions and navigational commands that exploit the degrees of freedom that disabled individuals are most capable to coordinate. We present a new framework for allowing spinal cord injured persons to control powered wheelchairs through signals derived from their residual mobility. The main novelty of this approach lies in substituting the more common joystick controllers of powered wheelchairs with a sensor shirt. This allows the whole upper body of the user to operate as an adaptive joystick. Considerations about learning and risks have lead us to develop a safe testing environment in 3D Virtual Reality. A Personal Augmented Reality Immersive System (PARIS) allows us to analyse learning skills and provide users with an adequate training to control a simulated wheelchair through the signals generated by body motions in a safe environment. We provide a description of the basic theory, of the development phases and of the operation of the complete system. We also present preliminary results illustrating the processing of the data and supporting of the feasibility of this approach.
Efficient collaborative decision making is an important challenge for multiagent systems. Finding optimal joint actions is especially challenging when each agent has only imperfect information about the state of its environment. Such problems can be modeled as collaborative Bayesian games in which each agent receives private information in the form of its type. However, representing and solving such games requires space and computation time exponential in the number of agents. This article introduces collaborative graphical Bayesian games (CGBGs), which facilitate more efficient collaborative decision making by decomposing the global payoff function as the sum of local payoff functions that depend on only a few agents. We propose a framework for the efficient solution of CGBGs based on the insight that they posses two different types of independence, which we call agent independence and type independence. In particular, we present a factor graph representation that captures both forms of independence and thus enables efficient solutions. In addition, we show how this representation can provide leverage in sequential tasks by using it to construct a novel method for decentralized partially observable Markov decision processes. Experimental results in both random and benchmark tasks demonstrate the improved scalability of our methods compared to several existing alternatives.
Specifying and implementing flexible human-computer dialogs, such as those used in kiosks and smart phone apps, is challenging because of the numerous and varied directions in which each user might steer a dialog. The objective of this research is to improve dialog specification and implementation. To do so we enriched a notation based on concepts from programming languages, especially partial evaluation, for specifying a variety of unsolicited reporting, mixed-initiative dialogs in a concise representation that serves as a design for dialog implementation. We also built a dialog mining system that extracts a specification in this notation from requirements. To demonstrate that such a specification provides a design for dialog implementation, we built a system that automatically generates an implementation of the dialog, called a stager, from it. These two components constitute a dialog modeling toolkit that automates dialog specification and implementation. These results provide a proof of concept and demonstrate the study of dialog specification and implementation from a programming languages perspective. The ubiquity of dialogs in domains such as travel, education, and health care combined with the demand for smart phone apps provide a landscape for further investigation of these results.
Crowdsourcing websites (e.g. Yahoo! Answers, Amazon Mechanical Turk, and etc.) emerged in recent years that allow requesters from all around the world to post tasks and seek help from an equally global pool of workers. However, intrinsic incentive problems reside in crowdsourcing applications as workers and requester are selfish and aim to strategically maximize their own benefit. In this paper, we propose to provide incentives for workers to exert effort using a novel game-theoretic model based on repeated games. As there is always a gap in the social welfare between the non-cooperative equilibria emerging when workers pursue their self-interests and the desirable Pareto efficient outcome, we propose a novel class of incentive protocols based on social norms which integrates reputation mechanisms into the existing pricing schemes currently implemented on crowdsourcing websites, in order to improve the performance of the non-cooperative equilibria emerging in such applications. We first formulate the exchanges on a crowdsourcing website as a two-sided market where requesters and workers are matched and play gift-giving games repeatedly. Subsequently, we study the protocol designer's problem of finding an optimal and sustainable (equilibrium) protocol which achieves the highest social welfare for that website. We prove that the proposed incentives protocol can make the website operate close to Pareto efficiency. Moreover, we also examine an alternative scenario, where the protocol designer aims at maximizing the revenue of the website and evaluate the performance of the optimal protocol.
We propose a dynamic logic of lying, wherein a 'lie that phi' (where phi is a formula in the logic) is an action in the sense of dynamic modal logic, that is interpreted as a state transformer relative to the formula phi. The states that are being transformed are pointed Kripke models encoding the uncertainty of agents about their beliefs. Lies can be about factual propositions but also about modal formulas, such as the beliefs of other agents or the belief consequences of the lies of other agents. We distinguish (i) an outside observer who is lying to an agent that is modelled in the system, from (ii) one agent who is lying to another agent, and where both are modelled in the system. For either case, we further distinguish (iii) the agent who believes everything that it is told (even at the price of inconsistency), from (iv) the agent who only believes what it is told if that is consistent with its current beliefs, and from (v) the agent who believes everything that it is told by consistently revising its current beliefs. The logics have complete axiomatizations, which can most elegantly be shown by way of their embedding in what is known as action model logic or the extension of that logic to belief revision.
Given a set of several inputs into a system (e.g., independent variables characterizing stimuli) and a set of several stochastically non-independent outputs (e.g., random variables describing different aspects of responses), how can one determine, for each of the outputs, which of the inputs it is influenced by? The problem has applications ranging from modeling pairwise comparisons to reconstructing mental processing architectures to conjoint testing. A necessary and sufficient condition for a given pattern of selective influences is provided by the Joint Distribution Criterion, according to which the problem of "what influences what" is equivalent to that of the existence of a joint distribution for a certain set of random variables. For inputs and outputs with finite sets of values this criterion translates into a test of consistency of a certain system of linear equations and inequalities (Linear Feasibility Test) which can be performed by means of linear programming. The Joint Distribution Criterion also leads to a metatheoretical principle for generating a broad class of necessary conditions (tests) for diagrams of selective influences. Among them is the class of distance-type tests based on the observation that certain functionals on jointly distributed random variables satisfy triangle inequality.
There is little research concerning comparisons and combination of System Dynamics Simulation (SDS) and Agent Based Simulation (ABS). ABS is a paradigm used in many levels of abstraction, including those levels covered by SDS. We believe that the establishment of frameworks for the choice between these two simulation approaches would contribute to the simulation research. Hence, our work aims for the establishment of directions for the choice between SDS and ABS approaches for immune system-related problems. Previously, we compared the use of ABS and SDS for modelling agents' behaviour in an environment with nomovement or interactions between these agents. We concluded that for these types of agents it is preferable to use SDS, as it takes up less computational resources and produces the same results as those obtained by the ABS model. In order to move this research forward, our next research question is: if we introduce interactions between these agents will SDS still be the most appropriate paradigm to be used? To answer this question for immune system simulation problems, we will use, as case studies, models involving interactions between tumour cells and immune effector cells. Experiments show that there are cases where SDS and ABS can not be used interchangeably, and therefore, their comparison is not straightforward.
Dung's famous abstract argumentation frameworks represent the core formalism for many problems and applications in the field of argumentation which significantly evolved within the last decade. Recent work in the field has thus focused on implementations for these frameworks, whereby one of the main approaches is to use Answer-Set Programming (ASP). While some of the argumentation semantics can be nicely expressed within the ASP language, others required rather cumbersome encoding techniques. Recent advances in ASP systems, in particular, the metasp optimization frontend for the ASP-package gringo/claspD provides direct commands to filter answer sets satisfying certain subset-minimality (or -maximality) constraints. This allows for much simpler encodings compared to the ones in standard ASP language. In this paper, we experimentally compare the original encodings (for the argumentation semantics based on preferred, semi-stable, and respectively, stage extensions) with new metasp encodings. Moreover, we provide novel encodings for the recently introduced resolution-based grounded semantics. Our experimental results indicate that the metasp approach works well in those cases where the complexity of the encoded problem is adequately mirrored within the metasp approach.
To reduce datacenter energy consumption and cost, current practice has considered demand-proportional resource provisioning schemes, where servers are turned on/off according to the load of requests. Most existing work considers instantaneous (Internet) requests only, which are explicitly or implicitly assumed to be delay-sensitive. On the other hand, in datacenters, there exist a vast amount of delay-tolerant jobs, such as background/maintainance jobs. In this paper, we explicitly differentiate delay-sensitive jobs and delay tolerant jobs. We focus on the problem of using delay-tolerant jobs to fill the extra capacity of datacenters, referred to as trough/valley filling. Giving a higher priority to delay-sensitive jobs, our schemes complement to most existing demand-proportional resource provisioning schemes. Our goal is to design intelligent trough filling mechanisms that are energy efficient and also achieve good delay performance. Specifically, we propose two joint dynamic speed scaling and traffic shifting schemes, one subgradient-based and the other queue-based. Our schemes assume little statistical information of the system, which is usually difficult to obtain in practice. In both schemes, energy cost saving comes from dynamic speed scaling, statistical multiplexing, electricity price diversity, and service efficiency diversity. In addition, good delay performance is achieved in the queue-based scheme via load shifting and capacity allocation based on queue conditions. Practical issues that may arise in datacenter networks are considered, including capacity and bandwidth constraint, service agility constraint, and load shifting cost. We use both artificial and real datacenter traces to evaluate the proposed schemes.
Obtaining the set of cosmological parameters consistent with observational data is an important exercise in current cosmological research. It involves finding the global maximum of the likelihood function in the multi-dimensional parameter space. Currently sampling based methods, which are in general stochastic in nature, like Markov-Chain Monte Carlo(MCMC), are being commonly used for parameter estimation. The beauty of stochastic methods is that the computational cost grows, at the most, linearly in place of exponentially (as in grid based approaches) with the dimensionality of the search space. MCMC methods sample the full joint probability distribution (posterior) from which one and two dimensional probability distributions, best fit (average) values of parameters and then error bars can be computed. In the present work we demonstrate the application of another stochastic method, named Particle Swarm Optimization (PSO), that is widely used in the field of engineering and artificial intelligence, for cosmological parameter estimation from WMAP seven years data. We find that there is a good agreement between the values of the best fit parameters obtained from PSO and publicly available code COSMOMC. However, there is a slight disagreement between error bars mainly due to the fact that errors are computed differently in PSO. Apart from presenting the results of our exercise, we also discuss the merits of PSO and explain its usefulness in more extensive search in higher dimensional parameter space.
We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One method of increasing the expressiveness of learned hypotheses in this context is to expand the feature set to include conjunctions of basic features. This can be done explicitly or where possible by using a kernel function. Focusing on the well known Perceptron and Winnow algorithms, the paper demonstrates a tradeoff between the computational efficiency with which the algorithm can be run over the expanded feature space and the generalization ability of the corresponding learning algorithm. We first describe several kernel functions which capture either limited forms of conjunctions or all conjunctions. We show that these kernels can be used to efficiently run the Perceptron algorithm over a feature space of exponentially many conjunctions; however we also show that using such kernels, the Perceptron algorithm can provably make an exponential number of mistakes even when learning simple functions. We then consider the question of whether kernel functions can analogously be used to run the multiplicative-update Winnow algorithm over an expanded feature space of exponentially many conjunctions. Known upper bounds imply that the Winnow algorithm can learn Disjunctive Normal Form (DNF) formulae with a polynomial mistake bound in this setting. However, we prove that it is computationally hard to simulate Winnows behavior for learning DNF over such a feature set. This implies that the kernel functions which correspond to running Winnow for this problem are not efficiently computable, and that there is no general construction that can run Winnow with kernels.
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with respect to a policy as the probability of entering such a state when the policy is pursued. We consider the problem of finding good policies whose risk is smaller than some user-specified threshold, and formalize it as a constrained MDP with two criteria. The first criterion corresponds to the value function originally given. We will show that the risk can be formulated as a second criterion function based on a cumulative return, whose definition is independent of the original value function. We present a model free, heuristic reinforcement learning algorithm that aims at finding good deterministic policies. It is based on weighting the original value function and the risk. The weight parameter is adapted in order to find a feasible solution for the constrained problem that has a good performance with respect to the value function. The algorithm was successfully applied to the control of a feed tank with stochastic inflows that lies upstream of a distillation column. This control task was originally formulated as an optimal control problem with chance constraints, and it was solved under certain assumptions on the model to obtain an optimal solution. The power of our learning algorithm is that it can be used even when some of these restrictive assumptions are relaxed.
We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by theories of perception, the model consists of two interacting pathways: identity and control, intended to mirror the what and where pathways in neuroscience models. The identity pathway models object appearance and performs classification using deep (factored)-Restricted Boltzmann Machines. At each point in time the observations consist of foveated images, with decaying resolution toward the periphery of the gaze. The control pathway models the location, orientation, scale and speed of the attended object. The posterior distribution of these states is estimated with particle filtering. Deeper in the control pathway, we encounter an attentional mechanism that learns to select gazes so as to minimize tracking uncertainty. Unlike in our previous work, we introduce gaze selection strategies which operate in the presence of partial information and on a continuous action space. We show that a straightforward extension of the existing approach to the partial information setting results in poor performance, and we propose an alternative method based on modeling the reward surface as a Gaussian Process. This approach gives good performance in the presence of partial information and allows us to expand the action space from a small, discrete set of fixation points to a continuous domain.
In answer-set programming (ASP), the solutions of a problem are encoded in dedicated models, called answer sets, of a logical theory. These answer sets are computed from the program that represents the theory by means of an ASP solver and returned to the user as sets of ground first-order literals. As this type of representation is often cumbersome for the user to interpret, tools like ASPVIZ and IDPDraw were developed that allow for visualising answer sets. The tool Kara, introduced in this paper, follows these approaches, using ASP itself as a language for defining visualisations of interpretations. Unlike existing tools that position graphic primitives according to static coordinates only, Kara allows for more high-level specifications, supporting graph structures, grids, and relative positioning of graphical elements. Moreover, generalising the functionality of previous tools, Kara provides modifiable visualisations such that interpretations can be manipulated by graphically editing their visualisations. This is realised by resorting to abductive reasoning techniques. Kara is part of SeaLion, a forthcoming integrated development environment (IDE) for ASP.
This paper proposes a novel latent semantic learning method for extracting high-level features (i.e. latent semantics) from a large vocabulary of abundant mid-level features (i.e. visual keywords) with structured sparse representation, which can help to bridge the semantic gap in the challenging task of human action recognition. To discover the manifold structure of midlevel features, we develop a spectral embedding approach to latent semantic learning based on L1-graph, without the need to tune any parameter for graph construction as a key step of manifold learning. More importantly, we construct the L1-graph with structured sparse representation, which can be obtained by structured sparse coding with its structured sparsity ensured by novel L1-norm hypergraph regularization over mid-level features. In the new embedding space, we learn latent semantics automatically from abundant mid-level features through spectral clustering. The learnt latent semantics can be readily used for human action recognition with SVM by defining a histogram intersection kernel. Different from the traditional latent semantic analysis based on topic models, our latent semantic learning method can explore the manifold structure of mid-level features in both L1-graph construction and spectral embedding, which results in compact but discriminative high-level features. The experimental results on the commonly used KTH action dataset and unconstrained YouTube action dataset show the superior performance of our method.
This paper studies the topic modeling problem of tagged documents and images. Higher-order relations among tagged documents and images are major and ubiquitous characteristics, and play positive roles in extracting reliable and interpretable topics. In this paper, we propose the tag-topic models (TTM) to depict such higher-order topic structural dependencies within the Markov random field (MRF) framework. First, we use the novel factor graph representation of latent Dirichlet allocation (LDA)-based topic models from the MRF perspective, and present an efficient loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Second, we propose the factor hypergraph representation of TTM, and focus on both pairwise and higher-order relation modeling among tagged documents and images. Efficient loopy BP algorithm is developed to learn TTM, which encourages the topic labeling smoothness among tagged documents and images. Extensive experimental results confirm the incorporation of higher-order relations to be effective in enhancing the overall topic modeling performance, when compared with current state-of-the-art topic models, in many text and image mining tasks of broad interests such as word and link prediction, document classification, and tag recommendation.
Effective coordination of agents actions in partially-observable domains is a major challenge of multi-agent systems research. To address this, many researchers have developed techniques that allow the agents to make decisions based on estimates of the states and actions of other agents that are typically learnt using some form of machine learning algorithm. Nevertheless, many of these approaches fail to provide an actual means by which the necessary information is made available so that the estimates can be learnt. To this end, we argue that cooperative communication of state information between agents is one such mechanism. However, in a dynamically changing environment, the accuracy and timeliness of this communicated information determine the fidelity of the learned estimates and the usefulness of the actions taken based on these. Given this, we propose a novel information-sharing protocol, post-task-completion sharing, for the distribution of state information. We then show, through a formal analysis, the improvement in the quality of estimates produced using our strategy over the widely used protocol of sharing information between nearest neighbours. Moreover, communication heuristics designed around our information-sharing principle are subjected to empirical evaluation along with other benchmark strategies (including Littmans Q-routing and Stones TPOT-RL) in a simulated call-routing application. These studies, conducted across a range of environmental settings, show that, compared to the different benchmarks used, our strategy generates an improvement of up to 60% in the call connection rate; of more than 1000% in the ability to connect long-distance calls; and incurs as low as 0.25 of the message overhead.
One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations.
Description logic programs (dl-programs) under the answer set semantics formulated by Eiter {\em et al.} have been considered as a prominent formalism for integrating rules and ontology knowledge bases. A question of interest has been whether dl-programs can be captured in a general formalism of nonmonotonic logic. In this paper, we study the possibility of embedding dl-programs into default logic. We show that dl-programs under the strong and weak answer set semantics can be embedded in default logic by combining two translations, one of which eliminates the constraint operator from nonmonotonic dl-atoms and the other translates a dl-program into a default theory. For dl-programs without nonmonotonic dl-atoms but with the negation-as-failure operator, our embedding is polynomial, faithful, and modular. In addition, our default logic encoding can be extended in a simple way to capture recently proposed weakly well-supported answer set semantics, for arbitrary dl-programs. These results reinforce the argument that default logic can serve as a fruitful foundation for query-based approaches to integrating ontology and rules. With its simple syntax and intuitive semantics, plus available computational results, default logic can be considered an attractive approach to integration of ontology and rules.
Electronic government (e-government) has been one of the most active areas of ontology development during the past six years. In e-government, ontologies are being used to describe and specify e-government services (e-services) because they enable easy composition, matching, mapping and merging of various e-government services. More importantly, they also facilitate the semantic integration and interoperability of e-government services. However, it is still unclear in the current literature how an existing ontology building methodology can be applied to develop semantic ontology models in a government service domain. In this paper the Uschold and King ontology building methodology is applied to develop semantic ontology models in a government service domain. Firstly, the Uschold and King methodology is presented, discussed and applied to build a government domain ontology. Secondly, the domain ontology is evaluated for semantic consistency using its semi-formal representation in Description Logic. Thirdly, an alignment of the domain ontology with the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) upper level ontology is drawn to allow its wider visibility and facilitate its integration with existing metadata standard. Finally, the domain ontology is formally written in Web Ontology Language (OWL) to enable its automatic processing by computers. The study aims to provide direction for the application of existing ontology building methodologies in the Semantic Web development processes of e-government domain specific ontology models; which would enable their repeatability in other e-government projects and strengthen the adoption of semantic technologies in e-government.
RGB-D cameras, which give an RGB image to- gether with depths, are becoming increasingly popular for robotic perception. In this paper, we address the task of detecting commonly found objects in the 3D point cloud of indoor scenes obtained from such cameras. Our method uses a graphical model that captures various features and contextual relations, including the local visual appearance and shape cues, object co-occurence relationships and geometric relationships. With a large number of object classes and relations, the model's parsimony becomes important and we address that by using multiple types of edge potentials. We train the model using a maximum-margin learning approach. In our experiments over a total of 52 3D scenes of homes and offices (composed from about 550 views), we get a performance of 84.06% and 73.38% in labeling office and home scenes respectively for 17 object classes each. We also present a method for a robot to search for an object using the learned model and the contextual information available from the current labelings of the scene. We applied this algorithm successfully on a mobile robot for the task of finding 12 object classes in 10 different offices and achieved a precision of 97.56% with 78.43% recall.
A famous result by Jeavons, Cohen, and Gyssens shows that every constraint satisfaction problem (CSP) where the constraints are preserved by a semi-lattice operation can be solved in polynomial time. This is one of the basic facts for the so-called universal-algebraic approach to a systematic theory of tractability and hardness in finite domain constraint satisfaction. Not surprisingly, the theorem of Jeavons et al. fails for arbitrary infinite domain CSPs. Many CSPs of practical interest, though, and in particular those CSPs that are motivated by qualitative reasoning calculi from Artificial Intelligence, can be formulated with constraint languages that are rather well-behaved from a model-theoretic point of view. In particular, the automorphism group of these constraint languages tends to be large in the sense that the number of orbits of n-subsets of the automorphism group is bounded by some function in n. In this paper we present a generalization of the theorem by Jeavons et al. to infinite domain CSPs where the number of orbits of n-subsets grows sub-exponentially in n, and prove that preservation under a semi-lattice operation for such CSPs implies polynomial-time tractability. Unlike the result of Jeavons et al., this includes many CSPs that cannot be solved by Datalog.
A few decades of work in the AI field have focused efforts on developing a new generation of systems which can acquire knowledge via interaction with the world. Yet, until very recently, most such attempts were underpinned by research which predominantly regarded linguistic phenomena as separated from the brain and body. This could lead one into believing that to emulate linguistic behaviour, it suffices to develop 'software' operating on abstract representations that will work on any computational machine. This picture is inaccurate for several reasons, which are elucidated in this paper and extend beyond sensorimotor and semantic resonance. Beginning with a review of research, I list several heterogeneous arguments against disembodied language, in an attempt to draw conclusions for developing embodied multisensory agents which communicate verbally and non-verbally with their environment. Without taking into account both the architecture of the human brain, and embodiment, it is unrealistic to replicate accurately the processes which take place during language acquisition, comprehension, production, or during non-linguistic actions. While robots are far from isomorphic with humans, they could benefit from strengthened associative connections in the optimization of their processes and their reactivity and sensitivity to environmental stimuli, and in situated human-machine interaction. The concept of multisensory integration should be extended to cover linguistic input and the complementary information combined from temporally coincident sensory impressions.
Character posing is of interest in computer animation. It is difficult due to its dependence on inverse kinematics (IK) techniques and articulate property of human characters . To solve the IK problem, classical methods that rely on numerical solutions often suffer from the under-determination problem and can not guarantee naturalness. Existing data-driven methods address this problem by learning from motion capture data. When facing a large variety of poses however, these methods may not be able to capture the pose styles or be applicable in real-time environment. Inspired from the low-rank motion de-noising and completion model in \cite{lai2011motion}, we propose a novel model for character posing based on sparse coding. Unlike conventional approaches, our model directly captures the pose styles in Euclidean space to provide intuitive training error measurements and facilitate pose synthesis. A pose dictionary is learned in training stage and based on it natural poses are synthesized to satisfy users' constraints . We compare our model with existing models for tasks of pose de-noising and completion. Experiments show our model obtains lower de-noising and completion error. We also provide User Interface(UI) examples illustrating that our model is effective for interactive character posing.
Text mining is becoming vital as Web 2.0 offers collaborative content creation and sharing. Now Researchers have growing interest in text mining methods for discovering knowledge. Text mining researchers come from variety of areas like: Natural Language Processing, Computational Linguistic, Machine Learning, and Statistics. A typical text mining application involves preprocessing of text, stemming and lemmatization, tagging and annotation, deriving knowledge patterns, evaluating and interpreting the results. There are numerous approaches for performing text mining tasks, like: clustering, categorization, sentimental analysis, and summarization. There is a growing need to standardize the evaluation of these tasks. One major component of establishing standardization is to provide standard datasets for these tasks. Although there are various standard datasets available for traditional text mining tasks, but there are very few and expensive datasets for blog-mining task. Blogs, a new genre in web 2.0 is a digital diary of web user, which has chronological entries and contains a lot of useful knowledge, thus offers a lot of challenges and opportunities for text mining. In this paper, we report a new indigenous dataset for Pakistani Political Blogosphere. The paper describes the process of data collection, organization, and standardization. We have used this dataset for carrying out various text mining tasks for blogosphere, like: blog-search, political sentiments analysis and tracking, identification of influential blogger, and clustering of the blog-posts. We wish to offer this dataset free for others who aspire to pursue further in this domain.
The bi-objective winner determination problem (2WDP-SC) of a combinatorial procurement auction for transport contracts is characterized by a set B of bundle bids, with each bundle bid b in B consisting of a bidding carrier c_b, a bid price p_b, and a set tau_b transport contracts which is a subset of the set T of tendered transport contracts. Additionally, the transport quality q_{t,c_b} is given which is expected to be realized when a transport contract t is executed by a carrier c_b. The task of the auctioneer is to find a set X of winning bids (X subset B), such that each transport contract is part of at least one winning bid, the total procurement costs are minimized, and the total transport quality is maximized. This article presents a metaheuristic approach for the 2WDP-SC which integrates the greedy randomized adaptive search procedure with a two-stage candidate component selection procedure, large neighborhood search, and self-adaptive parameter setting in order to find a competitive set of non-dominated solutions. The heuristic outperforms all existing approaches. For seven small benchmark instances, the heuristic is the sole approach that finds all Pareto-optimal solutions. For 28 out of 30 large instances, none of the existing approaches is able to compute a solution that dominates a solution found by the proposed heuristic.
Multilabel classification is a relatively recent subfield of machine learning. Unlike to the classical approach, where instances are labeled with only one category, in multilabel classification, an arbitrary number of categories is chosen to label an instance. Due to the problem complexity (the solution is one among an exponential number of alternatives), a very common solution (the binary method) is frequently used, learning a binary classifier for every category, and combining them all afterwards. The assumption taken in this solution is not realistic, and in this work we give examples where the decisions for all the labels are not taken independently, and thus, a supervised approach should learn those existing relationships among categories to make a better classification. Therefore, we show here a generic methodology that can improve the results obtained by a set of independent probabilistic binary classifiers, by using a combination procedure with a classifier trained on the co-occurrences of the labels. We show an exhaustive experimentation in three different standard corpora of labeled documents (Reuters-21578, Ohsumed-23 and RCV1), which present noticeable improvements in all of them, when using our methodology, in three probabilistic base classifiers.
This paper develops generalizations of empowerment to continuous states. Empowerment is a recently introduced information-theoretic quantity motivated by hypotheses about the efficiency of the sensorimotor loop in biological organisms, but also from considerations stemming from curiosity-driven learning. Empowemerment measures, for agent-environment systems with stochastic transitions, how much influence an agent has on its environment, but only that influence that can be sensed by the agent sensors. It is an information-theoretic generalization of joint controllability (influence on environment) and observability (measurement by sensors) of the environment by the agent, both controllability and observability being usually defined in control theory as the dimensionality of the control/observation spaces. Earlier work has shown that empowerment has various interesting and relevant properties, e.g., it allows us to identify salient states using only the dynamics, and it can act as intrinsic reward without requiring an external reward. However, in this previous work empowerment was limited to the case of small-scale and discrete domains and furthermore state transition probabilities were assumed to be known. The goal of this paper is to extend empowerment to the significantly more important and relevant case of continuous vector-valued state spaces and initially unknown state transition probabilities. The continuous state space is addressed by Monte-Carlo approximation; the unknown transitions are addressed by model learning and prediction for which we apply Gaussian processes regression with iterated forecasting. In a number of well-known continuous control tasks we examine the dynamics induced by empowerment and include an application to exploration and online model learning.
In the contexts of automated reasoning and formal verification, important decision problems are effectively encoded into Satisfiability Modulo Theories (SMT). In the last decade efficient SMT solvers have been developed for several theories of practical interest (e.g., linear arithmetic, arrays, bit-vectors). Surprisingly, very few work has been done to extend SMT to deal with optimization problems; in particular, we are not aware of any work on SMT solvers able to produce solutions which minimize cost functions over arithmetical variables. This is unfortunate, since some problems of interest require this functionality. In this paper we start filling this gap. We present and discuss two general procedures for leveraging SMT to handle the minimization of LA(Q) cost functions, combining SMT with standard minimization techniques. We have implemented the proposed approach within the MathSAT SMT solver. Due to the lack of competitors in AR and SMT domains, we experimentally evaluated our implementation against state-of-the-art tools for the domain of linear generalized disjunctive programming (LGDP), which is closest in spirit to our domain, on sets of problems which have been previously proposed as benchmarks for the latter tools. The results show that our tool is very competitive with, and often outperforms, these tools on these problems, clearly demonstrating the potential of the approach.
Achieving joint objectives by teams of cooperative planning agents requires significant coordination and communication efforts. For a single-agent system facing a plan failure in a dynamic environment, arguably, attempts to repair the failed plan in general do not straightforwardly bring any benefit in terms of time complexity. However, in multi-agent settings the communication complexity might be of a much higher importance, possibly a high communication overhead might be even prohibitive in certain domains. We hypothesize that in decentralized systems, where coordination is enforced to achieve joint objectives, attempts to repair failed multi-agent plans should lead to lower communication overhead than replanning from scratch. The contribution of the presented paper is threefold. Firstly, we formally introduce the multi-agent plan repair problem and formally present the core hypothesis underlying our work. Secondly, we propose three algorithms for multi-agent plan repair reducing the problem to specialized instances of the multi-agent planning problem. Finally, we present results of experimental validation confirming the core hypothesis of the paper.
It is a high-quality algorithm for hierarchical clustering of large software source code. This effectively allows to break the complexity of tens of millions lines of source code, so that a human software engineer can comprehend a software system at high level by means of looking at its architectural diagram that is reconstructed automatically from the source code of the software system. The architectural diagram shows a tree of subsystems having OOP classes in its leaves (in the other words, a nested software decomposition). The tool reconstructs the missing (inconsistent/incomplete/inexistent) architectural documentation for a software system from its source code. This facilitates software maintenance: change requests can be performed substantially faster. Simply speaking, this unique tool allows to lift the comprehensible grain of object-oriented software systems from OOP class-level to subsystem-level. It is estimated that a commercial tool, developed on the basis of this work, will reduce software maintenance expenses 10 times on the current needs, and will allow to implement next-generation software systems which are currently too complex to be within the range of human comprehension, therefore can't yet be designed or implemented. Implemented prototype in Open Source: http://sourceforge.net/p/insoar/code-0/1/tree/
In this paper we present {\em refinement modal logic}. A refinement is like a bisimulation, except that from the three relational requirements only `atoms' and `back' need to be satisfied. Our logic contains a new operator 'all' in addition to the standard modalities 'box' for each agent. The operator 'all' acts as a quantifier over the set of all refinements of a given model. As a variation on a bisimulation quantifier, this refinement operator or refinement quantifier 'all' can be seen as quantifying over a variable not occurring in the formula bound by it. The logic combines the simplicity of multi-agent modal logic with some powers of monadic second-order quantification. We present a sound and complete axiomatization of multi-agent refinement modal logic. We also present an extension of the logic to the modal mu-calculus, and an axiomatization for the single-agent version of this logic. Examples and applications are also discussed: to software verification and design (the set of agents can also be seen as a set of actions), and to dynamic epistemic logic. We further give detailed results on the complexity of satisfiability, and on succinctness.
The assignment of tasks to multiple resources becomes an interesting game theoretic problem, when both the task owner and the resources are strategic. In the classical, nonstrategic setting, where the states of the tasks and resources are observable by the controller, this problem is that of finding an optimal policy for a Markov decision process (MDP). When the states are held by strategic agents, the problem of an efficient task allocation extends beyond that of solving an MDP and becomes that of designing a mechanism. Motivated by this fact, we propose a general mechanism which decides on an allocation rule for the tasks and resources and a payment rule to incentivize agents' participation and truthful reports. In contrast to related dynamic strategic control problems studied in recent literature, the problem studied here has interdependent values: the benefit of an allocation to the task owner is not simply a function of the characteristics of the task itself and the allocation, but also of the state of the resources. We introduce a dynamic extension of Mezzetti's two phase mechanism for interdependent valuations. In this changed setting, the proposed dynamic mechanism is efficient, within period ex-post incentive compatible, and within period ex-post individually rational.
Knuth (1990) introduced the class of nested formulas and showed that their satisfiability can be decided in polynomial time. We show that, parameterized by the size of a smallest strong backdoor set to the target class of nested formulas, checking the satisfiability of any CNF formula is fixed-parameter tractable. Thus, for any k>0, the satisfiability problem can be solved in polynomial time for any formula F for which there exists a variable set B of size at most k such that for every truth assignment t to B, the formula F[t] is nested; moreover, the degree of the polynomial is independent of k. Our algorithm uses the grid-minor theorem of Robertson and Seymour (1986) to either find that the incidence graph of the formula has bounded treewidth - a case that is solved using model checking for monadic second order logic - or to find many vertex-disjoint obstructions in the incidence graph. For the latter case, new combinatorial arguments are used to find a small backdoor set. Combining both cases leads to an approximation algorithm producing a strong backdoor set whose size is upper bounded by a function of the optimum. Going through all assignments to this set of variables and using Knuth's algorithm, the satisfiability of the input formula is decided.
Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function evaluations are possible. Under such a setting, BO could choose to either sequentially evaluate the function, one input at a time and wait for the output of the function before making the next selection, or evaluate the function at a batch of multiple inputs at once. These two different settings are commonly referred to as the sequential and batch settings of Bayesian Optimization. In general, the sequential setting leads to better optimization performance as each function evaluation is selected with more information, whereas the batch setting has an advantage in terms of the total experimental time (the number of iterations). In this work, our goal is to combine the strength of both settings. Specifically, we systematically analyze Bayesian optimization using Gaussian process as the posterior estimator and provide a hybrid algorithm that, based on the current state, dynamically switches between a sequential policy and a batch policy with variable batch sizes. We provide theoretical justification for our algorithm and present experimental results on eight benchmark BO problems. The results show that our method achieves substantial speedup (up to %78) compared to a pure sequential policy, without suffering any significant performance loss.
This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uniform combination solution that has proven to be difficult to improve upon in the past, as well as other algorithms for learning kernels based on convex combinations of base kernels in both classification and regression. Our algorithms are based on the notion of centered alignment which is used as a similarity measure between kernels or kernel matrices. We present a number of novel algorithmic, theoretical, and empirical results for learning kernels based on our notion of centered alignment. In particular, we describe efficient algorithms for learning a maximum alignment kernel by showing that the problem can be reduced to a simple QP and discuss a one-stage algorithm for learning both a kernel and a hypothesis based on that kernel using an alignment-based regularization. Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment. We also report the results of experiments with our centered alignment-based algorithms in both classification and regression.
In the paper, frameworks for electronic shopping of composite (modular) products are described: (a) multicriteria selection (product is considered as a whole system, it is a traditional approach), (b) combinatorial synthesis (composition) of the product from its components, (c) aggregation of the product from several selected products/prototypes. The following product model is examined: (i) general tree-like structure, (ii) set of system parts/components (leaf nodes), (iii) design alternatives (DAs) for each component, (iv) ordinal priorities for DAs, and (v) estimates of compatibility between DAs for different components. The combinatorial synthesis is realized as morphological design of a composite (modular) product or an extended composite product (e.g., product and support services as financial instruments). Here the solving process is based on Hierarchical Morphological Multicriteria Design (HMMD): (i) multicriteria selection of alternatives for system parts, (ii) composing the selected alternatives into a resultant combination (while taking into account ordinal quality of the alternatives above and their compatibility). The aggregation framework is based on consideration of aggregation procedures, for example: (i) addition procedure: design of a products substructure or an extended substructure ('kernel') and addition of elements, and (ii) design procedure: design of the composite solution based on all elements of product superstructure. Applied numerical examples (e.g., composite product, extended composite product, product repair plan, and product trajectory) illustrate the proposed approaches.
We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable corresponding to the mixture components is hidden and each mixture component over the observed variables can have a potentially different Markov graph structure and parameters. We propose a novel approach for estimating the mixture components, and our output is a tree-mixture model which serves as a good approximation to the underlying graphical model mixture. Our method is efficient when the union graph, which is the union of the Markov graphs of the mixture components, has sparse vertex separators between any pair of observed variables. This includes tree mixtures and mixtures of bounded degree graphs. For such models, we prove that our method correctly recovers the union graph structure and the tree structures corresponding to maximum-likelihood tree approximations of the mixture components. The sample and computational complexities of our method scale as $\poly(p, r)$, for an $r$-component mixture of $p$-variate graphical models. We further extend our results to the case when the union graph has sparse local separators between any pair of observed variables, such as mixtures of locally tree-like graphs, and the mixture components are in the regime of correlation decay.
In Multi-Source Feedback or 360 Degree Feedback, data on the performance of an individual are collected systematically from a number of stakeholders and are used for improving performance. The 360-Degree Feedback approach provides a consistent management philosophy meeting the criterion outlined previously. The 360-degree feedback appraisal process describes a human resource methodology that is frequently used for both employee appraisal and employee development. Used in employee performance appraisals, the 360-degree feedback methodology is differentiated from traditional, top-down appraisal methods in which the supervisor responsible for the appraisal provides the majority of the data. Instead it seeks to use information gained from other sources to provide a fuller picture of employees' performances. Similarly, when this technique used in employee development it augments employees' perceptions of training needs with those of the people with whom they interact. The 360-degree feedback based appraisal is a comprehensive method where in the feedback about the employee comes from all the sources that come into contact with the employee on his/her job. The respondents for an employee can be her/his peers, managers, subordinates team members, customers, suppliers and vendors. Hence anyone who comes into contact with the employee, the 360 degree appraisal has four components that include self-appraisal, superior's appraisal, subordinate's appraisal student's appraisal and peer's appraisal .The proposed system is an attempt to implement the 360 degree feedback based appraisal system in academics especially engineering colleges.
To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable non-parametric approach to automatically learn the structural dynamics of the network and individual nodes. Roles may represent structural or behavioral patterns such as the center of a star, peripheral nodes, or bridge nodes that connect different communities. Our novel approach learns the appropriate structural role dynamics for any arbitrary network and tracks the changes over time. In particular, we uncover the specific global network dynamics and the local node dynamics of a technological, communication, and social network. We identify interesting node and network patterns such as stationary and non-stationary roles, spikes/steps in role-memberships (perhaps indicating anomalies), increasing/decreasing role trends, among many others. Our results indicate that the nodes in each of these networks have distinct connectivity patterns that are non-stationary and evolve considerably over time. Overall, the experiments demonstrate the effectiveness of our approach for fast mining and tracking of the dynamics in large networks. Furthermore, the dynamic structural representation provides a basis for building more sophisticated models and tools that are fast for exploring large dynamic networks.
Bayesian structure learning is the NP-hard problem of discovering a Bayesian network that optimally represents a given set of training data. In this paper we study the computational worst-case complexity of exact Bayesian structure learning under graph theoretic restrictions on the super-structure. The super-structure (a concept introduced by Perrier, Imoto, and Miyano, JMLR 2008) is an undirected graph that contains as subgraphs the skeletons of solution networks. Our results apply to several variants of score-based Bayesian structure learning where the score of a network decomposes into local scores of its nodes. Results: We show that exact Bayesian structure learning can be carried out in non-uniform polynomial time if the super-structure has bounded treewidth and in linear time if in addition the super-structure has bounded maximum degree. We complement this with a number of hardness results. We show that both restrictions (treewidth and degree) are essential and cannot be dropped without loosing uniform polynomial time tractability (subject to a complexity-theoretic assumption). Furthermore, we show that the restrictions remain essential if we do not search for a globally optimal network but we aim to improve a given network by means of at most k arc additions, arc deletions, or arc reversals (k-neighborhood local search).
Gaussian processes (GPs) provide a probabilistic nonparametric representation of functions in regression, classification, and other problems. Unfortunately, exact learning with GPs is intractable for large datasets. A variety of approximate GP methods have been proposed that essentially map the large dataset into a small set of basis points. Among them, two state-of-the-art methods are sparse pseudo-input Gaussian process (SPGP) (Snelson and Ghahramani, 2006) and variablesigma GP (VSGP) Walder et al. (2008), which generalizes SPGP and allows each basis point to have its own length scale. However, VSGP was only derived for regression. In this paper, we propose a new sparse GP framework that uses expectation propagation to directly approximate general GP likelihoods using a sparse and smooth basis. It includes both SPGP and VSGP for regression as special cases. Plus as an EP algorithm, it inherits the ability to process data online. As a particular choice of approximating family, we blur each basis point with a Gaussian distribution that has a full covariance matrix representing the data distribution around that basis point; as a result, we can summarize local data manifold information with a small set of basis points. Our experiments demonstrate that this framework outperforms previous GP classification methods on benchmark datasets in terms of minimizing divergence to the non-sparse GP solution as well as lower misclassification rate.
Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.
The problem of structure estimation in graphical models with latent variables is considered. We characterize conditions for tractable graph estimation and develop efficient methods with provable guarantees. We consider models where the underlying Markov graph is locally tree-like, and the model is in the regime of correlation decay. For the special case of the Ising model, the number of samples $n$ required for structural consistency of our method scales as $n=\Omega(\theta_{\min}^{-\delta\eta(\eta+1)-2}\log p)$, where p is the number of variables, $\theta_{\min}$ is the minimum edge potential, $\delta$ is the depth (i.e., distance from a hidden node to the nearest observed nodes), and $\eta$ is a parameter which depends on the bounds on node and edge potentials in the Ising model. Necessary conditions for structural consistency under any algorithm are derived and our method nearly matches the lower bound on sample requirements. Further, the proposed method is practical to implement and provides flexibility to control the number of latent variables and the cycle lengths in the output graph.
I present a new approach to recover the primordial density fluctuations and the cosmic web structure underlying a galaxy distribution. The method is based on sampling Gaussian fields which are compatible with a galaxy distribution and a structure formation model. This is achieved by splitting the inversion problem into two Gibbs-sampling steps: the first being a Gaussianisation step transforming a distribution of point sources at Lagrangian positions -which are not a priori given- into a linear alias-free Gaussian field. This step is based on Hamiltonian sampling with a Gaussian-Poisson model. The second step consists on a likelihood comparison in which the set of matter tracers at the initial conditions is constrained on the galaxy distribution and the assumed structure formation model. For computational reasons second order Lagrangian Perturbation Theory is used. However, the presented approach is flexible to adopt any structure formation model. A semi-analytic halo-model based galaxy mock catalog is taken to demonstrate that the recovered initial conditions are closely unbiased with respect to the actual ones from the corresponding N-body simulation down to scales of a ~ 5 Mpc/h. The cross-correlation between them shows a substantial gain of information, being at k ~ 0.3 h/Mpc more than doubled. In addition the initial conditions are extremely well Gaussian distributed and the power-spectra follow the shape of the linear power-spectrum being very close to the actual one from the simulation down to scales of k ~ 1 h/Mpc.
Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed.
We propose a graphical model for representing networks of stochastic processes, the minimal generative model graph. It is based on reduced factorizations of the joint distribution over time. We show that under appropriate conditions, it is unique and consistent with another type of graphical model, the directed information graph, which is based on a generalization of Granger causality. We demonstrate how directed information quantifies Granger causality in a particular sequential prediction setting. We also develop efficient methods to estimate the topological structure from data that obviate estimating the joint statistics. One algorithm assumes upper-bounds on the degrees and uses the minimal dimension statistics necessary. In the event that the upper-bounds are not valid, the resulting graph is nonetheless an optimal approximation. Another algorithm uses near-minimal dimension statistics when no bounds are known but the distribution satisfies a certain criterion. Analogous to how structure learning algorithms for undirected graphical models use mutual information estimates, these algorithms use directed information estimates. We characterize the sample-complexity of two plug-in directed information estimators and obtain confidence intervals. For the setting when point estimates are unreliable, we propose an algorithm that uses confidence intervals to identify the best approximation that is robust to estimation error. Lastly, we demonstrate the effectiveness of the proposed algorithms through analysis of both synthetic data and real data from the Twitter network. In the latter case, we identify which news sources influence users in the network by merely analyzing tweet times.
We present an automated classification of 2165 \textit{Kepler} eclipsing binary (EB) light curves that accompanied the second \textit{Kepler} data release. The light curves are classified using Locally Linear Embedding, a general nonlinear dimensionality reduction tool, into morphology types (detached, semi-detached, overcontact, ellipsoidal). The method, related to a more widely used Principal Component Analysis, produces a lower-dimensional representation of the input data while preserving local geometry and, consequently, the similarity between neighboring data points. We use this property to reduce the dimensionality in a series of steps to a one-dimensional manifold and classify light curves with a single parameter that is a measure of "detachedness" of the system. This fully automated classification correlates well with the manual determination of morphology from the data release, and also efficiently highlights any misclassified objects. Once a lower-dimensional projection space is defined, the classification of additional light curves runs in a negligible time and the method can therefore be used as a fully automated classifier in pipeline structures. The classifier forms a tier of the \textit{Kepler} EB pipeline that pre-processes light curves for the artificial intelligence based parameter estimator.
Novel research in the field of Linked Data focuses on the problem of entity summarization. This field addresses the problem of ranking features according to their importance for the task of identifying a particular entity. Next to a more human friendly presentation, these summarizations can play a central role for semantic search engines and semantic recommender systems. In current approaches, it has been tried to apply entity summarization based on patterns that are inherent to the regarded data. The proposed approach of this paper focuses on the movie domain. It utilizes usage data in order to support measuring the similarity between movie entities. Using this similarity it is possible to determine the k-nearest neighbors of an entity. This leads to the idea that features that entities share with their nearest neighbors can be considered as significant or important for these entities. Additionally, we introduce a downgrading factor (similar to TF-IDF) in order to overcome the high number of commonly occurring features. We exemplify the approach based on a movie-ratings dataset that has been linked to Freebase entities.
We present an approach to labeling short video clips with English verbs as event descriptions. A key distinguishing aspect of this work is that it labels videos with verbs that describe the spatiotemporal interaction between event participants, humans and objects interacting with each other, abstracting away all object-class information and fine-grained image characteristics, and relying solely on the coarse-grained motion of the event participants. We apply our approach to a large set of 22 distinct verb classes and a corpus of 2,584 videos, yielding two surprising outcomes. First, a classification accuracy of greater than 70% on a 1-out-of-22 labeling task and greater than 85% on a variety of 1-out-of-10 subsets of this labeling task is independent of the choice of which of two different time-series classifiers we employ. Second, we achieve this level of accuracy using a highly impoverished intermediate representation consisting solely of the bounding boxes of one or two event participants as a function of time. This indicates that successful event recognition depends more on the choice of appropriate features that characterize the linguistic invariants of the event classes than on the particular classifier algorithms.
The application of reinforcement learning algorithms onto real life problems always bears the challenge of filtering the environmental state out of raw sensor readings. While most approaches use heuristics, biology suggests that there must exist an unsupervised method to construct such filters automatically. Besides the extraction of environmental states, the filters have to represent them in a fashion that support modern reinforcement algorithms. Many popular algorithms use a linear architecture, so one should aim at filters that have good approximation properties in combination with linear functions. This thesis wants to propose the unsupervised method slow feature analysis (SFA) for this task. Presented with a random sequence of sensor readings, SFA learns a set of filters. With growing model complexity and training examples, the filters converge against trigonometric polynomial functions. These are known to possess excellent approximation capabilities and should therfore support the reinforcement algorithms well. We evaluate this claim on a robot. The task is to learn a navigational control in a simple environment using the least square policy iteration (LSPI) algorithm. The only accessible sensor is a head mounted video camera, but without meaningful filtering, video images are not suited as LSPI input. We will show that filters learned by SFA, based on a random walk video of the robot, allow the learned control to navigate successfully in ca. 80% of the test trials.
A relatively recent advance in cognitive neuroscience has been multi-voxel pattern analysis (MVPA), which enables researchers to decode brain states and/or the type of information represented in the brain during a cognitive operation. MVPA methods utilize machine learning algorithms to distinguish among types of information or cognitive states represented in the brain, based on distributed patterns of neural activity. In the current investigation, we propose a new approach for representation of neural data for pattern analysis, namely a Mesh Learning Model. In this approach, at each time instant, a star mesh is formed around each voxel, such that the voxel corresponding to the center node is surrounded by its p-nearest neighbors. The arc weights of each mesh are estimated from the voxel intensity values by least squares method. The estimated arc weights of all the meshes, called Mesh Arc Descriptors (MADs), are then used to train a classifier, such as Neural Networks, k-Nearest Neighbor, Na\"ive Bayes and Support Vector Machines. The proposed Mesh Model was tested on neuroimaging data acquired via functional magnetic resonance imaging (fMRI) during a recognition memory experiment using categorized word lists, employing a previously established experimental paradigm (\"Oztekin & Badre, 2011). Results suggest that the proposed Mesh Learning approach can provide an effective algorithm for pattern analysis of brain activity during cognitive processing.
Methods for automated discovery of causal relationships from non-interventional data have received much attention recently. A widely used and well understood model family is given by linear acyclic causal models (recursive structural equation models). For Gaussian data both constraint-based methods (Spirtes et al., 1993; Pearl, 2000) (which output a single equivalence class) and Bayesian score-based methods (Geiger and Heckerman, 1994) (which assign relative scores to the equivalence classes) are available. On the contrary, all current methods able to utilize non-Gaussianity in the data (Shimizu et al., 2006; Hoyer et al., 2008) always return only a single graph or a single equivalence class, and so are fundamentally unable to express the degree of certainty attached to that output. In this paper we develop a Bayesian score-based approach able to take advantage of non-Gaussianity when estimating linear acyclic causal models, and we empirically demonstrate that, at least on very modest size networks, its accuracy is as good as or better than existing methods. We provide a complete code package (in R) which implements all algorithms and performs all of the analysis provided in the paper, and hope that this will further the application of these methods to solving causal inference problems.
The choice of the kernel is critical to the success of many learning algorithms but it is typically left to the user. Instead, the training data can be used to learn the kernel by selecting it out of a given family, such as that of non-negative linear combinations of p base kernels, constrained by a trace or L1 regularization. This paper studies the problem of learning kernels with the same family of kernels but with an L2 regularization instead, and for regression problems. We analyze the problem of learning kernels with ridge regression. We derive the form of the solution of the optimization problem and give an efficient iterative algorithm for computing that solution. We present a novel theoretical analysis of the problem based on stability and give learning bounds for orthogonal kernels that contain only an additive term O(pp/m) when compared to the standard kernel ridge regression stability bound. We also report the results of experiments indicating that L1 regularization can lead to modest improvements for a small number of kernels, but to performance degradations in larger-scale cases. In contrast, L2 regularization never degrades performance and in fact achieves significant improvements with a large number of kernels.
Evaluating conjunctive queries and solving constraint satisfaction problems are fundamental problems in database theory and artificial intelligence, respectively. These problems are NP-hard, so that several research efforts have been made in the literature for identifying tractable classes, known as islands of tractability, as well as for devising clever heuristics for solving efficiently real-world instances. Many heuristic approaches are based on enforcing on the given instance a property called local consistency, where (in database terms) each tuple in every query atom matches at least one tuple in every other query atom. Interestingly, it turns out that, for many well-known classes of queries, such as for the acyclic queries, enforcing local consistency is even sufficient to solve the given instance correctly. However, the precise power of such a procedure was unclear, but for some very restricted cases. The paper provides full answers to the long-standing questions about the precise power of algorithms based on enforcing local consistency. The classes of instances where enforcing local consistency turns out to be a correct query-answering procedure are however not efficiently recognizable. In fact, the paper finally focuses on certain subclasses defined in terms of the novel notion of greedy tree projections. These latter classes are shown to be efficiently recognizable and strictly larger than most islands of tractability known so far, both in the general case of tree projections and for specific structural decomposition methods.
We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational representations. kLog allows users to specify learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming, and deductive databases. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph --- in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed numerical and symbolic data, as well as background knowledge in the form of Prolog or Datalog programs as in inductive logic programming systems. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. We also report about empirical comparisons, showing that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials.
I am most honoured to have the privilege to present the Foreword to this fascinating and wonderfully varied collection of contributions, concerning the nature of computation and of its deep connection with the operation of those basic laws, known or yet unknown, governing the universe in which we live. Fundamentally deep questions are indeed being grappled with here, and the fact that we find so many different viewpoints is something to be expected, since, in truth, we know little about the foundational nature and origins of these basic laws, despite the immense precision that we so often find revealed in them. Accordingly, it is not surprising that within the viewpoints expressed here is some unabashed speculation, occasionally bordering on just partially justified guesswork, while elsewhere we find a good deal of precise reasoning, some in the form of rigorous mathematical theorems. Both of these are as should be, for without some inspired guesswork we cannot have new ideas as to where look in order to make genuinely new progress, and without precise mathematical reasoning, no less than in precise observation, we cannot know when we are right -- or, more usually, when we are wrong.
In order to involve user knowledge in determining equality of sets, which may not be equal in the mathematical sense, three types of approximate (rough) equalities were introduced by Novotny and Pawlak ([8, 9, 10]). These notions were generalized by Tripathy, Mitra and Ojha ([13]), who introduced the concepts of approximate (rough) equivalences of sets. Rough equivalences capture equality of sets at a higher level than rough equalities. More properties of these concepts were established in [14]. Combining the conditions for the two types of approximate equalities, two more approximate equalities were introduced by Tripathy [12] and a comparative analysis of their relative efficiency was provided. In [15], the four types of approximate equalities were extended by considering rough fuzzy sets instead of only rough sets. In fact the concepts of leveled approximate equalities were introduced and properties were studied. In this paper we proceed further by introducing and studying the approximate equalities based on rough intuitionistic fuzzy sets instead of rough fuzzy sets. That is we introduce the concepts of approximate (rough)equalities of intuitionistic fuzzy sets and study their properties. We provide some real life examples to show the applications of rough equalities of fuzzy sets and rough equalities of intuitionistic fuzzy sets.
Within the framework proposed in this paper, we address the issue of extending the certain networks to a fuzzy certain networks in order to cope with a vagueness and limitations of existing models for decision under imprecise and uncertain knowledge. This paper proposes a framework that combines two disciplines to exploit their own advantages in uncertain and imprecise knowledge representation problems. The framework proposed is a possibilistic logic based one in which Bayesian nodes and their properties are represented by local necessity-valued knowledge base. Data in properties are interpreted as set of valuated formulas. In our contribution possibilistic Bayesian networks have a qualitative part and a quantitative part, represented by local knowledge bases. The general idea is to study how a fusion of these two formalisms would permit representing compact way to solve efficiently problems for knowledge representation. We show how to apply possibility and necessity measures to the problem of knowledge representation with large scale data. On the other hand fuzzification of crisp certainty degrees to fuzzy variables improves the quality of the network and tends to bring smoothness and robustness in the network performance. The general aim is to provide a new approach for decision under uncertainty that combines three methodologies: Bayesian networks certainty distribution and fuzzy logic.
Pertinence Feedback is a technique that enables a user to interactively express his information requirement by modifying his original query formulation with further information. This information is provided by explicitly confirming the pertinent of some indicating objects and/or goals extracted by the system. Obviously the user cannot mark objects and/or goals as pertinent until some are extracted, so the first search has to be initiated by a query and the initial query specification has to be good enough to pick out some pertinent objects and/or goals from the Semantic Network. In this paper we present a short survey of fuzzy and Semantic approaches to Knowledge Extraction. The goal of such approaches is to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. It has long been recognised that interactivity improves the effectiveness of Knowledge Extraction systems. Novice user's queries are the most natural and interactive medium of communication and recent progress in recognition is making it possible to build systems that interact with the user. However, given the typical novice user's queries submitted to Knowledge Extraction Systems, it is easy to imagine that the effects of goal recognition errors in novice user's queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of possibility theory in classical Knowledge Extraction techniques for novice user's query processing is more robust than the use of the probability theory. Moreover, both possibilistic and probabilistic pertinence feedback can be effectively employed to improve the effectiveness of novice user's query processing.
We analyze different aspects of our quantum modeling approach of human concepts, and more specifically focus on the quantum effects of contextuality, interference, entanglement and emergence, illustrating how each of them makes its appearance in specific situations of the dynamics of human concepts and their combinations. We point out the relation of our approach, which is based on an ontology of a concept as an entity in a state changing under influence of a context, with the main traditional concept theories, i.e. prototype theory, exemplar theory and theory theory. We ponder about the question why quantum theory performs so well in its modeling of human concepts, and shed light on this question by analyzing the role of complex amplitudes, showing how they allow to describe interference in the statistics of measurement outcomes, while in the traditional theories statistics of outcomes originates in classical probability weights, without the possibility of interference. The relevance of complex numbers, the appearance of entanglement, and the role of Fock space in explaining contextual emergence, all as unique features of the quantum modeling, are explicitly revealed in this paper by analyzing human concepts and their dynamics.
The similarity between trajectory patterns in clustering has played an important role in discovering movement behaviour of different groups of mobile objects. Several approaches have been proposed to measure the similarity between sequences in trajectory data. Most of these measures are based on Euclidean space or on spatial network and some of them have been concerned with temporal aspect or ordering types. However, they are not appropriate to characteristics of spatiotemporal mobility patterns in wireless networks. In this paper, we propose a new similarity measure for mobility patterns in cellular space of wireless network. The framework for constructing our measure is composed of two phases as follows. First, we present formal definitions to capture mathematically two spatial and temporal similarity measures for mobility patterns. And then, we define the total similarity measure by means of a weighted combination of these similarities. The truth of the partial and total similarity measures are proved in mathematics. Furthermore, instead of the time interval or ordering, our work makes use of the timestamp at which two mobility patterns share the same cell. A case study is also described to give a comparison of the combination measure with other ones.
This paper presents a method of optimization, based on both Bayesian Analysis technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical System we use learn by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When novice's query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user's Network or an object in the user's Network, the system infer that this new verb corresponds to one of the known goal. With the learning of new words in natural language as the interpretation, which was produced in agreement with the user, the system improves its representation scheme at each experiment with a new user and, in addition, takes advantage of previous discussions with users. The semantic Net of user objects thus obtained by these kinds of learning is not always optimal because some relationships between couple of user objects can be generalized and others suppressed according to values of forces that characterize them. Indeed, to simplify the obtained Net, we propose to proceed to an inductive Bayesian analysis, on the Net obtained from Gallois lattice. The objective of this analysis can be seen as an operation of filtering of the obtained descriptive graph.
Answer Set Programming (ASP) is a well-established paradigm of declarative programming in close relationship with other declarative formalisms such as SAT Modulo Theories, Constraint Handling Rules, FO(.), PDDL and many others. Since its first informal editions, ASP systems have been compared in the now well-established ASP Competition. The Third (Open) ASP Competition, as the sequel to the ASP Competitions Series held at the University of Potsdam in Germany (2006-2007) and at the University of Leuven in Belgium in 2009, took place at the University of Calabria (Italy) in the first half of 2011. Participants competed on a pre-selected collection of benchmark problems, taken from a variety of domains as well as real world applications. The Competition ran on two tracks: the Model and Solve (M&S) Track, based on an open problem encoding, and open language, and open to any kind of system based on a declarative specification paradigm; and the System Track, run on the basis of fixed, public problem encodings, written in a standard ASP language. This paper discusses the format of the Competition and the rationale behind it, then reports the results for both tracks. Comparison with the second ASP competition and state-of-the-art solutions for some of the benchmark domains is eventually discussed. To appear in Theory and Practice of Logic Programming (TPLP).
Parameter estimation in Markov random fields (MRFs) is a difficult task, in which inference over the network is run in the inner loop of a gradient descent procedure. Replacing exact inference with approximate methods such as loopy belief propagation (LBP) can suffer from poor convergence. In this paper, we provide a different approach for combining MRF learning and Bethe approximation. We consider the dual of maximum likelihood Markov network learning - maximizing entropy with moment matching constraints - and then approximate both the objective and the constraints in the resulting optimization problem. Unlike previous work along these lines (Teh & Welling, 2003), our formulation allows parameter sharing between features in a general log-linear model, parameter regularization and conditional training. We show that piecewise training (Sutton & McCallum, 2005) is a very restricted special case of this formulation. We study two optimization strategies: one based on a single convex approximation and one that uses repeated convex approximations. We show results on several real-world networks that demonstrate that these algorithms can significantly outperform learning with loopy and piecewise. Our results also provide a framework for analyzing the trade-offs of different relaxations of the entropy objective and of the constraints.
Much recent work has concerned sparse approximations to speed up the Gaussian process regression from the unfavorable O(n3) scaling in computational time to O(nm2). Thus far, work has concentrated on models with one covariance function. However, in many practical situations additive models with multiple covariance functions may perform better, since the data may contain both long and short length-scale phenomena. The long length-scales can be captured with global sparse approximations, such as fully independent conditional (FIC), and the short length-scales can be modeled naturally by covariance functions with compact support (CS). CS covariance functions lead to naturally sparse covariance matrices, which are computationally cheaper to handle than full covariance matrices. In this paper, we propose a new sparse Gaussian process model with two additive components: FIC for the long length-scales and CS covariance function for the short length-scales. We give theoretical and experimental results and show that under certain conditions the proposed model has the same computational complexity as FIC. We also compare the model performance of the proposed model to additive models approximated by fully and partially independent conditional (PIC). We use real data sets and show that our model outperforms FIC and PIC approximations for data sets with two additive phenomena.
We consider online planning in Markov decision processes (MDPs). In online planning, the agent focuses on its current state only, deliberates about the set of possible policies from that state onwards and, when interrupted, uses the outcome of that exploratory deliberation to choose what action to perform next. The performance of algorithms for online planning is assessed in terms of simple regret, which is the agent's expected performance loss when the chosen action, rather than an optimal one, is followed. To date, state-of-the-art algorithms for online planning in general MDPs are either best effort, or guarantee only polynomial-rate reduction of simple regret over time. Here we introduce a new Monte-Carlo tree search algorithm, BRUE, that guarantees exponential-rate reduction of simple regret and error probability. This algorithm is based on a simple yet non-standard state-space sampling scheme, MCTS2e, in which different parts of each sample are dedicated to different exploratory objectives. Our empirical evaluation shows that BRUE not only provides superior performance guarantees, but is also very effective in practice and favorably compares to state-of-the-art. We then extend BRUE with a variant of "learning by forgetting." The resulting set of algorithms, BRUE(alpha), generalizes BRUE, improves the exponential factor in the upper bound on its reduction rate, and exhibits even more attractive empirical performance.
In the recent advancement of multimedia technologies, it becomes a major concern of detecting visual attention regions in the field of image processing. The popularity of the terminal devices in a heterogeneous environment of the multimedia technology gives us enough scope for the betterment of image visualization. Although there exist numerous methods, feature based image extraction becomes a popular one in the field of image processing. The objective of image segmentation is the domain-independent partition of the image into a set of regions, which are visually distinct and uniform with respect to some property, such as grey level, texture or colour. Segmentation and subsequent extraction can be considered the first step and key issue in object recognition, scene understanding and image analysis. Its application area encompasses mobile devices, industrial quality control, medical appliances, robot navigation, geophysical exploration, military applications, etc. In all these areas, the quality of the final results depends largely on the quality of the preprocessing work. Most of the times, acquiring spurious-free preprocessing data requires a lot of application cum mathematical intensive background works. We propose a feature based fuzzy rule guided novel technique that is functionally devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE) and Peak Signal to Noise Ratio (PSNR).
We systematically investigate the complexity of model checking the existential positive fragment of first-order logic. In particular, for a set of existential positive sentences, we consider model checking where the sentence is restricted to fall into the set; a natural question is then to classify which sentence sets are tractable and which are intractable. With respect to fixed-parameter tractability, we give a general theorem that reduces this classification question to the corresponding question for primitive positive logic, for a variety of representations of structures. This general theorem allows us to deduce that an existential positive sentence set having bounded arity is fixed-parameter tractable if and only if each sentence is equivalent to one in bounded-variable logic. We then use the lens of classical complexity to study these fixed-parameter tractable sentence sets. We show that such a set can be NP-complete, and consider the length needed by a translation from sentences in such a set to bounded-variable logic; we prove superpolynomial lower bounds on this length using the theory of compilability, obtaining an interesting type of formula size lower bound. Overall, the tools, concepts, and results of this article set the stage for the future consideration of the complexity of model checking on more expressive logics.
In this work we present an algorithm for covering continuous connected domains by ant-like robots with very limited capabilities. The robots can mark visited places with pheromone marks and sense the level of the pheromone in their local neighborhood. In case of multiple robots these pheromone marks can be sensed by all robots and provide the only way of (indirect) communication between the robots. The robots are assumed to be memoryless, and to have no global information such as the domain map, their own position (either absolute or relative), total marked area percentage, maximal pheromone level, etc.. Despite the robots' simplicity, we show that they are able, by running a very simple rule of behavior, to ensure efficient covering of arbitrary connected domains, including non-planar and multidimensional ones. The novelty of our algorithm lies in the fact that, unlike previously proposed methods, our algorithm works on continuous domains without relying on some "induced" underlying graph, that effectively reduces the problem to a discrete case of graph covering. The algorithm guarantees complete coverage of any connected domain. We also prove that the algorithm is noise immune, i.e., it is able to cope with any initial pheromone profile (noise). In addition the algorithm provides a bounded constant time between two successive visits of the robot, and thus, is suitable for patrolling or surveillance applications.
Online sequence prediction is the problem of predicting the next element of a sequence given previous elements. This problem has been extensively studied in the context of individual sequence prediction, where no prior assumptions are made on the origin of the sequence. Individual sequence prediction algorithms work quite well for long sequences, where the algorithm has enough time to learn the temporal structure of the sequence. However, they might give poor predictions for short sequences. A possible remedy is to rely on the general model of prediction with expert advice, where the learner has access to a set of $r$ experts, each of which makes its own predictions on the sequence. It is well known that it is possible to predict almost as well as the best expert if the sequence length is order of $\log(r)$. But, without firm prior knowledge on the problem, it is not clear how to choose a small set of {\em good} experts. In this paper we describe and analyze a new algorithm that learns a good set of experts using a training set of previously observed sequences. We demonstrate the merits of our approach by applying it on the task of click prediction on the web.
We consider the problem of parameter estimation using weakly supervised datasets, where a training sample consists of the input and a partially specified annotation, which we refer to as the output. The missing information in the annotation is modeled using latent variables. Previous methods overburden a single distribution with two separate tasks: (i) modeling the uncertainty in the latent variables during training; and (ii) making accurate predictions for the output and the latent variables during testing. We propose a novel framework that separates the demands of the two tasks using two distributions: (i) a conditional distribution to model the uncertainty of the latent variables for a given input-output pair; and (ii) a delta distribution to predict the output and the latent variables for a given input. During learning, we encourage agreement between the two distributions by minimizing a loss-based dissimilarity coefficient. Our approach generalizes latent SVM in two important ways: (i) it models the uncertainty over latent variables instead of relying on a pointwise estimate; and (ii) it allows the use of loss functions that depend on latent variables, which greatly increases its applicability. We demonstrate the efficacy of our approach on two challenging problems---object detection and action detection---using publicly available datasets.
Sparse coding is an unsupervised learning algorithm that learns a succinct high-level representation of the inputs given only unlabeled data; it represents each input as a sparse linear combination of a set of basis functions. Originally applied to modeling the human visual cortex, sparse coding has also been shown to be useful for self-taught learning, in which the goal is to solve a supervised classification task given access to additional unlabeled data drawn from different classes than that in the supervised learning problem. Shift-invariant sparse coding (SISC) is an extension of sparse coding which reconstructs a (usually time-series) input using all of the basis functions in all possible shifts. In this paper, we present an efficient algorithm for learning SISC bases. Our method is based on iteratively solving two large convex optimization problems: The first, which computes the linear coefficients, is an L1-regularized linear least squares problem with potentially hundreds of thousands of variables. Existing methods typically use a heuristic to select a small subset of the variables to optimize, but we present a way to efficiently compute the exact solution. The second, which solves for bases, is a constrained linear least squares problem. By optimizing over complex-valued variables in the Fourier domain, we reduce the coupling between the different variables, allowing the problem to be solved efficiently. We show that SISC's learned high-level representations of speech and music provide useful features for classification tasks within those domains. When applied to classification, under certain conditions the learned features outperform state of the art spectral and cepstral features.
Instrumental variables have proven useful, in particular within the social sciences and economics, for making inference about the causal effect of a random variable, B, on another random variable, C, in the presence of unobserved confounders. In the case where relationships are linear, causal effects can be identified exactly from studying the regression of C on A and the regression of B on A, where A is the instrument. In the more general case, bounds have been developed in the literature for the causal effect of B on C, given observational data on the joint distribution of C, B and A. Using an approach based on the analysis of convex polytopes, we develop bounds for the same causal effect when given data on (C,A) and (B,A) only. The bounds developed are thus in direct analogy to the standard use of instruments in econometrics, but we make no assumption of linearity. Use of the bounds is illustrated for experiments with partial compliance. The bounds are, for example, relevant in genetic epidemiology, where the 'Mendelian instrument' S represents a genotype, and where joint data on all of C, B and A may rarely be available but studies involving pairs of these may be abundant. Other examples of bounding causal effects are considered to show that the method applies to DAGs in general, subject to certain conditions.
In many application domains, such as computational biology, the goal of graphical model structure learning is to uncover discrete relationships between entities. For example, in our problem of interest concerning HIV vaccine design, we want to infer which HIV peptides interact with which immune system molecules (HLA molecules). For problems of this nature, we are interested in determining the number of nonspurious arcs in a learned graphical model. We describe both a Bayesian and frequentist approach to this problem. In the Bayesian approach, we use the posterior distribution over model structures to compute the expected number of true arcs in a learned model. In the frequentist approach, we develop a method based on the concept of the False Discovery Rate. On synthetic data sets generated from models similar to the ones learned, we find that both the Bayesian and frequentist approaches yield accurate estimates of the number of non-spurious arcs. In addition, we speculate that the frequentist approach, which is non-parametric, may outperform the parametric Bayesian approach in situations where the models learned are less representative of the data. Finally, we apply the frequentist approach to our problem of HIV vaccine design.
Assistive systems for persons with cognitive disabilities (e.g. dementia) are difficult to build due to the wide range of different approaches people can take to accomplishing the same task, and the significant uncertainties that arise from both the unpredictability of client's behaviours and from noise in sensor readings. Partially observable Markov decision process (POMDP) models have been used successfully as the reasoning engine behind such assistive systems for small multi-step tasks such as hand washing. POMDP models are a powerful, yet flexible framework for modelling assistance that can deal with uncertainty and utility. Unfortunately, POMDPs usually require a very labour intensive, manual procedure for their definition and construction. Our previous work has described a knowledge driven method for automatically generating POMDP activity recognition and context sensitive prompting systems for complex tasks. We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The spreadsheet-like result of the analysis does not correspond to the POMDP model directly and the translation to a formal POMDP representation is required. To date, this translation had to be performed manually by a trained POMDP expert. In this paper, we formalise and automate this translation process using a probabilistic relational model (PRM) encoded in a relational database. We demonstrate the method by eliciting three assistance tasks from non-experts. We validate the resulting POMDP models using case-based simulations to show that they are reasonable for the domains. We also show a complete case study of a designer specifying one database, including an evaluation in a real-life experiment with a human actor.
Representations are internal models of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether or not they are necessary or even essential for intelligent behavior. We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks---an artificial neural network and a network of hidden Markov gates---to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation, and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts (features of the environment) our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system, and should be predictive of an agent's long-term adaptive success.
Discriminative linear models are a popular tool in machine learning. These can be generally divided into two types: The first is linear classifiers, such as support vector machines, which are well studied and provide state-of-the-art results. One shortcoming of these models is that their output (known as the 'margin') is not calibrated, and cannot be translated naturally into a distribution over the labels. Thus, it is difficult to incorporate such models as components of larger systems, unlike probabilistic based approaches. The second type of approach constructs class conditional distributions using a nonlinearity (e.g. log-linear models), but is occasionally worse in terms of classification error. We propose a supervised learning method which combines the best of both approaches. Specifically, our method provides a distribution over the labels, which is a linear function of the model parameters. As a consequence, differences between probabilities are linear functions, a property which most probabilistic models (e.g. log-linear) do not have. Our model assumes that classes correspond to linear subspaces (rather than to half spaces). Using a relaxed projection operator, we construct a measure which evaluates the degree to which a given vector 'belongs' to a subspace, resulting in a distribution over labels. Interestingly, this view is closely related to similar concepts in quantum detection theory. The resulting models can be trained either to maximize the margin or to optimize average likelihood measures. The corresponding optimization problems are semidefinite programs which can be solved efficiently. We illustrate the performance of our algorithm on real world datasets, and show that it outperforms 2nd order kernel methods.
Many tasks require finding groups of elements in a matrix of numbers, symbols or class likelihoods. One approach is to use efficient bi- or tri-linear factorization techniques including PCA, ICA, sparse matrix factorization and plaid analysis. These techniques are not appropriate when addition and multiplication of matrix elements are not sensibly defined. More directly, methods like bi-clustering can be used to classify matrix elements, but these methods make the overly-restrictive assumption that the class of each element is a function of a row class and a column class. We introduce a general computational problem, `matrix tile analysis' (MTA), which consists of decomposing a matrix into a set of non-overlapping tiles, each of which is defined by a subset of usually nonadjacent rows and columns. MTA does not require an algebra for combining tiles, but must search over discrete combinations of tile assignments. Exact MTA is a computationally intractable integer programming problem, but we describe an approximate iterative technique and a computationally efficient sum-product relaxation of the integer program. We compare the effectiveness of these methods to PCA and plaid on hundreds of randomly generated tasks. Using double-gene-knockout data, we show that MTA finds groups of interacting yeast genes that have biologically-related functions.
Recently, a theory for stochastic optimal control in non-linear dynamical systems in continuous space-time has been developed (Kappen, 2005). We apply this theory to collaborative multi-agent systems. The agents evolve according to a given non-linear dynamics with additive Wiener noise. Each agent can control its own dynamics. The goal is to minimize the accumulated joint cost, which consists of a state dependent term and a term that is quadratic in the control. We focus on systems of non-interacting agents that have to distribute themselves optimally over a number of targets, given a set of end-costs for the different possible agent-target combinations. We show that optimal control is the combinatorial sum of independent single-agent single-target optimal controls weighted by a factor proportional to the end-costs of the different combinations. Thus, multi-agent control is related to a standard graphical model inference problem. The additional computational cost compared to single-agent control is exponential in the tree-width of the graph specifying the combinatorial sum times the number of targets. We illustrate the result by simulations of systems with up to 42 agents.
We present a machine learning approach for estimating the second derivative of a drivable surface, its roughness. Robot perception generally focuses on the first derivative, obstacle detection. However, the second derivative is also important due to its direct relation (with speed) to the shock the vehicle experiences. Knowing the second derivative allows a vehicle to slow down in advance of rough terrain. Estimating the second derivative is challenging due to uncertainty. For example, at range, laser readings may be so sparse that significant information about the surface is missing. Also, a high degree of precision is required in projecting laser readings. This precision may be unavailable due to latency or error in the pose estimation. We model these sources of error as a multivariate polynomial. Its coefficients are learned using the shock data as ground truth -- the accelerometers are used to train the lasers. The resulting classifier operates on individual laser readings from a road surface described by a 3D point cloud. The classifier identifies sections of road where the second derivative is likely to be large. Thus, the vehicle can slow down in advance, reducing the shock it experiences. The algorithm is an evolution of one we used in the 2005 DARPA Grand Challenge. We analyze it using data from that route.
Combinatorial optimization is widely applied in a number of areas nowadays. Unfortunately, many combinatorial optimization problems are NP-hard which usually means that they are unsolvable in practice. However, it is often unnecessary to have an exact solution. In this case one may use heuristic approach to obtain a near-optimal solution in some reasonable time. We focus on two combinatorial optimization problems, namely the Generalized Traveling Salesman Problem and the Multidimensional Assignment Problem. The first problem is an important generalization of the Traveling Salesman Problem; the second one is a generalization of the Assignment Problem for an arbitrary number of dimensions. Both problems are NP-hard and have hosts of applications. In this work, we discuss different aspects of heuristics design and evaluation. A broad spectrum of related subjects, covered in this research, includes test bed generation and analysis, implementation and performance issues, local search neighborhoods and efficient exploration algorithms, metaheuristics design and population sizing in memetic algorithm. The most important results are obtained in the areas of local search and memetic algorithms for the considered problems. In both cases we have significantly advanced the existing knowledge on the local search neighborhoods and algorithms by systematizing and improving the previous results. We have proposed a number of efficient heuristics which dominate the existing algorithms in a wide range of time/quality requirements. Several new approaches, introduced in our memetic algorithms, make them the state-of-the-art metaheuristics for the corresponding problems. Population sizing is one of the most promising among these approaches; it is expected to be applicable to virtually any memetic algorithm.
Ontologies are key enablers for sharing precise and machine-understandable semantics among different applications and parties. Yet, for ontologies to meet these expectations, their quality must be of a good standard. The quality of an ontology is strongly based on the design method employed. This paper addresses the design problems related to the modelling of ontologies, with specific concentration on the issues related to the quality of the conceptualisations produced. The paper aims to demonstrate the impact of the modelling paradigm adopted on the quality of ontological models and, consequently, the potential impact that such a decision can have in relation to the development of software applications. To this aim, an ontology that is conceptualised based on the Object-Role Modelling (ORM) approach (a representative of endurantism) is re-engineered into a one modelled on the basis of the Object Paradigm (OP) (a representative of perdurantism). Next, the two ontologies are analytically compared using the specified criteria. The conducted comparison highlights that using the OP for ontology conceptualisation can provide more expressive, reusable, objective and temporal ontologies than those conceptualised on the basis of the ORM approach.
Dynamic treatment regimes operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function takes as input up-to-date patient information and gives as output a single recommended treatment. Current methods for estimating optimal dynamic treatment regimes, for example Q-learning, require the specification of a single outcome by which the `goodness' of competing dynamic treatment regimes are measured. However, this is an over-simplification of the goal of clinical decision making, which aims to balance several potentially competing outcomes. For example, often a balance must be struck between treatment effectiveness and side-effect burden. We propose a method for constructing dynamic treatment regimes that accommodates competing outcomes by recommending sets of treatments at each decision point. Formally, we construct a sequence of set-valued functions that take as input up-to-date patient information and give as output a recommended subset of the possible treatments. For a given patient history, the recommended set of treatments contains all treatments that are not inferior according to any of the competing outcomes. When there is more than one decision point, constructing these set-valued functions requires solving a non-trivial enumeration problem. We offer an exact enumeration algorithm by recasting the problem as a linear mixed integer program. The proposed methods are illustrated using data from a depression study and the CATIE schizophrenia study.
PIDE is a general framework for document-oriented prover interaction and integration, based on a bilingual architecture that combines ML and Scala. The overall aim is to connect LCF-style provers like Isabelle (or Coq or HOL) with sophisticated front-end technology on the JVM platform, overcoming command-line interaction at last. The present system description specifically covers Isabelle/jEdit as part of the official release of Isabelle2011-1 (October 2011). It is a concrete Prover IDE implementation based on Isabelle/PIDE library modules (implemented in Scala) on the one hand, and the well-known text editor framework of jEdit (implemented in Java) on the other hand. The interaction model of our Prover IDE follows the idea of continuous proof checking: the theory source text is annotated by semantic information by the prover as it becomes available incrementally. This works via an asynchronous protocol that neither blocks the editor nor stops the prover from exploiting parallelism on multi-core hardware. The jEdit GUI provides standard metaphors for augmented text editing (highlighting, squiggles, tooltips, hyperlinks etc.) that we have instrumented to render the formal content from the prover context. Further refinement of the jEdit display engine via suitable plugins and fonts approximates mathematical rendering in the text buffer, including symbols from the TeX repertoire, and sub-/superscripts. Isabelle/jEdit is presented here both as a usable interface for current Isabelle, and as a reference application to inspire further projects based on PIDE.
We address the problem of unsupervised learning of complex articulated object models from 3D range data. We describe an algorithm whose input is a set of meshes corresponding to different configurations of an articulated object. The algorithm automatically recovers a decomposition of the object into approximately rigid parts, the location of the parts in the different object instances, and the articulated object skeleton linking the parts. Our algorithm first registers allthe meshes using an unsupervised non-rigid technique described in a companion paper. It then segments the meshes using a graphical model that captures the spatial contiguity of parts. The segmentation is done using the EM algorithm, iterating between finding a decomposition of the object into rigid parts, and finding the location of the parts in the object instances. Although the graphical model is densely connected, the object decomposition step can be performed optimally and efficiently, allowing us to identify a large number of object parts while avoiding local maxima. We demonstrate the algorithm on real world datasets, recovering a 15-part articulated model of a human puppet from just 7 different puppet configurations, as well as a 4 part model of a fiexing arm where significant non-rigid deformation was present.
Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins not with a training sample, but instead with resources that it can use to obtain information to help identify the optimal model. To better understand this task, this paper presents and analyses the simplified "(budgeted) active model selection" version, which captures the pure exploration aspect of many active learning problems in a clean and simple problem formulation. Here the learner can use a fixed budget of "model probes" (where each probe evaluates the specified model on a random indistinguishable instance) to identify which of a given set of possible models has the highest expected accuracy. Our goal is a policy that sequentially determines which model to probe next, based on the information observed so far. We present a formal description of this task, and show that it is NPhard in general. We then investigate a number of algorithms for this task, including several existing ones (eg, "Round-Robin", "Interval Estimation", "Gittins") as well as some novel ones (e.g., "Biased-Robin"), describing first their approximation properties and then their empirical performance on various problem instances. We observe empirically that the simple biased-robin algorithm significantly outperforms the other algorithms in the case of identical costs and priors.
Haplotypes, the global patterns of DNA sequence variation, have important implications for identifying complex traits. Recently, blocks of limited haplotype diversity have been discovered in human chromosomes, intensifying the research on modelling the block structure as well as the transitions or co-occurrence of the alleles in these blocks as a way to compress the variability and infer the associations more robustly. The haplotype block structure analysis is typically complicated by the fact that the phase information for each SNP is missing, i.e., the observed allele pairs are not given in a consistent order across the sequence. The techniques for circumventing this require additional information, such as family data, or a more complex sequencing procedure. In this paper we present a hierarchical statistical model and the associated learning and inference algorithms that simultaneously deal with the allele ambiguity per locus, missing data, block estimation, and the complex trait association. While the blo structure may differ from the structures inferred by other methods, which use the pedigree information or previously known alleles, the parameters we estimate, including the learned block structure and the estimated block transitions per locus, define a good model of variability in the set. The method is completely datadriven and can detect Chron's disease from the SNP data taken from the human chromosome 5q31 with the detection rate of 80% and a small error variance.
One of the major problems in modeling natural signals is that signals with very similar structure may locally have completely different measurements, e.g., images taken under different illumination conditions, or the speech signal captured in different environments. While there have been many successful attempts to address these problems in application-specific settings, we believe that underlying a large set of problems in signal representation is a representational deficiency of intensity-derived local measurements that are the basis of most efficient models. We argue that interesting structure in signals is better captured when the signal is de- fined as a matrix whose entries are discrete indices to a separate palette of possible measurements. In order to model the variability in signal structure, we define a signal class not by a single index map, but by a probability distribution over the index maps, which can be estimated from the data, and which we call probabilistic index maps. The existing algorithm can be adapted to work with this representation. Furthermore, the probabilistic index map representation leads to algorithms with computational costs proportional to either the size of the palette or the log of the size of the palette, making the cost of significantly increased invariance to non-structural changes quite bearable. We illustrate the benefits of the probabilistic index map representation in several applications in computer vision and speech processing.
The lack of interoperability between mobile cellular access networks has long been a challenging obstacle, which telecommunication engineering is trying to overcome. In second generation networks for example, this problem lies in the fact that there are multiple standards. Each of these standards can operate in the same frequency range. However, each utilizes a different Radio Technology and Modulation Scheme, which are characteristics of the standard. Therefore, the lack of interoperability in 2G occurs because of the lack of standardization. Interoperability within 3G networks is limited to a few operating modes using different Radio Transmission Technologies that are not inter-operable. Thus, interoperability remains an issue for 3G. 4G technology even being successful in its various trials cannot guarantee the interoperability. This is within each network generation; meanwhile between heterogeneous network generations the situation seems to be worst. This approach is first to analyze the structure, inputs, and outputs of three different cellular technologies, performing a domain analysis (of this subset of technologies) and producing a feature model of the domain. Finally, we sought to build an ontology capable of providing a common view of the domain, providing an effective representation of relations between representations of corresponding concepts in different cellular technologies.
The exploration/exploitation (E/E) dilemma arises naturally in many subfields of Science. Multi-armed bandit problems formalize this dilemma in its canonical form. Most current research in this field focuses on generic solutions that can be applied to a wide range of problems. However, in practice, it is often the case that a form of prior information is available about the specific class of target problems. Prior knowledge is rarely used in current solutions due to the lack of a systematic approach to incorporate it into the E/E strategy. To address a specific class of E/E problems, we propose to proceed in three steps: (i) model prior knowledge in the form of a probability distribution over the target class of E/E problems; (ii) choose a large hypothesis space of candidate E/E strategies; and (iii), solve an optimization problem to find a candidate E/E strategy of maximal average performance over a sample of problems drawn from the prior distribution. We illustrate this meta-learning approach with two different hypothesis spaces: one where E/E strategies are numerically parameterized and another where E/E strategies are represented as small symbolic formulas. We propose appropriate optimization algorithms for both cases. Our experiments, with two-armed Bernoulli bandit problems and various playing budgets, show that the meta-learnt E/E strategies outperform generic strategies of the literature (UCB1, UCB1-Tuned, UCB-v, KL-UCB and epsilon greedy); they also evaluate the robustness of the learnt E/E strategies, by tests carried out on arms whose rewards follow a truncated Gaussian distribution.
Today's conventional search engines hardly do provide the essential content relevant to the user's search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query.
Creating and monitoring competitive and cost-effective pay-per-click advertisement campaigns through the web-search channel is a resource demanding task in terms of expertise and effort. Assisting or even automating the work of an advertising specialist will have an unrivaled commercial value. In this paper we propose a methodology, an architecture, and a fully functional framework for semi- and fully- automated creation, monitoring, and optimization of cost-efficient pay-per-click campaigns with budget constraints. The campaign creation module generates automatically keywords based on the content of the web page to be advertised extended with corresponding ad-texts. These keywords are used to create automatically the campaigns fully equipped with the appropriate values set. The campaigns are uploaded to the auctioneer platform and start running. The optimization module focuses on the learning process from existing campaign statistics and also from applied strategies of previous periods in order to invest optimally in the next period. The objective is to maximize the performance (i.e. clicks, actions) under the current budget constraint. The fully functional prototype is experimentally evaluated on real world Google AdWords campaigns and presents a promising behavior with regards to campaign performance statistics as it outperforms systematically the competing manually maintained campaigns.
Inferring probabilistic networks from data is a notoriously difficult task. Under various goodness-of-fit measures, finding an optimal network is NP-hard, even if restricted to polytrees of bounded in-degree. Polynomial-time algorithms are known only for rare special cases, perhaps most notably for branchings, that is, polytrees in which the in-degree of every node is at most one. Here, we study the complexity of finding an optimal polytree that can be turned into a branching by deleting some number of arcs or nodes, treated as a parameter. We show that the problem can be solved via a matroid intersection formulation in polynomial time if the number of deleted arcs is bounded by a constant. The order of the polynomial time bound depends on this constant, hence the algorithm does not establish fixed-parameter tractability when parameterized by the number of deleted arcs. We show that a restricted version of the problem allows fixed-parameter tractability and hence scales well with the parameter. We contrast this positive result by showing that if we parameterize by the number of deleted nodes, a somewhat more powerful parameter, the problem is not fixed-parameter tractable, subject to a complexity-theoretic assumption.
Fuzzy rule based classification systems are one of the most popular fuzzy modeling systems used in pattern classification problems. This paper investigates the effect of applying nine different T-norms in fuzzy rule based classification systems. In the recent researches, fuzzy versions of confidence and support merits from the field of data mining have been widely used for both rules selecting and weighting in the construction of fuzzy rule based classification systems. For calculating these merits the product has been usually used as a T-norm. In this paper different T-norms have been used for calculating the confidence and support measures. Therefore, the calculations in rule selection and rule weighting steps (in the process of constructing the fuzzy rule based classification systems) are modified by employing these T-norms. Consequently, these changes in calculation results in altering the overall accuracy of rule based classification systems. Experimental results obtained on some well-known data sets show that the best performance is produced by employing the Aczel-Alsina operator in terms of the classification accuracy, the second best operator is Dubois-Prade and the third best operator is Dombi. In experiments, we have used 12 data sets with numerical attributes from the University of California, Irvine machine learning repository (UCI).
With such increasing popularity and availability of digital text data, authorships of digital texts can not be taken for granted due to the ease of copying and parsing. This paper presents a new text style analysis called natural frequency zoned word distribution analysis (NFZ-WDA), and then a basic authorship attribution scheme and an open authorship attribution scheme for digital texts based on the analysis. NFZ-WDA is based on the observation that all authors leave distinct intrinsic word usage traces on texts written by them and these intrinsic styles can be identified and employed to analyze the authorship. The intrinsic word usage styles can be estimated through the analysis of word distribution within a text, which is more than normal word frequency analysis and can be expressed as: which groups of words are used in the text; how frequently does each group of words occur; how are the occurrences of each group of words distributed in the text. Next, the basic authorship attribution scheme and the open authorship attribution scheme provide solutions for both closed and open authorship attribution problems. Through analysis and extensive experimental studies, this paper demonstrates the efficiency of the proposed method for authorship attribution.
A major computational burden, while performing document clustering, is the calculation of similarity measure between a pair of documents. Similarity measure is a function that assign a real number between 0 and 1 to a pair of documents, depending upon the degree of similarity between them. A value of zero means that the documents are completely dissimilar whereas a value of one indicates that the documents are practically identical. Traditionally, vector-based models have been used for computing the document similarity. The vector-based models represent several features present in documents. These approaches to similarity measures, in general, cannot account for the semantics of the document. Documents written in human languages contain contexts and the words used to describe these contexts are generally semantically related. Motivated by this fact, many researchers have proposed semantic-based similarity measures by utilizing text annotation through external thesauruses like WordNet (a lexical database). In this paper, we define a semantic similarity measure based on documents represented in topic maps. Topic maps are rapidly becoming an industrial standard for knowledge representation with a focus for later search and extraction. The documents are transformed into a topic map based coded knowledge and the similarity between a pair of documents is represented as a correlation between the common patterns. The experimental studies on the text mining datasets reveal that this new similarity measure is more effective as compared to commonly used similarity measures in text clustering.
Much current research in AI and games is being devoted to Monte Carlo search (MCS) algorithms. While the quest for a single unified MCS algorithm that would perform well on all problems is of major interest for AI, practitioners often know in advance the problem they want to solve, and spend plenty of time exploiting this knowledge to customize their MCS algorithm in a problem-driven way. We propose an MCS algorithm discovery scheme to perform this in an automatic and reproducible way. We first introduce a grammar over MCS algorithms that enables inducing a rich space of candidate algorithms. Afterwards, we search in this space for the algorithm that performs best on average for a given distribution of training problems. We rely on multi-armed bandits to approximately solve this optimization problem. The experiments, generated on three different domains, show that our approach enables discovering algorithms that outperform several well-known MCS algorithms such as Upper Confidence bounds applied to Trees and Nested Monte Carlo search. We also show that the discovered algorithms are generally quite robust with respect to changes in the distribution over the training problems.
Direct policy search (DPS) and look-ahead tree (LT) policies are two widely used classes of techniques to produce high performance policies for sequential decision-making problems. To make DPS approaches work well, one crucial issue is to select an appropriate space of parameterized policies with respect to the targeted problem. A fundamental issue in LT approaches is that, to take good decisions, such policies must develop very large look-ahead trees which may require excessive online computational resources. In this paper, we propose a new hybrid policy learning scheme that lies at the intersection of DPS and LT, in which the policy is an algorithm that develops a small look-ahead tree in a directed way, guided by a node scoring function that is learned through DPS. The LT-based representation is shown to be a versatile way of representing policies in a DPS scheme, while at the same time, DPS enables to significantly reduce the size of the look-ahead trees that are required to take high-quality decisions. We experimentally compare our method with two other state-of-the-art DPS techniques and four common LT policies on four benchmark domains and show that it combines the advantages of the two techniques from which it originates. In particular, we show that our method: (1) produces overall better performing policies than both pure DPS and pure LT policies, (2) requires a substantially smaller number of policy evaluations than other DPS techniques, (3) is easy to tune and (4) results in policies that are quite robust with respect to perturbations of the initial conditions.
As an important tool for information filtering in the era of socialized web, recommender systems have witnessed rapid development in the last decade. As benefited from the better interpretability, neighborhood-based collaborative filtering techniques, such as item-based collaborative filtering adopted by Amazon, have gained a great success in many practical recommender systems. However, the neighborhood-based collaborative filtering method suffers from the rating bound problem, i.e., the rating on a target item that this method estimates is bounded by the observed ratings of its all neighboring items. Therefore, it cannot accurately estimate the unobserved rating on a target item, if its ground truth rating is actually higher (lower) than the highest (lowest) rating over all items in its neighborhood. In this paper, we address this problem by formalizing rating estimation as a task of recovering a scalar rating function. With a linearity assumption, we infer all the ratings by optimizing the low-order norm, e.g., the $l_1/2$-norm, of the second derivative of the target scalar function, while remaining its observed ratings unchanged. Experimental results on three real datasets, namely Douban, Goodreads and MovieLens, demonstrate that the proposed approach can well overcome the rating bound problem. Particularly, it can significantly improve the accuracy of rating estimation by 37% than the conventional neighborhood-based methods.
We propose parametric constructive Kripke-semantics for multi-agent KD45-belief and S5-knowledge in terms of elementary set-theoretic constructions of two basic functional building blocks, namely bias (or viewpoint) and visibility, functioning also as the parameters of the doxastic and epistemic accessibility relation. The doxastic accessibility relates two possible worlds whenever the application of the composition of bias with visibility to the first world is equal to the application of visibility to the second world. The epistemic accessibility is the transitive closure of the union of our doxastic accessibility and its converse. Therefrom, accessibility relations for common and distributed belief and knowledge can be constructed in a standard way. As a result, we obtain a general definition of knowledge in terms of belief that enables us to view S5-knowledge as accurate (unbiased and thus true) KD45-belief, negation-complete belief and knowledge as exact KD45-belief and S5-knowledge, respectively, and perfect S5-knowledge as precise (exact and accurate) KD45-belief, and all this generically for arbitrary functions of bias and visibility. Our results can be seen as a semantic complement to previous foundational results by Halpern et al. about the (un)definability and (non-)reducibility of knowledge in terms of and to belief, respectively.
The aim of this paper is to develop a methodology that is useful for analysing from a microeconomic perspective the incentives to entry, permanence and exit in the market for pharmaceutical generics under fuzzy conditions. In an empirical application of our proposed methodology, the potential towards permanence of labs with different characteristics has been estimated. The case we deal with is set in an open market where global players diversify into different national markets of pharmaceutical generics. Risk issues are significantly important in deterring decision makers from expanding in the generic pharmaceutical business. However, not all players are affected in the same way and/or to the same extent. Small, non-diversified generics labs are in the worse position. We have highlighted that the expected NPV and the number of generics in the portfolio of a pharmaceutical lab are important variables, but that it is also important to consider the degree of diversification. Labs with a higher potential for diversification across markets have an advantage over smaller labs. We have described a fuzzy decision support system based on the Mamdani model in order to determine the incentives for a laboratory to remain in the market both when it is stable and when it is growing.
Cultural algorithm is a kind of evolutionary algorithm inspired from societal evolution and is composed of a belief space, a population space and a protocol that enables exchange of knowledge between these sources. Knowledge created in the population space is accepted into the belief space while this collective knowledge from these sources is combined to influence the decisions of the individual agents in solving problems. Classification rules comes under descriptive knowledge discovery in data mining and are the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to users. The rules are evaluated using these properties namely the rule metrics. In the current study a Cultural Algorithm Toolkit for Classification Rule Mining (CAT-CRM) is proposed which allows the user to control three different set of parameters namely the evolutionary parameters, the rule parameters as well as agent parameters and hence can be used for experimenting with an evolutionary system, a rule mining system or an agent based social system. Results of experiments conducted to observe the effect of different number and type of metrics on the performance of the algorithm on bench mark data sets is reported.
Efficient ontology debugging is a cornerstone for many activities in the context of the Semantic Web, especially when automatic tools produce (parts of) ontologies such as in the field of ontology matching. The best currently known interactive debugging systems rely upon some meta information in terms of fault probabilities, which can speed up the debugging procedure in the good case, but can also have negative impact on the performance in the bad case. The problem is that assessment of the meta information is only possible a-posteriori. Consequently, as long as the actual fault is unknown, there is always some risk of suboptimal interactive diagnoses discrimination. As an alternative, one might prefer to rely on a tool which pursues a no-risk strategy. In this case, however, possibly well-chosen meta information cannot be exploited, resulting again in inefficient debugging actions. In this work we present a reinforcement learning strategy that continuously adapts its behavior depending on the performance achieved and minimizes the risk of using low-quality meta information. Therefore, this method is suitable for application scenarios where reliable a-priori fault estimates are difficult to obtain. Using problematic ontologies in the field of ontology matching, we show that the proposed risk-aware query strategy outperforms both active learning approaches and no-risk strategies on average in terms of required amount of user interaction.
A central problem of surveillance is to monitor multiple targets moving in a large-scale, obstacle-ridden environment with occlusions. This paper presents a novel principled Partially Observable Markov Decision Process-based approach to coordinating and controlling a network of active cameras for tracking and observing multiple mobile targets at high resolution in such surveillance environments. Our proposed approach is capable of (a) maintaining a belief over the targets' states (i.e., locations, directions, and velocities) to track them, even when they may not be observed directly by the cameras at all times, (b) coordinating the cameras' actions to simultaneously improve the belief over the targets' states and maximize the expected number of targets observed with a guaranteed resolution, and (c) exploiting the inherent structure of our surveillance problem to improve its scalability (i.e., linear time) in the number of targets to be observed. Quantitative comparisons with state-of-the-art multi-camera coordination and control techniques show that our approach can achieve higher surveillance quality in real time. The practical feasibility of our approach is also demonstrated using real AXIS 214 PTZ cameras
Covering is a common type of data structure and covering-based rough set theory is an efficient tool to process this data. Lattice is an important algebraic structure and used extensively in investigating some types of generalized rough sets. In this paper, we propose two family of sets and study the conditions that these two sets become some lattice structures. These two sets are consisted by the fixed point of the lower approximations of the first type and the sixth type of covering-based rough sets, respectively. These two sets are called the fixed point set of neighborhoods and the fixed point set of covering, respectively. First, for any covering, the fixed point set of neighborhoods is a complete and distributive lattice, at the same time, it is also a double p-algebra. Especially, when the neighborhood forms a partition of the universe, the fixed point set of neighborhoods is both a boolean lattice and a double Stone algebra. Second, for any covering, the fixed point set of covering is a complete lattice.When the covering is unary, the fixed point set of covering becomes a distributive lattice and a double p-algebra. a distributive lattice and a double p-algebra when the covering is unary. Especially, when the reduction of the covering forms a partition of the universe, the fixed point set of covering is both a boolean lattice and a double Stone algebra.
We design temporal description logics suitable for reasoning about temporal conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. In the temporal dimension, they capture future and past temporal operators on concepts, flexible and rigid roles, the operators `always' and `some time' on roles, data assertions for particular moments of time and global concept inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,<), satisfying the constant domain assumption. We prove that the most expressive of our temporal description logics (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turn out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions we obtain logics whose complexity ranges between PSpace and NLogSpace. These positive results were obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models.
Taaable is a case-based reasoning system that adapts cooking recipes to user constraints. Within it, the preparation part of recipes is formalised as a graph. This graph is a semantic representation of the sequence of instructions composing the cooking process and is used to compute the procedure adaptation, conjointly with the textual adaptation. It is composed of cooking actions and ingredients, among others, represented as vertices, and semantic relations between those, shown as arcs, and is built automatically thanks to natural language processing. The results of the automatic annotation process is often a disconnected graph, representing an incomplete annotation, or may contain errors. Therefore, a validating and correcting step is required. In this paper, we present an existing graphic tool named \kcatos, conceived for representing and editing decision trees, and show how it has been adapted and integrated in WikiTaaable, the semantic wiki in which the knowledge used by Taaable is stored. This interface provides the wiki users with a way to correct the case representation of the cooking process, improving at the same time the quality of the knowledge about cooking procedures stored in WikiTaaable.
The measurement error with normal distribution is universal in applications. Generally, smaller measurement error requires better instrument and higher test cost. In decision making based on attribute values of objects, we shall select an attribute subset with appropriate measurement error to minimize the total test cost. Recently, error-range-based covering rough set with uniform distribution error was proposed to investigate this issue. However, the measurement errors satisfy normal distribution instead of uniform distribution which is rather simple for most applications. In this paper, we introduce normal distribution measurement errors to covering-based rough set model, and deal with test-cost-sensitive attribute reduction problem in this new model. The major contributions of this paper are four-fold. First, we build a new data model based on normal distribution measurement errors. With the new data model, the error range is an ellipse in a two-dimension space. Second, the covering-based rough set with normal distribution measurement errors is constructed through the "3-sigma" rule. Third, the test-cost-sensitive attribute reduction problem is redefined on this covering-based rough set. Fourth, a heuristic algorithm is proposed to deal with this problem. The algorithm is tested on ten UCI (University of California - Irvine) datasets. The experimental results show that the algorithm is more effective and efficient than the existing one. This study is a step toward realistic applications of cost-sensitive learning.
A fundamental question in systems biology is the construction and training to data of mathematical models. Logic formalisms have become very popular to model signaling networks because their simplicity allows us to model large systems encompassing hundreds of proteins. An approach to train (Boolean) logic models to high-throughput phospho-proteomics data was recently introduced and solved using optimization heuristics based on stochastic methods. Here we demonstrate how this problem can be solved using Answer Set Programming (ASP), a declarative problem solving paradigm, in which a problem is encoded as a logical program such that its answer sets represent solutions to the problem. ASP has significant improvements over heuristic methods in terms of efficiency and scalability, it guarantees global optimality of solutions as well as provides a complete set of solutions. We illustrate the application of ASP with in silico cases based on realistic networks and data.
The notion of meta-mining has appeared recently and extends the traditional meta-learning in two ways. First it does not learn meta-models that provide support only for the learning algorithm selection task but ones that support the whole data-mining process. In addition it abandons the so called black-box approach to algorithm description followed in meta-learning. Now in addition to the datasets, algorithms also have descriptors, workflows as well. For the latter two these descriptions are semantic, describing properties of the algorithms. With the availability of descriptors both for datasets and data mining workflows the traditional modelling techniques followed in meta-learning, typically based on classification and regression algorithms, are no longer appropriate. Instead we are faced with a problem the nature of which is much more similar to the problems that appear in recommendation systems. The most important meta-mining requirements are that suggestions should use only datasets and workflows descriptors and the cold-start problem, e.g. providing workflow suggestions for new datasets. In this paper we take a different view on the meta-mining modelling problem and treat it as a recommender problem. In order to account for the meta-mining specificities we derive a novel metric-based-learning recommender approach. Our method learns two homogeneous metrics, one in the dataset and one in the workflow space, and a heterogeneous one in the dataset-workflow space. All learned metrics reflect similarities established from the dataset-workflow preference matrix. We demonstrate our method on meta-mining over biological (microarray datasets) problems. The application of our method is not limited to the meta-mining problem, its formulations is general enough so that it can be applied on problems with similar requirements.
Answer Set Programming (ASP) is a well-known problem solving approach based on nonmonotonic logic programs and efficient solvers. To enable access to external information, HEX-programs extend programs with external atoms, which allow for a bidirectional communication between the logic program and external sources of computation (e.g., description logic reasoners and Web resources). Current solvers evaluate HEX-programs by a translation to ASP itself, in which values of external atoms are guessed and verified after the ordinary answer set computation. This elegant approach does not scale with the number of external accesses in general, in particular in presence of nondeterminism (which is instrumental for ASP). In this paper, we present a novel, native algorithm for evaluating HEX-programs which uses learning techniques. In particular, we extend conflict-driven ASP solving techniques, which prevent the solver from running into the same conflict again, from ordinary to HEX-programs. We show how to gain additional knowledge from external source evaluations and how to use it in a conflict-driven algorithm. We first target the uninformed case, i.e., when we have no extra information on external sources, and then extend our approach to the case where additional meta-information is available. Experiments show that learning from external sources can significantly decrease both the runtime and the number of considered candidate compatible sets.
Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors can affect posterior distributions through Bayes' rule, imposing posterior regularization is arguably more direct and in some cases more natural and general. In this paper, we present regularized Bayesian inference (RegBayes), a novel computational framework that performs posterior inference with a regularization term on the desired post-data posterior distribution under an information theoretical formulation. RegBayes is more flexible than the procedure that elicits expert knowledge via priors, and it covers both directed Bayesian networks and undirected Markov networks whose Bayesian formulation results in hybrid chain graph models. When the regularization is induced from a linear operator on the posterior distributions, such as the expectation operator, we present a general convex-analysis theorem to characterize the solution of RegBayes. Furthermore, we present two concrete examples of RegBayes, infinite latent support vector machines (iLSVM) and multi-task infinite latent support vector machines (MT-iLSVM), which explore the large-margin idea in combination with a nonparametric Bayesian model for discovering predictive latent features for classification and multi-task learning, respectively. We present efficient inference methods and report empirical studies on several benchmark datasets, which appear to demonstrate the merits inherited from both large-margin learning and Bayesian nonparametrics. Such results were not available until now, and contribute to push forward the interface between these two important subfields, which have been largely treated as isolated in the community.
The fundamental, powerful process of computation in the brain has been widely misunderstood. The paper [1] associates the general failure to build intelligent thinking machines with current reductionist principles of temporal coding and advocates for a change in paradigm regarding the brain analogy. Since fragments of information are stored in proteins which can shift between several structures to perform their function, the biological substrate is actively involved in physical computation. The intrinsic nonlinear dynamics of action potentials and synaptic activities maintain physical interactions within and between neurons in the brain. During these events the required information is exchanged between molecular structures (proteins) which store fragments of information and the generated electric flux which carries and integrates information in the brain. The entire process of physical interaction explains how the brain actively creates or experiences meaning. This process of interaction during an action potential generation can be simply seen as the moment when the neuron solves a many-body problem. A neuroelectrodynamic theory shows that the neuron solves equations rather than exclusively computes functions. With the main focus on temporal patterns, the spike timing dogma (STD) has neglected important forms of computation which do occur inside neurons. In addition, artificial neural models have missed the most important part since the real super-computing power of the brain has its origins in computations that occur within neurons.
Android and Facebook provide third-party applications with access to users' private data and the ability to perform potentially sensitive operations (e.g., post to a user's wall or place phone calls). As a security measure, these platforms restrict applications' privileges with permission systems: users must approve the permissions requested by applications before the applications can make privacy- or security-relevant API calls. However, recent studies have shown that users often do not understand permission requests and lack a notion of typicality of requests. As a first step towards simplifying permission systems, we cluster a corpus of 188,389 Android applications and 27,029 Facebook applications to find patterns in permission requests. Using a method for Boolean matrix factorization for finding overlapping clusters, we find that Facebook permission requests follow a clear structure that exhibits high stability when fitted with only five clusters, whereas Android applications demonstrate more complex permission requests. We also find that low-reputation applications often deviate from the permission request patterns that we identified for high-reputation applications suggesting that permission request patterns are indicative for user satisfaction or application quality.
Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping. It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and then update the clustering model. By alternating the learning process between views, this approach produces a unified clustering model that is consistent with all views. We show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views. Our evaluation reveals that the propagated constraints have high precision with respect to the true clusters in the data, explaining their benefit to clustering performance in both single- and multi-view learning scenarios.
In social networks, information and influence diffuse among users as cascades. While the importance of studying cascades has been recognized in various applications, it is difficult to observe the complete structure of cascades in practice. Moreover, much less is known on how to infer cascades based on partial observations. In this paper we study the cascade inference problem following the independent cascade model, and provide a full treatment from complexity to algorithms: (a) We propose the idea of consistent trees as the inferred structures for cascades; these trees connect source nodes and observed nodes with paths satisfying the constraints from the observed temporal information. (b) We introduce metrics to measure the likelihood of consistent trees as inferred cascades, as well as several optimization problems for finding them. (c) We show that the decision problems for consistent trees are in general NP-complete, and that the optimization problems are hard to approximate. (d) We provide approximation algorithms with performance guarantees on the quality of the inferred cascades, as well as heuristics. We experimentally verify the efficiency and effectiveness of our inference algorithms, using real and synthetic data.
Quick Summary is an innovate implementation of an automatic document summarizer that inputs a document in the English language and evaluates each sentence. The scanner or evaluator determines criteria based on its grammatical structure and place in the paragraph. The program then asks the user to specify the number of sentences the person wishes to highlight. For example should the user ask to have three of the most important sentences, it would highlight the first and most important sentence in green. Commonly this is the sentence containing the conclusion. Then Quick Summary finds the second most important sentence usually called a satellite and highlights it in yellow. This is usually the topic sentence. Then the program finds the third most important sentence and highlights it in red. The implementations of this technology are useful in a society of information overload when a person typically receives 42 emails a day (Microsoft). The paper also is a candid look at difficulty that machine learning has in textural translating. However, it speaks on how to overcome the obstacles that historically prevented progress. This paper proposes mathematical meta-data criteria that justify the place of importance of a sentence. Just as tools for the study of relational symmetry in bio-informatics, this tool seeks to classify words with greater clarity. "Survey Finds Workers Average Only Three Productive Days per Week." Microsoft News Center. Microsoft. Web. 31 Mar. 2012.
Congestion games model a wide variety of real-world resource congestion problems, such as selfish network routing, traffic route guidance in congested areas, taxi fleet optimization and crowd movement in busy areas. However, existing research in congestion games assumes: (a) deterministic movement of agents between resources; and (b) perfect rationality (i.e. maximizing their own expected value) of all agents. Such assumptions are not reasonable in dynamic domains where decision support has to be provided to humans. For instance, in optimizing the performance of a taxi fleet serving a city, movement of taxis can be involuntary or nondeterministic (decided by the specific customer who hires the taxi) and more importantly, taxi drivers may not follow advice provided by the decision support system (due to bounded rationality of humans). To that end, we contribute: (a) a general framework for representing congestion games under uncertainty for populations with assorted notions of rationality. (b) a scalable approach for solving the decision problem for perfectly rational agents which are in the mix with boundedly rational agents; and (c) a detailed evaluation on a synthetic and realworld data set to illustrate the usefulness of our new approach with respect to key social welfare metrics in the context of an assorted human-agent population. An interesting result from our experiments on a real-world taxi fleet optimization problem is that it is better (in terms of revenue and operational efficiency) for taxi drivers to follow perfectly rational strategies irrespective of the percentage of drivers not following the advice.
A determinantal point process (DPP) is a random process useful for modeling the combinatorial problem of subset selection. In particular, DPPs encourage a random subset Y to contain a diverse set of items selected from a base set Y. For example, we might use a DPP to display a set of news headlines that are relevant to a user's interests while covering a variety of topics. Suppose, however, that we are asked to sequentially select multiple diverse sets of items, for example, displaying new headlines day-by-day. We might want these sets to be diverse not just individually but also through time, offering headlines today that are unlike the ones shown yesterday. In this paper, we construct a Markov DPP (M-DPP) that models a sequence of random sets {Yt}. The proposed M-DPP defines a stationary process that maintains DPP margins. Crucially, the induced union process Zt = Yt u Yt-1 is also marginally DPP-distributed. Jointly, these properties imply that the sequence of random sets are encouraged to be diverse both at a given time step as well as across time steps. We describe an exact, efficient sampling procedure, and a method for incrementally learning a quality measure over items in the base set Y based on external preferences. We apply the M-DPP to the task of sequentially displaying diverse and relevant news articles to a user with topic preferences.
In spectral clustering, one defines a similarity matrix for a collection of data points, transforms the matrix to get the Laplacian matrix, finds the eigenvectors of the Laplacian matrix, and obtains a partition of the data using the leading eigenvectors. The last step is sometimes referred to as rounding, where one needs to decide how many leading eigenvectors to use, to determine the number of clusters, and to partition the data points. In this paper, we propose a novel method for rounding. The method differs from previous methods in three ways. First, we relax the assumption that the number of clusters equals the number of eigenvectors used. Second, when deciding the number of leading eigenvectors to use, we not only rely on information contained in the leading eigenvectors themselves, but also use subsequent eigenvectors. Third, our method is model-based and solves all the three subproblems of rounding using a class of graphical models called latent tree models. We evaluate our method on both synthetic and real-world data. The results show that our method works correctly in the ideal case where between-clusters similarity is 0, and degrades gracefully as one moves away from the ideal case.
Latent variable models are used to estimate variables of interest quantities which are observable only up to some measurement error. In many studies, such variables are known but not precisely quantifiable (such as "job satisfaction" in social sciences and marketing, "analytical ability" in educational testing, or "inflation" in economics). This leads to the development of measurement instruments to record noisy indirect evidence for such unobserved variables such as surveys, tests and price indexes. In such problems, there are postulated latent variables and a given measurement model. At the same time, other unantecipated latent variables can add further unmeasured confounding to the observed variables. The problem is how to deal with unantecipated latents variables. In this paper, we provide a method loosely inspired by canonical correlation that makes use of background information concerning the "known" latent variables. Given a partially specified structure, it provides a structure learning approach to detect "unknown unknowns," the confounding effect of potentially infinitely many other latent variables. This is done without explicitly modeling such extra latent factors. Because of the special structure of the problem, we are able to exploit a new variation of composite likelihood fitting to efficiently learn this structure. Validation is provided with experiments in synthetic data and the analysis of a large survey done with a sample of over 100,000 staff members of the National Health Service of the United Kingdom.
Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since it allows to discover non-destructively spectral characteristics of plants governed primarily by scattering and absorption characteristics of the leaf internal structure and biochemical constituents. Several drought stress indices have been derived using hyper-spectral imaging. However, they are typically based on few hyper-spectral images only, rely on interpretations of experts, and consider few wavelengths only. In this study, we present the first data-driven approach to discovering spectral drought stress indices, treating it as an unsupervised labeling problem at massive scale. To make use of short range dependencies of spectral wavelengths, we develop an online variational Bayes algorithm for latent Dirichlet allocation with convolved Dirichlet regularizer. This approach scales to massive datasets and, hence, provides a more objective complement to plant physiological practices. The spectral topics found conform to plant physiological knowledge and can be computed in a fraction of the time compared to existing LDA approaches.
In time series analysis research there is a strong interest in discrete representations of real valued data streams. One approach that emerged over a decade ago and is still considered state-of-the-art is the Symbolic Aggregate Approximation algorithm. This discretization algorithm was the first symbolic approach that mapped a real-valued time series to a symbolic representation that was guaranteed to lower-bound Euclidean distance. The interest of this paper concerns the SAX assumption of data being highly Gaussian and the use of the standard normal curve to choose partitions to discretize the data. Though not necessarily, but generally, and certainly in its canonical form, the SAX approach chooses partitions on the standard normal curve that would produce an equal probability for each symbol in a finite alphabet to occur. This procedure is generally valid as a time series is normalized before the rest of the SAX algorithm is applied. However there exists a caveat to this assumption of equi-probability due to the intermediate step of Piecewise Aggregate Approximation (PAA). What we will show in this paper is that when PAA is applied the distribution of the data is indeed altered, resulting in a shrinking standard deviation that is proportional to the number of points used to create a segment of the PAA representation and the degree of auto-correlation within the series. Data that exhibits statistically significant auto-correlation is less affected by this shrinking distribution. As the standard deviation of the data contracts, the mean remains the same, however the distribution is no longer standard normal and therefore the partitions based on the standard normal curve are no longer valid for the assumption of equal probability.
Our broader goal is to automatically translate English sentences into formulas in appropriate knowledge representation languages as a step towards understanding and thus answering questions with respect to English text. Our focus in this paper is on the language of Answer Set Programming (ASP). Our approach to translate sentences to ASP rules is inspired by Montague's use of lambda calculus formulas as meaning of words and phrases. With ASP as the target language the meaning of words and phrases are ASP-lambda formulas. In an earlier work we illustrated our approach by manually developing a dictionary of words and their ASP-lambda formulas. However such an approach is not scalable. In this paper our focus is on two algorithms that allow one to construct ASP-lambda formulas in an inverse manner. In particular the two algorithms take as input two lambda-calculus expressions G and H and compute a lambda-calculus expression F such that F with input as G, denoted by F@G, is equal to H; and similarly G@F = H. We present correctness and complexity results about these algorithms. To do that we develop the notion of typed ASP-lambda calculus theories and their orders and use it in developing the completeness results. (To appear in Theory and Practice of Logic Programming.)
In the family of Learning Classifier Systems, the classifier system XCS has been successfully used for many applications. However, the standard XCS has no memory mechanism and can only learn optimal policy in Markov environments, where the optimal action is determined solely by the state of current sensory input. In practice, most environments are partially observable environments on agent's sensation, which are also known as non-Markov environments. Within these environments, XCS either fails, or only develops a suboptimal policy, since it has no memory. In this work, we develop a new classifier system based on XCS to tackle this problem. It adds an internal message list to XCS as the memory list to record input sensation history, and extends a small number of classifiers with memory conditions. The classifier's memory condition, as a foothold to disambiguate non-Markov states, is used to sense a specified element in the memory list. Besides, a detection method is employed to recognize non-Markov states in environments, to avoid these states controlling over classifiers' memory conditions. Furthermore, four sets of different complex maze environments have been tested by the proposed method. Experimental results show that our system is one of the best techniques to solve partially observable environments, compared with some well-known classifier systems proposed for these environments.
The propositional planning problem is a notoriously difficult computational problem. Downey et al. (1999) initiated the parameterized analysis of planning (with plan length as the parameter) and B\"ackstr\"om et al. (2012) picked up this line of research and provided an extensive parameterized analysis under various restrictions, leaving open only one stubborn case. We continue this work and provide a full classification. In particular, we show that the case when actions have no preconditions and at most $e$ postconditions is fixed-parameter tractable if $e\leq 2$ and W[1]-complete otherwise. We show fixed-parameter tractability by a reduction to a variant of the Steiner Tree problem; this problem has been shown fixed-parameter tractable by Guo et al. (2007). If a problem is fixed-parameter tractable, then it admits a polynomial-time self-reduction to instances whose input size is bounded by a function of the parameter, called the kernel. For some problems, this function is even polynomial which has desirable computational implications. Recent research in parameterized complexity has focused on classifying fixed-parameter tractable problems on whether they admit polynomial kernels or not. We revisit all the previously obtained restrictions of planning that are fixed-parameter tractable and show that none of them admits a polynomial kernel unless the polynomial hierarchy collapses to its third level.
The systematic biases seen in people's probability judgments are typically taken as evidence that people do not reason about probability using the rules of probability theory, but instead use heuristics which sometimes yield reasonable judgments and sometimes systematic biases. This view has had a major impact in economics, law, medicine, and other fields; indeed, the idea that people cannot reason with probabilities has become a widespread truism. We present a simple alternative to this view, where people reason about probability according to probability theory but are subject to random variation or noise in the reasoning process. In this account the effect of noise is cancelled for some probabilistic expressions: analysing data from two experiments we find that, for these expressions, people's probability judgments are strikingly close to those required by probability theory. For other expressions this account produces systematic deviations in probability estimates. These deviations explain four reliable biases in human probabilistic reasoning (conservatism, subadditivity, conjunction and disjunction fallacies). These results suggest that people's probability judgments embody the rules of probability theory, and that biases in those judgments are due to the effects of random noise.
Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm's runtime as a function of problem-specific instance features. Such models have important applications to algorithm analysis, portfolio-based algorithm selection, and the automatic configuration of parameterized algorithms. Over the past decade, a wide variety of techniques have been studied for building such models. Here, we describe extensions and improvements of existing models, new families of models, and -- perhaps most importantly -- a much more thorough treatment of algorithm parameters as model inputs. We also comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) problems. We evaluate these innovations through the largest empirical analysis of its kind, comparing to a wide range of runtime modelling techniques from the literature. Our experiments consider 11 algorithms and 35 instance distributions; they also span a very wide range of SAT, MIP, and TSP instances, with the least structured having been generated uniformly at random and the most structured having emerged from real industrial applications. Overall, we demonstrate that our new models yield substantially better runtime predictions than previous approaches in terms of their generalization to new problem instances, to new algorithms from a parameterized space, and to both simultaneously.
We introduce a new model of membership query (MQ) learning, where the learning algorithm is restricted to query points that are \emph{close} to random examples drawn from the underlying distribution. The learning model is intermediate between the PAC model (Valiant, 1984) and the PAC+MQ model (where the queries are allowed to be arbitrary points). Membership query algorithms are not popular among machine learning practitioners. Apart from the obvious difficulty of adaptively querying labelers, it has also been observed that querying \emph{unnatural} points leads to increased noise from human labelers (Lang and Baum, 1992). This motivates our study of learning algorithms that make queries that are close to examples generated from the data distribution. We restrict our attention to functions defined on the $n$-dimensional Boolean hypercube and say that a membership query is local if its Hamming distance from some example in the (random) training data is at most $O(\log(n))$. We show the following results in this model: (i) The class of sparse polynomials (with coefficients in R) over $\{0,1\}^n$ is polynomial time learnable under a large class of \emph{locally smooth} distributions using $O(\log(n))$-local queries. This class also includes the class of $O(\log(n))$-depth decision trees. (ii) The class of polynomial-sized decision trees is polynomial time learnable under product distributions using $O(\log(n))$-local queries. (iii) The class of polynomial size DNF formulas is learnable under the uniform distribution using $O(\log(n))$-local queries in time $n^{O(\log(\log(n)))}$. (iv) In addition we prove a number of results relating the proposed model to the traditional PAC model and the PAC+MQ model.
In this paper secured wireless communication using fuzzy logic based high speed public key cryptography (FLHSPKC) has been proposed by satisfying the major issues likes computational safety, power management and restricted usage of memory in wireless communication. Wireless Sensor Network (WSN) has several major constraints likes inadequate source of energy, restricted computational potentiality and limited memory. Though conventional Elliptic Curve Cryptography (ECC) which is a sort of public key cryptography used in wireless communication provides equivalent level of security like other existing public key algorithm using smaller parameters than other but this traditional ECC does not take care of all these major limitations in WSN. In conventional ECC consider Elliptic curve point p, an arbitrary integer k and modulus m, ECC carry out scalar multiplication kP mod m, which takes about 80% of key computation time on WSN. In this paper proposed FLHSPKC scheme provides some novel strategy including novel soft computing based strategy to speed up scalar multiplication in conventional ECC and which in turn takes shorter computational time and also satisfies power consumption restraint, limited usage of memory without hampering the security level. Performance analysis of the different strategies under FLHSPKC scheme and comparison study with existing conventional ECC methods has been done.
The paper addresses the modular design of composite solving strategies for multicriteria ranking (sorting). Here a 'scale of creativity' that is close to creative levels proposed by Altshuller is used as the reference viewpoint: (i) a basic object, (ii) a selected object, (iii) a modified object, and (iv) a designed object (e.g., composition of object components). These levels maybe used in various parts of decision support systems (DSS) (e.g., information, operations, user). The paper focuses on the more creative above-mentioned level (i.e., composition or combinatorial synthesis) for the operational part (i.e., composite solving strategy). This is important for a search/exploration mode of decision making process with usage of various procedures and techniques and analysis/integration of obtained results. The paper describes methodological issues of decision technology and synthesis of composite strategy for multicriteria ranking. The synthesis of composite strategies is based on 'hierarchical morphological multicriteria design' (HMMD) which is based on selection and combination of design alternatives (DAs) (here: local procedures or techniques) while taking into account their quality and quality of their interconnections (IC). A new version of HMMD with interval multiset estimates for DAs is used. The operational environment of DSS COMBI for multicriteria ranking, consisting of a morphology of local procedures or techniques (as design alternatives DAs), is examined as a basic one.
We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to $k$ sites, and the sites are required to communicate with each other via the coordinator. At each time-step $t$, one of the $k$ site nodes has to pick an expert from the set ${1, ..., n}$, and the same site receives information about payoffs of all experts for that round. The goal of the distributed system is to minimize regret at time horizon $T$, while simultaneously keeping communication to a minimum. The two extreme solutions to this problem are: (i) Full communication: This essentially simulates the non-distributed setting to obtain the optimal $O(\sqrt{\log(n)T})$ regret bound at the cost of $T$ communication. (ii) No communication: Each site runs an independent copy : the regret is $O(\sqrt{log(n)kT})$ and the communication is 0. This paper shows the difficulty of simultaneously achieving regret asymptotically better than $\sqrt{kT}$ and communication better than $T$. We give a novel algorithm that for an oblivious adversary achieves a non-trivial trade-off: regret $O(\sqrt{k^{5(1+\epsilon)/6} T})$ and communication $O(T/k^{\epsilon})$, for any value of $\epsilon \in (0, 1/5)$. We also consider a variant of the model, where the coordinator picks the expert. In this model, we show that the label-efficient forecaster of Cesa-Bianchi et al. (2005) already gives us strategy that is near optimal in regret vs communication trade-off.
Optimization problems associated with the interaction of linked particles are at the heart of polymer science, protein folding and other important problems in the physical sciences. In this review we explain how to recast these problems as constraint satisfaction problems such as linear programming, maximum satisfiability, and pseudo-boolean optimization. By encoding problems this way, one can leverage substantial insight and powerful solvers from the computer science community which studies constraint programming for diverse applications such as logistics, scheduling, artificial intelligence, and circuit design. We demonstrate how to constrain and embed lattice heteropolymer problems using several strategies. Each strikes a unique balance between number of constraints, complexity of constraints, and number of variables. Finally, we show how to reduce the locality of couplings in these energy functions so they can be realized as Hamiltonians on existing quantum annealing machines. We intend that this review be used as a case study for encoding related combinatorial optimization problems in a form suitable for adiabatic quantum optimization.
Information-Geometric Optimization (IGO) is a unified framework of stochastic algorithms for optimization problems. Given a family of probability distributions, IGO turns the original optimization problem into a new maximization problem on the parameter space of the probability distributions. IGO updates the parameter of the probability distribution along the natural gradient, taken with respect to the Fisher metric on the parameter manifold, aiming at maximizing an adaptive transform of the objective function. IGO recovers several known algorithms as particular instances: for the family of Bernoulli distributions IGO recovers PBIL, for the family of Gaussian distributions the pure rank-mu CMA-ES update is recovered, and for exponential families in expectation parametrization the cross-entropy/ML method is recovered. This article provides a theoretical justification for the IGO framework, by proving that any step size not greater than 1 guarantees monotone improvement over the course of optimization, in terms of q-quantile values of the objective function f. The range of admissible step sizes is independent of f and its domain. We extend the result to cover the case of different step sizes for blocks of the parameters in the IGO algorithm. Moreover, we prove that expected fitness improves over time when fitness-proportional selection is applied, in which case the RPP algorithm is recovered.
Given the limited performance of 2D cellular automata in terms of space when the number of documents increases and in terms of visualization clusters, our motivation was to experiment these cellular automata by increasing the size to view the impact of size on quality of results. The representation of textual data was carried out by a vector model whose components are derived from the overall balancing of the used corpus, Term Frequency Inverse Document Frequency (TF-IDF). The WorldNet thesaurus has been used to address the problem of the lemmatization of the words because the representation used in this study is that of the bags of words. Another independent method of the language was used to represent textual records is that of the n-grams. Several measures of similarity have been tested. To validate the classification we have used two measures of assessment based on the recall and precision (f-measure and entropy). The results are promising and confirm the idea to increase the dimension to the problem of the spatiality of the classes. The results obtained in terms of purity class (i.e. the minimum value of entropy) shows that the number of documents over longer believes the results are better for 3D cellular automata, which was not obvious to the 2D dimension. In terms of spatial navigation, cellular automata provide very good 3D performance visualization than 2D cellular automata.
This paper introduces TwitterPaul, a system designed to make use of Social Media data to help to predict game outcomes for the 2010 FIFA World Cup tournament. To this end, we extracted over 538K mentions to football games from a large sample of tweets that occurred during the World Cup, and we classified into different types with a precision of up to 88%. The different mentions were aggregated in order to make predictions about the outcomes of the actual games. We attempt to learn which Twitter users are accurate predictors and explore several techniques in order to exploit this information to make more accurate predictions. We compare our results to strong baselines and against the betting line (prediction market) and found that the quality of extractions is more important than the quantity, suggesting that high precision methods working on a medium-sized dataset are preferable over low precision methods that use a larger amount of data. Finally, by aggregating some classes of predictions, the system performance is close to the one of the betting line. Furthermore, we believe that this domain independent framework can help to predict other sports, elections, product release dates and other future events that people talk about in social media.
Smartphone technology is more and more becoming the predominant communication tool for people across the world. People use their smartphones to keep their contact data, to browse the internet, to exchange messages, to keep notes, carry their personal files and documents, etc. Users while browsing are also capable of shopping online, thus provoking a need to type their credit card numbers and security codes. As the smartphones are becoming widespread so do the security threats and vulnerabilities facing this technology. Recent news and articles indicate huge increase in malware and viruses for operating systems employed on smartphones (primarily Android and iOS). Major limitations of smartphone technology are its processing power and its scarce energy source since smartphones rely on battery usage. Since smartphones are devices which change their network location as the user moves between different places, intrusion detection systems for smartphone technology are most often classified as IDSs designed for mobile ad-hoc networks. The aim of this research is to give a brief overview of IDS technology, give an overview of major machine learning and pattern recognition algorithms used in IDS technologies, give an overview of security models of iOS and Android and propose a new host-based IDS model for smartphones and create proof-of-concept application for Android platform for the newly proposed model. Keywords: IDS, SVM, Android, iOS;
Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs). In this paper, we extend the research over BS switching operations, which should match up with traffic load variations. Instead of depending on the dynamic traffic loads which are still quite challenging to precisely forecast, we firstly formulate the traffic variations as a Markov decision process. Afterwards, in order to foresightedly minimize the energy consumption of RANs, we design a reinforcement learning framework based BS switching operation scheme. Furthermore, to avoid the underlying curse of dimensionality in reinforcement learning, a transfer actor-critic algorithm (TACT), which utilizes the transferred learning expertise in historical periods or neighboring regions, is proposed and provably converges. In the end, we evaluate our proposed scheme by extensive simulations under various practical configurations and show that the proposed TACT algorithm contributes to a performance jumpstart and demonstrates the feasibility of significant energy efficiency improvement at the expense of tolerable delay performance.
The considerable mathematical knowledge encoded by the Flyspeck project is combined with external automated theorem provers (ATPs) and machine-learning premise selection methods trained on the proofs, producing an AI system capable of answering a wide range of mathematical queries automatically. The performance of this architecture is evaluated in a bootstrapping scenario emulating the development of Flyspeck from axioms to the last theorem, each time using only the previous theorems and proofs. It is shown that 39% of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation. The necessary work involves: (i) an implementation of sound translations of the HOL Light logic to ATP formalisms: untyped first-order, polymorphic typed first-order, and typed higher-order, (ii) export of the dependency information from HOL Light and ATP proofs for the machine learners, and (iii) choice of suitable representations and methods for learning from previous proofs, and their integration as advisors with HOL Light. This work is described and discussed here, and an initial analysis of the body of proofs that were found fully automatically is provided.
In game theory, an Evolutionarily Stable Set (ES set) is a set of Nash Equilibrium (NE) strategies that give the same payoffs. Similar to an Evolutionarily Stable Strategy (ES strategy), an ES set is also a strict NE. This work investigates the evolutionary stability of classical and quantum strategies in the quantum penny flip games. In particular, we developed an evolutionary game theory model to conduct a series of simulations where a population of mixed classical strategies from the ES set of the game were invaded by quantum strategies. We found that when only one of the two players' mixed classical strategies were invaded, the results were different. In one case, due to the interference phenomenon of superposition, quantum strategies provided more payoff, hence successfully replaced the mixed classical strategies in the ES set. In the other case, the mixed classical strategies were able to sustain the invasion of quantum strategies and remained in the ES set. Moreover, when both players' mixed classical strategies were invaded by quantum strategies, a new quantum ES set emerged. The strategies in the quantum ES set give both players payoff 0, which is the same as the payoff of the strategies in the mixed classical ES set of this game.
Training a Support Vector Machine (SVM) requires the solution of a quadratic programming problem (QP) whose computational complexity becomes prohibitively expensive for large scale datasets. Traditional optimization methods cannot be directly applied in these cases, mainly due to memory restrictions. By adopting a slightly different objective function and under mild conditions on the kernel used within the model, efficient algorithms to train SVMs have been devised under the name of Core Vector Machines (CVMs). This framework exploits the equivalence of the resulting learning problem with the task of building a Minimal Enclosing Ball (MEB) problem in a feature space, where data is implicitly embedded by a kernel function. In this paper, we improve on the CVM approach by proposing two novel methods to build SVMs based on the Frank-Wolfe algorithm, recently revisited as a fast method to approximate the solution of a MEB problem. In contrast to CVMs, our algorithms do not require to compute the solutions of a sequence of increasingly complex QPs and are defined by using only analytic optimization steps. Experiments on a large collection of datasets show that our methods scale better than CVMs in most cases, sometimes at the price of a slightly lower accuracy. As CVMs, the proposed methods can be easily extended to machine learning problems other than binary classification. However, effective classifiers are also obtained using kernels which do not satisfy the condition required by CVMs and can thus be used for a wider set of problems.
For effective autonomous navigation,estimation of the pose of the robot is essential at every sampling time. For computing an accurate estimation,odometric error needs to be reduced with the help of data from external sensor. In this work, a technique has been developed for accurate pose estimation of mobile robot by using Laser Range data. The technique is robust to noisy data, which may contain considerable amount of outliers. A grey image is formed from laser range data and the key points from this image are extracted by Harris corner detector. The matching of the key points from consecutive data sets have been done while outliers have been rejected by RANSAC method. Robot state is measured by the correspondence between the two sets of keypoints. Finally, optimal robot state is estimated by Extended Kalman Filter. The technique has been applied to an operational robot in the laboratory environment to show the robustness of the technique in presence of noisy sensor data. The performance of this new technique has been compared with that of conventional ICP method. Through this method, effective and accurate navigation has been achieved even in presence of substantial noise in the sensor data at the cost of a small amount of additional computational complexity.
During years 2008 to 2011 author gives several courses on Foundations of Scientific Research at Computer Science Faculty of the National Aviation University in Kiev. This text presents material to lectures of the courses. It consists of 18 sections and some ideas of the manual can be seen from their titles. These include: General notions about scientific research. Ontologies and upper ontologies. Ontologies of object domains. Examples of Research Activity. Some Notions of the Theory of Finite and Discrete Sets. Algebraic Operations and Algebraic Structures. Elements of the Theory of Graphs and Nets. Scientific activity on the example of Information and its investigation. Scientific research in Artificial Intelligence. Compilers and compilation. Objective, Concepts and History of Computer security. Methodological and categorical apparatus of scientific research. Methodology and methods of scientific research. Scientific idea and significance of scientific research. Forms of scientific knowledge organization and principles of scientific research. Theoretical study, applied study and creativity. Types of scientific research: theoretical study, applied study. Types of scientific research: forms of representation of material. Some sections of the text contain enough material to lectures, but in some cases these are sketchs without references to Foundations of Research Activities. Really this is the first version of the manual and author plans to edit, modify and extend the version. Some reasons impose the author to post it as e-print. . Author compiled material from many sources and hope that it gives various points of view on Foundations of Research Activities.
The monotone duality problem is defined as follows: Given two monotone formulas f and g in iredundant DNF, decide whether f and g are dual. This problem is the same as duality testing for hypergraphs, that is, checking whether a hypergraph H consists of precisely all minimal transversals of a simple hypergraph G. By exploiting a recent problem-decomposition method by Boros and Makino (ICALP 2009), we show that duality testing for hypergraphs, and thus for monotone DNFs, is feasible in DSPACE[log^2 n], i.e., in quadratic logspace. As the monotone duality problem is equivalent to a number of problems in the areas of databases, data mining, and knowledge discovery, the results presented here yield new complexity results for those problems, too. For example, it follows from our results that whenever for a Boolean-valued relation (whose attributes represent items), a number of maximal frequent itemsets and a number of minimal infrequent itemsets are known, then it can be decided in quadratic logspace whether there exist additional frequent or infrequent itemsets.
Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is able to capture both local and global structure similarity information is proposed for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segments from the time series as words. The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series. Although the temporal order of the local segments is ignored, the bag-of-words representation is able to capture high-level structural information because both local and global structural information are well utilized. The performance of the bag-of-words model is validated on three datasets extracted from real EEG and ECG signals. The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag-of-words model such as local segment length and codebook size, but also robust to noise.
Tree projections provide a mathematical framework that encompasses all the various (purely) structural decomposition methods that have been proposed in the literature to single out classes of nearly-acyclic (hyper)graphs, such as the tree decomposition method, which is the most powerful decomposition method on graphs, and the (generalized) hypertree decomposition method, which is its natural counterpart on arbitrary hypergraphs. The paper analyzes this framework, by focusing in particular on "minimal" tree projections, that is, on tree projections without useless redundancies. First, it is shown that minimal tree projections enjoy a number of properties that are usually required for normal form decompositions in various structural decomposition methods. In particular, they enjoy the same kind of connection properties as (minimal) tree decompositions of graphs, with the result being tight in the light of the negative answer that is provided to the open question about whether they enjoy a slightly stronger notion of connection property, defined to speed-up the computation of hypertree decompositions. Second, it is shown that tree projections admit a natural game-theoretic characterization in terms of the Captain and Robber game. In this game, as for the Robber and Cops game characterizing tree decompositions, the existence of winning strategies implies the existence of monotone ones. As a special case, the Captain and Robber game can be used to characterize the generalized hypertree decomposition method, where such a game-theoretic characterization was missing and asked for. Besides their theoretical interest, these results have immediate algorithmic applications both for the general setting and for structural decomposition methods that can be recast in terms of tree projections.
We exploit the redundancy and volume of information on the web to build a computerized player for the ABC TV game show 'Who Wants To Be A Millionaire?' The player consists of a question-answering module and a decision-making module. The question-answering module utilizes question transformation techniques, natural language parsing, multiple information retrieval algorithms, and multiple search engines; results are combined in the spirit of ensemble learning using an adaptive weighting scheme. Empirically, the system correctly answers about 75% of questions from the Millionaire CD-ROM, 3rd edition - general-interest trivia questions often about popular culture and common knowledge. The decision-making module chooses from allowable actions in the game in order to maximize expected risk-adjusted winnings, where the estimated probability of answering correctly is a function of past performance and confidence in in correctly answering the current question. When given a six question head start (i.e., when starting from the $2,000 level), we find that the system performs about as well on average as humans starting at the beginning. Our system demonstrates the potential of simple but well-chosen techniques for mining answers from unstructured information such as the web.
Collaborative filtering is a very useful general technique for exploiting the preference patterns of a group of users to predict the utility of items to a particular user. Previous research has studied several probabilistic graphic models for collaborative filtering with promising results. However, while these models have succeeded in capturing the similarity among users and items in one way or the other, none of them has considered the fact that users with similar interests in items can have very different rating patterns; some users tend to assign a higher rating to all items than other users. In this paper, we propose and study of two new graphic models that address the distinction between user preferences and ratings. In one model, called the decoupled model, we introduce two different variables to decouple a users preferences FROM his ratings. IN the other, called the preference model, we model the orderings OF items preferred BY a USER, rather than the USERs numerical ratings of items. Empirical study over two datasets of movie ratings shows that appropriate modeling of the distinction between user preferences and ratings improves the performance substantially and consistently. Specifically, the proposed decoupled model outperforms all five existing approaches that we compare with significantly, but the preference model is not very successful. These results suggest that explicit modeling of the underlying user preferences is very important for collaborative filtering, but we can not afford ignoring the rating information completely.
Collaborative filtering (CF) and content-based filtering (CBF) have widely been used in information filtering applications. Both approaches have their strengths and weaknesses which is why researchers have developed hybrid systems. This paper proposes a novel approach to unify CF and CBF in a probabilistic framework, named collaborative ensemble learning. It uses probabilistic SVMs to model each user's profile (as CBF does).At the prediction phase, it combines a society OF users profiles, represented by their respective SVM models, to predict an active users preferences(the CF idea).The combination scheme is embedded in a probabilistic framework and retains an intuitive explanation.Moreover, collaborative ensemble learning does not require a global training stage and thus can incrementally incorporate new data.We report results based on two data sets. For the Reuters-21578 text data set, we simulate user ratings under the assumption that each user is interested in only one category. In the second experiment, we use users' opinions on a set of 642 art images that were collected through a web-based survey. For both data sets, collaborative ensemble achieved excellent performance in terms of recommendation accuracy.
The mean field methods, which entail approximating intractable probability distributions variationally with distributions from a tractable family, enjoy high efficiency, guaranteed convergence, and provide lower bounds on the true likelihood. But due to requirement for model-specific derivation of the optimization equations and unclear inference quality in various models, it is not widely used as a generic approximate inference algorithm. In this paper, we discuss a generalized mean field theory on variational approximation to a broad class of intractable distributions using a rich set of tractable distributions via constrained optimization over distribution spaces. We present a class of generalized mean field (GMF) algorithms for approximate inference in complex exponential family models, which entails limiting the optimization over the class of cluster-factorizable distributions. GMF is a generic method requiring no model-specific derivations. It factors a complex model into a set of disjoint variable clusters, and uses a set of canonical fix-point equations to iteratively update the cluster distributions, and converge to locally optimal cluster marginals that preserve the original dependency structure within each cluster, hence, fully decomposed the overall inference problem. We empirically analyzed the effect of different tractable family (clusters of different granularity) on inference quality, and compared GMF with BP on several canonical models. Possible extension to higher-order MF approximation is also discussed.
Methods for learning Bayesian network structure can discover dependency structure between observed variables, and have been shown to be useful in many applications. However, in domains that involve a large number of variables, the space of possible network structures is enormous, making it difficult, for both computational and statistical reasons, to identify a good model. In this paper, we consider a solution to this problem, suitable for domains where many variables have similar behavior. Our method is based on a new class of models, which we call module networks. A module network explicitly represents the notion of a module - a set of variables that have the same parents in the network and share the same conditional probability distribution. We define the semantics of module networks, and describe an algorithm that learns a module network from data. The algorithm learns both the partitioning of the variables into modules and the dependency structure between the variables. We evaluate our algorithm on synthetic data, and on real data in the domains of gene expression and the stock market. Our results show that module networks generalize better than Bayesian networks, and that the learned module network structure reveals regularities that are obscured in learned Bayesian networks.
The Semantic Web ontology language OWL 2 DL comes with a variety of language features that enable sophisticated and practically useful modeling. However, the use of these features has been severely restricted in order to retain decidability of the language. For example, OWL 2 DL does not allow a property to be both transitive and asymmetric, which would be desirable, e.g., for representing an ancestor relation. In this paper, we argue that the so-called global restrictions of OWL 2 DL preclude many useful forms of modeling, by providing a catalog of basic modeling patterns that would be available in OWL 2 DL if the global restrictions were discarded. We then report on the results of evaluating several state-of-the-art OWL 2 DL reasoners on problems that use combinations of features in a way that the global restrictions are violated. The systems turn out to rely heavily on the global restrictions and are thus largely incapable of coping with the modeling patterns. Next we show how off-the-shelf first-order logic theorem proving technology can be used to perform reasoning in the OWL 2 direct semantics, the semantics that underlies OWL 2 DL, but without requiring the global restrictions. Applying a naive proof-of-concept implementation of this approach to the test problems was successful in all cases. Based on our observations, we make suggestions for future lines of research on expressive description logic-style OWL reasoning.
The chase algorithm is a fundamental tool for query evaluation and query containment under constraints, where the constraints are (sub-classes of) tuple-generating dependencies (TGDs) and equality generating depencies (EGDs). So far, most of the research on this topic has focused on cases where the chase procedure terminates, with some notable exceptions. In this paper we take a general approach, and we propose large classes of TGDs under which the chase does not always terminate. Our languages, in particular, are inspired by guarded logic: we show that by enforcing syntactic properties on the form of the TGDs, we are able to ensure decidability of the problem of answering conjunctive queries despite the non-terminating chase. We provide tight complexity bounds for the problem of conjunctive query evaluation for several classes of TGDs. We then introduce EGDs, and provide a condition under which EGDs do not interact with TGDs, and therefore do not take part in query answering. We show applications of our classes of constraints to the problem of answering conjunctive queries under F-Logic Lite, a recently introduced ontology language, and under prominent tractable Description Logics languages. All the results in this paper immediately extend to the problem of conjunctive query containment.
Nowadays, huge efforts are made to modernize the air traffic management systems to cope with uncertainty, complexity and sub-optimality. An answer is to enhance the information sharing between the stakeholders. This paper introduces a framework that bridges the gap between air traffic management and air traffic control on the one hand, and bridges the gap between the ground, the approach and the en-route centers on the other hand. An original system is presented, that has three essential components: the trajectory models, the optimization process, and the monitoring process. The uncertainty of the trajectory is modeled with a Bayesian Network, where the nodes are associated to two types of random variables: the time of overflight on metering points of the airspace, and the traveling time of the routes linking these points. The resulting Bayesian Network covers the complete airspace, and Monte- Carlo simulations are done to estimate the probabilities of sector congestion and delays. On top of this trajectory model, an optimization process minimizes these probabilities by tuning the parameters of the Bayesian trajectory model related to overflight times on metering points. The last component is the monitoring process, that continuously updates the situation of the airspace, modifying the trajectories uncertainties according to actual positions of aircraft. After each update, a new optimal set of overflight times is computed, and can be communicated to the controllers as clearances for the aircraft pilots. The paper presents a formal specification of this global optimization problem, whose underlying rationale was derived with the help of air traffic controllers at Thales Air Systems.
This paper presents a model based on an hybrid system to numerically simulate the climbing phase of an aircraft. This model is then used within a trajectory prediction tool. Finally, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm is used to tune five selected parameters, and thus improve the accuracy of the model. Incorporated within a trajectory prediction tool, this model can be used to derive the order of magnitude of the prediction error over time, and thus the domain of validity of the trajectory prediction. A first validation experiment of the proposed model is based on the errors along time for a one-time trajectory prediction at the take off of the flight with respect to the default values of the theoretical BADA model. This experiment, assuming complete information, also shows the limit of the model. A second experiment part presents an on-line trajectory prediction, in which the prediction is continuously updated based on the current aircraft position. This approach raises several issues, for which improvements of the basic model are proposed, and the resulting trajectory prediction tool shows statistically significantly more accurate results than those of the default model.
Software design is crucial to successful software development, yet is a demanding multi-objective problem for software engineers. In an attempt to assist the software designer, interactive (i.e. human in-the-loop) meta-heuristic search techniques such as evolutionary computing have been applied and show promising results. Recent investigations have also shown that Ant Colony Optimization (ACO) can outperform evolutionary computing as a potential search engine for interactive software design. With a limited computational budget, ACO produces superior candidate design solutions in a smaller number of iterations. Building on these findings, we propose a novel interactive ACO (iACO) approach to assist the designer in early lifecycle software design, in which the search is steered jointly by subjective designer evaluation as well as machine fitness functions relating the structural integrity and surrogate elegance of software designs. Results show that iACO is speedy, responsive and highly effective in enabling interactive, dynamic multi-objective search in early lifecycle software design. Study participants rate the iACO search experience as compelling. Results of machine learning of fitness measure weightings indicate that software design elegance does indeed play a significant role in designer evaluation of candidate software design. We conclude that the evenness of the number of attributes and methods among classes (NAC) is a significant surrogate elegance measure, which in turn suggests that this evenness of distribution, when combined with structural integrity, is an implicit but crucial component of effective early lifecycle software design.
In science and engineering, intelligent processing of complex signals such as images, sound or language is often performed by a parameterized hierarchy of nonlinear processing layers, sometimes biologically inspired. Hierarchical systems (or, more generally, nested systems) offer a way to generate complex mappings using simple stages. Each layer performs a different operation and achieves an ever more sophisticated representation of the input, as, for example, in an deep artificial neural network, an object recognition cascade in computer vision or a speech front-end processing. Joint estimation of the parameters of all the layers and selection of an optimal architecture is widely considered to be a difficult numerical nonconvex optimization problem, difficult to parallelize for execution in a distributed computation environment, and requiring significant human expert effort, which leads to suboptimal systems in practice. We describe a general mathematical strategy to learn the parameters and, to some extent, the architecture of nested systems, called the method of auxiliary coordinates (MAC). This replaces the original problem involving a deeply nested function with a constrained problem involving a different function in an augmented space without nesting. The constrained problem may be solved with penalty-based methods using alternating optimization over the parameters and the auxiliary coordinates. MAC has provable convergence, is easy to implement reusing existing algorithms for single layers, can be parallelized trivially and massively, applies even when parameter derivatives are not available or not desirable, and is competitive with state-of-the-art nonlinear optimizers even in the serial computation setting, often providing reasonable models within a few iterations.
A plethora of words are used to describe the spectrum of human emotions, but how many emotions are there really, and how do they interact? Over the past few decades, several theories of emotion have been proposed, each based around the existence of a set of 'basic emotions', and each supported by an extensive variety of research including studies in facial expression, ethology, neurology and physiology. Here we present research based on a theory that people transmit their understanding of emotions through the language they use surrounding emotion keywords. Using a labelled corpus of over 21,000 tweets, six of the basic emotion sets proposed in existing literature were analysed using Latent Semantic Clustering (LSC), evaluating the distinctiveness of the semantic meaning attached to the emotional label. We hypothesise that the more distinct the language is used to express a certain emotion, then the more distinct the perception (including proprioception) of that emotion is, and thus more 'basic'. This allows us to select the dimensions best representing the entire spectrum of emotion. We find that Ekman's set, arguably the most frequently used for classifying emotions, is in fact the most semantically distinct overall. Next, taking all analysed (that is, previously proposed) emotion terms into account, we determine the optimal semantically irreducible basic emotion set using an iterative LSC algorithm. Our newly-derived set (Accepting, Ashamed, Contempt, Interested, Joyful, Pleased, Sleepy, Stressed) generates a 6.1% increase in distinctiveness over Ekman's set (Angry, Disgusted, Joyful, Sad, Scared). We also demonstrate how using LSC data can help visualise emotions. We introduce the concept of an Emotion Profile and briefly analyse compound emotions both visually and mathematically.
Visual features can help predict if a manipulation behavior will succeed at a given location. For example, the success of a behavior that flips light switches depends on the location of the switch. Within this paper, we present methods that enable a mobile manipulator to autonomously learn a function that takes an RGB image and a registered 3D point cloud as input and returns a 3D location at which a manipulation behavior is likely to succeed. Given a pair of manipulation behaviors that can change the state of the world between two sets (e.g., light switch up and light switch down), classifiers that detect when each behavior has been successful, and an initial hint as to where one of the behaviors will be successful, the robot autonomously trains a pair of support vector machine (SVM) classifiers by trying out the behaviors at locations in the world and observing the results. When an image feature vector associated with a 3D location is provided as input to one of the SVMs, the SVM predicts if the associated manipulation behavior will be successful at the 3D location. To evaluate our approach, we performed experiments with a PR2 robot from Willow Garage in a simulated home using behaviors that flip a light switch, push a rocker-type light switch, and operate a drawer. By using active learning, the robot efficiently learned SVMs that enabled it to consistently succeed at these tasks. After training, the robot also continued to learn in order to adapt in the event of failure.
Submodular functions have many applications. Matchings have many applications. The bitext word alignment problem can be modeled as the problem of maximizing a nonnegative, monotone, submodular function constrained to matchings in a complete bipartite graph where each vertex corresponds to a word in the two input sentences and each edge represents a potential word-to-word translation. We propose a more general problem of maximizing a nonnegative, monotone, submodular function defined on the edge set of a complete graph constrained to matchings; we call this problem the CSM-Matching problem. CSM-Matching also generalizes the maximum-weight matching problem, which has a polynomial-time algorithm; however, we show that it is NP-hard to approximate CSM-Matching within a factor of e/(e-1) by reducing the max k-cover problem to it. Our main result is a simple, greedy, 3-approximation algorithm for CSM-Matching. Then we reduce CSM-Matching to maximizing a nonnegative, monotone, submodular function over two matroids, i.e., CSM-2-Matroids. CSM-2-Matroids has a (2+epsilon)-approximation algorithm - called LSV2. We show that we can find a (4+epsilon)-approximate solution to CSM-Matching using LSV2. We extend this approach to similar problems.
Travel sharing, i.e., the problem of finding parts of routes which can be shared by several travellers with different points of departure and destinations, is a complex multiagent problem that requires taking into account individual agents' preferences to come up with mutually acceptable joint plans. In this paper, we apply state-of-the-art planning techniques to real-world public transportation data to evaluate the feasibility of multiagent planning techniques in this domain. The potential application value of improving travel sharing technology has great application value due to its ability to reduce the environmental impact of travelling while providing benefits to travellers at the same time. We propose a three-phase algorithm that utilises performant single-agent planners to find individual plans in a simplified domain first, then merges them using a best-response planner which ensures resulting solutions are individually rational, and then maps the resulting plan onto the full temporal planning domain to schedule actual journeys. The evaluation of our algorithm on real-world, multi-modal public transportation data for the United Kingdom shows linear scalability both in the scenario size and in the number of agents, where trade-offs have to be made between total cost improvement, the percentage of feasible timetables identified for journeys, and the prolongation of these journeys. Our system constitutes the first implementation of strategic multiagent planning algorithms in large-scale domains and provides insights into the engineering process of translating general domain-independent multiagent planning algorithms to real-world applications.
In probabilistic approaches to classification and information extraction, one typically builds a statistical model of words under the assumption that future data will exhibit the same regularities as the training data. In many data sets, however, there are scope-limited features whose predictive power is only applicable to a certain subset of the data. For example, in information extraction from web pages, word formatting may be indicative of extraction category in different ways on different web pages. The difficulty with using such features is capturing and exploiting the new regularities encountered in previously unseen data. In this paper, we propose a hierarchical probabilistic model that uses both local/scope-limited features, such as word formatting, and global features, such as word content. The local regularities are modeled as an unobserved random parameter which is drawn once for each local data set. This random parameter is estimated during the inference process and then used to perform classification with both the local and global features--- a procedure which is akin to automatically retuning the classifier to the local regularities on each newly encountered web page. Exact inference is intractable and we present approximations via point estimates and variational methods. Empirical results on large collections of web data demonstrate that this method significantly improves performance from traditional models of global features alone.
In this paper we propose a measure of clustering quality or accuracy that is appropriate in situations where it is desirable to evaluate a clustering algorithm by somehow comparing the clusters it produces with ``ground truth' consisting of classes assigned to the patterns by manual means or some other means in whose veracity there is confidence. Such measures are refered to as ``external'. Our measure also has the characteristic of allowing clusterings with different numbers of clusters to be compared in a quantitative and principled way. Our evaluation scheme quantitatively measures how useful the cluster labels of the patterns are as predictors of their class labels. In cases where all clusterings to be compared have the same number of clusters, the measure is equivalent to the mutual information between the cluster labels and the class labels. In cases where the numbers of clusters are different, however, it computes the reduction in the number of bits that would be required to encode (compress) the class labels if both the encoder and decoder have free acccess to the cluster labels. To achieve this encoding the estimated conditional probabilities of the class labels given the cluster labels must also be encoded. These estimated probabilities can be seen as a model for the class labels and their associated code length as a model cost.
In this paper, by adopting a coherence-based probabilistic approach to default reasoning, we focus the study on the logical operation of quasi conjunction and the Goodman-Nguyen inclusion relation for conditional events. We recall that quasi conjunction is a basic notion for defining consistency of conditional knowledge bases. By deepening some results given in a previous paper we show that, given any finite family of conditional events F and any nonempty subset S of F, the family F p-entails the quasi conjunction C(S); then, given any conditional event E|H, we analyze the equivalence between p-entailment of E|H from F and p-entailment of E|H from C(S), where S is some nonempty subset of F. We also illustrate some alternative theorems related with p-consistency and p-entailment. Finally, we deepen the study of the connections between the notions of p-entailment and inclusion relation by introducing for a pair (F,E|H) the (possibly empty) class K of the subsets S of F such that C(S) implies E|H. We show that the class K satisfies many properties; in particular K is additive and has a greatest element which can be determined by applying a suitable algorithm.
Within the area of computational models of argumentation, the instantiation-based approach is gaining more and more attention, not at least because meaningful input for Dung's abstract frameworks is provided in that way. In a nutshell, the aim of instantiation-based argumentation is to form, from a given knowledge base, a set of arguments and to identify the conflicts between them. The resulting network is then evaluated by means of extension-based semantics on an abstract level, i.e. on the resulting graph. While several systems are nowadays available for the latter step, the automation of the instantiation process itself has received less attention. In this work, we provide a novel approach to construct and visualize an argumentation framework from a given knowledge base. The system we propose relies on Answer-Set Programming and follows a two-step approach. A first program yields the logic-based arguments as its answer-sets; a second program is then used to specify the relations between arguments based on the answer-sets of the first program. As it turns out, this approach not only allows for a flexible and extensible tool for instantiation-based argumentation, but also provides a new method for answer-set visualization in general.
Previous research into the relation between ASP and classical logic has identified at least two different ways in which the former extends the latter. First, ASP program typically contain sets of rules that can be naturally interpreted as inductive definitions, and the language FO(ID) has shown that such inductive definitions can elegantly be added to classical logic in a modular way. Second, there is of course also the well-known epistemic component of ASP, which was mainly emphasized in the early papers on stable model semantics. To investigate whether this kind of knowledge can also, and in a similarly modular way, be added to classical logic, the language of Ordered Epistemic Logic was presented in recent work. However, this logic views the epistemic component as entirely separate from the inductive definition component, thus ignoring any possible interplay between the two. In this paper, we present a language that extends the inductive definition construct found in FO(ID) with an epistemic component, making such interplay possible. The eventual goal of this work is to discover whether it is really appropriate to view the epistemic component and the inductive definition component of ASP as two separate extensions of classical logic, or whether there is also something of importance in the combination of the two.
Artifact systems are a novel paradigm for specifying and implementing business processes described in terms of interacting modules called artifacts. Artifacts consist of data and lifecycles, accounting respectively for the relational structure of the artifacts' states and their possible evolutions over time. In this paper we put forward artifact-centric multi-agent systems, a novel formalisation of artifact systems in the context of multi-agent systems operating on them. Differently from the usual process-based models of services, the semantics we give explicitly accounts for the data structures on which artifact systems are defined. We study the model checking problem for artifact-centric multi-agent systems against specifications written in a quantified version of temporal-epistemic logic expressing the knowledge of the agents in the exchange. We begin by noting that the problem is undecidable in general. We then identify two noteworthy restrictions, one syntactical and one semantical, that enable us to find bisimilar finite abstractions and therefore reduce the model checking problem to the instance on finite models. Under these assumptions we show that the model checking problem for these systems is EXPSPACE-complete. We then introduce artifact-centric programs, compact and declarative representations of the programs governing both the artifact system and the agents. We show that, while these in principle generate infinite-state systems, under natural conditions their verification problem can be solved on finite abstractions that can be effectively computed from the programs. Finally we exemplify the theoretical results of the paper through a mainstream procurement scenario from the artifact systems literature.
Recently, there has been a burst in the number of research projects on human computation via crowdsourcing. Multiple choice (or labeling) questions could be referred to as a common type of problem which is solved by this approach. As an application, crowd labeling is applied to find true labels for large machine learning datasets. Since crowds are not necessarily experts, the labels they provide are rather noisy and erroneous. This challenge is usually resolved by collecting multiple labels for each sample, and then aggregating them to estimate the true label. Although the mechanism leads to high-quality labels, it is not actually cost-effective. As a result, efforts are currently made to maximize the accuracy in estimating true labels, while fixing the number of acquired labels. This paper surveys methods to aggregate redundant crowd labels in order to estimate unknown true labels. It presents a unified statistical latent model where the differences among popular methods in the field correspond to different choices for the parameters of the model. Afterwards, algorithms to make inference on these models will be surveyed. Moreover, adaptive methods which iteratively collect labels based on the previously collected labels and estimated models will be discussed. In addition, this paper compares the distinguished methods, and provides guidelines for future work required to address the current open issues.
In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum.
Feature selection aims to select the smallest subset of features for a specified level of performance. The optimal achievable classification performance on a feature subset is summarized by its Receiver Operating Curve (ROC). When infinite data is available, the Neyman- Pearson (NP) design procedure provides the most efficient way of obtaining this curve. In practice the design procedure is applied to density estimates from finite data sets. We perform a detailed statistical analysis of the resulting error propagation on finite alphabets. We show that the estimated performance curve (EPC) produced by the design procedure is arbitrarily accurate given sufficient data, independent of the size of the feature set. However, the underlying likelihood ranking procedure is highly sensitive to errors that reduces the probability that the EPC is in fact the ROC. In the worst case, guaranteeing that the EPC is equal to the ROC may require data sizes exponential in the size of the feature set. These results imply that in theory the NP design approach may only be valid for characterizing relatively small feature subsets, even when the performance of any given classifier can be estimated very accurately. We discuss the practical limitations for on-line methods that ensures that the NP procedure operates in a statistically valid region.
Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to this, in supervised model selection it is a priori known that the chosen model will be used in the future for prediction tasks involving more ``focused' predictive distributions. Although focused predictive distributions can be produced from the joint probability distribution by marginalization, in practice the best model in the unsupervised sense does not necessarily perform well in supervised domains. In particular, the standard marginal likelihood score is a criterion for the unsupervised task, and, although frequently used for supervised model selection also, does not perform well in such tasks. In this paper we study the performance of the marginal likelihood score empirically in supervised Bayesian network selection tasks by using a large number of publicly available classification data sets, and compare the results to those obtained by alternative model selection criteria, including empirical crossvalidation methods, an approximation of a supervised marginal likelihood measure, and a supervised version of Dawids prequential(predictive sequential) principle.The results demonstrate that the marginal likelihood score does NOT perform well FOR supervised model selection, WHILE the best results are obtained BY using Dawids prequential r napproach.
This paper is about metric data structures in high-dimensional or non-Euclidean space that permit cached sufficient statistics accelerations of learning algorithms. It has recently been shown that for less than about 10 dimensions, decorating kd-trees with additional "cached sufficient statistics" such as first and second moments and contingency tables can provide satisfying acceleration for a very wide range of statistical learning tasks such as kernel regression, locally weighted regression, k-means clustering, mixture modeling and Bayes Net learning. In this paper, we begin by defining the anchors hierarchy - a fast data structure and algorithm for localizing data based only on a triangle-inequality-obeying distance metric. We show how this, in its own right, gives a fast and effective clustering of data. But more importantly we show how it can produce a well-balanced structure similar to a Ball-Tree (Omohundro, 1991) or a kind of metric tree (Uhlmann, 1991; Ciaccia, Patella, & Zezula, 1997) in a way that is neither "top-down" nor "bottom-up" but instead "middle-out". We then show how this structure, decorated with cached sufficient statistics, allows a wide variety of statistical learning algorithms to be accelerated even in thousands of dimensions.
The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple users to recommend items of interest to other users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method called emph{personality diagnosis (PD)}. Given a user's preferences for some items, we compute the probability that he or she is of the same "personality type" as other users, and, in turn, the probability that he or she will like new items. PD retains some of the advantages of traditional similarity-weighting techniques in that all data is brought to bear on each prediction and new data can be added easily and incrementally. Additionally, PD has a meaningful probabilistic interpretation, which may be leveraged to justify, explain, and augment results. We report empirical results on the EachMovie database of movie ratings, and on user profile data collected from the CiteSeer digital library of Computer Science research papers. The probabilistic framework naturally supports a variety of descriptive measurements - in particular, we consider the applicability of a value of information (VOI) computation.
"Information Processing" is a recently launched buzzword whose meaning is vague and obscure even for the majority of its users. The reason for this is the lack of a suitable definition for the term "information". In my attempt to amend this bizarre situation, I have realized that, following the insights of Kolmogorov's Complexity theory, information can be defined as a description of structures observable in a given data set. Two types of structures could be easily distinguished in every data set - in this regard, two types of information (information descriptions) should be designated: physical information and semantic information. Kolmogorov's theory also posits that the information descriptions should be provided as a linguistic text structure. This inevitably leads us to an assertion that information processing has to be seen as a kind of text processing. The idea is not new - inspired by the observation that human information processing is deeply rooted in natural language handling customs, Lotfi Zadeh and his followers have introduced the so-called "Computing With Words" paradigm. Despite of promotional efforts, the idea is not taking off yet. The reason - a lack of a coherent understanding of what should be called "information", and, as a result, misleading research roadmaps and objectives. I hope my humble attempt to clarify these issues would be helpful in avoiding common traps and pitfalls.
This paper presents a method to compute automatically topological relations using SWRL rules. The calculation of these rules is based on the definition of a Selective Nef Complexes Nef Polyhedra structure generated from standard Polyhedron. The Selective Nef Complexes is a data model providing a set of binary Boolean operators such as Union, Difference, Intersection and Symmetric difference, and unary operators such as Interior, Closure and Boundary. In this work, these operators are used to compute topological relations between objects defined by the constraints of the 9 Intersection Model (9-IM) from Egenhofer. With the help of these constraints, we defined a procedure to compute the topological relations on Nef polyhedra. These topological relationships are Disjoint, Meets, Contains, Inside, Covers, CoveredBy, Equals and Overlaps, and defined in a top-level ontology with a specific semantic definition on relation such as Transitive, Symmetric, Asymmetric, Functional, Reflexive, and Irreflexive. The results of the computation of topological relationships are stored in an OWL-DL ontology allowing after what to infer on these new relationships between objects. In addition, logic rules based on the Semantic Web Rule Language allows the definition of logic programs that define which topological relationships have to be computed on which kind of objects with specific attributes. For instance, a "Building" that overlaps a "Railway" is a "RailStation".
The dependency graph is a data architecture that models all the dependencies between the different types of assets in the game. It depicts the dependency-based relationships between the assets of a game. For example, a player must construct an arsenal before he can build weapons. It is vital that the dependency graph of a game is designed logically to ensure a logical sequence of game play. However, a mere logical dependency graph is not sufficient in sustaining the players' enduring interests in a game, which brings the problem of game balancing into picture. The issue of game balancing arises when the players do not feel the chances of winning the game over their AI opponents who are more skillful in the game play. At the current state of research, the architecture of dependency graph is monolithic for the players. The sequence of asset possession is always foreseeable because there is only a single dependency graph. Game balancing is impossible when the assets of AI players are overwhelmingly outnumbering that of human players. This paper proposes a parallel architecture of dependency graph for the AI players and human players. Instead of having a single dependency graph, a parallel architecture is proposed where the dependency graph of AI player is adjustable with that of human player using a support dependency as a game balancing mechanism. This paper exhibits that the parallel dependency graph helps to improve game balancing.
Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to this, in supervised model selection it is a priori known that the chosen model will be used in the future for prediction tasks involving more ``focused' predictive distributions. Although focused predictive distributions can be produced from the joint probability distribution by marginalization, in practice the best model in the unsupervised sense does not necessarily perform well in supervised domains. In particular, the standard marginal likelihood score is a criterion for the unsupervised task, and, although frequently used for supervised model selection also, does not perform well in such tasks. In this paper we study the performance of the marginal likelihood score empirically in supervised Bayesian network selection tasks by using a large number of publicly available classification data sets, and compare the results to those obtained by alternative model selection criteria, including empirical crossvalidation methods, an approximation of a supervised marginal likelihood measure, and a supervised version of Dawids prequential(predictive sequential) principle.The results demonstrate that the marginal likelihood score does NOT perform well FOR supervised model selection, WHILE the best results are obtained BY using Dawids prequential r napproach.
Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metrics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.
We investigate a class of hierarchical mixtures-of-experts (HME) models where exponential family regression models with generalized linear mean functions of the form psi(ga+fx^Tfgb) are mixed. Here psi(...) is the inverse link function. Suppose the true response y follows an exponential family regression model with mean function belonging to a class of smooth functions of the form psi(h(fx)) where h(...)in W_2^infty (a Sobolev class over [0,1]^{s}). It is shown that the HME probability density functions can approximate the true density, at a rate of O(m^{-2/s}) in L_p norm, and at a rate of O(m^{-4/s}) in Kullback-Leibler divergence. These rates can be achieved within the family of HME structures with no more than s-layers, where s is the dimension of the predictor fx. It is also shown that likelihood-based inference based on HME is consistent in recovering the truth, in the sense that as the sample size n and the number of experts m both increase, the mean square error of the predicted mean response goes to zero. Conditions for such results to hold are stated and discussed.
A key problem of robotic environmental sensing and monitoring is that of active sensing: How can a team of robots plan the most informative observation paths to minimize the uncertainty in modeling and predicting an environmental phenomenon? This paper presents two principled approaches to efficient information-theoretic path planning based on entropy and mutual information criteria for in situ active sensing of an important broad class of widely-occurring environmental phenomena called anisotropic fields. Our proposed algorithms are novel in addressing a trade-off between active sensing performance and time efficiency. An important practical consequence is that our algorithms can exploit the spatial correlation structure of Gaussian process-based anisotropic fields to improve time efficiency while preserving near-optimal active sensing performance. We analyze the time complexity of our algorithms and prove analytically that they scale better than state-of-the-art algorithms with increasing planning horizon length. We provide theoretical guarantees on the active sensing performance of our algorithms for a class of exploration tasks called transect sampling, which, in particular, can be improved with longer planning time and/or lower spatial correlation along the transect. Empirical evaluation on real-world anisotropic field data shows that our algorithms can perform better or at least as well as the state-of-the-art algorithms while often incurring a few orders of magnitude less computational time, even when the field conditions are less favorable.
Assignment methods are at the heart of many algorithms for unsupervised learning and clustering - in particular, the well-known K-means and Expectation-Maximization (EM) algorithms. In this work, we study several different methods of assignment, including the "hard" assignments used by K-means and the ?soft' assignments used by EM. While it is known that K-means minimizes the distortion on the data and EM maximizes the likelihood, little is known about the systematic differences of behavior between the two algorithms. Here we shed light on these differences via an information-theoretic analysis. The cornerstone of our results is a simple decomposition of the expected distortion, showing that K-means (and its extension for inferring general parametric densities from unlabeled sample data) must implicitly manage a trade-off between how similar the data assigned to each cluster are, and how the data are balanced among the clusters. How well the data are balanced is measured by the entropy of the partition defined by the hard assignments. In addition to letting us predict and verify systematic differences between K-means and EM on specific examples, the decomposition allows us to give a rather general argument showing that K ?means will consistently find densities with less "overlap" than EM. We also study a third natural assignment method that we call posterior assignment, that is close in spirit to the soft assignments of EM, but leads to a surprisingly different algorithm.
Over the years, numerous experiments have been accumulated to show that cooperation is not casual and depends on the payoffs of the game. These findings suggest that humans have attitude to cooperation by nature and the same person may act more or less cooperatively depending on the particular payoffs. In other words, people do not act a priori as single agents, but they forecast how the game would be played if they formed coalitions and then they play according to their best forecast. In this paper we formalize this idea and we define a new solution concept for one-shot normal form games. We prove that this \emph{cooperative equilibrium} exists for all finite games and it explains a number of different experimental findings, such as (1) the rate of cooperation in the Prisoner's dilemma depends on the cost-benefit ratio; (2) the rate of cooperation in the Traveler's dilemma depends on the bonus/penalty; (3) the rate of cooperation in the Publig Goods game depends on the pro-capite marginal return and on the numbers of players; (4) the rate of cooperation in the Bertrand competition depends on the number of players; (5) players tend to be fair in the bargaining problem; (6) players tend to be fair in the Ultimatum game; (7) players tend to be altruist in the Dictator game; (8) offers in the Ultimatum game are larger than offers in the Dictator game.
We aim at providing a foundation of a theory of "good" SAT representations F of boolean functions f. We argue that the hierarchy UC_k of unit-refutation complete clause-sets of level k, introduced by the authors, provides the most basic target classes, that is, F in UC_k is to be achieved for k as small as feasible. If F does not contain new variables, i.e., F is equivalent (as a CNF) to f, then F in UC_1 is similar to "achieving (generalised) arc consistency" known from the literature (it is somewhat weaker, but theoretically much nicer to handle). We show that for polysize representations of boolean functions in this sense, the hierarchy UC_k is strict. The boolean functions for these separations are "doped" minimally unsatisfiable clause-sets of deficiency 1; these functions have been introduced in [Sloan, Soerenyi, Turan, 2007], and we generalise their construction and show a correspondence to a strengthened notion of irredundant sub-clause-sets. Turning from lower bounds to upper bounds, we believe that many common CNF representations fit into the UC_k scheme, and we give some basic tools to construct representations in UC_1 with new variables, based on the Tseitin translation. Note that regarding new variables the UC_1-representations are stronger than mere "arc consistency", since the new variables are not excluded from consideration.
One of the most challenging problems in recommender systems based on the collaborative filtering (CF) concept is data sparseness, i.e., limited user preference data is available for making recommendations. Cross-domain collaborative filtering (CDCF) has been studied as an effective mechanism to alleviate data sparseness of one domain using the knowledge about user preferences from other domains. A key question to be answered in the context of CDCF is what common characteristics can be deployed to link different domains for effective knowledge transfer. In this paper, we assess the usefulness of user-contributed (social) tags in this respect. We do so by means of the Generalized Tag-induced Cross-domain Collaborative Filtering (GTagCDCF) approach that we propose in this paper and that we developed based on the general collective matrix factorization framework. Assessment is done by a series of experiments, using publicly available CF datasets that represent three cross-domain cases, i.e., two two-domain cases and one three-domain case. A comparative analysis on two-domain cases involving GTagCDCF and several state-of-the-art CDCF approaches indicates the increased benefit of using social tags as representatives of explicit links between domains for CDCF as compared to the implicit links deployed by the existing CDCF methods. In addition, we show that users from different domains can already benefit from GTagCDCF if they only share a few common tags. Finally, we use the three-domain case to validate the robustness of GTagCDCF with respect to the scale of datasets and the varying number of domains.
Saliency detection has been an intuitive way to provide useful cues for object detection and segmentation, as desired for many vision and graphics applications. In this paper, we provided a robust method for salient object detection and segmentation. Other than using various pixel-level contrast definitions, we exploited global image structures and proposed a new geodesic method dedicated for salient object detection. In the proposed approach, a new geodesic scheme, namely geodesic tunneling is proposed to tackle with textures and local chaotic structures. With our new geodesic approach, a geodesic saliency map is estimated in correspondence to spatial structures in an image. Experimental evaluation on a salient object benchmark dataset validated that our algorithm consistently outperformed a number of the state-of-art saliency methods, yielding higher precision and better recall rates. With the robust saliency estimation, we also present an unsupervised hierarchical salient object cut scheme simply using adaptive saliency thresholding, which attained the highest score in our F-measure test. We also applied our geodesic cut scheme to a number of image editing tasks as demonstrated in additional experiments.
The marginal maximum a posteriori probability (MAP) estimation problem, which calculates the mode of the marginal posterior distribution of a subset of variables with the remaining variables marginalized, is an important inference problem in many models, such as those with hidden variables or uncertain parameters. Unfortunately, marginal MAP can be NP-hard even on trees, and has attracted less attention in the literature compared to the joint MAP (maximization) and marginalization problems. We derive a general dual representation for marginal MAP that naturally integrates the marginalization and maximization operations into a joint variational optimization problem, making it possible to easily extend most or all variational-based algorithms to marginal MAP. In particular, we derive a set of "mixed-product" message passing algorithms for marginal MAP, whose form is a hybrid of max-product, sum-product and a novel "argmax-product" message updates. We also derive a class of convergent algorithms based on proximal point methods, including one that transforms the marginal MAP problem into a sequence of standard marginalization problems. Theoretically, we provide guarantees under which our algorithms give globally or locally optimal solutions, and provide novel upper bounds on the optimal objectives. Empirically, we demonstrate that our algorithms significantly outperform the existing approaches, including a state-of-the-art algorithm based on local search methods.
Every cellular network deployment requires planning and optimization in order to provide adequate coverage, capacity, and quality of service (QoS). Optimization mobile radio network planning is a very complex task, as many aspects must be taken into account. With the rapid development in mobile network we need effective network planning tool to satisfy the need of customers. However, deciding upon the optimum placement for the base stations (BS s) to achieve best services while reducing the cost is a complex task requiring vast computational resource. This paper introduces the spatial clustering to solve the Mobile Networking Planning problem. It addresses antenna placement problem or the cell planning problem, involves locating and configuring infrastructure for mobile networks by modified the original Partitioning Around Medoids PAM algorithm. M-PAM (Modified Partitioning Around Medoids) has been proposed to satisfy the requirements and constraints. PAM needs to specify number of clusters (k) before starting to search for the best locations of base stations. The M-PAM algorithm uses the radio network planning to determine k. We calculate for each cluster its coverage and capacity and determine if they satisfy the mobile requirements, if not we will increase (k) and reapply algorithms depending on two methods for clustering. Implementation of this algorithm to a real case study is presented. Experimental results and analysis indicate that the M-PAM algorithm when applying method two is effective in case of heavy load distribution, and leads to minimum number of base stations, which directly affected onto the cost of planning the network.
Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which requires statistical distributions of travel times, rather than just mean values. We propose a method to estimate travel time distributions on large-scale road networks, using probe vehicle data collected from GPS. We present a framework that works with large input of data, and scales linearly with the size of the network. Leveraging the planar topology of the graph, the method computes efficiently the time correlations between neighboring streets. First, raw probe vehicle traces are compressed into pairs of travel times and number of stops for each traversed road segment using a `stop-and-go' algorithm developed for this work. The compressed data is then used as input for training a path travel time model, which couples a Markov model along with a Gaussian Markov random field. Finally, scalable inference algorithms are developed for obtaining path travel time distributions from the composite MM-GMRF model. We illustrate the accuracy and scalability of our model on a 505,000 road link network spanning the San Francisco Bay Area.
This paper deals with chain graphs under the Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability distribution is faithful to. Moreover, we show that the extension of Meek's conjecture to AMP chain graphs does not hold, which compromises the development of efficient and correct score+search learning algorithms under assumptions weaker than faithfulness. We also introduce a new family of graphical models that consists of undirected and bidirected edges. We name this new family maximal covariance-concentration graphs (MCCGs) because it includes both covariance and concentration graphs as subfamilies. However, every MCCG can be seen as the result of marginalizing out some nodes in an AMP CG. We describe global, local and pairwise Markov properties for MCCGs and prove their equivalence. We characterize when two MCCGs are Markov equivalent, and show that every Markov equivalence class of MCCGs has a distinguished member. We present a constraint based algorithm for learning a MCCG a given probability distribution is faithful to. Finally, we present a graphical criterion for reading dependencies from a MCCG of a probability distribution that satisfies the graphoid properties, weak transitivity and composition. We prove that the criterion is sound and complete in certain sense.
Introduction. Case Based Reasoning (CBR) is an emerg- ing decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs) and a label (output). Extracting useful information from this database can help the CBR system providing more reliable results on the yet to be solved cases. Objective. For that purpose we suggest a general frame- work where a CBR system, viz. K-Nearest Neighbor (K-NN) algorithm, is combined with various information obtained from a Logistic Regression (LR) model. Methods. LR is applied, on the case database, to assign weights to the attributes as well as the solved cases. Thus, five possible decision making systems based on K-NN and/or LR were identified: a standalone K-NN, a standalone LR and three soft K-NN algorithms that rely on the weights based on the results of the LR. The evaluation of the described approaches is performed in the field of renal transplant access waiting list. Results and conclusion. The results show that our suggested approach, where the K-NN algorithm relies on both weighted attributes and cases, can efficiently deal with non relevant attributes, whereas the four other approaches suffer from this kind of noisy setups. The robustness of this approach suggests interesting perspectives for medical problem solving tools using CBR methodology.
We consider the problem of creating fair course timetables in the setting of a university. Our motivation is to improve the overall satisfaction of individuals concerned (students, teachers, etc.) by providing a fair timetable to them. The central idea is that undesirable arrangements in the course timetable, i.e., violations of soft constraints, should be distributed in a fair way among the individuals. We propose two formulations for the fair course timetabling problem that are based on max-min fairness and Jain's fairness index, respectively. Furthermore, we present and experimentally evaluate an optimization algorithm based on simulated annealing for solving max-min fair course timetabling problems. The new contribution is concerned with measuring the energy difference between two timetables, i.e., how much worse a timetable is compared to another timetable with respect to max-min fairness. We introduce three different energy difference measures and evaluate their impact on the overall algorithm performance. The second proposed problem formulation focuses on the tradeoff between fairness and the total amount of soft constraint violations. Our experimental evaluation shows that the known best solutions to the ITC2007 curriculum-based course timetabling instances are quite fair with respect to Jain's fairness index. However, the experiments also show that the fairness can be improved further for only a rather small increase in the total amount of soft constraint violations.
Bayesian Reinforcement Learning (RL) is capable of not only incorporating domain knowledge, but also solving the exploration-exploitation dilemma in a natural way. As Bayesian RL is intractable except for special cases, previous work has proposed several approximation methods. However, these methods are usually too sensitive to parameter values, and finding an acceptable parameter setting is practically impossible in many applications. In this paper, we propose a new algorithm that greedily approximates Bayesian RL to achieve robustness in parameter space. We show that for a desired learning behavior, our proposed algorithm has a polynomial sample complexity that is lower than those of existing algorithms. We also demonstrate that the proposed algorithm naturally outperforms other existing algorithms when the prior distributions are not significantly misleading. On the other hand, the proposed algorithm cannot handle greatly misspecified priors as well as the other algorithms can. This is a natural consequence of the fact that the proposed algorithm is greedier than the other algorithms. Accordingly, we discuss a way to select an appropriate algorithm for different tasks based on the algorithms' greediness. We also introduce a new way of simplifying Bayesian planning, based on which future work would be able to derive new algorithms.
VT (Viterbi training), or hard EM, is an efficient way of parameter learning for probabilistic models with hidden variables. Given an observation $y$, it searches for a state of hidden variables $x$ that maximizes $p(x,y \mid \theta)$ by coordinate ascent on parameters $\theta$ and $x$. In this paper we introduce VT to PRISM, a logic-based probabilistic modeling system for generative models. VT improves PRISM in three ways. First VT in PRISM converges faster than EM in PRISM due to the VT's termination condition. Second, parameters learned by VT often show good prediction performance compared to those learned by EM. We conducted two parsing experiments with probabilistic grammars while learning parameters by a variety of inference methods, i.e.\ VT, EM, MAP and VB. The result is that VT achieved the best parsing accuracy among them in both experiments. Also we conducted a similar experiment for classification tasks where a hidden variable is not a prediction target unlike probabilistic grammars. We found that in such a case VT does not necessarily yield superior performance. Third since VT always deals with a single probability of a single explanation, Viterbi explanation, the exclusiveness condition that is imposed on PRISM programs is no more required if we learn parameters by VT. Last but not least we can say that as VT in PRISM is general and applicable to any PRISM program, it largely reduces the need for the user to develop a specific VT algorithm for a specific model. Furthermore since VT in PRISM can be used just by setting a PRISM flag appropriately, it makes VT easily accessible to (probabilistic) logic programmers. To appear in Theory and Practice of Logic Programming (TPLP).
Associative classification is a recent and rewarding technique which integrates association rule mining and classification to a model for prediction and achieves maximum accuracy. Associative classifiers are especially fit to applications where maximum accuracy is desired to a model for prediction. There are many domains such as medical where the maximum accuracy of the model is desired. Heart disease is a single largest cause of death in developed countries and one of the main contributors to disease burden in developing countries. Mortality data from the registrar general of India shows that heart disease are a major cause of death in India, and in Andhra Pradesh coronary heart disease cause about 30%of deaths in rural areas. Hence there is a need to develop a decision support system for predicting heart disease of a patient. In this paper we propose efficient associative classification algorithm using genetic approach for heart disease prediction. The main motivation for using genetic algorithm in the discovery of high level prediction rules is that the discovered rules are highly comprehensible, having high predictive accuracy and of high interestingness values. Experimental Results show that most of the classifier rules help in the best prediction of heart disease which even helps doctors in their diagnosis decisions.
Associative memories store content in such a way that the content can be later retrieved by presenting the memory with a small portion of the content, rather than presenting the memory with an address as in more traditional memories. Associative memories are used as building blocks for algorithms within database engines, anomaly detection systems, compression algorithms, and face recognition systems. A classical example of an associative memory is the Hopfield neural network. Recently, Gripon and Berrou have introduced an alternative construction which builds on ideas from the theory of error correcting codes and which greatly outperforms the Hopfield network in capacity, diversity, and efficiency. In this paper we implement a variation of the Gripon-Berrou associative memory on a general purpose graphical processing unit (GPU). The work of Gripon and Berrou proposes two retrieval rules, sum-of-sum and sum-of-max. The sum-of-sum rule uses only matrix-vector multiplication and is easily implemented on the GPU. The sum-of-max rule is much less straightforward to implement because it involves non-linear operations. However, the sum-of-max rule gives significantly better retrieval error rates. We propose a hybrid rule tailored for implementation on a GPU which achieves a 880-fold speedup without sacrificing any accuracy.
Establishing arc consistency on two relational structures is one of the most popular heuristics for the constraint satisfaction problem. We aim at determining the time complexity of arc consistency testing. The input structures $G$ and $H$ can be supposed to be connected colored graphs, as the general problem reduces to this particular case. We first observe the upper bound $O(e(G)v(H)+v(G)e(H))$, which implies the bound $O(e(G)e(H))$ in terms of the number of edges and the bound $O((v(G)+v(H))^3)$ in terms of the number of vertices. We then show that both bounds are tight up to a constant factor as long as an arc consistency algorithm is based on constraint propagation (like any algorithm currently known). Our argument for the lower bounds is based on examples of slow constraint propagation. We measure the speed of constraint propagation observed on a pair $G,H$ by the size of a proof, in a natural combinatorial proof system, that Spoiler wins the existential 2-pebble game on $G,H$. The proof size is bounded from below by the game length $D(G,H)$, and a crucial ingredient of our analysis is the existence of $G,H$ with $D(G,H)=\Omega(v(G)v(H))$. We find one such example among old benchmark instances for the arc consistency problem and also suggest a new, different construction.
Orthogonality is a discipline of programming that in a syntactic manner guarantees determinism of functional specifications. Essentially, orthogonality avoids, on the one side, the inherent ambiguity of non determinism, prohibiting the existence of different rules that specify the same function and that may apply simultaneously (non-ambiguity), and, on the other side, it eliminates the possibility of occurrence of repetitions of variables in the left-hand side of these rules (left linearity). In the theory of term rewriting systems (TRSs) determinism is captured by the well-known property of confluence, that basically states that whenever different computations or simplifications from a term are possible, the computed answers should coincide. Although the proofs are technically elaborated, confluence is well-known to be a consequence of orthogonality. Thus, orthogonality is an important mathematical discipline intrinsic to the specification of recursive functions that is naturally applied in functional programming and specification. Starting from a formalization of the theory of TRSs in the proof assistant PVS, this work describes how confluence of orthogonal TRSs has been formalized, based on axiomatizations of properties of rules, positions and substitutions involved in parallel steps of reduction, in this proof assistant. Proofs for some similar but restricted properties such as the property of confluence of non-ambiguous and (left and right) linear TRSs have been fully formalized.
Web service composition is the process of synthesizing a new composite service using a set of available Web services in order to satisfy a client request that cannot be treated by any available Web services. The Web services space is a dynamic environment characterized by a huge number of elements. Furthermore, many Web services are offering similar functionalities. In this paper we propose a model for Web service composition designed to address the scale effect and the redundancy issue. The Web services space is represented by a two-layered network architecture. A concrete similarity network layer organizes the Web services operations into communities of functionally similar operations. An abstract interaction network layer represents the composition relationships between the sets of communities. Composition synthesis is performed by a two-phased graph search algorithm. First, the interaction network is mined in order to discover abstract solutions to the request goal. Then, the abstract compositions are instantiated with concrete operations selected from the similarity network. This strategy allows an efficient exploration of the Web services space. Furthermore, operations grouped in a community can be easily substituted if necessary during the composition's synthesis's process.
The highest level of mathematics research is traditionally seen as a solitary activity. Yet new innovations by mathematicians themselves are starting to harness the power of social computation to create new modes of mathematical production. We study the effectiveness of one such system, and make proposals for enhancement, drawing on AI and computer based mathematics. We analyse the content of a sample of questions and responses in the community question answering system for research mathematicians, math-overflow. We find that mathoverflow is very effective, with 90% of our sample of questions answered completely or in part. A typical response is an informal dialogue, allowing error and speculation, rather than rigorous mathematical argument: 37% of our sample discussions acknowledged error. Responses typically present information known to the respondent, and readily checked by other users: thus the effectiveness of mathoverflow comes from information sharing. We conclude that extending and the power and reach of mathoverflow through a combination of people and machines raises new challenges for artificial intelligence and computational mathematics, in particular how to handle error, analogy and informal reasoning.
Complex networks refer to large-scale graphs with nontrivial connection patterns. The salient and interesting features that the complex network study offer in comparison to graph theory are the emphasis on the dynamical properties of the networks and the ability of inherently uncovering pattern formation of the vertices. In this paper, we present a hybrid data classification technique combining a low level and a high level classifier. The low level term can be equipped with any traditional classification techniques, which realize the classification task considering only physical features (e.g., geometrical or statistical features) of the input data. On the other hand, the high level term has the ability of detecting data patterns with semantic meanings. In this way, the classification is realized by means of the extraction of the underlying network's features constructed from the input data. As a result, the high level classification process measures the compliance of the test instances with the pattern formation of the training data. Out of various high level perspectives that can be utilized to capture semantic meaning, we utilize the dynamical features that are generated from a tourist walker in a networked environment. Specifically, a weighted combination of transient and cycle lengths generated by the tourist walk is employed for that end. Interestingly, our study shows that the proposed technique is able to further improve the already optimized performance of traditional classification techniques.
In this work, we investigate a novel semantic approach for pattern discovery in trajectories that, relying on ontologies, enhances object movement information with event semantics. The approach can be applied to the detection of movement patterns and behaviors whenever the semantics of events occurring along the trajectory is, explicitly or implicitly, available. In particular, we tested it against an exacting case scenario in maritime surveillance, i.e., the discovery of suspicious container transportations. The methodology we have developed entails the formalization of the application domain through a domain ontology, extending the Moving Object Ontology (MOO) described in this paper. Afterwards, movement patterns have to be formalized, either as Description Logic (DL) axioms or queries, enabling the retrieval of the trajectories that follow the patterns. In our experimental evaluation, we have considered a real world dataset of 18 Million of container events describing the deed undertaken in a port to accomplish the shipping (e.g., loading on a vessel, export operation). Leveraging events, we have reconstructed almost 300 thousand container trajectories referring to 50 thousand containers travelling along three years. We have formalized the anomalous itinerary patterns as DL axioms, testing different ontology APIs and DL reasoners to retrieve the suspicious transportations. Our experiments demonstrate that the approach is feasible and efficient. In particular, the joint use of Pellet and SPARQL-DL enables to detect the trajectories following a given pattern in a reasonable time with big size datasets.
The extended mind hypothesis has stimulated much interest in cognitive science. However, its core claim, i.e. that the process of cognition can extend beyond the brain via the body and into the environment, has been heavily criticized. A prominent critique of this claim holds that when some part of the world is coupled to a cognitive system this does not necessarily entail that the part is also constitutive of that cognitive system. This critique is known as the "coupling-constitution fallacy". In this paper we respond to this reductionist challenge by using an evolutionary robotics approach to create a minimal model of two acoustically coupled agents. We demonstrate how the interaction process as a whole has properties that cannot be reduced to the contributions of the isolated agents. We also show that the neural dynamics of the coupled agents has formal properties that are inherently impossible for those neural networks in isolation. By keeping the complexity of the model to an absolute minimum, we are able to illustrate how the coupling-constitution fallacy is in fact based on an inadequate understanding of the constitutive role of nonlinear interactions in dynamical systems theory.
Recent research in robot exploration and mapping has focused on sampling environmental hotspot fields. This exploration task is formalized by Low, Dolan, and Khosla (2008) in a sequential decision-theoretic planning under uncertainty framework called MASP. The time complexity of solving MASP approximately depends on the map resolution, which limits its use in large-scale, high-resolution exploration and mapping. To alleviate this computational difficulty, this paper presents an information-theoretic approach to MASP (iMASP) for efficient adaptive path planning; by reformulating the cost-minimizing iMASP as a reward-maximizing problem, its time complexity becomes independent of map resolution and is less sensitive to increasing robot team size as demonstrated both theoretically and empirically. Using the reward-maximizing dual, we derive a novel adaptive variant of maximum entropy sampling, thus improving the induced exploration policy performance. It also allows us to establish theoretical bounds quantifying the performance advantage of optimal adaptive over non-adaptive policies and the performance quality of approximately optimal vs. optimal adaptive policies. We show analytically and empirically the superior performance of iMASP-based policies for sampling the log-Gaussian process to that of policies for the widely-used Gaussian process in mapping the hotspot field. Lastly, we provide sufficient conditions that, when met, guarantee adaptivity has no benefit under an assumed environment model.
The efficiency of current cargo screening processes at sea and air ports is unknown as no benchmarks exists against which they could be measured. Some manufacturer benchmarks exist for individual sensors but we have not found any benchmarks that take a holistic view of the screening procedures assessing a combination of sensors and also taking operator variability into account. Just adding up resources and manpower used is not an effective way for assessing systems where human decision-making and operator compliance to rules play a vital role. For such systems more advanced assessment methods need to be used, taking into account that the cargo screening process is of a dynamic and stochastic nature. Our project aim is to develop a decision support tool (cargo-screening system simulator) that will map the right technology and manpower to the right commodity-threat combination in order to maximize detection rates. In this paper we present a project outline and highlight the research challenges we have identified so far. In addition we introduce our first case study, where we investigate the cargo screening process at the ferry port in Calais.
Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.
We present in this paper a new approach for the automatic annotation of medical images, using the approach of "bag-of-words" to represent the visual content of the medical image combined with text descriptors based approach tf.idf and reduced by latent semantic to extract the co-occurrence between terms and visual terms. A medical report is composed of a text describing a medical image. First, we are interested to index the text and extract all relevant terms using a thesaurus containing MeSH medical concepts. In a second phase, the medical image is indexed while recovering areas of interest which are invariant to change in scale, light and tilt. To annotate a new medical image, we use the approach of "bagof-words" to recover the feature vector. Indeed, we use the vector space model to retrieve similar medical image from the database training. The calculation of the relevance value of an image to the query image is based on the cosine function. We conclude with an experiment carried out on five types of radiological imaging to evaluate the performance of our system of medical annotation. The results showed that our approach works better with more images from the radiology of the skull.
This manuscript discusses computation of the Partition Function (PF) and the Minimum Weight Perfect Matching (MWPM) on arbitrary, non-bipartite graphs. We present two novel problem formulations - one for computing the PF of a Perfect Matching (PM) and one for finding MWPMs - that build upon the inter-related Bethe Free Energy, Belief Propagation (BP), Loop Calculus (LC), Integer Linear Programming (ILP) and Linear Programming (LP) frameworks. First, we describe an extension of the LC framework to the PM problem. The resulting formulas, coined (fractional) Bootstrap-BP, express the PF of the original model via the BFE of an alternative PM problem. We then study the zero-temperature version of this Bootstrap-BP formula for approximately solving the MWPM problem. We do so by leveraging the Bootstrap-BP formula to construct a sequence of MWPM problems, where each new problem in the sequence is formed by contracting odd-sized cycles (or blossoms) from the previous problem. This Bootstrap-and-Contract procedure converges reliably and generates an empirically tight upper bound for the MWPM. We conclude by discussing the relationship between our iterative procedure and the famous Blossom Algorithm of Edmonds '65 and demonstrate the performance of the Bootstrap-and-Contract approach on a variety of weighted PM problems.
To determine the 3D conformation of proteins is a necessity to understand their functions or interactions with other molecules. It is commonly admitted that, when proteins fold from their primary linear structures to their final 3D conformations, they tend to choose the ones that minimize their free energy. To find the 3D conformation of a protein knowing its amino acid sequence, bioinformaticians use various models of different resolutions and artificial intelligence tools, as the protein folding prediction problem is a NP complete one. More precisely, to determine the backbone structure of the protein using the low resolution models (2D HP square and 3D HP cubic), by finding the conformation that minimize free energy, is intractable exactly. Both the proof of NP-completeness and the 2D prediction consider that acceptable conformations have to satisfy a self-avoiding walk (SAW) requirement, as two different amino acids cannot occupy a same position in the lattice. It is shown in this document that the SAW requirement considered when proving NP-completeness is different from the SAW requirement used in various prediction programs, and that they are different from the real biological requirement. Indeed, the proof of NP completeness and the predictions in silico consider conformations that are not possible in practice. Consequences of this fact are investigated in this research work.
We consider a voting setting where candidates have preferences about the outcome of the election and are free to join or leave the election. The corresponding candidacy game, where candidates choose strategically to participate or not, has been studied %initially by Dutta et al., who showed that no non-dictatorial voting procedure satisfying unanimity is candidacy-strategyproof, that is, is such that the joint action where all candidates enter the election is always a pure strategy Nash equilibrium. Dutta et al. also showed that for some voting tree procedures, there are candidacy games with no pure Nash equilibria, and that for the rule that outputs the sophisticated winner of voting by successive elimination, all games have a pure Nash equilibrium. No results were known about other voting rules. Here we prove several such results. For four candidates, the message is, roughly, that most scoring rules (with the exception of Borda) do not guarantee the existence of a pure Nash equilibrium but that Condorcet-consistent rules, for an odd number of voters, do. For five candidates, most rules we study no longer have this guarantee. Finally, we identify one prominent rule that guarantees the existence of a pure Nash equilibrium for any number of candidates (and for an odd number of voters): the Copeland rule. We also show that under mild assumptions on the voting rule, the existence of strong equilibria cannot be guaranteed.
Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster-Shafer theory of evidence and Shannon's entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework.
Logic programs under the stable model semantics, or answer-set programs, provide an expressive rule-based knowledge representation framework, featuring a formal, declarative and well-understood semantics. However, handling the evolution of rule bases is still a largely open problem. The AGM framework for belief change was shown to give inappropriate results when directly applied to logic programs under a non-monotonic semantics such as the stable models. The approaches to address this issue, developed so far, proposed update semantics based on manipulating the syntactic structure of programs and rules. More recently, AGM revision has been successfully applied to a significantly more expressive semantic characterisation of logic programs based on SE-models. This is an important step, as it changes the focus from the evolution of a syntactic representation of a rule base to the evolution of its semantic content. In this paper, we borrow results from the area of belief update to tackle the problem of updating (instead of revising) answer-set programs. We prove a representation theorem which makes it possible to constructively define any operator satisfying a set of postulates derived from Katsuno and Mendelzon's postulates for belief update. We define a specific operator based on this theorem, examine its computational complexity and compare the behaviour of this operator with syntactic rule update semantics from the literature. Perhaps surprisingly, we uncover a serious drawback of all rule update operators based on Katsuno and Mendelzon's approach to update and on SE-models.
Answer Set Programming (ASP) is a truly-declarative programming paradigm proposed in the area of non-monotonic reasoning and logic programming, that has been recently employed in many applications. The development of efficient ASP systems is, thus, crucial. Having in mind the task of improving the solving methods for ASP, there are two usual ways to reach this goal: $(i)$ extending state-of-the-art techniques and ASP solvers, or $(ii)$ designing a new ASP solver from scratch. An alternative to these trends is to build on top of state-of-the-art solvers, and to apply machine learning techniques for choosing automatically the "best" available solver on a per-instance basis. In this paper we pursue this latter direction. We first define a set of cheap-to-compute syntactic features that characterize several aspects of ASP programs. Then, we apply classification methods that, given the features of the instances in a {\sl training} set and the solvers' performance on these instances, inductively learn algorithm selection strategies to be applied to a {\sl test} set. We report the results of a number of experiments considering solvers and different training and test sets of instances taken from the ones submitted to the "System Track" of the 3rd ASP Competition. Our analysis shows that, by applying machine learning techniques to ASP solving, it is possible to obtain very robust performance: our approach can solve more instances compared with any solver that entered the 3rd ASP Competition. (To appear in Theory and Practice of Logic Programming (TPLP).)
We propose a decomposition of the max-min fair curriculum-based course timetabling (MMF-CB-CTT) problem. The decomposition models the room assignment subproblem as a generalized lexicographic bottleneck optimization problem (LBOP). We show that the generalized LBOP can be solved efficiently if the corresponding sum optimization problem can be solved efficiently. As a consequence, the room assignment subproblem of the MMF-CB-CTT problem can be solved efficiently. We use this insight to improve a previously proposed heuristic algorithm for the MMF-CB-CTT problem. Our experimental results indicate that using the new decomposition improves the performance of the algorithm on most of the 21 ITC2007 test instances with respect to the quality of the best solution found. Furthermore, we introduce a measure of the quality of a solution to a max-min fair optimization problem. This measure helps to overcome some limitations imposed by the qualitative nature of max-min fairness and aids the statistical evaluation of the performance of randomized algorithms for such problems. We use this measure to show that using the new decomposition the algorithm outperforms the original one on most instances with respect to the average solution quality.
Determination of dietary food consumed a day for patients with diseases in general, greatly affect the health of the body and the healing process, is no exception for people with kidney disease and urinary tract. This paper presents the determination of diet composition in the form of food subtance for people with kidney and urinary tract diseases with a genetic fuzzy approach. This approach combines fuzzy logic and genetic algorithms, which utilizing fuzzy logic fuzzy tools and techniques to model the components of the genetic algorithm and adapting genetic algorithm control parameters, with the aim of improving system performance. The Mamdani fuzzy inference model and fuzzy rules based on population parameters and generation are used to determine the probability of crossover and mutation, and was using In this study, 400 food survey data along with their substances was used as test material. From the data, a varying amount of population is established. Each chromosome has 10 genes in which the value of each gene indicates the index number of foodstuffs in the database. The fuzzy genetic approach produces 10 best food substance and their compositions. The composition of these foods has nutritional value in accordance with the number of calories needed by people with kidney and urinary tract diseases by type of food.
Smart home technology is a better choice for the people to care about security, comfort and power saving as well. It is required to develop technologies that recognize the Activities of Daily Living (ADLs) of the residents at home and detect the abnormal behavior in the individual's patterns. Data mining techniques such as Frequent pattern mining (FPM), High Utility Pattern (HUP) Mining were used to find those activity patterns from the collected sensor data. But applying the above technique for Activity Recognition from the temporal sensor data stream is highly complex and challenging task. So, a new approach is proposed for activity recognition from sensor data stream which is achieved by constructing Frequent Pattern Stream tree (FPS - tree). FPS is a sliding window based approach to discover the recent activity patterns over time from data streams. The proposed work aims at identifying the frequent pattern of the user from the sensor data streams which are later modeled for activity recognition. The proposed FPM algorithm uses a data structure called Linked Sensor Data Stream (LSDS) for storing the sensor data stream information which increases the efficiency of frequent pattern mining algorithm through both space and time. The experimental results show the efficiency of the proposed algorithm and this FPM is further extended for applying for power efficiency using HUP to detect the high usage of power consumption of residents at smart home.
The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple probabilistic graphics programs that define flexible generative models and to automatically invert them to interpret real-world images. Generative probabilistic graphics programs consist of a stochastic scene generator, a renderer based on graphics software, a stochastic likelihood model linking the renderer's output and the data, and latent variables that adjust the fidelity of the renderer and the tolerance of the likelihood model. Representations and algorithms from computer graphics, originally designed to produce high-quality images, are instead used as the deterministic backbone for highly approximate and stochastic generative models. This formulation combines probabilistic programming, computer graphics, and approximate Bayesian computation, and depends only on general-purpose, automatic inference techniques. We describe two applications: reading sequences of degraded and adversarially obscured alphanumeric characters, and inferring 3D road models from vehicle-mounted camera images. Each of the probabilistic graphics programs we present relies on under 20 lines of probabilistic code, and supports accurate, approximately Bayesian inferences about ambiguous real-world images.
The intuitive notion of evidence has both semantic and syntactic features. In this paper, we develop an {\em evidence logic} for epistemic agents faced with possibly contradictory evidence from different sources. The logic is based on a neighborhood semantics, where a neighborhood $N$ indicates that the agent has reason to believe that the true state of the world lies in $N$. Further notions of relative plausibility between worlds and beliefs based on the latter ordering are then defined in terms of this evidence structure, yielding our intended models for evidence-based beliefs. In addition, we also consider a second more general flavor, where belief and plausibility are modeled using additional primitive relations, and we prove a representation theorem showing that each such general model is a $p$-morphic image of an intended one. This semantics invites a number of natural special cases, depending on how uniform we make the evidence sets, and how coherent their total structure. We give a structural study of the resulting `uniform' and `flat' models. Our main result are sound and complete axiomatizations for the logics of all four major model classes with respect to the modal language of evidence, belief and safe belief. We conclude with an outlook toward logics for the dynamics of changing evidence, and the resulting language extensions and connections with logics of plausibility change.
Due to dynamic network conditions, routing is the most critical part in WMNs and needs to be optimised. The routing strategies developed for WMNs must be efficient to make it an operationally self configurable network. Thus we need to resort to near shortest path evaluation. This lays down the requirement of some soft computing approaches such that a near shortest path is available in an affordable computing time. This paper proposes a Fuzzy Logic based integrated cost measure in terms of delay, throughput and jitter. Based upon this distance (cost) between two adjacent nodes we evaluate minimal shortest path that updates routing tables. We apply two recent soft computing approaches namely Big Bang Big Crunch (BB-BC) and Biogeography Based Optimization (BBO) approaches to enumerate shortest or near short paths. BB-BC theory is related with the evolution of the universe whereas BBO is inspired by dynamical equilibrium in the number of species on an island. Both the algorithms have low computational time and high convergence speed. Simulation results show that the proposed routing algorithms find the optimal shortest path taking into account three most important parameters of network dynamics. It has been further observed that for the shortest path problem BB-BC outperforms BBO in terms of speed and percent error between the evaluated minimal path and the actual shortest path.
Dynamic behaviour of a WMN imposes stringent constraints on the routing policy of the network. In the shortest path based routing the shortest paths needs to be evaluated within a given time frame allowed by the WMN dynamics. The exact reasoning based shortest path evaluation methods usually fail to meet this rigid requirement. Thus, requiring some soft computing based approaches which can replace "best for sure" solutions with "good enough" solutions. This paper proposes a framework for optimal routing in the WMNs; where we investigate the suitability of Big Bang-Big Crunch (BB-BC), a soft computing based approach to evaluate shortest/near-shortest path. In order to make routing optimal we first propose to replace distance between the adjacent nodes with an integrated cost measure that takes into account throughput, delay, jitter and residual energy of a node. A fuzzy logic based inference mechanism evaluates this cost measure at each node. Using this distance measure we apply BB-BC optimization algorithm to evaluate shortest/near shortest path to update the routing tables periodically as dictated by network requirements. A large number of simulations were conducted and it has been observed that BB-BC algorithm appears to be a high potential candidate suitable for routing in WMNs.
Philosophers writing about the ravens paradox often note that Nicod's Condition (NC) holds given some set of background information, and fails to hold against others, but rarely go any further. That is, it is usually not explored which background information makes NC true or false. The present paper aims to fill this gap. For us, "(objective) background knowledge" is restricted to information that can be expressed as probability events. Any other configuration is regarded as being subjective and a property of the a priori probability distribution. We study NC in two specific settings. In the first case, a complete description of some individuals is known, e.g. one knows of each of a group of individuals whether they are black and whether they are ravens. In the second case, the number of individuals having a particular property is given, e.g. one knows how many ravens or how many black things there are (in the relevant population). While some of the most famous answers to the paradox are measure-dependent, our discussion is not restricted to any particular probability measure. Our most interesting result is that in the second setting, NC violates a simple kind of inductive inference (namely projectability). Since relative to NC, this latter rule is more closely related to, and more directly justified by our intuitive notion of inductive reasoning, this tension makes a case against the plausibility of NC. In the end, we suggest that the informal representation of NC may seem to be intuitively plausible because it can easily be mistaken for reasoning by analogy.
The language of probability is used to define several different types of conditional statements. There are four principal types: subjunctive, material, existential, and feasibility. Two further types of conditionals are defined using the propositional calculus and Boole's mathematical logic: truth-functional and Boolean feasibility (which turn out to be special cases of probabilistic conditionals). Each probabilistic conditional is quantified by a fractional parameter between zero and one that says whether it is purely affirmative, purely negative, or intermediate in its sense. Conditionals can be specialized further by their content to express factuality and counterfactuality, and revised or reformulated to account for exceptions and confounding factors. The various conditionals have distinct mathematical representations: through intermediate probability expressions and logical formulas, each conditional is eventually translated into a set of polynomial equations and inequalities (with real coefficients). The polynomial systems from different types of conditionals exhibit different patterns of behavior, concerning for example opposing conditionals or false antecedents. Interesting results can be computed from the relevant polynomial systems using well-known methods from algebra and computer science. Among other benefits, the proposed framework of analysis offers paraconsistent procedures for logical deduction that produce such familiar results as modus ponens, transitivity, disjunction introduction, and disjunctive syllogism; all while avoiding any explosion of consequences from inconsistent premises. Several example problems from Goodman and Adams are analyzed. A new perspective called polylogicism is presented: mathematical logic that respects the diversity among conditionals in particular and logic problems in general.
This paper establishes theoretical bonafides for implicit concurrent multivariate effect evaluation--implicit concurrency for short---a broad and versatile computational learning efficiency thought to underlie general-purpose, non-local, noise-tolerant optimization in genetic algorithms with uniform crossover (UGAs). We demonstrate that implicit concurrency is indeed a form of efficient learning by showing that it can be used to obtain close-to-optimal bounds on the time and queries required to approximately correctly solve a constrained version (k=7, \eta=1/5) of a recognizable computational learning problem: learning parities with noisy membership queries. We argue that a UGA that treats the noisy membership query oracle as a fitness function can be straightforwardly used to approximately correctly learn the essential attributes in O(log^1.585 n) queries and O(n log^1.585 n) time, where n is the total number of attributes. Our proof relies on an accessible symmetry argument and the use of statistical hypothesis testing to reject a global null hypothesis at the 10^-100 level of significance. It is, to the best of our knowledge, the first relatively rigorous identification of efficient computational learning in an evolutionary algorithm on a non-trivial learning problem.
Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses on a particular approach for this purpose called independence-based learning. Such approach guarantees the learning of the correct structure efficiently, whenever data is sufficient for representing the underlying distribution. However, an important issue of such approach is that the learned structures are encoded in an undirected graph. The problem with graphs is that they cannot encode some types of independence relations, such as the context-specific independences. They are a particular case of conditional independences that is true only for a certain assignment of its conditioning set, in contrast to conditional independences that must hold for all its assignments. In this work we present CSPC, an independence-based algorithm for learning structures that encode context-specific independences, and encoding them in a log-linear model, instead of a graph. The central idea of CSPC is combining the theoretical guarantees provided by the independence-based approach with the benefits of representing complex structures by using features in a log-linear model. We present experiments in a synthetic case, showing that CSPC is more accurate than the state-of-the-art IB algorithms when the underlying distribution contains CSIs.
Planning is a notoriously difficult computational problem of high worst-case complexity. Researchers have been investing significant efforts to develop heuristics or restrictions to make planning practically feasible. Case-based planning is a heuristic approach where one tries to reuse previous experience when solving similar problems in order to avoid some of the planning effort. Plan reuse may offer an interesting alternative to plan generation in some settings. We provide theoretical results that identify situations in which plan reuse is provably tractable. We perform our analysis in the framework of parameterized complexity, which supports a rigorous worst-case complexity analysis that takes structural properties of the input into account in terms of parameters. A central notion of parameterized complexity is fixed-parameter tractability which extends the classical notion of polynomial-time tractability by utilizing the effect of structural properties of the problem input. We draw a detailed map of the parameterized complexity landscape of several variants of problems that arise in the context of case-based planning. In particular, we consider the problem of reusing an existing plan, imposing various restrictions in terms of parameters, such as the number of steps that can be added to the existing plan to turn it into a solution of the planning instance at hand.
Verification of multi-agents systems (MAS) has been recently studied taking into account the need of expressing resource bounds. Several logics for specifying properties of MAS have been presented in quite a variety of scenarios with bounded resources. In this paper, we study a different formalism, called Priced Resource-Bounded Alternating-time Temporal Logic (PRBATL), whose main novelty consists in moving the notion of resources from a syntactic level (part of the formula) to a semantic one (part of the model). This allows us to track the evolution of the resource availability along the computations and provides us with a formalisms capable to model a number of real-world scenarios. Two relevant aspects are the notion of global availability of the resources on the market, that are shared by the agents, and the notion of price of resources, depending on their availability. In a previous work of ours, an initial step towards this new formalism was introduced, along with an EXPTIME algorithm for the model checking problem. In this paper we better analyze the features of the proposed formalism, also in comparison with previous approaches. The main technical contribution is the proof of the EXPTIME-hardness of the the model checking problem for PRBATL, based on a reduction from the acceptance problem for Linearly-Bounded Alternating Turing Machines. In particular, since the problem has multiple parameters, we show two fixed-parameter reductions.
The magnetic permeability of a ferrite is an important factor in designing devices such as inductors, transformers, and microwave absorbing materials among others. Due to this, it is advisable to study the magnetic permeability of a ferrite as a function of frequency. When an excitation that corresponds to a harmonic magnetic field \textbf{H} is applied to the system, this system responds with a magnetic flux density \textbf{B}; the relation between these two vectors can be expressed as \textbf{B}=$\mu(\omega)$ \textbf{H} . Where $\mu$ is the magnetic permeability. In this paper, ferrites were considered linear, homogeneous, and isotropic materials. A magnetic permeability model was applied to NiZn ferrites doped with Yttrium. The parameters of the model were adjusted using the Genetic Algorithm. In the computer science field of artificial intelligence, Genetic Algorithms and Machine Learning does rely upon nature's bounty for both inspiration nature's and mechanisms. Genetic Algorithms are probabilistic search procedures which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. For the numerical fitting usually is used a nonlinear least square method, this algorithm is based on calculus by starting from an initial set of variable values. This approach is mathematically elegant compared to the exhaustive or random searches but tends easily to get stuck in local minima. On the other hand, random methods use some probabilistic calculations to find variable sets. They tend to be slower but have greater success at finding the global minimum regardless of the initial values of the variables
Time-series classification has attracted considerable research attention due to the various domains where time-series data are observed, ranging from medicine to econometrics. Traditionally, the focus of time-series classification has been on short time-series data composed of a unique pattern with intraclass pattern distortions and variations, while recently there have been attempts to focus on longer series composed of various local patterns. This study presents a novel method which can detect local patterns in long time-series via fitting local polynomial functions of arbitrary degrees. The coefficients of the polynomial functions are converted to symbolic words via equivolume discretizations of the coefficients' distributions. The symbolic polynomial words enable the detection of similar local patterns by assigning the same words to similar polynomials. Moreover, a histogram of the frequencies of the words is constructed from each time-series' bag of words. Each row of the histogram enables a new representation for the series and symbolize the existence of local patterns and their frequencies. Experimental evidence demonstrates outstanding results of our method compared to the state-of-art baselines, by exhibiting the best classification accuracies in all the datasets and having statistically significant improvements in the absolute majority of experiments.
We provide a systematic analysis of levels of integration between discrete high-level reasoning and continuous low-level reasoning to address hybrid planning problems in robotics. We identify four distinct strategies for such an integration: (i) low-level checks are done for all possible cases in advance and then this information is used during plan generation, (ii) low-level checks are done exactly when they are needed during the search for a plan, (iii) first all plans are computed and then infeasible ones are filtered, and (iv) by means of replanning, after finding a plan, low-level checks identify whether it is infeasible or not; if it is infeasible, a new plan is computed considering the results of previous low- level checks. We perform experiments on hybrid planning problems in robotic manipulation and legged locomotion domains considering these four methods of integration, as well as some of their combinations. We analyze the usefulness of levels of integration in these domains, both from the point of view of computational efficiency (in time and space) and from the point of view of plan quality relative to its feasibility. We discuss advantages and disadvantages of each strategy in the light of experimental results and provide some guidelines on choosing proper strategies for a given domain.
There has been significant interest in crowdsourcing and human computation. One subclass of human computation applications are those directed at tasks that involve planning (e.g. travel planning) and scheduling (e.g. conference scheduling). Much of this work appears outside the traditional automated planning forums, and at the outset it is not clear whether automated planning has much of a role to play in these human computation systems. Interestingly however, work on these systems shows that even primitive forms of automated oversight of the human planner does help in significantly improving the effectiveness of the humans/crowd. In this paper, we will argue that the automated oversight used in these systems can be viewed as a primitive automated planner, and that there are several opportunities for more sophisticated automated planning in effectively steering crowdsourced planning. Straightforward adaptation of current planning technology is however hampered by the mismatch between the capabilities of human workers and automated planners. We identify two important challenges that need to be overcome before such adaptation of planning technology can occur: (i) interpreting the inputs of the human workers (and the requester) and (ii) steering or critiquing the plans being produced by the human workers armed only with incomplete domain and preference models. In this paper, we discuss approaches for handling these challenges, and characterize existing human computation systems in terms of the specific choices they make in handling these challenges.
When eliciting opinions from a group of experts, traditional devices used to promote honest reporting assume that there is an observable future outcome. In practice, however, this assumption is not always reasonable. In this paper, we propose a scoring method built on strictly proper scoring rules to induce honest reporting without assuming observable outcomes. Our method provides scores based on pairwise comparisons between the reports made by each pair of experts in the group. For ease of exposition, we introduce our scoring method by illustrating its application to the peer-review process. In order to do so, we start by modeling the peer-review process using a Bayesian model where the uncertainty regarding the quality of the manuscript is taken into account. Thereafter, we introduce our scoring method to evaluate the reported reviews. Under the assumptions that reviewers are Bayesian decision-makers and that they cannot influence the reviews of other reviewers, we show that risk-neutral reviewers strictly maximize their expected scores by honestly disclosing their reviews. We also show how the group's scores can be used to find a consensual review. Experimental results show that encouraging honest reporting through the proposed scoring method creates more accurate reviews than the traditional peer-review process.
The theory of natural selection cannot describe how early life evolved, in part because acquired characteristics are passed on through horizontal exchange. It has been proposed that culture, like life, began with the emergence of autopoietic form, thus its evolution too cannot be described by natural selection. The evolution of autopoietic form can be described using a framework referred to as Context-driven Actualization of Potential (CAP), which grew out of a generalization of the formalisms of quantum mechanics, and encompasses nondeterministic as well as deterministic change of state. The autopoietic structure that evolves through culture is the mind, or more accurately the conceptual network that yields an individual's internal model of the world. A branch of CAP research referred to as the state-context-property (SCOP) formalism provides a mathematical framework for reconciling the stability of conceptual structure with its susceptibility to context-driven change. The combination of two or more concepts (an extreme case of contextual influence), as occurs in insight, is modeled as a state of entanglement. Theoretical and empirical findings are presented that challenge assumptions underlying virtually all of cognitive science, such as the notion of spreading activation and the assumption that cognitive processes can be described with a Kolmogorovian probability model.
We investigate a method to deal with congestion of sectors and delays in the tactical phase of air traffic flow and capacity management. It relies on temporal objectives given for every point of the flight plans and shared among the controllers in order to create a collaborative environment. This would enhance the transition from the network view of the flow management to the local view of air traffic control. Uncertainty is modeled at the trajectory level with temporal information on the boundary points of the crossed sectors and then, we infer the probabilistic occupancy count. Therefore, we can model the accuracy of the trajectory prediction in the optimization process in order to fix some safety margins. On the one hand, more accurate is our prediction; more efficient will be the proposed solutions, because of the tighter safety margins. On the other hand, when uncertainty is not negligible, the proposed solutions will be more robust to disruptions. Furthermore, a multiobjective algorithm is used to find the tradeoff between the delays and congestion, which are antagonist in airspace with high traffic density. The flow management position can choose manually, or automatically with a preference-based algorithm, the adequate solution. This method is tested against two instances, one with 10 flights and 5 sectors and one with 300 flights and 16 sectors.
In this paper we study a particular aspect of the urban community policing: routine patrol route planning. We seek routes that guarantee visibility, as this has a sizable impact on the community perceived safety, allowing quick emergency responses and providing surveillance of selected sites (e.g., hospitals, schools). The planning is restricted to the availability of vehicles and strives to achieve balanced routes. We study an adaptation of the model for the multi-vehicle covering tour problem, in which a set of locations must be visited, whereas another subset must be close enough to the planned routes. It constitutes an NP-complete integer programming problem. Suboptimal solutions are obtained with several heuristics, some adapted from the literature and others developed by us. We solve some adapted instances from TSPLIB and an instance with real data, the former being compared with results from literature, and latter being compared with empirical data.
This paper presents a novel meta algorithm, Partition-Merge (PM), which takes existing centralized algorithms for graph computation and makes them distributed and faster. In a nutshell, PM divides the graph into small subgraphs using our novel randomized partitioning scheme, runs the centralized algorithm on each partition separately, and then stitches the resulting solutions to produce a global solution. We demonstrate the efficiency of the PM algorithm on two popular problems: computation of Maximum A Posteriori (MAP) assignment in an arbitrary pairwise Markov Random Field (MRF), and modularity optimization for community detection. We show that the resulting distributed algorithms for these problems essentially run in time linear in the number of nodes in the graph, and perform as well -- or even better -- than the original centralized algorithm as long as the graph has geometric structures. Here we say a graph has geometric structures, or polynomial growth property, when the number of nodes within distance r of any given node grows no faster than a polynomial function of r. More precisely, if the centralized algorithm is a C-factor approximation with constant C \ge 1, the resulting distributed algorithm is a (C+\delta)-factor approximation for any small \delta>0; but if the centralized algorithm is a non-constant (e.g. logarithmic) factor approximation, then the resulting distributed algorithm becomes a constant factor approximation. For general graphs, we compute explicit bounds on the loss of performance of the resulting distributed algorithm with respect to the centralized algorithm.
In addition to their limpid interface with semantics, categorial grammars enjoy another important property: learnability. This was first noticed by Buskowsky and Penn and further studied by Kanazawa, for Bar-Hillel categorial grammars. What about Lambek categorial grammars? In a previous paper we showed that product free Lambek grammars where learnable from structured sentences, the structures being incomplete natural deductions. These grammars were shown to be unlearnable from strings by Foret and Le Nir. In the present paper we show that Lambek grammars, possibly with product, are learnable from proof frames that are incomplete proof nets. After a short reminder on grammatical inference \`a la Gold, we provide an algorithm that learns Lambek grammars with product from proof frames and we prove its convergence. We do so for 1-valued also known as rigid Lambek grammars with product, since standard techniques can extend our result to $k$-valued grammars. Because of the correspondence between cut-free proof nets and normal natural deductions, our initial result on product free Lambek grammars can be recovered. We are sad to dedicate the present paper to Philippe Darondeau, with whom we started to study such questions in Rennes at the beginning of the millennium, and who passed away prematurely. We are glad to dedicate the present paper to Jim Lambek for his 90 birthday: he is the living proof that research is an eternal learning process.
Small groups of interneurons, abbreviated by CPG for central pattern generators, are arranged into neural networks to generate a variety of core bursting rhythms with specific phase-locked states, on distinct time scales, that govern vital motor behaviors in invertebrates such as chewing, swimming, etc. These movements in lower level animals mimic motions of organs in higher animals due to evolutionarily conserved mechanisms. Hence, various neurological diseases can be linked to abnormal movement of body parts that are regulated by a malfunctioning CPG. In this paper, we, being inspired by recent experimental studies of neuronal activity patterns recorded from a swimming motion CPG of the sea slug {\it Melibe leonina}, examine a mathematical model of a 4-cell network that can plausibly and stably underlie the observed bursting rhythm. We develop a dynamical systems framework for explaining the existence and robustness of phase-locked states in activity patterns produced by the modeled CPGs. The proposed tools can be used for identifying core components for other CPG networks with reliable bursting outcomes and specific phase relationships between the interneurons. Our findings can be employed for identifying or implementing the conditions for normal and pathological functioning of basic CPGs of animals and artificially intelligent prosthetics that can regulate various movements.
State-of-the-art algorithms for industrial instances of MaxSAT problem rely on iterative calls to a SAT solver. Preprocessing is crucial for the acceleration of SAT solving, and the key preprocessing techniques rely on the application of resolution and subsumption elimination. Additionally, satisfiability-preserving clause elimination procedures are often used. Since MaxSAT computation typically involves a large number of SAT calls, we are interested in whether an input instance to a MaxSAT problem can be preprocessed up-front, i.e. prior to running the MaxSAT solver, rather than (or, in addition to) during each iterative SAT solver call. The key requirement in this setting is that the preprocessing has to be sound, i.e. so that the solution can be reconstructed correctly and efficiently after the execution of a MaxSAT algorithm on the preprocessed instance. While, as we demonstrate in this paper, certain clause elimination procedures are sound for MaxSAT, it is well-known that this is not the case for resolution and subsumption elimination. In this paper we show how to adapt these preprocessing techniques to MaxSAT. To achieve this we recast the MaxSAT problem in a recently introduced labelled-CNF framework, and show that within the framework the preprocessing techniques can be applied soundly. Furthermore, we show that MaxSAT algorithms restated in the framework have a natural implementation on top of an incremental SAT solver. We evaluate the prototype implementation of a MaxSAT algorithm WMSU1 in this setting, demonstrate the effectiveness of preprocessing, and show overall improvement with respect to non-incremental versions of the algorithm on some classes of problems.
Even though modern service-oriented and data-oriented architectures promise to deliver loosely coupled control systems, they are inherently brittle as they commonly depend on a priori agreed interfaces and data models. At the same time, the Semantic Web and a whole set of accompanying standards and tools are emerging, advocating ontologies as the basis for knowledge exchange. In this paper we aim to identify a number of key ideas from the myriad of knowledge-based practices that can readily be implemented by control systems today. We demonstrate with a practical example (a three-channel imager for the Mercator Telescope) how ontologies developed in the Web Ontology Language (OWL) can serve as a meta-model for our instrument, covering as many engineering aspects of the project as needed. We show how a concrete system model can be built on top of this meta-model via a set of Domain Specific Languages (DSLs), supporting both formal verification and the generation of software and documentation artifacts. Finally we reason how the available semantics can be exposed at run-time by adding a "semantic layer" that can be browsed, queried, monitored etc. by any OPC UA-enabled client.
Probabilistic inference over large data sets is a challenging data management problem since exact inference is generally #P-hard and is most often solved approximately with sampling-based methods today. This paper proposes an alternative approach for approximate evaluation of conjunctive queries with standard relational databases: In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking their minimum. We provide an algorithm that takes into account important schema information to enumerate only the minimal necessary plans among all possible plans. Importantly, this algorithm is a strict generalization of all known PTIME self-join-free conjunctive queries: A query is in PTIME if and only if our algorithm returns one single plan. Furthermore, our approach is a generalization of a family of efficient ranking methods from graphs to hypergraphs. We also adapt three relational query optimization techniques to evaluate all necessary plans very fast. We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over non-probabilistic methods for ranking query answers. We also note that the techniques developed in this paper apply immediately to lifted inference from statistical relational models since lifted inference corresponds to PTIME plans in probabilistic databases.
Designing algorithms capable of efficiently constructing minimal models of CNFs is an important task in AI. This paper provides new results along this research line and presents new algorithms for performing minimal model finding and checking over positive propositional CNFs and model minimization over propositional CNFs. An algorithmic schema, called the Generalized Elimination Algorithm (GEA) is presented, that computes a minimal model of any positive CNF. The schema generalizes the Elimination Algorithm (EA) [BP97], which computes a minimal model of positive head-cycle-free (HCF) CNF theories. While the EA always runs in polynomial time in the size of the input HCF CNF, the complexity of the GEA depends on the complexity of the specific eliminating operator invoked therein, which may in general turn out to be exponential. Therefore, a specific eliminating operator is defined by which the GEA computes, in polynomial time, a minimal model for a class of CNF that strictly includes head-elementary-set-free (HEF) CNF theories [GLL06], which form, in their turn, a strict superset of HCF theories. Furthermore, in order to deal with the high complexity associated with recognizing HEF theories, an "incomplete" variant of the GEA (called IGEA) is proposed: the resulting schema, once instantiated with an appropriate elimination operator, always constructs a model of the input CNF, which is guaranteed to be minimal if the input theory is HEF. In the light of the above results, the main contribution of this work is the enlargement of the tractability frontier for the minimal model finding and checking and the model minimization problems.
Human activity and environment produces sounds such as, at home, the noise produced by water, cough, or television. These sounds can be used to determine the activity in the environment. The objective is to monitor a person's activity or determine his environment using a single low cost microphone by sound analysis. The purpose is to adapt programs to the activity or environment or detect abnormal situations. Some patterns of over expressed repeatedly in the sequences of recognized sounds inter and intra environment allow to characterize activities such as the entrance of a person in the house, or a tv program watched. We first manually annotated 1500 sounds of daily life activity of old persons living at home recognized sounds. Then we inferred an ontology and enriched the database of annotation with a crowed sourced manual annotation of 7500 sounds to help with the annotation of the most frequent sounds. Using learning sound algorithms, we defined 50 types of the most frequent sounds. We used this set of recognizable sounds as a base to tag sounds and put tags on them. By using over expressed number of motifs of sequences of the tags, we were able to categorize using only a single low-cost microphone, complex activities of daily life of a persona at home as watching TV, entrance in the apartment of a person, or phone conversation including detecting unknown activities as repeated tasks performed by users.
Ontology matching finds correspondences between similar entities of different ontologies. Two ontologies may be similar in some aspects such as structure, semantic etc. Most ontology matching systems integrate multiple matchers to extract all the similarities that two ontologies may have. Thus, we face a major problem to aggregate different similarities. Some matching systems use experimental weights for aggregation of similarities among different matchers while others use machine learning approaches and optimization algorithms to find optimal weights to assign to different matchers. However, both approaches have their own deficiencies. In this paper, we will point out the problems and shortcomings of current similarity aggregation strategies. Then, we propose a new strategy, which enables us to utilize the structural information of ontologies to get weights of matchers, for the similarity aggregation task. For achieving this goal, we create a new Ontology Matching system which it uses three available matchers, namely GMO, ISub and VDoc. We have tested our similarity aggregation strategy on the OAEI 2012 data set. Experimental results show significant improvements in accuracies of several cases, especially in matching the classes of ontologies. We will compare the performance of our similarity aggregation strategy with other well-known strategies
We present algorithms to effectively represent a set of Markov decision processes (MDPs), whose optimal policies have already been learned, by a smaller source subset for lifelong, policy-reuse-based transfer learning in reinforcement learning. This is necessary when the number of previous tasks is large and the cost of measuring similarity counteracts the benefit of transfer. The source subset forms an `$\epsilon$-net' over the original set of MDPs, in the sense that for each previous MDP $M_p$, there is a source $M^s$ whose optimal policy has $<\epsilon$ regret in $M_p$. Our contributions are as follows. We present EXP-3-Transfer, a principled policy-reuse algorithm that optimally reuses a given source policy set when learning for a new MDP. We present a framework to cluster the previous MDPs to extract a source subset. The framework consists of (i) a distance $d_V$ over MDPs to measure policy-based similarity between MDPs; (ii) a cost function $g(\cdot)$ that uses $d_V$ to measure how good a particular clustering is for generating useful source tasks for EXP-3-Transfer and (iii) a provably convergent algorithm, MHAV, for finding the optimal clustering. We validate our algorithms through experiments in a surveillance domain.
Many optimization tasks have to be handled in noisy environments, where we cannot obtain the exact evaluation of a solution but only a noisy one. For noisy optimization tasks, evolutionary algorithms (EAs), a kind of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on empirical studying and designing EAs for noisy optimization, while, the theoretical counterpart has been little investigated. In this paper, we investigate a largely ignored question, i.e., whether an optimization problem will always become harder for EAs in a noisy environment. We prove that the answer is negative, with respect to the measurement of the expected running time. The result implies that, for optimization tasks that have already been quite hard to solve, the noise may not have a negative effect, and the easier a task the more negatively affected by the noise. On a representative problem where the noise has a strong negative effect, we examine two commonly employed mechanisms in EAs dealing with noise, the re-evaluation and the threshold selection strategies. The analysis discloses that the two strategies, however, both are not effective, i.e., they do not make the EA more noise tolerant. We then find that a small modification of the threshold selection allows it to be proven as an effective strategy for dealing with the noise in the problem.
Answer Set Programming (ASP) is a popular framework for modeling combinatorial problems. However, ASP cannot easily be used for reasoning about uncertain information. Possibilistic ASP (PASP) is an extension of ASP that combines possibilistic logic and ASP. In PASP a weight is associated with each rule, where this weight is interpreted as the certainty with which the conclusion can be established when the body is known to hold. As such, it allows us to model and reason about uncertain information in an intuitive way. In this paper we present new semantics for PASP, in which rules are interpreted as constraints on possibility distributions. Special models of these constraints are then identified as possibilistic answer sets. In addition, since ASP is a special case of PASP in which all the rules are entirely certain, we obtain a new characterization of ASP in terms of constraints on possibility distributions. This allows us to uncover a new form of disjunction, called weak disjunction, that has not been previously considered in the literature. In addition to introducing and motivating the semantics of weak disjunction, we also pinpoint its computational complexity. In particular, while the complexity of most reasoning tasks coincides with standard disjunctive ASP, we find that brave reasoning for programs with weak disjunctions is easier.
In this paper, we consider active information acquisition when the prediction model is meant to be applied on a targeted subset of the population. The goal is to label a pre-specified fraction of customers in the target or test set by iteratively querying for information from the non-target or training set. The number of queries is limited by an overall budget. Arising in the context of two rather disparate applications- banking and medical diagnosis, we pose the active information acquisition problem as a constrained optimization problem. We propose two greedy iterative algorithms for solving the above problem. We conduct experiments with synthetic data and compare results of our proposed algorithms with few other baseline approaches. The experimental results show that our proposed approaches perform better than the baseline schemes.
Improving the throughput of molecular docking, a computationally intensive phase of the virtual screening process, is a highly sought area of research since it has a significant weight in the drug designing process. With such improvements, the world might find cures for incurable diseases like HIV disease and Cancer sooner. Our approach presented in this paper is to utilize a multi-core environment to introduce Data Level Parallelism (DLP) to the Autodock Vina software, which is a widely used for molecular docking software. Autodock Vina already exploits Instruction Level Parallelism (ILP) in multi-core environments and therefore optimized for such environments. However, with the results we have obtained, it can be clearly seen that our approach has enhanced the throughput of the already optimized software by more than six times. This will dramatically reduce the time consumed for the lead identification phase in drug designing along with the shift in the processor technology from multi-core to many-core of the current era. Therefore, we believe that the contribution of this project will effectively make it possible to expand the number of small molecules docked against a drug target and improving the chances to design drugs for incurable diseases.
Bike sharing systems are a very popular means to provide bikes to citizens in a simple and cheap way. The idea is to install bike stations at various points in the city, from which a registered user can easily loan a bike by removing it from a specialized rack. After the ride, the user may return the bike at any station (if there is a free rack). Services of this kind are mainly public or semi-public, often aimed at increasing the attractiveness of non-motorized means of transportation, and are usually free, or almost free, of charge for the users. Depending on their location, bike stations have specific patterns regarding when they are empty or full. For instance, in cities where most jobs are located near the city centre, the commuters cause certain peaks in the morning: the central bike stations are filled, while the stations in the outskirts are emptied. Furthermore, stations located on top of a hill are more likely to be empty, since users are less keen on cycling uphill to return the bike, and often leave their bike at a more reachable station. These issues result in substantial user dissatisfaction which may eventually cause the users to abandon the service. This is why nowadays most bike sharing system providers take measures to rebalance them. Over the last few years, balancing bike sharing systems (BBSS) has become increasingly studied in optimization. As such, generating meaningful instance to serve as a benchmark for the proposed approaches is an important task. In this technical report we describe the procedure we used to generate BBSS problem instances from data of the CitiBike NYC bike sharing system.
The number of malicious software (malware) is growing out of control. Syntactic signature based detection cannot cope with such growth and manual construction of malware signature databases needs to be replaced by computer learning based approaches. Currently, a single modern signature capturing the semantics of a malicious behavior can be used to replace an arbitrarily large number of old-fashioned syntactical signatures. However teaching computers to learn such behaviors is a challenge. Existing work relies on dynamic analysis to extract malicious behaviors, but such technique does not guarantee the coverage of all behaviors. To sidestep this limitation we show how to learn malware signatures using static reachability analysis. The idea is to model binary programs using pushdown systems (that can be used to model the stack operations occurring during the binary code execution), use reachability analysis to extract behaviors in the form of trees, and use subtrees that are common among the trees extracted from a training set of malware files as signatures. To detect malware we propose to use a tree automaton to compactly store malicious behavior trees and check if any of the subtrees extracted from the file under analysis is malicious. Experimental data shows that our approach can be used to learn signatures from a training set of malware files and use them to detect a test set of malware that is 5 times the size of the training set.
We consider the discrete assignment problem in which agents express ordinal preferences over objects and these objects are allocated to the agents in a fair manner. We use the stochastic dominance relation between fractional or randomized allocations to systematically define varying notions of proportionality and envy-freeness for discrete assignments. The computational complexity of checking whether a fair assignment exists is studied for these fairness notions. We also characterize the conditions under which a fair assignment is guaranteed to exist. For a number of fairness concepts, polynomial-time algorithms are presented to check whether a fair assignment exists. Our algorithmic results also extend to the case of unequal entitlements of agents. Our NP-hardness result, which holds for several variants of envy-freeness, answers an open question posed by Bouveret, Endriss, and Lang (ECAI 2010). We also propose fairness concepts that always suggest a non-empty set of assignments with meaningful fairness properties. Among these concepts, optimal proportionality and optimal weak proportionality appear to be desirable fairness concepts.
Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches.
Ontology Learning (OL) is the computational task of generating a knowledge base in the form of an ontology given an unstructured corpus whose content is in natural language (NL). Several works can be found in this area most of which are limited to statistical and lexico-syntactic pattern matching based techniques Light-Weight OL. These techniques do not lead to very accurate learning mostly because of several linguistic nuances in NL. Formal OL is an alternative (less explored) methodology were deep linguistics analysis is made using theory and tools found in computational linguistics to generate formal axioms and definitions instead simply inducing a taxonomy. In this paper we propose "Description Logic (DL)" based formal OL framework for learning factual IS-A type sentences in English. We claim that semantic construction of IS-A sentences is non trivial. Hence, we also claim that such sentences requires special studies in the context of OL before any truly formal OL can be proposed. We introduce a learner tool, called DLOL_IS-A, that generated such ontologies in the owl format. We have adopted "Gold Standard" based OL evaluation on IS-A rich WCL v.1.1 dataset and our own Community representative IS-A dataset. We observed significant improvement of DLOL_IS-A when compared to the light-weight OL tool Text2Onto and formal OL tool FRED.
A Constraint Satisfaction Problem (CSP) is a framework used for modeling and solving constrained problems. Tree-search algorithms like backtracking try to construct a solution to a CSP by selecting the variables of the problem one after another. The order in which these algorithm select the variables potentially have significant impact on the search performance. Various heuristics have been proposed for choosing good variable ordering. Many powerful variable ordering heuristics weigh the constraints first and then utilize the weights for selecting good order of the variables. Constraint weighting are basically employed to identify global bottlenecks in a CSP. In this paper, we propose a new approach for learning weights for the constraints using competitive coevolutionary Genetic Algorithm (GA). Weights learned by the coevolutionary GA later help to make better choices for the first few variables in a search. In the competitive coevolutionary GA, constraints and candidate solutions for a CSP evolve together through an inverse fitness interaction process. We have conducted experiments on several random, quasi-random and patterned instances to measure the efficiency of the proposed approach. The results and analysis show that the proposed approach is good at learning weights to distinguish the hard constraints for quasi-random instances and forced satisfiable random instances generated with the Model RB. For other type of instances, RNDI still seems to be the best approach as our experiments show.
These are the proceedings of the Second Workshop on GRAPH Inspection and Traversal Engineering (GRAPHITE 2013), which took place on March 24, 2013 in Rome, Italy, as a satellite event of the 16th European Joint Conferences on Theory and Practice of Software (ETAPS 2013). The topic of the GRAPHITE workshop is graph analysis in all its forms in computer science. Graphs are used to represent data in many application areas, and they are subjected to various computational algorithms in order to acquire the desired information. These graph algorithms tend to have common characteristics, such as duplicate detection to guarantee their termination, independent of their application domain. Over the past few years, it has been shown that the scalability of such algorithms can be dramatically improved by using, e.g., external memory, by exploiting parallel architectures, such as clusters, multi-core CPUs, and graphics processing units, and by using heuristics to guide the search. Novel techniques to further scale graph search algorithms, and new applications of graph search are within the scope of this workshop. Another topic of interest of the event is more related to the structural properties of graphs: which kind of graph characteristics are relevant for a particular application area, and how can these be measured? Finally, any novel way of using graphs for a particular application area is on topic. The goal of this event is to gather scientists from different communities, such as model checking, artificial intelligence planning, game playing, and algorithm engineering, who do research on graph search algorithms, such that awareness of each others' work is increased.
In this paper we examine the usefulness of two classes of algorithms Distance Methods, Discrete Character Methods (Felsenstein and Felsenstein 2003) widely used in genetics, for predicting the family relationships among a set of related languages and therefore, diachronic language change. Applying these algorithms to the data on the numbers of shared cognates- with-change and changed as well as unchanged cognates for a group of six languages belonging to a Dravidian language sub-family given in Krishnamurti et al. (1983), we observed that the resultant phylogenetic trees are largely in agreement with the linguistic family tree constructed using the comparative method of reconstruction with only a few minor differences. Furthermore, we studied these minor differences and found that they were cases of genuine ambiguity even for a well-trained historical linguist. We evaluated the trees obtained through our experiments using a well-defined criterion and report the results here. We finally conclude that quantitative methods like the ones we examined are quite useful in predicting family relationships among languages. In addition, we conclude that a modest degree of confidence attached to the intuition that there could indeed exist a parallelism between the processes of linguistic and genetic change is not totally misplaced.
Investigation of the underlying physics or biology from empirical data requires a quantifiable notion of similarity - when do two observed data sets indicate nearly identical generating processes, and when they do not. The discriminating characteristics to look for in data is often determined by heuristics designed by experts, $e.g.$, distinct shapes of "folded" lightcurves may be used as "features" to classify variable stars, while determination of pathological brain states might require a Fourier analysis of brainwave activity. Finding good features is non-trivial. Here, we propose a universal solution to this problem: we delineate a principle for quantifying similarity between sources of arbitrary data streams, without a priori knowledge, features or training. We uncover an algebraic structure on a space of symbolic models for quantized data, and show that such stochastic generators may be added and uniquely inverted; and that a model and its inverse always sum to the generator of flat white noise. Therefore, every data stream has an anti-stream: data generated by the inverse model. Similarity between two streams, then, is the degree to which one, when summed to the other's anti-stream, mutually annihilates all statistical structure to noise. We call this data smashing. We present diverse applications, including disambiguation of brainwaves pertaining to epileptic seizures, detection of anomalous cardiac rhythms, and classification of astronomical objects from raw photometry. In our examples, the data smashing principle, without access to any domain knowledge, meets or exceeds the performance of specialized algorithms tuned by domain experts.
Constraints have played an important role in the construction of GUIs, where they are mainly used to define the layout of the widgets. Resizing behavior is very important in GUIs because areas have domain specific parameters such as form the resizing of windows. If linear objective function is used and window is resized then error is not distributed equally. To distribute the error equally, a quadratic objective function is introduced. Different algorithms are widely used for solving linear constraints and quadratic problems in a variety of different scientific areas. The linear relxation, Kaczmarz, direct and linear programming methods are common methods for solving linear constraints for GUI layout. The interior point and active set methods are most commonly used techniques to solve quadratic programming problems. Current constraint solvers designed for GUI layout do not use interior point methods for solving a quadratic objective function subject to linear equality and inequality constraints. In this paper, performance aspects and the convergence speed of interior point and active set methods are compared along with one most commonly used linear programming method when they are implemented for graphical user interface layout. The performance and convergence of the proposed algorithms are evaluated empirically using randomly generated UI layout specifications of various sizes. The results show that the interior point algorithms perform significantly better than the Simplex method and QOCA-solver, which uses the active set method implementation for solving quadratic optimization.
In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings.
Many computer programs have graphical user interfaces (GUIs), which need good layout to make efficient use of the available screen real estate. Most GUIs do not have a fixed layout, but are resizable and able to adapt themselves. Constraints are a powerful tool for specifying adaptable GUI layouts: they are used to specify a layout in a general form, and a constraint solver is used to find a satisfying concrete layout, e.g.\ for a specific GUI size. The constraint solver has to calculate a new layout every time a GUI is resized or changed, so it needs to be efficient to ensure a good user experience. One approach for constraint solvers is based on the Gauss-Seidel algorithm and successive over-relaxation (SOR). Our observation is that a solution after resizing or changing is similar in structure to a previous solution. Thus, our hypothesis is that we can increase the computational performance of an SOR-based constraint solver if we reuse the solution of a previous layout to warm-start the solving of a new layout. In this paper we report on experiments to test this hypothesis experimentally for three common use cases: big-step resizing, small-step resizing and constraint change. In our experiments, we measured the solving time for randomly generated GUI layout specifications of various sizes. For all three cases we found that the performance is improved if an existing solution is used as a starting solution for a new layout.
The problem of defining and locating free will (FW) in physics is studied. On basis of logical paradoxes, we argue that FW has a meta-theoretic character, like the concept of truth in Tarski's undefinability theorem. Free will exists relative to a base theory if there is freedom to deviate from the deterministic or indeterministic dynamics in the theory, with the deviations caused by parameters (representing will) in the meta-theory. By contrast, determinism and indeterminism do not require meta-theoretic considerations in their formalization, making FW a fundamentally new causal primitive. FW exists relative to the meta-theory if there is freedom for deviation, due to higher-order causes. Absolute free will, which corresponds to our intuitive introspective notion of free will, exists if this meta-theoretic hierarchy is infinite. We argue that this hierarchy corresponds to higher levels of uncomputability. In other words, at any finitely high order in the hierarchy, there are uncomputable deviations from the law at that order. Applied to the human condition, the hierarchy corresponds to deeper levels of the subconscious or unconscious mind. Possible ramifications of our model for physics, neuroscience and artificial intelligence (AI) are briefly considered.
Following the "decomposition-and-ensemble" principle, the empirical mode decomposition (EMD)-based modeling framework has been widely used as a promising alternative for nonlinear and nonstationary time series modeling and prediction. The end effect, which occurs during the sifting process of EMD and is apt to distort the decomposed sub-series and hurt the modeling process followed, however, has been ignored in previous studies. Addressing the end effect issue, this study proposes to incorporate end condition methods into EMD-based decomposition and ensemble modeling framework for one- and multi-step ahead time series prediction. Four well-established end condition methods, Mirror method, Coughlin's method, Slope-based method, and Rato's method, are selected, and support vector regression (SVR) is employed as the modeling technique. For the purpose of justification and comparison, well-known NN3 competition data sets are used and four well-established prediction models are selected as benchmarks. The experimental results demonstrated that significant improvement can be achieved by the proposed EMD-based SVR models with end condition methods. The EMD-SBM-SVR model and EMD-Rato-SVR model, in particular, achieved the best prediction performances in terms of goodness of forecast measures and equality of accuracy of competing forecasts test.
We consider schemes for obtaining truthful reports on a common but hidden signal from large groups of rational, self-interested agents. One example are online feedback mechanisms, where users provide observations about the quality of a product or service so that other users can have an accurate idea of what quality they can expect. However, (i) providing such feedback is costly, and (ii) there are many motivations for providing incorrect feedback. Both problems can be addressed by reward schemes which (i) cover the cost of obtaining and reporting feedback, and (ii) maximize the expected reward of a rational agent who reports truthfully. We address the design of such incentive-compatible rewards for feedback generated in environments with pure adverse selection. Here, the correlation between the true knowledge of an agent and her beliefs regarding the likelihoods of reports of other agents can be exploited to make honest reporting a Nash equilibrium. In this paper we extend existing methods for designing incentive-compatible rewards by also considering collusion. We analyze different scenarios, where, for example, some or all of the agents collude. For each scenario we investigate whether a collusion-resistant, incentive-compatible reward scheme exists, and use automated mechanism design to specify an algorithm for deriving an efficient reward mechanism.
This paper presents a new method for inferring the semantic properties of documents by leveraging free-text keyphrase annotations. Such annotations are becoming increasingly abundant due to the recent dramatic growth in semi-structured, user-generated online content. One especially relevant domain is product reviews, which are often annotated by their authors with pros/cons keyphrases such as a real bargain or good value. These annotations are representative of the underlying semantic properties; however, unlike expert annotations, they are noisy: lay authors may use different labels to denote the same property, and some labels may be missing. To learn using such noisy annotations, we find a hidden paraphrase structure which clusters the keyphrases. The paraphrase structure is linked with a latent topic model of the review texts, enabling the system to predict the properties of unannotated documents and to effectively aggregate the semantic properties of multiple reviews. Our approach is implemented as a hierarchical Bayesian model with joint inference. We find that joint inference increases the robustness of the keyphrase clustering and encourages the latent topics to correlate with semantically meaningful properties. Multiple evaluations demonstrate that our model substantially outperforms alternative approaches for summarizing single and multiple documents into a set of semantically salient keyphrases.
Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive compatible, direct mechanisms that are efficient (i.e., maximise social utility) and individually rational (i.e., agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will "always" successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications, where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agents probability of succeeding at a given task are often captured and reasoned about using the notion of "trust". Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called "trust-based mechanisms", that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^5 possible allocations in 40 seconds).
Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic-oriented, informative multi-document summarization where the goal is to produce a single text as a compressed version of a set of documents with a minimum loss of relevant information. In this paper, we experiment with one empirical method and two unsupervised statistical machine learning techniques: K-means and Expectation Maximization (EM), for computing relative importance of the sentences. We compare the results of these approaches. Our experiments show that the empirical approach outperforms the other two techniques and EM performs better than K-means. However, the performance of these approaches depends entirely on the feature set used and the weighting of these features. In order to measure the importance and relevance to the user query we extract different kinds of features (i.e. lexical, lexical semantic, cosine similarity, basic element, tree kernel based syntactic and shallow-semantic) for each of the document sentences. We use a local search technique to learn the weights of the features. To the best of our knowledge, no study has used tree kernel functions to encode syntactic/semantic information for more complex tasks such as computing the relatedness between the query sentences and the document sentences in order to generate query-focused summaries (or answers to complex questions). For each of our methods of generating summaries (i.e. empirical, K-means and EM) we show the effects of syntactic and shallow-semantic features over the bag-of-words (BOW) features.
A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation.
Vast amounts of text on the Web are unstructured and ungrammatical, such as classified ads, auction listings, forum postings, etc. We call such text "posts." Despite their inconsistent structure and lack of grammar, posts are full of useful information. This paper presents work on semi-automatically building tables of relational information, called "reference sets," by analyzing such posts directly. Reference sets can be applied to a number of tasks such as ontology maintenance and information extraction. Our reference-set construction method starts with just a small amount of background knowledge, and constructs tuples representing the entities in the posts to form a reference set. We also describe an extension to this approach for the special case where even this small amount of background knowledge is impossible to discover and use. To evaluate the utility of the machine-constructed reference sets, we compare them to manually constructed reference sets in the context of reference-set-based information extraction. Our results show the reference sets constructed by our method outperform manually constructed reference sets. We also compare the reference-set-based extraction approach using the machine-constructed reference set to supervised extraction approaches using generic features. These results demonstrate that using machine-constructed reference sets outperforms the supervised methods, even though the supervised methods require training data.
In the usual models of cooperative game theory, the outcome of a coalition formation process is either the grand coalition or a coalition structure that consists of disjoint coalitions. However, in many domains where coalitions are associated with tasks, an agent may be involved in executing more than one task, and thus may distribute his resources among several coalitions. To tackle such scenarios, we introduce a model for cooperative games with overlapping coalitions--or overlapping coalition formation (OCF) games. We then explore the issue of stability in this setting. In particular, we introduce a notion of the core, which generalizes the corresponding notion in the traditional (non-overlapping) scenario. Then, under some quite general conditions, we characterize the elements of the core, and show that any element of the core maximizes the social welfare. We also introduce a concept of balancedness for overlapping coalitional games, and use it to characterize coalition structures that can be extended to elements of the core. Finally, we generalize the notion of convexity to our setting, and show that under some natural assumptions convex games have a non-empty core. Moreover, we introduce two alternative notions of stability in OCF that allow a wider range of deviations, and explore the relationships among the corresponding definitions of the core, as well as the classic (non-overlapping) core and the Aubin core. We illustrate the general properties of the three cores, and also study them from a computational perspective, thus obtaining additional insights into their fundamental structure.
Weighted voting is a classic model of cooperation among agents in decision-making domains. In such games, each player has a weight, and a coalition of players wins the game if its total weight meets or exceeds a given quota. A players power in such games is usually not directly proportional to his weight, and is measured by a power index, the most prominent among which are the Shapley-Shubik index and the Banzhaf index.In this paper, we investigate by how much a player can change his power, as measured by the Shapley-Shubik index or the Banzhaf index, by means of a false-name manipulation, i.e., splitting his weight among two or more identities. For both indices, we provide upper and lower bounds on the effect of weight-splitting. We then show that checking whether a beneficial split exists is NP-hard, and discuss efficient algorithms for restricted cases of this problem, as well as randomized algorithms for the general case. We also provide an experimental evaluation of these algorithms. Finally, we examine related forms of manipulative behavior, such as annexation, where a player subsumes other players, or merging, where several players unite into one. We characterize the computational complexity of such manipulations and provide limits on their effects. For the Banzhaf index, we describe a new paradox, which we term the Annexation Non-monotonicity Paradox.
There has been significant recent interest in game-theoretic approaches to security, with much of the recent research focused on utilizing the leader-follower Stackelberg game model. Among the major applications are the ARMOR program deployed at LAX Airport and the IRIS program in use by the US Federal Air Marshals (FAMS). The foundational assumption for using Stackelberg games is that security forces (leaders), acting first, commit to a randomized strategy; while their adversaries (followers) choose their best response after surveillance of this randomized strategy. Yet, in many situations, a leader may face uncertainty about the follower's surveillance capability. Previous work fails to address how a leader should compute her strategy given such uncertainty. We provide five contributions in the context of a general class of security games. First, we show that the Nash equilibria in security games are interchangeable, thus alleviating the equilibrium selection problem. Second, under a natural restriction on security games, any Stackelberg strategy is also a Nash equilibrium strategy; and furthermore, the solution is unique in a class of security games of which ARMOR is a key exemplar. Third, when faced with a follower that can attack multiple targets, many of these properties no longer hold. Fourth, we show experimentally that in most (but not all) games where the restriction does not hold, the Stackelberg strategy is still a Nash equilibrium strategy, but this is no longer true when the attacker can attack multiple targets. Finally, as a possible direction for future research, we propose an extensive-form game model that makes the defender's uncertainty about the attacker's ability to observe explicit.
The problem of adversarial multi-robot patrol has gained interest in recent years, mainly due to its immediate relevance to various security applications. In this problem, robots are required to repeatedly visit a target area in a way that maximizes their chances of detecting an adversary trying to penetrate through the patrol path. When facing a strong adversary that knows the patrol strategy of the robots, if the robots use a deterministic patrol algorithm, then in many cases it is easy for the adversary to penetrate undetected (in fact, in some of those cases the adversary can guarantee penetration). Therefore this paper presents a non-deterministic patrol framework for the robots. Assuming that the strong adversary will take advantage of its knowledge and try to penetrate through the patrols weakest spot, hence an optimal algorithm is one that maximizes the chances of detection in that point. We therefore present a polynomial-time algorithm for determining an optimal patrol under the Markovian strategy assumption for the robots, such that the probability of detecting the adversary in the patrols weakest spot is maximized. We build upon this framework and describe an optimal patrol strategy for several robotic models based on their movement abilities (directed or undirected) and sensing abilities (perfect or imperfect), and in different environment models - either patrol around a perimeter (closed polygon) or an open fence (open polyline).
In automatic summarization, centrality-as-relevance means that the most important content of an information source, or a collection of information sources, corresponds to the most central passages, considering a representation where such notion makes sense (graph, spatial, etc.). We assess the main paradigms, and introduce a new centrality-based relevance model for automatic summarization that relies on the use of support sets to better estimate the relevant content. Geometric proximity is used to compute semantic relatedness. Centrality (relevance) is determined by considering the whole input source (and not only local information), and by taking into account the existence of minor topics or lateral subjects in the information sources to be summarized. The method consists in creating, for each passage of the input source, a support set consisting only of the most semantically related passages. Then, the determination of the most relevant content is achieved by selecting the passages that occur in the largest number of support sets. This model produces extractive summaries that are generic, and language- and domain-independent. Thorough automatic evaluation shows that the method achieves state-of-the-art performance, both in written text, and automatically transcribed speech summarization, including when compared to considerably more complex approaches.
The Aviation Safety Reporting System collects voluntarily submitted reports on aviation safety incidents to facilitate research work aiming to reduce such incidents. To effectively reduce these incidents, it is vital to accurately identify why these incidents occurred. More precisely, given a set of possible causes, or shaping factors, this task of cause identification involves identifying all and only those shaping factors that are responsible for the incidents described in a report. We investigate two approaches to cause identification. Both approaches exploit information provided by a semantic lexicon, which is automatically constructed via Thelen and Riloffs Basilisk framework augmented with our linguistic and algorithmic modifications. The first approach labels a report using a simple heuristic, which looks for the words and phrases acquired during the semantic lexicon learning process in the report. The second approach recasts cause identification as a text classification problem, employing supervised and transductive text classification algorithms to learn models from incident reports labeled with shaping factors and using the models to label unseen reports. Our experiments show that both the heuristic-based approach and the learning-based approach (when given sufficient training data) outperform the baseline system significantly.
Today, mobile robots are expected to carry out increasingly complex tasks in multifarious, real-world environments. Often, the tasks require a certain semantic understanding of the workspace. Consider, for example, spoken instructions from a human collaborator referring to objects of interest; the robot must be able to accurately detect these objects to correctly understand the instructions. However, existing object detection, while competent, is not perfect. In particular, the performance of detection algorithms is commonly sensitive to the position of the sensor relative to the objects in the scene. This paper presents an online planning algorithm which learns an explicit model of the spatial dependence of object detection and generates plans which maximize the expected performance of the detection, and by extension the overall plan performance. Crucially, the learned sensor model incorporates spatial correlations between measurements, capturing the fact that successive measurements taken at the same or nearby locations are not independent. We show how this sensor model can be incorporated into an efficient forward search algorithm in the information space of detected objects, allowing the robot to generate motion plans efficiently. We investigate the performance of our approach by addressing the tasks of door and text detection in indoor environments and demonstrate significant improvement in detection performance during task execution over alternative methods in simulated and real robot experiments.
Many relations of scientific interest are nonlinear, and even in linear systems distributions are often non-Gaussian, for example in fMRI BOLD data. A class of search procedures for causal relations in high dimensional data relies on sample derived conditional independence decisions. The most common applications rely on Gaussian tests that can be systematically erroneous in nonlinear non-Gaussian cases. Recent work (Gretton et al. (2009), Tillman et al. (2009), Zhang et al. (2011)) has proposed conditional independence tests using Reproducing Kernel Hilbert Spaces (RKHS). Among these, perhaps the most efficient has been KCI (Kernel Conditional Independence, Zhang et al. (2011)), with computational requirements that grow effectively at least as O(N3), placing it out of range of large sample size analysis, and restricting its applicability to high dimensional data sets. We propose a class of O(N2) tests using conditional correlation independence (CCI) that require a few seconds on a standard workstation for tests that require tens of minutes to hours for the KCI method, depending on degree of parallelization, with similar accuracy. For accuracy on difficult nonlinear, non-Gaussian data sets, we also compare a recent test due to Harris & Drton (2012), applicable to nonlinear, non-Gaussian distributions in the Gaussian copula, as well as to partial correlation, a linear Gaussian test.
Large bilingual parallel texts (also known as bitexts) are usually stored in a compressed form, and previous work has shown that they can be more efficiently compressed if the fact that the two texts are mutual translations is exploited. For example, a bitext can be seen as a sequence of biwords ---pairs of parallel words with a high probability of co-occurrence--- that can be used as an intermediate representation in the compression process. However, the simple biword approach described in the literature can only exploit one-to-one word alignments and cannot tackle the reordering of words. We therefore introduce a generalization of biwords which can describe multi-word expressions and reorderings. We also describe some methods for the binary compression of generalized biword sequences, and compare their performance when different schemes are applied to the extraction of the biword sequence. In addition, we show that this generalization of biwords allows for the implementation of an efficient algorithm to look on the compressed bitext for words or text segments in one of the texts and retrieve their counterpart translations in the other text ---an application usually referred to as translation spotting--- with only some minor modifications in the compression algorithm.
The computation of relatedness between two fragments of text in an automated manner requires taking into account a wide range of factors pertaining to the meaning the two fragments convey, and the pairwise relations between their words. Without doubt, a measure of relatedness between text segments must take into account both the lexical and the semantic relatedness between words. Such a measure that captures well both aspects of text relatedness may help in many tasks, such as text retrieval, classification and clustering. In this paper we present a new approach for measuring the semantic relatedness between words based on their implicit semantic links. The approach exploits only a word thesaurus in order to devise implicit semantic links between words. Based on this approach, we introduce Omiotis, a new measure of semantic relatedness between texts which capitalizes on the word-to-word semantic relatedness measure (SR) and extends it to measure the relatedness between texts. We gradually validate our method: we first evaluate the performance of the semantic relatedness measure between individual words, covering word-to-word similarity and relatedness, synonym identification and word analogy; then, we proceed with evaluating the performance of our method in measuring text-to-text semantic relatedness in two tasks, namely sentence-to-sentence similarity and paraphrase recognition. Experimental evaluation shows that the proposed method outperforms every lexicon-based method of semantic relatedness in the selected tasks and the used data sets, and competes well against corpus-based and hybrid approaches.
Quality of General Game Playing (GGP) matches suffers from slow state-switching and weak knowledge modules. Instantiation and Propositional Networks offer great performance gains over Prolog-based reasoning, but do not scale well. In this publication mGDL, a variant of GDL stripped of function constants, has been defined as a basis for simple reasoning machines. mGDL allows to easily map rules to C++ functions. 253 out of 270 tested GDL rule sheets conformed to mGDL without any modifications; the rest required minor changes. A revised (m)GDL to C++ translation scheme has been reevaluated; it brought gains ranging from 28% to 7300% over YAP Prolog, managing to compile even demanding rule sheets under few seconds. For strengthening game knowledge, spatial features inspired by similar successful techniques from computer Go have been proposed. For they required an Euclidean metric, a small board extension to GDL has been defined through a set of ground atomic sentences. An SGA-based genetic algorithm has been designed for tweaking game parameters and conducting self-plays, so the features could be mined from meaningful game records. The approach has been tested on a small cluster, giving performance gains up to 20% more wins against the baseline UCT player. Implementations of proposed ideas constitutes the core of GGP Spatium - a small C++/Python GGP framework, created for developing compact GGP Players and problem solvers.
In the last decade, a lot of effort has been put into securing software application during development in the software industry. Software security is a research field in this area which looks at how security can be weaved into software at each phase of software development lifecycle (SDLC). The use of attack patterns is one of the approaches that have been proposed for integrating security during the design phase of SDLC. While this approach help developers in identify security flaws in their software designs, the need to apply the proper security capability that will mitigate the threat identified is very important. To assist in this area, the uses of security patterns have been proposed to help developers to identify solutions to recurring security problems. However due to different types of security patterns and their taxonomy, software developers are faced with the challenge of finding and selecting appropriate security patterns that addresses the security risks in their design. In this paper, we propose a tool based on Neural Network for proposing solutions in form of security patterns to threats in attack patterns matching attacking patterns. From the result of performance of the neural network, we found out that the neural network was able to match attack patterns to security patterns that can mitigate the threat in the attack pattern. With this information developers are better informed in making decision on the solution for securing their application.
Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as Question Answering, Information Extraction, and Summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, we have concentrated our efforts to reduce the need for annotated data as much as possible. This paper presents two different algorithms towards this goal. The first algorithm is a weakly supervised machine learning approach for classification of temporal relations between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, inspired by the hypothesis of "one type of temporal relation per discourse, it extracts useful information from a cluster of topically related documents. We show that by combining the global information of such a cluster with local decisions of a general classifier, a bootstrapping cross-document classifier can be built to extract temporal relations between events. Our experiments show that without any additional annotated data, the accuracy of the proposed algorithm is higher than that of several previous successful systems. The second proposed method for temporal relation extraction is based on the expectation maximization (EM) algorithm. Within EM, we used different techniques such as a greedy best-first search and integer linear programming for temporal inconsistency removal. We think that the experimental results of our EM based algorithm, as a first step toward a fully unsupervised temporal relation extraction method, is encouraging.
We give the analysis of the computational complexity of coalition structure generation over graphs. Given an undirected graph G = (N,E) and a valuation function v : P(N) \to R over the subsets of nodes, the problem is to find a partition of N into connected subsets, that maximises the sum of the components values. This problem is generally NP-complete; in particular, it is hard for a defined class of valuation functions which are independent of disconnected members - that is, two nodes have no effect on each other's marginal contribution to their vertex separator. Nonetheless, for all such functions we provide bounds on the complexity of coalition structure generation over general and minor-free graphs. Our proof is constructive and yields algorithms for solving corresponding instances of the problem. Furthermore, we derive linear time bounds for graphs of bounded treewidth. However, as we show, the problem remains NP-complete for planar graphs, and hence, for any K_k minor free graphs where k \geq 5. Moreover, a 3-SAT problem with m clauses can be represented by a coalition structure generation problem over a planar graph with O(m^2) nodes. Importantly, our hardness result holds for a particular subclass of valuation functions, termed edge sum, where the value of each subset of nodes is simply determined by the sum of given weights of the edges in the induced subgraph.
To tackle the vocabulary problem in conversational systems, previous work has applied unsupervised learning approaches on co-occurring speech and eye gaze during interaction to automatically acquire new words. Although these approaches have shown promise, several issues related to human language behavior and human-machine conversation have not been addressed. First, psycholinguistic studies have shown certain temporal regularities between human eye movement and language production. While these regularities can potentially guide the acquisition process, they have not been incorporated in the previous unsupervised approaches. Second, conversational systems generally have an existing knowledge base about the domain and vocabulary. While the existing knowledge can potentially help bootstrap and constrain the acquired new words, it has not been incorporated in the previous models. Third, eye gaze could serve different functions in human-machine conversation. Some gaze streams may not be closely coupled with speech stream, and thus are potentially detrimental to word acquisition. Automated recognition of closely-coupled speech-gaze streams based on conversation context is important. To address these issues, we developed new approaches that incorporate user language behavior, domain knowledge, and conversation context in word acquisition. We evaluated these approaches in the context of situated dialogue in a virtual world. Our experimental results have shown that incorporating the above three types of contextual information significantly improves word acquisition performance.
We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X_1 into a resource-rich language Y given a bi-text containing a limited number of parallel sentences for X_1-Y and a larger bi-text for X_2-Y for some resource-rich language X_2 that is closely related to X_1. This is achieved by taking advantage of the opportunities that vocabulary overlap and similarities between the languages X_1 and X_2 in spelling, word order, and syntax offer: (1) we improve the word alignments for the resource-poor language, (2) we further augment it with additional translation options, and (3) we take care of potential spelling differences through appropriate transliteration. The evaluation for Indonesian- >English using Malay and for Spanish -> English using Portuguese and pretending Spanish is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points, respectively, which is an improvement over the best rivaling approaches, while using much less additional data. Overall, our method cuts the amount of necessary "real training data by a factor of 2--5.
The thyroid, an endocrine gland that secretes hormones in the blood, circulates its products to all tissues of the body, where they control vital functions in every cell. Normal levels of thyroid hormone help the brain, heart, intestines, muscles and reproductive system function normally. Thyroid hormones control the metabolism of the body. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Therefore, the correct diagnosis of these diseases is very important topic. In this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features (LHNFCSF) is presented for diagnosis of thyroid diseases. The performance evaluation of this system is estimated by using classification accuracy and k-fold cross-validation. The results indicated that the classification accuracy without feature selection was 98.6047% and 97.6744% during training and testing phases, respectively with RMSE of 0.02335. After applying feature selection algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase. However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for each class, 90.6977% using two clusters, 91.8605% using three clusters and 97.6744% using four clusters for each class and 12 fuzzy rules. The obtained classification accuracy was very promising with regard to the other classification applications in literature for this problem.
Biological organisms are composed of numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for diagnosing diseases, and drug development. Scientists studying these processes have identified various pathways responsible for drug metabolism, and signal transduction, etc. Newer techniques and speed improvements have resulted in deeper knowledge about these pathways, resulting in refined models that tend to be large and complex, making it difficult for a person to remember all aspects of it. Thus, computer models are needed to analyze them. We want to build such a system that allows modeling of biological systems and pathways in such a way that we can answer questions about them. Many existing models focus on structural and/or factoid questions, using surface-level knowledge that does not require understanding the underlying model. We believe these are not the kind of questions that a biologist may ask someone to test their understanding of the biological processes. We want our system to answer the kind of questions a biologist may ask. Such questions appear in early college level text books. Thus the main goal of our thesis is to develop a system that allows us to encode knowledge about biological pathways and answer such questions about them demonstrating understanding of the pathway. To that end, we develop a language that will allow posing such questions and illustrate the utility of our framework with various applications in the biological domain. We use some existing tools with modifications to accomplish our goal. Finally, we apply our system to real world applications by extracting pathway knowledge from text and answering questions related to drug development.
Alignment-free sequence analysis approaches provide important alternatives over multiple sequence alignment (MSA) in biological sequence analysis because alignment-free approaches have low computation complexity and are not dependent on high level of sequence identity, however, most of the existing alignment-free methods do not employ true full information content of sequences and thus can not accurately reveal similarities and differences among DNA sequences. We present a novel alignment-free computational method for sequence analysis based on Ramanujan-Fourier transform (RFT), in which complete information of DNA sequences is retained. We represent DNA sequences as four binary indicator sequences and apply RFT on the indicator sequences to convert them into frequency domain. The Euclidean distance of the complete RFT coefficients of DNA sequences are used as similarity measure. To address the different lengths in Euclidean space of RFT coefficients, we pad zeros to short DNA binary sequences so that the binary sequences equal the longest length in the comparison sequence data. Thus, the DNA sequences are compared in the same dimensional frequency space without information loss. We demonstrate the usefulness of the proposed method by presenting experimental results on hierarchical clustering of genes and genomes. The proposed method opens a new channel to biological sequence analysis, classification, and structural module identification.
Optimizing an interactive system against a predefined online metric is particularly challenging, when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different search result page for the same query, but we normally cannot infer reliably from search log how users would react to the new result page. Consequently, it appears impossible to accurately estimate online metrics that depend on user feedback, unless the new engine is run to serve users and compared with a baseline in an A/B test. This approach, while valid and successful, is unfortunately expensive and time-consuming. In this paper, we propose to address this problem using causal inference techniques, under the contextual-bandit framework. This approach effectively allows one to run (potentially infinitely) many A/B tests offline from search log, making it possible to estimate and optimize online metrics quickly and inexpensively. Focusing on an important component in a commercial search engine, we show how these ideas can be instantiated and applied, and obtain very promising results that suggest the wide applicability of these techniques.
Scientific practice typically involves repeatedly studying a system, each time trying to unravel a different perspective. In each study, the scientist may take measurements under different experimental conditions (interventions, manipulations, perturbations) and measure different sets of quantities (variables). The result is a collection of heterogeneous data sets coming from different data distributions. In this work, we present algorithm COmbINE, which accepts a collection of data sets over overlapping variable sets under different experimental conditions; COmbINE then outputs a summary of all causal models indicating the invariant and variant structural characteristics of all models that simultaneously fit all of the input data sets. COmbINE converts estimated dependencies and independencies in the data into path constraints on the data-generating causal model and encodes them as a SAT instance. The algorithm is sound and complete in the sample limit. To account for conflicting constraints arising from statistical errors, we introduce a general method for sorting constraints in order of confidence, computed as a function of their corresponding p-values. In our empirical evaluation, COmbINE outperforms in terms of efficiency the only pre-existing similar algorithm; the latter additionally admits feedback cycles, but does not admit conflicting constraints which hinders the applicability on real data. As a proof-of-concept, COmbINE is employed to co-analyze 4 real, mass-cytometry data sets measuring phosphorylated protein concentrations of overlapping protein sets under 3 different interventions.
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the analytical challenge exists in double. The use of effective sampling technique in classification algorithms always yields good prediction accuracy. The SEER public use cancer database provides various prominent class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling techniques in classifying the prognosis variable and propose an ideal sampling method based on the outcome of the experimentation. In the first phase of this work the traditional random sampling and stratified sampling techniques have been used. At the next level the balanced stratified sampling with variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been focused on performing the pre_processing of the SEER data set. The classification model for experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with three traditional classifiers namely Decision Tree, Naive Bayes and K-Nearest Neighbor. The three prognosis factors survival, stage and metastasis have been used as class labels for experimental comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model as the sample size increases, but the traditional approach fluctuates before the optimum results.
Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine the probability of query answers in PLP, is intractable in general, thereby motivating the need for approximate techniques. In this paper, we present a technique for approximate inference of conditional probabilities for PLP queries. It is an Adaptive Markov Chain Monte Carlo (MCMC) technique, where the distribution from which samples are drawn is modified as the Markov Chain is explored. In particular, the distribution is progressively modified to increase the likelihood that a generated sample is consistent with evidence. In our context, each sample is uniquely characterized by the outcomes of a set of random variables. Inspired by reinforcement learning, our technique propagates rewards to random variable/outcome pairs used in a sample based on whether the sample was consistent or not. The cumulative rewards of each outcome is used to derive a new "adapted distribution" for each random variable. For a sequence of samples, the distributions are progressively adapted after each sample. For a query with "Markovian evaluation structure", we show that the adapted distribution of samples converges to the query's conditional probability distribution. For Markovian queries, we present a modified adaptation process that can be used in adaptive MCMC as well as adaptive independent sampling. We empirically evaluate the effectiveness of the adaptive sampling methods for queries with and without Markovian evaluation structure.
In this paper we introduce a Bayesian framework for solving a class of problems termed Multi-agent Inverse Reinforcement Learning (MIRL). Compared to the well-known Inverse Reinforcement Learning (IRL) problem, MIRL is formalized in the context of a stochastic game rather than a Markov decision process (MDP). Games bring two primary challenges: First, the concept of optimality, central to MDPs, loses its meaning and must be replaced with a more general solution concept, such as the Nash equilibrium. Second, the non-uniqueness of equilibria means that in MIRL, in addition to multiple reasonable solutions for a given inversion model, there may be multiple inversion models that are all equally sensible approaches to solving the problem. We establish a theoretical foundation for competitive two-agent MIRL problems and propose a Bayesian optimization algorithm to solve the problem. We focus on the case of two-person zero-sum stochastic games, developing a generative model for the likelihood of unknown rewards of agents given observed game play assuming that the two agents follow a minimax bipolicy. As a numerical illustration, we apply our method in the context of an abstract soccer game. For the soccer game, we investigate relationships between the extent of prior information and the quality of learned rewards. Results suggest that covariance structure is more important than mean value in reward priors.
We describe Venture, an interactive virtual machine for probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language descended from Lisp. Unlike Church, Venture also provides a compositional language for custom inference strategies built out of scalable exact and approximate techniques. We also describe four key aspects of Venture's implementation that build on ideas from probabilistic graphical models. First, we describe the stochastic procedure interface (SPI) that specifies and encapsulates primitive random variables. The SPI supports custom control flow, higher-order probabilistic procedures, partially exchangeable sequences and ``likelihood-free'' stochastic simulators. It also supports external models that do inference over latent variables hidden from Venture. Second, we describe probabilistic execution traces (PETs), which represent execution histories of Venture programs. PETs capture conditional dependencies, existential dependencies and exchangeable coupling. Third, we describe partitions of execution histories called scaffolds that factor global inference problems into coherent sub-problems. Finally, we describe a family of stochastic regeneration algorithms for efficiently modifying PET fragments contained within scaffolds. Stochastic regeneration linear runtime scaling in cases where many previous approaches scaled quadratically. We show how to use stochastic regeneration and the SPI to implement general-purpose inference strategies such as Metropolis-Hastings, Gibbs sampling, and blocked proposals based on particle Markov chain Monte Carlo and mean-field variational inference techniques.
This paper introduces a new paradigm for minimax game-tree search algo- rithms. MT is a memory-enhanced version of Pearls Test procedure. By changing the way MT is called, a number of best-first game-tree search algorithms can be simply and elegantly constructed (including SSS*). Most of the assessments of minimax search algorithms have been based on simulations. However, these simulations generally do not address two of the key ingredients of high performance game-playing programs: iterative deepening and memory usage. This paper presents experimental data from three game-playing programs (checkers, Othello and chess), covering the range from low to high branching factor. The improved move ordering due to iterative deepening and memory usage results in significantly different results from those portrayed in the literature. Whereas some simulations show Alpha-Beta expanding almost 100% more leaf nodes than other algorithms [12], our results showed variations of less than 20%. One new instance of our framework (MTD-f) out-performs our best alpha- beta searcher (aspiration NegaScout) on leaf nodes, total nodes and execution time. To our knowledge, these are the first reported results that compare both depth-first and best-first algorithms given the same amount of memory
In 1979 Stockman introduced the SSS* minimax search algorithm that domi- nates Alpha-Beta in the number of leaf nodes expanded. Further investigation of the algorithm showed that it had three serious drawbacks, which prevented its use by practitioners: it is difficult to understand, it has large memory requirements, and it is slow. This paper presents an alternate formulation of SSS*, in which it is implemented as a series of Alpha-Beta calls that use a transposition table (AB- SSS*). The reformulation solves all three perceived drawbacks of SSS*, making it a practical algorithm. Further, because the search is now based on Alpha-Beta, the extensive research on minimax search enhancements can be easily integrated into AB-SSS*. To test AB-SSS* in practise, it has been implemented in three state-of-the- art programs: for checkers, Othello and chess. AB-SSS* is comparable in performance to Alpha-Beta on leaf node count in all three games, making it a viable alternative to Alpha-Beta in practise. Whereas SSS* has usually been regarded as being entirely different from Alpha-Beta, it turns out to be just an Alpha-Beta enhancement, like null-window searching. This runs counter to published simulation results. Our research leads to the surprising result that iterative deepening versions of Alpha-Beta can expand fewer leaf nodes than iterative deepening versions of SSS* due to dynamic move re-ordering.
Knuth and Moore presented a theoretical lower bound on the number of leaves that any fixed-depth minimax tree-search algorithm traversing a uniform tree must explore, the so-called minimal tree. Since real-life minimax trees are not uniform, the exact size of this tree is not known for most applications. Further, most games have transpositions, implying that there exists a minimal graph which is smaller than the minimal tree. For three games (chess, Othello and checkers) we compute the size of the minimal tree and the minimal graph. Empirical evidence shows that in all three games, enhanced Alpha-Beta search is capable of building a tree that is close in size to that of the minimal graph. Hence, it appears game-playing programs build nearly optimal search trees. However, the conventional definition of the minimal graph is wrong. There are ways in which the size of the minimal graph can be reduced: by maximizing the number of transpositions in the search, and generating cutoffs using branches that lead to smaller search trees. The conventional definition of the minimal graph is just a left-most approximation. Calculating the size of the real minimal graph is too computationally intensive. However, upper bound approximations show it to be significantly smaller than the left-most minimal graph. Hence, it appears that game-playing programs are not searching as efficiently as is widely believed. Understanding the left-most and real minimal search graphs leads to some new ideas for enhancing Alpha-Beta search. One of them, enhanced transposition cutoffs, is shown to significantly reduce search tree size.
We present a polyphonic MIDI score-following algorithm capable of following performances with arbitrary repeats and skips, based on a probabilistic model of musical performances. It is attractive in practical applications of score following to handle repeats and skips which may be made arbitrarily during performances, but the algorithms previously described in the literature cannot be applied to scores of practical length due to problems with large computational complexity. We propose a new type of hidden Markov model (HMM) as a performance model which can describe arbitrary repeats and skips including performer tendencies on distributed score positions before and after them, and derive an efficient score-following algorithm that reduces computational complexity without pruning. A theoretical discussion on how much such information on performer tendencies improves the score-following results is given. The proposed score-following algorithm also admits performance mistakes and is demonstrated to be effective in practical situations by carrying out evaluations with human performances. The proposed HMM is potentially valuable for other topics in information processing and we also provide a detailed description of inference algorithms.
One important challenge for probabilistic logics is reasoning with very large knowledge bases (KBs) of imperfect information, such as those produced by modern web-scale information extraction systems. One scalability problem shared by many probabilistic logics is that answering queries involves "grounding" the query---i.e., mapping it to a propositional representation---and the size of a "grounding" grows with database size. To address this bottleneck, we present a first-order probabilistic language called ProPPR in which that approximate "local groundings" can be constructed in time independent of database size. Technically, ProPPR is an extension to stochastic logic programs (SLPs) that is biased towards short derivations; it is also closely related to an earlier relational learning algorithm called the path ranking algorithm (PRA). We show that the problem of constructing proofs for this logic is related to computation of personalized PageRank (PPR) on a linearized version of the proof space, and using on this connection, we develop a proveably-correct approximate grounding scheme, based on the PageRank-Nibble algorithm. Building on this, we develop a fast and easily-parallelized weight-learning algorithm for ProPPR. In experiments, we show that learning for ProPPR is orders magnitude faster than learning for Markov logic networks; that allowing mutual recursion (joint learning) in KB inference leads to improvements in performance; and that ProPPR can learn weights for a mutually recursive program with hundreds of clauses, which define scores of interrelated predicates, over a KB containing one million entities.
Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels --- voice call, short message, and email --- to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a "rich club" maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy.
Face verification remains a challenging problem in very complex conditions with large variations such as pose, illumination, expression, and occlusions. This problem is exacerbated when we rely unrealistically on a single training data source, which is often insufficient to cover the intrinsically complex face variations. This paper proposes a principled multi-task learning approach based on Discriminative Gaussian Process Latent Variable Model, named GaussianFace, to enrich the diversity of training data. In comparison to existing methods, our model exploits additional data from multiple source-domains to improve the generalization performance of face verification in an unknown target-domain. Importantly, our model can adapt automatically to complex data distributions, and therefore can well capture complex face variations inherent in multiple sources. Extensive experiments demonstrate the effectiveness of the proposed model in learning from diverse data sources and generalize to unseen domain. Specifically, the accuracy of our algorithm achieves an impressive accuracy rate of 98.52% on the well-known and challenging Labeled Faces in the Wild (LFW) benchmark. For the first time, the human-level performance in face verification (97.53%) on LFW is surpassed.
Scoring systems are an extremely important class of election systems. A length-$m$ (so-called) scoring vector applies only to $m$-candidate elections. To handle general elections, one must use a family of vectors, one per length. The most elegant approach to making sure such families are "family-like" is the recently introduced notion of (polynomial-time uniform) pure scoring rules [Betzler and Dorn 2010], where each scoring vector is obtained from its precursor by adding one new coefficient. We obtain the first dichotomy theorem for pure scoring rules for a control problem. In particular, for constructive control by adding voters (CCAV), we show that CCAV is solvable in polynomial time for $k$-approval with $k \leq 3$, $k$-veto with $k \leq 2$, every pure scoring rule in which only the two top-rated candidates gain nonzero scores, and a particular rule that is a "hybrid" of 1-approval and 1-veto. For all other pure scoring rules, CCAV is NP-complete. We also investigate the descriptive richness of different models for defining pure scoring rules, proving how more rule-generation time gives more rules, proving that rationals give more rules than do the natural numbers, and proving that some restrictions previously thought to be "w.l.o.g." in fact do lose generality.
We study techniques to incentivize self-interested agents to form socially desirable solutions in scenarios where they benefit from mutual coordination. Towards this end, we consider coordination games where agents have different intrinsic preferences but they stand to gain if others choose the same strategy as them. For non-trivial versions of our game, stable solutions like Nash Equilibrium may not exist, or may be socially inefficient even when they do exist. This motivates us to focus on designing efficient algorithms to compute (almost) stable solutions like Approximate Equilibrium that can be realized if agents are provided some additional incentives. Our results apply in many settings like adoption of new products, project selection, and group formation, where a central authority can direct agents towards a strategy but agents may defect if they have better alternatives. We show that for any given instance, we can either compute a high quality approximate equilibrium or a near-optimal solution that can be stabilized by providing small payments to some players. We then generalize our model to encompass situations where player relationships may exhibit complementarities and present an algorithm to compute an Approximate Equilibrium whose stability factor is linear in the degree of complementarity. Our results imply that a little influence is necessary in order to ensure that selfish players coordinate and form socially efficient solutions.
Research on multi-agent planning has been popular in recent years. While previous research has been motivated by the understanding that, through cooperation, multi-agent systems can achieve tasks that are unachievable by single-agent systems, there are no formal characterizations of situations where cooperation is required to achieve a goal, thus warranting the application of multi-agent systems. In this paper, we provide such a formal discussion from the planning aspect. We first show that determining whether there is required cooperation (RC) is intractable is general. Then, by dividing the problems that require cooperation (referred to as RC problems) into two classes -- problems with heterogeneous and homogeneous agents, we aim to identify all the conditions that can cause RC in these two classes. We establish that when none of these identified conditions hold, the problem is single-agent solvable. Furthermore, with a few assumptions, we provide an upper bound on the minimum number of agents required for RC problems with homogeneous agents. This study not only provides new insights into multi-agent planning, but also has many applications. For example, in human-robot teaming, when a robot cannot achieve a task, it may be due to RC. In such cases, the human teammate should be informed and, consequently, coordinate with other available robots for a solution.
A {\it dynamic reasoning system} (DRS) is an adaptation of a conventional formal logical system that explicitly portrays reasoning as a temporal activity, with each extralogical input to the system and each inference rule application being viewed as occurring at a distinct time step. Every DRS incorporates some well-defined logic together with a controller that serves to guide the reasoning process in response to user inputs. Logics are generic, whereas controllers are application-specific. Every controller does, nonetheless, provide an algorithm for nonmonotonic belief revision. The general notion of a DRS comprises a framework within which one can formulate the logic and algorithms for a given application and prove that the algorithms are correct, i.e., that they serve to (i) derive all salient information and (ii) preserve the consistency of the belief set. This paper illustrates the idea with ordinary first-order predicate calculus, suitably modified for the present purpose, and an example. The example revisits some classic nonmonotonic reasoning puzzles (Opus the Penguin, Nixon Diamond) and shows how these can be resolved in the context of a DRS, using an expanded version of first-order logic that incorporates typed predicate symbols. All concepts are rigorously defined and effectively computable, thereby providing the foundation for a future software implementation.
Runtime monitoring is one of the central tasks to provide operational decision support to running business processes, and check on-the-fly whether they comply with constraints and rules. We study runtime monitoring of properties expressed in LTL on finite traces (LTLf) and in its extension LDLf. LDLf is a powerful logic that captures all monadic second order logic on finite traces, which is obtained by combining regular expressions and LTLf, adopting the syntax of propositional dynamic logic (PDL). Interestingly, in spite of its greater expressivity, LDLf has exactly the same computational complexity of LTLf. We show that LDLf is able to capture, in the logic itself, not only the constraints to be monitored, but also the de-facto standard RV-LTL monitors. This makes it possible to declaratively capture monitoring metaconstraints, and check them by relying on usual logical services instead of ad-hoc algorithms. This, in turn, enables to flexibly monitor constraints depending on the monitoring state of other constraints, e.g., "compensation" constraints that are only checked when others are detected to be violated. In addition, we devise a direct translation of LDLf formulas into nondeterministic automata, avoiding to detour to Buechi automata or alternating automata, and we use it to implement a monitoring plug-in for the PROM suite.
Outlier detection in a large-scale database is a significant and complex issue in knowledge discovering field. As the data distributions are obscure and uncertain in high dimensional space, most existing solutions try to solve the issue taking into account the two intuitive points: first, outliers are extremely far away from other points in high dimensional space; second, outliers are detected obviously different in projected-dimensional subspaces. However, for a complicated case that outliers are hidden inside the normal points in all dimensions, existing detection methods fail to find such inner outliers. In this paper, we propose a method with twice dimension-projections, which integrates primary subspace outlier detection and secondary point-projection between subspaces, and sums up the multiple weight values for each point. The points are computed with local density ratio separately in twice-projected dimensions. After the process, outliers are those points scoring the largest values of weight. The proposed method succeeds to find all inner outliers on the synthetic test datasets with the dimension varying from 100 to 10000. The experimental results also show that the proposed algorithm can work in low dimensional space and can achieve perfect performance in high dimensional space. As for this reason, our proposed approach has considerable potential to apply it in multimedia applications helping to process images or video with large-scale attributes.
Answer Set Programming (ASP) is logic programming under the stable model or answer set semantics. During the last decade, this paradigm has seen several extensions by generalizing the notion of atom used in these programs. Among these, there are aggregate atoms, HEX atoms, generalized quantifiers, and abstract constraints. In this paper we refer to these constructs collectively as generalized atoms. The idea common to all of these constructs is that their satisfaction depends on the truth values of a set of (non-generalized) atoms, rather than the truth value of a single (non-generalized) atom. Motivated by several examples, we argue that for some of the more intricate generalized atoms, the previously suggested semantics provide unintuitive results and provide an alternative semantics, which we call supportedly stable or SFLP answer sets. We show that it is equivalent to the major previously proposed semantics for programs with convex generalized atoms, and that it in general admits more intended models than other semantics in the presence of non-convex generalized atoms. We show that the complexity of supportedly stable models is on the second level of the polynomial hierarchy, similar to previous proposals and to stable models of disjunctive logic programs. Given these complexity results, we provide a compilation method that compactly transforms programs with generalized atoms in disjunctive normal form to programs without generalized atoms. Variants are given for the new supportedly stable and the existing FLP semantics, for which a similar compilation technique has not been known so far.
In this paper, a mathematical theory of learning is proposed that has many parallels with information theory. We consider Vapnik's General Setting of Learning in which the learning process is defined to be the act of selecting a hypothesis in response to a given training set. Such hypothesis can, for example, be a decision boundary in classification, a set of centroids in clustering, or a set of frequent item-sets in association rule mining. Depending on the hypothesis space and how the final hypothesis is selected, we show that a learning process can be assigned a numeric score, called learning capacity, which is analogous to Shannon's channel capacity and satisfies similar interesting properties as well such as the data-processing inequality and the information-cannot-hurt inequality. In addition, learning capacity provides the tightest possible bound on the difference between true risk and empirical risk of the learning process for all loss functions that are parametrized by the chosen hypothesis. It is also shown that the notion of learning capacity equivalently quantifies how sensitive the choice of the final hypothesis is to a small perturbation in the training set. Consequently, algorithmic stability is both necessary and sufficient for generalization. While the theory does not rely on concentration inequalities, we finally show that analogs to classical results in learning theory using the Probably Approximately Correct (PAC) model can be immediately deduced using this theory, and conclude with information-theoretic bounds to learning capacity.
The maximum mean discrepancy (MMD) is a recently proposed test statistic for two-sample test. Its quadratic time complexity, however, greatly hampers its availability to large-scale applications. To accelerate the MMD calculation, in this study we propose an efficient method called FastMMD. The core idea of FastMMD is to equivalently transform the MMD with shift-invariant kernels into the amplitude expectation of a linear combination of sinusoid components based on Bochner's theorem and Fourier transform (Rahimi & Recht, 2007). Taking advantage of sampling of Fourier transform, FastMMD decreases the time complexity for MMD calculation from $O(N^2 d)$ to $O(L N d)$, where $N$ and $d$ are the size and dimension of the sample set, respectively. Here $L$ is the number of basis functions for approximating kernels which determines the approximation accuracy. For kernels that are spherically invariant, the computation can be further accelerated to $O(L N \log d)$ by using the Fastfood technique (Le et al., 2013). The uniform convergence of our method has also been theoretically proved in both unbiased and biased estimates. We have further provided a geometric explanation for our method, namely ensemble of circular discrepancy, which facilitates us to understand the insight of MMD, and is hopeful to help arouse more extensive metrics for assessing two-sample test. Experimental results substantiate that FastMMD is with similar accuracy as exact MMD, while with faster computation speed and lower variance than the existing MMD approximation methods.
Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP$^2$ for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.
In the event that a bacteriological or chemical toxin is intro- duced to a water distribution network, a large population of consumers may become exposed to the contaminant. A contamination event may be poorly predictable dynamic process due to the interactions of consumers and utility managers during an event. Consumers that become aware of a threat may select protective actions that change their water demands from typical demand patterns, and new hydraulic conditions can arise that differ from conditions that are predicted when demands are considered as exogenous inputs. Consequently, the movement of the contaminant plume in the pipe network may shift from its expected trajectory. A sociotechnical model is developed here to integrate agent-based models of consumers with an engineering water distribution system model and capture the dynamics between consumer behaviors and the water distribution system for predicting contaminant transport and public exposure. Consumers are simulated as agents with behaviors defined for water use activities, mobility, word-of-mouth communication, and demand reduction, based on a set of rules representing an agents autonomy and reaction to health impacts, the environment, and the actions of other agents. As consumers decrease their water use, the demand exerted on the water distribution system is updated; as the flow directions and volumes shift in response, the location of the contaminant plume is updated and the amount of contaminant consumed by each agent is calculated. The framework is tested through simulating realistic contamination scenarios for a virtual city and water distribution system.
Answer Set Programming (ASP) is a powerful modelling formalism that is very efficient in solving combinatorial problems. ASP solvers implement the stable model semantics that eliminates circular derivations between Boolean variables from the solutions of a logic program. Due to this, ASP solvers are better suited than propositional satisfiability (SAT) and Constraint Programming (CP) solvers to solve a certain class of problems whose specification includes inductive definitions such as reachability in a graph. On the other hand, ASP solvers suffer from the grounding bottleneck that occurs due to their inability to model finite domain variables. Furthermore, the existing stable model semantics are not sufficient to disallow circular reasoning on the bounds of numeric variables. An example where this is required is in modelling shortest paths between nodes in a graph. Just as reachability can be encoded as an inductive definition with one or more base cases and recursive rules, shortest paths between nodes can also be modelled with similar base cases and recursive rules for their upper bounds. This deficiency of stable model semantics introduces another type of grounding bottleneck in ASP systems that cannot be removed by naively merging ASP with CP solvers, but requires a theoretical extension of the semantics from Booleans and normal rules to bounds over numeric variables and more general rules. In this work, we propose Bound Founded Answer Set Programming (BFASP) that resolves this issue and consequently, removes all types of grounding bottleneck inherent in ASP systems.
Bayesian model averaging (BMA) is the state of the art approach for overcoming model uncertainty. Yet, especially on small data sets, the results yielded by BMA might be sensitive to the prior over the models. Credal Model Averaging (CMA) addresses this problem by substituting the single prior over the models by a set of priors (credal set). Such approach solves the problem of how to choose the prior over the models and automates sensitivity analysis. We discuss various CMA algorithms for building an ensemble of logistic regressors characterized by different sets of covariates. We show how CMA can be appropriately tuned to the case in which one is prior-ignorant and to the case in which instead domain knowledge is available. CMA detects prior-dependent instances, namely instances in which a different class is more probable depending on the prior over the models. On such instances CMA suspends the judgment, returning multiple classes. We thoroughly compare different BMA and CMA variants on a real case study, predicting presence of Alpine marmot burrows in an Alpine valley. We find that BMA is almost a random guesser on the instances recognized as prior-dependent by CMA.
Answer Set Programming (ASP) is a well-established paradigm of declarative programming that has been developed in the field of logic programming and nonmonotonic reasoning. Advances in ASP solving technology are customarily assessed in competition events, as it happens for other closely-related problem-solving technologies like SAT/SMT, QBF, Planning and Scheduling. ASP Competitions are (usually) biennial events; however, the Fifth ASP Competition departs from tradition, in order to join the FLoC Olympic Games at the Vienna Summer of Logic 2014, which is expected to be the largest event in the history of logic. This edition of the ASP Competition series is jointly organized by the University of Calabria (Italy), the Aalto University (Finland), and the University of Genova (Italy), and is affiliated with the 30th International Conference on Logic Programming (ICLP 2014). It features a completely re-designed setup, with novelties involving the design of tracks, the scoring schema, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. Benchmark domains are taken from past editions, and best system packages submitted in 2013 are compared with new versions and solvers. To appear in Theory and Practice of Logic Programming (TPLP).
The belief bias effect is a phenomenon which occurs when we think that we judge an argument based on our reasoning, but are actually influenced by our beliefs and prior knowledge. Evans, Barston and Pollard carried out a psychological syllogistic reasoning task to prove this effect. Participants were asked whether they would accept or reject a given syllogism. We discuss one specific case which is commonly assumed to be believable but which is actually not logically valid. By introducing abnormalities, abduction and background knowledge, we adequately model this case under the weak completion semantics. Our formalization reveals new questions about possible extensions in abductive reasoning. For instance, observations and their explanations might include some relevant prior abductive contextual information concerning some side-effect or leading to a contestable or refutable side-effect. A weaker notion indicates the support of some relevant consequences by a prior abductive context. Yet another definition describes jointly supported relevant consequences, which captures the idea of two observations containing mutually supportive side-effects. Though motivated with and exemplified by the running psychology application, the various new general abductive context definitions are introduced here and given a declarative semantics for the first time, and have a much wider scope of application. Inspection points, a concept introduced by Pereira and Pinto, allows us to express these definitions syntactically and intertwine them into an operational semantics.
This work deals with the problem of combining reactive features, such as the ability to respond to events and define complex events, with the execution of transactions over general Knowledge Bases (KBs). With this as goal, we build on Transaction Logic (TR), a logic precisely designed to model and execute transactions in KBs defined by arbitrary logic theories. In it, transactions are written in a logic-programming style, by combining primitive update operations over a general KB, with the usual logic programming connectives and some additional connectives e.g. to express sequence of actions. While TR is a natural choice to deal with transactions, it remains the question whether TR can be used to express complex events, but also to deal simultaneously with the detection of complex events and the execution of transactions. In this paper we show that the former is possible while the latter is not. For that, we start by illustrating how TR can express complex events, and in particular, how SNOOP event expressions can be translated in the logic. Afterwards, we show why TR fails to deal with the two issues together, and to solve the intended problem propose Transaction Logic with Events, its syntax, model theory and executional semantics. The achieved solution is a non-monotonic extension of TR, which guarantees that every complex event detected in a transaction is necessarily responded.
The stable model (SM) semantics lacks the properties of existence, relevance and cumulativity. If we prospectively consider the class of conservative extensions of SM semantics (i.e., semantics that for each normal logic program P retrieve a superset of the set of stable models of P), one may wander how do the semantics of this class behave in what concerns the aforementioned properties. That is the type of issue dealt with in this paper. We define a large class of conservative extensions of the SM semantics, dubbed affix stable model semantics, ASM, and study the above referred properties into two non-disjoint subfamilies of the class ASM, here dubbed ASMh and ASMm. From this study a number of results stem which facilitate the assessment of semantics in the class ASMh U ASMm with respect to the properties of existence, relevance and cumulativity, whilst unveiling relations among these properties. As a result of the approach taken in our work, light is shed on the characterization of the SM semantics, as we show that the properties of (lack of) existence and (lack of) cautious monotony are equivalent, which opposes statements on this issue that may be found in the literature; we also characterize the relevance failure of SM semantics in a more clear way than usually stated in the literature.
In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. We construct distributional thesauri based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we compare these sense clusters of two different time points to find if (i) there is birth of a new sense or (ii) if an older sense has got split into more than one sense or (iii) if a newer sense has been formed from the joining of older senses or (iv) if a particular sense has died. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet. Manual evaluation indicates that the algorithm could correctly identify 60.4% birth cases from a set of 48 randomly picked samples and 57% split/join cases from a set of 21 randomly picked samples. Remarkably, in 44% cases the birth of a novel sense is attested by WordNet, while in 46% cases and 43% cases split and join are respectively confirmed by WordNet. Our approach can be applied for lexicography, as well as for applications like word sense disambiguation or semantic search.
Shapleys impossibility result indicates that the two-person bargaining problem has no non-trivial ordinal solution with the traditional game-theoretic bargaining model. Although the result is no longer true for bargaining problems with more than two agents, none of the well known bargaining solutions are ordinal. Searching for meaningful ordinal solutions, especially for the bilateral bargaining problem, has been a challenging issue in bargaining theory for more than three decades. This paper proposes a logic-based ordinal solution to the bilateral bargaining problem. We argue that if a bargaining problem is modeled in terms of the logical relation of players physical negotiation items, a meaningful bargaining solution can be constructed based on the ordinal structure of bargainers preferences. We represent bargainers demands in propositional logic and bargainers preferences over their demands in total preorder. We show that the solution satisfies most desirable logical properties, such as individual rationality (logical version), consistency, collective rationality as well as a few typical game-theoretic properties, such as weak Pareto optimality and contraction invariance. In addition, if all players demand sets are logically closed, the solution satisfies a fixed-point condition, which says that the outcome of a negotiation is the result of mutual belief revision. Finally, we define various decision problems in relation to our bargaining model and study their computational complexity.
We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human. The learning takes place completely automatically, without any human intervention. First, we describe the clustering of demonstrated action sequences into different human types using an unsupervised learning algorithm. These demonstrated sequences are also used by the robot to learn a reward function that is representative for each type, through the employment of an inverse reinforcement learning algorithm. The learned model is then used as part of a Mixed Observability Markov Decision Process formulation, wherein the human type is a partially observable variable. With this framework, we can infer, either offline or online, the human type of a new user that was not included in the training set, and can compute a policy for the robot that will be aligned to the preference of this new user and will be robust to deviations of the human actions from prior demonstrations. Finally we validate the approach using data collected in human subject experiments, and conduct proof-of-concept demonstrations in which a person performs a collaborative task with a small industrial robot.
Advances in high energy physics have created the need to increase computational capacity. Project HEPGAME was composed to address this challenge. One of the issues is that numerical integration of expressions of current interest have millions of terms and takes weeks to compute. We have investigated ways to simplify these expressions, using Horner schemes and common subexpression elimination. Our approach applies MCTS, a search procedure that has been successful in AI. We use it to find near-optimal Horner schemes. Although MCTS finds better solutions, this approach gives rise to two further challenges. (1) MCTS (with UCT) introduces a constant, $C_p$ that governs the balance between exploration and exploitation. This constant has to be tuned manually. (2) There should be more guided exploration at the bottom of the tree, since the current approach reduces the quality of the solution towards the end of the expression. We investigate NMCS (Nested Monte Carlo Search) to address both issues, but find that NMCS is computationally unfeasible for our problem. Then, we modify the MCTS formula by introducing a dynamic exploration-exploitation parameter $T$ that decreases linearly with the iteration number. Consequently, we provide a performance analysis. We observe that a variable $C_p$ solves our domain: it yields more exploration at the bottom and as a result the tuning problem has been simplified. The region in $C_p$ for which good values are found is increased by more than a tenfold. This result encourages us to continue our research to solve other prominent problems in High Energy Physics.
Semantic composition is the task of understanding the meaning of text by composing the meanings of the individual words in the text. Semantic decomposition is the task of understanding the meaning of an individual word by decomposing it into various aspects (factors, constituents, components) that are latent in the meaning of the word. We take a distributional approach to semantics, in which a word is represented by a context vector. Much recent work has considered the problem of recognizing compositions and decompositions, but we tackle the more difficult generation problem. For simplicity, we focus on noun-modifier bigrams and noun unigrams. A test for semantic composition is, given context vectors for the noun and modifier in a noun-modifier bigram ("red salmon"), generate a noun unigram that is synonymous with the given bigram ("sockeye"). A test for semantic decomposition is, given a context vector for a noun unigram ("snifter"), generate a noun-modifier bigram that is synonymous with the given unigram ("brandy glass"). With a vocabulary of about 73,000 unigrams from WordNet, there are 73,000 candidate unigram compositions for a bigram and 5,300,000,000 (73,000 squared) candidate bigram decompositions for a unigram. We generate ranked lists of potential solutions in two passes. A fast unsupervised learning algorithm generates an initial list of candidates and then a slower supervised learning algorithm refines the list. We evaluate the candidate solutions by comparing them to WordNet synonym sets. For decomposition (unigram to bigram), the top 100 most highly ranked bigrams include a WordNet synonym of the given unigram 50.7% of the time. For composition (bigram to unigram), the top 100 most highly ranked unigrams include a WordNet synonym of the given bigram 77.8% of the time.
Recently, multiple formulations of vision problems as probabilistic inversions of generative models based on computer graphics have been proposed. However, applications to 3D perception from natural images have focused on low-dimensional latent scenes, due to challenges in both modeling and inference. Accounting for the enormous variability in 3D object shape and 2D appearance via realistic generative models seems intractable, as does inverting even simple versions of the many-to-many computations that link 3D scenes to 2D images. This paper proposes and evaluates an approach that addresses key aspects of both these challenges. We show that it is possible to solve challenging, real-world 3D vision problems by approximate inference in generative models for images based on rendering the outputs of probabilistic CAD (PCAD) programs. Our PCAD object geometry priors generate deformable 3D meshes corresponding to plausible objects and apply affine transformations to place them in a scene. Image likelihoods are based on similarity in a feature space based on standard mid-level image representations from the vision literature. Our inference algorithm integrates single-site and locally blocked Metropolis-Hastings proposals, Hamiltonian Monte Carlo and discriminative data-driven proposals learned from training data generated from our models. We apply this approach to 3D human pose estimation and object shape reconstruction from single images, achieving quantitative and qualitative performance improvements over state-of-the-art baselines.
Recent work introduced Generalized First Order Decision Diagrams (GFODD) as a knowledge representation that is useful in mechanizing decision theoretic planning in relational domains. GFODDs generalize function-free first order logic and include numerical values and numerical generalizations of existential and universal quantification. Previous work presented heuristic inference algorithms for GFODDs and implemented these heuristics in systems for decision theoretic planning. In this paper, we study the complexity of the computational problems addressed by such implementations. In particular, we study the evaluation problem, the satisfiability problem, and the equivalence problem for GFODDs under the assumption that the size of the intended model is given with the problem, a restriction that guarantees decidability. Our results provide a complete characterization placing these problems within the polynomial hierarchy. The same characterization applies to the corresponding restriction of problems in first order logic, giving an interesting new avenue for efficient inference when the number of objects is bounded. Our results show that for $\Sigma_k$ formulas, and for corresponding GFODDs, evaluation and satisfiability are $\Sigma_k^p$ complete, and equivalence is $\Pi_{k+1}^p$ complete. For $\Pi_k$ formulas evaluation is $\Pi_k^p$ complete, satisfiability is one level higher and is $\Sigma_{k+1}^p$ complete, and equivalence is $\Pi_{k+1}^p$ complete.
An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.
The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In order to apply the rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented in this paper, along with the concept of a generalized revision algorithm for knowledge bases (Horn or Horn logic with stratified negation). We show that knowledge base dynamics has an interesting connection with kernel change via hitting set and abduction. In this paper, we show how techniques from disjunctive logic programming can be used for efficient (deductive) database updates. The key idea is to transform the given database together with the update request into a disjunctive (datalog) logic program and apply disjunctive techniques (such as minimal model reasoning) to solve the original update problem. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of a hitting set is carried out through a hyper tableaux calculus and magic set that is focused on the goal of minimality.
Counterexample-guided inductive synthesis CEGIS is used to synthesize programs from a candidate space of programs. The technique is guaranteed to terminate and synthesize the correct program if the space of candidate programs is finite. But the technique may or may not terminate with the correct program if the candidate space of programs is infinite. In this paper, we perform a theoretical analysis of counterexample-guided inductive synthesis technique. We investigate whether the set of candidate spaces for which the correct program can be synthesized using CEGIS depends on the counterexamples used in inductive synthesis, that is, whether there are good mistakes which would increase the synthesis power. We investigate whether the use of minimal counterexamples instead of arbitrary counterexamples expands the set of candidate spaces of programs for which inductive synthesis can successfully synthesize a correct program. We consider two kinds of counterexamples: minimal counterexamples and history bounded counterexamples. The history bounded counterexample used in any iteration of CEGIS is bounded by the examples used in previous iterations of inductive synthesis. We examine the relative change in power of inductive synthesis in both cases. We show that the synthesis technique using minimal counterexamples MinCEGIS has the same synthesis power as CEGIS but the synthesis technique using history bounded counterexamples HCEGIS has different power than that of CEGIS, but none dominates the other.
Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule.
In this paper we address the problem of planning in rich domains, where knowledge representation is a key aspect for managing the complexity and size of the planning domain. We follow the approach of Description Logic (DL) based Dynamic Knowledge Bases, where a state of the world is represented concisely by a (possibly changing) ABox and a (fixed) TBox containing the axioms, and actions that allow to change the content of the ABox. The plan goal is given in terms of satisfaction of a DL query. In this paper we start from a traditional forward planning algorithm and we propose a much more efficient variant by combining backward and forward search. In particular, we propose a Backward State-space Reduction technique that consists in two phases: first, an Abstract Planning Graph P is created by using the Abstract Backward Planning Algorithm (ABP), then the abstract planning graph P is instantiated into a corresponding planning graph P by using the Forward Plan Instantiation Algorithm (FPI). The advantage is that in the preliminary ABP phase we produce a symbolic plan that is a pattern to direct the search of the concrete plan. This can be seen as a kind of informed search where the preliminary backward phase is useful to discover properties of the state-space that can be used to direct the subsequent forward phase. We evaluate the effectiveness of our ABP+FPI algorithm in the reduction of the explored planning domain by comparing it to a standard forward planning algorithm and applying both of them to a concrete business case study.
This paper deals with the relations among structural, topological, and chemical properties of the E.Coli proteome from the vantage point of the solubility/aggregation propensity of proteins. Each E.Coli protein is initially represented according to its known folded 3D shape. This step consists in representing the available E.Coli proteins in terms of graphs. We first analyze those graphs by considering pure topological characterizations, i.e., by analyzing the mass fractal dimension and the distribution underlying both shortest paths and vertex degrees. Results confirm the general architectural principles of proteins. Successively, we focus on the statistical properties of a representation of such graphs in terms of vectors composed of several numerical features, which we extracted from their structural representation. We found that protein size is the main discriminator for the solubility, while however there are other factors that help explaining the solubility degree. We finally analyze such data through a novel one-class classifier, with the aim of discriminating among very and poorly soluble proteins. Results are encouraging and consolidate the potential of pattern recognition techniques when employed to describe complex biological systems.
Bots are, for many Web and social media users, the source of many dangerous attacks or the carrier of unwanted messages, such as spam. Nevertheless, crawlers and software agents are a precious tool for analysts, and they are continuously executed to collect data or to test distributed applications. However, no one knows which is the real potential of a bot whose purpose is to control a community, to manipulate consensus, or to influence user behavior. It is commonly believed that the better an agent simulates human behavior in a social network, the more it can succeed to generate an impact in that community. We contribute to shed light on this issue through an online social experiment aimed to study to what extent a bot with no trust, no profile, and no aims to reproduce human behavior, can become popular and influential in a social media. Results show that a basic social probing activity can be used to acquire social relevance on the network and that the so-acquired popularity can be effectively leveraged to drive users in their social connectivity choices. We also register that our bot activity unveiled hidden social polarization patterns in the community and triggered an emotional response of individuals that brings to light subtle privacy hazards perceived by the user base.
The strong solutions of Nine Men's Morris and its variant, Lasker Morris are well-known results (the starting positions are draws). We re-examined both of these games, and calculated extended strong solutions for them. By this we mean the game-theoretic values of all possible game states that could be reached from certain starting positions where the number of stones to be placed by the players is different from the standard rules. These were also calculated for a previously unsolved third variant, Morabaraba, with interesting results: most of the starting positions where the players can place an equal number of stones (including the standard starting position) are wins for the first player (as opposed to the above games, where these are usually draws). We also developed a multi-valued retrograde analysis, and used it as a basis for an algorithm for solving these games ultra-strongly. This means that when our program is playing against a fallible opponent, it has a greater chance of achieving a better result than the game-theoretic value, compared to randomly selecting between "just strongly" optimal moves. Previous attempts on ultra-strong solutions used local heuristics or learning during games, but we incorporated our algorithm into the retrograde analysis.
Due to advances in sensors, growing large and complex medical image data have the ability to visualize the pathological change in the cellular or even the molecular level or anatomical changes in tissues and organs. As a consequence, the medical images have the potential to enhance diagnosis of disease, prediction of clinical outcomes, characterization of disease progression, management of health care and development of treatments, but also pose great methodological and computational challenges for representation and selection of features in image cluster analysis. To address these challenges, we first extend one dimensional functional principal component analysis to the two dimensional functional principle component analyses (2DFPCA) to fully capture space variation of image signals. Image signals contain a large number of redundant and irrelevant features which provide no additional or no useful information for cluster analysis. Widely used methods for removing redundant and irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on how to select penalty parameters and a threshold for selecting features. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attention in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image cluster analysis. The proposed method is applied to ovarian and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis.
After experimentation with other designs, the major search engines converged on the weighted, generalized second-price auction (wGSP) for selling keyword advertisements. Notably, this convergence occurred before position auctions were well understood (or, indeed, widely studied) theoretically. While much progress has been made since, theoretical analysis is still not able to settle the question of why search engines found wGSP preferable to other position auctions. We approach this question in a new way, adopting a new analytical paradigm we dub "computational mechanism analysis." By sampling position auction games from a given distribution, encoding them in a computationally efficient representation language, computing their Nash equilibria, and then calculating economic quantities of interest, we can quantitatively answer questions that theoretical methods have not. We considered seven widely studied valuation models from the literature and three position auction variants (generalized first price, unweighted generalized second price, and wGSP). We found that wGSP consistently showed the best ads of any position auction, measured both by social welfare and by relevance (expected number of clicks). Even in models where wGSP was already known to have bad worse-case efficiency, we found that it almost always performed well on average. In contrast, we found that revenue was extremely variable across auction mechanisms, and was highly sensitive to equilibrium selection, the preference model, and the valuation distribution.
Exact Bayesian structure discovery in Bayesian networks requires exponential time and space. Using dynamic programming (DP), the fastest known sequential algorithm computes the exact posterior probabilities of structural features in $O(2(d+1)n2^n)$ time and space, if the number of nodes (variables) in the Bayesian network is $n$ and the in-degree (the number of parents) per node is bounded by a constant $d$. Here we present a parallel algorithm capable of computing the exact posterior probabilities for all $n(n-1)$ edges with optimal parallel space efficiency and nearly optimal parallel time efficiency. That is, if $p=2^k$ processors are used, the run-time reduces to $O(5(d+1)n2^{n-k}+k(n-k)^d)$ and the space usage becomes $O(n2^{n-k})$ per processor. Our algorithm is based the observation that the subproblems in the sequential DP algorithm constitute a $n$-$D$ hypercube. We take a delicate way to coordinate the computation of correlated DP procedures such that large amount of data exchange is suppressed. Further, we develop parallel techniques for two variants of the well-known \emph{zeta transform}, which have applications outside the context of Bayesian networks. We demonstrate the capability of our algorithm on datasets with up to 33 variables and its scalability on up to 2048 processors. We apply our algorithm to a biological data set for discovering the yeast pheromone response pathways.
This paper addresses the path selection problem from a known source to the destination in dense networks. The proposed solution for route discovery uses the genetic algorithm approach for a QoS based network. The multi point crossover and mutation helps in determining the optimal path and alternate path when required. The input to the genetic algorithm is a learnt module which is a part of the cognitive router that takes care of four QoS parameters. Here the set of nodes selected for routing is determined by delay, jitter and loss. On this graded surface of nodes selected, the bandwidth parameter is considered for path selection. The aim of the approach is to occupy the maximized bandwidth along the forward channels and minimize the route length. The population size is considered as fixed nodes participating in the network scenario, which will be limited to a known size of topology. The simulated results show that by using genetic algorithm (GA) approach the probability of convergence to shortest path is higher.
In many applications that require matrix solutions of minimal rank, the underlying cost function is non-convex leading to an intractable, NP-hard optimization problem. Consequently, the convex nuclear norm is frequently used as a surrogate penalty term for matrix rank. The problem is that in many practical scenarios there is no longer any guarantee that we can correctly estimate generative low-rank matrices of interest, theoretical special cases notwithstanding. Consequently, this paper proposes an alternative empirical Bayesian procedure build upon a variational approximation that, unlike the nuclear norm, retains the same globally minimizing point estimate as the rank function under many useful constraints. However, locally minimizing solutions are largely smoothed away via marginalization, allowing the algorithm to succeed when standard convex relaxations completely fail. While the proposed methodology is generally applicable to a wide range of low-rank applications, we focus our attention on the robust principal component analysis problem (RPCA), which involves estimating an unknown low-rank matrix with unknown sparse corruptions. Theoretical and empirical evidence are presented to show that our method is potentially superior to related MAP-based approaches, for which the convex principle component pursuit (PCP) algorithm (Candes et al., 2011) can be viewed as a special case.
Editing faces in videos is a popular yet challenging aspect of computer vision and graphics, which encompasses several applications including facial attractiveness enhancement, makeup transfer, face replacement, and expression manipulation. Simply applying image-based warping algorithms to video-based face editing produces temporal incoherence in the synthesized videos because it is impossible to consistently localize facial features in two frames representing two different faces in two different videos (or even two consecutive frames representing the same face in one video). Therefore, high performance face editing usually requires significant manual manipulation. In this paper we propose a novel temporal-spatial-smooth warping (TSSW) algorithm to effectively exploit the temporal information in two consecutive frames, as well as the spatial smoothness within each frame. TSSW precisely estimates two control lattices in the horizontal and vertical directions respectively from the corresponding control lattices in the previous frame, by minimizing a novel energy function that unifies a data-driven term, a smoothness term, and feature point constraints. Corresponding warping surfaces then precisely map source frames to the target frames. Experimental testing on facial attractiveness enhancement, makeup transfer, face replacement, and expression manipulation demonstrates that the proposed approaches can effectively preserve spatial smoothness and temporal coherence in editing facial geometry, skin detail, identity, and expression, which outperform the existing face editing methods. In particular, TSSW is robust to subtly inaccurate localization of feature points and is a vast improvement over image-based warping methods.
We evaluate a version of the recently-proposed classification system named Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a classification system for patterns represented as labeled graphs. However, since ODSE is founded on the dissimilarity space representation of the input data, the classifier can be easily adapted to any input domain where it is possible to define a meaningful dissimilarity measure. Here we demonstrate the effectiveness of the ODSE classifier for sequences by considering an application dealing with the recognition of the solubility degree of the Escherichia coli proteome. Solubility, or analogously aggregation propensity, is an important property of protein molecules, which is intimately related to the mechanisms underlying the chemico-physical process of folding. Each protein of our dataset is initially associated with a solubility degree and it is represented as a sequence of symbols, denoting the 20 amino acid residues. The herein obtained computational results, which we stress that have been achieved with no context-dependent tuning of the ODSE system, confirm the validity and generality of the ODSE-based approach for structured data classification.
We propose and evaluate a number of solutions to the problem of calculating the cost to serve each location in a single-vehicle transport setting. Such cost to serve analysis has application both strategically and operationally in transportation. The problem is formally given by the traveling salesperson game (TSG), a cooperative total utility game in which agents correspond to locations in a traveling salesperson problem (TSP). The cost to serve a location is an allocated portion of the cost of an optimal tour. The Shapley value is one of the most important normative division schemes in cooperative games, giving a principled and fair allocation both for the TSG and more generally. We consider a number of direct and sampling-based procedures for calculating the Shapley value, and present the first proof that approximating the Shapley value of the TSG within a constant factor is NP-hard. Treating the Shapley value as an ideal baseline allocation, we then develop six proxies for that value which are relatively easy to compute. We perform an experimental evaluation using Synthetic Euclidean games as well as games derived from real-world tours calculated for fast-moving consumer goods scenarios. Our experiments show that several computationally tractable allocation techniques correspond to good proxies for the Shapley value.
In this paper, we address the knowledge engineering problems for hypothesis generation motivated by applications that require timely exploration of hypotheses under unreliable observations. We looked at two applications: malware detection and intensive care delivery. In intensive care, the goal is to generate plausible hypotheses about the condition of the patient from clinical observations and further refine these hypotheses to create a recovery plan for the patient. Similarly, preventing malware spread within a corporate network involves generating hypotheses from network traffic data and selecting preventive actions. To this end, building on the already established characterization and use of AI planning for similar problems, we propose use of planning for the hypothesis generation problem. However, to deal with uncertainty, incomplete model description and unreliable observations, we need to use a planner capable of generating multiple high-quality plans. To capture the model description we propose a language called LTS++ and a web-based tool that enables the specification of the LTS++ model and a set of observations. We also proposed a 9-step process that helps provide guidance to the domain expert in specifying the LTS++ model. The hypotheses are then generated by running a planner on the translated LTS++ model and the provided trace. The hypotheses can be visualized and shown to the analyst or can be further investigated automatically.
We introduce the notion of online reactive planning with sensing actions for systems with temporal logic constraints in partially observable and dynamic environments. With incomplete information on the dynamic environment, reactive controller synthesis amounts to solving a two-player game with partial observations, which has impractically computational complexity. To alleviate the high computational burden, online replanning via sensing actions avoids solving the strategy in the reactive system under partial observations. Instead, we only solve for a strategy that ensures a given temporal logic specification can be satisfied had the system have complete observations of its environment. Such a strategy is then transformed into one which makes control decisions based on the observed sequence of states (of the interacting system and its environment). When the system encounters a belief---a set including all possible hypotheses the system has for the current state---for which the observation-based strategy is undefined, a sequence of sensing actions are triggered, chosen by an active sensing strategy, to reduce the uncertainty in the system's belief. We show that by alternating between the observation-based strategy and the active sensing strategy, under a mild technical assumption of the set of sensors in the system, the given temporal logic specification can be satisfied with probability 1.
In image classification, visual separability between different object categories is highly uneven, and some categories are more difficult to distinguish than others. Such difficult categories demand more dedicated classifiers. However, existing deep convolutional neural networks (CNN) are trained as flat N-way classifiers, and few efforts have been made to leverage the hierarchical structure of categories. In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy. An HD-CNN separates easy classes using a coarse category classifier while distinguishing difficult classes using fine category classifiers. During HD-CNN training, component-wise pretraining is followed by global finetuning with a multinomial logistic loss regularized by a coarse category consistency term. In addition, conditional executions of fine category classifiers and layer parameter compression make HD-CNNs scalable for large-scale visual recognition. We achieve state-of-the-art results on both CIFAR100 and large-scale ImageNet 1000-class benchmark datasets. In our experiments, we build up three different HD-CNNs and they lower the top-1 error of the standard CNNs by 2.65%, 3.1% and 1.1%, respectively.
While most Bayesian nonparametric models in machine learning have focused on the Dirichlet process, the beta process, or their variants, the gamma process has recently emerged as a useful nonparametric prior in its own right. Current inference schemes for models involving the gamma process are restricted to MCMC-based methods, which limits their scalability. In this paper, we present a variational inference framework for models involving gamma process priors. Our approach is based on a novel stick-breaking constructive definition of the gamma process. We prove correctness of this stick-breaking process by using the characterization of the gamma process as a completely random measure (CRM), and we explicitly derive the rate measure of our construction using Poisson process machinery. We also derive error bounds on the truncation of the infinite process required for variational inference, similar to the truncation analyses for other nonparametric models based on the Dirichlet and beta processes. Our representation is then used to derive a variational inference algorithm for a particular Bayesian nonparametric latent structure formulation known as the infinite Gamma-Poisson model, where the latent variables are drawn from a gamma process prior with Poisson likelihoods. Finally, we present results for our algorithms on nonnegative matrix factorization tasks on document corpora, and show that we compare favorably to both sampling-based techniques and variational approaches based on beta-Bernoulli priors.
We propose a framework grounded in Logic Programming for representing and reasoning about business processes from both the procedural and ontological point of views. In particular, our goal is threefold: (1) define a logical language and a formal semantics for process models enriched with ontology-based annotations; (2) provide an effective inference mechanism that supports the combination of reasoning services dealing with the structural definition of a process model, its behavior, and the domain knowledge related to the participating business entities; (3) implement such a theoretical framework into a process modeling and reasoning platform. To this end we define a process ontology coping with a relevant fragment of the popular BPMN modeling notation. The behavioral semantics of a process is defined as a state transition system by following an approach similar to the Fluent Calculus, and allows us to specify state change in terms of preconditions and effects of the enactment of activities. Then we show how the procedural process knowledge can be seamlessly integrated with the domain knowledge specified by using the OWL 2 RL rule-based ontology language. Our framework provides a wide range of reasoning services, including CTL model checking, which can be performed by using standard Logic Programming inference engines through a goal-oriented, efficient, sound and complete evaluation procedure. We also present a software environment implementing the proposed framework, and we report on an experimental evaluation of the system, whose results are encouraging and show the viability of the approach.
In part one of the Critique of Judgment, Immanuel Kant wrote that "the judgment of taste...is not a cognitive judgment, and so not logical, but is aesthetic."\cite{Kant} While the condition of aesthetic discernment has long been the subject of philosophical discourse, the role of the arbiters of that judgment has more often been assumed than questioned. The art historian, critic, connoisseur, and curator have long held the esteemed position of the aesthetic judge, their training, instinct, and eye part of the inimitable subjective processes that Kant described as occurring upon artistic evaluation. Although the concept of intangible knowledge in regard to aesthetic theory has been much explored, little discussion has arisen in response to the development of new types of artificial intelligence as a challenge to the seemingly ineffable abilities of the human observer. This paper examines the developments in the field of computer vision analysis of paintings from canonical movements with the history of Western art and the reaction of art historians to the application of this technology in the field. Through an investigation of the ethical consequences of this innovative technology, the unquestioned authority of the art expert is challenged and the subjective nature of aesthetic judgment is brought to philosophical scrutiny once again.
Given recent advances in information technology and artificial intelligence, web-based education systems have became complementary and, in some cases, viable alternatives to traditional classroom teaching. The popularity of these systems stems from their ability to make education available to a large demographics (see MOOCs). However, existing systems do not take advantage of the personalization which becomes possible when web-based education is offered: they continue to be one-size-fits-all. In this paper, we aim to provide a first systematic method for designing a personalized web-based education system. Personalizing education is challenging: (i) students need to be provided personalized teaching and training depending on their contexts (e.g. classes already taken, methods of learning preferred, etc.), (ii) for each specific context, the best teaching and training method (e.g type and order of teaching materials to be shown) must be learned, (iii) teaching and training should be adapted online, based on the scores/feedback (e.g. tests, quizzes, final exam, likes/dislikes etc.) of the students. Our personalized online system, e-Tutor, is able to address these challenges by learning how to adapt the teaching methodology (in this case what sequence of teaching material to present to a student) to maximize her performance in the final exam, while minimizing the time spent by the students to learn the course (and possibly dropouts). We illustrate the efficiency of the proposed method on a real-world eTutor platform which is used for remedial training for a Digital Signal Processing (DSP) course.
Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image tagging, and web search. Machine learning techniques, referred to as learning-to-match in this paper, have been successfully applied to the problems. Among them, a class of state-of-the-art methods, named feature-based matrix factorization, formalize the task as an extension to matrix factorization by incorporating auxiliary features into the model. Unfortunately, making those algorithms scale to real world problems is challenging, and simple parallelization strategies fail due to the complex cross talking patterns between sub-tasks. In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization. Our algorithm, based on coordinate descent, can easily handle hundreds of millions of instances and features on a single machine. The key recipe of this algorithm is an iterative relaxation of the objective to facilitate parallel updates of parameters, with guaranteed convergence on minimizing the original objective function. Experimental results demonstrate that the proposed method is effective on a wide range of matching problems, with efficiency significantly improved upon the baselines while accuracy retained unchanged.
Functional Magnetic Resonance Imaging (fMRI) is a powerful non-invasive tool for localizing and analyzing brain activity. This study focuses on one very important aspect of the functional properties of human brain, specifically the estimation of the level of parallelism when performing complex cognitive tasks. Using fMRI as the main modality, the human brain activity is investigated through a purely data-driven signal processing and dimensionality analysis approach. Specifically, the fMRI signal is treated as a multi-dimensional data space and its intrinsic `complexity' is studied via dataset fractal analysis and blind-source separation (BSS) methods. One simulated and two real fMRI datasets are used in combination with Independent Component Analysis (ICA) and fractal analysis for estimating the intrinsic (true) dimensionality, in order to provide data-driven experimental evidence on the number of independent brain processes that run in parallel when visual or visuo-motor tasks are performed. Although this number is can not be defined as a strict threshold but rather as a continuous range, when a specific activation level is defined, a corresponding number of parallel processes or the casual equivalent of `cpu cores' can be detected in normal human brain activity.
As language and visual understanding by machines progresses rapidly, we are observing an increasing interest in holistic architectures that tightly interlink both modalities in a joint learning and inference process. This trend has allowed the community to progress towards more challenging and open tasks and refueled the hope at achieving the old AI dream of building machines that could pass a turing test in open domains. In order to steadily make progress towards this goal, we realize that quantifying performance becomes increasingly difficult. Therefore we ask how we can precisely define such challenges and how we can evaluate different algorithms on this open tasks? In this paper, we summarize and discuss such challenges as well as try to give answers where appropriate options are available in the literature. We exemplify some of the solutions on a recently presented dataset of question-answering task based on real-world indoor images that establishes a visual turing challenge. Finally, we argue despite the success of unique ground-truth annotation, we likely have to step away from carefully curated dataset and rather rely on 'social consensus' as the main driving force to create suitable benchmarks. Providing coverage in this inherently ambiguous output space is an emerging challenge that we face in order to make quantifiable progress in this area.
One important challenge for a set of agents to achieve more efficient collaboration is for these agents to maintain proper models of each other. An important aspect of these models of other agents is that they are often partial and incomplete. Thus far, there are two common representations of agent models: MDP based and action based, which are both based on action modeling. In many applications, agent models may not have been given, and hence must be learnt. While it may seem convenient to use either MDP based or action based models for learning, in this paper, we introduce a new representation based on capability models, which has several unique advantages. First, we show that learning capability models can be performed efficiently online via Bayesian learning, and the learning process is robust to high degrees of incompleteness in plan execution traces (e.g., with only start and end states). While high degrees of incompleteness in plan execution traces presents learning challenges for MDP based and action based models, capability models can still learn to {\em abstract} useful information out of these traces. As a result, capability models are useful in applications in which such incompleteness is common, e.g., robot learning human model from observations and interactions. Furthermore, when used in multi-agent planning (with each agent modeled separately), capability models provide flexible abstraction of actions. The limitation, however, is that the synthesized plan is incomplete and abstract.
We study the data space $D$ of any given data set $X$ and explain how functions and relations are defined over $D$. From $D$ and for a specific domain $\Delta$ we construct the information space $I$ of $X$ by interpreting variables, functions, and explicit relations over $D$ in $\Delta$ and by including other relations that $D$ implies under the interpretation in $\Delta$. Then from $I$ we build up the knowledge space $K$ of $X$ as the product of two spaces $K_T$ and $K_P$, where $K_T$ is obtained from $I$ by using the induction principle to generalize propositional relations to quantified relations, the deduction principle to generate new relations, and standard mechanisms to validate relations and $K_P$ is the space of specifications of methods with operational instructions which are valid in $K_T$. Through our construction of the three topological spaces the following key observation is made clear: the retrieval of information from the given data set for $\Delta$ consists essentially in mining domain objects and relations, and the discovery of knowledge from the retrieved information consists essentially in applying the induction and deduction principles to generate propositions, synthesizing and modeling the information to generate specifications of methods with operational instructions, and validating the propositions and specifications. Based on this observation, efficient approaches may be designed to discover profound knowledge automatically from simple data, as demonstrated by the result of our study in the case of geometry.
Answering conjunctive queries (CQs) over $\mathcal{EL}$ knowledge bases (KBs) with complex role inclusions is PSPACE-hard and in PSPACE in certain cases; however, if complex role inclusions are restricted to role transitivity, the tight upper complexity bound has so far been unknown. Furthermore, the existing algorithms cannot handle reflexive roles, and they are not practicable. Finally, the problem is tractable for acyclic CQs and $\mathcal{ELH}$, and NP-complete for unrestricted CQs and $\mathcal{ELHO}$ KBs. In this paper we complete the complexity landscape of CQ answering for several important cases. In particular, we present a practicable NP algorithm for answering CQs over $\mathcal{ELHO}^s$ KBs---a logic containing all of OWL 2 EL, but with complex role inclusions restricted to role transitivity. Our preliminary evaluation suggests that the algorithm can be suitable for practical use. Moreover, we show that, even for a restricted class of so-called arborescent acyclic queries, CQ answering over $\mathcal{EL}$ KBs becomes NP-hard in the presence of either transitive or reflexive roles. Finally, we show that answering arborescent CQs over $\mathcal{ELHO}$ KBs is tractable, whereas answering acyclic CQs is NP-hard.
This short paper concerns discretization schemes for representing and computing approximate Nash equilibria, with emphasis on graphical games, but briefly touching on normal-form and poly-matrix games. The main technical contribution is a representation theorem that informally states that to account for every exact Nash equilibrium using a nearby approximate Nash equilibrium on a grid over mixed strategies, a uniform discretization size linear on the inverse of the approximation quality and natural game-representation parameters suffices. For graphical games, under natural conditions, the discretization is logarithmic in the game-representation size, a substantial improvement over the linear dependency previously required. The paper has five other objectives: (1) given the venue, to highlight the important, but often ignored, role that work on constraint networks in AI has in simplifying the derivation and analysis of algorithms for computing approximate Nash equilibria; (2) to summarize the state-of-the-art on computing approximate Nash equilibria, with emphasis on relevance to graphical games; (3) to help clarify the distinction between sparse-discretization and sparse-support techniques; (4) to illustrate and advocate for the deliberate mathematical simplicity of the formal proof of the representation theorem; and (5) to list and discuss important open problems, emphasizing graphical-game generalizations, which the AI community is most suitable to solve.
Human beings do not observe the world from the outside, but rather are fully embedded in it. The sciences, however, often give the observer both a "god's eye" perspective and substantial a~priori knowledge. Motivated by W. Ross Ashby's statement, "the theory of the Black Box is merely the theory of real objects or systems, when close attention is given to the question, relating object and observer, about what information comes from the object, and how it is obtained" (Introduction to Cybernetics, 1956, p. 110), I develop here an alternate picture of the world as a black box to which the observer is coupled. Within this framework I prove purely-classical analogs of the "no-go" theorems of quantum theory. Focussing on the question of identifying macroscopic objects, such as laboratory apparatus or even other observers, I show that the standard quantum formalism of superposition is required to adequately represent the classical information that an observer can obtain. I relate these results to supporting considerations from evolutionary biology, cognitive and developmental psychology, and artificial intelligence.
We propose a scalable temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. The model assumes that each user lies in an unobserved latent space and interactions are more likely to form between similar users in the latent space representation. In addition, the model allows each user to gradually move its position in the latent space as the network structure evolves over time. We present a global optimization algorithm to effectively infer the temporal latent space, with a quadratic convergence rate. Two alternative optimization algorithms with local and incremental updates are also proposed, allowing the model to scale to larger networks without compromising prediction accuracy. Empirically, we demonstrate that our model, when evaluated on a number of real-world dynamic networks, significantly outperforms existing approaches for temporal link prediction in terms of both scalability and predictive power.
The automatic design of controllers for mobile robots usually requires two stages. In the first stage,sensorial data are preprocessed or transformed into high level and meaningful values of variables whichare usually defined from expert knowledge. In the second stage, a machine learning technique is applied toobtain a controller that maps these high level variables to the control commands that are actually sent tothe robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learningstage in order to get controllers directly starting from sensorial raw data with no expert knowledgeinvolved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules(QFRs), that are able to transform low-level input variables into high-level input variables, reducingthe dimensionality through summarization. The proposed learning algorithm, called Iterative QuantifiedFuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with differentstructures, and can manage linguistic variables with multiple granularities. The algorithm has been testedwith the implementation of the wall-following behavior both in several realistic simulated environmentswith different complexity and on a Pioneer 3-AT robot in two real environments. Results have beencompared with several well-known learning algorithms combined with different data preprocessingtechniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover,three real world applications for which IQFRL plays a central role are also presented: path and objecttracking with static and moving obstacles avoidance.
We investigate the potential of using ordinal peer grading for the evaluation of students in massive online open courses (MOOCs). According to such grading schemes, each student receives a few assignments (by other students) which she has to rank. Then, a global ranking (possibly translated into numerical scores) is produced by combining the individual ones. This is a novel application area for social choice concepts and methods where the important problem to be solved is as follows: how should the assignments be distributed so that the collected individual rankings can be easily merged into a global one that is as close as possible to the ranking that represents the relative performance of the students in the assignment? Our main theoretical result suggests that using very simple ways to distribute the assignments so that each student has to rank only $k$ of them, a Borda-like aggregation method can recover a $1-O(1/k)$ fraction of the true ranking when each student correctly ranks the assignments she receives. Experimental results strengthen our analysis further and also demonstrate that the same method is extremely robust even when students have imperfect capabilities as graders. We believe that our results provide strong evidence that ordinal peer grading can be a highly effective and scalable solution for evaluation in MOOCs.
A wide range of evidence points toward the existence of a common algorithm underlying the processing of information throughout the cerebral cortex. Several hypothesized features of this cortical algorithm are reviewed, including sparse distributed representation, Bayesian inference, hierarchical organization composed of alternating template matching and pooling layers, temporal slowness and predictive coding. Hierarchical Temporal Memory (HTM) is a family of learning algorithms and corresponding theories of cortical function that embodies these principles. HTM has previously been applied mainly to perceptual tasks typical of posterior cortex. In order to evaluate HTM as a candidate model of cortical function, it is necessary also to investigate its compatibility with the requirements of frontal cortical function. To this end, a variety of models of frontal cortical function are reviewed and integrated, to arrive at the hypothesis that frontal functions including attention, working memory and action selection depend largely upon the same basic algorithms as do posterior functions, with the notable additions of a mechanism for the active maintenance of representations and of multiple cortico-striato-thalamo-cortical loops that allow communication between regions of frontal cortex to be gated in an adaptive manner. Computational models of this system are reviewed. Finally, there is a discussion of how HTM can contribute to the understanding of frontal cortical function, and of what the requirements of frontal cortical function mean for the future development of HTM.
Nonparametric two sample testing deals with the question of consistently deciding if two distributions are different, given samples from both, without making any parametric assumptions about the form of the distributions. The current literature is split into two kinds of tests - those which are consistent without any assumptions about how the distributions may differ (\textit{general} alternatives), and those which are designed to specifically test easier alternatives, like a difference in means (\textit{mean-shift} alternatives). The main contribution of this paper is to explicitly characterize the power of a popular nonparametric two sample test, designed for general alternatives, under a mean-shift alternative in the high-dimensional setting. Specifically, we explicitly derive the power of the linear-time Maximum Mean Discrepancy statistic using the Gaussian kernel, where the dimension and sample size can both tend to infinity at any rate, and the two distributions differ in their means. As a corollary, we find that if the signal-to-noise ratio is held constant, then the test's power goes to one if the number of samples increases faster than the dimension increases. This is the first explicit power derivation for a general nonparametric test in the high-dimensional setting, and also the first analysis of how tests designed for general alternatives perform when faced with easier ones.
In railway operations, a timetable is established to determine the departure and arrival times for the trains or other rolling stock at the different stations or relevant points inside the rail network or a subset of this network. The elaboration of this timetable is done to respond to the commercial requirements for both passenger and freight traffic, but also it must respect a set of security and capacity constraints associated with the railway network, rolling stock and legislation. Combining these requirements and constraints, as well as the important number of trains and schedules to plan, makes the preparation of a feasible timetable a complex and time-consuming process, that normally takes several months to be completed. This article addresses the problem of generating periodic timetables, which means that the involved trains operate in a recurrent pattern. For instance, the trains belonging to the same train line, depart from some station every 15 minutes or one hour. To tackle the problem, we present a constraint-based model suitable for this kind of problem. Then, we propose a genetic algorithm, allowing a rapid generation of feasible periodic timetables. Finally, two case studies are presented, the first, describing a sub-set of the Netherlands rail network, and the second a large portion of the Nord-pas-de-Calais regional rail network, both of them are then solved using our algorithm and the results are presented and discussed.
Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this paper we show the first application of the Behaviour Tree framework to a real robotic platform using the Evolutionary Robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behaviour as compared to the traditional Neural Network formulation. As a result, the behaviour is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-gram DelFly Explorer flapping wing Micro Air Vehicle equipped with a 4-gram onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the optimised behaviour in simulation is 88% and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.
Within the Kolmogorov theory of probability, Bayes' rule allows one to perform statistical inference by relating conditional probabilities to unconditional probabilities. As we show here, however, there is a continuous set of alternative inference rules that yield the same results, and that may have computational or practical advantages for certain problems. We formulate generalized axioms for probability theory, according to which the reverse conditional probability distribution P(B|A) is not specified by the forward conditional probability distribution P(A|B) and the marginals P(A) and P(B). Thus, in order to perform statistical inference, one must specify an additional "inference axiom," which relates P(B|A) to P(A|B), P(A), and P(B). We show that when Bayes' rule is chosen as the inference axiom, the axioms are equivalent to the classical Kolmogorov axioms. We then derive consistency conditions on the inference axiom, and thereby characterize the set of all possible rules for inference. The set of "first-order" inference axioms, defined as the set of axioms in which P(B|A) depends on the first power of P(A|B), is found to be a 1-simplex, with Bayes' rule at one of the extreme points. The other extreme point, the "inversion rule," is studied in depth.
Chaos provides many interesting properties that can be used to achieve computational tasks. Such properties are sensitivity to initial conditions, space filling, control and synchronization. Chaotic neural models have been devised to exploit such properties. In this paper, a chaotic spiking neuron model is investigated experimentally. This investigation is performed to understand the dynamic behaviours of the model. The aim of this research is to investigate the dynamics of the nonlinear dynamic state neuron (NDS) experimentally. The experimental approach has revealed some quantitative and qualitative properties of the NDS model such as the control mechanism, the reset mechanism, and the way the model may exhibit dynamic behaviours in phase space. It is shown experimentally in this paper that both the reset mechanism and the self-feed back control mechanism are important for the NDS model to work and to stabilise to one of the large number of available unstable periodic orbits (UPOs) that are embedded in its attractor. The experimental investigation suggests that the internal dynamics of the NDS neuron provide a rich set of dynamic behaviours that can be controlled and stabilised. These wide range of dynamic behaviours may be exploited to carry out information processing tasks.
Planned experiments are the gold standard in reliably comparing the causal effect of switching from a baseline policy to a new policy. One critical shortcoming of classical experimental methods, however, is that they typically do not take into account the dynamic nature of response to policy changes. For instance, in an experiment where we seek to understand the effects of a new ad pricing policy on auction revenue, agents may adapt their bidding in response to the experimental pricing changes. Thus, causal effects of the new pricing policy after such adaptation period, the {\em long-term causal effects}, are not captured by the classical methodology even though they clearly are more indicative of the value of the new policy. Here, we formalize a framework to define and estimate long-term causal effects of policy changes in multiagent economies. Central to our approach is behavioral game theory, which we leverage to formulate the ignorability assumptions that are necessary for causal inference. Under such assumptions we estimate long-term causal effects through a latent space approach, where a behavioral model of how agents act conditional on their latent behaviors is combined with a temporal model of how behaviors evolve over time.
Participatory democracy advances in virtually all governments and especially in South America which exhibits a mixed culture and social predisposition. This article presents the "Social Participation Ontology" (OPS from the Brazilian name \emph{Ontologia de Participa\c{c}\~ao Social}) implemented in compliance with the Web Ontology Language standard (OWL) for fostering social participation, specially in virtual platforms. The entities and links of OPS were defined based on an extensive collaboration of specialists. It is shown that OPS is instrumental for information retrieval from the contents of the portal, both in terms of the actors (at various levels) as well as mechanisms and activities. Significantly, OPS is linked to other OWL ontologies as an upper ontology and via FOAF and BFO as higher upper ontologies, which yields sound organization and access of knowledge and data. In order to illustrate the usefulness of OPS, we present results on ontological expansion and integration with other ontologies and data. Ongoing work involves further adoption of OPS by the official Brazilian federal portal for social participation and NGO s, and further linkage to other ontologies for social participation.
Reservoir computing is a recent bio-inspired approach for processing time-dependent signals. It has enabled a breakthrough in analog information processing, with several experiments, both electronic and optical, demonstrating state-of-the-art performances for hard tasks such as speech recognition, time series prediction and nonlinear channel equalization. A proof-of-principle experiment using a linear optical circuit on a photonic chip to process digital signals was recently reported. Here we present a photonic implementation of a reservoir computer based on a coherently driven passive fiber cavity processing analog signals. Our experiment has error rate as low or lower than previous experiments on a wide variety of tasks, and also has lower power consumption. Furthermore, the analytical model describing our experiment is also of interest, as it constitutes a very simple high performance reservoir computer algorithm. The present experiment, given its good performances, low energy consumption and conceptual simplicity, confirms the great potential of photonic reservoir computing for information processing applications ranging from artificial intelligence to telecommunications
Understanding infant development is one of the greatest scientific challenges of contemporary science. A large source of difficulty comes from the fact that the development of skills in infants results from the interactions of multiple mechanisms at multiple spatio-temporal scales. The concepts of "innate" or "acquired" are not any more adequate tools for explanations, which call for a shift from reductionist to systemic accounts. To address this challenge, building and experimenting with robots modeling the growing infant brain and body is crucial. Systemic explanations of pattern formation in sensorimotor, cognitive and social development, viewed as a complex dynamical system, require the use of formal models based on mathematics, algorithms and robots. Formulating hypothesis about development using such models, and exploring them through experiments, allows us to consider in detail the interaction between many mechanisms and parameters. This complements traditional experimental methods in psychology and neuroscience where only a few variables can be studied at the same time. Furthermore, the use of robots is of particular importance. The laws of physics generate everywhere around us spontaneous patterns in the inorganic world. They also strongly impact the living, and in particular constrain and guide infant development through the properties of its (changing) body in interaction with the physical environment. Being able to consider the body as an experimental variable, something that can be systematically changed in order to study the impact on skill formation, has been a dream to many developmental scientists. This is today becoming possible with developmental robotics.
In view of the paradigm shift that makes science ever more data-driven, in this thesis we propose a synthesis method for encoding and managing large-scale deterministic scientific hypotheses as uncertain and probabilistic data. In the form of mathematical equations, hypotheses symmetrically relate aspects of the studied phenomena. For computing predictions, however, deterministic hypotheses can be abstracted as functions. We build upon Simon's notion of structural equations in order to efficiently extract the (so-called) causal ordering between variables, implicit in a hypothesis structure (set of mathematical equations). We show how to process the hypothesis predictive structure effectively through original algorithms for encoding it into a set of functional dependencies (fd's) and then performing causal reasoning in terms of acyclic pseudo-transitive reasoning over fd's. Such reasoning reveals important causal dependencies implicit in the hypothesis predictive data and guide our synthesis of a probabilistic database. Like in the field of graphical models in AI, such a probabilistic database should be normalized so that the uncertainty arisen from competing hypotheses is decomposed into factors and propagated properly onto predictive data by recovering its joint probability distribution through a lossless join. That is motivated as a design-theoretic principle for data-driven hypothesis management and predictive analytics. The method is applicable to both quantitative and qualitative deterministic hypotheses and demonstrated in realistic use cases from computational science.
Web services allow communication between heterogeneous systems in a distributed environment. Their enormous success and their increased use led to the fact that thousands of Web services are present on the Internet. This significant number of Web services which not cease to increase has led to problems of the difficulty in locating and classifying web services, these problems are encountered mainly during the operations of web services discovery and substitution. Traditional ways of search based on keywords are not successful in this context, their results do not support the structure of Web services and they consider in their search only the identifiers of the web service description language (WSDL) interface elements. The methods based on semantics (WSDLS, OWLS, SAWSDL...) which increase the WSDL description of a Web service with a semantic description allow raising partially this problem, but their complexity and difficulty delays their adoption in real cases. Measuring the similarity between the web services interfaces is the most suitable solution for this kind of problems, it will classify available web services so as to know those that best match the searched profile and those that do not match. Thus, the main goal of this work is to study the degree of similarity between any two web services by offering a new method that is more effective than existing works.
In the domain of online advertising, our aim is to serve the best ad to a user who visits a certain webpage, to maximize the chance of a desired action to be performed by this user after seeing the ad. While it is possible to generate a different prediction model for each user to tell if he/she will act on a given ad, the prediction result typically will be quite unreliable with huge variance, since the desired actions are extremely sparse, and the set of users is huge (hundreds of millions) and extremely volatile, i.e., a lot of new users are introduced everyday, or are no longer valid. In this paper we aim to improve the accuracy in finding users who will perform the desired action, by assigning each user to a cluster, where the number of clusters is much smaller than the number of users (in the order of hundreds). Each user will fall into the same cluster with another user if their event history are similar. For this purpose, we modify the probabilistic latent semantic analysis (pLSA) model by assuming the independence of the user and the cluster id, given the history of events. This assumption helps us to identify a cluster of a new user without re-clustering all the users. We present the details of the algorithm we employed as well as the distributed implementation on Hadoop, and some initial results on the clusters that were generated by the algorithm.
This thesis contributes to ongoing research related to the categorical compositional model for natural language of Coecke, Sadrzadeh and Clark in three ways: Firstly, I propose a concrete instantiation of the abstract framework based on Frobenius algebras (joint work with Sadrzadeh). The theory improves shortcomings of previous proposals, extends the coverage of the language, and is supported by experimental work that improves existing results. The proposed framework describes a new class of compositional models that find intuitive interpretations for a number of linguistic phenomena. Secondly, I propose and evaluate in practice a new compositional methodology which explicitly deals with the different levels of lexical ambiguity (joint work with Pulman). A concrete algorithm is presented, based on the separation of vector disambiguation from composition in an explicit prior step. Extensive experimental work shows that the proposed methodology indeed results in more accurate composite representations for the framework of Coecke et al. in particular and every other class of compositional models in general. As a last contribution, I formalize the explicit treatment of lexical ambiguity in the context of the categorical framework by resorting to categorical quantum mechanics (joint work with Coecke). In the proposed extension, the concept of a distributional vector is replaced with that of a density matrix, which compactly represents a probability distribution over the potential different meanings of the specific word. Composition takes the form of quantum measurements, leading to interesting analogies between quantum physics and linguistics.
Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional neural network (CNN) trained on images, and then a maximum entropy (ME) language model is used to arrange these words into a coherent sentence. The second uses the penultimate activation layer of the CNN as input to a recurrent neural network (RNN) that then generates the caption sequence. In this paper, we compare the merits of these different language modeling approaches for the first time by using the same state-of-the-art CNN as input. We examine issues in the different approaches, including linguistic irregularities, caption repetition, and data set overlap. By combining key aspects of the ME and RNN methods, we achieve a new record performance over previously published results on the benchmark COCO dataset. However, the gains we see in BLEU do not translate to human judgments.
We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. The algorithm builds a forest of trees whose nodes store previously seen examples. It can be shown data points one at a time and updates itself incrementally, hence it is naturally online. Few instance-based algorithms have this property while being simultaneously fast, which the BF is. This is crucial for applications where one needs to respond to input data in real time. The number of children of each node is not set beforehand but obtained from the training procedure, which makes the algorithm very flexible with regards to what data manifolds it can learn. We test its generalization performance and speed on a range of benchmark datasets and detail in which settings it outperforms the state of the art. Empirically we find that training time scales as O(DNlog(N)) and testing as O(Dlog(N)), where D is the dimensionality and N the amount of data,
The basic indicators of a researcher's productivity and impact are still the number of publications and their citation counts. These metrics are clear, straightforward, and easy to obtain. When a ranking of scholars is needed, for instance in grant, award, or promotion procedures, their use is the fastest and cheapest way of prioritizing some scientists over others. However, due to their nature, there is a danger of oversimplifying scientific achievements. Therefore, many other indicators have been proposed including the usage of the PageRank algorithm known for the ranking of webpages and its modifications suited to citation networks. Nevertheless, this recursive method is computationally expensive and even if it has the advantage of favouring prestige over popularity, its application should be well justified, particularly when compared to the standard citation counts. In this study, we analyze three large datasets of computer science papers in the categories of artificial intelligence, software engineering, and theory and methods and apply 12 different ranking methods to the citation networks of authors. We compare the resulting rankings with self-compiled lists of outstanding researchers selected as frequent editorial board members of prestigious journals in the field and conclude that there is no evidence of PageRank-based methods outperforming simple citation counts.
The Stable Matching Problem with Couples (SMP-C) is a ubiquitous real-world extension of the stable matching problem (SMP) involving complementarities. Although SMP can be solved in polynomial time, SMP-C is NP-Complete. Hence, it is not clear which, if any, of the theoretical results surrounding the canonical SMP problem apply in this setting. In this paper, we use a recently-developed SAT encoding to solve SMP-C exactly. This allows us to enumerate all stable matchings for any given instance of SMP-C. With this tool, we empirically evaluate some of the properties that have been hypothesized to hold for SMP-C. We take particular interest in investigating if, as the size of the market grows, the percentage of instances with unique stable matchings also grows. While we did not find this trend among the random problem instances we sampled, we did find that the percentage of instances with an resident optimal matching seems to more closely follow the trends predicted by previous conjectures. We also define and investigate resident Pareto optimal stable matchings, finding that, even though this is important desideratum for the deferred acceptance style algorithms previously designed to solve SMP-C, they do not always find one. We also investigate strategy-proofness for SMP-C, showing that even if only one stable matching exists, residents still have incentive to misreport their preferences. However, if a problem has a resident optimal stable matching, we show that residents cannot manipulate via truncation.
Formal synthesis is the process of generating a program satisfying a high-level formal specification. In recent times, effective formal synthesis methods have been proposed based on the use of inductive learning. We refer to this class of methods that learn programs from examples as formal inductive synthesis. In this paper, we present a theoretical framework for formal inductive synthesis. We discuss how formal inductive synthesis differs from traditional machine learning. We then describe oracle-guided inductive synthesis (OGIS), a framework that captures a family of synthesizers that operate by iteratively querying an oracle. An instance of OGIS that has had much practical impact is counterexample-guided inductive synthesis (CEGIS). We present a theoretical characterization of CEGIS for learning any program that computes a recursive language. In particular, we analyze the relative power of CEGIS variants where the types of counterexamples generated by the oracle varies. We also consider the impact of bounded versus unbounded memory available to the learning algorithm. In the special case where the universe of candidate programs is finite, we relate the speed of convergence to the notion of teaching dimension studied in machine learning theory. Altogether, the results of the paper take a first step towards a theoretical foundation for the emerging field of formal inductive synthesis.
We focus on the problem of finding a non-linear classification function that lies in a Reproducing Kernel Hilbert Space (RKHS) both from the primal point of view (finding a perfect separator when one exists) and the dual point of view (giving a certificate of non-existence), with special focus on generalizations of two classical schemes - the Perceptron (primal) and Von-Neumann (dual) algorithms. We cast our problem as one of maximizing the regularized normalized hard-margin ($\rho$) in an RKHS and %use the Representer Theorem to rephrase it in terms of a Mahalanobis dot-product/semi-norm associated with the kernel's (normalized and signed) Gram matrix. We derive an accelerated smoothed algorithm with a convergence rate of $\tfrac{\sqrt {\log n}}{\rho}$ given $n$ separable points, which is strikingly similar to the classical kernelized Perceptron algorithm whose rate is $\tfrac1{\rho^2}$. When no such classifier exists, we prove a version of Gordan's separation theorem for RKHSs, and give a reinterpretation of negative margins. This allows us to give guarantees for a primal-dual algorithm that halts in $\min\{\tfrac{\sqrt n}{|\rho|}, \tfrac{\sqrt n}{\epsilon}\}$ iterations with a perfect separator in the RKHS if the primal is feasible or a dual $\epsilon$-certificate of near-infeasibility.
Interesting theoretical associations have been established by recent papers between the fields of active learning and stochastic convex optimization due to the common role of feedback in sequential querying mechanisms. In this paper, we continue this thread in two parts by exploiting these relations for the first time to yield novel algorithms in both fields, further motivating the study of their intersection. First, inspired by a recent optimization algorithm that was adaptive to unknown uniform convexity parameters, we present a new active learning algorithm for one-dimensional thresholds that can yield minimax rates by adapting to unknown noise parameters. Next, we show that one can perform $d$-dimensional stochastic minimization of smooth uniformly convex functions when only granted oracle access to noisy gradient signs along any coordinate instead of real-valued gradients, by using a simple randomized coordinate descent procedure where each line search can be solved by $1$-dimensional active learning, provably achieving the same error convergence rate as having the entire real-valued gradient. Combining these two parts yields an algorithm that solves stochastic convex optimization of uniformly convex and smooth functions using only noisy gradient signs by repeatedly performing active learning, achieves optimal rates and is adaptive to all unknown convexity and smoothness parameters.
A fundamental challenge in developing high-impact machine learning technologies is balancing the need to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured and large scale, from social and biological networks, to knowledge graphs and the Web, to images, video, and natural language. In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data. The first, hinge-loss Markov random fields (HL-MRFs), is a new kind of probabilistic graphical model that generalizes different approaches to convex inference. We unite three approaches from the randomized algorithms, probabilistic graphical models, and fuzzy logic communities, showing that all three lead to the same inference objective. We then define HL-MRFs by generalizing this unified objective. The second new formalism, probabilistic soft logic (PSL), is a probabilistic programming language that makes HL-MRFs easy to define using a syntax based on first-order logic. We introduce an algorithm for inferring most-probable variable assignments (MAP inference) that is much more scalable than general-purpose convex optimization methods, because it uses message passing to take advantage of sparse dependency structures. We then show how to learn the parameters of HL-MRFs. The learned HL-MRFs are as accurate as analogous discrete models, but much more scalable. Together, these algorithms enable HL-MRFs and PSL to model rich, structured data at scales not previously possible.
Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the RankSVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.
We recently performed cognitive experiments on conjunctions and negations of two concepts with the aim of investigating the combination problem of concepts. Our experiments confirmed the deviations (conceptual vagueness, underextension, overextension, etc.) from the rules of classical (fuzzy) logic and probability theory observed by several scholars in concept theory, while our data were successfully modeled in a quantum-theoretic framework developed by ourselves. In this paper, we isolate a new, very stable and systematic pattern of violation of classicality that occurs in concept combinations. In addition, the strength and regularity of this non-classical effect leads us to believe that it occurs at a more fundamental level than the deviations observed up to now. It is our opinion that we have identified a deep non-classical mechanism determining not only how concepts are combined but, rather, how they are formed. We show that this effect can be faithfully modeled in a two-sector Fock space structure, and that it can be exactly explained by assuming that human thought is the supersposition of two processes, a 'logical reasoning', guided by 'logic', and a 'conceptual reasoning' guided by 'emergence', and that the latter generally prevails over the former. All these findings provide a new fundamental support to our quantum-theoretic approach to human cognition.
The paper introduces a new modular action language, ALM, and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993; 1998) in which a high-level action language is used as a front end for a logic programming system description. The resulting logic programming representation is used to perform various computational tasks. The methodology based on existing action languages works well for small and even medium size systems, but is not meant to deal with larger systems that require structuring of knowledge. ALM is meant to remedy this problem. Structuring of knowledge in ALM is supported by the concepts of module (a formal description of a specific piece of knowledge packaged as a unit), module hierarchy, and library, and by the division of a system description of ALM into two parts: theory and structure. A theory consists of one or more modules with a common theme, possibly organized into a module hierarchy based on a dependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them. Structures are used to describe the domain's objects. These features, together with the means for defining classes of a domain as special cases of previously defined ones, facilitate the stepwise development, testing, and readability of a knowledge base, as well as the creation of knowledge representation libraries. To appear in Theory and Practice of Logic Programming (TPLP).
The proliferation of the web presents an unsolved problem of automatically analyzing billions of pages of natural language. We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters. It does this on a single mid-range machine using efficient algorithms and compressed document representations. It is applied to two web-scale crawls covering tens of terabytes. ClueWeb09 and ClueWeb12 contain 500 and 733 million web pages and were clustered into 500,000 to 700,000 clusters. To the best of our knowledge, such fine grained clustering has not been previously demonstrated. Previous approaches clustered a sample that limits the maximum number of discoverable clusters. The proposed EM-tree algorithm uses the entire collection in clustering and produces several orders of magnitude more clusters than the existing algorithms. Fine grained clustering is necessary for meaningful clustering in massive collections where the number of distinct topics grows linearly with collection size. These fine-grained clusters show an improved cluster quality when assessed with two novel evaluations using ad hoc search relevance judgments and spam classifications for external validation. These evaluations solve the problem of assessing the quality of clusters where categorical labeling is unavailable and unfeasible.
The choice of approximate posterior distribution is one of the core problems in variational inference. Most applications of variational inference employ simple families of posterior approximations in order to allow for efficient inference, focusing on mean-field or other simple structured approximations. This restriction has a significant impact on the quality of inferences made using variational methods. We introduce a new approach for specifying flexible, arbitrarily complex and scalable approximate posterior distributions. Our approximations are distributions constructed through a normalizing flow, whereby a simple initial density is transformed into a more complex one by applying a sequence of invertible transformations until a desired level of complexity is attained. We use this view of normalizing flows to develop categories of finite and infinitesimal flows and provide a unified view of approaches for constructing rich posterior approximations. We demonstrate that the theoretical advantages of having posteriors that better match the true posterior, combined with the scalability of amortized variational approaches, provides a clear improvement in performance and applicability of variational inference.
Short Message Service (SMS) based Information Systems (SMSbIS) provide an excellent alternative to a traditional approach of obtaining specific information by direct (through phone) or indirect (IVRS, Web, Email) probing. Information and communication technology and far reaching mobile penetration has opened this new research trend Number of key players in Search industry including Microsoft and Google are attracted by the expected increase in volume of use of such applications. The wide range of applications and their public acceptance has motivated researchers to work in this research domain. Several applications such as SMS based information access using database management services, SMS based information retrieval through internet (search engine), SMS based information extraction, question answering, image retrieval etc. have been emerged. With the aim to understand the functionality involved in these systems, an extensive review of a few of these SMSbISs has been planned and executed by us. These systems are classified into four categories based on the objectives and domains of the applications. As a result of this study a well structured functional model is presented here. The model is evaluated in different dimensions, which is presented in this paper. In addition to this a chronological progress with respect to research and development in this upcoming field is compiled in this paper. Such an extensive review presented in this paper would definitely help the researchers and developers to understand the technical aspects of this field. The functional framework presented here would be useful to the system designers to design and develop an SMS based Information System of any specific domain.
The Web has made it possible to harness human cognition en masse to achieve new capabilities. Some of these successes are well known; for example Wikipedia has become the go-to place for basic information on all things; Duolingo engages millions of people in real-life translation of text, while simultaneously teaching them to speak foreign languages; and fold.it has enabled public-driven scientific discoveries by recasting complex biomedical challenges into popular online puzzle games. These and other early successes hint at the tremendous potential for future crowd-powered capabilities for the benefit of health, education, science, and society. In the process, a new field called Human Computation has emerged to better understand, replicate, and improve upon these successes through scientific research. Human Computation refers to the science that underlies online crowd-powered systems and was the topic of a recent visioning activity in which a representative cross-section of researchers, industry practitioners, visionaries, funding agency representatives, and policy makers came together to understand what makes crowd-powered systems successful. Teams of experts considered past, present, and future human computation systems to explore which kinds of crowd-powered systems have the greatest potential for societal impact and which kinds of research will best enable the efficient development of new crowd-powered systems to achieve this impact. This report summarize the products and findings of those activities as well as the unconventional process and activities employed by the workshop, which were informed by human computation research.
Stackelberg security game models and associated computational tools have seen deployment in a number of high-consequence security settings, such as LAX canine patrols and Federal Air Marshal Service. These models focus on isolated systems with only one defender, despite being part of a more complex system with multiple players. Furthermore, many real systems such as transportation networks and the power grid exhibit interdependencies between targets and, consequently, between decision makers jointly charged with protecting them. To understand such multidefender strategic interactions present in security, we investigate game theoretic models of security games with multiple defenders. Unlike most prior analysis, we focus on the situations in which each defender must protect multiple targets, so that even a single defender's best response decision is, in general, highly non-trivial. We start with an analytical investigation of multidefender security games with independent targets, offering an equilibrium and price-of-anarchy analysis of three models with increasing generality. In all models, we find that defenders have the incentive to over-protect targets, at times significantly. Additionally, in the simpler models, we find that the price of anarchy is unbounded, linearly increasing both in the number of defenders and the number of targets per defender. Considering interdependencies among targets, we develop a novel mixed-integer linear programming formulation to compute a defender's best response, and make use of this formulation in approximating Nash equilibria of the game. We apply this approach towards computational strategic analysis of several models of networks representing interdependencies, including real-world power networks. Our analysis shows how network structure and the probability of failure spread determine the propensity of defenders to over- or under-invest in security.
Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling.
Achieving efficient and scalable exploration in complex domains poses a major challenge in reinforcement learning. While Bayesian and PAC-MDP approaches to the exploration problem offer strong formal guarantees, they are often impractical in higher dimensions due to their reliance on enumerating the state-action space. Hence, exploration in complex domains is often performed with simple epsilon-greedy methods. In this paper, we consider the challenging Atari games domain, which requires processing raw pixel inputs and delayed rewards. We evaluate several more sophisticated exploration strategies, including Thompson sampling and Boltzman exploration, and propose a new exploration method based on assigning exploration bonuses from a concurrently learned model of the system dynamics. By parameterizing our learned model with a neural network, we are able to develop a scalable and efficient approach to exploration bonuses that can be applied to tasks with complex, high-dimensional state spaces. In the Atari domain, our method provides the most consistent improvement across a range of games that pose a major challenge for prior methods. In addition to raw game-scores, we also develop an AUC-100 metric for the Atari Learning domain to evaluate the impact of exploration on this benchmark.
In this paper we propose the approach for constructing partitionings of hard variants of the Boolean satisfiability problem (SAT). Such partitionings can be used for solving corresponding SAT instances in parallel. For the same SAT instance one can construct different partitionings, each of them is a set of simplified versions of the original SAT instance. The effectiveness of an arbitrary partitioning is determined by the total time of solving of all SAT instances from it. We suggest the approach, based on the Monte Carlo method, for estimating time of processing of an arbitrary partitioning. With each partitioning we associate a point in the special finite search space. The estimation of effectiveness of the particular partitioning is the value of predictive function in the corresponding point of this space. The problem of search for an effective partitioning can be formulated as a problem of optimization of the predictive function. We use metaheuristic algorithms (simulated annealing and tabu search) to move from point to point in the search space. In our computational experiments we found partitionings for SAT instances encoding problems of inversion of some cryptographic functions. Several of these SAT instances with realistic predicted solving time were successfully solved on a computing cluster and in the volunteer computing project SAT@home. The solving time agrees well with estimations obtained by the proposed method.
As software systems are getting increasingly connected, there is a need for equipping nonmonotonic logic programs with access to external sources that are possibly remote and may contain information in heterogeneous formats. To cater for this need, HEX programs were designed as a generalization of answer set programs with an API style interface that allows to access arbitrary external sources, providing great flexibility. Efficient evaluation of such programs however is challenging, and it requires to interleave external computation and model building; to decide when to switch between these tasks is difficult, and existing approaches have limited scalability in many real-world application scenarios. We present a new approach for the evaluation of logic programs with external source access, which is based on a configurable framework for dividing the non-ground program into possibly overlapping smaller parts called evaluation units. The latter will be processed by interleaving external evaluation and model building using an evaluation graph and a model graph, respectively, and by combining intermediate results. Experiments with our prototype implementation show a significant improvement compared to previous approaches. While designed for HEX-programs, the new evaluation approach may be deployed to related rule-based formalisms as well.
Our goal is to answer elementary-level science questions using knowledge extracted automatically from science textbooks, expressed in a subset of first-order logic. Given the incomplete and noisy nature of these automatically extracted rules, Markov Logic Networks (MLNs) seem a natural model to use, but the exact way of leveraging MLNs is by no means obvious. We investigate three ways of applying MLNs to our task. In the first, we simply use the extracted science rules directly as MLN clauses. Unlike typical MLN applications, our domain has long and complex rules, leading to an unmanageable number of groundings. We exploit the structure present in hard constraints to improve tractability, but the formulation remains ineffective. In the second approach, we instead interpret science rules as describing prototypical entities, thus mapping rules directly to grounded MLN assertions, whose constants are then clustered using existing entity resolution methods. This drastically simplifies the network, but still suffers from brittleness. Finally, our third approach, called Praline, uses MLNs to align the lexical elements as well as define and control how inference should be performed in this task. Our experiments, demonstrating a 15\% accuracy boost and a 10x reduction in runtime, suggest that the flexibility and different inference semantics of Praline are a better fit for the natural language question answering task.
In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is described explicitly by a list of attributes. Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets. In this paper a new prototype-based clustering method, named Evidential C-Medoids (ECMdd), which is an extension of Fuzzy C-Medoids (FCMdd) on the theoretical framework of belief functions is proposed. In ECMdd, medoids are utilized as the prototypes to represent the detected classes, including specific classes and imprecise classes. Specific classes are for the data which are distinctly far from the prototypes of other classes, while imprecise classes accept the objects that may be close to the prototypes of more than one class. This soft decision mechanism could make the clustering results more cautious and reduce the misclassification rates. Experiments in synthetic and real data sets are used to illustrate the performance of ECMdd. The results show that ECMdd could capture well the uncertainty in the internal data structure. Moreover, it is more robust to the initializations compared with FCMdd.
Many algorithms have been parallelized successfully on the Intel Xeon Phi coprocessor, especially those with regular, balanced, and predictable data access patterns and instruction flows. Irregular and unbalanced algorithms are harder to parallelize efficiently. They are, for instance, present in artificial intelligence search algorithms such as Monte Carlo Tree Search (MCTS). In this paper we study the scaling behavior of MCTS, on a highly optimized real-world application, on real hardware. The Intel Xeon Phi allows shared memory scaling studies up to 61 cores and 244 hardware threads. We compare work-stealing (Cilk Plus and TBB) and work-sharing (FIFO scheduling) approaches. Interestingly, we find that a straightforward thread pool with a work-sharing FIFO queue shows the best performance. A crucial element for this high performance is the controlling of the grain size, an approach that we call Grain Size Controlled Parallel MCTS. Our subsequent comparing with the Xeon CPUs shows an even more comprehensible distinction in performance between different threading libraries. We achieve, to the best of our knowledge, the fastest implementation of a parallel MCTS on the 61 core Intel Xeon Phi using a real application (47 relative to a sequential run).
Real-time bidding (RTB) has become one of the largest online advertising markets in the world. Today the bid price per ad impression is typically decided by the expected value of how it can lead to a desired action event (e.g., registering an account or placing a purchase order) to the advertiser. However, this industry standard approach to decide the bid price does not consider the actual effect of the ad shown to the user, which should be measured based on the performance lift among users who have been or have not been exposed to a certain treatment of ads. In this paper, we propose a new bidding strategy and prove that if the bid price is decided based on the performance lift rather than absolute performance value, advertisers can actually gain more action events. We describe the modeling methodology to predict the performance lift and demonstrate the actual performance gain through blind A/B test with real ad campaigns in an industry-leading Demand-Side Platform (DSP). We also discuss the relationship between attribution models and bidding strategies. We prove that, to move the DSPs to bid based on performance lift, they should be rewarded according to the relative performance lift they contribute.
The visualization of an image collection is the process of displaying a collection of images on a screen under some specific layout requirements. This paper focuses on an important problem that is not well addressed by the previous methods: visualizing image collections into arbitrary layout shapes while arranging images according to user-defined semantic or visual correlations (e.g., color or object category). To this end, we first propose a property-based tree construction scheme to organize images of a collection into a tree structure according to user-defined properties. In this way, images can be adaptively placed with the desired semantic or visual correlations in the final visualization layout. Then, we design a two-step visualization optimization scheme to further optimize image layouts. As a result, multiple layout effects including layout shape and image overlap ratio can be effectively controlled to guarantee a satisfactory visualization. Finally, we also propose a tree-transfer scheme such that visualization layouts can be adaptively changed when users select different "images of interest". We demonstrate the effectiveness of our proposed approach through the comparisons with state-of-the-art visualization techniques.
Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal operating strategy, which could be integrated to building's energy management system (BEMS). This paper proposes a prediction model for building energy consumption using support vector machine (SVM). Data-driven model, for instance, SVM is very sensitive to the selection of training data. Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model. In addition, to encompass thermal inertia of building, pseudo dynamic model is applied since it takes into account information of transition of energy consumption effects and occupancy profile. Relevant days data selection and whole training data model is applied to the case studies of Ecole des Mines de Nantes, France Office building. The results showed that support vector machine based on relevant data selection method is able to predict the energy consumption of building with a high accuracy in compare to whole data training. In addition, relevant data selection method is computationally cheaper (around 8 minute training time) in contrast to whole data training (around 31 hour for weekend and 116 hour for working days) and reveals realistic control implementation for online system as well.
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead, even in the absence of intent, to a lack of fairness, i.e., their outcomes can disproportionately hurt (or, benefit) particular groups of people sharing one or more sensitive attributes (e.g., race, sex). In this paper, we introduce a flexible mechanism to design fair classifiers by leveraging a novel intuitive measure of decision boundary (un)fairness. We instantiate this mechanism with two well-known classifiers, logistic regression and support vector machines, and show on real-world data that our mechanism allows for a fine-grained control on the degree of fairness, often at a small cost in terms of accuracy.
Disjunctive Answer Set Programming is a powerful declarative programming paradigm with complexity beyond NP. Identifying classes of programs for which the consistency problem is in NP is of interest from the theoretical standpoint and can potentially lead to improvements in the design of answer set programming solvers. One of such classes consists of dual-normal programs, where the number of positive body atoms in proper rules is at most one. Unlike other classes of programs, dual-normal programs have received little attention so far. In this paper we study this class. We relate dual-normal programs to propositional theories and to normal programs by presenting several inter-translations. With the translation from dual-normal to normal programs at hand, we introduce the novel class of body-cycle free programs, which are in many respects dual to head-cycle free programs. We establish the expressive power of dual-normal programs in terms of SE- and UE-models, and compare them to normal programs. We also discuss the complexity of deciding whether dual-normal programs are strongly and uniformly equivalent.
The Support Vector Machine (SVM) method has been widely used in numerous classification tasks. The main idea of this algorithm is based on the principle of the margin maximization to find an hyperplane which separates the data into two different classes.In this paper, SVM is applied to phoneme recognition task. However, in many real-world problems, each phoneme in the data set for recognition problems may differ in the degree of significance due to noise, inaccuracies, or abnormal characteristics; All those problems can lead to the inaccuracies in the prediction phase. Unfortunately, the standard formulation of SVM does not take into account all those problems and, in particular, the variation in the speech input. This paper presents a new formulation of SVM (B-SVM) that attributes to each phoneme a confidence degree computed based on its geometric position in the space. Then, this degree is used in order to strengthen the class membership of the tested phoneme. Hence, we introduce a reformulation of the standard SVM that incorporates the degree of belief. Experimental performance on TIMIT database shows the effectiveness of the proposed method B-SVM on a phoneme recognition problem.
Words (phrases or symbols) play a key role in human life. Word (phrase or symbol) representation is the fundamental problem for knowledge representation and understanding. A word (phrase or symbol) usually represents a name of a category. However, it is always a challenge that how to represent a category can make it easily understood. In this paper, a new representation for a category is discussed, which can be considered a generalization of classic set. In order to reduce representation complexity, the economy principle of category representation is proposed. The proposed category representation provides a powerful tool for analyzing conceptual systems, relations between words, communication, knowledge, situations. More specifically, the conceptual system, word relations and communication are mathematically defined and classified such as ideal conceptual system, perfect communication and so on; relation between words and sentences is also studied, which shows that knowledge are words. Furthermore, how conceptual systems and words depend on situations is presented, and how truth is defined is also discussed.
Approximate Newton methods are a standard optimization tool which aim to maintain the benefits of Newton's method, such as a fast rate of convergence, whilst alleviating its drawbacks, such as computationally expensive calculation or estimation of the inverse Hessian. In this work we investigate approximate Newton methods for policy optimization in Markov Decision Processes (MDPs). We first analyse the structure of the Hessian of the objective function for MDPs. We show that, like the gradient, the Hessian exhibits useful structure in the context of MDPs and we use this analysis to motivate two Gauss-Newton Methods for MDPs. Like the Gauss-Newton method for non-linear least squares, these methods involve approximating the Hessian by ignoring certain terms in the Hessian which are difficult to estimate. The approximate Hessians possess desirable properties, such as negative definiteness, and we demonstrate several important performance guarantees including guaranteed ascent directions, invariance to affine transformation of the parameter space, and convergence guarantees. We finally provide a unifying perspective of key policy search algorithms, demonstrating that our second Gauss-Newton algorithm is closely related to both the EM-algorithm and natural gradient ascent applied to MDPs, but performs significantly better in practice on a range of challenging domains.
The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.
This paper examines the role and efficiency of the non-convex loss functions for binary classification problems. In particular, we investigate how to design a simple and effective boosting algorithm that is robust to the outliers in the data. The analysis of the role of a particular non-convex loss for prediction accuracy varies depending on the diminishing tail properties of the gradient of the loss -- the ability of the loss to efficiently adapt to the outlying data, the local convex properties of the loss and the proportion of the contaminated data. In order to use these properties efficiently, we propose a new family of non-convex losses named $\gamma$-robust losses. Moreover, we present a new boosting framework, {\it Arch Boost}, designed for augmenting the existing work such that its corresponding classification algorithm is significantly more adaptable to the unknown data contamination. Along with the Arch Boosting framework, the non-convex losses lead to the new class of boosting algorithms, named adaptive, robust, boosting (ARB). Furthermore, we present theoretical examples that demonstrate the robustness properties of the proposed algorithms. In particular, we develop a new breakdown point analysis and a new influence function analysis that demonstrate gains in robustness. Moreover, we present new theoretical results, based only on local curvatures, which may be used to establish statistical and optimization properties of the proposed Arch boosting algorithms with highly non-convex loss functions. Extensive numerical calculations are used to illustrate these theoretical properties and reveal advantages over the existing boosting methods when data exhibits a number of outliers.
Purpose: In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem. Methods: This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction. Conclusion: We investigate how adding spatial feature coordinates (i.e. $i$, $j$, $k$) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain. Results: As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.
Answer set programming is a declarative programming paradigm oriented towards difficult combinatorial search problems. A fundamental task in answer set programming is to compute stable models, i.e., solutions of logic programs. Answer set solvers are the programs that perform this task. The problem of deciding whether a disjunctive program has a stable model is $\Sigma^P_2$-complete. The high complexity of reasoning within disjunctive logic programming is responsible for few solvers capable of dealing with such programs, namely DLV, GnT, Cmodels, CLASP and WASP. In this paper we show that transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers can be adapted for disjunctive answer set solvers. Transition systems give a unifying perspective and bring clarity in the description and comparison of solvers. They can be effectively used for analyzing, comparing and proving correctness of search algorithms as well as inspiring new ideas in the design of disjunctive answer set solvers. In this light, we introduce a general template, which accounts for major techniques implemented in disjunctive solvers. We then illustrate how this general template captures solvers DLV, GnT and Cmodels. We also show how this framework provides a convenient tool for designing new solving algorithms by means of combinations of techniques employed in different solvers.
Transferring knowledge from prior source tasks in solving a new target task can be useful in several learning applications. The application of transfer poses two serious challenges which have not been adequately addressed. First, the agent should be able to avoid negative transfer, which happens when the transfer hampers or slows down the learning instead of helping it. Second, the agent should be able to selectively transfer, which is the ability to select and transfer from different and multiple source tasks for different parts of the state space of the target task. We propose A2T (Attend, Adapt and Transfer), an attentive deep architecture which adapts and transfers from these source tasks. Our model is generic enough to effect transfer of either policies or value functions. Empirical evaluations on different learning algorithms show that A2T is an effective architecture for transfer by being able to avoid negative transfer while transferring selectively from multiple source tasks in the same domain.
Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations; examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. Rewarding these characteristics is formalized in NAB, using a scoring algorithm designed for streaming data. NAB evaluates detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and give results and analyses for several open source, commercially-used algorithms. The goal for NAB is to provide a standard, open source framework with which the research community can compare and evaluate different algorithms for detecting anomalies in streaming data.
The increasing complexity of deep learning architectures is resulting in training time requiring weeks or even months. This slow training is due in part to vanishing gradients, in which the gradients used by back-propagation are extremely large for weights connecting deep layers (layers near the output layer), and extremely small for shallow layers (near the input layer); this results in slow learning in the shallow layers. Additionally, it has also been shown that in highly non-convex problems, such as deep neural networks, there is a proliferation of high-error low curvature saddle points, which slows down learning dramatically. In this paper, we attempt to overcome the two above problems by proposing an optimization method for training deep neural networks which uses learning rates which are both specific to each layer in the network and adaptive to the curvature of the function, increasing the learning rate at low curvature points. This enables us to speed up learning in the shallow layers of the network and quickly escape high-error low curvature saddle points. We test our method on standard image classification datasets such as MNIST, CIFAR10 and ImageNet, and demonstrate that our method increases accuracy as well as reduces the required training time over standard algorithms.
The only rigorous approaches for achieving a numerical proof of optimality in global optimization are interval-based methods that interleave branching of the search-space and pruning of the subdomains that cannot contain an optimal solution. State-of-the-art solvers generally integrate local optimization algorithms to compute a good upper bound of the global minimum over each subspace. In this document, we propose a cooperative framework in which interval methods cooperate with evolutionary algorithms. The latter are stochastic algorithms in which a population of candidate solutions iteratively evolves in the search-space to reach satisfactory solutions. Within our cooperative solver Charibde, the evolutionary algorithm and the interval-based algorithm run in parallel and exchange bounds, solutions and search-space in an advanced manner via message passing. A comparison of Charibde with state-of-the-art interval-based solvers (GlobSol, IBBA, Ibex) and NLP solvers (Couenne, BARON) on a benchmark of difficult COCONUT problems shows that Charibde is highly competitive against non-rigorous solvers and converges faster than rigorous solvers by an order of magnitude.
We analyze the RI-TIMEXes in temporally annotated corpora and propose two hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative domain: the anchor point hypothesis and the anchor relation hypothesis. We annotate the RI-TIMEXes in three corpora to study the characteristics of RI-TMEXes in different domains. This informed the design of our RI-TIMEX normalization system for the clinical domain, which consists of an anchor point classifier, an anchor relation classifier and a rule-based RI-TIMEX text span parser. We experiment with different feature sets and perform error analysis for each system component. The annotation confirmed the hypotheses that we can simplify the RI-TIMEXes normalization task using two multi-label classifiers. Our system achieves anchor point classification, anchor relation classification and rule-based parsing accuracy of 74.68%, 87.71% and 57.2% (82.09% under relaxed matching criteria) respectively on the held-out test set of the 2012 i2b2 temporal relation challenge. Experiments with feature sets reveals some interesting findings such as the verbal tense feature does not inform the anchor relation classification in clinical narratives as much as the tokens near the RI-TIMEX. Error analysis shows that underrepresented anchor point and anchor relation classes are difficult to detect. We formulate the RI-TIMEX normalization problem as a pair of multi-label classification problems. Considering only the RI-TIMEX extraction and normalization, the system achieves statistically significant improvement over the RI-TIMEX results of the best systems in the 2012 i2b2 challenge.
Latent variable models have accumulated a considerable amount of interest from the industry and academia for their versatility in a wide range of applications. A large amount of effort has been made to develop systems that is able to extend the systems to a large scale, in the hope to make use of them on industry scale data. In this paper, we describe a system that operates at a scale orders of magnitude higher than previous works, and an order of magnitude faster than state-of-the-art system at the same scale, at the same time showing more robustness and more accurate results. Our system uses a number of advances in distributed inference: high performance in synchronization of sufficient statistics with relaxed consistency model; fast sampling, using the Metropolis-Hastings-Walker method to overcome dense generative models; statistical modeling, moving beyond Latent Dirichlet Allocation (LDA) to Pitman-Yor distributions (PDP) and Hierarchical Dirichlet Process (HDP) models; sophisticated parameter projection schemes, to resolve the conflicts within the constraint between parameters arising from the relaxed consistency model. This work significantly extends the domain of applicability of what is commonly known as the Parameter Server. We obtain results with up to hundreds billion oftokens, thousands of topics, and a vocabulary of a few million token-types, using up to 60,000 processor cores operating on a production cluster of a large Internet company. This demonstrates the feasibility to scale to problems orders of magnitude larger than any previously published work.
Evidence for small amounts of very hot plasma has been found in active regions and might be the indication of an impulsive heating, released at spatial scales smaller than the cross section of a single loop. We investigate the heating and substructure of coronal loops in the core of one such active region by analyzing the light curves in the smallest resolution elements of solar observations in two EUV channels (94 A and 335 A) from the Atmospheric Imaging Assembly on-board the Solar Dynamics Observatory. We model the evolution of a bundle of strands heated by a storm of nanoflares by means of a hydrodynamic 0D loop model (EBTEL). The light curves obtained from the random combination of those of single strands are compared to the observed light curves either in a single pixel or in a row of pixels, simultaneously in the two channels and using two independent methods: an artificial intelligent system (Probabilistic Neural Network, PNN) and a simple cross-correlation technique. We explore the space of the parameters to constrain the distribution of the heat pulses, their duration and their spatial size, and, as a feedback on the data, their signatures on the light curves. From both methods the best agreement is obtained for a relatively large population of events (1000) with a short duration (less than 1 min) and a relatively shallow distribution (power law with index 1.5) in a limited energy range (1.5 decades). The feedback on the data indicates that bumps in the light curves, especially in the 94 A channel, are signatures of a heating excess that occurred a few minutes before.
Recently, there has been significant progress in understanding reinforcement learning in discounted infinite-horizon Markov decision processes (MDPs) by deriving tight sample complexity bounds. However, in many real-world applications, an interactive learning agent operates for a fixed or bounded period of time, for example tutoring students for exams or handling customer service requests. Such scenarios can often be better treated as episodic fixed-horizon MDPs, for which only looser bounds on the sample complexity exist. A natural notion of sample complexity in this setting is the number of episodes required to guarantee a certain performance with high probability (PAC guarantee). In this paper, we derive an upper PAC bound $\tilde O(\frac{|\mathcal S|^2 |\mathcal A| H^2}{\epsilon^2} \ln\frac 1 \delta)$ and a lower PAC bound $\tilde \Omega(\frac{|\mathcal S| |\mathcal A| H^2}{\epsilon^2} \ln \frac 1 {\delta + c})$ that match up to log-terms and an additional linear dependency on the number of states $|\mathcal S|$. The lower bound is the first of its kind for this setting. Our upper bound leverages Bernstein's inequality to improve on previous bounds for episodic finite-horizon MDPs which have a time-horizon dependency of at least $H^3$.
We consider the problem of learning causal networks with interventions, when each intervention is limited in size under Pearl's Structural Equation Model with independent errors (SEM-IE). The objective is to minimize the number of experiments to discover the causal directions of all the edges in a causal graph. Previous work has focused on the use of separating systems for complete graphs for this task. We prove that any deterministic adaptive algorithm needs to be a separating system in order to learn complete graphs in the worst case. In addition, we present a novel separating system construction, whose size is close to optimal and is arguably simpler than previous work in combinatorics. We also develop a novel information theoretic lower bound on the number of interventions that applies in full generality, including for randomized adaptive learning algorithms. For general chordal graphs, we derive worst case lower bounds on the number of interventions. Building on observations about induced trees, we give a new deterministic adaptive algorithm to learn directions on any chordal skeleton completely. In the worst case, our achievable scheme is an $\alpha$-approximation algorithm where $\alpha$ is the independence number of the graph. We also show that there exist graph classes for which the sufficient number of experiments is close to the lower bound. In the other extreme, there are graph classes for which the required number of experiments is multiplicatively $\alpha$ away from our lower bound. In simulations, our algorithm almost always performs very close to the lower bound, while the approach based on separating systems for complete graphs is significantly worse for random chordal graphs.
Recent applications of Stackelberg Security Games (SSG), from wildlife crime to urban crime, have employed machine learning tools to learn and predict adversary behavior using available data about defender-adversary interactions. Given these recent developments, this paper commits to an approach of directly learning the response function of the adversary. Using the PAC model, this paper lays a firm theoretical foundation for learning in SSGs (e.g., theoretically answer questions about the numbers of samples required to learn adversary behavior) and provides utility guarantees when the learned adversary model is used to plan the defender's strategy. The paper also aims to answer practical questions such as how much more data is needed to improve an adversary model's accuracy. Additionally, we explain a recently observed phenomenon that prediction accuracy of learned adversary behavior is not enough to discover the utility maximizing defender strategy. We provide four main contributions: (1) a PAC model of learning adversary response functions in SSGs; (2) PAC-model analysis of the learning of key, existing bounded rationality models in SSGs; (3) an entirely new approach to adversary modeling based on a non-parametric class of response functions with PAC-model analysis and (4) identification of conditions under which computing the best defender strategy against the learned adversary behavior is indeed the optimal strategy. Finally, we conduct experiments with real-world data from a national park in Uganda, showing the benefit of our new adversary modeling approach and verification of our PAC model predictions.
Classification is a fundamental task in machine learning and data mining. Existing classification methods are designed to classify unknown instances within a set of previously known training classes. Such a classification takes the form of a prediction within a closed-set of classes. However, a more realistic scenario that fits real-world applications is to consider the possibility of encountering instances that do not belong to any of the training classes, $i.e.$, an open-set classification. In such situation, existing closed-set classifiers will assign a training label to these instances resulting in a misclassification. In this paper, we introduce Galaxy-X, a novel multi-class classification approach for open-set recognition problems. For each class of the training set, Galaxy-X creates a minimum bounding hyper-sphere that encompasses the distribution of the class by enclosing all of its instances. In such manner, our method is able to distinguish instances resembling previously seen classes from those that are of unknown ones. To adequately evaluate open-set classification, we introduce a novel evaluation procedure. Experimental results on benchmark datasets show the efficiency of our approach in classifying novel instances from known as well as unknown classes.
Deviations from rational decision-making due to limited computational resources have been studied in the field of bounded rationality, originally proposed by Herbert Simon. There have been a number of different approaches to model bounded rationality ranging from optimality principles to heuristics. Here we take an information-theoretic approach to bounded rationality, where information-processing costs are measured by the relative entropy between a posterior decision strategy and a given fixed prior strategy. In the case of multiple environments, it can be shown that there is an optimal prior rendering the bounded rationality problem equivalent to the rate distortion problem for lossy compression in information theory. Accordingly, the optimal prior and posterior strategies can be computed by the well-known Blahut-Arimoto algorithm which requires the computation of partition sums over all possible outcomes and cannot be applied straightforwardly to continuous problems. Here we derive a sampling-based alternative update rule for the adaptation of prior behaviors of decision-makers and we show convergence to the optimal prior predicted by rate distortion theory. Importantly, the update rule avoids typical infeasible operations such as the computation of partition sums. We show in simulations a proof of concept for discrete action and environment domains. This approach is not only interesting as a generic computational method, but might also provide a more realistic model of human decision-making processes occurring on a fast and a slow time scale.
A recommender system is an information filtering technology which can be used to predict preference ratings of items (products, services, movies, etc) and/or to output a ranking of items that are likely to be of interest to the user. Context-aware recommender systems (CARS) learn and predict the tastes and preferences of users by incorporating available contextual information in the recommendation process. One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems. Considering this scenario, in this paper, we propose to use contextual information from topic hierarchies of the items (web pages) to improve the performance of context-aware recommender systems. The topic hierarchies are constructed by an extension of the LUPI-based Incremental Hierarchical Clustering method that considers three types of information: traditional bag-of-words (technical information), and the combination of named entities (privileged information I) with domain terms (privileged information II). We evaluated the contextual information in four context-aware recommender systems. Different weights were assigned to each type of information. The empirical results demonstrated that topic hierarchies with the combination of the two kinds of privileged information can provide better recommendations.
Multi-person event recognition is a challenging task, often with many people active in the scene but only a small subset contributing to an actual event. In this paper, we propose a model which learns to detect events in such videos while automatically "attending" to the people responsible for the event. Our model does not use explicit annotations regarding who or where those people are during training and testing. In particular, we track people in videos and use a recurrent neural network (RNN) to represent the track features. We learn time-varying attention weights to combine these features at each time-instant. The attended features are then processed using another RNN for event detection/classification. Since most video datasets with multiple people are restricted to a small number of videos, we also collected a new basketball dataset comprising 257 basketball games with 14K event annotations corresponding to 11 event classes. Our model outperforms state-of-the-art methods for both event classification and detection on this new dataset. Additionally, we show that the attention mechanism is able to consistently localize the relevant players.
A theoretical framework that supports automated construction of dynamic prime models purely from experimental time series data has been invented and developed, which can automatically generate (construct) data-driven models of any time series data in seconds. This has resulted in the formulation and formalisation of new reverse engineering and dynamic methods for automated systems modelling of complex systems, including complex biological, financial, control, and artificial neural network systems. The systems/model theory behind the invention has been formalised as a new, effective and robust system identification strategy complementary to process-based modelling. The proposed dynamic modelling and network inference solutions often involve tackling extremely difficult parameter estimation challenges, inferring unknown underlying network structures, and unsupervised formulation and construction of smart and intelligent ODE models of complex systems. In underdetermined conditions, i.e., cases of dealing with how best to instantaneously and rapidly construct data-consistent prime models of unknown (or well-studied) complex system from small-sized time series data, inference of unknown underlying network of interaction is more challenging. This article reports a robust step-by-step mathematical and computational analysis of the entire prime model construction process that determines a model from data in less than a minute.
Most machine learning models are static, but the world is dynamic, and increasing online deployment of learned models gives increasing urgency to the development of efficient and effective mechanisms to address learning in the context of non-stationary distributions, or as it is commonly called concept drift. However, the key issue of characterizing the different types of drift that can occur has not previously been subjected to rigorous definition and analysis. In particular, while some qualitative drift categorizations have been proposed, few have been formally defined, and the quantitative descriptions required for precise and objective understanding of learner performance have not existed. We present the first comprehensive framework for quantitative analysis of drift. This supports the development of the first comprehensive set of formal definitions of types of concept drift. The formal definitions clarify ambiguities and identify gaps in previous definitions, giving rise to a new comprehensive taxonomy of concept drift types and a solid foundation for research into mechanisms to detect and address concept drift.
Learning about the social structure of hidden and hard-to-reach populations --- such as drug users and sex workers --- is a major goal of epidemiological and public health research on risk behaviors and disease prevention. Respondent-driven sampling (RDS) is a peer-referral process widely used by many health organizations, where research subjects recruit other subjects from their social network. In such surveys, researchers observe who recruited whom, along with the time of recruitment and the total number of acquaintances (network degree) of respondents. However, due to privacy concerns, the identities of acquaintances are not disclosed. In this work, we show how to reconstruct the underlying network structure through which the subjects are recruited. We formulate the dynamics of RDS as a continuous-time diffusion process over the underlying graph and derive the likelihood for the recruitment time series under an arbitrary recruitment time distribution. We develop an efficient stochastic optimization algorithm called RENDER (REspoNdent-Driven nEtwork Reconstruction) that finds the network that best explains the collected data. We support our analytical results through an exhaustive set of experiments on both synthetic and real data.
In the task of Object Recognition, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose regression using these approaches has received relatively much less attention. In this paper we show how deep architectures, specifically Convolutional Neural Networks (CNN), can be adapted to the task of simultaneous categorization and pose estimation of objects. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations of CNNs represent object pose information and how this contradicts with object category representations. We extensively experiment on two recent large and challenging multi-view datasets. Our models achieve better than state-of-the-art performance on both datasets.
We address the problem of Visual Question Answering (VQA), which requires joint image and language understanding to answer a question about a given photograph. Recent approaches have applied deep image captioning methods based on convolutional-recurrent networks to this problem, but have failed to model spatial inference. To remedy this, we propose a model we call the Spatial Memory Network and apply it to the VQA task. Memory networks are recurrent neural networks with an explicit attention mechanism that selects certain parts of the information stored in memory. Our Spatial Memory Network stores neuron activations from different spatial regions of the image in its memory, and uses the question to choose relevant regions for computing the answer, a process of which constitutes a single "hop" in the network. We propose a novel spatial attention architecture that aligns words with image patches in the first hop, and obtain improved results by adding a second attention hop which considers the whole question to choose visual evidence based on the results of the first hop. To better understand the inference process learned by the network, we design synthetic questions that specifically require spatial inference and visualize the attention weights. We evaluate our model on two published visual question answering datasets, DAQUAR [1] and VQA [2], and obtain improved results compared to a strong deep baseline model (iBOWIMG) which concatenates image and question features to predict the answer [3].
Traditional algorithms for robots who need to integrate into a wireless network often focus on one specific task. In this work we want to develop simple, adaptive and reusable algorithms for real world applications for this scenario. Starting with the most basic task for mobile wireless network nodes, finding the position of another node, we introduce an algorithm able to solve this task. We then show how this algorithm can readily be employed to solve a large number of other related tasks like finding the optimal position to bridge two static network nodes. For this we first introduce a meta-algorithm inspired by autonomous robot learning strategies and the concept of internal models which yields a class of source seeking algorithms for mobile nodes. The effectiveness of this algorithm is demonstrated in real world experiments using a physical mobile robot and standard 802.11 wireless LAN in an office environment. We also discuss the differences to conventional algorithms and give the robotics perspective on this class of algorithms. Then we proceed to show how more complex tasks, which might be encountered by mobile nodes, can be encoded in the same framework and how the introduced algorithm can solve them. These tasks can be direct (cross layer) optimization tasks or can also encode more complex tasks like bridging two network nodes. We choose the bridging scenario as an example, implemented on a real physical robot, and show how the robot can solve it in a real world experiment.
In practice, there are often explicit constraints on what representations or decisions are acceptable in an application of machine learning. For example it may be a legal requirement that a decision must not favour a particular group. Alternatively it can be that that representation of data must not have identifying information. We address these two related issues by learning flexible representations that minimize the capability of an adversarial critic. This adversary is trying to predict the relevant sensitive variable from the representation, and so minimizing the performance of the adversary ensures there is little or no information in the representation about the sensitive variable. We demonstrate this adversarial approach on two problems: making decisions free from discrimination and removing private information from images. We formulate the adversarial model as a minimax problem, and optimize that minimax objective using a stochastic gradient alternate min-max optimizer. We demonstrate the ability to provide discriminant free representations for standard test problems, and compare with previous state of the art methods for fairness, showing statistically significant improvement across most cases. The flexibility of this method is shown via a novel problem: removing annotations from images, from unaligned training examples of annotated and unannotated images, and with no a priori knowledge of the form of annotation provided to the model.
Computer system monitoring generates huge amounts of logs that record the interaction of system entities. How to query such data to better understand system behaviors and identify potential system risks and malicious behaviors becomes a challenging task for system administrators due to the dynamics and heterogeneity of the data. System monitoring data are essentially heterogeneous temporal graphs with nodes being system entities and edges being their interactions over time. Given the complexity of such graphs, it becomes time-consuming for system administrators to manually formulate useful queries in order to examine abnormal activities, attacks, and vulnerabilities in computer systems. In this work, we investigate how to query temporal graphs and treat query formulation as a discriminative temporal graph pattern mining problem. We introduce TGMiner to mine discriminative patterns from system logs, and these patterns can be taken as templates for building more complex queries. TGMiner leverages temporal information in graphs to prune graph patterns that share similar growth trend without compromising pattern quality. Experimental results on real system data show that TGMiner is 6-32 times faster than baseline methods. The discovered patterns were verified by system experts; they achieved high precision (97%) and recall (91%).
Methods for learning word representations using large text corpora have received much attention lately due to their impressive performance in numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection. Despite their success, these data-driven word representation learning methods do not consider the rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNet that represent the meanings of words by defining the various relationships that exist among the words in a language. We consider the question, can we improve the word representations learnt using a corpora by integrating the knowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predicts the co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon. We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus. Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into word representations on several benchmark datasets for semantic similarity and word analogy.
We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input. Raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. This intermediate representation contains important topological information and provides useful cues for reasoning about joint locations. The mapping from raw depth to segmentation maps is learned in a semi/weakly-supervised way from two different datasets: (i) a synthetic dataset created through a rendering pipeline including densely labeled ground truth (pixelwise segmentations); and (ii) a dataset with real images for which ground truth joint positions are available, but not dense segmentations. Loss for training on real images is generated from a patch-wise restoration process, which aligns tentative segmentation maps with a large dictionary of synthetic poses. The underlying premise is that the domain shift between synthetic and real data is smaller in the intermediate representation, where labels carry geometric and topological meaning, than in the raw input domain. Experiments on the NYU dataset show that the proposed training method decreases error on joints over direct regression of joints from depth data by 15.7%.
Non-sentential utterances (NSUs) are utterances that lack a complete sentential form but whose meaning can be inferred from the dialogue context, such as "OK", "where?", "probably at his apartment". The interpretation of non-sentential utterances is an important problem in computational linguistics since they constitute a frequent phenomena in dialogue and they are intrinsically context-dependent. The interpretation of NSUs is the task of retrieving their full semantic content from their form and the dialogue context. The first half of this thesis is devoted to the NSU classification task. Our work builds upon Fern\'andez et al. (2007) which present a series of machine-learning experiments on the classification of NSUs. We extended their approach with a combination of new features and semi-supervised learning techniques. The empirical results presented in this thesis show a modest but significant improvement over the state-of-the-art classification performance. The consecutive, yet independent, problem is how to infer an appropriate semantic representation of such NSUs on the basis of the dialogue context. Fern\'andez (2006) formalizes this task in terms of "resolution rules" built on top of the Type Theory with Records (TTR). Our work is focused on the reimplementation of the resolution rules from Fern\'andez (2006) with a probabilistic account of the dialogue state. The probabilistic rules formalism Lison (2014) is particularly suited for this task because, similarly to the framework developed by Ginzburg (2012) and Fern\'andez (2006), it involves the specification of update rules on the variables of the dialogue state to capture the dynamics of the conversation. However, the probabilistic rules can also encode probabilistic knowledge, thereby providing a principled account of ambiguities in the NSU resolution process.
We introduce the concept of continuous transportation task to the context of multi-agent systems. A continuous transportation task is one in which a multi-agent team visits a number of fixed locations, picks up objects, and delivers them to a final destination. The goal is to maximize the rate of transportation while the objects are replenished over time. Examples of problems that need continuous transportation are foraging, area sweeping, and first/last mile problem. Previous approaches typically neglect the interference and are highly dependent on communications among agents. Some also incorporate an additional reconnaissance agent to gather information. In this paper, we present a hybrid of centralized and distributed approaches that minimize the interference and communications in the multi-agent team without the need for a reconnaissance agent. We contribute two partitioning-transportation algorithms inspired by existing algorithms, and contribute one novel online partitioning-transportation algorithm with information gathering in the multi-agent team. Our algorithms have been implemented and tested extensively in the simulation. The results presented in this paper demonstrate the effectiveness of our algorithms that outperform the existing algorithms, even without any communications between the agents and without the presence of a reconnaissance agent.
This paper proposes algorithms for learning two-level Boolean rules in Conjunctive Normal Form (CNF, i.e. AND-of-ORs) or Disjunctive Normal Form (DNF, i.e. OR-of-ANDs) as a type of human-interpretable classification model, aiming for a favorable trade-off between the classification accuracy and the simplicity of the rule. Two formulations are proposed. The first is an integer program whose objective function is a combination of the total number of errors and the total number of features used in the rule. We generalize a previously proposed linear programming (LP) relaxation from one-level to two-level rules. The second formulation replaces the 0-1 classification error with the Hamming distance from the current two-level rule to the closest rule that correctly classifies a sample. Based on this second formulation, block coordinate descent and alternating minimization algorithms are developed. Experiments show that the two-level rules can yield noticeably better performance than one-level rules due to their dramatically larger modeling capacity, and the two algorithms based on the Hamming distance formulation are generally superior to the other two-level rule learning methods in our comparison. A proposed approach to binarize any fractional values in the optimal solutions of LP relaxations is also shown to be effective.
Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Latent variable models are well suited to deal with the high dimensionality and sparsity of typical knowledge graphs. In recent publications the embedding models were extended to also consider time evolutions, time patterns and subsymbolic representations. In this paper we map embedding models, which were developed purely as solutions to technical problems for modelling temporal knowledge graphs, to various cognitive memory functions, in particular to semantic and concept memory, episodic memory, sensory memory, short-term memory, and working memory. We discuss learning, query answering, the path from sensory input to semantic decoding, and the relationship between episodic memory and semantic memory. We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models.
Human language is recognized as a very complex domain since decades. No computer system has been able to reach human levels of performance so far. The only known computational system capable of proper language processing is the human brain. While we gather more and more data about the brain, its fundamental computational processes still remain obscure. The lack of a sound computational brain theory also prevents the fundamental understanding of Natural Language Processing. As always when science lacks a theoretical foundation, statistical modeling is applied to accommodate as many sampled real-world data as possible. An unsolved fundamental issue is the actual representation of language (data) within the brain, denoted as the Representational Problem. Starting with Jeff Hawkins' Hierarchical Temporal Memory (HTM) theory, a consistent computational theory of the human cortex, we have developed a corresponding theory of language data representation: The Semantic Folding Theory. The process of encoding words, by using a topographic semantic space as distributional reference frame into a sparse binary representational vector is called Semantic Folding and is the central topic of this document. Semantic Folding describes a method of converting language from its symbolic representation (text) into an explicit, semantically grounded representation that can be generically processed by Hawkins' HTM networks. As it turned out, this change in representation, by itself, can solve many complex NLP problems by applying Boolean operators and a generic similarity function like the Euclidian Distance. Many practical problems of statistical NLP systems, like the high cost of computation, the fundamental incongruity of precision and recall , the complex tuning procedures etc., can be elegantly overcome by applying Semantic Folding.
This paper addresses the general problem of reinforcement learning (RL) in partially observable environments. In 2013, our large RL recurrent neural networks (RNNs) learned from scratch to drive simulated cars from high-dimensional video input. However, real brains are more powerful in many ways. In particular, they learn a predictive model of their initially unknown environment, and somehow use it for abstract (e.g., hierarchical) planning and reasoning. Guided by algorithmic information theory, we describe RNN-based AIs (RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending sequences of tasks, some of them provided by the user, others invented by the RNNAI itself in a curious, playful fashion, to improve its RNN-based world model. Unlike our previous model-building RNN-based RL machines dating back to 1990, the RNNAI learns to actively query its model for abstract reasoning and planning and decision making, essentially "learning to think." The basic ideas of this report can be applied to many other cases where one RNN-like system exploits the algorithmic information content of another. They are taken from a grant proposal submitted in Fall 2014, and also explain concepts such as "mirror neurons." Experimental results will be described in separate papers.
Bicycle-sharing systems, which can provide shared bike usage services for the public, have been launched in many big cities. In bicycle-sharing systems, people can borrow and return bikes at any stations in the service region very conveniently. Therefore, bicycle-sharing systems are normally used as a short-distance trip supplement for private vehicles as well as regular public transportation. Meanwhile, for stations located at different places in the service region, the bike usages can be quite skewed and imbalanced. Some stations have too many incoming bikes and get jammed without enough docks for upcoming bikes, while some other stations get empty quickly and lack enough bikes for people to check out. Therefore, inferring the potential destinations and arriving time of each individual trip beforehand can effectively help the service providers schedule manual bike re-dispatch in advance. In this paper, we will study the individual trip prediction problem for bicycle-sharing systems. To address the problem, we study a real-world bicycle-sharing system and analyze individuals' bike usage behaviors first. Based on the analysis results, a new trip destination prediction and trip duration inference model will be introduced. Experiments conducted on a real-world bicycle-sharing system demonstrate the effectiveness of the proposed model.
The constraint satisfaction problem (CSP) involves deciding, given a set of variables and a set of constraints on the variables, whether or not there is an assignment to the variables satisfying all of the constraints. One formulation of the CSP is as the problem of deciding, given a pair (G,H) of relational structures, whether or not there is a homomorphism from the first structure to the second structure. The CSP is in general NP-hard; a common way to restrict this problem is to fix the second structure H, so that each structure H gives rise to a problem CSP(H). The problem family CSP(H) has been studied using an algebraic approach, which links the algorithmic and complexity properties of each problem CSP(H) to a set of operations, the so-called polymorphisms of H. Certain types of polymorphisms are known to imply the polynomial-time tractability of $CSP(H)$, and others are conjectured to do so. This article systematically studies---for various classes of polymorphisms---the computational complexity of deciding whether or not a given structure H admits a polymorphism from the class. Among other results, we prove the NP-completeness of deciding a condition conjectured to characterize the tractable problems CSP(H), as well as the NP-completeness of deciding if CSP(H) has bounded width.
The main advantage of Constraint Programming (CP) approaches for sequential pattern mining (SPM) is their modularity, which includes the ability to add new constraints (regular expressions, length restrictions, etc). The current best CP approach for SPM uses a global constraint (module) that computes the projected database and enforces the minimum frequency; it does this with a filtering algorithm similar to the PrefixSpan method. However, the resulting system is not as scalable as some of the most advanced mining systems like Zaki's cSPADE. We show how, using techniques from both data mining and CP, one can use a generic constraint solver and yet outperform existing specialized systems. This is mainly due to two improvements in the module that computes the projected frequencies: first, computing the projected database can be sped up by pre-computing the positions at which an symbol can become unsupported by a sequence, thereby avoiding to scan the full sequence each time; and second by taking inspiration from the trailing used in CP solvers to devise a backtracking-aware data structure that allows fast incremental storing and restoring of the projected database. Detailed experiments show how this approach outperforms existing CP as well as specialized systems for SPM, and that the gain in efficiency translates directly into increased efficiency for other settings such as mining with regular expressions.
Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is crucial. The process of extracting features is highly linked to dimensionality reduction as it implies the transformation of the data from a sparse high-dimensional space, to higher level meaningful abstractions. This dissertation employs Neural Networks for distributed paragraph representations, and Latent Dirichlet Allocation to capture higher level features of paragraph vectors. Although Neural Networks for distributed paragraph representations are considered the state of the art for extracting paragraph vectors, we show that a quick topic analysis model such as Latent Dirichlet Allocation can provide meaningful features too. We evaluate the two methods on the CMU Movie Summary Corpus, a collection of 25,203 movie plot summaries extracted from Wikipedia. Finally, for both approaches, we use K-Nearest Neighbors to discover similar movies, and plot the projected representations using T-Distributed Stochastic Neighbor Embedding to depict the context similarities. These similarities, expressed as movie distances, can be used for movies recommendation. The recommended movies of this approach are compared with the recommended movies from IMDB, which use a collaborative filtering recommendation approach, to show that our two models could constitute either an alternative or a supplementary recommendation approach.
Residual learning has recently surfaced as an effective means of constructing very deep neural networks for object recognition. However, current incarnations of residual networks do not allow for the modeling and integration of complex relations between closely coupled recognition tasks or across domains. Such problems are often encountered in multimedia applications involving large-scale content recognition. We propose a novel extension of residual learning for deep networks that enables intuitive learning across multiple related tasks using cross-connections called cross-residuals. These cross-residuals connections can be viewed as a form of in-network regularization and enables greater network generalization. We show how cross-residual learning (CRL) can be integrated in multitask networks to jointly train and detect visual concepts across several tasks. We present a single multitask cross-residual network with >40% less parameters that is able to achieve competitive, or even better, detection performance on a visual sentiment concept detection problem normally requiring multiple specialized single-task networks. The resulting multitask cross-residual network also achieves better detection performance by about 10.4% over a standard multitask residual network without cross-residuals with even a small amount of cross-task weighting.
What is the right supervisory signal to train visual representations? Current approaches in computer vision use category labels from datasets such as ImageNet to train ConvNets. However, in case of biological agents, visual representation learning does not require millions of semantic labels. We argue that biological agents use physical interactions with the world to learn visual representations unlike current vision systems which just use passive observations (images and videos downloaded from web). For example, babies push objects, poke them, put them in their mouth and throw them to learn representations. Towards this goal, we build one of the first systems on a Baxter platform that pushes, pokes, grasps and observes objects in a tabletop environment. It uses four different types of physical interactions to collect more than 130K datapoints, with each datapoint providing supervision to a shared ConvNet architecture allowing us to learn visual representations. We show the quality of learned representations by observing neuron activations and performing nearest neighbor retrieval on this learned representation. Quantitatively, we evaluate our learned ConvNet on image classification tasks and show improvements compared to learning without external data. Finally, on the task of instance retrieval, our network outperforms the ImageNet network on recall@1 by 3%
Representation and learning of commonsense knowledge is one of the foundational problems in the quest to enable deep language understanding. This issue is particularly challenging for understanding casual and correlational relationships between events. While this topic has received a lot of interest in the NLP community, research has been hindered by the lack of a proper evaluation framework. This paper attempts to address this problem with a new framework for evaluating story understanding and script learning: the 'Story Cloze Test'. This test requires a system to choose the correct ending to a four-sentence story. We created a new corpus of ~50k five-sentence commonsense stories, ROCStories, to enable this evaluation. This corpus is unique in two ways: (1) it captures a rich set of causal and temporal commonsense relations between daily events, and (2) it is a high quality collection of everyday life stories that can also be used for story generation. Experimental evaluation shows that a host of baselines and state-of-the-art models based on shallow language understanding struggle to achieve a high score on the Story Cloze Test. We discuss these implications for script and story learning, and offer suggestions for deeper language understanding.
Machine learning techniques are often used in computer vision due to their ability to leverage large amounts of training data to improve performance. Unfortunately, most generic object trackers are still trained from scratch online and do not benefit from the large number of videos that are readily available for offline training. We propose a method for offline training of neural networks that can track novel objects at test-time at 100 fps. Our tracker is significantly faster than previous methods that use neural networks for tracking, which are typically very slow to run and not practical for real-time applications. Our tracker uses a simple feed-forward network with no online training required. The tracker learns a generic relationship between object motion and appearance and can be used to track novel objects that do not appear in the training set. We test our network on a standard tracking benchmark to demonstrate our tracker's state-of-the-art performance. Further, our performance improves as we add more videos to our offline training set. To the best of our knowledge, our tracker is the first neural-network tracker that learns to track generic objects at 100 fps.
In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises---termed deep knowledge tracing or DKT---has demonstrated a stunning performance advantage over the mainstay of the field, Bayesian knowledge tracing or BKT. In this article, we attempt to understand the basis for DKT's advantage by considering the sources of statistical regularity in the data that DKT can leverage but which BKT cannot. We hypothesize four forms of regularity that BKT fails to exploit: recency effects, the contextualized trial sequence, inter-skill similarity, and individual variation in ability. We demonstrate that when BKT is extended to allow it more flexibility in modeling statistical regularities---using extensions previously proposed in the literature---BKT achieves a level of performance indistinguishable from that of DKT. We argue that while DKT is a powerful, useful, general-purpose framework for modeling student learning, its gains do not come from the discovery of novel representations---the fundamental advantage of deep learning. To answer the question posed in our title, knowledge tracing may be a domain that does not require `depth'; shallow models like BKT can perform just as well and offer us greater interpretability and explanatory power.
The AUV three-dimension path planning in complex turbulent underwater environment is investigated in this research, in which static current map data and uncertain static-moving time variant obstacles are taken into account. Robustness of AUVs path planning to this strong variability is known as a complex NP-hard problem and is considered a critical issue to ensure vehicles safe deployment. Efficient evolutionary techniques have substantial potential of handling NP hard complexity of path planning problem as more powerful and fast algorithms among other approaches for mentioned problem. For the purpose of this research Differential Evolution (DE) technique is conducted to solve the AUV path planning problem in a realistic underwater environment. The path planners designed in this paper are capable of extracting feasible areas of a real map to determine the allowed spaces for deployment, where coastal area, islands, static/dynamic obstacles and ocean current is taken into account and provides the efficient path with a small computation time. The results obtained from analyze of experimental demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles path planning capability in coping undesired current, using useful current flow, and avoid colliding collision boundaries in a real-time manner. The proposed approach is also flexible and strictly respects to vehicle's kinematic constraints resisting current instabilities.
We present a method for the classification of multi-labelled text documents explicitly designed for data stream applications that require to process a virtually infinite sequence of data using constant memory and constant processing time. Our method is composed of an online procedure used to efficiently map text into a low-dimensional feature space and a partition of this space into a set of regions for which the system extracts and keeps statistics used to predict multi-label text annotations. Documents are fed into the system as a sequence of words, mapped to a region of the partition, and annotated using the statistics computed from the labelled instances colliding in the same region. This approach is referred to as clashing. We illustrate the method in real-world text data, comparing the results with those obtained using other text classifiers. In addition, we provide an analysis about the effect of the representation space dimensionality on the predictive performance of the system. Our results show that the online embedding indeed approximates the geometry of the full corpus-wise TF and TF-IDF space. The model obtains competitive F measures with respect to the most accurate methods, using significantly fewer computational resources. In addition, the method achieves a higher macro-averaged F measure than methods with similar running time. Furthermore, the system is able to learn faster than the other methods from partially labelled streams.
Peer review, evaluation, and selection is a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of procedures or mechanisms for peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.
We consider the well-studied cake cutting problem in which the goal is to find an envy-free allocation based on queries from $n$ agents. The problem has received attention in computer science, mathematics, and economics. It has been a major open problem whether there exists a discrete and bounded envy-free protocol. We resolve the problem by proposing a discrete and bounded envy-free protocol for any number of agents. The maximum number of queries required by the protocol is $n^{n^{n^{n^{n^n}}}}$. We additionally show that even if we do not run our protocol to completion, it can find in at most $n^3{(n^2)}^n$ queries a partial allocation of the cake that achieves proportionality (each agent gets at least $1/n$ of the value of the whole cake) and envy-freeness. Finally we show that an envy-free partial allocation can be computed in at most $n^3{(n^2)}^n$ queries such that each agent gets a connected piece that gives the agent at least $1/(3n)$ of the value of the whole cake.
A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics.
Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i.e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position. Based on this idea, we propose a novel deep architecture, namely Match-SRNN, to model the recursive matching structure. Firstly, a tensor is constructed to capture the word level interactions. Then a spatial RNN is applied to integrate the local interactions recursively, with importance determined by four types of gates. Finally, the matching score is calculated based on the global interaction. We show that, after degenerated to the exact matching scenario, Match-SRNN can approximate the dynamic programming process of longest common subsequence. Thus, there exists a clear interpretation for Match-SRNN. Our experiments on two semantic matching tasks showed the effectiveness of Match-SRNN, and its ability of visualizing the learned matching structure.
Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous service robots that can operate in real environments for extended periods. In the STRANDS project we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots, and deploying these systems for long-term installations in security and care environments. Over four deployments, our robots have been operational for a combined duration of 104 days autonomously performing end-user defined tasks, covering 116km in the process. In this article we describe the approach we have used to enable long-term autonomous operation in everyday environments, and how our robots are able to use their long run times to improve their own performance.
In multiagent systems, we often have a set of agents each of which have a preference ordering over a set of items and one would like to know these preference orderings for various tasks, for example, data analysis, preference aggregation, voting etc. However, we often have a large number of items which makes it impractical to ask the agents for their complete preference ordering. In such scenarios, we usually elicit these agents' preferences by asking (a hopefully small number of) comparison queries --- asking an agent to compare two items. Prior works on preference elicitation focus on unrestricted domain and the domain of single peaked preferences and show that the preferences in single peaked domain can be elicited by much less number of queries compared to unrestricted domain. We extend this line of research and study preference elicitation for single peaked preferences on trees which is a strict superset of the domain of single peaked preferences. We show that the query complexity crucially depends on the number of leaves, the path cover number, and the distance from path of the underlying single peaked tree, whereas the other natural parameters like maximum degree, diameter, pathwidth do not play any direct role in determining query complexity. We then investigate the query complexity for finding a weak Condorcet winner for preferences single peaked on a tree and show that this task has much less query complexity than preference elicitation. Here again we observe that the number of leaves in the underlying single peaked tree and the path cover number of the tree influence the query complexity of the problem.
Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in commercial and industrial environment. In this paper we present an interesting application of the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model equipped by renewable sources and an energy storage system, taking into account the economical profit in energy trading with the main-grid. In particular, this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform decision making in the microgrid. The HGA rationale focuses on a particular encoding scheme, based on control genes and parametric genes applied to the optimization of the FIS parameters, allowing to perform a reduction in the structural complexity of the RB. This approach will be referred in the following as fuzzy-HGA. Results are compared with a simpler approach based on a classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned, while the number of fuzzy rules is fixed in advance. Experiments shows how the fuzzy-HGA approach adopted for the synthesis of the proposed controller outperforms the classic fuzzy-GA scheme, increasing the accounting profit by 67\% in the considered energy trading problem yielding at the same time a simpler RB.
An Autonomous Underwater Vehicle (AUV) needs to acquire a certain degree of autonomy for any particular underwater mission to fulfill the mission objectives successfully and ensure its safety in all stages of the mission in a large scale operating filed. In this paper, a novel combinatorial conflict-free-task assignment strategy consisting an interactive engagement of a local path planner and an adaptive global route planner, is introduced. The method is established upon the heuristic search potency of the Particle Swarm Optimisation (PSO) algorithm to address the discrete nature of routing-task assignment approach and the complexity of NP-hard path planning problem. The proposed hybrid method is highly efficient for having a reactive guidance framework that guarantees successful completion of missions specifically in cluttered environments. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of simulation studies are undertaken. The results of simulations declare that the proposed method is reliable and robust, particularly in dealing with uncertainties, and it can significantly enhance the level of vehicle's autonomy by relying on its reactive nature and capability of providing fast feasible solutions.
In this work we present a novel end-to-end framework for tracking and classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments. The approach deploys a recurrent neural network to filter an input stream of raw laser measurements in order to directly infer object locations, along with their identity in both visible and occluded areas. To achieve this we first train the network using unsupervised Deep Tracking, a recently proposed theoretical framework for end-to-end space occupancy prediction. We show that by learning to track on a large amount of unsupervised data, the network creates a rich internal representation of its environment which we in turn exploit through the principle of inductive transfer of knowledge to perform the task of it's semantic classification. As a result, we show that only a small amount of labelled data suffices to steer the network towards mastering this additional task. Furthermore we propose a novel recurrent neural network architecture specifically tailored to tracking and semantic classification in real-world robotics applications. We demonstrate the tracking and classification performance of the method on real-world data collected at a busy road junction. Our evaluation shows that the proposed end-to-end framework compares favourably to a state-of-the-art, model-free tracking solution and that it outperforms a conventional one-shot training scheme for semantic classification.
Eliciting the preferences of a set of agents over a set of alternatives is a problem of fundamental importance in social choice theory. Prior work on this problem has studied the query complexity of preference elicitation for the unrestricted domain and for the domain of single peaked preferences. In this paper, we consider the domain of single crossing preference profiles and study the query complexity of preference elicitation under various settings. We consider two distinct situations: when an ordering of the voters with respect to which the profile is single crossing is known versus when it is unknown. We also consider different access models: when the votes can be accessed at random, as opposed to when they are coming in a pre-defined sequence. In the sequential access model, we distinguish two cases when the ordering is known: the first is that sequence in which the votes appear is also a single-crossing order, versus when it is not. The main contribution of our work is to provide polynomial time algorithms with low query complexity for preference elicitation in all the above six cases. Further, we show that the query complexities of our algorithms are optimal up to constant factors for all but one of the above six cases. We then present preference elicitation algorithms for profiles which are close to being single crossing under various notions of closeness, for example, single crossing width, minimum number of candidates | voters whose deletion makes a profile single crossing.
Automatic image annotation has been an important research topic in facilitating large scale image management and retrieval. Existing methods focus on learning image-tag correlation or correlation between tags to improve annotation accuracy. However, most of these methods evaluate their performance using top-k retrieval performance, where k is fixed. Although such setting gives convenience for comparing different methods, it is not the natural way that humans annotate images. The number of annotated tags should depend on image contents. Inspired by the recent progress in machine translation and image captioning, we propose a novel Recurrent Image Annotator (RIA) model that forms image annotation task as a sequence generation problem so that RIA can natively predict the proper length of tags according to image contents. We evaluate the proposed model on various image annotation datasets. In addition to comparing our model with existing methods using the conventional top-k evaluation measures, we also provide our model as a high quality baseline for the arbitrary length image tagging task. Moreover, the results of our experiments show that the order of tags in training phase has a great impact on the final annotation performance.
While probability theory is normally applied to external environments, there has been some recent interest in probabilistic modeling of the outputs of computations that are too expensive to run. Since mathematical logic is a powerful tool for reasoning about computer programs, we consider this problem from the perspective of integrating probability and logic. Recent work on assigning probabilities to mathematical statements has used the concept of coherent distributions, which satisfy logical constraints such as the probability of a sentence and its negation summing to one. Although there are algorithms which converge to a coherent probability distribution in the limit, this yields only weak guarantees about finite approximations of these distributions. In our setting, this is a significant limitation: Coherent distributions assign probability one to all statements provable in a specific logical theory, such as Peano Arithmetic, which can prove what the output of any terminating computation is; thus, a coherent distribution must assign probability one to the output of any terminating computation. To model uncertainty about computations, we propose to work with approximations to coherent distributions. We introduce inductive coherence, a strengthening of coherence that provides appropriate constraints on finite approximations, and propose an algorithm which satisfies this criterion.
A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is referred to as a Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in \mathcal{S}}\phi_{l}(x_l)$, where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll d$. Assuming $\phi_l$'s and $\mathcal{S}$ to be unknown, the problem of estimating $f$ from its samples has been studied extensively. In this work, we consider a generalized SPAM, allowing for second order interaction terms. For some $\mathcal{S}_1 \subset [d], \mathcal{S}_2 \subset {[d] \choose 2}$, the function $f$ is assumed to be of the form: $$f(\mathbf{x}) = \sum_{p \in \mathcal{S}_1}\phi_{p} (x_p) + \sum_{(l,l^{\prime}) \in \mathcal{S}_2}\phi_{(l,l^{\prime})} (x_{l},x_{l^{\prime}}).$$ Assuming $\phi_{p},\phi_{(l,l^{\prime})}$, $\mathcal{S}_1$ and, $\mathcal{S}_2$ to be unknown, we provide a randomized algorithm that queries $f$ and exactly recovers $\mathcal{S}_1,\mathcal{S}_2$. Consequently, this also enables us to estimate the underlying $\phi_p, \phi_{(l,l^{\prime})}$. We derive sample complexity bounds for our scheme and also extend our analysis to include the situation where the queries are corrupted with noise -- either stochastic, or arbitrary but bounded. Lastly, we provide simulation results on synthetic data, that validate our theoretical findings.
The field of iterated belief change has focused mainly on revision, with the other main operator of AGM belief change theory, i.e. contraction, receiving relatively little attention. In this paper we extend the Harper Identity from single-step change to define iterated contraction in terms of iterated revision. Specifically, just as the Harper Identity provides a recipe for defining the belief set resulting from contracting A in terms of (i) the initial belief set and (ii) the belief set resulting from revision by not-A, we look at ways to define the plausibility ordering over worlds resulting from contracting A in terms of (iii) the initial plausibility ordering, and (iv) the plausibility ordering resulting from revision by not-A. After noting that the most straightforward such extension leads to a trivialisation of the space of permissible orderings, we provide a family of operators for combining plausibility orderings that avoid such a result. These operators are characterised in our domain of interest by a pair of intuitively compelling properties, which turn out to enable the derivation of a number of iterated contraction postulates from postulates for iterated revision. We finish by observing that a salient member of this family allows for the derivation of counterparts for contraction of some well known iterated revision operators, as well as for defining new iterated contraction operators.
It is important to have multi-agent robotic system specifications that ensure correctness properties of safety and liveness. As these systems have concurrency, and often have dynamic environment, the formal specification and verification of these systems along with step-wise refinement from abstract to concrete concepts play a major role in system correctness. Formal verification is used for exhaustive investigation of the system space thus ensuring that undetected failures in the behavior are excluded. We construct the system incrementally from subcomponents, based on software architecture. The challenge is to develop a safe multi-agent robotic system, more specifically to ensure the correctness properties of safety and liveness. Formal specifications based on model-checking are flexible, have a concrete syntax, and play vital role in correctness of a multi-agent robotic system. To formally verify safety and liveness of such systems is important because they have high concurrency and in most of the cases have dynamic environment. We have considered a case-study of a multi-agent robotic system for the transport of stock between storehouses to exemplify our formal approach. Our proposed development approach allows for formal verification during specification definition. The development process has been classified in to four major phases of requirement specifications, verification specifications, architecture specifications and implementation.
Two important requirements when aggregating the preferences of multiple agents are that the outcome should be economically efficient and the aggregation mechanism should not be manipulable. In this paper, we provide a computer-aided proof of a sweeping impossibility using these two conditions for randomized aggregation mechanisms. More precisely, we show that every efficient aggregation mechanism can be manipulated for all expected utility representations of the agents' preferences. This settles an open problem and strengthens a number of existing theorems, including statements that were shown within the special domain of assignment. Our proof is obtained by formulating the claim as a satisfiability problem over predicates from real-valued arithmetic, which is then checked using an SMT (satisfiability modulo theories) solver. In order to verify the correctness of the result, a minimal unsatisfiable set of constraints returned by the SMT solver was translated back into a proof in higher-order logic, which was automatically verified by an interactive theorem prover. To the best of our knowledge, this is the first application of SMT solvers in computational social choice.
We consider the problem of selecting the best variable-value strategy for solving a given problem in constraint programming. We show that the recent Embarrassingly Parallel Search method (EPS) can be used for this purpose. EPS proposes to solve a problem by decomposing it in a lot of subproblems and to give them on-demand to workers which run in parallel. Our method uses a part of these subproblems as a simple sample as defined in statistics for comparing some strategies in order to select the most promising one that will be used for solving the remaining subproblems. For each subproblem of the sample, the parallelism helps us to control the running time of the strategies because it gives us the possibility to introduce timeouts by stopping a strategy when it requires more than twice the time of the best one. Thus, we can deal with the great disparity in solving times for the strategies. The selections we made are based on the Wilcoxon signed rank tests because no assumption has to be made on the distribution of the solving times and because these tests can deal with the censored data that we obtain after introducing timeouts. The experiments we performed on a set of classical benchmarks for satisfaction and optimization problems show that our method obtain good performance by selecting almost all the time the best variable-value strategy and by almost never choosing a variable-value strategy which is dramatically slower than the best one. Our method also outperforms the portfolio approach consisting in running some strategies in parallel and is competitive with the multi armed bandit framework.
Privacy has traditionally been a major motivation for decentralized problem solving. However, even though several metrics have been proposed to quantify it, none of them is easily integrated with common solvers. Constraint programming is a fundamental paradigm used to approach various families of problems. We introduce Utilitarian Distributed Constraint Satisfaction Problems (UDisCSP) where the utility of each state is estimated as the difference between the the expected rewards for agreements on assignments for shared variables, and the expected cost of privacy loss. Therefore, a traditional DisCSP with privacy requirements is viewed as a planning problem. The actions available to agents are: communication and local inference. Common decentralized solvers are evaluated here from the point of view of their interpretation as greedy planners. Further, we investigate some simple extensions where these solvers start taking into account the utility function. In these extensions we assume that the planning problem is further restricting the set of communication actions to only the communication primitives present in the corresponding solver protocols. The solvers obtained for the new type of problems propose the action (communication/inference) to be performed in each situation, defining thereby the policy.
Group discussions are essential for organizing every aspect of modern life, from faculty meetings to senate debates, from grant review panels to papal conclaves. While costly in terms of time and organization effort, group discussions are commonly seen as a way of reaching better decisions compared to solutions that do not require coordination between the individuals (e.g. voting)---through discussion, the sum becomes greater than the parts. However, this assumption is not irrefutable: anecdotal evidence of wasteful discussions abounds, and in our own experiments we find that over 30% of discussions are unproductive. We propose a framework for analyzing conversational dynamics in order to determine whether a given task-oriented discussion is worth having or not. We exploit conversational patterns reflecting the flow of ideas and the balance between the participants, as well as their linguistic choices. We apply this framework to conversations naturally occurring in an online collaborative world exploration game developed and deployed to support this research. Using this setting, we show that linguistic cues and conversational patterns extracted from the first 20 seconds of a team discussion are predictive of whether it will be a wasteful or a productive one.
Tensor factorization is a powerful tool to analyse multi-way data. Compared with traditional multi-linear methods, nonlinear tensor factorization models are capable of capturing more complex relationships in the data. However, they are computationally expensive and may suffer severe learning bias in case of extreme data sparsity. To overcome these limitations, in this paper we propose a distributed, flexible nonlinear tensor factorization model. Our model can effectively avoid the expensive computations and structural restrictions of the Kronecker-product in existing TGP formulations, allowing an arbitrary subset of tensorial entries to be selected to contribute to the training. At the same time, we derive a tractable and tight variational evidence lower bound (ELBO) that enables highly decoupled, parallel computations and high-quality inference. Based on the new bound, we develop a distributed inference algorithm in the MapReduce framework, which is key-value-free and can fully exploit the memory cache mechanism in fast MapReduce systems such as SPARK. Experimental results fully demonstrate the advantages of our method over several state-of-the-art approaches, in terms of both predictive performance and computational efficiency. Moreover, our approach shows a promising potential in the application of Click-Through-Rate (CTR) prediction for online advertising.
This paper is motivated by a series of (related) questions as to whether a computer can have pleasure and pain, what pleasure (and intensity of pleasure) is, and, ultimately, what concepts of emotion are. To determine what an emotion is, is a matter of conceptualization, namely, understanding and explicitly encoding the concept of emotion as people use it in everyday life. This is a notoriously difficult problem (Frijda, 1986, Fehr \& Russell, 1984). This paper firstly shows why this is a difficult problem by aligning it with the conceptualization of a few other so called semantic primitives such as "EXIST", "FORCE", "BIG" (plus "LIMIT"). The definitions of these thought-to-be-indefinable concepts, given in this paper, show what formal definitions of concepts look like and how concepts are constructed. As a by-product, owing to the explicit account of the meaning of "exist", the famous dispute between Einstein and Bohr is naturally resolved from linguistic point of view. Secondly, defending Frijda's view that emotion is action tendency (or Ryle's behavioral disposition (propensity)), we give a list of emotions defined in terms of action tendency. In particular, the definitions of pleasure and the feeling of beauty are presented. Further, we give a formal definition of "action tendency", from which the concept of "intensity" of emotions (including pleasure) is naturally derived in a formal fashion. The meanings of "wish", "wait", "good", "hot" are analyzed.
Despite being the standard loss function to train multi-class neural networks, the log-softmax has two potential limitations. First, it involves computations that scale linearly with the number of output classes, which can restrict the size of problems we are able to tackle with current hardware. Second, it remains unclear how close it matches the task loss such as the top-k error rate or other non-differentiable evaluation metrics which we aim to optimize ultimately. In this paper, we introduce an alternative classification loss function, the Z-loss, which is designed to address these two issues. Unlike the log-softmax, it has the desirable property of belonging to the spherical loss family (Vincent et al., 2015), a class of loss functions for which training can be performed very efficiently with a complexity independent of the number of output classes. We show experimentally that it significantly outperforms the other spherical loss functions previously investigated. Furthermore, we show on a word language modeling task that it also outperforms the log-softmax with respect to certain ranking scores, such as top-k scores, suggesting that the Z-loss has the flexibility to better match the task loss. These qualities thus makes the Z-loss an appealing candidate to train very efficiently large output networks such as word-language models or other extreme classification problems. On the One Billion Word (Chelba et al., 2014) dataset, we are able to train a model with the Z-loss 40 times faster than the log-softmax and more than 4 times faster than the hierarchical softmax.
In existing literature, while approximate approaches based on Monte-Carlo simulation technique have been proposed to compute the semantics of probabilistic argumentation, how to improve the efficiency of computation without using simulation technique is still an open problem. In this paper, we address this problem from the following two perspectives. First, conceptually, we define specific properties to characterize the subgraphs of a PrAG with respect to a given extension, such that the probability of a set of arguments E being an extension can be defined in terms of these properties, without (or with less) construction of subgraphs. Second, computationally, we take preferred semantics as an example, and develop algorithms to evaluate the efficiency of our approach. The results show that our approach not only dramatically decreases the time for computing p(E^\sigma), but also has an attractive property, which is contrary to that of existing approaches: the denser the edges of a PrAG are or the bigger the size of a given extension E is, the more efficient our approach computes p(E^\sigma). Meanwhile, it is shown that under complete and preferred semantics, the problems of determining p(E^\sigma) are fixed-parameter tractable.
Steering a complex system towards a desired outcome is a challenging task. The lack of clarity on the system's exact architecture and the often scarce scientific data upon which to base the operationalisation of the dynamic rules that underpin the interactions between participant entities are two contributing factors. We describe an analytical approach that builds on Fuzzy Cognitive Mapping (FCM) to address the latter and represent the system as a complex network. We apply results from network controllability to address the former and determine minimal control configurations - subsets of factors, or system levers, which comprise points for strategic intervention in steering the system. We have implemented the combination of these techniques in an analytical tool that runs in the browser, and generates all minimal control configurations of a complex network. We demonstrate our approach by reporting on our experience of working alongside industrial, local-government, and NGO stakeholders in the Humber region, UK. Our results are applied to the decision-making process involved in the transition of the region to a bio-based economy.
Answer Set Programming (ASP) is a popular logic programming paradigm that has been applied for solving a variety of complex problems. Among the most challenging real-world applications of ASP are two industrial problems defined by Siemens: the Partner Units Problem (PUP) and the Combined Configuration Problem (CCP). The hardest instances of PUP and CCP are out of reach for state-of-the-art ASP solvers. Experiments show that the performance of ASP solvers could be significantly improved by embedding domain-specific heuristics, but a proper effective integration of such criteria in off-the-shelf ASP implementations is not obvious. In this paper the combination of ASP and domain-specific heuristics is studied with the goal of effectively solving real-world problem instances of PUP and CCP. As a byproduct of this activity, the ASP solver WASP was extended with an interface that eases embedding new external heuristics in the solver. The evaluation shows that our domain-heuristic-driven ASP solver finds solutions for all the real-world instances of PUP and CCP ever provided by Siemens. This paper is under consideration for acceptance in TPLP.
Word puzzles and the problem of their representations in logic languages have received considerable attention in the last decade (Ponnuru et al. 2004; Shapiro 2011; Baral and Dzifcak 2012; Schwitter 2013). Of special interest is the problem of generating such representations directly from natural language (NL) or controlled natural language (CNL). An interesting variation of this problem, and to the best of our knowledge, scarcely explored variation in this context, is when the input information is inconsistent. In such situations, the existing encodings of word puzzles produce inconsistent representations and break down. In this paper, we bring the well-known type of paraconsistent logics, called Annotated Predicate Calculus (APC) (Kifer and Lozinskii 1992), to bear on the problem. We introduce a new kind of non-monotonic semantics for APC, called consistency preferred stable models and argue that it makes APC into a suitable platform for dealing with inconsistency in word puzzles and, more generally, in NL sentences. We also devise a number of general principles to help the user choose among the different representations of NL sentences, which might seem equivalent but, in fact, behave differently when inconsistent information is taken into account. These principles can be incorporated into existing CNL translators, such as Attempto Controlled English (ACE) (Fuchs et al. 2008) and PENG Light (White and Schwitter 2009). Finally, we show that APC with the consistency preferred stable model semantics can be equivalently embedded in ASP with preferences over stable models, and we use this embedding to implement this version of APC in Clingo (Gebser et al. 2011) and its Asprin add-on (Brewka et al. 2015).
Some argue that biologically inspired algorithms are the future of solving difficult problems in computer science. Others strongly believe that the future lies in the exploration of mathematical foundations of problems at hand. The field of computer security tends to accept the latter view as a more appropriate approach due to its more workable validation and verification possibilities. The lack of rigorous scientific practices prevalent in biologically inspired security research does not aid in presenting bio-inspired security approaches as a viable way of dealing with complex security problems. This chapter introduces a biologically inspired algorithm, called the Self Organising Map (SOM), that was developed by Teuvo Kohonen in 1981. Since the algorithm's inception it has been scrutinised by the scientific community and analysed in more than 4000 research papers, many of which dealt with various computer security issues, from anomaly detection, analysis of executables all the way to wireless network monitoring. In this chapter a review of security related SOM research undertaken in the past is presented and analysed. The algorithm's biological analogies are detailed and the author's view on the future possibilities of this successful bio-inspired approach are given. The SOM algorithm's close relation to a number of vital functions of the human brain and the emergence of multi-core computer architectures are the two main reasons behind our assumption that the future of the SOM algorithm and its variations is promising, notably in the field of computer security.
Answer set programming (ASP) is a well-established logic programming language that offers an intuitive, declarative syntax for problem solving. In its traditional application, a fixed ASP program for a given problem is designed and the actual instance of the problem is fed into the program as a set of facts. This approach typically results in programs with comparably short and simple rules. However, as is known from complexity analysis, such an approach limits the expressive power of ASP; in fact, an entire NP-check can be encoded into a single large rule body of bounded arity that performs both a guess and a check within the same rule. Here, we propose a novel paradigm for encoding hard problems in ASP by making explicit use of large rules which depend on the actual instance of the problem. We illustrate how this new encoding paradigm can be used, providing examples of problems from the first, second, and even third level of the polynomial hierarchy. As state-of-the-art solvers are tuned towards short rules, rule decomposition is a key technique in the practical realization of our approach. We also provide some preliminary benchmarks which indicate that giving up the convenient way of specifying a fixed program can lead to a significant speed-up. This paper is under consideration for acceptance into TPLP.
In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy. This paper is under consideration for acceptance in TPLP.
Adaptive behavior is mainly the result of adaptive brains. We go a step beyond and claim that the brain does not only adapt to its surrounding reality but rather, it builds itself up to constructs its own reality. That is, rather than just trying to passively understand its environment, the brain is the architect of its own reality in an active process where its internal models of the external world frame how its new interactions with the environment are assimilated. These internal models represent relevant predictive patterns of interaction all over the different brain structures: perceptual, sensorimotor, motor, etc. The emergence of adaptive behavior arises from this self-constructive nature of the brain, based on the following principles of organization: self-experimental, self- growing, and self-repairing. Self-experimental, since to ensure survival, the self-constructive brain (SCB) is an active machine capable of performing experiments of its own interactions with the environment by mental simulation. Self-growing, since it dynamically and incrementally constructs internal structures in order to build a model of the world as it gathers statistics from its interactions with the environment. Self-repairing, since to survive the SCB must also be robust and capable of finding ways to repair parts of previously working structures and hence re-construct a previous relevant pattern of activity.
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a domain, more accurate joint probability distributions can be obtained for inference tasks or, more directly, for interpreting the most significant relations among the variables. Recently, several researchers have investigated techniques for automatically learning the structure from data by obtaining the probabilistic maximum-a-posteriori structure given the available data. However, all the approximations proposed decompose the posterior of the whole structure into local sub-problems, by assuming that the posteriors of the Markov blankets of all the variables are mutually independent. In this work, we propose a scoring function for relaxing such assumption. The Blankets Joint Posterior score computes the joint posterior of structures as a joint distribution of the collection of its Markov blankets. Essentially, the whole posterior is obtained by computing the posterior of the blanket of each variable as a conditional distribution that takes into account information from other blankets in the network. We show in our experimental results that the proposed approximation can improve the sample complexity of state-of-the-art scores when learning complex networks, where the independence assumption between blanket variables is clearly incorrect.
Several methods exist to infer causal networks from massive volumes of observational data. However, almost all existing methods require a considerable length of time series data to capture cause and effect relationships. In contrast, memory-less transition networks or Markov Chain data, which refers to one-step transitions to and from an event, have not been explored for causality inference even though such data is widely available. We find that causal network can be inferred from characteristics of four unique distribution zones around each event. We call this Composition of Transitions and show that cause, effect, and random events exhibit different behavior in their compositions. We applied machine learning models to learn these different behaviors and to infer causality. We name this new method Causality Inference using Composition of Transitions (CICT). To evaluate CICT, we used an administrative inpatient healthcare dataset to set up a network of patients transitions between different diagnoses. We show that CICT is highly accurate in inferring whether the transition between a pair of events is causal or random and performs well in identifying the direction of causality in a bi-directional association.
Deriving an effective facial expression recognition component is important for a successful human-computer interaction system. Nonetheless, recognizing facial expression remains a challenging task. This paper describes a novel approach towards facial expression recognition task. The proposed method is motivated by the success of Convolutional Neural Networks (CNN) on the face recognition problem. Unlike other works, we focus on achieving good accuracy while requiring only a small sample data for training. Scale Invariant Feature Transform (SIFT) features are used to increase the performance on small data as SIFT does not require extensive training data to generate useful features. In this paper, both Dense SIFT and regular SIFT are studied and compared when merged with CNN features. Moreover, an aggregator of the models is developed. The proposed approach is tested on the FER-2013 and CK+ datasets. Results demonstrate the superiority of CNN with Dense SIFT over conventional CNN and CNN with SIFT. The accuracy even increased when all the models are aggregated which generates state-of-art results on FER-2013 and CK+ datasets, where it achieved 73.4% on FER-2013 and 99.1% on CK+.
We propose stochastic rank-$1$ bandits, a class of online learning problems where at each step a learning agent chooses a pair of row and column arms, and receives the product of their values as a reward. The main challenge of the problem is that the individual values of the row and column are unobserved. We assume that these values are stochastic and drawn independently. We propose a computationally-efficient algorithm for solving our problem, which we call Rank1Elim. We derive a $O((K + L) (1 / \Delta) \log n)$ upper bound on its $n$-step regret, where $K$ is the number of rows, $L$ is the number of columns, and $\Delta$ is the minimum of the row and column gaps; under the assumption that the mean row and column rewards are bounded away from zero. To the best of our knowledge, we present the first bandit algorithm that finds the maximum entry of a rank-$1$ matrix whose regret is linear in $K + L$, $1 / \Delta$, and $\log n$. We also derive a nearly matching lower bound. Finally, we evaluate Rank1Elim empirically on multiple problems. We observe that it leverages the structure of our problems and can learn near-optimal solutions even if our modeling assumptions are mildly violated.
Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been already sequenced, the biological functions of all of their genes are still unknown. Since experimentally studying the functions of those genes, one by one, would be unfeasible, new computational methods for gene functions inference are needed. We present here a novel computational approach for inferring biological function for a set of genes with previously unknown function, given a set of genes with well-known information. This approach is based on the premise that genes with similar behaviour should be grouped together. This is known as the guilt-by-association principle. Thus, it is possible to take advantage of clustering techniques to obtain groups of unknown genes that are co-clustered with genes that have well-known semantic information (GO annotations). Meaningful knowledge to infer unknown semantic information can therefore be provided by these well-known genes. We provide a method to explore the potential function of new genes according to those currently annotated. The results obtained indicate that the proposed approach could be a useful and effective tool when used by biologists to guide the inference of biological functions for recently discovered genes. Our work sets an important landmark in the field of identifying unknown gene functions through clustering, using an external source of biological input. A simple web interface to this proposal can be found at http://fich.unl.edu.ar/sinc/webdemo/gamma-am/.
Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human cognition due to the scarcity of data per subject and the variability of brain anatomy and functional response across subjects. Recent work on latent factor models shows promising results in this task but this approach does not preserve spatial locality in the brain. We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality. We first do this directly by combining a recent factor method known as a shared response model with searchlight analysis. Then we design a multi-view convolutional autoencoder for the same task. Both approaches preserve spatial locality and have competitive or better performance compared with standard searchlight analysis and the shared response model applied across the whole brain. We also report a system design to handle the computational challenge of training the convolutional autoencoder.
Planning plays an important role in the broad class of decision theory. Planning has drawn much attention in recent work in the robotics and sequential decision making areas. Recently, Reinforcement Learning (RL), as an agent-environment interaction problem, has brought further attention to planning methods. Generally in RL, one can assume a generative model, e.g. graphical models, for the environment, and then the task for the RL agent is to learn the model parameters and find the optimal strategy based on these learnt parameters. Based on environment behavior, the agent can assume various types of generative models, e.g. Multi Armed Bandit for a static environment, or Markov Decision Process (MDP) for a dynamic environment. The advantage of these popular models is their simplicity, which results in tractable methods of learning the parameters and finding the optimal policy. The drawback of these models is again their simplicity: these models usually underfit and underestimate the actual environment behavior. For example, in robotics, the agent usually has noisy observations of the environment inner state and MDP is not a suitable model. More complex models like Partially Observable Markov Decision Process (POMDP) can compensate for this drawback. Fitting this model to the environment, where the partial observation is given to the agent, generally gives dramatic performance improvement, sometimes unbounded improvement, compared to MDP. In general, finding the optimal policy for the POMDP model is computationally intractable and fully non convex, even for the class of memoryless policies. The open problem is to come up with a method to find an exact or an approximate optimal stochastic memoryless policy for POMDP models.
State-of-the-art answer set programming (ASP) solvers rely on a program called a grounder to convert non-ground programs containing variables into variable-free, propositional programs. The size of this grounding depends heavily on the size of the non-ground rules, and thus, reducing the size of such rules is a promising approach to improve solving performance. To this end, in this paper we announce lpopt, a tool that decomposes large logic programming rules into smaller rules that are easier to handle for current solvers. The tool is specifically tailored to handle the standard syntax of the ASP language (ASP-Core) and makes it easier for users to write efficient and intuitive ASP programs, which would otherwise often require significant hand-tuning by expert ASP engineers. It is based on an idea proposed by Morak and Woltran (2012) that we extend significantly in order to handle the full ASP syntax, including complex constructs like aggregates, weak constraints, and arithmetic expressions. We present the algorithm, the theoretical foundations on how to treat these constructs, as well as an experimental evaluation showing the viability of our approach.
Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff poses challenges in medicine where both accuracy and interpretability are important. We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention. RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.
In this work a discrete counterpart to the continuous harmonic potential field approach is suggested. The extension to the discrete case makes use of the strong relation HPF-based planning has to connectionist artificial intelligence (AI). Connectionist AI systems are networks of simple, interconnected processors running in parallel within the confines of the environment in which the planning action is to be synthesized. It is not hard to see that such a paradigm naturally lends itself to planning on weighted graphs where the processors may be seen as the vertices of the graph and the relations among them as its edges. Electrical networks are an effective realization of connectionist AI. The utility of the discrete HPF (DHPF) approach is demonstrated in three ways. First, the capability of the DHPF approach to generate new, abstract, planning techniques is demonstrated by constructing a novel, efficient, optimal, discrete planning method called the M* algorithm. Also, its ability to augment the capabilities of existing planners is demonstrated by suggesting a generic solution to the lower bound problem faced by the A* algorithm. The DHPF approach is shown to be useful in solving specific planning problems in communication. It is demonstrated that the discrete HPF paradigm can support routing on-the-fly while the network is still in a transient state. It is shown by simulation that if a path to the target always exist and the switching delays in the routers are negligible, a packet will reach its destination despite the changes in the network which may simultaneously take place while the packet is being routed.
Partial monitoring games are repeated games where the learner receives feedback that might be different from adversary's move or even the reward gained by the learner. Recently, a general model of combinatorial partial monitoring (CPM) games was proposed \cite{lincombinatorial2014}, where the learner's action space can be exponentially large and adversary samples its moves from a bounded, continuous space, according to a fixed distribution. The paper gave a confidence bound based algorithm (GCB) that achieves $O(T^{2/3}\log T)$ distribution independent and $O(\log T)$ distribution dependent regret bounds. The implementation of their algorithm depends on two separate offline oracles and the distribution dependent regret additionally requires existence of a unique optimal action for the learner. Adopting their CPM model, our first contribution is a Phased Exploration with Greedy Exploitation (PEGE) algorithmic framework for the problem. Different algorithms within the framework achieve $O(T^{2/3}\sqrt{\log T})$ distribution independent and $O(\log^2 T)$ distribution dependent regret respectively. Crucially, our framework needs only the simpler "argmax" oracle from GCB and the distribution dependent regret does not require existence of a unique optimal action. Our second contribution is another algorithm, PEGE2, which combines gap estimation with a PEGE algorithm, to achieve an $O(\log T)$ regret bound, matching the GCB guarantee but removing the dependence on size of the learner's action space. However, like GCB, PEGE2 requires access to both offline oracles and the existence of a unique optimal action. Finally, we discuss how our algorithm can be efficiently applied to a CPM problem of practical interest: namely, online ranking with feedback at the top.
Deep learning has been popularized by its recent successes on challenging artificial intelligence problems. One of the reasons for its dominance is also an ongoing challenge: the need for immense amounts of computational power. Hardware architects have responded by proposing a wide array of promising ideas, but to date, the majority of the work has focused on specific algorithms in somewhat narrow application domains. While their specificity does not diminish these approaches, there is a clear need for more flexible solutions. We believe the first step is to examine the characteristics of cutting edge models from across the deep learning community. Consequently, we have assembled Fathom: a collection of eight archetypal deep learning workloads for study. Each of these models comes from a seminal work in the deep learning community, ranging from the familiar deep convolutional neural network of Krizhevsky et al., to the more exotic memory networks from Facebook's AI research group. Fathom has been released online, and this paper focuses on understanding the fundamental performance characteristics of each model. We use a set of application-level modeling tools built around the TensorFlow deep learning framework in order to analyze the behavior of the Fathom workloads. We present a breakdown of where time is spent, the similarities between the performance profiles of our models, an analysis of behavior in inference and training, and the effects of parallelism on scaling.
Sequential data modeling and analysis have become indispensable tools for analyzing sequential data such as time-series data because a larger amount of sensed event data have become available. These methods capture the sequential structure of data of interest, such as input- output relationship and correlation among datasets. However, since most studies in this area are specialized or limited for their respective applications, rigorous requirement analysis on such a model has not been examined in a general point of view. Hence, we particularly examine the structure of sequential data, and extract the necessity of "state duration" and "state duration" of events for efficient and rich representation of sequential data. Specifically addressing the hidden semi-Markov model (HSMM) that represents such state duration inside a model, we attempt to newly add representational capability of state interval of events onto HSMM. To this end, we propose two extended models; one is interval state hidden semi-Markov model (IS-HSMM) to express the length of state interval with a special state node designated as "interval state node". The other is interval length probability hidden semi-Markov model (ILP-HSMM) which repre- sents the length of state interval with a new probabilistic parameter "interval length probability." From exhaustive simulations, we show superior performances of the proposed models in comparison with HSMM. To the best of our knowledge, our proposed models are the first extensions of HMM to support state interval representation as well as state duration representation.
Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features to form different views of it. Many approaches have been proposed to improve clustering performance by exploring and integrating heterogeneous information underlying different views. In this paper, we propose a new multi-view fuzzy clustering approach called MinimaxFCM by using minimax optimization based on well-known Fuzzy c means. In MinimaxFCM the consensus clustering results are generated based on minimax optimization in which the maximum disagreements of different weighted views are minimized. Moreover, the weight of each view can be learned automatically in the clustering process. In addition, there is only one parameter to be set besides the fuzzifier. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed MinimaxFCM are provided here. Experimental studies on nine multi-view data sets including real world image and document data sets have been conducted. We observed that MinimaxFCM outperforms related multi-view clustering approaches in terms of clustering accuracy, demonstrating the great potential of MinimaxFCM for multi-view data analysis.
A great video title describes the most salient event compactly and captures the viewer's attention. In contrast, video captioning tends to generate sentences that describe the video as a whole. Although generating a video title automatically is a very useful task, it is much less addressed than video captioning. We address video title generation for the first time by proposing two methods that extend state-of-the-art video captioners to this new task. First, we make video captioners highlight sensitive by priming them with a highlight detector. Our framework allows for jointly training a model for title generation and video highlight localization. Second, we induce high sentence diversity in video captioners, so that the generated titles are also diverse and catchy. This means that a large number of sentences might be required to learn the sentence structure of titles. Hence, we propose a novel sentence augmentation method to train a captioner with additional sentence-only examples that come without corresponding videos. We collected a large-scale Video Titles in the Wild (VTW) dataset of 18100 automatically crawled user-generated videos and titles. On VTW, our methods consistently improve title prediction accuracy, and achieve the best performance in both automatic and human evaluation. Finally, our sentence augmentation method also outperforms the baselines on the M-VAD dataset.
ANDy , Activity Networks with Delays, is a discrete time framework aimed at the qualitative modelling of time-dependent activities. The modular and concise syntax makes ANDy suitable for an easy and natural modelling of time-dependent biological systems (i.e., regulatory pathways). Activities involve entities playing the role of activators, inhibitors or products of biochemical network operation. Activities may have given duration, i.e., the time required to obtain results. An entity may represent an object (e.g., an agent, a biochemical species or a family of thereof) with a local attribute, a state denoting its level (e.g., concentration, strength). Entities levels may change as a result of an activity or may decay gradually as time passes by. The semantics of ANDy is formally given via high-level Petri nets ensuring this way some modularity. As main results we show that ANDy systems have finite state representations even for potentially infinite processes and it well adapts to the modelling of toxic behaviours. As an illustration, we present a classification of toxicity properties and give some hints on how they can be verified with existing tools on ANDy systems. A small case study on blood glucose regulation is provided to exemplify the ANDy framework and the toxicity properties.
Relation classification is associated with many potential applications in the artificial intelligence area. Recent approaches usually leverage neural networks based on structure features such as syntactic or dependency features to solve this problem. However, high-cost structure features make such approaches inconvenient to be directly used. In addition, structure features are probably domain-dependent. Therefore, this paper proposes a bi-directional long-short-term-memory recurrent-neural-network (Bi-LSTM-RNN) model based on low-cost sequence features to address relation classification. This model divides a sentence or text segment into five parts, namely two target entities and their three contexts. It learns the representations of entities and their contexts, and uses them to classify relations. We evaluate our model on two standard benchmark datasets in different domains, namely SemEval-2010 Task 8 and BioNLP-ST 2016 Task BB3. In the former dataset, our model achieves comparable performance compared with other models using sequence features. In the latter dataset, our model obtains the third best results compared with other models in the official evaluation. Moreover, we find that the context between two target entities plays the most important role in relation classification. Furthermore, statistic experiments show that the context between two target entities can be used as an approximate replacement of the shortest dependency path when dependency parsing is not used.
An important role carried out by cyber-security experts is the assessment of proposed computer systems, during their design stage. This task is fraught with difficulties and uncertainty, making the knowledge provided by human experts essential for successful assessment. Today, the increasing number of progressively complex systems has led to an urgent need to produce tools that support the expert-led process of system-security assessment. In this research, we use weighted averages (WAs) and ordered weighted averages (OWAs) with evolutionary algorithms (EAs) to create aggregation operators that model parts of the assessment process. We show how individual overall ratings for security components can be produced from ratings of their characteristics, and how these individual overall ratings can be aggregated to produce overall rankings of potential attacks on a system. As well as the identification of salient attacks and weak points in a prospective system, the proposed method also highlights which factors and security components contribute most to a component's difficulty and attack ranking respectively. A real world scenario is used in which experts were asked to rank a set of technical attacks, and to answer a series of questions about the security components that are the subject of the attacks. The work shows how finding good aggregation operators, and identifying important components and factors of a cyber-security problem can be automated. The resulting operators have the potential for use as decision aids for systems designers and cyber-security experts, increasing the amount of assessment that can be achieved with the limited resources available.
With the constant growth of the World Wide Web and the number of documents in different languages accordingly, the need for reliable language detection tools has increased as well. Platforms such as Twitter with predominantly short texts are becoming important information resources, which additionally imposes the need for short texts language detection algorithms. In this paper, we show how incorporating personalized user-specific information into the language detection algorithm leads to an important improvement of detection results. To choose the best algorithm for language detection for short text messages, we investigate several machine learning approaches. These approaches include the use of the well-known classifiers such as SVM and logistic regression, a dictionary based approach, and a probabilistic model based on modified Kneser-Ney smoothing. Furthermore, the extension of the probabilistic model to include additional user-specific information such as evidence accumulation per user and user interface language is explored, with the goal of improving the classification performance. The proposed approaches are evaluated on randomly collected Twitter data containing Latin as well as non-Latin alphabet languages and the quality of the obtained results is compared, followed by the selection of the best performing algorithm. This algorithm is then evaluated against two already existing general language detection tools: Chromium Compact Language Detector 2 (CLD2) and langid, where our method significantly outperforms the results achieved by both of the mentioned methods. Additionally, a preview of benefits and possible applications of having a reliable language detection algorithm is given.
The tremendous success of ImageNet-trained deep features on a wide range of transfer tasks begs the question: what are the properties of the ImageNet dataset that are critical for learning good, general-purpose features? This work provides an empirical investigation of various facets of this question: Is more pre-training data always better? How does feature quality depend on the number of training examples per class? Does adding more object classes improve performance? For the same data budget, how should the data be split into classes? Is fine-grained recognition necessary for learning good features? Given the same number of training classes, is it better to have coarse classes or fine-grained classes? Which is better: more classes or more examples per class? To answer these and related questions, we pre-trained CNN features on various subsets of the ImageNet dataset and evaluated transfer performance on PASCAL detection, PASCAL action classification, and SUN scene classification tasks. Our overall findings suggest that most changes in the choice of pre-training data long thought to be critical do not significantly affect transfer performance.? Given the same number of training classes, is it better to have coarse classes or fine-grained classes? Which is better: more classes or more examples per class?
The amount of completely sequenced chloroplast genomes increases rapidly every day, leading to the possibility to build large-scale phylogenetic trees of plant species. Considering a subset of close plant species defined according to their chloroplasts, the phylogenetic tree that can be inferred by their core genes is not necessarily well supported, due to the possible occurrence of problematic genes (i.e., homoplasy, incomplete lineage sorting, horizontal gene transfers, etc.) which may blur the phylogenetic signal. However, a trustworthy phylogenetic tree can still be obtained provided such a number of blurring genes is reduced. The problem is thus to determine the largest subset of core genes that produces the best-supported tree. To discard problematic genes and due to the overwhelming number of possible combinations, this article focuses on how to extract the largest subset of sequences in order to obtain the most supported species tree. Due to computational complexity, a distributed Binary Particle Swarm Optimization (BPSO) is proposed in sequential and distributed fashions. Obtained results from both versions of the BPSO are compared with those computed using an hybrid approach embedding both genetic algorithms and statistical tests. The proposal has been applied to different cases of plant families, leading to encouraging results for these families.
Many problems, especially those with a composite structure, can naturally be expressed in higher order logic. From a KR perspective modeling these problems in an intuitive way is a challenging task. In this paper we study the graph mining problem as an example of a higher order problem. In short, this problem asks us to find a graph that frequently occurs as a subgraph among a set of example graphs. We start from the problem's mathematical definition to solve it in three state-of-the-art specification systems. For IDP and ASP, which have no native support for higher order logic, we propose the use of encoding techniques such as the disjoint union technique and the saturation technique. ProB benefits from the higher order support for sets. We compare the performance of the three approaches to get an idea of the overhead of the higher order support. We propose higher-order language extensions for IDP-like specification languages and discuss what kind of solver support is needed. Native higher order shifts the burden of rewriting specifications using encoding techniques from the user to the solver itself.
Over the years, nonmonotonic rules have proven to be a very expressive and useful knowledge representation paradigm. They have recently been used to complement the expressive power of Description Logics (DLs), leading to the study of integrative formal frameworks, generally referred to as hybrid knowledge bases, where both DL axioms and rules can be used to represent knowledge. The need to use these hybrid knowledge bases in dynamic domains has called for the development of update operators, which, given the substantially different way Description Logics and rules are usually updated, has turned out to be an extremely difficult task. In [SL10], a first step towards addressing this problem was taken, and an update operator for hybrid knowledge bases was proposed. Despite its significance -- not only for being the first update operator for hybrid knowledge bases in the literature, but also because it has some applications - this operator was defined for a restricted class of problems where only the ABox was allowed to change, which considerably diminished its applicability. Many applications that use hybrid knowledge bases in dynamic scenarios require both DL axioms and rules to be updated. In this paper, motivated by real world applications, we introduce an update operator for a large class of hybrid knowledge bases where both the DL component as well as the rule component are allowed to dynamically change. We introduce splitting sequences and splitting theorem for hybrid knowledge bases, use them to define a modular update semantics, investigate its basic properties, and illustrate its use on a realistic example about cargo imports.
Developing smart house systems has been a great challenge for researchers and engineers in this area because of the high cost of implementation and evaluation process of these systems, while being very time consuming. Testing a designed smart house before actually building it is considered as an obstacle towards an efficient smart house project. This is because of the variety of sensors, home appliances and devices available for a real smart environment. In this paper, we present the design and implementation of a multi-purpose smart house simulation system for designing and simulating all aspects of a smart house environment. This simulator provides the ability to design the house plan and different virtual sensors and appliances in a two dimensional model of the virtual house environment. This simulator can connect to any external smart house remote controlling system, providing evaluation capabilities to their system much easier than before. It also supports detailed adding of new emerging sensors and devices to help maintain its compatibility with future simulation needs. Scenarios can also be defined for testing various possible combinations of device states; so different criteria and variables can be simply evaluated without the need of experimenting on a real environment.
Using an interactive theorem prover to reason about programs involves a sequence of interactions where the user challenges the theorem prover with conjectures. Invariably, many of the conjectures posed are in fact false, and users often spend considerable effort examining the theorem prover's output before realizing this. We present a synergistic integration of testing with theorem proving, implemented in the ACL2 Sedan (ACL2s), for automatically generating concrete counterexamples. Our method uses the full power of the theorem prover and associated libraries to simplify conjectures; this simplification can transform conjectures for which finding counterexamples is hard into conjectures where finding counterexamples is trivial. In fact, our approach even leads to better theorem proving, e.g. if testing shows that a generalization step leads to a false conjecture, we force the theorem prover to backtrack, allowing it to pursue more fruitful options that may yield a proof. The focus of the paper is on the engineering of a synergistic integration of testing with interactive theorem proving; this includes extending ACL2 with new functionality that we expect to be of general interest. We also discuss our experience in using ACL2s to teach freshman students how to reason about their programs.
We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a reproducing kernel Hilbert space (RKHS), and message updates are simple linear operations in the RKHS. KBP makes none of the assumptions commonly required in classical BP algorithms: the variables need not arise from a finite domain or a Gaussian distribution, nor must their relations take any particular parametric form. Rather, the relations between variables are represented implicitly, and are learned nonparametrically from training data. KBP has the advantage that it may be used on any domain where kernels are defined (Rd, strings, groups), even where explicit parametric models are not known, or closed form expressions for the BP updates do not exist. The computational cost of message updates in KBP is polynomial in the training data size. We also propose a constant time approximate message update procedure by representing messages using a small number of basis functions. In experiments, we apply KBP to image denoising, depth prediction from still images, and protein configuration prediction: KBP is faster than competing classical and nonparametric approaches (by orders of magnitude, in some cases), while providing significantly more accurate results.
This paper applies machine learning and the mathematics of chaos to the task of designing indoor rock-climbing routes. Chaotic variation has been used to great advantage on music and dance, but the challenges here are quite different, beginning with the representation. We present a formalized system for transcribing rock climbing problems, then describe a variation generator that is designed to support human route-setters in designing new and interesting climbing problems. This variation generator, termed Strange Beta, combines chaos and machine learning, using the former to introduce novelty and the latter to smooth transitions in a manner that is consistent with the style of the climbs This entails parsing the domain-specific natural language that rock climbers use to describe routes and movement and then learning the patterns in the results. We validated this approach with a pilot study in a small university rock climbing gym, followed by a large blinded study in a commercial climbing gym, in cooperation with experienced climbers and expert route setters. The results show that {\sc Strange Beta} can help a human setter produce routes that are at least as good as, and in some cases better than, those produced in the traditional manner.
The distribution semantics is one of the most prominent approaches for the combination of logic programming and probability theory. Many languages follow this semantics, such as Independent Choice Logic, PRISM, pD, Logic Programs with Annotated Disjunctions (LPADs) and ProbLog. When a program contains functions symbols, the distribution semantics is well-defined only if the set of explanations for a query is finite and so is each explanation. Well-definedness is usually either explicitly imposed or is achieved by severely limiting the class of allowed programs. In this paper we identify a larger class of programs for which the semantics is well-defined together with an efficient procedure for computing the probability of queries. Since LPADs offer the most general syntax, we present our results for them, but our results are applicable to all languages under the distribution semantics. We present the algorithm "Probabilistic Inference with Tabling and Answer subsumption" (PITA) that computes the probability of queries by transforming a probabilistic program into a normal program and then applying SLG resolution with answer subsumption. PITA has been implemented in XSB and tested on six domains: two with function symbols and four without. The execution times are compared with those of ProbLog, cplint and CVE, PITA was almost always able to solve larger problems in a shorter time, on domains with and without function symbols.
We study the performance of different message passing algorithms in the two dimensional Edwards Anderson model. We show that the standard Belief Propagation (BP) algorithm converges only at high temperature to a paramagnetic solution. Then, we test a Generalized Belief Propagation (GBP) algorithm, derived from a Cluster Variational Method (CVM) at the plaquette level. We compare its performance with BP and with other algorithms derived under the same approximation: Double Loop (DL) and a two-ways message passing algorithm (HAK). The plaquette-CVM approximation improves BP in at least three ways: the quality of the paramagnetic solution at high temperatures, a better estimate (lower) for the critical temperature, and the fact that the GBP message passing algorithm converges also to non paramagnetic solutions. The lack of convergence of the standard GBP message passing algorithm at low temperatures seems to be related to the implementation details and not to the appearance of long range order. In fact, we prove that a gauge invariance of the constrained CVM free energy can be exploited to derive a new message passing algorithm which converges at even lower temperatures. In all its region of convergence this new algorithm is faster than HAK and DL by some orders of magnitude.
Given a collection of objects and an associated similarity measure, the all-pairs similarity search problem asks us to find all pairs of objects with similarity greater than a certain user-specified threshold. Locality-sensitive hashing (LSH) based methods have become a very popular approach for this problem. However, most such methods only use LSH for the first phase of similarity search - i.e. efficient indexing for candidate generation. In this paper, we present BayesLSH, a principled Bayesian algorithm for the subsequent phase of similarity search - performing candidate pruning and similarity estimation using LSH. A simpler variant, BayesLSH-Lite, which calculates similarities exactly, is also presented. BayesLSH is able to quickly prune away a large majority of the false positive candidate pairs, leading to significant speedups over baseline approaches. For BayesLSH, we also provide probabilistic guarantees on the quality of the output, both in terms of accuracy and recall. Finally, the quality of BayesLSH's output can be easily tuned and does not require any manual setting of the number of hashes to use for similarity estimation, unlike standard approaches. For two state-of-the-art candidate generation algorithms, AllPairs and LSH, BayesLSH enables significant speedups, typically in the range 2x-20x for a wide variety of datasets.
This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy approach and a Bayesian model averaging approach. Both ideas can be easily applied on top of EM, while the entropy idea can be also implemented in a more sophisticated way, through a dedicated non-linear solver. A vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum (penalized) log-likelihood and maximum a posteriori estimates. In particular, if EM is adopted as optimization engine, the model averaging approach is the best performing one; its performance is matched by the entropy approach when implemented using the non-linear solver. The results suggest that the applicability of these ideas is immediate (they are easy to implement and to integrate in currently available inference engines) and that they constitute a better way to learn Bayesian network parameters.
Scene understanding includes many related sub-tasks, such as scene categorization, depth estimation, object detection, etc. Each of these sub-tasks is often notoriously hard, and state-of-the-art classifiers already exist for many of them. These classifiers operate on the same raw image and provide correlated outputs. It is desirable to have an algorithm that can capture such correlation without requiring any changes to the inner workings of any classifier. We propose Feedback Enabled Cascaded Classification Models (FE-CCM), that jointly optimizes all the sub-tasks, while requiring only a `black-box' interface to the original classifier for each sub-task. We use a two-layer cascade of classifiers, which are repeated instantiations of the original ones, with the output of the first layer fed into the second layer as input. Our training method involves a feedback step that allows later classifiers to provide earlier classifiers information about which error modes to focus on. We show that our method significantly improves performance in all the sub-tasks in the domain of scene understanding, where we consider depth estimation, scene categorization, event categorization, object detection, geometric labeling and saliency detection. Our method also improves performance in two robotic applications: an object-grasping robot and an object-finding robot.
Recently, quantitative versions of the Gibbard-Satterthwaite theorem were proven for $k=3$ alternatives by Friedgut, Kalai, Keller and Nisan and for neutral functions on $k \geq 4$ alternatives by Isaksson, Kindler and Mossel. We prove a quantitative version of the Gibbard-Satterthwaite theorem for general social choice functions for any number $k \geq 3$ of alternatives. In particular we show that for a social choice function $f$ on $k \geq 3$ alternatives and $n$ voters, which is $\epsilon$-far from the family of nonmanipulable functions, a uniformly chosen voter profile is manipulable with probability at least inverse polynomial in $n$, $k$, and $\epsilon^{-1}$. Removing the neutrality assumption of previous theorems is important for multiple reasons. For one, it is known that there is a conflict between anonymity and neutrality, and since most common voting rules are anonymous, they cannot always be neutral. Second, virtual elections are used in many applications in artificial intelligence, where there are often restrictions on the outcome of the election, and so neutrality is not a natural assumption in these situations. Ours is a unified proof which in particular covers all previous cases established before. The proof crucially uses reverse hypercontractivity in addition to several ideas from the two previous proofs. Much of the work is devoted to understanding functions of a single voter, and in particular we also prove a quantitative Gibbard-Satterthwaite theorem for one voter.
The CDOI outcome measure - a patient-reported outcome (PRO) instrument utilizing direct client feedback - was implemented in a large, real-world behavioral healthcare setting in order to evaluate previous findings from smaller controlled studies. PROs provide an alternative window into treatment effectiveness based on client perception and facilitate detection of problems/symptoms for which there is no discernible measure (e.g. pain). The principal focus of the study was to evaluate the utility of the CDOI for predictive modeling of outcomes in a live clinical setting. Implementation factors were also addressed within the framework of the Theory of Planned Behavior by linking adoption rates to implementation practices and clinician perceptions. The results showed that the CDOI does contain significant capacity to predict outcome delta over time based on baseline and early change scores in a large, real-world clinical setting, as suggested in previous research. The implementation analysis revealed a number of critical factors affecting successful implementation and adoption of the CDOI outcome measure, though there was a notable disconnect between clinician intentions and actual behavior. Most importantly, the predictive capacity of the CDOI underscores the utility of direct client feedback measures such as PROs and their potential use as the basis for next generation clinical decision support tools and personalized treatment approaches.
The basis of the method proposed in this article is the idea that information is one of the most important factors in strategic decisions, including decisions in computer chess and other strategy games. The model proposed in this article and the algorithm described are based on the idea of a information theoretic basis of decision in strategy games . The model generalizes and provides a mathematical justification for one of the most popular search algorithms used in leading computer chess programs, the fractional ply scheme. However, despite its success in leading computer chess applications, until now few has been published about this method. The article creates a fundamental basis for this method in the axioms of information theory, then derives the principles used in programming the search and describes mathematically the form of the coefficients. One of the most important parameters of the fractional ply search is derived from fundamental principles. Until now this coefficient has been usually handcrafted or determined from intuitive elements or data mining. There is a deep, information theoretical justification for such a parameter. In one way the method proposed is a generalization of previous methods. More important, it shows why the fractional depth ply scheme is so powerful. It is because the algorithm navigates along the lines where the highest information gain is possible. A working and original implementation has been written and tested for this algorithm and is provided in the appendix. The article is essentially self-contained and gives proper background knowledge and references. The assumptions are intuitive and in the direction expected and described intuitively by great champions of chess.
Adaptation to changing environments is a hallmark of biological systems. Diversity in traits is necessary for adaptation and can influence the survival of a population faced with novelty. In habitats that remain stable over many generations, stabilizing selection reduces trait differences within populations, thereby appearing to remove the diversity needed for heritable adaptive responses in new environments. Paradoxically, field studies have documented numerous populations under long periods of stabilizing selection and evolutionary stasis that have rapidly evolved under changed environmental conditions. In this article, we review how cryptic genetic variation (CGV) resolves this diversity paradox by allowing populations in a stable environment to gradually accumulate hidden genetic diversity that is revealed as trait differences when environments change. Instead of being in conflict, environmental stasis supports CGV accumulation and thus appears to facilitate rapid adaptation in new environments as suggested by recent CGV studies. Similarly, degeneracy has been found to support both genetic and non-genetic adaptation at many levels of biological organization. Degenerate, as opposed to diverse or redundant, ensembles appear functionally redundant in certain environmental contexts but functionally diverse in others. CGV and degeneracy paradigms for adaptation are integrated in this review, revealing a common set of principles that support adaptation at multiple levels of biological organization. Though a discussion of simulation studies, molecular-based experimental systems, principles from population genetics, and field experiments, we demonstrate that CGV and degeneracy reflect complementary top-down and bottom-up, respectively, conceptualizations of the same basic phenomenon and arguably capture a universal feature of biological adaptive processes.
In this paper, we study CPU utilization time patterns of several Map-Reduce applications. After extracting running patterns of several applications, the patterns with their statistical information are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications along with its statistical information are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW) is utilized for such comparison; a statistical analysis is then applied to DTWs' outcomes to select the most suitable candidates. Moreover, under a hypothesis, another algorithm is proposed to classify applications under similar CPU utilization patterns. Three widely used text processing applications (WordCount, Distributed Grep, and Terasort) and another application (Exim Mainlog parsing) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on 5-node Map-Reduce platform
Atomizing various Web activities by replacing human to human interactions on the Internet has been made indispensable due to its enormous growth. However, bots also known as Web-bots which have a malicious intend and pretending to be humans pose a severe threat to various services on the Internet that implicitly assume a human interaction. Accordingly, Web service providers before allowing access to such services use various Human Interaction Proof's (HIPs) to authenticate that the user is a human and not a bot. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a class of HIPs tests and are based on Artificial Intelligence. These tests are easier for humans to qualify and tough for bots to simulate. Several Web services use CAPTCHAs as a defensive mechanism against automated Web-bots. In this paper, we review the existing CAPTCHA schemes that have been proposed or are being used to protect various Web services. We classify them in groups and compare them with each other in terms of security and usability. We present general method used to generate and break text-based and image-based CAPTCHAs. Further, we discuss various security and usability issues in CAPTCHA design and provide guidelines for improving their robustness and usability.
Importance of document clustering is now widely acknowledged by researchers for better management, smart navigation, efficient filtering, and concise summarization of large collection of documents like World Wide Web (WWW). The next challenge lies in semantically performing clustering based on the semantic contents of the document. The problem of document clustering has two main components: (1) to represent the document in such a form that inherently captures semantics of the text. This may also help to reduce dimensionality of the document, and (2) to define a similarity measure based on the semantic representation such that it assigns higher numerical values to document pairs which have higher semantic relationship. Feature space of the documents can be very challenging for document clustering. A document may contain multiple topics, it may contain a large set of class-independent general-words, and a handful class-specific core-words. With these features in mind, traditional agglomerative clustering algorithms, which are based on either Document Vector model (DVM) or Suffix Tree model (STC), are less efficient in producing results with high cluster quality. This paper introduces a new approach for document clustering based on the Topic Map representation of the documents. The document is being transformed into a compact form. A similarity measure is proposed based upon the inferred information through topic maps data and structures. The suggested method is implemented using agglomerative hierarchal clustering and tested on standard Information retrieval (IR) datasets. The comparative experiment reveals that the proposed approach is effective in improving the cluster quality.
Document clustering as an unsupervised approach extensively used to navigate, filter, summarize and manage large collection of document repositories like the World Wide Web (WWW). Recently, focuses in this domain shifted from traditional vector based document similarity for clustering to suffix tree based document similarity, as it offers more semantic representation of the text present in the document. In this paper, we compare and contrast two recently introduced approaches to document clustering based on suffix tree data model. The first is an Efficient Phrase based document clustering, which extracts phrases from documents to form compact document representation and uses a similarity measure based on common suffix tree to cluster the documents. The second approach is a frequent word/word meaning sequence based document clustering, it similarly extracts the common word sequence from the document and uses the common sequence/ common word meaning sequence to perform the compact representation, and finally, it uses document clustering approach to cluster the compact documents. These algorithms are using agglomerative hierarchical document clustering to perform the actual clustering step, the difference in these approaches are mainly based on extraction of phrases, model representation as a compact document, and the similarity measures used for clustering. This paper investigates the computational aspect of the two algorithms, and the quality of results they produced.
The causal inference literature has provided a clear formal definition of confounding expressed in terms of counterfactual independence. The literature has not, however, come to any consensus on a formal definition of a confounder, as it has given priority to the concept of confounding over that of a confounder. We consider a number of candidate definitions arising from various more informal statements made in the literature. We consider the properties satisfied by each candidate definition, principally focusing on (i) whether under the candidate definition control for all "confounders" suffices to control for "confounding" and (ii) whether each confounder in some context helps eliminate or reduce confounding bias. Several of the candidate definitions do not have these two properties. Only one candidate definition of those considered satisfies both properties. We propose that a "confounder" be defined as a pre-exposure covariate C for which there exists a set of other covariates X such that effect of the exposure on the outcome is unconfounded conditional on (X,C) but such that for no proper subset of (X,C) is the effect of the exposure on the outcome unconfounded given the subset. We also provide a conditional analogue of the above definition; and we propose a variable that helps reduce bias but not eliminate bias be referred to as a "surrogate confounder." These definitions are closely related to those given by Robins and Morgenstern [Comput. Math. Appl. 14 (1987) 869-916]. The implications that hold among the various candidate definitions are discussed.
The computational characterization of game-theoretic solution concepts is a central topic in artificial intelligence, with the aim of developing computationally efficient tools for finding optimal ways to behave in strategic interactions. The central solution concept in game theory is Nash equilibrium (NE). However, it fails to capture the possibility that agents can form coalitions (even in the 2-agent case). Strong Nash equilibrium (SNE) refines NE to this setting. It is known that finding an SNE is NP-complete when the number of agents is constant. This hardness is solely due to the existence of mixed-strategy SNEs, given that the problem of enumerating all pure-strategy SNEs is trivially in P. Our central result is that, in order for a game to have at least one non-pure-strategy SNE, the agents' payoffs restricted to the agents' supports must, in the case of 2 agents, lie on the same line, and, in the case of n agents, lie on an (n - 1)-dimensional hyperplane. Leveraging this result, we provide two contributions. First, we develop worst-case instances for support-enumeration algorithms. These instances have only one SNE and the support size can be chosen to be of any size-in particular, arbitrarily large. Second, we prove that, unlike NE, finding an SNE is in smoothed polynomial time: generic game instances (i.e., all instances except knife-edge cases) have only pure-strategy SNEs.
Pattern-Based Constraint Satisfaction and Logic Puzzles develops a pure logic, pattern-based perspective of solving the finite Constraint Satisfaction Problem (CSP), with emphasis on finding the "simplest" solution. Different ways of reasoning with the constraints are formalised by various families of "resolution rules", each of them carrying its own notion of simplicity. A large part of the book illustrates the power of the approach by applying it to various popular logic puzzles. It provides a unified view of how to model and solve them, even though they involve very different types of constraints: obvious symmetric ones in Sudoku, non-symmetric but transitive ones (inequalities) in Futoshiki, topological and geometric ones in Map colouring, Numbrix and Hidato, and even much more complex non-binary arithmetic ones in Kakuro (or Cross Sums). It also shows that the most familiar techniques for these puzzles can indeed be understood as mere application-specific presentations of the general rules. Sudoku is used as the main example throughout the book, making it also an advanced level sequel to "The Hidden Logic of Sudoku" (another book by the same author), with: many examples of relationships among different rules and of exceptional situations; comparisons of the resolution potential of various families of rules; detailed statistics of puzzles hardness; analysis of extreme instances.
The Decision Support System (DSS) contains more than one antecedent and the degrees of strength of the antecedents need to be combined to determine the overall strength of the rule consequent. The membership values of the linguistic variables in Fuzzy have to be combined using an aggregation operator. But it is not feasible to predefine the form of aggregation operators in decision making. Instead, each rule should be found based on the feeling of the experts and on their actual decision pattern over the set of typical examples. Thus this work illustrates how the choice of aggregation operators is intended to mimic human decision making and can be selected and adjusted to fit empirical data, a series of test cases. Both parametrized and nonparametrized aggregation operators are adapted to fit empirical data. Moreover, they provided compensatory properties and, therefore, seemed to produce a better decision support system. To solve the problem, a threshold point from the output of the aggregation operators is chosen as the separation point between two classes. The best achieved accuracy is chosen as the appropriate aggregation operator. Thus a medical decision can be generated which is very close to a practitioner's guideline.
In this Part II, we apply the general theory developed in Part I to a detailed analysis of the Constraint Satisfaction Problem (CSP). We show how specific types of resolution rules can be defined. In particular, we introduce the general notions of a chain and a braid. As in Part I, these notions are illustrated in detail with the Sudoku example - a problem known to be NP-complete and which is therefore typical of a broad class of hard problems. For Sudoku, we also show how far one can go in 'approximating' a CSP with a resolution theory and we give an empirical statistical analysis of how the various puzzles, corresponding to different sets of entries, can be classified along a natural scale of complexity. For any CSP, we also prove the confluence property of some Resolution Theories based on braids and we show how it can be used to define different resolution strategies. Finally, we prove that, in any CSP, braids have the same solving capacity as Trial-and-Error (T&E) with no guessing and we comment this result in the Sudoku case.
Trustworthiness especially for service oriented system is very important topic now a day in IT field of the whole world. There are many successful E-commerce organizations presently run in the whole world, but E-commerce has not reached its full potential. The main reason behind this is lack of Trust of people in e-commerce. Again, proper models are still absent for calculating trust of different e-commerce organizations. Most of the present trust models are subjective and have failed to account vagueness and ambiguity of different domain. In this paper we have proposed a new fuzzy logic based Certain Trust model which considers these ambiguity and vagueness of different domain. Fuzzy Based Certain Trust Model depends on some certain values given by experts and developers. can be applied in a system like cloud computing, internet, website, e-commerce, etc. to ensure trustworthiness of these platforms. In this paper we show, although fuzzy works with uncertainties, proposed model works with some certain values. Some experimental results and validation of the model with linguistics terms are shown at the last part of the paper.
An undirected graphical model is a joint probability distribution defined on an undirected graph G*, where the vertices in the graph index a collection of random variables and the edges encode conditional independence relationships among random variables. The undirected graphical model selection (UGMS) problem is to estimate the graph G* given observations drawn from the undirected graphical model. This paper proposes a framework for decomposing the UGMS problem into multiple subproblems over clusters and subsets of the separators in a junction tree. The junction tree is constructed using a graph that contains a superset of the edges in G*. We highlight three main properties of using junction trees for UGMS. First, different regularization parameters or different UGMS algorithms can be used to learn different parts of the graph. This is possible since the subproblems we identify can be solved independently of each other. Second, under certain conditions, a junction tree based UGMS algorithm can produce consistent results with fewer observations than the usual requirements of existing algorithms. Third, both our theoretical and experimental results show that the junction tree framework does a significantly better job at finding the weakest edges in a graph than existing methods. This property is a consequence of both the first and second properties. Finally, we note that our framework is independent of the choice of the UGMS algorithm and can be used as a wrapper around standard UGMS algorithms for more accurate graph estimation.
The paper describes development (improvement/extension) approaches for composite (modular) systems (as combinatorial reengineering). The following system improvement/extension actions are considered: (a) improvement of systems component(s) (e.g., improvement of a system component, replacement of a system component); (b) improvement of system component interconnection (compatibility); (c) joint improvement improvement of system components(s) and their interconnection; (d) improvement of system structure (replacement of system part(s), addition of a system part, deletion of a system part, modification of system structure). The study of system improvement approaches involve some crucial issues: (i) scales for evaluation of system components and component compatibility (quantitative scale, ordinal scale, poset-like scale, scale based on interval multiset estimate), (ii) evaluation of integrated system quality, (iii) integration methods to obtain the integrated system quality. The system improvement/extension strategies can be examined as seleciton/combination of the improvement action(s) above and as modification of system structure. The strategies are based on combinatorial optimization problems (e.g., multicriteria selection, knapsack problem, multiple choice problem, combinatorial synthesis based on morphological clique problem, assignment/reassignment problem, graph recoloring problem, spanning problems, hotlink assignment). Here, heuristics are used. Various system improvement/extension strategies are presented including illustrative numerical examples.
We consider stochastic strongly convex optimization with a complex inequality constraint. This complex inequality constraint may lead to computationally expensive projections in algorithmic iterations of the stochastic gradient descent~(SGD) methods. To reduce the computation costs pertaining to the projections, we propose an Epoch-Projection Stochastic Gradient Descent~(Epro-SGD) method. The proposed Epro-SGD method consists of a sequence of epochs; it applies SGD to an augmented objective function at each iteration within the epoch, and then performs a projection at the end of each epoch. Given a strongly convex optimization and for a total number of $T$ iterations, Epro-SGD requires only $\log(T)$ projections, and meanwhile attains an optimal convergence rate of $O(1/T)$, both in expectation and with a high probability. To exploit the structure of the optimization problem, we propose a proximal variant of Epro-SGD, namely Epro-ORDA, based on the optimal regularized dual averaging method. We apply the proposed methods on real-world applications; the empirical results demonstrate the effectiveness of our methods.
In the domain of Computing with words (CW), fuzzy linguistic approaches are known to be relevant in many decision-making problems. Indeed, they allow us to model the human reasoning in replacing words, assessments, preferences, choices, wishes... by ad hoc variables, such as fuzzy sets or more sophisticated variables. This paper focuses on a particular model: Herrera & Martinez' 2-tuple linguistic model and their approach to deal with unbalanced linguistic term sets. It is interesting since the computations are accomplished without loss of information while the results of the decision-making processes always refer to the initial linguistic term set. They propose a fuzzy partition which distributes data on the axis by using linguistic hierarchies to manage the non-uniformity. However, the required input (especially the density around the terms) taken by their fuzzy partition algorithm may be considered as too much demanding in a real-world application, since density is not always easy to determine. Moreover, in some limit cases (especially when two terms are very closed semantically to each other), the partition doesn't comply with the data themselves, it isn't close to the reality. Therefore we propose to modify the required input, in order to offer a simpler and more faithful partition. We have added an extension to the package jFuzzyLogic and to the corresponding script language FCL. This extension supports both 2-tuple models: Herrera & Martinez' and ours. In addition to the partition algorithm, we present two aggregation algorithms: the arithmetic means and the addition. We also discuss these kinds of 2-tuple models.
We reconsider the stochastic (sub)gradient approach to the unconstrained primal L1-SVM optimization. We observe that if the learning rate is inversely proportional to the number of steps, i.e., the number of times any training pattern is presented to the algorithm, the update rule may be transformed into the one of the classical perceptron with margin in which the margin threshold increases linearly with the number of steps. Moreover, if we cycle repeatedly through the possibly randomly permuted training set the dual variables defined naturally via the expansion of the weight vector as a linear combination of the patterns on which margin errors were made are shown to obey at the end of each complete cycle automatically the box constraints arising in dual optimization. This renders the dual Lagrangian a running lower bound on the primal objective tending to it at the optimum and makes available an upper bound on the relative accuracy achieved which provides a meaningful stopping criterion. In addition, we propose a mechanism of presenting the same pattern repeatedly to the algorithm which maintains the above properties. Finally, we give experimental evidence that algorithms constructed along these lines exhibit a considerably improved performance.
Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks such as computing the marginals given evidence and learning from (partial) interpretations have not really been addressed for probabilistic logic programs before. The first contribution of this paper is a suite of efficient algorithms for various inference tasks. It is based on a conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs Expectation Maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state-of-the-art in probabilistic logic programming and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.
Researchers since at least Darwin have debated whether and to what extent emotions are universal or culture-dependent. However, previous studies have primarily focused on facial expressions and on a limited set of emotions. Given that emotions have a substantial impact on human lives, evidence for cultural emotional relativity might be derived by applying distributional semantics techniques to a text corpus of self-reported behaviour. Here, we explore this idea by measuring the valence and arousal of the twelve most popular emotion keywords expressed on the micro-blogging site Twitter. We do this in three geographical regions: Europe, Asia and North America. We demonstrate that in our sample, the valence and arousal levels of the same emotion keywords differ significantly with respect to these geographical regions --- Europeans are, or at least present themselves as more positive and aroused, North Americans are more negative and Asians appear to be more positive but less aroused when compared to global valence and arousal levels of the same emotion keywords. Our work is the first in kind to programatically map large text corpora to a dimensional model of affect.
The junction tree algorithm is a way of computing marginals of boolean multivariate probability distributions that factorise over sets of random variables. The junction tree algorithm first constructs a tree called a junction tree who's vertices are sets of random variables. The algorithm then performs a generalised version of belief propagation on the junction tree. The Shafer-Shenoy and Hugin architectures are two ways to perform this belief propagation that tradeoff time and space complexities in different ways: Hugin propagation is at least as fast as Shafer-Shenoy propagation and in the cases that we have large vertices of high degree is significantly faster. However, this speed increase comes at the cost of an increased space complexity. This paper first introduces a simple novel architecture, ARCH-1, which has the best of both worlds: the speed of Hugin propagation and the low space requirements of Shafer-Shenoy propagation. A more complicated novel architecture, ARCH-2, is then introduced which has, up to a factor only linear in the maximum cardinality of any vertex, time and space complexities at least as good as ARCH-1 and in the cases that we have large vertices of high degree is significantly faster than ARCH-1.
With the proliferation of its applications in various industries, sentiment analysis by using publicly available web data has become an active research area in text classification during these years. It is argued by researchers that semi-supervised learning is an effective approach to this problem since it is capable to mitigate the manual labeling effort which is usually expensive and time-consuming. However, there was a long-term debate on the effectiveness of unlabeled data in text classification. This was partially caused by the fact that many assumptions in theoretic analysis often do not hold in practice. We argue that this problem may be further understood by adding an additional dimension in the experiment. This allows us to address this problem in the perspective of bias and variance in a broader view. We show that the well-known performance degradation issue caused by unlabeled data can be reproduced as a subset of the whole scenario. We argue that if the bias-variance trade-off is to be better balanced by a more effective feature selection method unlabeled data is very likely to boost the classification performance. We then propose a feature selection framework in which labeled and unlabeled training samples are both considered. We discuss its potential in achieving such a balance. Besides, the application in financial sentiment analysis is chosen because it not only exemplifies an important application, the data possesses better illustrative power as well. The implications of this study in text classification and financial sentiment analysis are both discussed.
The Bayesian approach to machine learning amounts to computing posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables. There is a trend in machine learning towards expressing Bayesian models as probabilistic programs. As a foundation for this kind of programming, we propose a core functional calculus with primitives for sampling prior distributions and observing variables. We define measure-transformer combinators inspired by theorems in measure theory, and use these to give a rigorous semantics to our core calculus. The original features of our semantics include its support for discrete, continuous, and hybrid measures, and, in particular, for observations of zero-probability events. We compile our core language to a small imperative language that is processed by an existing inference engine for factor graphs, which are data structures that enable many efficient inference algorithms. This allows efficient approximate inference of posterior marginal distributions, treating thousands of observations per second for large instances of realistic models.
What are the cognitive after-effects of making a similarity judgement? What, cognitively, is left behind and what effect might these residues have on subsequent processing? In this paper, we probe for such after-effects using a visual search task, performed after a task in which pictures of real-world objects were compared. So, target objects were first presented in a comparison task (e.g., rate the similarity of this object to another) thus, presumably, modifying some of their features before asking people to visually search for the same object in complex scenes (with distractors and camouflaged backgrounds). As visual search is known to be influenced by the features of target objects, then any after-effects of the comparison task should be revealed in subsequent visual searches. Results showed that when people previously rated an object as being high on a scale (e.g., colour similarity or general similarity) then visual search is inhibited (slower RTs and more saccades in eye-tracking) relative to an object being rated as low in the same scale. There was also some evidence that different comparison tasks (e.g., compare on colour or compare on general similarity) have differential effects on visual search.
In this paper, a new structure of cooperative learning automata so-called extended learning automata (eDLA) is introduced. Based on the proposed structure, a new iterative randomized heuristic algorithm for finding optimal sub-graph in a stochastic edge-weighted graph through sampling is proposed. It has been shown that the proposed algorithm based on new networked-structure can be to solve the optimization problems on stochastic graph through less number of sampling in compare to standard sampling. Stochastic graphs are graphs in which the edges have an unknown distribution probability weights. Proposed algorithm uses an eDLA to find a policy that leads to an induced sub-graph that satisfies some restrictions such as minimum or maximum weight (length). At each stage of the proposed algorithm, eDLA determines which edges to be sampled. This eDLA-based proposed sampling method may result in decreasing unnecessary samples and hence decreasing the time that algorithm requires for finding the optimal sub-graph. It has been shown that proposed method converge to optimal solution, furthermore the probability of this convergence can be made arbitrarily close to 1 by using a sufficiently small learning rate. A new variance-aware threshold value was proposed that can be improving significantly convergence rate of the proposed eDLA-based algorithm. It has been shown that the proposed algorithm is competitive in terms of the quality of the solution
Floating-point computations are quickly finding their way in the design of safety- and mission-critical systems, despite the fact that designing floating-point algorithms is significantly more difficult than designing integer algorithms. For this reason, verification and validation of floating-point computations is a hot research topic. An important verification technique, especially in some industrial sectors, is testing. However, generating test data for floating-point intensive programs proved to be a challenging problem. Existing approaches usually resort to random or search-based test data generation, but without symbolic reasoning it is almost impossible to generate test inputs that execute complex paths controlled by floating-point computations. Moreover, as constraint solvers over the reals or the rationals do not natively support the handling of rounding errors, the need arises for efficient constraint solvers over floating-point domains. In this paper, we present and fully justify improved algorithms for the propagation of arithmetic IEEE 754 binary floating-point constraints. The key point of these algorithms is a generalization of an idea by B. Marre and C. Michel that exploits a property of the representation of floating-point numbers.
This paper summarizes efforts to computationally model two transitions in the evolution of human creativity: its origins about two million years ago, and the 'big bang' of creativity about 50,000 years ago. Using a computational model of cultural evolution in which neural network based agents evolve ideas for actions through invention and imitation, we tested the hypothesis that human creativity began with onset of the capacity for recursive recall. We compared runs in which agents were limited to single-step actions to runs in which they used recursive recall to chain simple actions into complex ones. Chaining resulted in higher diversity, open-ended novelty, no ceiling on the mean fitness of actions, and greater ability to make use of learning. Using a computational model of portrait painting, we tested the hypothesis that the explosion of creativity in the Middle/Upper Paleolithic was due to onset of con-textual focus: the capacity to shift between associative and analytic thought. This resulted in faster convergence on portraits that resembled the sitter, employed painterly techniques, and were rated as preferable. We conclude that recursive recall and contextual focus provide a computationally plausible explanation of how humans evolved the means to transform this planet.
The Shapley value---probably the most important normative payoff division scheme in coalitional games---has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. For instance, in the case of unweighted networks our algorithms are able to return the exact solution about 1600 times faster than the Monte Carlo approximation, even if we allow for a generous 10% error margin for the latter method.
Many feature subset selection (FSS) algorithms have been proposed, but not all of them are appropriate for a given feature selection problem. At the same time, so far there is rarely a good way to choose appropriate FSS algorithms for the problem at hand. Thus, FSS algorithm automatic recommendation is very important and practically useful. In this paper, a meta learning based FSS algorithm automatic recommendation method is presented. The proposed method first identifies the data sets that are most similar to the one at hand by the k-nearest neighbor classification algorithm, and the distances among these data sets are calculated based on the commonly-used data set characteristics. Then, it ranks all the candidate FSS algorithms according to their performance on these similar data sets, and chooses the algorithms with best performance as the appropriate ones. The performance of the candidate FSS algorithms is evaluated by a multi-criteria metric that takes into account not only the classification accuracy over the selected features, but also the runtime of feature selection and the number of selected features. The proposed recommendation method is extensively tested on 115 real world data sets with 22 well-known and frequently-used different FSS algorithms for five representative classifiers. The results show the effectiveness of our proposed FSS algorithm recommendation method.
We consider how selfish agents are likely to share revenues derived from maintaining connectivity between important network servers. We model a network where a failure of one node may disrupt communication between other nodes as a cooperative game called the vertex Connectivity Game (CG). In this game, each agent owns a vertex, and controls all the edges going to and from that vertex. A coalition of agents wins if it fully connects a certain subset of vertices in the graph, called the primary vertices. Power indices measure an agents ability to affect the outcome of the game. We show that in our domain, such indices can be used to both determine the fair share of the revenues an agent is entitled to, and identify significant possible points of failure affecting the reliability of communication in the network. We show that in general graphs, calculating the Shapley and Banzhaf power indices is #P-complete, but suggest a polynomial algorithm for calculating them in trees. We also investigate finding stable payoff divisions of the revenues in CGs, captured by the game theoretic solution of the core, and its relaxations, the epsilon-core and least core. We show a polynomial algorithm for computing the core of a CG, but show that testing whether an imputation is in the epsilon-core is coNP-complete. Finally, we show that for trees, it is possible to test for epsilon-core imputations in polynomial time.
To achieve an optimal outcome in many situations, agents need to choose distinct actions from one another. This is the case notably in many resource allocation problems, where a single resource can only be used by one agent at a time. How shall a designer of a multi-agent system program its identical agents to behave each in a different way? From a game theoretic perspective, such situations lead to undesirable Nash equilibria. For example consider a resource allocation game in that two players compete for an exclusive access to a single resource. It has three Nash equilibria. The two pure-strategy NE are efficient, but not fair. The one mixed-strategy NE is fair, but not efficient. Aumanns notion of correlated equilibrium fixes this problem: It assumes a correlation device that suggests each agent an action to take. However, such a "smart" coordination device might not be available. We propose using a randomly chosen, "stupid" integer coordination signal. "Smart" agents learn which action they should use for each value of the coordination signal. We present a multi-agent learning algorithm that converges in polynomial number of steps to a correlated equilibrium of a channel allocation game, a variant of the resource allocation game. We show that the agents learn to play for each coordination signal value a randomly chosen pure-strategy Nash equilibrium of the game. Therefore, the outcome is an efficient correlated equilibrium. This CE becomes more fair as the number of the available coordination signal values increases.
Content-based and collaborative filtering methods are the most successful solutions in recommender systems. Content based method is based on items attributes. This method checks the features of users favourite items and then proposes the items which have the most similar characteristics with those items. Collaborative filtering method is based on the determination of similar items or similar users, which are called item-based and user-based collaborative filtering, respectively.In this paper we propose a hybrid method that integrates collaborative filtering and content-based methods. The proposed method can be viewed as user-based Collaborative filtering technique. However to find users with similar taste with active user, we used content features of the item under investigation to put more emphasis on users rating for similar items. In other words two users are similar if their ratings are similar on items that have similar context. This is achieved by assigning a weight to each rating when calculating the similarity of two users.We used movielens data set to access the performance of the proposed method in comparison with basic user-based collaborative filtering and other popular methods.
We describe a probabilistic framework for synthesizing control policies for general multi-robot systems, given environment and sensor models and a cost function. Decentralized, partially observable Markov decision processes (Dec-POMDPs) are a general model of decision processes where a team of agents must cooperate to optimize some objective (specified by a shared reward or cost function) in the presence of uncertainty, but where communication limitations mean that the agents cannot share their state, so execution must proceed in a decentralized fashion. While Dec-POMDPs are typically intractable to solve for real-world problems, recent research on the use of macro-actions in Dec-POMDPs has significantly increased the size of problem that can be practically solved as a Dec-POMDP. We describe this general model, and show how, in contrast to most existing methods that are specialized to a particular problem class, it can synthesize control policies that use whatever opportunities for coordination are present in the problem, while balancing off uncertainty in outcomes, sensor information, and information about other agents. We use three variations on a warehouse task to show that a single planner of this type can generate cooperative behavior using task allocation, direct communication, and signaling, as appropriate.
Unsupervised ranking faces one critical challenge in evaluation applications, that is, no ground truth is available. When PageRank and its variants show a good solution in related subjects, they are applicable only for ranking from link-structure data. In this work, we focus on unsupervised ranking from multi-attribute data which is also common in evaluation tasks. To overcome the challenge, we propose five essential meta-rules for the design and assessment of unsupervised ranking approaches: scale and translation invariance, strict monotonicity, linear/nonlinear capacities, smoothness, and explicitness of parameter size. These meta-rules are regarded as high level knowledge for unsupervised ranking tasks. Inspired by the works in [8] and [14], we propose a ranking principal curve (RPC) model, which learns a one-dimensional manifold function to perform unsupervised ranking tasks on multi-attribute observations. Furthermore, the RPC is modeled to be a cubic B\'ezier curve with control points restricted in the interior of a hypercube, thereby complying with all the five meta-rules to infer a reasonable ranking list. With control points as the model parameters, one is able to understand the learned manifold and to interpret the ranking list semantically. Numerical experiments of the presented RPC model are conducted on two open datasets of different ranking applications. In comparison with the state-of-the-art approaches, the new model is able to show more reasonable ranking lists.
In the last decade, scenario-based serious-games have become a main tool for learning new skills and capabilities. An important factor in the development of such systems is the overhead in time, cost and human resources to manually create the content for these scenarios. We focus on how to create content for scenarios in medical, military, commerce and gaming applications where maintaining the integrity and coherence of the content is integral for the system's success. To do so, we present an automatic method for generating content about everyday activities through combining computer science techniques with the crowd. We use the crowd in three basic ways: to capture a database of scenarios of everyday activities, to generate a database of likely replacements for specific events within that scenario, and to evaluate the resulting scenarios. We found that the generated scenarios were rated as reliable and consistent by the crowd when compared to the scenarios that were originally captured. We also compared the generated scenarios to those created by traditional planning techniques. We found that both methods were equally effective in generated reliable and consistent scenarios, yet the main advantages of our approach is that the content we generate is more varied and much easier to create. We have begun integrating this approach within a scenario-based training application for novice investigators within the law enforcement departments to improve their questioning skills.
As digital games continue to be explored as solutions to educational and behavioural challenges, the need for evaluation methodologies which support both the unique nature of the format and the need for comparison with other approaches continues to increase. In this workshop paper, a range of challenges are described related specifically to the case of cultural learning using digital games, in terms of how it may best be assessed, understood, and sustained through an iterative process supported by research. An evaluation framework is proposed, identifying metrics for reach and impact and their associated challenges, as well as presenting ethical considerations and the means to utilize evaluation outcomes within an iterative cycle, and to provide feedback to learners. Presenting as a case study a serious game from the Mobile Assistance for Social Inclusion and Empowerment of Immigrants with Persuasive Learning Technologies and Social Networks (MASELTOV) project, the use of the framework in the context of an integrative project is discussed, with emphasis on the need to view game-based learning as a blended component of the cultural learning process, rather than a standalone solution. The particular case of mobile gaming is also considered within this case study, providing a platform by which to deliver and update content in response to evaluation outcomes. Discussion reflects upon the general challenges related to the assessment of cultural learning, and behavioural change in more general terms, suggesting future work should address the need to provide sustainable, research-driven platforms for game-based learning content.
We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning method, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using a functional neighbourhood concept. In order to define the functional neighbourhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighbouring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Local Relational Features (FC-LRF) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-nearest neighbour (k-nn) and Support Vector Machine (SVM), are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62%-71% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40%-48%, for ten semantic categories.
Event recognition systems rely on properly engineered knowledge bases of event definitions to infer occurrences of events in time. The manual development of such knowledge is a tedious and error-prone task, thus event-based applications may benefit from automated knowledge construction techniques, such as Inductive Logic Programming (ILP), which combines machine learning with the declarative and formal semantics of First-Order Logic. However, learning temporal logical formalisms, which are typically utilized by logic-based Event Recognition systems is a challenging task, which most ILP systems cannot fully undertake. In addition, event-based data is usually massive and collected at different times and under various circumstances. Ideally, systems that learn from temporal data should be able to operate in an incremental mode, that is, revise prior constructed knowledge in the face of new evidence. Most ILP systems are batch learners, in the sense that in order to account for new evidence they have no alternative but to forget past knowledge and learn from scratch. Given the increased inherent complexity of ILP and the volumes of real-life temporal data, this results to algorithms that scale poorly. In this work we present an incremental method for learning and revising event-based knowledge, in the form of Event Calculus programs. The proposed algorithm relies on abductive-inductive learning and comprises a scalable clause refinement methodology, based on a compressive summarization of clause coverage in a stream of examples. We present an empirical evaluation of our approach on real and synthetic data from activity recognition and city transport applications.
Hospital readmission has become a critical metric of quality and cost of healthcare. Medicare anticipates that nearly $17 billion is paid out on the 20% of patients who are readmitted within 30 days of discharge. Although several interventions such as transition care management and discharge reengineering have been practiced in recent years, the effectiveness and sustainability depends on how well they can identify and target patients at high risk of rehospitalization. Based on the literature, most current risk prediction models fail to reach an acceptable accuracy level; none of them considers patient's history of readmission and impacts of patient attribute changes over time; and they often do not discriminate between planned and unnecessary readmissions. Tackling such drawbacks, we develop a new readmission metric based on administrative data that can identify potentially avoidable readmissions from all other types of readmission. We further propose a tree based classification method to estimate the predicted probability of readmission that can directly incorporate patient's history of readmission and risk factors changes over time. The proposed methods are validated with 2011-12 Veterans Health Administration data from inpatients hospitalized for heart failure, acute myocardial infarction, pneumonia, or chronic obstructive pulmonary disease in the State of Michigan. Results shows improved discrimination power compared to the literature (c-statistics>80%) and good calibration.
Although many algorithms for the multi-armed bandit problem are well-understood theoretically, empirical confirmation of their effectiveness is generally scarce. This paper presents a thorough empirical study of the most popular multi-armed bandit algorithms. Three important observations can be made from our results. Firstly, simple heuristics such as epsilon-greedy and Boltzmann exploration outperform theoretically sound algorithms on most settings by a significant margin. Secondly, the performance of most algorithms varies dramatically with the parameters of the bandit problem. Our study identifies for each algorithm the settings where it performs well, and the settings where it performs poorly. Thirdly, the algorithms' performance relative each to other is affected only by the number of bandit arms and the variance of the rewards. This finding may guide the design of subsequent empirical evaluations. In the second part of the paper, we turn our attention to an important area of application of bandit algorithms: clinical trials. Although the design of clinical trials has been one of the principal practical problems motivating research on multi-armed bandits, bandit algorithms have never been evaluated as potential treatment allocation strategies. Using data from a real study, we simulate the outcome that a 2001-2002 clinical trial would have had if bandit algorithms had been used to allocate patients to treatments. We find that an adaptive trial would have successfully treated at least 50% more patients, while significantly reducing the number of adverse effects and increasing patient retention. At the end of the trial, the best treatment could have still been identified with a high level of statistical confidence. Our findings demonstrate that bandit algorithms are attractive alternatives to current adaptive treatment allocation strategies.
In this paper we propose a structural parameter of CNF formulas and use it to identify instances of weighted MaxSAT and #SAT that can be solved in polynomial time. Given a CNF formula we say that a set of clauses is precisely satisfiable if there is some complete assignment satisfying these clauses only. Let the ps-value of the formula be the number of precisely satisfiable sets of clauses. Applying the notion of branch decompositions to CNF formulas and using ps-value as cut function, we define the ps-width of a formula. For a formula given with a decomposition of polynomial ps-width we show dynamic programming algorithms solving weighted MaxSAT and #SAT in polynomial time. Combining with results of 'Belmonte and Vatshelle, Graph classes with structured neighborhoods and algorithmic applications, Theor. Comput. Sci. 511: 54-65 (2013)' we get polynomial-time algorithms solving weighted MaxSAT and #SAT for some classes of structured CNF formulas. For example, we get $O(m^2(m + n)s)$ algorithms for formulas $F$ of $m$ clauses and $n$ variables and size $s$, if $F$ has a linear ordering of the variables and clauses such that for any variable $x$ occurring in clause $C$, if $x$ appears before $C$ then any variable between them also occurs in $C$, and if $C$ appears before $x$ then $x$ occurs also in any clause between them. Note that the class of incidence graphs of such formulas do not have bounded clique-width.
Sponsored search is an important monetization channel for search engines, in which an auction mechanism is used to select the ads shown to users and determine the prices charged from advertisers. There have been several pieces of work in the literature that investigate how to design an auction mechanism in order to optimize the revenue of the search engine. However, due to some unrealistic assumptions used, the practical values of these studies are not very clear. In this paper, we propose a novel \emph{game-theoretic machine learning} approach, which naturally combines machine learning and game theory, and learns the auction mechanism using a bilevel optimization framework. In particular, we first learn a Markov model from historical data to describe how advertisers change their bids in response to an auction mechanism, and then for any given auction mechanism, we use the learnt model to predict its corresponding future bid sequences. Next we learn the auction mechanism through empirical revenue maximization on the predicted bid sequences. We show that the empirical revenue will converge when the prediction period approaches infinity, and a Genetic Programming algorithm can effectively optimize this empirical revenue. Our experiments indicate that the proposed approach is able to produce a much more effective auction mechanism than several baselines.
The satisfiability problem for SPARQL patterns is undecidable in general, since the expressive power of SPARQL 1.0 is comparable with that of the relational algebra. The goal of this paper is to delineate the boundary of decidability of satisfiability in terms of the constraints allowed in filter conditions. The classes of constraints considered are bound-constraints, negated bound-constraints, equalities, nonequalities, constant-equalities, and constant-nonequalities. The main result of the paper can be summarized by saying that, as soon as inconsistent filter conditions can be formed, satisfiability is undecidable. The key insight in each case is to find a way to emulate the set difference operation. Undecidability can then be obtained from a known undecidability result for the algebra of binary relations with union, composition, and set difference. When no inconsistent filter conditions can be formed, satisfiability is efficiently decidable by simple checks on bound variables and on the use of literals. The paper also points out that satisfiability for the so-called `well-designed' patterns can be decided by a check on bound variables and a check for inconsistent filter conditions.
We perform two experiments with the aim to investigate the effects of negation on the combination of natural concepts. In the first experiment, we test the membership weights of a list of exemplars with respect to two concepts, e.g., {\it Fruits} and {\it Vegetables}, and their conjunction {\it Fruits And Vegetables}. In the second experiment, we test the membership weights of the same list of exemplars with respect to the same two concepts, but negating the second, e.g., {\it Fruits} and {\it Not Vegetables}, and again their conjunction {\it Fruits And Not Vegetables}. The collected data confirm existing results on conceptual combination, namely, they show dramatic deviations from the predictions of classical (fuzzy set) logic and probability theory. More precisely, they exhibit conceptual vagueness, gradeness of membership, overextension and double overextension of membership weights with respect to the given conjunctions. Then, we show that the quantum probability model in Fock space recently elaborated to model Hampton's data on concept conjunction (Hampton, 1988a) and disjunction (Hampton, 1988b) faithfully accords with the collected data. Our quantum-theoretic modeling enables to describe these non-classical effects in terms of genuine quantum effects, namely `contextuality', `superposition', `interference' and `emergence'. The obtained results confirm and strenghten the analysis in Aerts (2009a) and Sozzo (2014) on the identification of quantum aspects in experiments on conceptual vagueness. Our results can be inserted within the general research on the identification of quantum structures in cognitive and decision processes.
This paper investigates the impact of query topology on the difficulty of answering conjunctive queries in the presence of OWL 2 QL ontologies. Our first contribution is to clarify the worst-case size of positive existential (PE), non-recursive Datalog (NDL), and first-order (FO) rewritings for various classes of tree-like conjunctive queries, ranging from linear queries to bounded treewidth queries. Perhaps our most surprising result is a superpolynomial lower bound on the size of PE-rewritings that holds already for linear queries and ontologies of depth 2. More positively, we show that polynomial-size NDL-rewritings always exist for tree-shaped queries with a bounded number of leaves (and arbitrary ontologies), and for bounded treewidth queries paired with bounded depth ontologies. For FO-rewritings, we equate the existence of polysize rewritings with well-known problems in Boolean circuit complexity. As our second contribution, we analyze the computational complexity of query answering and establish tractability results (either NL- or LOGCFL-completeness) for a range of query-ontology pairs. Combining our new results with those from the literature yields a complete picture of the succinctness and complexity landscapes for the considered classes of queries and ontologies.
Hotspot detection aims at identifying subgroups in the observations that are unexpected, with respect to the some baseline information. For instance, in disease surveillance, the purpose is to detect sub-regions in spatiotemporal space, where the count of reported diseases (e.g. Cancer) is higher than expected, with respect to the population. The state-of-the-art method for this kind of problem is the Space-Time Scan Statistics (STScan), which exhaustively search the whole space through a sliding window looking for significant spatiotemporal clusters. STScan makes some restrictive assumptions about the distribution of data, the shape of the hotspots and the quality of data, which can be unrealistic for some nontraditional data sources. A novel methodology called EigenSpot is proposed where instead of an exhaustive search over the space, tracks the changes in a space-time correlation structure. Not only does the new approach presents much more computational efficiency, but also makes no assumption about the data distribution, hotspot shape or the data quality. The principal idea is that with the joint combination of abnormal elements in the principal spatial and the temporal singular vectors, the location of hotspots in the spatiotemporal space can be approximated. A comprehensive experimental evaluation, both on simulated and real data sets reveals the effectiveness of the proposed method.
Faces are a class of visual stimuli with unique significance, for a variety of reasons. They are ubiquitous throughout the course of a person's life, and face recognition is crucial for daily social interaction. Faces are also unlike any other stimulus class in terms of certain physical stimulus characteristics. Furthermore, faces have been empirically found to elicit certain characteristic behavioral phenomena, which are widely held to be evidence of "holistic" processing of faces. However, little is known about the neural mechanisms underlying such holistic face processing. In other words, for the processing of faces by the primate visual system, the input and output characteristics are relatively well known, but the internal neural computations are not. The main aim of this work is to further the fundamental understanding of what causes the visual processing of faces to be different from that of objects. In this computational modeling work, we show that a single factor - "neural tuning size" - is able to account for three key phenomena that are characteristic of face processing, namely the Composite Face Effect (CFE), Face Inversion Effect (FIE) and Whole-Part Effect (WPE). Our computational proof-of-principle provides specific neural tuning properties that correspond to the poorly-understood notion of holistic face processing, and connects these neural properties to psychophysical behavior. Overall, our work provides a unified and parsimonious theoretical account for the disparate empirical data on face-specific processing, deepening the fundamental understanding of face processing.
The semantics of determiner phrases, be they definite de- scriptions, indefinite descriptions or quantified noun phrases, is often as- sumed to be a fully solved question: common nouns are properties, and determiners are generalised quantifiers that apply to two predicates: the property corresponding to the common noun and the one corresponding to the verb phrase. We first present a criticism of this standard view. Firstly, the semantics of determiners does not follow the syntactical structure of the sentence. Secondly the standard interpretation of the indefinite article cannot ac- count for nominal sentences. Thirdly, the standard view misses the linguis- tic asymmetry between the two properties of a generalised quantifier. In the sequel, we propose a treatment of determiners and quantifiers as Hilbert terms in a richly typed system that we initially developed for lexical semantics, using a many sorted logic for semantical representations. We present this semantical framework called the Montagovian generative lexicon and show how these terms better match the syntactical structure and avoid the aforementioned problems of the standard approach. Hilbert terms rather differ from choice functions in that there is one polymorphic operator and not one operator per formula. They also open an intriguing connection between the logic for meaning assembly, the typed lambda calculus handling compositionality and the many-sorted logic for semantical representations. Furthermore epsilon terms naturally introduce type-judgements and confirm the claim that type judgment are a form of presupposition.
The search for binary sequences with a high figure of merit, known as the low autocorrelation binary sequence ($labs$}) problem, represents a formidable computational challenge. To mitigate the computational constraints of the problem, we consider solvers that accept odd values of sequence length $L$ and return solutions for skew-symmetric binary sequences only -- with the consequence that not all best solutions under this constraint will be optimal for each $L$. In order to improve both, the search for best merit factor $and$ the asymptotic runtime performance, we instrumented three stochastic solvers, the first two are state-of-the-art solvers that rely on variants of memetic and tabu search ($lssMAts$ and $lssRRts$), the third solver ($lssOrel$) organizes the search as a sequence of independent contiguous self-avoiding walk segments. By adapting a rigorous statistical methodology to performance testing of all three combinatorial solvers, experiments show that the solver with the best asymptotic average-case performance, $lssOrel\_8 = 0.000032*1.1504^L$, has the best chance of finding solutions that improve, as $L$ increases, figures of merit reported to date. The same methodology can be applied to engineering new $labs$ solvers that may return merit factors even closer to the conjectured asymptotic value of 12.3248.
We present a simple noise-robust margin-based active learning algorithm to find homogeneous (passing the origin) linear separators and analyze its error convergence when labels are corrupted by noise. We show that when the imposed noise satisfies the Tsybakov low noise condition (Mammen, Tsybakov, and others 1999; Tsybakov 2004) the algorithm is able to adapt to unknown level of noise and achieves optimal statistical rate up to poly-logarithmic factors. We also derive lower bounds for margin based active learning algorithms under Tsybakov noise conditions (TNC) for the membership query synthesis scenario (Angluin 1988). Our result implies lower bounds for the stream based selective sampling scenario (Cohn 1990) under TNC for some fairly simple data distributions. Quite surprisingly, we show that the sample complexity cannot be improved even if the underlying data distribution is as simple as the uniform distribution on the unit ball. Our proof involves the construction of a well separated hypothesis set on the d-dimensional unit ball along with carefully designed label distributions for the Tsybakov noise condition. Our analysis might provide insights for other forms of lower bounds as well.
Video Surveillance is a fast evolving field of research and development (R&D) driven by the urgent need for public security and safety (due to the growing threats of terrorism, vandalism, and anti-social behavior). Traditionally, surveillance systems are comprised of two components - video cameras distributed over the guarded area and human observer watching and analyzing the incoming video. Explosive growth of installed cameras and limited human operator's ability to process the delivered video content raise an urgent demand for developing surveillance systems with human like cognitive capabilities, that is - Cognitive surveillance systems. The growing interest in this issue is testified by the tens of workshops, symposiums and conferences held over the world each year. The IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS) is certainly one of them. However, for unknown reasons, the term Cognitive Surveillance does never appear among its topics. As to me, the explanation for this is simple - the complexity and the indefinable nature of the term "Cognition". In this paper, I am trying to resolve the problem providing a novel definition of cognition equally suitable for biological as well as technological applications. I hope my humble efforts will be helpful.
Methods for combining predictions from different models in a supervised learning setting must somehow estimate/predict the quality of a model's predictions at unknown future inputs. Many of these methods (often implicitly) make the assumption that the test inputs are identical to the training inputs, which is seldom reasonable. By failing to take into account that prediction will generally be harder for test inputs that did not occur in the training set, this leads to the selection of too complex models. Based on a novel, unbiased expression for KL divergence, we propose XAIC and its special case FAIC as versions of AIC intended for prediction that use different degrees of knowledge of the test inputs. Both methods substantially differ from and may outperform all the known versions of AIC even when the training and test inputs are iid, and are especially useful for deterministic inputs and under covariate shift. Our experiments on linear models suggest that if the test and training inputs differ substantially, then XAIC and FAIC predictively outperform AIC, BIC and several other methods including Bayesian model averaging.
We present a physics inspired heuristic method for solving combinatorial optimization problems. Our approach is specifically motivated by the desire to avoid trapping in metastable local minima- a common occurrence in hard problems with multiple extrema. Our method involves (i) coupling otherwise independent simulations of a system ("replicas") via geometrical distances as well as (ii) probabilistic inference applied to the solutions found by individual replicas. The {\it ensemble} of replicas evolves as to maximize the inter-replica correlation while simultaneously minimize the local intra-replica cost function (e.g., the total path length in the Traveling Salesman Problem within each replica). We demonstrate how our method improves the performance of rudimentary local optimization schemes long applied to the NP hard Traveling Salesman Problem. In particular, we apply our method to the well-known "$k$-opt" algorithm and examine two particular cases- $k=2$ and $k=3$. With the aid of geometrical coupling alone, we are able to determine for the optimum tour length on systems up to $280$ cities (an order of magnitude larger than the largest systems typically solved by the bare $k=3$ opt). The probabilistic replica-based inference approach improves $k-opt$ even further and determines the optimal solution of a problem with $318$ cities and find tours whose total length is close to that of the optimal solutions for other systems with a larger number of cities.
It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios. Efficient implementations of score-based structure learning benefit from past and current research in optimisation theory, which can be adapted to the task by using the network score as the objective function to maximise. This is not true for approaches based on conditional independence tests, called constraint-based learning algorithms. The only optimisation in widespread use, backtracking, leverages the symmetries implied by the definitions of neighbourhood and Markov blanket. In this paper we illustrate how backtracking is implemented in recent versions of the bnlearn R package, and how it degrades the stability of Bayesian network structure learning for little gain in terms of speed. As an alternative, we describe a software architecture and framework that can be used to parallelise constraint-based structure learning algorithms (also implemented in bnlearn) and we demonstrate its performance using four reference networks and two real-world data sets from genetics and systems biology. We show that on modern multi-core or multiprocessor hardware parallel implementations are preferable over backtracking, which was developed when single-processor machines were the norm.
In this paper, the Dempster-Shafer method is employed as the theoretical basis for creating data classification systems. Testing is carried out using three popular (multiple attribute) benchmark datasets that have two, three and four classes. In each case, a subset of the available data is used for training to establish thresholds, limits or likelihoods of class membership for each attribute, and hence create mass functions that establish probability of class membership for each attribute of the test data. Classification of each data item is achieved by combination of these probabilities via Dempster's Rule of Combination. Results for the first two datasets show extremely high classification accuracy that is competitive with other popular methods. The third dataset is non-numerical and difficult to classify, but good results can be achieved provided the system and mass functions are designed carefully and the right attributes are chosen for combination. In all cases the Dempster-Shafer method provides comparable performance to other more popular algorithms, but the overhead of generating accurate mass functions increases the complexity with the addition of new attributes. Overall, the results suggest that the D-S approach provides a suitable framework for the design of classification systems and that automating the mass function design and calculation would increase the viability of the algorithm for complex classification problems.
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach for training GP-LVMs is based on maximum likelihood, where the latent projection variables are maximized over rather than integrated out. In this paper we present a Bayesian method for training GP-LVMs by introducing a non-standard variational inference framework that allows to approximately integrate out the latent variables and subsequently train a GP-LVM by maximizing an analytic lower bound on the exact marginal likelihood. We apply this method for learning a GP-LVM from iid observations and for learning non-linear dynamical systems where the observations are temporally correlated. We show that a benefit of the variational Bayesian procedure is its robustness to overfitting and its ability to automatically select the dimensionality of the nonlinear latent space. The resulting framework is generic, flexible and easy to extend for other purposes, such as Gaussian process regression with uncertain inputs and semi-supervised Gaussian processes. We demonstrate our method on synthetic data and standard machine learning benchmarks, as well as challenging real world datasets, including high resolution video data.
Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.
We consider the problem of learning the canonical parameters specifying an undirected graphical model (Markov random field) from the mean parameters. For graphical models representing a minimal exponential family, the canonical parameters are uniquely determined by the mean parameters, so the problem is feasible in principle. The goal of this paper is to investigate the computational feasibility of this statistical task. Our main result shows that parameter estimation is in general intractable: no algorithm can learn the canonical parameters of a generic pair-wise binary graphical model from the mean parameters in time bounded by a polynomial in the number of variables (unless RP = NP). Indeed, such a result has been believed to be true (see the monograph by Wainwright and Jordan (2008)) but no proof was known. Our proof gives a polynomial time reduction from approximating the partition function of the hard-core model, known to be hard, to learning approximate parameters. Our reduction entails showing that the marginal polytope boundary has an inherent repulsive property, which validates an optimization procedure over the polytope that does not use any knowledge of its structure (as required by the ellipsoid method and others).
In 2013 Intel introduced the Xeon Phi, a new parallel co-processor board. The Xeon Phi is a cache-coherent many-core shared memory architecture claiming CPU-like versatility, programmability, high performance, and power efficiency. The first published micro-benchmark studies indicate that many of Intel's claims appear to be true. The current paper is the first study on the Phi of a complex artificial intelligence application. It contains an open source MCTS application for playing tournament quality Go (an oriental board game). We report the first speedup figures for up to 240 parallel threads on a real machine, allowing a direct comparison to previous simulation studies. After a substantial amount of work, we observed that performance scales well up to 32 threads, largely confirming previous simulation results of this Go program, although the performance surprisingly deteriorates between 32 and 240 threads. Furthermore, we report (1) unexpected performance anomalies between the Xeon Phi and Xeon CPU for small problem sizes and small numbers of threads, and (2) that performance is sensitive to scheduling choices. Achieving good performance on the Xeon Phi for complex programs is not straightforward; it requires a deep understanding of (1) search patterns, (2) of scheduling, and (3) of the architecture and its many cores and caches. In practice, the Xeon Phi is less straightforward to program for than originally envisioned by Intel.
This paper introduces a multi-period inspector scheduling problem (MPISP), which is a new variant of the multi-trip vehicle routing problem with time windows (VRPTW). In the MPISP, each inspector is scheduled to perform a route in a given multi-period planning horizon. At the end of each period, each inspector is not required to return to the depot but has to stay at one of the vertices for recuperation. If the remaining time of the current period is insufficient for an inspector to travel from his/her current vertex $A$ to a certain vertex B, he/she can choose either waiting at vertex A until the start of the next period or traveling to a vertex C that is closer to vertex B. Therefore, the shortest transit time between any vertex pair is affected by the length of the period and the departure time. We first describe an approach of computing the shortest transit time between any pair of vertices with an arbitrary departure time. To solve the MPISP, we then propose several local search operators adapted from classical operators for the VRPTW and integrate them into a tabu search framework. In addition, we present a constrained knapsack model that is able to produce an upper bound for the problem. Finally, we evaluate the effectiveness of our algorithm with extensive experiments based on a set of test instances. Our computational results indicate that our approach generates high-quality solutions.
Local field potentials (LFPs) sampled with extracellular electrodes are frequently used as a measure of population neuronal activity. However, relating such measurements to underlying neuronal behaviour and connectivity is non-trivial. To help study this link, we developed the Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX). We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons. We then developed VERTEX as an easy-to-use Matlab tool for simulating LFPs from large populations (>100 000 neurons). A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue. Our model, with virtual electrodes placed anywhere in 3D, allows direct comparisons with the in vitro recording setup. We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.
We consider \textit{anytime} linear prediction in the common machine learning setting, where features are in groups that have costs. We achieve anytime (or interruptible) predictions by sequencing the computation of feature groups and reporting results using the computed features at interruption. We extend Orthogonal Matching Pursuit (OMP) and Forward Regression (FR) to learn the sequencing greedily under this group setting with costs. We theoretically guarantee that our algorithms achieve near-optimal linear predictions at each budget when a feature group is chosen. With a novel analysis of OMP, we improve its theoretical bound to the same strength as that of FR. In addition, we develop a novel algorithm that consumes cost $4B$ to approximate the optimal performance of \textit{any} cost $B$, and prove that with cost less than $4B$, such an approximation is impossible. To our knowledge, these are the first anytime bounds at \textit{all} budgets. We test our algorithms on two real-world data-sets and evaluate them in terms of anytime linear prediction performance against cost-weighted Group Lasso and alternative greedy algorithms.
Active security is mainly concerned with performing one or more security functions when a host in a communication network is subject to an attack. Such security functions include appropriate actions against attackers. To properly afford active security actions a set of software subsystems should be integrated together so that they can automatically detect and appropriately address any vulnerability in the underlying network. This work presents integrated model for active security response model. The proposed model introduces Active Response Mechanism (ARM) for tracing anonymous attacks in the network back to their source. This work is motivated by the increased frequency and sophistication of denial-of-service attacks and by the difficulty in tracing packets with incorrect, or "spoofed", source addresses. This paper presents within the proposed model two tracing approaches based on: 1.Sleepy Watermark Tracing (SWT) for unauthorized access attacks. 2.Probabilistic Packet Marking (PPM) in the network for Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. On the basis of the proposed model a cooperative network security tools such as firewall, intrusion detection system with IP tracing mechanism has been designed for taking a rapid active response against real IPs for attackers. The proposed model is able to detect network vulnerabilities, trace attack source IP and reconfigure the attacked subnetworks.
To analyze high-dimensional systems, many fields in science and engineering rely on high-level descriptions, sometimes called "macrostates," "coarse-grainings," or "effective theories". Examples of such descriptions include the thermodynamic properties of a large collection of point particles undergoing reversible dynamics, the variables in a macroeconomic model describing the individuals that participate in an economy, and the summary state of a cell composed of a large set of biochemical networks. Often these high-level descriptions are constructed without considering the ultimate reason for needing them in the first place. Here, we formalize and quantify one such purpose: the need to predict observables of interest concerning the high-dimensional system with as high accuracy as possible, while minimizing the computational cost of doing so. The resulting State Space Compression (SSC) framework provides a guide for how to solve for the {optimal} high-level description of a given dynamical system, rather than constructing it based on human intuition alone. In this preliminary report, we introduce SSC, and illustrate it with several information-theoretic quantifications of "accuracy", all with different implications for the optimal compression. We also discuss some other possible applications of SSC beyond the goal of accurate prediction. These include SSC as a measure of the complexity of a dynamical system, and as a way to quantify information flow between the scales of a system.
Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of 2-, 4-, and 8-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can in principle be scaled to larger number of qubits, and may provide a new route to accelerate machine learning.
Probabilistic graphical models such as Bayesian Networks are one of the most powerful structures known by the Computer Science community for deriving probabilistic inferences. However, modern cognitive psychology has revealed that human decisions could not follow the rules of classical probability theory, because humans cannot process large amounts of data in order to make judgements. Consequently, the inferences performed are based on limited data coupled with several heuristics, leading to violations of the law of total probability. This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making. Quantum probability theory was developed in order to accommodate the paradoxical findings that the classical theory could not explain. Recent findings in cognitive psychology revealed that quantum probability can fully describe human decisions in an elegant framework. Their findings suggest that, before taking a decision, human thoughts are seen as superposed waves that can interfere with each other, influencing the final decision. In this work, we propose a new Bayesian Network based on the psychological findings of cognitive scientists. We made experiments with two very well known Bayesian Networks from the literature. The results obtained revealed that the quantum like Bayesian Network can affect drastically the probabilistic inferences, specially when the levels of uncertainty of the network are very high (no pieces of evidence observed). When the levels of uncertainty are very low, then the proposed quantum like network collapses to its classical counterpart.
Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.
The Turing machine, as it was presented by Turing himself, models the calculations done by a person. This means that we can compute whatever any Turing machine can compute, and therefore we are Turing complete. The question addressed here is why, Why are we Turing complete? Being Turing complete also means that somehow our brain implements the function that a universal Turing machine implements. The point is that evolution achieved Turing completeness, and then the explanation should be evolutionary, but our explanation is mathematical. The trick is to introduce a mathematical theory of problems, under the basic assumption that solving more problems provides more survival opportunities. So we build a problem theory by fusing set and computing theories. Then we construct a series of resolvers, where each resolver is defined by its computing capacity, that exhibits the following property: all problems solved by a resolver are also solved by the next resolver in the series if certain condition is satisfied. The last of the conditions is to be Turing complete. This series defines a resolvers hierarchy that could be seen as a framework for the evolution of cognition. Then the answer to our question would be: to solve most problems. By the way, the problem theory defines adaptation, perception, and learning, and it shows that there are just three ways to resolve any problem: routine, trial, and analogy. And, most importantly, this theory demonstrates how problems can be used to found mathematics and computing on biology.
A database of fetal heart rate (FHR) time series measured from 7221 patients during labor is analyzed with the aim of learning the types of features of these recordings that are informative of low cord pH. Our 'highly comparative' analysis involves extracting over 9000 time-series analysis features from each FHR time series, including measures of autocorrelation, entropy, distribution, and various model fits. This diverse collection of features was developed in previous work, and is publicly available. We describe five features that most accurately classify a balanced training set of 59 'low pH' and 59 'normal pH' FHR recordings. We then describe five of the features with the strongest linear correlation to cord pH across the full dataset of FHR time series. The features identified in this work may be used as part of a system for guiding intervention during labor in future. This work successfully demonstrates the utility of comparing across a large, interdisciplinary literature on time-series analysis to automatically contribute new scientific results for specific biomedical signal processing challenges.
The FO Model Counting problem (FOMC) is the following: given a sentence $\Phi$ in FO and a number $n$, compute the number of models of $\Phi$ over a domain of size $n$; the Weighted variant (WFOMC) generalizes the problem by associating a weight to each tuple and defining the weight of a model to be the product of weights of its tuples. In this paper we study the complexity of the symmetric WFOMC, where all tuples of a given relation have the same weight. Our motivation comes from an important application, inference in Knowledge Bases with soft constraints, like Markov Logic Networks, but the problem is also of independent theoretical interest. We study both the data complexity, and the combined complexity of FOMC and WFOMC. For the data complexity we prove the existence of an FO$^{3}$ formula for which FOMC is #P$_1$-complete, and the existence of a Conjunctive Query for which WFOMC is #P$_1$-complete. We also prove that all $\gamma$-acyclic queries have polynomial time data complexity. For the combined complexity, we prove that, for every fragment FO$^{k}$, $k\geq 2$, the combined complexity of FOMC (or WFOMC) is #P-complete.
Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call "fooling images" (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.
Joint attention is a core, early-developing form of social interaction. It is based on our ability to discriminate the third party objects that other people are looking at. While it has been shown that people can accurately determine whether another person is looking directly at them versus away, little is known about human ability to discriminate a third person gaze directed towards objects that are further away, especially in unconstraint cases where the looker can move her head and eyes freely. In this paper we address this question by jointly exploring human psychophysics and a cognitively motivated computer vision model, which can detect the 3D direction of gaze from 2D face images. The synthesis of behavioral study and computer vision yields several interesting discoveries. (1) Human accuracy of discriminating targets 8{\deg}-10{\deg} of visual angle apart is around 40% in a free looking gaze task; (2) The ability to interpret gaze of different lookers vary dramatically; (3) This variance can be captured by the computational model; (4) Human outperforms the current model significantly. These results collectively show that the acuity of human joint attention is indeed highly impressive, given the computational challenge of the natural looking task. Moreover, the gap between human and model performance, as well as the variability of gaze interpretation across different lookers, require further understanding of the underlying mechanisms utilized by humans for this challenging task.
Human-robot interaction can be divided into two categories based on the physical distance between the human and robot: remote and proximal. In proximal interaction, the human and robot often engage in close coordination; in remote interaction, the human and robot are less coupled due to communication constraints. As a result, providing automation for the robot in remote interaction becomes more important. Thus far, human factor studies on automation in remote human-robot interaction have been restricted to various forms of supervision, in which the robot is essentially being used as a smart mobile manipulation platform with sensing capabilities. In this paper, we investigate the incorporation of general planning capability into the robot to facilitate peer-to-peer human-robot teaming, in which the human and robot are viewed as teammates that are physically separated. The human and robot share the same global goal and collaborate to achieve it. Note that humans may feel uncomfortable at such robot autonomy, which can potentially reduce teaming performance. One important difference between peer-to-peer teaming and supervised teaming is that an autonomous robot in peer-to-peer teaming can achieve the goal alone when the task information is completely specified. However, incompleteness often exists, which implies information asymmetry. While information asymmetry can be desirable sometimes, it may also lead to the robot choosing improper actions that negatively influence the teaming performance. We aim to investigate the various trade-offs, e.g., mental workload and situation awareness, between these two types of remote human-robot teaming.
Halpern and Pearl introduced a definition of actual causality; Eiter and Lukasiewicz showed that computing whether X=x is a cause of Y=y is NP-complete in binary models (where all variables can take on only two values) and\ Sigma_2^P-complete in general models. In the final version of their paper, Halpern and Pearl slightly modified the definition of actual cause, in order to deal with problems pointed by Hopkins and Pearl. As we show, this modification has a nontrivial impact on the complexity of computing actual cause. To characterize the complexity, a new family D_k^P, k= 1, 2, 3, ..., of complexity classes is introduced, which generalizes the class DP introduced by Papadimitriou and Yannakakis (DP is just D_1^P). %joe2 %We show that the complexity of computing causality is $\D_2$-complete %under the new definition. Chockler and Halpern \citeyear{CH04} extended the We show that the complexity of computing causality under the updated definition is $D_2^P$-complete. Chockler and Halpern extended the definition of causality by introducing notions of responsibility and blame. The complexity of determining the degree of responsibility and blame using the original definition of causality was completely characterized. Again, we show that changing the definition of causality affects the complexity, and completely characterize it using the updated definition.
Mastering the game of Go has remained a long standing challenge to the field of AI. Modern computer Go systems rely on processing millions of possible future positions to play well, but intuitively a stronger and more 'humanlike' way to play the game would be to rely on pattern recognition abilities rather then brute force computation. Following this sentiment, we train deep convolutional neural networks to play Go by training them to predict the moves made by expert Go players. To solve this problem we introduce a number of novel techniques, including a method of tying weights in the network to 'hard code' symmetries that are expect to exist in the target function, and demonstrate in an ablation study they considerably improve performance. Our final networks are able to achieve move prediction accuracies of 41.1% and 44.4% on two different Go datasets, surpassing previous state of the art on this task by significant margins. Additionally, while previous move prediction programs have not yielded strong Go playing programs, we show that the networks trained in this work acquired high levels of skill. Our convolutional neural networks can consistently defeat the well known Go program GNU Go, indicating it is state of the art among programs that do not use Monte Carlo Tree Search. It is also able to win some games against state of the art Go playing program Fuego while using a fraction of the play time. This success at playing Go indicates high level principles of the game were learned.
Causal models defined in terms of structural equations have proved to be quite a powerful way of representing knowledge regarding causality. However, a number of authors have given examples that seem to show that the Halpern-Pearl (HP) definition of causality gives intuitively unreasonable answers. Here it is shown that, for each of these examples, we can give two stories consistent with the description in the example, such that intuitions regarding causality are quite different for each story. By adding additional variables, we can disambiguate the stories. Moreover, in the resulting causal models, the HP definition of causality gives the intuitively correct answer. It is also shown that, by adding extra variables, a modification to the original HP definition made to deal with an example of Hopkins and Pearl may not be necessary. Given how much can be done by adding extra variables, there might be a concern that the notion of causality is somewhat unstable. Can adding extra variables in a "conservative" way (i.e., maintaining all the relations between the variables in the original model) cause the answer to the question "Is X=x a cause of Y=y" to alternate between "yes" and "no"? It is shown that we can have such alternation infinitely often, but if we take normality into consideration, we cannot. Indeed, under appropriate normality assumptions. adding an extra variable can change the answer from "yes" to "no", but after that, it cannot cannot change back to "yes".
Dropout is a simple but effective technique for learning in neural networks and other settings. A sound theoretical understanding of dropout is needed to determine when dropout should be applied and how to use it most effectively. In this paper we continue the exploration of dropout as a regularizer pioneered by Wager, et.al. We focus on linear classification where a convex proxy to the misclassification loss (i.e. the logistic loss used in logistic regression) is minimized. We show: (a) when the dropout-regularized criterion has a unique minimizer, (b) when the dropout-regularization penalty goes to infinity with the weights, and when it remains bounded, (c) that the dropout regularization can be non-monotonic as individual weights increase from 0, and (d) that the dropout regularization penalty may not be convex. This last point is particularly surprising because the combination of dropout regularization with any convex loss proxy is always a convex function. In order to contrast dropout regularization with $L_2$ regularization, we formalize the notion of when different sources are more compatible with different regularizers. We then exhibit distributions that are provably more compatible with dropout regularization than $L_2$ regularization, and vice versa. These sources provide additional insight into how the inductive biases of dropout and $L_2$ regularization differ. We provide some similar results for $L_1$ regularization.
The process of multiple criteria decision making (MCDM) is of determining the best choice among all of the probable alternatives. The problem of supplier selection on which decision maker has usually vague and imprecise knowledge is a typical example of multi criteria group decision-making problem. The conventional crisp techniques has not much effective for solving MCDM problems because of imprecise or fuzziness nature of the linguistic assessments. To find the exact values for MCDM problems is both difficult and impossible in more cases in real world. So, it is more reasonable to consider the values of alternatives according to the criteria as single valued neutrosophic sets (SVNS). This paper deal with the technique for order preference by similarity to ideal solution (TOPSIS) approach and extend the TOPSIS method to MCDM problem with single valued neutrosophic information. The value of each alternative and the weight of each criterion are characterized by single valued neutrosophic numbers. Here, the importance of criteria and alternatives is identified by aggregating individual opinions of decision makers (DMs) via single valued neutrosophic weighted averaging (IFWA) operator. The proposed method is, easy use, precise and practical for solving MCDM problem with single valued neutrosophic data. Finally, to show the applicability of the developed method, a numerical experiment for supplier choice is given as an application of single valued neutrosophic TOPSIS method at end of this paper.
We investigate modal logics of high probability having two unary modal operators: an operator $K$ expressing probabilistic certainty and an operator $B$ expressing probability exceeding a fixed rational threshold $c\geq\frac 12$. Identifying knowledge with the former and belief with the latter, we may think of $c$ as the agent's betting threshold, which leads to the motto "belief is willingness to bet." The logic $\mathsf{KB.5}$ for $c=\frac 12$ has an $\mathsf{S5}$ $K$ modality along with a sub-normal $B$ modality that extends the minimal modal logic $\mathsf{EMND45}$ by way of four schemes relating $K$ and $B$, one of which is a complex scheme arising out of a theorem due to Scott. Lenzen was the first to use Scott's theorem to show that a version of this logic is sound and complete for the probability interpretation. We reformulate Lenzen's results and present them here in a modern and accessible form. In addition, we introduce a new epistemic neighborhood semantics that will be more familiar to modern modal logicians. Using Scott's theorem, we provide the Lenzen-derivative properties that must be imposed on finite epistemic neighborhood models so as to guarantee the existence of a probability measure respecting the neighborhood function in the appropriate way for threshold $c=\frac 12$. This yields a link between probabilistic and modal neighborhood semantics that we hope will be of use in future work on modal logics of qualitative probability. We leave open the question of which properties must be imposed on finite epistemic neighborhood models so as to guarantee existence of an appropriate probability measure for thresholds $c\neq\frac 12$.
While influence maximization in social networks has been studied extensively in computer science community for the last decade the focus has been on the progressive influence models, such as independent cascade (IC) and Linear threshold (LT) models, which cannot capture the reversibility of choices. In this paper, we present the Heat Conduction (HC) model which is a non-progressive influence model with real-world interpretations. We show that HC unifies, generalizes, and extends the existing nonprogressive models, such as the Voter model [1] and non-progressive LT [2]. We then prove that selecting the optimal seed set of influential nodes is NP-hard for HC but by establishing the submodularity of influence spread, we can tackle the influence maximization problem with a scalable and provably near-optimal greedy algorithm. We are the first to present a scalable solution for influence maximization under nonprogressive LT model, as a special case of the HC model. In sharp contrast to the other greedy influence maximization methods, our fast and efficient C2GREEDY algorithm benefits from two analytically computable steps: closed-form computation for finding the influence spread as well as the greedy seed selection. Through extensive experiments on several large real and synthetic networks, we show that C2GREEDY outperforms the state-of-the-art methods, in terms of both influence spread and scalability.
In the recent decade, with the enormous growth of digital content in internet and databases, sentiment analysis has received more and more attention between information retrieval and natural language processing researchers. Sentiment analysis aims to use automated tools to detect subjective information from reviews. One of the main challenges in sentiment analysis is feature selection. Feature selection is widely used as the first stage of analysis and classification tasks to reduce the dimension of problem, and improve speed by the elimination of irrelevant and redundant features. Up to now as there are few researches conducted on feature selection in sentiment analysis, there are very rare works for Persian sentiment analysis. This paper considers the problem of sentiment classification using different feature selection methods for online customer reviews in Persian language. Three of the challenges of Persian text are using of a wide variety of declensional suffixes, different word spacing and many informal or colloquial words. In this paper we study these challenges by proposing a model for sentiment classification of Persian review documents. The proposed model is based on lemmatization and feature selection and is employed Naive Bayes algorithm for classification. We evaluate the performance of the model on a manually gathered collection of cellphone reviews, where the results show the effectiveness of the proposed approaches.
In this work, we propose an abductive framework for biosignal interpretation, based on the concept of Temporal Abstraction Patterns. A temporal abstraction pattern defines an abstraction relation between an observation hypothesis and a set of observations constituting its evidence support. New observations are generated abductively from any subset of the evidence of a pattern, building an abstraction hierarchy of observations in which higher levels contain those observations with greater interpretative value of the physiological processes underlying a given signal. Non-monotonic reasoning techniques have been applied to this model in order to find the best interpretation of a set of initial observations, permitting even to correct these observations by removing, adding or modifying them in order to make them consistent with the available domain knowledge. Some preliminary experiments have been conducted to apply this framework to a well known and bounded problem: the QRS detection on ECG signals. The objective is not to provide a new better QRS detector, but to test the validity of an abductive paradigm. These experiments show that a knowledge base comprising just a few very simple rhythm abstraction patterns can enhance the results of a state of the art algorithm by significantly improving its detection F1-score, besides proving the ability of the abductive framework to correct both sensitivity and specificity failures.
Belief revision of knowledge bases represented by a set of sentences in a given logic has been extensively studied but for specific logics, mainly propositional, and also recently Horn and description logics. Here, we propose to generalize this operation from a model-theoretic point of view, by defining revision in an abstract model theory known under the name of satisfaction systems. In this framework, we generalize to any satisfaction systems the characterization of the well known AGM postulates given by Katsuno and Mendelzon for propositional logic in terms of minimal change among interpretations. Moreover, we study how to define revision, satisfying the AGM postulates, from relaxation notions that have been first introduced in description logics to define dissimilarity measures between concepts, and the consequence of which is to relax the set of models of the old belief until it becomes consistent with the new pieces of knowledge. We show how the proposed general framework can be instantiated in different logics such as propositional, first-order, description and Horn logics. In particular for description logics, we introduce several concrete relaxation operators tailored for the description logic $\ALC{}$ and its fragments $\EL{}$ and $\ELext{}$, discuss their properties and provide some illustrative examples.
Enforcing local consistencies in cost function networks is performed by applying so-called Equivalent Preserving Transformations (EPTs) to the cost functions. As EPTs transform the cost functions, they may break the property that was making local consistency enforcement tractable on a global cost function. A global cost function is called tractable projection-safe when applying an EPT to it is tractable and does not break the tractability property. In this paper, we prove that depending on the size r of the smallest scopes used for performing EPTs, the tractability of global cost functions can be preserved (r = 0) or destroyed (r > 1). When r = 1, the answer is indefinite. We show that on a large family of cost functions, EPTs can be computed via dynamic programming-based algorithms, leading to tractable projection-safety. We also show that when a global cost function can be decomposed into a Berge acyclic network of bounded arity cost functions, soft local consistencies such as soft Directed or Virtual Arc Consistency can directly emulate dynamic programming. These different approaches to decomposable cost functions are then embedded in a solver for extensive experiments that confirm the feasibility and efficiency of our proposal.
Sentiment analysis on user reviews helps to keep track of user reactions towards products, and make advices to users about what to buy. State-of-the-art review-level sentiment classification techniques could give pretty good precisions of above 90%. However, current phrase-level sentiment analysis approaches might only give sentiment polarity labelling precisions of around 70%~80%, which is far from satisfaction and restricts its application in many practical tasks. In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-level and review-level sentiment analysis. We investigate the inconsistency between the numerical star ratings and the sentiment orientation of textual user reviews. Although they have long been treated as identical, which serves as a basic assumption in previous work, we find that this assumption is not necessarily true. We further propose to leverage the results of review-level sentiment classification to boost the performance of phrase-level polarity labelling using a novel constrained convex optimization framework. Besides, the framework is capable of integrating various kinds of information sources and heuristics, while giving the global optimal solution due to its convexity. Experimental results on both English and Chinese reviews show that our framework achieves high labelling precisions of up to 89%, which is a significant improvement from current approaches.
Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.
Multiple hypothesis testing is a significant problem in nearly all neuroimaging studies. In order to correct for this phenomena, we require a reliable estimate of the Family-Wise Error Rate (FWER). The well known Bonferroni correction method, while simple to implement, is quite conservative, and can substantially under-power a study because it ignores dependencies between test statistics. Permutation testing, on the other hand, is an exact, non-parametric method of estimating the FWER for a given $\alpha$-threshold, but for acceptably low thresholds the computational burden can be prohibitive. In this paper, we show that permutation testing in fact amounts to populating the columns of a very large matrix ${\bf P}$. By analyzing the spectrum of this matrix, under certain conditions, we see that ${\bf P}$ has a low-rank plus a low-variance residual decomposition which makes it suitable for highly sub--sampled --- on the order of $0.5\%$ --- matrix completion methods. Based on this observation, we propose a novel permutation testing methodology which offers a large speedup, without sacrificing the fidelity of the estimated FWER. Our evaluations on four different neuroimaging datasets show that a computational speedup factor of roughly $50\times$ can be achieved while recovering the FWER distribution up to very high accuracy. Further, we show that the estimated $\alpha$-threshold is also recovered faithfully, and is stable.
The proliferation of heterogeneous data sources of semantic knowledge base intensifies the need of an automatic instance matching technique. However, the efficiency of instance matching is often influenced by the weight of a property associated to instances. Automatic weight generation is a non-trivial, however an important task in instance matching technique. Therefore, identifying an appropriate metric for generating weight for a property automatically is nevertheless a formidable task. In this paper, we investigate an approach of generating weights automatically by considering hypotheses: (1) the weight of a property is directly proportional to the ratio of the number of its distinct values to the number of instances contain the property, and (2) the weight is also proportional to the ratio of the number of distinct values of a property to the number of instances in a training dataset. The basic intuition behind the use of our approach is the classical theory of information content that infrequent words are more informative than frequent ones. Our mathematical model derives a metric for generating property weights automatically, which is applied in instance matching system to produce re-conciliated instances efficiently. Our experiments and evaluations show the effectiveness of our proposed metric of automatic weight generation for properties in an instance matching technique.
Monaural source separation is important for many real world applications. It is challenging because, with only a single channel of information available, without any constraints, an infinite number of solutions are possible. In this paper, we explore joint optimization of masking functions and deep recurrent neural networks for monaural source separation tasks, including monaural speech separation, monaural singing voice separation, and speech denoising. The joint optimization of the deep recurrent neural networks with an extra masking layer enforces a reconstruction constraint. Moreover, we explore a discriminative criterion for training neural networks to further enhance the separation performance. We evaluate the proposed system on the TSP, MIR-1K, and TIMIT datasets for speech separation, singing voice separation, and speech denoising tasks, respectively. Our approaches achieve 2.30--4.98 dB SDR gain compared to NMF models in the speech separation task, 2.30--2.48 dB GNSDR gain and 4.32--5.42 dB GSIR gain compared to existing models in the singing voice separation task, and outperform NMF and DNN baselines in the speech denoising task.
This book discusses computational curiosity, from the psychology of curiosity to the computational models of curiosity, and then showcases several interesting applications of computational curiosity. A brief overview of the book is given as follows. Chapter 1 discusses the underpinnings of curiosity in human beings, including the major categories of curiosity, curiosity-related emotions and behaviors, and the benefits of curiosity. Chapter 2 reviews the arousal theories of curiosity in psychology and summarizes a general two-step process model for computational curiosity. Base on the perspective of the two-step process model, Chapter 3 reviews and analyzes some of the traditional computational models of curiosity. Chapter 4 introduces a novel generic computational model of curiosity, which is developed based on the arousal theories of curiosity. After the discussion of computational models of curiosity, we outline the important applications where computational curiosity may bring significant impacts in Chapter 5. Chapter 6 discusses the application of the generic computational model of curiosity in a machine learning framework. Chapter 7 discusses the application of the generic computational model of curiosity in a recommender system. In Chapter 8 and Chapter 9, the generic computational model of curiosity is studied in two types of pedagogical agents. In Chapter 8, a curious peer learner is studied. It is a non-player character that aims to provide a believable virtual learning environment for users. In Chapter 9, a curious learning companion is studied. It aims to enhance users' learning experience through providing meaningful interactions with them. Chapter 10 discusses open questions in the research field of computation curiosity.
Recent years have seen the development of methods for multiagent planning under uncertainty that scale to tens or even hundreds of agents. However, most of these methods either make restrictive assumptions on the problem domain, or provide approximate solutions without any guarantees on quality. Methods in the former category typically build on heuristic search using upper bounds on the value function. Unfortunately, no techniques exist to compute such upper bounds for problems with non-factored value functions. To allow for meaningful benchmarking through measurable quality guarantees on a very general class of problems, this paper introduces a family of influence-optimistic upper bounds for factored decentralized partially observable Markov decision processes (Dec-POMDPs) that do not have factored value functions. Intuitively, we derive bounds on very large multiagent planning problems by subdividing them in sub-problems, and at each of these sub-problems making optimistic assumptions with respect to the influence that will be exerted by the rest of the system. We numerically compare the different upper bounds and demonstrate how we can achieve a non-trivial guarantee that a heuristic solution for problems with hundreds of agents is close to optimal. Furthermore, we provide evidence that the upper bounds may improve the effectiveness of heuristic influence search, and discuss further potential applications to multiagent planning.
We design mechanisms for online procurement of data held by strategic agents for machine learning tasks. The challenge is to use past data to actively price future data and give learning guarantees even when an agent's cost for revealing her data may depend arbitrarily on the data itself. We achieve this goal by showing how to convert a large class of no-regret algorithms into online posted-price and learning mechanisms. Our results in a sense parallel classic sample complexity guarantees, but with the key resource being money rather than quantity of data: With a budget constraint $B$, we give robust risk (predictive error) bounds on the order of $1/\sqrt{B}$. Because we use an active approach, we can often guarantee to do significantly better by leveraging correlations between costs and data. Our algorithms and analysis go through a model of no-regret learning with $T$ arriving pairs (cost, data) and a budget constraint of $B$. Our regret bounds for this model are on the order of $T/\sqrt{B}$ and we give lower bounds on the same order.
Automatic reconstruction of 3D models from images using multi-view Structure-from-Motion methods has been one of the most fruitful outcomes of computer vision. These advances combined with the growing popularity of Micro Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools ubiquitous for large number of Architecture, Engineering and Construction applications among audiences, mostly unskilled in computer vision. However, to obtain high-resolution and accurate reconstructions from a large-scale object using SfM, there are many critical constraints on the quality of image data, which often become sources of inaccuracy as the current 3D reconstruction pipelines do not facilitate the users to determine the fidelity of input data during the image acquisition. In this paper, we present and advocate a closed-loop interactive approach that performs incremental reconstruction in real-time and gives users an online feedback about the quality parameters like Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We also propose a novel multi-scale camera network design to prevent scene drift caused by incremental map building, and release the first multi-scale image sequence dataset as a benchmark. Further, we evaluate our system on real outdoor scenes, and show that our interactive pipeline combined with a multi-scale camera network approach provides compelling accuracy in multi-view reconstruction tasks when compared against the state-of-the-art methods.
Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.
Recursive neural models, which use syntactic parse trees to recursively generate representations bottom-up, are a popular architecture. But there have not been rigorous evaluations showing for exactly which tasks this syntax-based method is appropriate. In this paper we benchmark {\bf recursive} neural models against sequential {\bf recurrent} neural models (simple recurrent and LSTM models), enforcing apples-to-apples comparison as much as possible. We investigate 4 tasks: (1) sentiment classification at the sentence level and phrase level; (2) matching questions to answer-phrases; (3) discourse parsing; (4) semantic relation extraction (e.g., {\em component-whole} between nouns). Our goal is to understand better when, and why, recursive models can outperform simpler models. We find that recursive models help mainly on tasks (like semantic relation extraction) that require associating headwords across a long distance, particularly on very long sequences. We then introduce a method for allowing recurrent models to achieve similar performance: breaking long sentences into clause-like units at punctuation and processing them separately before combining. Our results thus help understand the limitations of both classes of models, and suggest directions for improving recurrent models.
The global influence of Big Data is not only growing but seemingly endless. The trend is leaning towards knowledge that is attained easily and quickly from massive pools of Big Data. Today we are living in the technological world that Dr. Usama Fayyad and his distinguished research fellows discussed in the introductory explanations of Knowledge Discovery in Databases (KDD) predicted nearly two decades ago. Indeed, they were precise in their outlook on Big Data analytics. In fact, the continued improvement of the interoperability of machine learning, statistics, database building and querying fused to create this increasingly popular science- Data Mining and Knowledge Discovery. The next generation computational theories are geared towards helping to extract insightful knowledge from even larger volumes of data at higher rates of speed. As the trend increases in popularity, the need for a highly adaptive solution for knowledge discovery will be necessary. In this research paper, we are introducing the investigation and development of 23 bit-questions for a Metaknowledge template for Big Data Processing and clustering purposes. This research aims to demonstrate the construction of this methodology and proves the validity and the beneficial utilization that brings Knowledge Discovery from Big Data.
This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across the literature and produce a large corpus of anomaly detection benchmarks that vary in their construction across several dimensions we deem important to real-world applications: (a) point difficulty, (b) relative frequency of anomalies, (c) clusteredness of anomalies, and (d) relevance of features. We apply a representative set of anomaly detection algorithms to this corpus, yielding a very large collection of experimental results. We analyze these results to understand many phenomena observed in previous work. First we observe the effects of experimental design on experimental results. Second, results are evaluated with two metrics, ROC Area Under the Curve and Average Precision. We employ statistical hypothesis testing to demonstrate the value (or lack thereof) of our benchmarks. We then offer several approaches to summarizing our experimental results, drawing several conclusions about the impact of our methodology as well as the strengths and weaknesses of some algorithms. Last, we compare results against a trivial solution as an alternate means of normalizing the reported performance of algorithms. The intended contributions of this article are many; in addition to providing a large publicly-available corpus of anomaly detection benchmarks, we provide an ontology for describing anomaly detection contexts, a methodology for controlling various aspects of benchmark creation, guidelines for future experimental design and a discussion of the many potential pitfalls of trying to measure success in this field.
Given a hierarchical plan (or schedule) with uncertain task times, we propose a deterministic polynomial (time and memory) algorithm for estimating the probability that its meets a deadline, or, alternately, that its {\em makespan} is less than a given duration. Approximation is needed as it is known that this problem is NP-hard even for sequential plans (just, a sum of random variables). In addition, we show two new complexity results: (1) Counting the number of events that do not cross deadline is \#P-hard; (2)~Computing the expected makespan of a hierarchical plan is NP-hard. For the proposed approximation algorithm, we establish formal approximation bounds and show that the time and memory complexities grow polynomially with the required accuracy, the number of nodes in the plan, and with the size of the support of the random variables that represent the durations of the primitive tasks. We examine these approximation bounds empirically and demonstrate, using task networks taken from the literature, how our scheme outperforms sampling techniques and exact computation in terms of accuracy and run-time. As the empirical data shows much better error bounds than guaranteed, we also suggest a method for tightening the bounds in some cases.
Poker is one of the most popular card games, whose rational investigation represents also one of the major challenges in several scientific areas, spanning from information theory and artificial intelligence to game theory and statistical physics. In principle, several variants of Poker can be identified, although all of them make use of money to make the challenge meaningful and, moreover, can be played in two different formats: tournament and cash game. An important issue when dealing with Poker is its classification, i.e., as a `skill game' or as gambling. Nowadays, its classification still represents an open question, having a long list of implications (e.g., legal and healthcare) that vary from country to country. In this study, we analyze Poker challenges, considering the cash game format, in terms of thermodynamics systems. Notably, we propose a framework to represent a cash game Poker challenge that, although based on a simplified scenario, allows both to obtain useful information for rounders (i.e., Poker players), and to evaluate the role of Poker room in this context. Finally, starting from a model based on thermodynamics, we show the evolution of a Poker challenge, making a direct connection with the probability theory underlying its dynamics and finding that, even if we consider these games as `skill games', to take a real profit from Poker is really hard.
Conjunctive database queries have been extended with a mechanism for object creation to capture important applications such as data exchange, data integration, and ontology-based data access. Object creation generates new object identifiers in the result, that do not belong to the set of constants in the source database. The new object identifiers can be also seen as Skolem terms. Hence, object-creating conjunctive queries can also be regarded as restricted second-order tuple-generating dependencies (SO tgds), considered in the data exchange literature. In this paper, we focus on the class of single-function object-creating conjunctive queries, or sifo CQs for short. We give a new characterization for oid-equivalence of sifo CQs that is simpler than the one given by Hull and Yoshikawa and places the problem in the complexity class NP. Our characterization is based on Cohen's equivalence notions for conjunctive queries with multiplicities. We also solve the logical entailment problem for sifo CQs, showing that also this problem belongs to NP. Results by Pichler et al. have shown that logical equivalence for more general classes of SO tgds is either undecidable or decidable with as yet unknown complexity upper bounds.
Motivated by online settings where users can provide explicit feedback about the relevance of products that are sequentially presented to them, we look at the recommendation process as a problem of dynamically optimizing this relevance feedback. Such an algorithm optimizes the fine tradeoff between presenting the products that are most likely to be relevant, and learning the preferences of the user so that more relevant recommendations can be made in the future. We assume a standard predictive model inspired by collaborative filtering, in which a user is sampled from a distribution over a set of possible types. For every product category, each type has an associated relevance feedback that is assumed to be binary: the category is either relevant or irrelevant. Assuming that the user stays for each additional recommendation opportunity with probability $\beta$ independent of the past, the problem is to find a policy that maximizes the expected number of recommendations that are deemed relevant in a session. We analyze this problem and prove key structural properties of the optimal policy. Based on these properties, we first present an algorithm that strikes a balance between recursion and dynamic programming to compute this policy. We further propose and analyze two heuristic policies: a `farsighted' greedy policy that attains at least $1-\beta$ factor of the optimal payoff, and a naive greedy policy that attains at least $\frac{1-\beta}{1+\beta}$ factor of the optimal payoff in the worst case. Extensive simulations show that these heuristics are very close to optimal in practice.
This paper investigates the effectiveness of factorial speech processing models in noise-robust automatic speech recognition tasks. For this purpose, the paper proposes an idealistic approach for modeling state-conditional observation distribution of factorial models based on weighted stereo samples. This approach is an extension to previous single pass retraining for ideal model compensation which is extended here to support multiple audio sources. Non-stationary noises can be considered as one of these audio sources with multiple states. Experiments of this paper over the set A of the Aurora 2 dataset show that recognition performance can be improved by this consideration. The improvement is significant in low signal to noise energy conditions, up to 4% absolute word recognition accuracy. In addition to the power of the proposed method in accurate representation of state-conditional observation distribution, it has an important advantage over previous methods by providing the opportunity to independently select feature spaces for both source and corrupted features. This opens a new window for seeking better feature spaces appropriate for noisy speech, independent from clean speech features.
We study sequential decision making in environments where rewards are only partially observed, but can be modeled as a function of observed contexts and the chosen action by the decision maker. This setting, known as contextual bandits, encompasses a wide variety of applications such as health care, content recommendation and Internet advertising. A central task is evaluation of a new policy given historic data consisting of contexts, actions and received rewards. The key challenge is that the past data typically does not faithfully represent proportions of actions taken by a new policy. Previous approaches rely either on models of rewards or models of the past policy. The former are plagued by a large bias whereas the latter have a large variance. In this work, we leverage the strengths and overcome the weaknesses of the two approaches by applying the doubly robust estimation technique to the problems of policy evaluation and optimization. We prove that this approach yields accurate value estimates when we have either a good (but not necessarily consistent) model of rewards or a good (but not necessarily consistent) model of past policy. Extensive empirical comparison demonstrates that the doubly robust estimation uniformly improves over existing techniques, achieving both lower variance in value estimation and better policies. As such, we expect the doubly robust approach to become common practice in policy evaluation and optimization.
We analyse in this paper the data collected in a set of experiments performed on human subjects on the combination of natural concepts. We investigate the mutual influence of conceptual conjunction and negation by measuring the membership weights of a list of exemplars with respect to two concepts, e.g., 'Fruits' and 'Vegetables', and their conjunction 'Fruits And Vegetables', but also their conjunction when one or both concepts are negated, namely, 'Fruits And Not Vegetables', 'Not Fruits And Vegetables' and 'Not Fruits And Not Vegetables'. Our findings sharpen existing analysis on conceptual combinations, revealing systematic and remarkable deviations from classical (fuzzy set) logic and probability theory. And, more important, our results give further considerable evidence to the validity of our quantum-theoretic framework for the combination of two concepts. Indeed, the representation of conceptual negation naturally arises from the general assumptions of our two-sector Fock space model, and this representation faithfully agrees with the collected data. In addition, we find a further significant deviation and a priori unexpected from classicality, which can exactly be explained by assuming that human reasoning is the superposition of an 'emergent reasoning' and a 'logical reasoning', and that these two processes can be successfully represented in a Fock space algebraic structure.
The idea of dynamic move chains has been described in a preceding paper [10]. Re-using an earlier piece of search allows the tree to be forward-pruned, which is known to be dangerous, because it can potentially remove new information that would only be realised through a more exhaustive search process. The justification is the integrity in the position and small changes between positions make it more likely that an earlier result still applies. Larger problems where exhaustive search is not possible would also like a method that can guess accurately. This paper has added to the forward-pruning technique by using 'move tables' that can act in the same way as Transposition Tables, but for moves not positions. They use an efficient memory structure and have put the design into the context of short or long-term memories. The long-term memory includes simply rote-learning of other players' games. The forward-pruning technique can also be fortified to help to remove some potential errors. Another idea is 'long branches'. This plays a short move sequence, before returning to a full search at the resulting leaf nodes. Therefore, with some configuration the dynamic tables can be reliably used and relatively independently of the position. This has advanced some of the future work theory of the earlier paper, and made more explicit where logical plans and more knowledge-based approaches might be applied. The author would argue that the process is a very human approach to searching for chess moves.
Dimension reduction is often needed in the area of data mining. The goal of these methods is to map the given high-dimensional data into a low-dimensional space preserving certain properties of the initial data. There are two kinds of techniques for this purpose. The first, projective methods, builds an explicit linear projection from the high-dimensional space to the low-dimensional one. On the other hand, the nonlinear methods utilizes nonlinear and implicit mapping between the two spaces. In both cases, the methods considered in literature have usually relied on computationally very intensive matrix factorizations, frequently the Singular Value Decomposition (SVD). The computational burden of SVD quickly renders these dimension reduction methods infeasible thanks to the ever-increasing sizes of the practical datasets. In this paper, we present a new decomposition strategy, Reduced Basis Decomposition (RBD), which is inspired by the Reduced Basis Method (RBM). Given $X$ the high-dimensional data, the method approximates it by $Y \, T (\approx X)$ with $Y$ being the low-dimensional surrogate and $T$ the transformation matrix. $Y$ is obtained through a greedy algorithm thus extremely efficient. In fact, it is significantly faster than SVD with comparable accuracy. $T$ can be computed on the fly. Moreover, unlike many compression algorithms, it easily finds the mapping for an arbitrary ``out-of-sample'' vector and it comes with an ``error indicator'' certifying the accuracy of the compression. Numerical results are shown validating these claims.
Deep Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, but still show room for improvement in object-detection tasks with many categories, in particular for cluttered scenes and occlusion. Modern detection algorithms like Regions with CNNs (Girshick et al., 2014) rely on Selective Search (Uijlings et al., 2013) to propose regions which with high probability represent objects, where in turn CNNs are deployed for classification. Selective Search represents a family of sophisticated algorithms that are engineered with multiple segmentation, appearance and saliency cues, typically coming with a significant run-time overhead. Furthermore, (Hosang et al., 2014) have shown that most methods suffer from low reproducibility due to unstable superpixels, even for slight image perturbations. Although CNNs are subsequently used for classification in top-performing object-detection pipelines, current proposal methods are agnostic to how these models parse objects and their rich learned representations. As a result they may propose regions which may not resemble high-level objects or totally miss some of them. To overcome these drawbacks we propose a boosting approach which directly takes advantage of hierarchical CNN features for detecting regions of interest fast. We demonstrate its performance on ImageNet 2013 detection benchmark and compare it with state-of-the-art methods.
Searching through a large volume of data is very critical for companies, scientists, and searching engines applications due to time complexity and memory complexity. In this paper, a new technique of generating FuzzyFind Dictionary for text mining was introduced. We simply mapped the 23 bits of the English alphabet into a FuzzyFind Dictionary or more than 23 bits by using more FuzzyFind Dictionary, and reflecting the presence or absence of particular letters. This representation preserves closeness of word distortions in terms of closeness of the created binary vectors within Hamming distance of 2 deviations. This paper talks about the Golay Coding Transformation Hash Table and how it can be used on a FuzzyFind Dictionary as a new technology for using in searching through big data. This method is introduced by linear time complexity for generating the dictionary and constant time complexity to access the data and update by new data sets, also updating for new data sets is linear time depends on new data points. This technique is based on searching only for letters of English that each segment has 23 bits, and also we have more than 23-bit and also it could work with more segments as reference table.
During last years poker has gained a lot of prestige in several countries and, beyond to be one of the most famous card games, it represents a modern challenge for scientists belonging to different communities, spanning from artificial intelligence to physics and from psychology to mathematics. Unlike games like chess, the task of classifying the nature of poker (i.e., as 'skill game' or gambling) seems really hard and it also constitutes a current problem, whose solution has several implications. In general, gambling offers equal winning probabilities both to rational players (i.e., those that use a strategy) and to irrational ones (i.e., those without a strategy). Therefore, in order to uncover the nature of poker, a viable way is comparing performances of rational versus irrational players during a series of challenges. Recently, a work on this topic revealed that rationality is a fundamental ingredient to succeed in poker tournaments. In this study we analyze a simple model of poker challenges by a statistical physics approach, with the aim to uncover the nature of this game. As main result we found that, under particular conditions, few irrational players can turn poker into gambling. Therefore, although rationality is a key ingredient to succeed in poker, also the format of challenges has an important role in these dynamics, as it can strongly influence the underlying nature of the game. The importance of our results lies on related implications, as for instance in identifying the limits poker can be considered as a `skill game' and, as a consequence, which kind of format must be chosen to devise algorithms able to face humans.
We present for the first time an asymptotic convergence analysis of two time-scale stochastic approximation driven by `controlled' Markov noise. In particular, both the faster and slower recursions have non-additive controlled Markov noise components in addition to martingale difference noise. We analyze the asymptotic behavior of our framework by relating it to limiting differential inclusions in both time-scales that are defined in terms of the ergodic occupation measures associated with the controlled Markov processes. Finally, we present a solution to the off-policy convergence problem for temporal difference learning with linear function approximation, using our results.
This paper investigates distributed cooperative learning algorithms for data processing in a network setting. Specifically, the extreme learning machine (ELM) is introduced to train a set of data distributed across several components, and each component runs a program on a subset of the entire data. In this scheme, there is no requirement for a fusion center in the network due to e.g., practical limitations, security, or privacy reasons. We first reformulate the centralized ELM training problem into a separable form among nodes with consensus constraints. Then, we solve the equivalent problem using distributed optimization tools. A new distributed cooperative learning algorithm based on ELM, called DC-ELM, is proposed. The architecture of this algorithm differs from that of some existing parallel/distributed ELMs based on MapReduce or cloud computing. We also present an online version of the proposed algorithm that can learn data sequentially in a one-by-one or chunk-by-chunk mode. The novel algorithm is well suited for potential applications such as artificial intelligence, computational biology, finance, wireless sensor networks, and so on, involving datasets that are often extremely large, high-dimensional and located on distributed data sources. We show simulation results on both synthetic and real-world data sets.
Traditional way of storing facts in triplets ({\it head\_entity, relation, tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively displayed and easily acquired by mankind, but hardly computed or even reasoned by AI machines. Inspired by the success in applying {\it Distributed Representations} to AI-related fields, recent studies expect to represent each entity and relation with a unique low-dimensional embedding, which is different from the symbolic and atomic framework of displaying knowledge in triplets. In this way, the knowledge computing and reasoning can be essentially facilitated by means of a simple {\it vector calculation}, i.e. ${\bf h} + {\bf r} \approx {\bf t}$. We thus contribute an effective model to learn better embeddings satisfying the formula by pulling the positive tail entities ${\bf t^{+}}$ to get together and close to {\bf h} + {\bf r} ({\it Nearest Neighbor}), and simultaneously pushing the negatives ${\bf t^{-}}$ away from the positives ${\bf t^{+}}$ via keeping a {\it Large Margin}. We also design a corresponding learning algorithm to efficiently find the optimal solution based on {\it Stochastic Gradient Descent} in iterative fashion. Quantitative experiments illustrate that our approach can achieve the state-of-the-art performance, compared with several latest methods on some benchmark datasets for two classical applications, i.e. {\it Link prediction} and {\it Triplet classification}. Moreover, we analyze the parameter complexities among all the evaluated models, and analytical results indicate that our model needs fewer computational resources on outperforming the other methods.
Identification of falls while performing normal activities of daily living (ADL) is important to ensure personal safety and well-being. However, falling is a short term activity that occurs infrequently. This poses a challenge to traditional classification algorithms, because there may be very little training data for falls (or none at all). This paper proposes an approach for the identification of falls using a wearable device in the absence of training data for falls but with plentiful data for normal ADL. We propose three `X-Factor' Hidden Markov Model (XHMMs) approaches. The XHMMs model unseen falls using "inflated" output covariances (observation models). To estimate the inflated covariances, we propose a novel cross validation method to remove "outliers" from the normal ADL that serve as proxies for the unseen falls and allow learning the XHMMs using only normal activities. We tested the proposed XHMM approaches on two activity recognition datasets and show high detection rates for falls in the absence of fall-specific training data. We show that the traditional method of choosing a threshold based on maximum of negative of log-likelihood to identify unseen falls is ill-posed for this problem. We also show that supervised classification methods perform poorly when very limited fall data are available during the training phase.
Nowadays data sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of Formal Concept Analysis (FCA) and its extension based on "pattern structures". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e., a data reduction of sequential structures), are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analyzing interesting patient patterns from a French healthcare data set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use case which is the main motivation for this work. Keywords: data mining; formal concept analysis; pattern structures; projections; sequences; sequential data.
We present a computational framework for automatically quantifying verbal and nonverbal behaviors in the context of job interviews. The proposed framework is trained by analyzing the videos of 138 interview sessions with 69 internship-seeking undergraduates at the Massachusetts Institute of Technology (MIT). Our automated analysis includes facial expressions (e.g., smiles, head gestures, facial tracking points), language (e.g., word counts, topic modeling), and prosodic information (e.g., pitch, intonation, and pauses) of the interviewees. The ground truth labels are derived by taking a weighted average over the ratings of 9 independent judges. Our framework can automatically predict the ratings for interview traits such as excitement, friendliness, and engagement with correlation coefficients of 0.75 or higher, and can quantify the relative importance of prosody, language, and facial expressions. By analyzing the relative feature weights learned by the regression models, our framework recommends to speak more fluently, use less filler words, speak as "we" (vs. "I"), use more unique words, and smile more. We also find that the students who were rated highly while answering the first interview question were also rated highly overall (i.e., first impression matters). Finally, our MIT Interview dataset will be made available to other researchers to further validate and expand our findings.
Evolutionary Algorithms (EAs) have been shown to be powerful tools for complex optimization problems, which are ubiquitous in both communication and big data analytics. This paper presents a new EA, namely Negatively Correlated Search (NCS), which maintains multiple individual search processes in parallel and models the search behaviors of individual search processes as probability distributions. NCS explicitly promotes negatively correlated search behaviors by encouraging differences among the probability distributions (search behaviors). By this means, individual search processes share information and cooperate with each other to search diverse regions of a search space, which makes NCS a promising method for non-convex optimization. The cooperation scheme of NCS could also be regarded as a novel diversity preservation scheme that, different from other existing schemes, directly promotes diversity at the level of search behaviors rather than merely trying to maintain diversity among candidate solutions. Empirical studies showed that NCS is competitive to well-established search methods in the sense that NCS achieved the best overall performance on 20 multimodal (non-convex) continuous optimization problems. The advantages of NCS over state-of-the-art approaches are also demonstrated with a case study on the synthesis of unequally spaced linear antenna arrays.
The amount of completely sequenced chloroplast genomes increases rapidly every day, leading to the possibility to build large scale phylogenetic trees of plant species. Considering a subset of close plant species defined according to their chloroplasts, the phylogenetic tree that can be inferred by their core genes is not necessarily well supported, due to the possible occurrence of "problematic" genes (i.e., homoplasy, incomplete lineage sorting, horizontal gene transfers, etc.) which may blur phylogenetic signal. However, a trustworthy phylogenetic tree can still be obtained if the number of problematic genes is low, the problem being to determine the largest subset of core genes that produces the best supported tree. To discard problematic genes and due to the overwhelming number of possible combinations, we propose an hybrid approach that embeds both genetic algorithms and statistical tests. Given a set of organisms, the result is a pipeline of many stages for the production of well supported phylogenetic trees. The proposal has been applied to different cases of plant families, leading to encouraging results for these families.
This paper addresses the task of time separated aerial image registration. The ability to solve this problem accurately and reliably is important for a variety of subsequent image understanding applications. The principal challenge lies in the extent and nature of transient appearance variation that a land area can undergo, such as that caused by the change in illumination conditions, seasonal variations, or the occlusion by non-persistent objects (people, cars). Our work introduces several novelties: (i) unlike all previous work on aerial image registration, we approach the problem using a set-based paradigm; (ii) we show how local, pair-wise constraints can be used to enforce a globally good registration using a constraints graph structure; (iii) we show how a simple holistic representation derived from raw aerial images can be used as a basic building block of the constraints graph in a manner which achieves both high registration accuracy and speed. We demonstrate: (i) that the proposed method outperforms the state-of-the-art for pair-wise registration already, achieving greater accuracy and reliability, while at the same time reducing the computational cost of the task; and (ii) that the increase in the number of available images in a set consistently reduces the average registration error.
In the classic AGM belief revision theory, beliefs are static and do not change their own shape. For instance, if p is accepted by a rational agent, it will remain p to the agent. But such rarely happens to us. Often, when we accept some information p, what is actually accepted is not the whole p, but only a portion of it; not necessarily because we select the portion but because p must be perceived. Only the perceived p is accepted; and the perception is subject to what we already believe (know). What may, however, happen to the rest of p that initially escaped our attention? In this work we argue that the invisible part is also accepted to the agent, if only unconsciously. Hence some parts of p are accepted as visible, while some other parts as latent, beliefs. The division is not static. As the set of beliefs changes, what were hidden may become visible. We present a perception-based belief theory that incorporates latent beliefs.
Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.
Constraint programming is a family of techniques for solving combinatorial problems, where the problem is modelled as a set of decision variables (typically with finite domains) and a set of constraints that express relations among the decision variables. One key concept in constraint programming is propagation: reasoning on a constraint or set of constraints to derive new facts, typically to remove values from the domains of decision variables. Specialised propagation algorithms (propagators) exist for many classes of constraints. The concept of support is pervasive in the design of propagators. Traditionally, when a domain value ceases to have support, it may be removed because it takes part in no solutions. Arc-consistency algorithms such as AC2001 make use of support in the form of a single domain value. GAC algorithms such as GAC-Schema use a tuple of values to support each literal. We generalize these notions of support in two ways. First, we allow a set of tuples to act as support. Second, the supported object is generalized from a set of literals (GAC-Schema) to an entire constraint or any part of it. We design a methodology for developing correct propagators using generalized support. A constraint is expressed as a family of support properties, which may be proven correct against the formal semantics of the constraint. Using Curry-Howard isomorphism to interpret constructive proofs as programs, we show how to derive correct propagators from the constructive proofs of the support properties. The framework is carefully designed to allow efficient algorithms to be produced. Derived algorithms may make use of dynamic literal triggers or watched literals for efficiency. Finally, two case studies of deriving efficient algorithms are given.
Social media is becoming an increasingly important source of information to complement traditional pharmacovigilance methods. In order to identify signals of potential adverse drug reactions, it is necessary to first identify medical concepts in the social media text. Most of the existing studies use dictionary-based methods which are not evaluated independently from the overall signal detection task. We compare different approaches to automatically identify and normalise medical concepts in consumer reviews in medical forums. Specifically, we implement several dictionary-based methods popular in the relevant literature, as well as a method we suggest based on a state-of-the-art machine learning method for entity recognition. MetaMap, a popular biomedical concept extraction tool, is used as a baseline. Our evaluations were performed in a controlled setting on a common corpus which is a collection of medical forum posts annotated with concepts and linked to controlled vocabularies such as MedDRA and SNOMED CT. To our knowledge, our study is the first to systematically examine the effect of popular concept extraction methods in the area of signal detection for adverse reactions. We show that the choice of algorithm or controlled vocabulary has a significant impact on concept extraction, which will impact the overall signal detection process. We also show that our proposed machine learning approach significantly outperforms all the other methods in identification of both adverse reactions and drugs, even when trained with a relatively small set of annotated text.
In this paper, we present a probabilistic framework for goal-driven spoken dialog systems. A new dynamic stochastic state (DS-state) is then defined to characterize the goal set of a dialog state at different stages of the dialog process. Furthermore, an entropy minimization dialog management(EMDM) strategy is also proposed to combine with the DS-states to facilitate a robust and efficient solution in reaching a user's goals. A Song-On-Demand task, with a total of 38117 songs and 12 attributes corresponding to each song, is used to test the performance of the proposed approach. In an ideal simulation, assuming no errors, the EMDM strategy is the most efficient goal-seeking method among all tested approaches, returning the correct song within 3.3 dialog turns on average. Furthermore, in a practical scenario, with top five candidates to handle the unavoidable automatic speech recognition (ASR) and natural language understanding (NLU) errors, the results show that only 61.7\% of the dialog goals can be successfully obtained in 6.23 dialog turns on average when random questions are asked by the system, whereas if the proposed DS-states are updated with the top 5 candidates from the SLU output using the proposed EMDM strategy executed at every DS-state, then a 86.7\% dialog success rate can be accomplished effectively within 5.17 dialog turns on average. We also demonstrate that entropy-based DM strategies are more efficient than non-entropy based DM. Moreover, using the goal set distributions in EMDM, the results are better than those without them, such as in sate-of-the-art database summary DM.
We consider existential rules (aka Datalog+) as a formalism for specifying ontologies. In recent years, many classes of existential rules have been exhibited for which conjunctive query (CQ) entailment is decidable. However, most of these classes cannot express transitivity of binary relations, a frequently used modelling construct. In this paper, we address the issue of whether transitivity can be safely combined with decidable classes of existential rules. First, we prove that transitivity is incompatible with one of the simplest decidable classes, namely aGRD (acyclic graph of rule dependencies), which clarifies the landscape of `finite expansion sets' of rules. Second, we show that transitivity can be safely added to linear rules (a subclass of guarded rules, which generalizes the description logic DL-Lite-R) in the case of atomic CQs, and also for general CQs if we place a minor syntactic restriction on the rule set. This is shown by means of a novel query rewriting algorithm that is specially tailored to handle transitivity rules. Third, for the identified decidable cases, we pinpoint the combined and data complexities of query entailment.
Particle Swarm Optimization (PSO) is a nature-inspired meta-heuristic for solving continuous optimization problems. In the literature, the potential of the particles of swarm has been used to show that slightly modified PSO guarantees convergence to local optima. Here we show that under specific circumstances the unmodified PSO, even with swarm parameters known (from the literature) to be good, almost surely does not yield convergence to a local optimum is provided. This undesirable phenomenon is called stagnation. For this purpose, the particles' potential in each dimension is analyzed mathematically. Additionally, some reasonable assumptions on the behavior if the particles' potential are made. Depending on the objective function and, interestingly, the number of particles, the potential in some dimensions may decrease much faster than in other dimensions. Therefore, these dimensions lose relevance, i.e., the contribution of their entries to the decisions about attractor updates becomes insignificant and, with positive probability, they never regain relevance. If Brownian Motion is assumed to be an approximation of the time-dependent drop of potential, practical, i.e., large values for this probability are calculated. Finally, on chosen multidimensional polynomials of degree two, experiments are provided showing that the required circumstances occur quite frequently. Furthermore, experiments are provided showing that even when the very simple sphere function is processed the described stagnation phenomenon occurs. Consequently, unmodified PSO does not converge to any local optimum of the chosen functions for tested parameter settings.
The Coalitional Manipulation (CM) problem has been studied extensively in the literature for many voting rules. The CM problem, however, has been studied only in the complete information setting, that is, when the manipulators know the votes of the non-manipulators. A more realistic scenario is an incomplete information setting where the manipulators do not know the exact votes of the non- manipulators but may have some partial knowledge of the votes. In this paper, we study a setting where the manipulators know a partial order for each voter that is consistent with the vote of that voter. In this setting, we introduce and study two natural computational problems - (1) Weak Manipulation (WM) problem where the manipulators wish to vote in a way that makes their preferred candidate win in at least one extension of the partial votes of the non-manipulators; (2) Strong Manipulation (SM) problem where the manipulators wish to vote in a way that makes their preferred candidate win in all possible extensions of the partial votes of the non-manipulators. We study the computational complexity of the WM and the SM problems for commonly used voting rules such as plurality, veto, k-approval, k-veto, maximin, Copeland, and Bucklin. Our key finding is that, barring a few exceptions, manipulation becomes a significantly harder problem in the setting of incomplete votes.
Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users.
We describe Quizz, a gamified crowdsourcing system that simultaneously assesses the knowledge of users and acquires new knowledge from them. Quizz operates by asking users to complete short quizzes on specific topics; as a user answers the quiz questions, Quizz estimates the user's competence. To acquire new knowledge, Quizz also incorporates questions for which we do not have a known answer; the answers given by competent users provide useful signals for selecting the correct answers for these questions. Quizz actively tries to identify knowledgeable users on the Internet by running advertising campaigns, effectively leveraging the targeting capabilities of existing, publicly available, ad placement services. Quizz quantifies the contributions of the users using information theory and sends feedback to the advertisingsystem about each user. The feedback allows the ad targeting mechanism to further optimize ad placement. Our experiments, which involve over ten thousand users, confirm that we can crowdsource knowledge curation for niche and specialized topics, as the advertising network can automatically identify users with the desired expertise and interest in the given topic. We present controlled experiments that examine the effect of various incentive mechanisms, highlighting the need for having short-term rewards as goals, which incentivize the users to contribute. Finally, our cost-quality analysis indicates that the cost of our approach is below that of hiring workers through paid-crowdsourcing platforms, while offering the additional advantage of giving access to billions of potential users all over the planet, and being able to reach users with specialized expertise that is not typically available through existing labor marketplaces.
A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives passive memristors and implements competitive learning in a compact circuit module, and enables in-situ plasticity in the memristor synapses. We demonstrate handwritten-digits recognition using the proposed architecture using transistor-level circuit simulations. As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive computing.
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data has led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimisers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.
The monolithic approach to policy representation in Markov Decision Processes (MDPs) looks for a single policy that can be represented as a function from states to actions. For the monolithic approach to succeed (and this is not always possible), a complex feature representation is often necessary since the policy is a complex object that has to prescribe what actions to take all over the state space. This is especially true in large domains with complicated dynamics. It is also computationally inefficient to both learn and plan in MDPs using a complex monolithic approach. We present a different approach where we restrict the policy space to policies that can be represented as combinations of simpler, parameterized skills---a type of temporally extended action, with a simple policy representation. We introduce Learning Skills via Bootstrapping (LSB) that can use a broad family of Reinforcement Learning (RL) algorithms as a "black box" to iteratively learn parametrized skills. Initially, the learned skills are short-sighted but each iteration of the algorithm allows the skills to bootstrap off one another, improving each skill in the process. We prove that this bootstrapping process returns a near-optimal policy. Furthermore, our experiments demonstrate that LSB can solve MDPs that, given the same representational power, could not be solved by a monolithic approach. Thus, planning with learned skills results in better policies without requiring complex policy representations.
This paper reveals the tree structure as an intermediate result of clustering by fast search and find of density peaks (DPCLUS), and explores the power of using this tree to perform hierarchical clustering. The array used to hold the index of the nearest higher-densitied object for each object can be transformed into a Leading Tree (LT), in which each parent node P leads its child nodes to join the same cluster as P itself, and the child nodes are sorted by their gamma values in descendant order to accelerate the disconnecting of root in each subtree. There are two major advantages with the LT: One is dramatically reducing the running time of assigning noncenter data points to their cluster ID, because the assigning process is turned into just disconnecting the links from each center to its parent. The other is that the tree model for representing clusters is more informative. Because we can check which objects are more likely to be selected as centers in finer grained clustering, or which objects reach to its center via less jumps. Experiment results and analysis show the effectiveness and efficiency of the assigning process with an LT.
Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. It is a nontrivial classification problem which has attracted many research. In recent years, there is a high exploitation of Artificial Intelligent algorithms in robotics applications. Inspired by the recent successes of deep learning methods, we propose an end-to-end learning approach for the place classification problem. With the deep architectures, this methodology automatically discovers features and contributes in general to higher classification accuracies. The pipeline of our approach is composed of three parts. Firstly, we construct multiple layers of laser range data to represent the environment information in different levels of granularity. Secondly, each layer of data is fed into a deep neural network model for classification, where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples. Finally, the predicted labels obtained from all the layers are fused based on confidence trees to maximize the overall confidence. Experimental results validate the effective- ness of our end-to-end place classification framework in which both the multi-layer structure and the graph regularization promote the classification performance. Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.
Owing to the rapidly growing multimedia content available on the Internet, extractive spoken document summarization, with the purpose of automatically selecting a set of representative sentences from a spoken document to concisely express the most important theme of the document, has been an active area of research and experimentation. On the other hand, word embedding has emerged as a newly favorite research subject because of its excellent performance in many natural language processing (NLP)-related tasks. However, as far as we are aware, there are relatively few studies investigating its use in extractive text or speech summarization. A common thread of leveraging word embeddings in the summarization process is to represent the document (or sentence) by averaging the word embeddings of the words occurring in the document (or sentence). Then, intuitively, the cosine similarity measure can be employed to determine the relevance degree between a pair of representations. Beyond the continued efforts made to improve the representation of words, this paper focuses on building novel and efficient ranking models based on the general word embedding methods for extractive speech summarization. Experimental results demonstrate the effectiveness of our proposed methods, compared to existing state-of-the-art methods.
Declarative spatial reasoning denotes the ability to (declaratively) specify and solve real-world problems related to geometric and qualitative spatial representation and reasoning within standard knowledge representation and reasoning (KR) based methods (e.g., logic programming and derivatives). One approach for encoding the semantics of spatial relations within a declarative programming framework is by systems of polynomial constraints. However, solving such constraints is computationally intractable in general (i.e. the theory of real-closed fields). We present a new algorithm, implemented within the declarative spatial reasoning system CLP(QS), that drastically improves the performance of deciding the consistency of spatial constraint graphs over conventional polynomial encodings. We develop pruning strategies founded on spatial symmetries that form equivalence classes (based on affine transformations) at the qualitative spatial level. Moreover, pruning strategies are themselves formalised as knowledge about the properties of space and spatial symmetries. We evaluate our algorithm using a range of benchmarks in the class of contact problems, and proofs in mereology and geometry. The empirical results show that CLP(QS) with knowledge-based spatial pruning outperforms conventional polynomial encodings by orders of magnitude, and can thus be applied to problems that are otherwise unsolvable in practice.
Modern conflict-driven clause-learning (CDCL) Boolean SAT solvers provide efficient automatic analysis of real-world feature models (FM) of systems ranging from cars to operating systems. It is well-known that solver-based analysis of real-world FMs scale very well even though SAT instances obtained from such FMs are large, and the corresponding analysis problems are known to be NP-complete. To better understand why SAT solvers are so effective, we systematically studied many syntactic and semantic characteristics of a representative set of large real-world FMs. We discovered that a key reason why large real-world FMs are easy-to-analyze is that the vast majority of the variables in these models are unrestricted, i.e., the models are satisfiable for both true and false assignments to such variables under the current partial assignment. Given this discovery and our understanding of CDCL SAT solvers, we show that solvers can easily find satisfying assignments for such models without too many backtracks relative to the model size, explaining why solvers scale so well. Further analysis showed that the presence of unrestricted variables in these real-world models can be attributed to their high-degree of variability. Additionally, we experimented with a series of well-known non-backtracking simplifications that are particularly effective in solving FMs. The remaining variables/clauses after simplifications, called the core, are so few that they are easily solved even with backtracking, further strengthening our conclusions.
This paper introduces a high-performance hybrid algorithm, called Hybrid Hypervolume Maximization Algorithm (H2MA), for multi-objective optimization that alternates between exploring the decision space and exploiting the already obtained non-dominated solutions. The proposal is centered on maximizing the hypervolume indicator, thus converting the multi-objective problem into a single-objective one. The exploitation employs gradient-based methods, but considering a single candidate efficient solution at a time, to overcome limitations associated with population-based approaches and also to allow an easy control of the number of solutions provided. There is an interchange between two steps. The first step is a deterministic local exploration, endowed with an automatic procedure to detect stagnation. When stagnation is detected, the search is switched to a second step characterized by a stochastic global exploration using an evolutionary algorithm. Using five ZDT benchmarks with 30 variables, the performance of the new algorithm is compared to state-of-the-art algorithms for multi-objective optimization, more specifically NSGA-II, SPEA2, and SMS-EMOA. The solutions found by the H2MA guide to higher hypervolume and smaller distance to the true Pareto frontier with significantly less function evaluations, even when the gradient is estimated numerically. Furthermore, although only continuous decision spaces have been considered here, discrete decision spaces could also have been treated, replacing gradient-based search by hill-climbing. Finally, a thorough explanation is provided to support the expressive gain in performance that was achieved.
It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time. With this ability, new class labels could be assigned to these inputs by a human operator, allowing them to be incorporated into the recognition function --- ideally under an efficient incremental update mechanism. While good algorithms that assume inputs from a fixed set of classes exist, e.g., artificial neural networks and kernel machines, it is not immediately obvious how to extend them to perform incremental learning in the presence of unknown query classes. Existing algorithms take little to no distributional information into account when learning recognition functions and lack a strong theoretical foundation. We address this gap by formulating a novel, theoretically sound classifier --- the Extreme Value Machine (EVM). The EVM has a well-grounded interpretation derived from statistical Extreme Value Theory (EVT), and is the first classifier to be able to perform nonlinear kernel-free variable bandwidth incremental learning. Compared to other classifiers in the same deep network derived feature space, the EVM is accurate and efficient on an established benchmark partition of the ImageNet dataset.
This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage. It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest. Our interestingness measure employs the partition approach. A pattern is interesting to the extent that it is more frequent than can be explained by assuming independence between any of the pairs of patterns from which it can be composed. The larger the support compared to the expectation under independence, the more interesting is the pattern. We build on these two elements to exactly extract the k sequential patterns with highest leverage, consistent with our definition of expected support. We conduct experiments on both synthetic data with known patterns and real-world datasets; both experiments confirm the consistency and relevance of our approach with regard to the state of the art. This article was published in Data Mining and Knowledge Discovery and is accessible at http://dx.doi.org/10.1007/s10618-016-0467-9.
Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper we review techniques exploiting the graph structure for exact inference, borrowed from optimisation and computer science. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated. The so-called treewidth of the graph characterises this algorithmic complexity: low-treewidth graphs can be processed efficiently. The first message that we illustrate is therefore the idea that for inference in graphical model, the number of variables is not the limiting factor, and it is worth checking for the treewidth before turning to approximate methods. We show how algorithms providing an upper bound of the treewidth can be exploited to derive a 'good' elimination order enabling to perform exact inference. The second message is that when the treewidth is too large, algorithms for approximate inference linked to the principle of variable elimination, such as loopy belief propagation and variational approaches, can lead to accurate results while being much less time consuming than Monte-Carlo approaches. We illustrate the techniques reviewed in this article on benchmarks of inference problems in genetic linkage analysis and computer vision, as well as on hidden variables restoration in coupled Hidden Markov Models.
We address the problem of belief revision of logic programs, i.e., how to incorporate to a logic program P a new logic program Q. Based on the structure of SE interpretations, Delgrande et al. adapted the well-known AGM framework to logic program (LP) revision. They identified the rational behavior of LP revision and introduced some specific operators. In this paper, a constructive characterization of all rational LP revision operators is given in terms of orderings over propositional interpretations with some further conditions specific to SE interpretations. It provides an intuitive, complete procedure for the construction of all rational LP revision operators and makes easier the comprehension of their semantic and computational properties. We give a particular consideration to logic programs of very general form, i.e., the generalized logic programs (GLPs). We show that every rational GLP revision operator is derived from a propositional revision operator satisfying the original AGM postulates. Interestingly, the further conditions specific to GLP revision are independent from the propositional revision operator on which a GLP revision operator is based. Taking advantage of our characterization result, we embed the GLP revision operators into structures of Boolean lattices, that allow us to bring to light some potential weaknesses in the adapted AGM postulates. To illustrate our claim, we introduce and characterize axiomatically two specific classes of (rational) GLP revision operators which arguably have a drastic behavior. We additionally consider two more restricted forms of logic programs, i.e., the disjunctive logic programs (DLPs) and the normal logic programs (NLPs) and adapt our characterization result to DLP and NLP revision operators.
Checking software application suitability using automated software tools has become a vital element for most organisations irrespective of whether they produce in-house software or simply customise off-the-shelf software applications for internal use. As software solutions become ever more complex, the industry becomes increasingly dependent on software automation tools, yet the brittle nature of the available software automation tools limits their effectiveness. Companies invest significantly in obtaining and implementing automation software but most of the tools fail to deliver when the cost of maintaining an effective automation test suite exceeds the cost and time that would have otherwise been spent on manual testing. A failing in the current generation of software automation tools is they do not adapt to unexpected modifications and obstructions without frequent (and time expensive) manual interference. Such issues are commonly acknowledged amongst industry practitioners, yet none of the current generation of tools have leveraged the advances in machine learning and artificial intelligence to address these problems. This paper proposes a framework solution that utilises machine learning concepts, namely fuzzy matching and error recovery. The suggested solution applies adaptive techniques to recover from unexpected obstructions that would otherwise have prevented the script from proceeding. Recovery details are presented to the user in a report which can be analysed to determine if the recovery procedure was acceptable and the framework will adapt future runs based on the decisions of the user. Using this framework, a practitioner can run the automated suits without human intervention while minimising the risk of schedule delays.
Purpose. Radiation therapy is a local treatment aimed at cells in and around a tumor. The goal of this study is to develop an algorithmic solution for predicting the position of a target in 3D in real time, aiming for the short fixed calibration time for each patient at the beginning of the procedure. Accurate predictions of lung tumor motion are expected to improve the precision of radiation treatment by controlling the position of a couch or a beam in order to compensate for respiratory motion during radiation treatment. Methods. For developing the algorithmic solution, data mining techniques are used. A model form from the family of exponential smoothing is assumed, and the model parameters are fitted by minimizing the absolute disposition error, and the fluctuations of the prediction signal (jitter). The predictive performance is evaluated retrospectively on clinical datasets capturing different behavior (being quiet, talking, laughing), and validated in real-time on a prototype system with respiratory motion imitation. Results. An algorithmic solution for respiratory motion prediction (called ExSmi) is designed. ExSmi achieves good accuracy of prediction (error $4-9$ mm/s) with acceptable jitter values (5-7 mm/s), as tested on out-of-sample data. The datasets, the code for algorithms and the experiments are openly available for research purposes on a dedicated website. Conclusions. The developed algorithmic solution performs well to be prototyped and deployed in applications of radiotherapy.
Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank problem in information retrieval. We consider popular listwise performance measures such as Normalized Discounted Cumulative Gain (NDCG) and Average Precision (AP). A modern perspective on perceptron for classification is that it is simply an instance of online gradient descent (OGD), during mistake rounds, using the hinge loss function. Motivated by this interpretation, we propose a novel family of listwise, large margin ranking surrogates. Members of this family can be thought of as analogs of the hinge loss. Exploiting a certain self-bounding property of the proposed family, we provide a guarantee on the cumulative NDCG (or AP) induced loss incurred by our perceptron-like algorithm. We show that, if there exists a perfect oracle ranker which can correctly rank each instance in an online sequence of ranking data, with some margin, the cumulative loss of perceptron algorithm on that sequence is bounded by a constant, irrespective of the length of the sequence. This result is reminiscent of Novikoff's convergence theorem for the classification perceptron. Moreover, we prove a lower bound on the cumulative loss achievable by any deterministic algorithm, under the assumption of existence of perfect oracle ranker. The lower bound shows that our perceptron bound is not tight, and we propose another, \emph{purely online}, algorithm which achieves the lower bound. We provide empirical results on simulated and large commercial datasets to corroborate our theoretical results.
Presently, a very large number of public and private data sets are available from local governments. In most cases, they are not semantically interoperable and a huge human effort would be needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity, to allow the data reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big data volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to a smart-city ontology, called KM4City (Knowledge Model for City), and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users via specific applications of public administration and enterprises. The paper presents the process adopted to produce the ontology and the big data architecture for the knowledge base feeding on the basis of open and private data, and the mechanisms adopted for the data verification, reconciliation and validation. Some examples about the possible usage of the coherent big data knowledge base produced are also offered and are accessible from the RDF-Store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection.
The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.
Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning. Besides storing facts about the world, schema-based knowledge graphs are backed by rich semantic descriptions of entities and relation-types that allow machines to understand the notion of things and their semantic relationships. In this work, we study how type-constraints can generally support the statistical modeling with latent variable models. More precisely, we integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches. Our experimental results show that prior knowledge on relation-types significantly improves these models up to 77% in link-prediction tasks. The achieved improvements are especially prominent when a low model complexity is enforced, a crucial requirement when these models are applied to very large datasets. Unfortunately, type-constraints are neither always available nor always complete e.g., they can become fuzzy when entities lack proper typing. We show that in these cases, it can be beneficial to apply a local closed-world assumption that approximates the semantics of relation-types based on observations made in the data.
Algorithms for hyperparameter optimization abound, all of which work well under different and often unverifiable assumptions. Motivated by the general challenge of sequentially choosing which algorithm to use, we study the more specific task of choosing among distributions to use for random hyperparameter optimization. This work is naturally framed in the extreme bandit setting, which deals with sequentially choosing which distribution from a collection to sample in order to minimize (maximize) the single best cost (reward). Whereas the distributions in the standard bandit setting are primarily characterized by their means, a number of subtleties arise when we care about the minimal cost as opposed to the average cost. For example, there may not be a well-defined "best" distribution as there is in the standard bandit setting. The best distribution depends on the rewards that have been obtained and on the remaining time horizon. Whereas in the standard bandit setting, it is sensible to compare policies with an oracle which plays the single best arm, in the extreme bandit setting, there are multiple sensible oracle models. We define a sensible notion of "extreme regret" in the extreme bandit setting, which parallels the concept of regret in the standard bandit setting. We then prove that no policy can asymptotically achieve no extreme regret.
The primary challenge of rocket propulsion is the burden of needing to accelerate the spacecraft's own fuel, resulting in only a logarithmic gain in maximum speed as propellant is added to the spacecraft. Light sails offer an attractive alternative in which fuel is not carried by the spacecraft, with acceleration being provided by an external source of light. By artificially illuminating the spacecraft with beamed radiation, speeds are only limited by the area of the sail, heat resistance of its material, and power use of the accelerating apparatus. In this paper, we show that leakage from a light sail propulsion apparatus in operation around a solar system analogue would be detectable. To demonstrate this, we model the launch and arrival of a microwave beam-driven light sail constructed for transit between planets in orbit around a single star, and find an optimal beam frequency on the order of tens of GHz. Leakage from these beams yields transients with flux densities of Jy and durations of tens of seconds at 100 pc. Because most travel within a planetary system would be conducted between the habitable worlds within that system, multiply-transiting exoplanetary systems offer the greatest chance of detection, especially when the planets are in projected conjunction as viewed from Earth. If interplanetary travel via beam-driven light sails is commonly employed in our galaxy, this activity could be revealed by radio follow-up of nearby transiting exoplanetary systems. The expected signal properties define a new strategy in the search for extraterrestrial intelligence (SETI).
A general tension-reduction (GTR) model was recently considered to derive quantum probabilities as (universal) averages over all possible forms of non-uniform fluctuations, and explain their considerable success in describing experimental situations also outside of the domain of physics, for instance in the ambit of quantum models of cognition and decision. Yet, this result also highlighted the possibility of observing violations of the predictions of the Born rule, in those situations where the averaging would not be large enough, or would be altered because of the combination of multiple measurements. In this article we show that this is indeed the case in typical psychological measurements exhibiting question order effects, by showing that their statistics of outcomes are inherently non-Hilbertian, and require the larger framework of the GTR-model to receive an exact mathematical description. We also consider another unsolved problem of quantum cognition: response replicability. It is has been observed that when question order effects and response replicability occur together, the situation cannot be handled anymore by quantum theory. However, we show that it can be easily and naturally described in the GTR-model. Based on these findings, we motivate the adoption in cognitive science of a hidden-measurements interpretation of the quantum formalism, and of its GTR-model generalization, as the natural interpretational framework explaining the data of psychological measurements on conceptual entities.
Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions.
This paper describes an architecture that combines the complementary strengths of probabilistic graphical models and declarative programming to enable robots to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluents and non-deterministic causal laws. This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, refining a coarse-resolution transition diagram of the domain to obtain a fine-resolution transition diagram. The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action probabilistically, the part of the fine-resolution transition diagram relevant to this action is identified, and a probabilistic representation of the uncertainty in sensing and actuation is included and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.
In this paper, we propose a model-based clustering method (TVClust) that robustly incorporates noisy side information as soft-constraints and aims to seek a consensus between side information and the observed data. Our method is based on a nonparametric Bayesian hierarchical model that combines the probabilistic model for the data instance and the one for the side-information. An efficient Gibbs sampling algorithm is proposed for posterior inference. Using the small-variance asymptotics of our probabilistic model, we then derive a new deterministic clustering algorithm (RDP-means). It can be viewed as an extension of K-means that allows for the inclusion of side information and has the additional property that the number of clusters does not need to be specified a priori. Empirical studies have been carried out to compare our work with many constrained clustering algorithms from the literature on both a variety of data sets and under a variety of conditions such as using noisy side information and erroneous k values. The results of our experiments show strong results for our probabilistic and deterministic approaches under these conditions when compared to other algorithms in the literature.
Consider a setting where selfish agents are to be assigned to coalitions or projects from a fixed set P. Each project k is characterized by a valuation function; v_k(S) is the value generated by a set S of agents working on project k. We study the following classic problem in this setting: "how should the agents divide the value that they collectively create?". One traditional approach in cooperative game theory is to study core stability with the implicit assumption that there are infinite copies of one project, and agents can partition themselves into any number of coalitions. In contrast, we consider a model with a finite number of non-identical projects; this makes computing both high-welfare solutions and core payments highly non-trivial. The main contribution of this paper is a black-box mechanism that reduces the problem of computing a near-optimal core stable solution to the purely algorithmic problem of welfare maximization; we apply this to compute an approximately core stable solution that extracts one-fourth of the optimal social welfare for the class of subadditive valuations. We also show much stronger results for several popular sub-classes: anonymous, fractionally subadditive, and submodular valuations, as well as provide new approximation algorithms for welfare maximization with anonymous functions. Finally, we establish a connection between our setting and the well-studied simultaneous auctions with item bidding; we adapt our results to compute approximate pure Nash equilibria for these auctions.
This paper addresses the problem of predicting the k events that are most likely to occur next, over historical real-time event streams. Existing approaches to causal prediction queries have a number of limitations. First, they exhaustively search over an acyclic causal network to find the most likely k effect events; however, data from real event streams frequently reflect cyclic causality. Second, they contain conservative assumptions intended to exclude all possible non-causal links in the causal network; it leads to the omission of many less-frequent but important causal links. We overcome these limitations by proposing a novel event precedence model and a run-time causal inference mechanism. The event precedence model constructs a first order absorbing Markov chain incrementally over event streams, where an edge between two events signifies a temporal precedence relationship between them, which is a necessary condition for causality. Then, the run-time causal inference mechanism learns causal relationships dynamically during query processing. This is done by removing some of the temporal precedence relationships that do not exhibit causality in the presence of other events in the event precedence model. This paper presents two query processing algorithms -- one performs exhaustive search on the model and the other performs a more efficient reduced search with early termination. Experiments using two real datasets (cascading blackouts in power systems and web page views) verify the effectiveness of the probabilistic top-k prediction queries and the efficiency of the algorithms. Specifically, the reduced search algorithm reduced runtime, relative to exhaustive search, by 25-80% (depending on the application) with only a small reduction in accuracy.
This work proposes a unified heuristic algorithm for a large class of earliness-tardiness (E-T) scheduling problems. We consider single/parallel machine E-T problems that may or may not consider some additional features such as idle time, setup times and release dates. In addition, we also consider those problems whose objective is to minimize either the total (average) weighted completion time or the total (average) weighted flow time, which arise as particular cases when the due dates of all jobs are either set to zero or to their associated release dates, respectively. The developed local search based metaheuristic framework is quite simple, but at the same time relies on sophisticated procedures for efficiently performing local search according to the characteristics of the problem. We present efficient move evaluation approaches for some parallel machine problems that generalize the existing ones for single machine problems. The algorithm was tested in hundreds of instances of several E-T problems and particular cases. The results obtained show that our unified heuristic is capable of producing high quality solutions when compared to the best ones available in the literature that were obtained by specific methods. Moreover, we provide an extensive annotated bibliography on the problems related to those considered in this work, where we not only indicate the approach(es) used in each publication, but we also point out the characteristics of the problem(s) considered. Beyond that, we classify the existing methods in different categories so as to have a better idea of the popularity of each type of solution procedure.
An approach for game bot detection in MMORPGs is proposed based on the analysis of game playing behavior. Since MMORPGs are large scale games, users can play in various ways. This variety in playing behavior makes it hard to detect game bots based on play behaviors. In order to cope with this problem, the proposed approach observes game playing behaviors of users and groups them by their behavioral similarities. Then, it develops a local bot detection model for each player group. Since the locally optimized models can more accurately detect game bots within each player group, the combination of those models brings about overall improvement. For a practical purpose of reducing the workloads of the game servers in service, the game data is collected at a low resolution in time. Behavioral features are selected and developed to accurately detect game bots with the low resolution data, considering common aspects of MMORPG playing. Through the experiment with the real data from a game currently in service, it is shown that the proposed local model approach yields more accurate results.
Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states. It is in general very challenging to construct and infer hidden states as they often depend on the agent's entire interaction history and may require substantial domain knowledge. In this work, we investigate a deep-learning approach to learning the representation of states in partially observable tasks, with minimal prior knowledge of the domain. In particular, we propose a new family of hybrid models that combines the strength of both supervised learning (SL) and reinforcement learning (RL), trained in a joint fashion: The SL component can be a recurrent neural networks (RNN) or its long short-term memory (LSTM) version, which is equipped with the desired property of being able to capture long-term dependency on history, thus providing an effective way of learning the representation of hidden states. The RL component is a deep Q-network (DQN) that learns to optimize the control for maximizing long-term rewards. Extensive experiments in a direct mailing campaign problem demonstrate the effectiveness and advantages of the proposed approach, which performs the best among a set of previous state-of-the-art methods.
This work presents and analyzes three convolutional neural network (CNN) models for efficient pixelwise classification of images. When using convolutional neural networks to classify single pixels in patches of a whole image, a lot of redundant computations are carried out when using sliding window networks. This set of new architectures solve this issue by either removing redundant computations or using fully convolutional architectures that inherently predict many pixels at once. The implementations of the three models are accessible through a new utility on top of the Caffe library. The utility provides support for a wide range of image input and output formats, pre-processing parameters and methods to equalize the label histogram during training. The Caffe library has been extended by new layers and a new backend for availability on a wider range of hardware such as CPUs and GPUs through OpenCL. On AMD GPUs, speedups of $54\times$ (SK-Net), $437\times$ (U-Net) and $320\times$ (USK-Net) have been observed, taking the SK equivalent SW (sliding window) network as the baseline. The label throughput is up to one megapixel per second. The analyzed neural networks have distinctive characteristics that apply during training or processing, and not every data set is suitable to every architecture. The quality of the predictions is assessed on two neural tissue data sets, of which one is the ISBI 2012 challenge data set. Two different loss functions, Malis loss and Softmax loss, were used during training. The whole pipeline, consisting of models, interface and modified Caffe library, is available as Open Source software under the working title Project Greentea.
Studies on computational neuroscience through functional magnetic resonance imaging (fMRI) and following biological inspired system stated that human action recognition in the brain of mammalian leads two distinct pathways in the model, which are specialized for analysis of motion (optic flow) and form information. Principally, we have defined a novel and robust form features applying active basis model as form extractor in form pathway in the biological inspired model. An unbalanced synergetic neural net-work classifies shapes and structures of human objects along with tuning its attention parameter by quantum particle swarm optimization (QPSO) via initiation of Centroidal Voronoi Tessellations. These tools utilized and justified as strong tools for following biological system model in form pathway. But the final decision has done by combination of ultimate outcomes of both pathways via fuzzy inference which increases novality of proposed model. Combination of these two brain pathways is done by considering each feature sets in Gaussian membership functions with fuzzy product inference method. Two configurations have been proposed for form pathway: applying multi-prototype human action templates using two time synergetic neural network for obtaining uniform template regarding each actions, and second scenario that it uses abstracting human action in four key-frames. Experimental results showed promising accuracy performance on different datasets (KTH and Weizmann).
The Singleton Property (SP) approach has been proposed for representing and querying metadata about RDF triples such as provenance, time, location, and evidence. In this approach, one singleton property is created to uniquely represent a relationship in a particular context, and in general, generates a large property hierarchy in the schema. It has become the subject of important questions from Semantic Web practitioners. Can an existing reasoner recognize the singleton property triples? And how? If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples? Or would the large property hierarchy affect the reasoners in some way? We address these questions in this paper and present our study about the reasoning aspects of the singleton properties. We propose a simple mechanism to enable existing reasoners to recognize the singleton property triples, as well as to infer the data triples described by the singleton property triples. We evaluate the effect of the singleton property triples in the reasoning processes by comparing the performance on RDF datasets with and without singleton properties. Our evaluation uses as benchmark the LUBM datasets and the LUBM-SP datasets derived from LUBM with temporal information added through singleton properties.
In many human brain network studies, we do not have sufficient number (n) of images relative to the number (p) of voxels due to the prohibitively expensive cost of scanning enough subjects. Thus, brain network models usually suffer the small-n large-p problem. Such a problem is often remedied by sparse network models, which are usually solved numerically by optimizing L1-penalties. Unfortunately, due to the computational bottleneck associated with optimizing L1-penalties, it is not practical to apply such methods to construct large-scale brain networks at the voxel-level. In this paper, we propose a new scalable sparse network model using cross-correlations that bypass the computational bottleneck. Our model can build sparse brain networks at the voxel level with p > 25000. Instead of using a single sparse parameter that may not be optimal in other studies and datasets, the computational speed gain enables us to analyze the collection of networks at every possible sparse parameter in a coherent mathematical framework via persistent homology. The method is subsequently applied in determining the extent of heritability on a functional brain network at the voxel-level for the first time using twin fMRI.
Systematic use of the published results of randomized clinical trials is increasingly important in evidence-based medicine. In order to collate and analyze the results from potentially numerous trials, evidence tables are used to represent trials concerning a set of interventions of interest. An evidence table has columns for the patient group, for each of the interventions being compared, for the criterion for the comparison (e.g. proportion who survived after 5 years from treatment), and for each of the results. Currently, it is a labour-intensive activity to read each published paper and extract the information for each field in an evidence table. There have been some NLP studies investigating how some of the features from papers can be extracted, or at least the relevant sentences identified. However, there is a lack of an NLP system for the systematic extraction of each item of information required for an evidence table. We address this need by a combination of a maximum entropy classifier, and integer linear programming. We use the later to handle constraints on what is an acceptable classification of the features to be extracted. With experimental results, we demonstrate substantial advantages in using global constraints (such as the features describing the patient group, and the interventions, must occur before the features describing the results of the comparison).
A generic algorithm for the extraction of probabilistic (Bayesian) information about model parameters from data is presented. The algorithm propagates an ensemble of particles in the product space of model parameters and outputs. Each particle update consists of a random jump in parameter space followed by a simulation of a model output and a Metropolis acceptance/rejection step based on a comparison of the simulated output to the data. The distance of a particle to the data is interpreted as an energy and the algorithm is reducing the associated temperature of the ensemble such that entropy production is minimized. If this simulated annealing is not too fast compared to the mixing speed in parameter space, the parameter marginal of the ensemble approaches the Bayesian posterior distribution. Annealing is adaptive and depends on certain extensive thermodynamic quantities that can easily be measured throughout run-time. In the general case, we propose annealing with a constant entropy production rate, which is optimal as long as annealing is not too fast. For the practically relevant special case of no prior knowledge, we derive an optimal fast annealing schedule with a non-constant entropy production rate. The algorithm does not require the calculation of the density of the model likelihood, which makes it interesting for Bayesian parameter inference with stochastic models, whose likelihood functions are typically very high dimensional integrals.
An open problem in robotics is that of using vision to identify a robot's own body and the world around it. Many models attempt to recover the traditional C-space parameters. Instead, we propose an alternative C-space by deriving generalized coordinates from $n$ images of the robot. We show that the space of such images is bijective to the motion space, so these images lie on a manifold $\mathcal{V}$ homeomorphic to the canonical C-space. We now approximate this manifold as a set of $n$ neighbourhood tangent spaces that result in a graph, which we call the Visual Roadmap (VRM). Given a new robot image, we perform inverse kinematics visually by interpolating between nearby images in the image space. Obstacles are projected onto the VRM in $O(n)$ time by superimposition of images, leading to the identification of collision poses. The edges joining the free nodes can now be checked with a visual local planner, and free-space motions computed in $O(nlogn)$ time. This enables us to plan paths in the image space for a robot manipulator with unknown link geometries, DOF, kinematics, obstacles, and camera pose. We sketch the proofs for the main theoretical ideas, identify the assumptions, and demonstrate the approach for both articulated and mobile robots. We also investigate the feasibility of the process by investigating various metrics and image sampling densities, and demonstrate it on simulated and real robots.
Protein-protein interaction (PPI) prediction is an important problem in machine learning and computational biology. However, there is no data set for training or evaluation purposes, where all the instances are accurately labeled. Instead, what is available are instances of positive class (with possibly noisy labels) and no instances of negative class. The non-availability of negative class data is typically handled with the observation that randomly chosen protein-pairs have a nearly 100% chance of being negative class, as only 1 in 1,500 protein pairs expected is expected to be an interacting pair. In this paper, we focused on the problem that non-availability of accurately labeled testing data sets in the domain of protein-protein interaction (PPI) prediction may lead to biased evaluation results. We first showed that not acknowledging the inherent skew in the interactome (i.e. rare occurrence of positive instances) leads to an over-estimated accuracy of the predictor. Then we show that, with the belief that positive interactions are a rare category, sampling random pairs of proteins excluding known interacting proteins set as the negative testing data set could lead to an under-estimated evaluation result. We formalized those two problems to validate the above claim, and based on the formalization, we proposed a balancing method to cancel out the over-estimation with under-estimation. Finally, our experiments validated the theoretical aspects and showed that this balancing evaluation could evaluate the exact performance without availability of golden standard data sets.
While most approaches to automatically recognizing entailment relations have used classifiers employing hand engineered features derived from complex natural language processing pipelines, in practice their performance has been only slightly better than bag-of-word pair classifiers using only lexical similarity. The only attempt so far to build an end-to-end differentiable neural network for entailment failed to outperform such a simple similarity classifier. In this paper, we propose a neural model that reads two sentences to determine entailment using long short-term memory units. We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases. Furthermore, we present a qualitative analysis of attention weights produced by this model, demonstrating such reasoning capabilities. On a large entailment dataset this model outperforms the previous best neural model and a classifier with engineered features by a substantial margin. It is the first generic end-to-end differentiable system that achieves state-of-the-art accuracy on a textual entailment dataset.
Max-product Belief Propagation (BP) is a popular message-passing algorithm for computing a Maximum-A-Posteriori (MAP) assignment over a distribution represented by a Graphical Model (GM). It has been shown that BP can solve a number of combinatorial optimization problems including minimum weight matching, shortest path, network flow and vertex cover under the following common assumption: the respective Linear Programming (LP) relaxation is tight, i.e., no integrality gap is present. However, when LP shows an integrality gap, no model has been known which can be solved systematically via sequential applications of BP. In this paper, we develop the first such algorithm, coined Blossom-BP, for solving the minimum weight matching problem over arbitrary graphs. Each step of the sequential algorithm requires applying BP over a modified graph constructed by contractions and expansions of blossoms, i.e., odd sets of vertices. Our scheme guarantees termination in O(n^2) of BP runs, where n is the number of vertices in the original graph. In essence, the Blossom-BP offers a distributed version of the celebrated Edmonds' Blossom algorithm by jumping at once over many sub-steps with a single BP. Moreover, our result provides an interpretation of the Edmonds' algorithm as a sequence of LPs.
This study explores the design and control of the behaviour of agents and robots using simple circuits of spiking neurons and Spike Timing Dependent Plasticity (STDP) as a mechanism of associative and unsupervised learning. Based on a "reward and punishment" classical conditioning, it is demonstrated that these robots learnt to identify and avoid obstacles as well as to identify and look for rewarding stimuli. Using the simulation and programming environment NetLogo, a software engine for the Integrate and Fire model was developed, which allowed us to monitor in discrete time steps the dynamics of each single neuron, synapse and spike in the proposed neural networks. These spiking neural networks (SNN) served as simple brains for the experimental robots. The Lego Mindstorms robot kit was used for the embodiment of the simulated agents. In this paper the topological building blocks are presented as well as the neural parameters required to reproduce the experiments. This paper summarizes the resulting behaviour as well as the observed dynamics of the neural circuits. The Internet-link to the NetLogo code is included in the annex.
Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.
This paper presents an ontology-based approach for the design of a collaborative business process model (CBP). This CBP is considered as a specification of needs in order to build a collaboration information system (CIS) for a network of organisations. The study is a part of a model driven engineering approach of the CIS in a specific enterprise interoperability framework that will be summarised. An adaptation of the Business Process Modeling Notation (BPMN) is used to represent the CBP model. We develop a knowledge-based system (KbS) which is composed of three main parts: knowledge gathering, knowledge representation and reasoning, and collaborative business process modelling. The first part starts from a high abstraction level where knowledge from business partners is captured. A collaboration ontology is defined in order to provide a structure to store and use the knowledge captured. In parallel, we try to reuse generic existing knowledge about business processes from the MIT Process Handbook repository. This results in a collaboration process ontology that is also described. A set of rules is defined in order to extract knowledge about fragments of the CBP model from the two previous ontologies. These fragments are finally assembled in the third part of the KbS. A prototype of the KbS has been developed in order to implement and support this approach. The prototype is a computer-aided design tool of the CBP. In this paper, we will present the theoretical aspects of each part of this KbS as well as the tools that we developed and used in order to support its functionalities.
The Mediation Information System Engineering project is currently finishing its second iteration (MISE 2.0). The main objective of this scientific project is to provide any emerging collaborative situation with methods and tools to deploy a Mediation Information System (MIS). MISE 2.0 aims at defining and designing a service-based platform, dedicated to initiating and supporting the interoperability of collaborative situations among potential partners. This MISE 2.0 platform implements a model-driven engineering approach to the design of a service-oriented MIS dedicated to supporting the collaborative situation. This approach is structured in three layers, each providing their own key innovative points: (i) the gathering of individual and collaborative knowledge to provide appropriate collaborative business behaviour (key point: knowledge management, including semantics, exploitation and capitalization), (ii) deployment of a mediation information system able to computerize the previously deduced collaborative processes (key point: the automatic generation of collaborative workflows, including connection with existing devices or services) (iii) the management of the agility of the obtained collaborative network of organizations (key point: supervision of collaborative situations and relevant exploitation of the gathered data). MISE covers business issues (through BPM), technical issues (through an SOA) and agility issues of collaborative situations (through EDA).
Symbolic (or Literal) Neutrosophic Theory is referring to the use of abstract symbols (i.e. the letters T, I, F, or their refined indexed letters Tj, Ik, Fl) in neutrosophics. We extend the dialectical triad thesis-antithesis-synthesis to the neutrosophic tetrad thesis-antithesis-neutrothesis-neutrosynthesis. The we introduce the neutrosophic system that is a quasi or (t,i,f) classical system, in the sense that the neutrosophic system deals with quasi-terms (concepts, attributes, etc.). Then the notions of Neutrosophic Axiom, Neutrosophic Deducibility, Degree of Contradiction (Dissimilarity) of Two Neutrosophic Axioms, etc. Afterwards a new type of structures, called (t, i, f) Neutrosophic Structures, and we show particular cases of such structures in geometry and in algebra. Also, a short history of the neutrosophic set, neutrosophic numerical components and neutrosophic literal components, neutrosophic numbers, etc. We construct examples of splitting the literal indeterminacy (I) into literal subindeterminacies (I1, I2, and so on, Ir), and to define a multiplication law of these literal subindeterminacies in order to be able to build refined I neutrosophic algebraic structures. We define three neutrosophic actions and their properties. We then introduce the prevalence order on T,I,F with respect to a given neutrosophic operator. And the refinement of neutrosophic entities A, neutA, and antiA. Then we extend the classical logical operators to neutrosophic literal (symbolic) logical operators and to refined literal (symbolic) logical operators, and we define the refinement neutrosophic literal (symbolic) space. We introduce the neutrosophic quadruple numbers (a+bT+cI+dF) and the refined neutrosophic quadruple numbers. Then we define an absorbance law, based on a prevalence order, in order to multiply the neutrosophic quadruple numbers.
We present a very general geometrico-dynamical description of physical or more abstract entities, called the 'general tension-reduction' (GTR) model, where not only states, but also measurement-interactions can be represented, and the associated outcome probabilities calculated. Underlying the model is the hypothesis that indeterminism manifests as a consequence of unavoidable fluctuations in the experimental context, in accordance with the 'hidden-measurements interpretation' of quantum mechanics. When the structure of the state space is Hilbertian, and measurements are of the 'universal' kind, i.e., are the result of an average over all possible ways of selecting an outcome, the GTR-model provides the same predictions of the Born rule, and therefore provides a natural completed version of quantum mechanics. However, when the structure of the state space is non-Hilbertian and/or not all possible ways of selecting an outcome are available to be actualized, the predictions of the model generally differ from the quantum ones, especially when sequential measurements are considered. Some paradigmatic examples will be discussed, taken from physics and human cognition. Particular attention will be given to some known psychological effects, like question order effects and response replicability, which we show are able to generate non-Hilbertian statistics. We also suggest a realistic interpretation of the GTR-model, when applied to human cognition and decision, which we think could become the generally adopted interpretative framework in quantum cognition research.
We proposed Neural Enquirer as a neural network architecture to execute a natural language (NL) query on a knowledge-base (KB) for answers. Basically, Neural Enquirer finds the distributed representation of a query and then executes it on knowledge-base tables to obtain the answer as one of the values in the tables. Unlike similar efforts in end-to-end training of semantic parsers, Neural Enquirer is fully "neuralized": it not only gives distributional representation of the query and the knowledge-base, but also realizes the execution of compositional queries as a series of differentiable operations, with intermediate results (consisting of annotations of the tables at different levels) saved on multiple layers of memory. Neural Enquirer can be trained with gradient descent, with which not only the parameters of the controlling components and semantic parsing component, but also the embeddings of the tables and query words can be learned from scratch. The training can be done in an end-to-end fashion, but it can take stronger guidance, e.g., the step-by-step supervision for complicated queries, and benefit from it. Neural Enquirer is one step towards building neural network systems which seek to understand language by executing it on real-world. Our experiments show that Neural Enquirer can learn to execute fairly complicated NL queries on tables with rich structures.
We first discuss certain problems with the classical probabilistic approach for assessing forensic evidence, in particular its inability to distinguish between lack of belief and disbelief, and its inability to model complete ignorance within a given population. We then discuss Shafer belief functions, a generalization of probability distributions, which can deal with both these objections. We use a calculus of belief functions which does not use the much criticized Dempster rule of combination, but only the very natural Dempster-Shafer conditioning. We then apply this calculus to some classical forensic problems like the various island problems and the problem of parental identification. If we impose no prior knowledge apart from assuming that the culprit or parent belongs to a given population (something which is possible in our setting), then our answers differ from the classical ones when uniform or other priors are imposed. We can actually retrieve the classical answers by imposing the relevant priors, so our setup can and should be interpreted as a generalization of the classical methodology, allowing more flexibility. We show how our calculus can be used to develop an analogue of Bayes' rule, with belief functions instead of classical probabilities. We also discuss consequences of our theory for legal practice.
There is a widespread need for statistical methods that can analyze high-dimensional datasets with- out imposing restrictive or opaque modeling assumptions. This paper describes a domain-general data analysis method called CrossCat. CrossCat infers multiple non-overlapping views of the data, each consisting of a subset of the variables, and uses a separate nonparametric mixture to model each view. CrossCat is based on approximately Bayesian inference in a hierarchical, nonparamet- ric model for data tables. This model consists of a Dirichlet process mixture over the columns of a data table in which each mixture component is itself an independent Dirichlet process mixture over the rows; the inner mixture components are simple parametric models whose form depends on the types of data in the table. CrossCat combines strengths of mixture modeling and Bayesian net- work structure learning. Like mixture modeling, CrossCat can model a broad class of distributions by positing latent variables, and produces representations that can be efficiently conditioned and sampled from for prediction. Like Bayesian networks, CrossCat represents the dependencies and independencies between variables, and thus remains accurate when there are multiple statistical signals. Inference is done via a scalable Gibbs sampling scheme; this paper shows that it works well in practice. This paper also includes empirical results on heterogeneous tabular data of up to 10 million cells, such as hospital cost and quality measures, voting records, unemployment rates, gene expression measurements, and images of handwritten digits. CrossCat infers structure that is consistent with accepted findings and common-sense knowledge in multiple domains and yields predictive accuracy competitive with generative, discriminative, and model-free alternatives.
Once known to be used exclusively in military domain, unmanned aerial vehicles (drones) have stepped up to become a part of new logistic method in commercial sector called "last-mile delivery". In this novel approach, small unmanned aerial vehicles (UAV), also known as drones, are deployed alongside with trucks to deliver goods to customers in order to improve the service quality or reduce the transportation cost. It gives rise to a new variant of the traveling salesman problem (TSP), of which we call TSP with drone (TSP-D). In this article, we consider a variant of TSP-D where the main objective is to minimize the total transportation cost. We also propose two heuristics: "Drone First, Truck Second" (DFTS) and "Truck First, Drone Second" (TFDS), to effectively solve the problem. The former constructs route for drone first while the latter constructs route for truck first. We solve a TSP to generate route for truck and propose a mixed integer programming (MIP) formulation with different profit functions to build route for drone. Numerical results obtained on many instances with different sizes and characteristics are presented. Recommendations on promising algorithm choices are also provided.
This paper presents a restricted visual Turing test (VTT) for story-line based deep understanding in long-term and multi-camera captured videos. Given a set of videos of a scene (such as a multi-room office, a garden, and a parking lot.) and a sequence of story-line based queries, the task is to provide answers either simply in binary form "true/false" (to a polar query) or in an accurate natural language description (to a non-polar query). Queries, polar or non-polar, consist of view-based queries which can be answered from a particular camera view and scene-centered queries which involves joint inference across different cameras. The story lines are collected to cover spatial, temporal and causal understanding of input videos. The data and queries distinguish our VTT from recently proposed visual question answering in images and video captioning. A vision system is proposed to perform joint video and query parsing which integrates different vision modules, a knowledge base and a query engine. The system provides unified interfaces for different modules so that individual modules can be reconfigured to test a new method. We provide a benchmark dataset and a toolkit for ontology guided story-line query generation which consists of about 93.5 hours videos captured in four different locations and 3,426 queries split into 127 story lines. We also provide a baseline implementation and result analyses.
In this dissertation, we analyze the computational properties of game-theoretic centrality measures. The key idea behind game-theoretic approach to network analysis is to treat nodes as players in a cooperative game, where the value of each coalition of nodes is determined by certain graph properties. Next, the centrality of any individual node is determined by a chosen game-theoretic solution concept (notably, the Shapley value) in the same way as the payoff of a player in a cooperative game. On one hand, the advantage of game-theoretic centrality measures is that nodes are ranked not only according to their individual roles but also according to how they contribute to the role played by all possible subsets of nodes. On the other hand, the disadvantage is that the game-theoretic solution concepts are typically computationally challenging. The main contribution of this dissertation is that we show that a wide variety of game-theoretic solution concepts on networks can be computed in polynomial time. Our focus is on centralities based on the Shapley value and its various extensions, such as the Semivalues and Coalitional Semivalues. Furthermore, we prove #P-hardness of computing the Shapley value in connectivity games and propose an algorithm to compute it. Finally, we analyse computational properties of generalized version of cooperative games in which order of player matters. We propose a new representation for such games, called generalized marginal contribution networks, that allows for polynomial computation in the size of the representation of two dedicated extensions of the Shapley value to this class of games.
To explore the hypothesis that KIC 8462852's aperiodic dimming is caused by artificial megastructures in orbit (Wright et al. 2015), rather than a natural cause such as cometary fragments in a highly elliptical orbit (Marengo et al. 2015), we searched for electromagnetic signals from KIC 8462852 indicative of extraterrestrial intelligence. The primary observations were in the visible optical regime using the Boquete Optical SETI Observatory in Panama. In addition, as a preparatory exercise for the possible future detection of a candidate signal (Heidmann 1991), three of six observing runs simultaneously searched radio frequencies at the Allen Telescope Array in California. No periodic optical signals greater than 67 photons/m2 within a time frame of 25 ns were seen. This limit corresponds to isotropic optical pulses of 8E22 joules. If, however, any inhabitants of KIC 8462852 were targeting our solar system (Shostak & Villard 2004), the required energy would be reduced greatly. The limits on narrowband radio signals were 180 - 300 Jy Hz at 1 and 8 GHz, respectively, corresponding to a transmitter with an effective isotropic radiated power of 4E15 W (and 7E15 W) at the distance of KIC 8462852. While these powers requirements are high, even modest targeting could - just as for optical signals - lower these numbers substantially.
Many scientific datasets are of high dimension, and the analysis usually requires visual manipulation by retaining the most important structures of data. Principal curve is a widely used approach for this purpose. However, many existing methods work only for data with structures that are not self-intersected, which is quite restrictive for real applications. A few methods can overcome the above problem, but they either require complicated human-made rules for a specific task with lack of convergence guarantee and adaption flexibility to different tasks, or cannot obtain explicit structures of data. To address these issues, we develop a new regularized principal graph learning framework that captures the local information of the underlying graph structure based on reversed graph embedding. As showcases, models that can learn a spanning tree or a weighted undirected $\ell_1$ graph are proposed, and a new learning algorithm is developed that learns a set of principal points and a graph structure from data, simultaneously. The new algorithm is simple with guaranteed convergence. We then extend the proposed framework to deal with large-scale data. Experimental results on various synthetic and six real world datasets show that the proposed method compares favorably with baselines and can uncover the underlying structure correctly.
The proliferation of contextualized knowledge in the Semantic Web (SW) has led to the popularity of knowledge formats such as \emph{quads} in the SW community. A quad is an extension of an RDF triple with contextual information of the triple. In this paper, we study the problem of query answering over quads augmented with forall-existential bridge rules that enable interoperability of reasoning between triples in various contexts. We call a set of quads together with such expressive bridge rules, a quad-system. Query answering over quad-systems is undecidable, in general. We derive decidable classes of quad-systems, for which query answering can be done using forward chaining. Sound, complete and terminating procedures, which are adaptations of the well known chase algorithm, are provided for these classes for deciding query entailment. Safe, msafe, and csafe class of quad-systems restrict the structure of blank nodes generated during the chase computation process to be directed acyclic graphs (DAGs) of bounded depth. RR and restricted RR classes do not allow the generation of blank nodes during the chase computation process. Both data and combined complexity of query entailment has been established for the classes derived. We further show that quad-systems are equivalent to forall-existential rules whose predicates are restricted to ternary arity, modulo polynomial time translations. We subsequently show that the technique of safety, strictly subsumes in expressivity, some of the well known and expressive techniques, such as joint acyclicity and model faithful acyclicity, used for decidability guarantees in the realm of forall-existential rules.
Background: Lung cancer was known as primary cancers and the survival rate of cancer is about 15%. Early detection of lung cancer is the leading factor in survival rate. All symptoms (features) of lung cancer do not appear until the cancer spreads to other areas. It needs an accurate early detection of lung cancer, for increasing the survival rate. For accurate detection, it need characterizes efficient features and delete redundancy features among all features. Feature selection is the problem of selecting informative features among all features. Materials and Methods: Lung cancer database consist of 32 patient records with 57 features. This database collected by Hong and Youngand indexed in the University of California Irvine repository. Experimental contents include the extracted from the clinical data and X-ray data, etc. The data described 3 types of pathological lung cancers and all features are taking an integer value 0-3. In our study, new method is proposed for identify efficient features of lung cancer. It is based on Hyper-Heuristic. Results: We obtained an accuracy of 80.63% using reduced 11 feature set. The proposed method compare to the accuracy of 5 machine learning feature selections. The accuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75. Conclusions: The proposed method has better performance with the highest level of accuracy. Therefore, the proposed model is recommended for identifying an efficient symptom of Disease. These finding are very important in health research, particularly in allocation of medical resources for patients who predicted as high-risks
Reverse engineering the brain is proving difficult, perhaps impossible. While many believe that this is just a matter of time and effort, a different approach might help. Here, we describe a very simple idea which explains the power of the brain as well as its structure, exploiting complex dynamics rather than abstracting it away. Just as a Turing Machine is a Universal Digital Computer operating in a world of symbols, we propose that the brain is a Universal Dynamical Systems Modeller, evolved bottom-up (itself using nested networks of interconnected, self-organised dynamical systems) to prosper in a world of dynamical systems. Recent progress in Applied Mathematics has produced startling evidence of what happens when abstract Dynamical Systems interact. Key latent information describing system A can be extracted by system B from very simple signals, and signals can be used by one system to control and manipulate others. Using these facts, we show how a region of the neocortex uses its dynamics to intrinsically "compute" about the external and internal world. Building on an existing "static" model of cortical computation (Hawkins' Hierarchical Temporal Memory - HTM), we describe how a region of neocortex can be viewed as a network of components which together form a Dynamical Systems modelling module, connected via sensory and motor pathways to the external world, and forming part of a larger dynamical network in the brain. Empirical modelling and simulations of Dynamical HTM are possible with simple extensions and combinations of currently existing open source software. We list a number of relevant projects.
Since the introduction of the stable marriage problem (SMP) by Gale and Shapley (1962), several variants and extensions have been investigated. While this variety is useful to widen the application potential, each variant requires a new algorithm for finding the stable matchings. To address this issue, we propose an encoding of the SMP using answer set programming (ASP), which can straightforwardly be adapted and extended to suit the needs of specific applications. The use of ASP also means that we can take advantage of highly efficient off-the-shelf solvers. To illustrate the flexibility of our approach, we show how our ASP encoding naturally allows us to select optimal stable matchings, i.e. matchings that are optimal according to some user-specified criterion. To the best of our knowledge, our encoding offers the first exact implementation to find sex-equal, minimum regret, egalitarian or maximum cardinality stable matchings for SMP instances in which individuals may designate unacceptable partners and ties between preferences are allowed. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
An active object recognition system has the advantage of being able to act in the environment to capture images that are more suited for training and that lead to better performance at test time. In this paper, we propose a deep convolutional neural network for active object recognition that simultaneously predicts the object label, and selects the next action to perform on the object with the aim of improving recognition performance. We treat active object recognition as a reinforcement learning problem and derive the cost function to train the network for joint prediction of the object label and the action. A generative model of object similarities based on the Dirichlet distribution is proposed and embedded in the network for encoding the state of the system. The training is carried out by simultaneously minimizing the label and action prediction errors using gradient descent. We empirically show that the proposed network is able to predict both the object label and the actions on GERMS, a dataset for active object recognition. We compare the test label prediction accuracy of the proposed model with Dirichlet and Naive Bayes state encoding. The results of experiments suggest that the proposed model equipped with Dirichlet state encoding is superior in performance, and selects images that lead to better training and higher accuracy of label prediction at test time.
We study Matching and other related problems in a partial information setting where the agents' utilities for being matched to other agents are hidden and the mechanism only has access to ordinal preference information. Our model is motivated by the fact that in many settings, agents cannot express the numerical values of their utility for different outcomes, but are still able to rank the outcomes in their order of preference. Specifically, we study problems where the ground truth exists in the form of a weighted graph, and look to design algorithms that approximate the true optimum matching using only the preference orderings for each agent (induced by the hidden weights) as input. If no restrictions are placed on the weights, then one cannot hope to do better than the simple greedy algorithm, which yields a half optimal matching. Perhaps surprisingly, we show that by imposing a little structure on the weights, we can improve upon the trivial algorithm significantly: we design a 1.6-approximation algorithm for instances where the hidden weights obey the metric inequality. Using our algorithms for matching as a black-box, we also design new approximation algorithms for other closely related problems: these include a a 3.2-approximation for the problem of clustering agents into equal sized partitions, a 4-approximation algorithm for Densest k-subgraph, and a 2.14-approximation algorithm for Max TSP. These results are the first non-trivial ordinal approximation algorithms for such problems, and indicate that we can design robust algorithms even when we are agnostic to the precise agent utilities.
Gaussian Processes (GPs) are widely used tools in statistics, machine learning, robotics, computer vision, and scientific computation. However, despite their popularity, they can be difficult to apply; all but the simplest classification or regression applications require specification and inference over complex covariance functions that do not admit simple analytical posteriors. This paper shows how to embed Gaussian processes in any higher-order probabilistic programming language, using an idiom based on memoization, and demonstrates its utility by implementing and extending classic and state-of-the-art GP applications. The interface to Gaussian processes, called gpmem, takes an arbitrary real-valued computational process as input and returns a statistical emulator that automatically improve as the original process is invoked and its input-output behavior is recorded. The flexibility of gpmem is illustrated via three applications: (i) robust GP regression with hierarchical hyper-parameter learning, (ii) discovering symbolic expressions from time-series data by fully Bayesian structure learning over kernels generated by a stochastic grammar, and (iii) a bandit formulation of Bayesian optimization with automatic inference and action selection. All applications share a single 50-line Python library and require fewer than 20 lines of probabilistic code each.
An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people's past choices can inform our inferences about their likes and preferences. If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning. That is, given a prior on utility functions and some observed choices, we invert an optimal decision-making process to infer a posterior distribution on utility functions. However, people often deviate from approximate optimality. They have false beliefs, their planning is sub-optimal, and their choices may be temporally inconsistent due to hyperbolic discounting and other biases. We demonstrate how to incorporate these deviations into algorithms for preference inference by constructing generative models of planning for agents who are subject to false beliefs and time inconsistency. We explore the inferences these models make about preferences, beliefs, and biases. We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. Results show that human subjects (like our model) explain choices in terms of systematic deviations from optimal behavior and suggest that they take such deviations into account when inferring preferences.
We study mechanisms for candidate selection that seek to minimize the social cost, where voters and candidates are associated with points in some underlying metric space. The social cost of a candidate is the sum of its distances to each voter. Some of our work assumes that these points can be modeled on a real line, but other results of ours are more general. A question closely related to candidate selection is that of minimizing the sum of distances for facility location. The difference is that in our setting there is a fixed set of candidates, whereas the large body of work on facility location seems to consider every point in the metric space to be a possible candidate. This gives rise to three types of mechanisms which differ in the granularity of their input space (voting, ranking and location mechanisms). We study the relationships between these three classes of mechanisms. While it may seem that Black's 1948 median algorithm is optimal for candidate selection on the line, this is not the case. We give matching upper and lower bounds for a variety of settings. In particular, when candidates and voters are on the line, our universally truthful spike mechanism gives a [tight] approximation of two. When assessing candidate selection mechanisms, we seek several desirable properties: (a) efficiency (minimizing the social cost) (b) truthfulness (dominant strategy incentive compatibility) and (c) simplicity (a smaller input space). We quantify the effect that truthfulness and simplicity impose on the efficiency.
Deep convolutional neural networks have led to breakthrough results in numerous practical machine learning tasks such as classification of images in the ImageNet data set, control-policy-learning to play Atari games or the board game Go, and image captioning. Many of these applications first perform feature extraction and then feed the results thereof into a trainable classifier. The mathematical analysis of deep convolutional neural networks for feature extraction was initiated by Mallat, 2012. Specifically, Mallat considered so-called scattering networks based on a wavelet transform followed by the modulus non-linearity in each network layer, and proved translation invariance (asymptotically in the wavelet scale parameter) and deformation stability of the corresponding feature extractor. This paper complements Mallat's results by developing a theory that encompasses general convolutional transforms, or in more technical parlance, general semi-discrete frames (including Weyl-Heisenberg filters, curvelets, shearlets, ridgelets, wavelets, and learned filters), general Lipschitz-continuous non-linearities (e.g., rectified linear units, shifted logistic sigmoids, hyperbolic tangents, and modulus functions), and general Lipschitz-continuous pooling operators emulating, e.g., sub-sampling and averaging. In addition, all of these elements can be different in different network layers. For the resulting feature extractor we prove a translation invariance result of vertical nature in the sense of the features becoming progressively more translation-invariant with increasing network depth, and we establish deformation sensitivity bounds that apply to signal classes such as, e.g., band-limited functions, cartoon functions, and Lipschitz functions.
Online reviews are often our first port of call when considering products and purchases online. When evaluating a potential purchase, we may have a specific query in mind, e.g. `will this baby seat fit in the overhead compartment of a 747?' or `will I like this album if I liked Taylor Swift's 1989?'. To answer such questions we must either wade through huge volumes of consumer reviews hoping to find one that is relevant, or otherwise pose our question directly to the community via a Q/A system. In this paper we hope to fuse these two paradigms: given a large volume of previously answered queries about products, we hope to automatically learn whether a review of a product is relevant to a given query. We formulate this as a machine learning problem using a mixture-of-experts-type framework---here each review is an `expert' that gets to vote on the response to a particular query; simultaneously we learn a relevance function such that `relevant' reviews are those that vote correctly. At test time this learned relevance function allows us to surface reviews that are relevant to new queries on-demand. We evaluate our system, Moqa, on a novel corpus of 1.4 million questions (and answers) and 13 million reviews. We show quantitatively that it is effective at addressing both binary and open-ended queries, and qualitatively that it surfaces reviews that human evaluators consider to be relevant.
In today's world, we follow news which is distributed globally. Significant events are reported by different sources and in different languages. In this work, we address the problem of tracking of events in a large multilingual stream. Within a recently developed system Event Registry we examine two aspects of this problem: how to compare articles in different languages and how to link collections of articles in different languages which refer to the same event. Taking a multilingual stream and clusters of articles from each language, we compare different cross-lingual document similarity measures based on Wikipedia. This allows us to compute the similarity of any two articles regardless of language. Building on previous work, we show there are methods which scale well and can compute a meaningful similarity between articles from languages with little or no direct overlap in the training data. Using this capability, we then propose an approach to link clusters of articles across languages which represent the same event. We provide an extensive evaluation of the system as a whole, as well as an evaluation of the quality and robustness of the similarity measure and the linking algorithm.
Attribute reduction is one of the most important topics in rough set theory. Heuristic attribute reduction algorithms have been presented to solve the attribute reduction problem. It is generally known that fitness functions play a key role in developing heuristic attribute reduction algorithms. The monotonicity of fitness functions can guarantee the validity of heuristic attribute reduction algorithms. In probabilistic rough set model, distribution reducts can ensure the decision rules derived from the reducts are compatible with those derived from the original decision table. However, there are few studies on developing heuristic attribute reduction algorithms for finding distribution reducts. This is partly due to the fact that there are no monotonic fitness functions that are used to design heuristic attribute reduction algorithms in probabilistic rough set model. The main objective of this paper is to develop heuristic attribute reduction algorithms for finding distribution reducts in probabilistic rough set model. For one thing, two monotonic fitness functions are constructed, from which equivalence definitions of distribution reducts can be obtained. For another, two modified monotonic fitness functions are proposed to evaluate the significance of attributes more effectively. On this basis, two heuristic attribute reduction algorithms for finding distribution reducts are developed based on addition-deletion method and deletion method. In particular, the monotonicity of fitness functions guarantees the rationality of the proposed heuristic attribute reduction algorithms. Results of experimental analysis are included to quantify the effectiveness of the proposed fitness functions and distribution reducts.
This paper discusses the representation of ontologies in the first-order logical environment FOLE (Kent 2013). An ontology defines the primitives with which to model the knowledge resources for a community of discourse (Gruber 2009). These primitives, consisting of classes, relationships and properties, are represented by the entity-relationship-attribute ERA data model (Chen 1976). An ontology uses formal axioms to constrain the interpretation of these primitives. In short, an ontology specifies a logical theory. This paper is the first in a series of three papers that provide a rigorous mathematical representation for the ERA data model in particular, and ontologies in general, within the first-order logical environment FOLE. The first two papers show how FOLE represents the formalism and semantics of (many-sorted) first-order logic in a classification form corresponding to ideas discussed in the Information Flow Framework (IFF). In particular, this first paper provides a foundation that connects elements of the ERA data model with components of the first-order logical environment FOLE, and the second paper provides a superstructure that extends FOLE to the formalisms of first-order logic. The third paper defines an interpretation of FOLE in terms of the transformational passage, first described in (Kent 2013), from the classification form of first-order logic to an equivalent interpretation form, thereby defining the formalism and semantics of first-order logical/relational database systems (Kent 2011). The FOLE representation follows a conceptual structures approach, that is completely compatible with formal concept analysis (Ganter and Wille 1999) and information flow (Barwise and Seligman 1997).
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, industrial plants and language models are only some of the many real-world tasks involving large numbers of discrete actions for which current methods are difficult or even often impossible to apply. An ability to generalize over the set of actions as well as sub-linear complexity relative to the size of the set are both necessary to handle such tasks. Current approaches are not able to provide both of these, which motivates the work in this paper. Our proposed approach leverages prior information about the actions to embed them in a continuous space upon which it can generalize. Additionally, approximate nearest-neighbor methods allow for logarithmic-time lookup complexity relative to the number of actions, which is necessary for time-wise tractable training. This combined approach allows reinforcement learning methods to be applied to large-scale learning problems previously intractable with current methods. We demonstrate our algorithm's abilities on a series of tasks having up to one million actions.
Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models,however, is the scalability. The Bayesian Network, which is one of the most common PGMs used in machine learning and data mining, demonstrates this limitation when the training data consists of random variables, each of them has a large set of possible values. In the big data era, one would expect new extensions to the existing PGMs to handle the massive amount of data produced these days by computers, sensors and other electronic devices. With hierarchical data - data that is arranged in a treelike structure with several levels - one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels. When modeling this kind of hierarchical data across large data sets, Bayesian Networks become infeasible for representing the probability distributions. In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space. The proposed model achieves perfect precision of 1.0 and high recall of 0.93 when it is used as multi-label classifier for the annotation of mass spectrometry data. On another data set of 1.5 billion search logs provided by CareerBuilder.com the model was able to predict latent semantic relationships between search keywords with accuracy up to 0.80.
The scientific community is becoming more and more interested in the research that applies the mathematical formalism of quantum theory to model human decision-making. In this paper, we provide the theoretical foundations of the quantum approach to cognition that we developed in Brussels. These foundations rest on the results of two decade studies on the axiomatic and operational-realistic approaches to the foundations of quantum physics. The deep analogies between the foundations of physics and cognition lead us to investigate the validity of quantum theory as a general and unitary framework for cognitive processes, and the empirical success of the Hilbert space models derived by such investigation provides a strong theoretical confirmation of this validity. However, two situations in the cognitive realm, 'question order effects' and 'response replicability', indicate that even the Hilbert space framework could be insufficient to reproduce the collected data. This does not mean that the mentioned operational-realistic approach would be incorrect, but simply that a larger class of measurements would be in force in human cognition, so that an extended quantum formalism may be needed to deal with all of them. As we will explain, the recently derived 'extended Bloch representation' of quantum theory (and the associated 'general tension-reduction' model) precisely provides such extended formalism, while remaining within the same unitary interpretative framework.
We study abduction in First Order Horn logic theories where all atoms can be abduced and we are looking for preferred solutions with respect to three objective functions: cardinality minimality, coherence, and weighted abduction. We represent this reasoning problem in Answer Set Programming (ASP), in order to obtain a flexible framework for experimenting with global constraints and objective functions, and to test the boundaries of what is possible with ASP. Realizing this problem in ASP is challenging as it requires value invention and equivalence between certain constants, because the Unique Names Assumption does not hold in general. To permit reasoning in cyclic theories, we formally describe fine-grained variations of limiting Skolemization. We identify term equivalence as a main instantiation bottleneck, and improve the efficiency of our approach with on-demand constraints that were used to eliminate the same bottleneck in state-of-the-art solvers. We evaluate our approach experimentally on the ACCEL benchmark for plan recognition in Natural Language Understanding. Our encodings are publicly available, modular, and our approach is more efficient than state-of-the-art solvers on the ACCEL benchmark.
We consider data in the form of pairwise comparisons of n items, with the goal of precisely identifying the top k items for some value of k < n, or alternatively, recovering a ranking of all the items. We analyze the Copeland counting algorithm that ranks the items in order of the number of pairwise comparisons won, and show it has three attractive features: (a) its computational efficiency leads to speed-ups of several orders of magnitude in computation time as compared to prior work; (b) it is robust in that theoretical guarantees impose no conditions on the underlying matrix of pairwise-comparison probabilities, in contrast to some prior work that applies only to the BTL parametric model; and (c) it is an optimal method up to constant factors, meaning that it achieves the information-theoretic limits for recovering the top k-subset. We extend our results to obtain sharp guarantees for approximate recovery under the Hamming distortion metric, and more generally, to any arbitrary error requirement that satisfies a simple and natural monotonicity condition.
In previous work, we proposed a logic-based framework in which computation is the execution of actions in an attempt to make reactive rules of the form if antecedent then consequent true in a canonical model of a logic program determined by an initial state, sequence of events, and the resulting sequence of subsequent states. In this model-theoretic semantics, reactive rules are the driving force, and logic programs play only a supporting role. In the canonical model, states, actions and other events are represented with timestamps. But in the operational semantics, for the sake of efficiency, timestamps are omitted and only the current state is maintained. State transitions are performed reactively by executing actions to make the consequents of rules true whenever the antecedents become true. This operational semantics is sound, but incomplete. It cannot make reactive rules true by preventing their antecedents from becoming true, or by proactively making their consequents true before their antecedents become true. In this paper, we characterize the notion of reactive model, and prove that the operational semantics can generate all and only such models. In order to focus on the main issues, we omit the logic programming component of the framework.
We propose a formal mathematical model for sparse representations and active dendrites in neocortex. Our model is inspired by recent experimental findings on active dendritic processing and NMDA spikes in pyramidal neurons. These experimental and modeling studies suggest that the basic unit of pattern memory in the neocortex is instantiated by small clusters of synapses operated on by localized non-linear dendritic processes. We derive a number of scaling laws that characterize the accuracy of such dendrites in detecting activation patterns in a neuronal population under adverse conditions. We introduce the union property which shows that synapses for multiple patterns can be randomly mixed together within a segment and still lead to highly accurate recognition. We describe simulation results that provide further insight into sparse representations as well as two primary results. First we show that pattern recognition by a neuron with active dendrites can be extremely accurate and robust with high dimensional sparse inputs even when using a tiny number of synapses to recognize large patterns. Second, equations representing recognition accuracy of a dendrite predict optimal NMDA spiking thresholds under a generous set of assumptions. The prediction tightly matches NMDA spiking thresholds measured in the literature. Our model matches many of the known properties of pyramidal neurons. As such the theory provides a mathematical framework for understanding the benefits and limits of sparse representations in cortical networks.
We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on trajectories; they are rather governed by the surrounding context of various objects and human interactions in the environment. We propose a coactive online learning framework for teaching preferences in contextually rich environments. The key novelty of our approach lies in the type of feedback expected from the user: the human user does not need to demonstrate optimal trajectories as training data, but merely needs to iteratively provide trajectories that slightly improve over the trajectory currently proposed by the system. We argue that this coactive preference feedback can be more easily elicited than demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds of our algorithm match the asymptotic rates of optimal trajectory algorithms. We implement our algorithm on two high degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings -- household chores and grocery store checkout -- and show that users are able to train the robot with just a few feedbacks (taking only a few minutes).\footnote{Parts of this work has been published at NIPS and ISRR conferences~\citep{Jain13,Jain13b}. This journal submission presents a consistent full paper, and also includes the proof of regret bounds, more details of the robotic system, and a thorough related work.}
The performance of deep neural networks is well-known to be sensitive to the setting of their hyperparameters. Recent advances in reverse-mode automatic differentiation allow for optimizing hyperparameters with gradients. The standard way of computing these gradients involves a forward and backward pass of computations. However, the backward pass usually needs to consume unaffordable memory to store all the intermediate variables to exactly reverse the forward training procedure. In this work we propose a simple but effective method, DrMAD, to distill the knowledge of the forward pass into a shortcut path, through which we approximately reverse the training trajectory. Experiments on several image benchmark datasets show that DrMAD is at least 45 times faster and consumes 100 times less memory compared to state-of-the-art methods for optimizing hyperparameters with minimal compromise to its effectiveness. To the best of our knowledge, DrMAD is the first research attempt to make it practical to automatically tune thousands of hyperparameters of deep neural networks. The code can be downloaded from https://github.com/bigaidream-projects/drmad
Popular online enrichment analysis tools from the field of molecular systems biology provide users with the ability to submit their experimental results as gene sets for individual analysis. Such queries are kept private, and have never before been considered as a resource for integrative analysis. By harnessing gene set query submissions from thousands of users, we aim to discover biological knowledge beyond the scope of an individual study. In this work, we investigated a large collection of gene sets submitted to the tool Enrichr by thousands of users. Based on co-occurrence, we constructed a global gene-gene association network. We interpret this inferred network as providing a summary of the structure present in this crowdsourced gene set library, and show that this network recapitulates known protein-protein interactions and functional associations between genes. This finding implies that this network also offers predictive value. Furthermore, we visualize this gene-gene association network using a new edge-pruning algorithm that retains both the local and global structures of large-scale networks. Our ability to make predictions for currently unknown gene associations, that may not be captured by individual researchers and data sources, is a demonstration of the potential of harnessing collective knowledge from users of popular tools in the field of molecular systems biology.
There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations. In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts. We formulate the manipulation planning as a structured prediction problem and learn to transfer manipulation strategy across different objects by embedding point-cloud, natural language, and manipulation trajectory data into a shared embedding space using a deep neural network. In order to learn semantically meaningful spaces throughout our network, we introduce a method for pre-training its lower layers for multimodal feature embedding and a method for fine-tuning this embedding space using a loss-based margin. In order to collect a large number of manipulation demonstrations for different objects, we develop a new crowd-sourcing platform called Robobarista. We test our model on our dataset consisting of 116 objects and appliances with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations. We further show that our robot with our model can even prepare a cup of a latte with appliances it has never seen before.
We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider scenarios where these plans must be generated in real-time, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Our approach is to pre-compute a library of "funnels" along different maneuvers of the system that the state is guaranteed to remain within (despite bounded disturbances) when the feedback controller corresponding to the maneuver is executed. We leverage powerful computational machinery from convex optimization (sums-of-squares programming in particular) to compute these funnels. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances. We demonstrate and validate our method using extensive hardware experiments on a small fixed-wing airplane avoiding obstacles at high speed (~12 mph), along with thorough simulation experiments of ground vehicle and quadrotor models navigating through cluttered environments. To our knowledge, these demonstrations constitute one of the first examples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in real-time in environments with complex geometric constraints.
Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives. Therefore, just like for power plants, highways, and myriad other engineered sociotechnical systems, we must consider the safety of systems involving machine learning. In this paper, we first discuss the definition of safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. Then we examine dimensions, such as the choice of cost function and the appropriateness of minimizing the empirical average training cost, along which certain real-world applications may not be completely amenable to the foundational principle of modern statistical machine learning: empirical risk minimization. In particular, we note an emerging dichotomy of applications: ones in which safety is important and risk minimization is not the complete story (we name these Type A applications), and ones in which safety is not so critical and risk minimization is sufficient (we name these Type B applications). Finally, we discuss how four different strategies for achieving safety in engineering (inherently safe design, safety reserves, safe fail, and procedural safeguards) can be mapped to the machine learning context through interpretability and causality of predictive models, objectives beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software.
The categorical compositional distributional model of natural language provides a conceptually motivated procedure to compute the meaning of sentences, given grammatical structure and the meanings of its words. This approach has outperformed other models in mainstream empirical language processing tasks. However, until recently it has lacked the crucial feature of lexical entailment -- as do other distributional models of meaning. In this paper we solve the problem of entailment for categorical compositional distributional semantics. Taking advantage of the abstract categorical framework allows us to vary our choice of model. This enables the introduction of a notion of entailment, exploiting ideas from the categorical semantics of partial knowledge in quantum computation. The new model of language uses density matrices, on which we introduce a novel robust graded order capturing the entailment strength between concepts. This graded measure emerges from a general framework for approximate entailment, induced by any commutative monoid. Quantum logic embeds in our graded order. Our main theorem shows that entailment strength lifts compositionally to the sentence level, giving a lower bound on sentence entailment. We describe the essential properties of graded entailment such as continuity, and provide a procedure for calculating entailment strength.
A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes. There is thus a growing need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussion on sites like Twitter and Facebook - before they get too influential. Spurred by such events, DARPA held a 4-week competition in February/March 2015 in which multiple teams supported by the DARPA Social Media in Strategic Communications program competed to identify a set of previously identified "influence bots" serving as ground truth on a specific topic within Twitter. Past work regarding influence bots often has difficulty supporting claims about accuracy, since there is limited ground truth (though some exceptions do exist [3,7]). However, with the exception of [3], no past work has looked specifically at identifying influence bots on a specific topic. This paper describes the DARPA Challenge and describes the methods used by the three top-ranked teams.
Teachable agents are computer agents based on the pedagogical concept of learning-by-teaching. During the tutoring process, where students take on the role of the tutor to teach a computer agent tutee, learners have been observed to gain deeper understanding of the subject matter. Teachable agents are commonly used in the areas of science and mathematics learning where learners are able to learn complex concepts and deep reasoning by teaching the teachable agent through graphic representation such as concept maps. Literature review on teachable agents as well as observations during field studies conducted by the researcher, have shown that many current teachable agents lack the interaction abilities required to keep learners engage in learning tasks. The result of this is learners deviating from the teaching process, and thus the learners are unable to benefit fully from learning with the teachable agent. The applications of teachable agents are restricted to the learning of academic subjects such as mathematics and science. In this book, we have proposed the Persuasive Teachable Agent (PTA), a teachable agent based on the theoretical framework of persuasion, computational and goal-oriented agent modelling. We argue that the PTA, an autonomous agent, capable of encouraging attitude and behavioural change can offer a more meaningful and engaging learning experiences for learners from different age groups. Based on the findings from our research we argue that persuasive feedback actions generated by the PTA provide significant influence over learner's decision to participate in intergenerational learning. The PTA plays a crucial role in the development of future persuasive technologies in artificially intelligent agents.
Goal models have been widely used in Computer Science to represent software requirements, business objectives, and design qualities. Existing goal modelling techniques, however, have shown limitations of expressiveness and/or tractability in coping with complex real-world problems. In this work, we exploit advances in automated reasoning technologies, notably Satisfiability and Optimization Modulo Theories (SMT/OMT), and we propose and formalize: (i) an extended modelling language for goals, namely the Constrained Goal Model (CGM), which makes explicit the notion of goal refinement and of domain assumption, allows for expressing preferences between goals and refinements, and allows for associating numerical attributes to goals and refinements for defining constraints and optimization goals over multiple objective functions, refinements and their numerical attributes; (ii) a novel set of automated reasoning functionalities over CGMs, allowing for automatically generating suitable refinements of input CGMs, under user-specified assumptions and constraints, that also maximize preferences and optimize given objective functions. We have implemented these modelling and reasoning functionalities in a tool, named CGM-Tool, using the OMT solver OptiMathSAT as automated reasoning backend. Moreover, we have conducted an experimental evaluation on large CGMs to support the claim that our proposal scales well for goal models with thousands of elements.
This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER's sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. While previous work presents influence maximizing techniques to choose intervention participants, they do not address three real-world issues: (i) they completely fail to scale up to real-world sizes; (ii) they do not handle deviations in execution of intervention plans; (iii) constructing real-world social networks is an expensive process. HEALER handles these issues via four major contributions: (i) HEALER casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; (ii) HEALER allows shelter officials to modify its recommendations, and updates its future plans in a deviation-tolerant manner; (iii) HEALER constructs social networks of homeless youth at low cost, using a Facebook application. Finally, (iv) we show hardness results for the problem that HEALER solves. HEALER will be deployed in the real world in early Spring 2016 and is currently undergoing testing at a homeless shelter.
Learning for maximizing AUC performance is an important research problem in Machine Learning and Artificial Intelligence. Unlike traditional batch learning methods for maximizing AUC which often suffer from poor scalability, recent years have witnessed some emerging studies that attempt to maximize AUC by single-pass online learning approaches. Despite their encouraging results reported, the existing online AUC maximization algorithms often adopt simple online gradient descent approaches that fail to exploit the geometrical knowledge of the data observed during the online learning process, and thus could suffer from relatively larger regret. To address the above limitation, in this work, we explore a novel algorithm of Adaptive Online AUC Maximization (AdaOAM) which employs an adaptive gradient method that exploits the knowledge of historical gradients to perform more informative online learning. The new adaptive updating strategy of the AdaOAM is less sensitive to the parameter settings and maintains the same time complexity as previous non-adaptive counterparts. Additionally, we extend the algorithm to handle high-dimensional sparse data (SAdaOAM) and address sparsity in the solution by performing lazy gradient updating. We analyze the theoretical bounds and evaluate their empirical performance on various types of data sets. The encouraging empirical results obtained clearly highlighted the effectiveness and efficiency of the proposed algorithms.
In this paper, we propose a novel unsupervised learning method for the lexical acquisition of words related to places visited by robots, from human continuous speech signals. We address the problem of learning novel words by a robot that has no prior knowledge of these words except for a primitive acoustic model. Further, we propose a method that allows a robot to effectively use the learned words and their meanings for self-localization tasks. The proposed method is nonparametric Bayesian spatial concept acquisition method (SpCoA) that integrates the generative model for self-localization and the unsupervised word segmentation in uttered sentences via latent variables related to the spatial concept. We implemented the proposed method SpCoA on SIGVerse, which is a simulation environment, and TurtleBot2, which is a mobile robot in a real environment. Further, we conducted experiments for evaluating the performance of SpCoA. The experimental results showed that SpCoA enabled the robot to acquire the names of places from speech sentences. They also revealed that the robot could effectively utilize the acquired spatial concepts and reduce the uncertainty in self-localization.
The current paper proposes a novel neural network model for recognizing visually perceived human actions. The proposed multiple spatio-temporal scales recurrent neural network (MSTRNN) model is derived by introducing multiple timescale recurrent dynamics to the conventional convolutional neural network model. One of the essential characteristics of the MSTRNN is that its architecture imposes both spatial and temporal constraints simultaneously on the neural activity which vary in multiple scales among different layers. As suggested by the principle of the upward and downward causation, it is assumed that the network can develop meaningful structures such as functional hierarchy by taking advantage of such constraints during the course of learning. To evaluate the characteristics of the model, the current study uses three types of human action video dataset consisting of different types of primitive actions and different levels of compositionality on them. The performance of the MSTRNN in testing with these dataset is compared with the ones by other representative deep learning models used in the field. The analysis of the internal representation obtained through the learning with the dataset clarifies what sorts of functional hierarchy can be developed by extracting the essential compositionality underlying the dataset.
Finding efficient and provable methods to solve non-convex optimization problems is an outstanding challenge in machine learning and optimization theory. A popular approach used to tackle non-convex problems is to use convex relaxation techniques to find a convex surrogate for the problem. Unfortunately, convex relaxations typically must be found on a problem-by-problem basis. Thus, providing a general-purpose strategy to estimate a convex relaxation would have a wide reaching impact. Here, we introduce Convex Relaxation Regression (CoRR), an approach for learning convex relaxations for a class of smooth functions. The main idea behind our approach is to estimate the convex envelope of a function $f$ by evaluating $f$ at a set of $T$ random points and then fitting a convex function to these function evaluations. We prove that with probability greater than $1-\delta$, the solution of our algorithm converges to the global optimizer of $f$ with error $\mathcal{O} \Big( \big(\frac{\log(1/\delta) }{T} \big)^{\alpha} \Big)$ for some $\alpha> 0$. Our approach enables the use of convex optimization tools to solve a class of non-convex optimization problems.
When data analysts train a classifier and check if its accuracy is significantly different from random guessing, they are implicitly and indirectly performing a hypothesis test (two sample testing) and it is of importance to ask whether this indirect method for testing is statistically optimal or not. Given that hypothesis tests attempt to maximize statistical power subject to a bound on the allowable false positive rate, while prediction attempts to minimize statistical risk on future predictions on unseen data, we wish to study whether a predictive approach for an ultimate aim of testing is prudent. We formalize this problem by considering the two-sample mean-testing setting where one must determine if the means of two Gaussians (with known and equal covariance) are the same or not, but the analyst indirectly does so by checking whether the accuracy achieved by Fisher's LDA classifier is significantly different from chance or not. Unexpectedly, we find that the asymptotic power of LDA's sample-splitting classification accuracy is actually minimax rate-optimal in terms of problem-dependent parameters. Since prediction is commonly thought to be harder than testing, it might come as a surprise to some that solving a harder problem does not create a information-theoretic bottleneck for the easier one. On the flip side, even though the power is rate-optimal, our derivation suggests that it may be worse by a small constant factor; hence practitioners must be wary of using (admittedly flexible) prediction methods on disguised testing problems.
The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks to utilize the information contained in vast document collections. General data mining methods based on machine learning face challenges with the scale of text data, posing a need for scalable text mining methods. This thesis proposes a solution to scalable text mining: generative models combined with sparse computation. A unifying formalization for generative text models is defined, bringing together research traditions that have used formally equivalent models, but ignored parallel developments. This framework allows the use of methods developed in different processing tasks such as retrieval and classification, yielding effective solutions across different text mining tasks. Sparse computation using inverted indices is proposed for inference on probabilistic models. This reduces the computational complexity of the common text mining operations according to sparsity, yielding probabilistic models with the scalability of modern search engines. The proposed combination provides sparse generative models: a solution for text mining that is general, effective, and scalable. Extensive experimentation on text classification and ranked retrieval datasets are conducted, showing that the proposed solution matches or outperforms the leading task-specific methods in effectiveness, with a order of magnitude decrease in classification times for Wikipedia article categorization with a million classes. The developed methods were further applied in two 2014 Kaggle data mining prize competitions with over a hundred competing teams, earning first and second places.
In clinical data sets we often find static information (e.g. patient gender, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.g. medications prescribed, tests performed, etc.). Recurrent Neural Networks (RNNs) have proven to be very successful for modelling sequences of data in many areas of Machine Learning. In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events. We work with a database collected in the Charit\'{e} Hospital in Berlin that contains complete information concerning patients that underwent a kidney transplantation. After the transplantation three main endpoints can occur: rejection of the kidney, loss of the kidney and death of the patient. Our goal is to predict, based on information recorded in the Electronic Health Record of each patient, whether any of those endpoints will occur within the next six or twelve months after each visit to the clinic. We compared different types of RNNs that we developed for this work, with a model based on a Feedforward Neural Network and a Logistic Regression model. We found that the RNN that we developed based on Gated Recurrent Units provides the best performance for this task. We also used the same models for a second task, i.e., next event prediction, and found that here the model based on a Feedforward Neural Network outperformed the other models. Our hypothesis is that long-term dependencies are not as relevant in this task.
In order to distribute the best arm identification task as close as possible to the user's devices, on the edge of the Radio Access Network, we propose a new problem setting, where distributed players collaborate to find the best arm. This architecture guarantees privacy to end-users since no events are stored. The only thing that can be observed by an adversary through the core network is aggregated information across users. We provide a first algorithm, Distributed Median Elimination, which is optimal in term of number of transmitted bits and near optimal in term of speed-up factor with respect to an optimal algorithm run independently on each player. In practice, this first algorithm cannot handle the trade-off between the communication cost and the speed-up factor, and requires some knowledge about the distribution of players. Extended Distributed Median Elimination overcomes these limitations, by playing in parallel different instances of Distributed Median Elimination and selecting the best one. Experiments illustrate and complete the analysis. According to the analysis, in comparison to Median Elimination performed on each player, the proposed algorithm shows significant practical improvements.
Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore how one can come to believe that other actors will coordinate, and with regard to systems involving humans, an important empirical question is how humans arrive at these expectations. We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. Our algorithm is valid in any environment with a finite state space, and extensions to certain countably infinite state spaces are likely possible. We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-agent collaboration.
We study a problem of allocating divisible jobs, arriving online, to workers in a crowdsourcing setting which involves learning two parameters of strategically behaving workers. Each job is split into a certain number of tasks that are then allocated to workers. Each arriving job has to be completed within a deadline and each task has to be completed satisfying an upper bound on probability of failure. The job population is homogeneous while the workers are heterogeneous in terms of costs, completion times, and times to failure. The job completion time and time to failure of each worker are stochastic with fixed but unknown means. The requester is faced with the challenge of learning two separate parameters of each (strategically behaving) worker simultaneously, namely, the mean job completion time and the mean time to failure. The time to failure of a worker depends on the duration of the task handled by the worker. Assuming non-strategic workers to start with, we solve this biparameter learning problem by applying the Robust UCB algorithm. Then, we non-trivially extend this algorithm to the setting where the workers are strategic about their costs. Our proposed mechanism is dominant strategy incentive compatible and ex-post individually rational with asymptotically optimal regret performance.
The exploration of social conversations for addressing patient's needs is an important analytical task in which many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in order to generate insight from social media data, which can be considered as one of the most challenging source of information available today due to its sheer volume and noise. This study is based on the work by Scott Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The mechanism for automatically identifying and generating a taxonomy from social conversations is developed and pressured tested using public data from media sites focused on the needs of cancer patients and their families. Moreover, a novel method for generating the category's label and the determination of an optimal number of categories is presented which extends Scott and Jeffrey's research in a meaningful way. We assume the reader is familiar with taxonomies, what they are and how they are used.
Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one. In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent.
Complex network topologies and hyperbolic geometry seem specularly connected, and one of the most fascinating and challenging problems of recent complex network theory is to map a given network to its hyperbolic space. The Popularity Similarity Optimization (PSO) model represents - at the moment - the climax of this theory. It suggests that the trade-off between node popularity and similarity is a mechanism to explain how complex network topologies emerge - as discrete samples - from the continuous world of hyperbolic geometry. The hyperbolic space seems appropriate to represent real complex networks. In fact, it preserves many of their fundamental topological properties, and can be exploited for real applications such as, among others, link prediction and community detection. Here, we observe for the first time that a topological-based machine learning class of algorithms - for nonlinear unsupervised dimensionality reduction - can directly approximate the network's node angular coordinates of the hyperbolic model into a two-dimensional space, according to a similar topological organization that we named angular coalescence. On the basis of this phenomenon, we propose a new class of algorithms that offers fast and accurate coalescent embedding of networks in the hyperbolic space even for graphs with thousands of nodes.
Automatic video keyword generation is one of the key ingredients in reducing the burden of security officers in analyzing surveillance videos. Keywords or attributes are generally chosen manually based on expert knowledge of surveillance. Most existing works primarily aim at either supervised learning approaches relying on extensive manual labelling or hierarchical probabilistic models that assume the features are extracted using the bag-of-words approach; thus limiting the utilization of the other features. To address this, we turn our attention to automatic attribute discovery approaches. However, it is not clear which automatic discovery approach can discover the most meaningful attributes. Furthermore, little research has been done on how to compare and choose the best automatic attribute discovery methods. In this paper, we propose a novel approach, based on the shared structure exhibited amongst meaningful attributes, that enables us to compare between different automatic attribute discovery approaches.We then validate our approach by comparing various attribute discovery methods such as PiCoDeS on two attribute datasets. The evaluation shows that our approach is able to select the automatic discovery approach that discovers the most meaningful attributes. We then employ the best discovery approach to generate keywords for videos recorded from a surveillance system. This work shows it is possible to massively reduce the amount of manual work in generating video keywords without limiting ourselves to a particular video feature descriptor.
This paper presents a strategy to guide a mobile ground robot equipped with a camera or depth sensor, in order to autonomously map the visible part of a bounded three-dimensional structure. We describe motion planning algorithms that determine appropriate successive viewpoints and attempt to fill holes automatically in a point cloud produced by the sensing and perception layer. The emphasis is on accurately reconstructing a 3D model of a structure of moderate size rather than mapping large open environments, with applications for example in architecture, construction and inspection. The proposed algorithms do not require any initialization in the form of a mesh model or a bounding box, and the paths generated are well adapted to situations where the vision sensor is used simultaneously for mapping and for localizing the robot, in the absence of additional absolute positioning system. We analyze the coverage properties of our policy, and compare its performance to the classic frontier based exploration algorithm. We illustrate its efficacy for different structure sizes, levels of localization accuracy and range of the depth sensor, and validate our design on a real-world experiment.
From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which computers understand a broad range of human behaviors. Today our systems fall short of these visions, however, because this range of behaviors is too large for designers or programmers to capture manually. In this paper, we instead demonstrate it is possible to mine a broad knowledge base of human behavior by analyzing more than one billion words of modern fiction. Our resulting knowledge base, Augur, trains vector models that can predict many thousands of user activities from surrounding objects in modern contexts: for example, whether a user may be eating food, meeting with a friend, or taking a selfie. Augur uses these predictions to identify actions that people commonly take on objects in the world and estimate a user's future activities given their current situation. We demonstrate Augur-powered, activity-based systems such as a phone that silences itself when the odds of you answering it are low, and a dynamic music player that adjusts to your present activity. A field deployment of an Augur-powered wearable camera resulted in 96% recall and 71% precision on its unsupervised predictions of common daily activities. A second evaluation where human judges rated the system's predictions over a broad set of input images found that 94% were rated sensible.
The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose modeling of a real-world MTD web application as a repeated Bayesian game. We then formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To incorporate this model into a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
In the study of human learning, there is broad evidence that our ability to retain information improves with repeated exposure and decays with delay since last exposure. This plays a crucial role in the design of educational software, leading to a trade-off between teaching new material and reviewing what has already been taught. A common way to balance this trade-off is spaced repetition, which uses periodic review of content to improve long-term retention. Though spaced repetition is widely used in practice, e.g., in electronic flashcard software, there is little formal understanding of the design of these systems. Our paper addresses this gap in three ways. First, we mine log data from spaced repetition software to establish the functional dependence of retention on reinforcement and delay. Second, we use this memory model to develop a stochastic model for spaced repetition systems. We propose a queueing network model of the Leitner system for reviewing flashcards, along with a heuristic approximation that admits a tractable optimization problem for review scheduling. Finally, we empirically evaluate our queueing model through a Mechanical Turk experiment, verifying a key qualitative prediction of our model: the existence of a sharp phase transition in learning outcomes upon increasing the rate of new item introductions.
Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked "What vehicle is the person riding?", computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) in order to answer correctly that "the person is riding a horse-drawn carriage". In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 100K images where each image has an average of 21 objects, 18 attributes, and 18 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answers.
We consider partially observable Markov decision processes (POMDPs) with a set of target states and positive integer costs associated with every transition. The traditional optimization objective (stochastic shortest path) asks to minimize the expected total cost until the target set is reached. We extend the traditional framework of POMDPs to model energy consumption, which represents a hard constraint. The energy levels may increase and decrease with transitions, and the hard constraint requires that the energy level must remain positive in all steps till the target is reached. First, we present a novel algorithm for solving POMDPs with energy levels, developing on existing POMDP solvers and using RTDP as its main method. Our second contribution is related to policy representation. For larger POMDP instances the policies computed by existing solvers are too large to be understandable. We present an automated procedure based on machine learning techniques that automatically extracts important decisions of the policy allowing us to compute succinct human readable policies. Finally, we show experimentally that our algorithm performs well and computes succinct policies on a number of POMDP instances from the literature that were naturally enhanced with energy levels.
Mutation is one of the most important stages of the genetic algorithm because of its impact on the exploration of global optima, and to overcome premature convergence. There are many types of mutation, and the problem lies in selection of the appropriate type, where the decision becomes more difficult and needs more trial and error. This paper investigates the use of more than one mutation operator to enhance the performance of genetic algorithms. Novel mutation operators are proposed, in addition to two selection strategies for the mutation operators, one of which is based on selecting the best mutation operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) were conducted to evaluate the proposed methods, and these were compared to the well-known exchange mutation and rearrangement mutation. The results show the importance of some of the proposed methods, in addition to the significant enhancement of the genetic algorithm's performance, particularly when using more than one mutation operator.
Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.
Neutrosophic set has the ability to handle uncertain, incomplete, inconsistent, indeterminate information in a more accurate way. In this paper, we proposed a neutrosophic recommender system to predict the diseases based on neutrosophic set which includes single-criterion neutrosophic recommender system (SC-NRS) and multi-criterion neutrosophic recommender system (MC-NRS). Further, we investigated some algebraic operations of neutrosophic recommender system such as union, complement, intersection, probabilistic sum, bold sum, bold intersection, bounded difference, symmetric difference, convex linear sum of min and max operators, Cartesian product, associativity, commutativity and distributive. Based on these operations, we studied the algebraic structures such as lattices, Kleen algebra, de Morgan algebra, Brouwerian algebra, BCK algebra, Stone algebra and MV algebra. In addition, we introduced several types of similarity measures based on these algebraic operations and studied some of their theoretic properties. Moreover, we accomplished a prediction formula using the proposed algebraic similarity measure. We also proposed a new algorithm for medical diagnosis based on neutrosophic recommender system. Finally to check the validity of the proposed methodology, we made experiments on the datasets Heart, RHC, Breast cancer, Diabetes and DMD. At the end, we presented the MSE and computational time by comparing the proposed algorithm with the relevant ones such as ICSM, DSM, CARE, CFMD, as well as other variants namely Variant 67, Variant 69, and Varian 71 both in tabular and graphical form to analyze the efficiency and accuracy. Finally we analyzed the strength of all 8 algorithms by ANOVA statistical tool.
In many common interactive scenarios, participants lack information about other participants, and specifically about the preferences of other participants. In this work, we model an extreme case of incomplete information, which we term games with type ambiguity, where a participant lacks even information enabling him to form a belief on the preferences of others. Under type ambiguity, one cannot analyze the scenario using the commonly used Bayesian framework, and therefore he needs to model the participants using a different decision model. In this work, we present the ${\rm MINthenMAX}$ decision model under ambiguity. This model is a refinement of Wald's MiniMax principle, which we show to be too coarse for games with type ambiguity. We characterize ${\rm MINthenMAX}$ as the finest refinement of the MiniMax principle that satisfies three properties we claim are necessary for games with type ambiguity. This prior-less approach we present her also follows the common practice in computer science of worst-case analysis. Finally, we define and analyze the corresponding equilibrium concept assuming all players follow ${\rm MINthenMAX}$. We demonstrate this equilibrium by applying it to two common economic scenarios: coordination games and bilateral trade. We show that in both scenarios, an equilibrium in pure strategies always exists and we analyze the equilibria.
We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space. Grasping unknown objects can be achieved by using a trial and error exploration strategy. Bayesian optimization is a sample efficient optimization algorithm that is especially suitable for this setups as it actively reduces the number of trials for learning about the function to optimize. In fact, this active object exploration is the same strategy that infants do to learn optimal grasps. One problem that arises while learning grasping policies is that some configurations of grasp parameters may be very sensitive to error in the relative pose between the object and robot end-effector. We call these configurations unsafe because small errors during grasp execution may turn good grasps into bad grasps. Therefore, to reduce the risk of grasp failure, grasps should be planned in safe areas. We propose a new algorithm, Unscented Bayesian optimization that is able to perform sample efficient optimization while taking into consideration input noise to find safe optima. The contribution of Unscented Bayesian optimization is twofold as if provides a new decision process that drives exploration to safe regions and a new selection procedure that chooses the optimal in terms of its safety without extra analysis or computational cost. Both contributions are rooted on the strong theory behind the unscented transformation, a popular nonlinear approximation method. We show its advantages with respect to the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations. The results highlights that our method achieves optimal and robust grasping policies after few trials while the selected grasps remain in safe regions.
We investigate a paradigm in multi-task reinforcement learning (MT-RL) in which an agent is placed in an environment and needs to learn to perform a series of tasks, within this space. Since the environment does not change, there is potentially a lot of common ground amongst tasks and learning to solve them individually seems extremely wasteful. In this paper, we explicitly model and learn this shared structure as it arises in the state-action value space. We will show how one can jointly learn optimal value-functions by modifying the popular Value-Iteration and Policy-Iteration procedures to accommodate this shared representation assumption and leverage the power of multi-task supervised learning. Finally, we demonstrate that the proposed model and training procedures, are able to infer good value functions, even under low samples regimes. In addition to data efficiency, we will show in our analysis, that learning abstractions of the state space jointly across tasks leads to more robust, transferable representations with the potential for better generalization. this shared representation assumption and leverage the power of multi-task supervised learning. Finally, we demonstrate that the proposed model and training procedures, are able to infer good value functions, even under low samples regimes. In addition to data efficiency, we will show in our analysis, that learning abstractions of the state space jointly across tasks leads to more robust, transferable representations with the potential for better generalization.
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. To train our network, we collected over 800,000 grasp attempts over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing.
Additive utility function models are widely used in multiple criteria decision analysis. In such models, a numerical value is associated to each alternative involved in the decision problem. It is computed by aggregating the scores of the alternative on the different criteria of the decision problem. The score of an alternative is determined by a marginal value function that evolves monotonically as a function of the performance of the alternative on this criterion. Determining the shape of the marginals is not easy for a decision maker. It is easier for him/her to make statements such as "alternative $a$ is preferred to $b$". In order to help the decision maker, UTA disaggregation procedures use linear programming to approximate the marginals by piecewise linear functions based only on such statements. In this paper, we propose to infer polynomials and splines instead of piecewise linear functions for the marginals. In this aim, we use semidefinite programming instead of linear programming. We illustrate this new elicitation method and present some experimental results.
Recent research has shown great progress on fine-grained entity typing. Most existing methods require pre-defining a set of types and training a multi-class classifier from a large labeled data set based on multi-level linguistic features. They are thus limited to certain domains, genres and languages. In this paper, we propose a novel unsupervised entity typing framework by combining symbolic and distributional semantics. We start from learning general embeddings for each entity mention, compose the embeddings of specific contexts using linguistic structures, link the mention to knowledge bases and learn its related knowledge representations. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework doesn't rely on any annotated data, predefined typing schema, or hand-crafted features, therefore it can be quickly adapted to a new domain, genre and language. Furthermore, it has great flexibility at incorporating linguistic structures (e.g., Abstract Meaning Representation (AMR), dependency relations) to improve specific context representation. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.
Nearly all previous work on geo-locating latent states and activities from social media confounds general discussions about activities, self-reports of users participating in those activities at times in the past or future, and self-reports made at the immediate time and place the activity occurs. Activities, such as alcohol consumption, may occur at different places and types of places, and it is important not only to detect the local regions where these activities occur, but also to analyze the degree of participation in them by local residents. In this paper, we develop new machine learning based methods for fine-grained localization of activities and home locations from Twitter data. We apply these methods to discover and compare alcohol consumption patterns in a large urban area, New York City, and a more suburban and rural area, Monroe County. We find positive correlations between the rate of alcohol consumption reported among a community's Twitter users and the density of alcohol outlets, demonstrating that the degree of correlation varies significantly between urban and suburban areas. While our experiments are focused on alcohol use, our methods for locating homes and distinguishing temporally-specific self-reports are applicable to a broad range of behaviors and latent states.
Two fundamental problems in computational game theory are computing a Nash equilibrium and learning to exploit opponents given observations of their play (opponent exploitation). The latter is perhaps even more important than the former: Nash equilibrium does not have a compelling theoretical justification in game classes other than two-player zero-sum, and for all games one can potentially do better by exploiting perceived weaknesses of the opponent than by following a static equilibrium strategy throughout the match. The natural setting for opponent exploitation is the Bayesian setting where we have a prior model that is integrated with observations to create a posterior opponent model that we respond to. The most natural, and a well-studied prior distribution is the Dirichlet distribution. An exact polynomial-time algorithm is known for best-responding to the posterior distribution for an opponent assuming a Dirichlet prior with multinomial sampling in normal-form games; however, for imperfect-information games the best known algorithm is based on approximating an infinite integral without theoretical guarantees. We present the first exact algorithm for a natural class of imperfect-information games. We demonstrate that our algorithm runs quickly in practice and outperforms the best prior approaches. We also present an algorithm for the uniform prior setting.
One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.
We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.
Humans have an impressive ability to reason about new concepts and experiences from just a single example. In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure, and then be able to generate compelling alternative variations of the concept. We develop machine learning systems with this important capacity by developing new deep generative models, models that combine the representational power of deep learning with the inferential power of Bayesian reasoning. We develop a class of sequential generative models that are built on the principles of feedback and attention. These two characteristics lead to generative models that are among the state-of-the art in density estimation and image generation. We demonstrate the one-shot generalization ability of our models using three tasks: unconditional sampling, generating new exemplars of a given concept, and generating new exemplars of a family of concepts. In all cases our models are able to generate compelling and diverse samples---having seen new examples just once---providing an important class of general-purpose models for one-shot machine learning.
This paper presents a Neural Aggregation Network (NAN) for video face recognition. The network takes a face video or face image set of a person with a variable number of face images as its input, and produces a compact, fixed-dimension feature representation for recognition. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which maps each face image to a feature vector. The aggregation module consists of two attention blocks which adaptively aggregate the feature vectors to form a single feature inside the convex hull spanned by them. Due to the attention mechanism, the aggregation is invariant to the image order. Our NAN is trained with a standard classification or verification loss without any extra supervision signal, and we found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces. The experiments on IJB-A, YouTube Face, Celebrity-1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state-of-the-art accuracy.
Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.
There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images. These tasks have focused on literal descriptions of the image. To move beyond the literal, we choose to explore how questions about an image are often directed at commonsense inference and the abstract events evoked by objects in the image. In this paper, we introduce the novel task of Visual Question Generation (VQG), where the system is tasked with asking a natural and engaging question when shown an image. We provide three datasets which cover a variety of images from object-centric to event-centric, with considerably more abstract training data than provided to state-of-the-art captioning systems thus far. We train and test several generative and retrieval models to tackle the task of VQG. Evaluation results show that while such models ask reasonable questions for a variety of images, there is still a wide gap with human performance which motivates further work on connecting images with commonsense knowledge and pragmatics. Our proposed task offers a new challenge to the community which we hope furthers interest in exploring deeper connections between vision & language.
We present a method for automatically generating repair feedback for syntax errors for introductory programming problems. Syntax errors constitute one of the largest classes of errors (34%) in our dataset of student submissions obtained from a MOOC course on edX. The previous techniques for generating automated feed- back on programming assignments have focused on functional correctness and style considerations of student programs. These techniques analyze the program AST of the program and then perform some dynamic and symbolic analyses to compute repair feedback. Unfortunately, it is not possible to generate ASTs for student pro- grams with syntax errors and therefore the previous feedback techniques are not applicable in repairing syntax errors. We present a technique for providing feedback on syntax errors that uses Recurrent neural networks (RNNs) to model syntactically valid token sequences. Our approach is inspired from the recent work on learning language models from Big Code (large code corpus). For a given programming assignment, we first learn an RNN to model all valid token sequences using the set of syntactically correct student submissions. Then, for a student submission with syntax errors, we query the learnt RNN model with the prefix to- ken sequence to predict token sequences that can fix the error by either replacing or inserting the predicted token sequence at the error location. We evaluate our technique on over 14, 000 student submissions with syntax errors. Our technique can completely re- pair 31.69% (4501/14203) of submissions with syntax errors and in addition partially correct 6.39% (908/14203) of the submissions.
We study methods for aggregating pairwise comparison data in order to estimate outcome probabilities for future comparisons among a collection of n items. Working within a flexible framework that imposes only a form of strong stochastic transitivity (SST), we introduce an adaptivity index defined by the indifference sets of the pairwise comparison probabilities. In addition to measuring the usual worst-case risk of an estimator, this adaptivity index also captures the extent to which the estimator adapts to instance-specific difficulty relative to an oracle estimator. We prove three main results that involve this adaptivity index and different algorithms. First, we propose a three-step estimator termed Count-Randomize-Least squares (CRL), and show that it has adaptivity index upper bounded as $\sqrt{n}$ up to logarithmic factors. We then show that that conditional on the hardness of planted clique, no computationally efficient estimator can achieve an adaptivity index smaller than $\sqrt{n}$. Second, we show that a regularized least squares estimator can achieve a poly-logarithmic adaptivity index, thereby demonstrating a $\sqrt{n}$-gap between optimal and computationally achievable adaptivity. Finally, we prove that the standard least squares estimator, which is known to be optimally adaptive in several closely related problems, fails to adapt in the context of estimating pairwise probabilities.
Integration between biology and information science benefits both fields. Many related models have been proposed, such as computational visual cognition models, computational motor control models, integrations of both and so on. In general, the robustness and precision of recognition is one of the key problems for object recognition models. In this paper, inspired by features of human recognition process and their biological mechanisms, a new integrated and dynamic framework is proposed to mimic the semantic extraction, concept formation and feature re-selection in human visual processing. The main contributions of the proposed model are as follows: (1) Semantic feature extraction: Local semantic features are learnt from episodic features that are extracted from raw images through a deep neural network; (2) Integrated concept formation: Concepts are formed with local semantic information and structural information learnt through network. (3) Feature re-selection: When ambiguity is detected during recognition process, distinctive features according to the difference between ambiguous candidates are re-selected for recognition. Experimental results on hand-written digits and facial shape dataset show that, compared with other methods, the new proposed model exhibits higher robustness and precision for visual recognition, especially in the condition when input samples are smantic ambiguous. Meanwhile, the introduced biological mechanisms further strengthen the interaction between neuroscience and information science.
In this paper, we present a preliminary work on an approach to fill the gap between logic-based argumentation and the numerous approaches to tackle the dynamics of abstract argumentation frameworks. Our idea is that, even when arguments and attacks are defined by means of a logical belief base, there may be some uncertainty about how accurate is the content of an argument, and so the presence (or absence) of attacks concerning it. We use enthymemes to illustrate this notion of uncertainty of arguments and attacks. Indeed, as argued in the literature, real arguments are often enthymemes instead of completely specified deductive arguments. This means that some parts of the pair (support, claim) may be missing because they are supposed to belong to some "common knowledge", and then should be deduced by the agent which receives the enthymeme. But the perception that agents have of the common knowledge may be wrong, and then a first agent may state an enthymeme that her opponent is not able to decode in an accurate way. It is likely that the decoding of the enthymeme by the agent leads to mistaken attacks between this new argument and the existing ones. In this case, the agent can receive some information about attacks or arguments acceptance statuses which disagree with her argumentation framework. We exemplify a way to incorporate this new piece of information by means of existing works on the dynamics of abstract argumentation frameworks.
Structural decomposition methods have been developed for identifying tractable classes of instances of fundamental problems in databases, such as conjunctive queries and query containment, of the constraint satisfaction problem in artificial intelligence, or more generally of the homomorphism problem over relational structures. Most structural decomposition methods can be characterized through hypergraph games that are variations of the Robber and Cops graph game that characterizes the notion of treewidth. In particular, decomposition trees somehow correspond to monotone winning strategies, where the escape space of the robber on the hypergraph is shrunk monotonically by the cops. In fact, unlike the treewidth case, there are hypergraphs where monotonic strategies do not exist, while the robber can be captured by means of more complex non-monotonic strategies. However, these powerful strategies do not correspond in general to valid decompositions. The paper provides a general way to exploit the power of non-monotonic strategies, by allowing a "disciplined" form of non-monotonicity, characteristic of cops playing in a greedy way. It is shown that deciding the existence of a (non-monotone) greedy winning strategy (and compute one, if any) is tractable. Moreover, despite their non-monotonicity, such strategies always induce valid decomposition trees, which can be computed efficiently based on them. As a consequence, greedy strategies allow us to define new islands of tractability for the considered problems properly including all previously known classes of tractable instances.
Visual recognition systems mounted on autonomous moving agents face the challenge of unconstrained data, but simultaneously have the opportunity to improve their performance by moving to acquire new views of test data. In this work, we first show how a recurrent neural network-based system may be trained to perform end-to-end learning of motion policies suited for this "active recognition" setting. Further, we hypothesize that active vision requires an agent to have the capacity to reason about the effects of its motions on its view of the world. To verify this hypothesis, we attempt to induce this capacity in our active recognition pipeline, by simultaneously learning to forecast the effects of the agent's motions on its internal representation of the environment conditional on all past views. Results across two challenging datasets confirm both that our end-to-end system successfully learns meaningful policies for active category recognition, and that "learning to look ahead" further boosts recognition performance.
Several `edge-discovery' applications over graph-based data models are known to have worst-case quadratic time complexity in the nodes, even if the discovered edges are sparse. One example is the generic link discovery problem between two graphs, which has invited research interest in several communities. Specific versions of this problem include link prediction in social networks, ontology alignment between metadata-rich RDF data, approximate joins, and entity resolution between instance-rich data. As large datasets continue to proliferate, reducing quadratic complexity to make the task practical is an important research problem. Within the entity resolution community, the problem is commonly referred to as blocking. A particular class of learnable blocking schemes is known as Disjunctive Normal Form (DNF) blocking schemes, and has emerged as state-of-the art for homogeneous (i.e. same-schema) tabular data. Despite the promise of these schemes, a formalism or learning framework has not been developed for them when input data instances are generic, attributed graphs possessing both node and edge heterogeneity. With such a development, the complexity-reducing scope of DNF schemes becomes applicable to a variety of problems, including entity resolution and type alignment between heterogeneous graphs, and link prediction in networks represented as attributed graphs. This paper presents a graph-theoretic formalism for DNF schemes, and investigates their learnability in an optimization framework. We also briefly describe an empirical case study encapsulating some of the principles in this paper.
The static bike rebalancing problem (SBRP) concerns the task of repositioning bikes among stations in self-service bike-sharing systems. This problem can be seen as a variant of the one-commodity pickup and delivery vehicle routing problem, where multiple visits are allowed to be performed at each station, i.e., the demand of a station is allowed to be split. Moreover, a vehicle may temporarily drop its load at a station, leaving it in excess or, alternatively, collect more bikes from a station (even all of them), thus leaving it in default. Both cases require further visits in order to meet the actual demands of such station. This paper deals with a particular case of the SBRP, in which only a single vehicle is available and the objective is to find a least-cost route that meets the demand of all stations and does not violate the minimum (zero) and maximum (vehicle capacity) load limits along the tour. Therefore, the number of bikes to be collected or delivered at each station should be appropriately determined in order to respect such constraints. We propose an iterated local search (ILS) based heuristic to solve the problem. The ILS algorithm was tested on 980 benchmark instances from the literature and the results obtained are quite competitive when compared to other existing methods. Moreover, our heuristic was capable of finding most of the known optimal solutions and also of improving the results on a number of open instances.
Humans demonstrate remarkable abilities to predict physical events in complex scenes. Two classes of models for physical scene understanding have recently been proposed: "Intuitive Physics Engines", or IPEs, which posit that people make predictions by running approximate probabilistic simulations in causal mental models similar in nature to video-game physics engines, and memory-based models, which make judgments based on analogies to stored experiences of previously encountered scenes and physical outcomes. Versions of the latter have recently been instantiated in convolutional neural network (CNN) architectures. Here we report four experiments that, to our knowledge, are the first rigorous comparisons of simulation-based and CNN-based models, where both approaches are concretely instantiated in algorithms that can run on raw image inputs and produce as outputs physical judgments such as whether a stack of blocks will fall. Both approaches can achieve super-human accuracy levels and can quantitatively predict human judgments to a similar degree, but only the simulation-based models generalize to novel situations in ways that people do, and are qualitatively consistent with systematic perceptual illusions and judgment asymmetries that people show.
In order to be effective teammates, robots need to be able to understand high-level human behavior to recognize, anticipate, and adapt to human motion. We have designed a new approach to enable robots to perceive human group motion in real-time, anticipate future actions, and synthesize their own motion accordingly. We explore this within the context of joint action, where humans and robots move together synchronously. In this paper, we present an anticipation method which takes high-level group behavior into account. We validate the method within a human-robot interaction scenario, where an autonomous mobile robot observes a team of human dancers, and then successfully and contingently coordinates its movements to "join the dance". We compared the results of our anticipation method to move the robot with another method which did not rely on high-level group behavior, and found our method performed better both in terms of more closely synchronizing the robot's motion to the team, and also exhibiting more contingent and fluent motion. These findings suggest that the robot performs better when it has an understanding of high-level group behavior than when it does not. This work will help enable others in the robotics community to build more fluent and adaptable robots in the future.
Machines of all kinds from vehicles to industrial equipment are increasingly instrumented with hundreds of sensors. Using such data to detect anomalous behaviour is critical for safety and efficient maintenance. However, anomalies occur rarely and with great variety in such systems, so there is often insufficient anomalous data to build reliable detectors. A standard approach to mitigate this problem is to use one class methods relying only on data from normal behaviour. Unfortunately, even these approaches are more likely to fail in the scenario of a dynamical system with manual control input(s). Normal behaviour in response to novel control input(s) might look very different to the learned detector which may be incorrectly detected as anomalous. In this paper, we address this issue by modelling time-series via Ordinary Differential Equations (ODE) and utilising such an ODE model to simulate the behaviour of dynamical systems under varying control inputs. The available data is then augmented with data generated from the ODE, and the anomaly detector is retrained on this augmented dataset. Experiments demonstrate that ODE-augmented training data allows better coverage of possible control input(s) and results in learning more accurate distinctions between normal and anomalous behaviour in time-series.
Epistemic logic has become a major field of philosophical logic ever since the groundbreaking work by Hintikka (1962). Despite its various successful applications in theoretical computer science, AI, and game theory, the technical development of the field has been mainly focusing on the propositional part, i.e., the propositional modal logics of "knowing that". However, knowledge is expressed in everyday life by using various other locutions such as "knowing whether", "knowing what", "knowing how" and so on (knowing-wh hereafter). Such knowledge expressions are better captured in quantified epistemic logic, as was already discussed by Hintikka (1962) and his sequel works at length. This paper aims to draw the attention back again to such a fascinating but largely neglected topic. We first survey what Hintikka and others did in the literature of quantified epistemic logic, and then advocate a new quantifier-free approach to study the epistemic logics of knowing-wh, which we believe can balance expressivity and complexity, and capture the essential reasoning patterns about knowing-wh. We survey our recent line of work on the epistemic logics of "knowing whether", "knowing what" and "knowing how" to demonstrate the use of this new approach.
We study the Bipartite Boolean Quadratic Programming Problem (BBQP) which is an extension of the well known Boolean Quadratic Programming Problem (BQP). Applications of the BBQP include mining discrete patterns from binary data, approximating matrices by rank-one binary matrices, computing the cut-norm of a matrix, and solving optimisation problems such as maximum weight biclique, bipartite maximum weight cut, maximum weight induced sub-graph of a bipartite graph, etc. For the BBQP, we first present several algorithmic components, specifically, hill climbers and mutations, and then show how to combine them in a high-performance metaheuristic. Instead of hand-tuning a standard metaheuristic to test the efficiency of the hybrid of the components, we chose to use an automated generation of a multi-component metaheuristic to save human time, and also improve objectivity in the analysis and comparisons of components. For this we designed a new metaheuristic schema which we call Conditional Markov Chain Search (CMCS). We show that CMCS is flexible enough to model several standard metaheuristics; this flexibility is controlled by multiple numeric parameters, and so is convenient for automated generation. We study the configurations revealed by our approach and show that the best of them outperforms the previous state-of-the-art BBQP algorithm by several orders of magnitude. In our experiments we use benchmark instances introduced in the preliminary version of this paper and described here, which have already become the de facto standard in the BBQP literature.
The generalized belief propagation (GBP), introduced by Yedidia et al., is an extension of the belief propagation (BP) algorithm, which is widely used in different problems involved in calculating exact or approximate marginals of probability distributions. In many problems, it has been observed that the accuracy of GBP considerably outperforms that of BP. However, because in general the computational complexity of GBP is higher than BP, its application is limited in practice. In this paper, we introduce a stochastic version of GBP called stochastic generalized belief propagation (SGBP) that can be considered as an extension to the stochastic BP (SBP) algorithm introduced by Noorshams et al. They have shown that SBP reduces the complexity per iteration of BP by an order of magnitude in alphabet size. In contrast to SBP, SGBP can reduce the computation complexity if certain topological conditions are met by the region graph associated to a graphical model. However, this reduction can be larger than only one order of magnitude in alphabet size. In this paper, we characterize these conditions and the amount of computation gain that we can obtain by using SGBP. Finally, using similar proof techniques employed by Noorshams et al., for general graphical models satisfy contraction conditions, we prove the asymptotic convergence of SGBP to the unique GBP fixed point, as well as providing non-asymptotic upper bounds on the mean square error and on the high probability error.
The Ant Colony System (ACS) is, next to Ant Colony Optimization (ACO) and the MAX-MIN Ant System (MMAS), one of the most efficient metaheuristic algorithms inspired by the behavior of ants. In this article we present three novel parallel versions of the ACS for the graphics processing units (GPUs). To the best of our knowledge, this is the first such work on the ACS which shares many key elements of the ACO and the MMAS, but differences in the process of building solutions and updating the pheromone trails make obtaining an efficient parallel version for the GPUs a difficult task. The proposed parallel versions of the ACS differ mainly in their implementations of the pheromone memory. The first two use the standard pheromone matrix, and the third uses a novel selective pheromone memory. Computational experiments conducted on several Travelling Salesman Problem (TSP) instances of sizes ranging from 198 to 2392 cities showed that the parallel ACS on Nvidia Kepler GK104 GPU (1536 CUDA cores) is able to obtain a speedup up to 24.29x vs the sequential ACS running on a single core of Intel Xeon E5-2670 CPU. The parallel ACS with the selective pheromone memory achieved speedups up to 16.85x, but in most cases the obtained solutions were of significantly better quality than for the sequential ACS.
Psychological research results have confirmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of visual emotion analysis. In particular, attempts have been made to analyze and predict people's emotional reaction towards images. To this end, different kinds of hand-tuned features are proposed. The results reported on several carefully selected and labeled small image data sets have confirmed the promise of such features. While the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks (CNNs), visual emotion analysis has not achieved the same level of success. This may be primarily due to the unavailability of confidently labeled and relatively large image data sets for visual emotion analysis. In this work, we introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set. We hope that this data set encourages further research on visual emotion analysis. We also perform extensive benchmarking analyses on this large data set using the state of the art methods including CNNs.
We address a question answering task on real-world images that is set up as a Visual Turing Test. By combining latest advances in image representation and natural language processing, we propose Ask Your Neurons, a scalable, jointly trained, end-to-end formulation to this problem. In contrast to previous efforts, we are facing a multi-modal problem where the language output (answer) is conditioned on visual and natural language inputs (image and question). We provide additional insights into the problem by analyzing how much information is contained only in the language part for which we provide a new human baseline. To study human consensus, which is related to the ambiguities inherent in this challenging task, we propose two novel metrics and collect additional answers which extend the original DAQUAR dataset to DAQUAR-Consensus. Moreover, we also extend our analysis to VQA, a large-scale question answering about images dataset, where we investigate some particular design choices and show the importance of stronger visual models. At the same time, we achieve strong performance of our model that still uses a global image representation. Finally, based on such analysis, we refine our Ask Your Neurons on DAQUAR, which also leads to a better performance on this challenging task.
Identifying context-specific entity networks from aggregated data is an important task, arising often in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming that can be solved efficiently in parallel. In addition to Gaussian data, SIMULE can also handle multivariate Nonparanormal data that greatly relaxes the normality assumption that many real-world applications do not follow. We provide a novel theoretical proof showing that SIMULE achieves a consistent result at the rate O(log(Kp)/n_{tot}). On multiple synthetic datasets and two biomedical datasets, SIMULE shows significant improvement over state-of-the-art multi-sGGM and single-UGM baselines.
Yield and quality improvement is of paramount importance to any manufacturing company. One of the ways of improving yield is through discovery of the root causal factors affecting yield. We propose the use of data-driven interpretable causal models to identify key factors affecting yield. We focus on factors that are measured in different stages of production and testing in the manufacturing cycle of a product. We apply causal structure learning techniques on real data collected from this line. Specifically, the goal of this work is to learn interpretable causal models from observational data produced by manufacturing lines. Emphasis has been given to the interpretability of the models to make them actionable in the field of manufacturing. We highlight the challenges presented by assembly line data and propose ways to alleviate them.We also identify unique characteristics of data originating from assembly lines and how to leverage them in order to improve causal discovery. Standard evaluation techniques for causal structure learning shows that the learned causal models seem to closely represent the underlying latent causal relationship between different factors in the production process. These results were also validated by manufacturing domain experts who found them promising. This work demonstrates how data mining and knowledge discovery can be used for root cause analysis in the domain of manufacturing and connected industry.
The estimation of class prevalence, i.e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc. For example, in sentiment analysis, the objective is often not to estimate whether a specific text conveys a positive or a negative sentiment, but rather estimate the overall distribution of positive and negative sentiments during an event window. A popular way of performing the above task, often dubbed quantification, is to use supervised learning to train a prevalence estimator from labeled data. Contemporary literature cites several performance measures used to measure the success of such prevalence estimators. In this paper we propose the first online stochastic algorithms for directly optimizing these quantification-specific performance measures. We also provide algorithms that optimize hybrid performance measures that seek to balance quantification and classification performance. Our algorithms present a significant advancement in the theory of multivariate optimization and we show, by a rigorous theoretical analysis, that they exhibit optimal convergence. We also report extensive experiments on benchmark and real data sets which demonstrate that our methods significantly outperform existing optimization techniques used for these performance measures.
Learning the true ordering between objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to natural language processing and search aggregation. We study the problem of unsupervised rank aggregation where no ground truth ordering information in available, neither about the true preference ordering between any set of objects nor about the quality of individual rank lists. Aggregating the often inconsistent and poor quality rank lists in such an unsupervised manner is a highly challenging problem, and standard consensus-based methods are often ill-defined, and difficult to solve. In this manuscript we propose a novel framework to bypass these issues by using object attributes to augment the standard rank aggregation framework. We design algorithms that learn joint models on both rank lists and object features to obtain an aggregated rank ordering that is more accurate and robust, and also helps weed out rank lists of dubious validity. We validate our techniques on synthetic datasets where our algorithm is able to estimate the true rank ordering even when the rank lists are corrupted. Experiments on three real datasets, MQ2008, MQ2008 and OHSUMED, show that using object features can result in significant improvement in performance over existing rank aggregation methods that do not use object information. Furthermore, when at least some of the rank lists are of high quality, our methods are able to effectively exploit their high expertise to output an aggregated rank ordering of great accuracy.
Databases in domains such as healthcare are routinely released to the public in aggregated form. Unfortunately, naive modeling with aggregated data may significantly diminish the accuracy of inferences at the individual level. This paper addresses the scenario where features are provided at the individual level, but the target variables are only available as histogram aggregates or order statistics. We consider a limiting case of generalized linear modeling when the target variables are only known up to permutation, and explore how this relates to permutation testing; a standard technique for assessing statistical dependency. Based on this relationship, we propose a simple algorithm to estimate the model parameters and individual level inferences via alternating imputation and standard generalized linear model fitting. Our results suggest the effectiveness of the proposed approach when, in the original data, permutation testing accurately ascertains the veracity of the linear relationship. The framework is extended to general histogram data with larger bins - with order statistics such as the median as a limiting case. Our experimental results on simulated data and aggregated healthcare data suggest a diminishing returns property with respect to the granularity of the histogram - when a linear relationship holds in the original data, the targets can be predicted accurately given relatively coarse histograms.
Time-frequency methods for vibration-based gearbox faults detection have been considered the most efficient method. Among these methods, continuous wavelet transform (CWT) as one of the best time-frequency method has been used for both stationary and transitory signals. Some deficiencies of CWT are problem of overlapping and distortion ofsignals. In this condition, a large amount of redundant information exists so that it may cause false alarm or misinterpretation of the operator. In this paper a modified method called Exact Wavelet Analysis is used to minimize the effects of overlapping and distortion in case of gearbox faults. To implement exact wavelet analysis, Particle Swarm Optimization (PSO) algorithm has been used for this purpose. This method have been implemented for the acceleration signals from 2D acceleration sensor acquired by Advantech PCI-1710 card from a gearbox test setup in Amirkabir University of Technology. Gearbox has been considered in both healthy and chipped tooth gears conditions. Kernelized Support Vector Machine (SVM) with radial basis functions has used the extracted features from exact wavelet analysis for classification. The efficiency of this classifier is then evaluated with the other signals acquired from the setup test. The results show that in comparison of CWT, PSO Exact Wavelet Transform has better ability in feature extraction in price of more computational effort. In addition, PSO exact wavelet has better speed comparing to Genetic Algorithm (GA) exact wavelet in condition of equal population because of factoring mutation and crossover in PSO algorithm. SVM classifier with the extracted features in gearbox shows very good results and its ability has been proved.
The rise in popularity and ubiquity of Twitter has made sentiment analysis of tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sentiment classification. On the other hand, what tweets lack in structure they make up with sheer volume and rich metadata. This metadata includes geolocation, temporal and author information. We hypothesize that sentiment is dependent on all these contextual factors. Different locations, times and authors have different emotional valences. In this paper, we explored this hypothesis by utilizing distant supervision to collect millions of labelled tweets from different locations, times and authors. We used this data to analyse the variation of tweet sentiments across different authors, times and locations. Once we explored and understood the relationship between these variables and sentiment, we used a Bayesian approach to combine these variables with more standard linguistic features such as n-grams to create a Twitter sentiment classifier. This combined classifier outperforms the purely linguistic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research.
Apnea-bradycardia is one of the major clinical early indicators of late-onset sepsis occurring in approximately 7% to 10% of all neonates and in more than 25% of very low birth weight infants in NICU. The objective of this paper was to determine if HRV, respiration and their relationships help to diagnose infection in premature infants via non-invasive ways in NICU. Therefore, we implement Mono-Channel (MC) and Bi-Channel (BC) Analysis in two groups: sepsis (S) vs. non-sepsis (NS). Firstly, we studied RR series not only by linear methods: time domain and frequency domain, but also by non-linear methods: chaos theory and information theory. The results show that alpha Slow, alpha Fast and Sample Entropy are significant parameters to distinguish S from NS. Secondly, the question about the functional coupling of HRV and nasal respiration is addressed. Local linear correlation coefficient r2t,f has been explored, while non-linear regression coefficient h2 was calculated in two directions. It is obvious that r2t,f within the third frequency band (0.2 7.9 billion triples. Further, we use the resulting SPARQL queries to mimic human associations with a Mean Average Precision (MAP) of 39.9 % and a Recall@10 of 63.9 %.
Data association, the reasoning over correspondence between targets and measurements, is a problem of fundamental importance in target tracking. Recently, belief propagation (BP) has emerged as a promising method for estimating the marginal probabilities of measurement to target association, providing fast, accurate estimates. The excellent performance of BP in the particular formulation used may be attributed to the convexity of the underlying free energy which it implicitly optimises. This paper studies multiple scan data association problems, i.e., problems that reason over correspondence between targets and several sets of measurements, which may correspond to different sensors or different time steps. We find that the multiple scan extension of the single scan BP formulation is non-convex and demonstrate the undesirable behaviour that can result. A convex free energy is constructed using the recently proposed fractional free energy (FFE). A convergent, BP-like algorithm is provided for the single scan FFE, and employed in optimising the multiple scan free energy using primal-dual coordinate ascent. Finally, based on a variational interpretation of joint probabilistic data association (JPDA), we develop a sequential variant of the algorithm that is similar to JPDA, but retains consistency constraints from prior scans. The performance of the proposed methods is demonstrated on a bearings only target localisation problem.
Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has been proposed for optimizing hyperparameters of various machine learning algorithms. Those methods adopt probabilistic surrogate models like Gaussian processes to approximate and minimize the validation error function of hyperparameter values. However, probabilistic surrogates require accurate estimates of sufficient statistics (e.g., covariance) of the error distribution and thus need many function evaluations with a sizeable number of hyperparameters. This makes them inefficient for optimizing hyperparameters of deep learning algorithms, which are highly expensive to evaluate. In this work, we propose a new deterministic and efficient hyperparameter optimization method that employs radial basis functions as error surrogates. The proposed mixed integer algorithm, called HORD, searches the surrogate for the most promising hyperparameter values through dynamic coordinate search and requires many fewer function evaluations. HORD does well in low dimensions but it is exceptionally better in higher dimensions. Extensive evaluations on MNIST and CIFAR-10 for four deep neural networks demonstrate HORD significantly outperforms the well-established Bayesian optimization methods such as GP, SMAC, and TPE. For instance, on average, HORD is more than 6 times faster than GP-EI in obtaining the best configuration of 19 hyperparameters.
Outlier detection is a fundamental data science task with applications ranging from data cleaning to network security. Given the fundamental nature of the task, this has been the subject of much research. Recently, a new class of outlier detection algorithms has emerged, called {\it contextual outlier detection}, and has shown improved performance when studying anomalous behavior in a specific context. However, as we point out in this article, such approaches have limited applicability in situations where the context is sparse (i.e. lacking a suitable frame of reference). Moreover, approaches developed to date do not scale to large datasets. To address these problems, here we propose a novel and robust approach alternative to the state-of-the-art called RObust Contextual Outlier Detection (ROCOD). We utilize a local and global behavioral model based on the relevant contexts, which is then integrated in a natural and robust fashion. We also present several optimizations to improve the scalability of the approach. We run ROCOD on both synthetic and real-world datasets and demonstrate that it outperforms other competitive baselines on the axes of efficacy and efficiency (40X speedup compared to modern contextual outlier detection methods). We also drill down and perform a fine-grained analysis to shed light on the rationale for the performance gains of ROCOD and reveal its effectiveness when handling objects with sparse contexts.
In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed. In multi-label classification, each of the input data sample belongs to one or more than one of the target labels. The traditional binary and multi-class classification where each sample belongs to only one target class forms the subset of multi-label classification. Multi-label classification problems are far more complex than binary and multi-class classification problems, as both the number of target labels and each of the target labels corresponding to each of the input samples are to be identified. The proposed work exploits the high-speed nature of the extreme learning machines to achieve real-time multi-label classification of streaming data. A new threshold-based online sequential learning algorithm is proposed for high speed and streaming data classification of multi-label problems. The proposed method is experimented with six different datasets from different application domains such as multimedia, text, and biology. The hamming loss, accuracy, training time and testing time of the proposed technique is compared with nine different state-of-the-art methods. Experimental studies shows that the proposed technique outperforms the existing multi-label classifiers in terms of performance and speed.
We present a loss function for neural networks that encompasses an idea of trivial versus non-trivial predictions, such that the network jointly determines its own prediction goals and learns to satisfy them. This permits the network to choose sub-sets of a problem which are most amenable to its abilities to focus on solving, while discarding 'distracting' elements that interfere with its learning. To do this, the network first transforms the raw data into a higher-level categorical representation, and then trains a predictor from that new time series to its future. To prevent a trivial solution of mapping the signal to zero, we introduce a measure of non-triviality via a contrast between the prediction error of the learned model with a naive model of the overall signal statistics. The transform can learn to discard uninformative and unpredictable components of the signal in favor of the features which are both highly predictive and highly predictable. This creates a coarse-grained model of the time-series dynamics, focusing on predicting the slowly varying latent parameters which control the statistics of the time-series, rather than predicting the fast details directly. The result is a semi-supervised algorithm which is capable of extracting latent parameters, segmenting sections of time-series with differing statistics, and building a higher-level representation of the underlying dynamics from unlabeled data.
Though deep learning has pushed the boundaries of classification forward, in recent years hints of the limits of standard classification have begun to emerge. Problems such as fooling, adding new classes over time, and the need to retrain learning models only for small changes to the original problem all point to a potential shortcoming in the classic classification regime, where a comprehensive a priori knowledge of the possible classes or concepts is critical. Without such knowledge, classifiers misjudge the limits of their knowledge and overgeneralization therefore becomes a serious obstacle to consistent performance. In response to these challenges, this paper extends the classic regime by reframing classification instead with the assumption that concepts present in the training set are only a sample of the hypothetical final set of concepts. To bring learning models into this new paradigm, a novel elaboration of standard architectures called the competitive overcomplete output layer (COOL) neural network is introduced. Experiments demonstrate the effectiveness of COOL by applying it to fooling, separable concept learning, one-class neural networks, and standard classification benchmarks. The results suggest that, unlike conventional classifiers, the amount of generalization in COOL networks can be tuned to match the problem.
The partially observable Markov decision process (POMDP) provides a principled general framework for planning under uncertainty, but solving POMDPs optimally is computationally intractable, due to the "curse of dimensionality" and the "curse of history". To overcome these challenges, we introduce the Determinized Sparse Partially Observable Tree (DESPOT), a sparse approximation of the standard belief tree, for online planning under uncertainty. A DESPOT focuses online planning on a set of randomly sampled scenarios and compactly captures the "execution" of all policies under these scenarios. We show that the best policy obtained from a DESPOT is near-optimal, with a regret bound that depends on the representation size of the optimal policy. Leveraging this result, we give an anytime online planning algorithm, which searches a DESPOT for a policy that optimizes a regularized objective function. Regularization balances the estimated value of a policy under the sampled scenarios and the policy size, thus avoiding overfitting. The algorithm demonstrates strong experimental results, compared with some of the best online POMDP algorithms available. It has also been incorporated into an autonomous driving system for real-time vehicle control. The source code for the algorithm is available online.
Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.
Statistical characteristics of network traffic have attracted a significant amount of research for automated network intrusion detection, some of which looked at applications of natural statistical laws such as Zipf's law, Benford's law and the Pareto distribution. In this paper, we present the application of Benford's law to a new network flow metric "flow size difference", which have not been studied before by other researchers, to build an unsupervised flow-based intrusion detection system (IDS). The method was inspired by our observation on a large number of TCP flow datasets where normal flows tend to follow Benford's law closely but malicious flows tend to deviate significantly from it. The proposed IDS is unsupervised, so it can be easily deployed without any training. It has two simple operational parameters with a clear semantic meaning, allowing the IDS operator to set and adapt their values intuitively to adjust the overall performance of the IDS. We tested the proposed IDS on two (one closed and one public) datasets, and proved its efficiency in terms of AUC (area under the ROC curve). Our work showed the "flow size difference" has a great potential to improve the performance of any flow-based network IDSs.
In recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. Bots are predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and sociality and it is hence natural to expect interactions between bots to be relatively predictable and uneventful. In this article, we analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other's edits over the period 2001-2010, model how pairs of bots interact over time, and identify different types of interaction trajectories. We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other's edits and these sterile "fights" may sometimes continue for years. Unlike humans on Wikipedia, bots' interactions tend to occur over longer periods of time and to be more reciprocated. Yet, just like humans, bots in different cultural environments may behave differently. Our research suggests that even relatively "dumb" bots may give rise to complex interactions, and this carries important implications for Artificial Intelligence research. Understanding what affects bot-bot interactions is crucial for managing social media well, providing adequate cyber-security, and designing well functioning autonomous vehicles.
Preference orderings are orderings of a set of items according to the preferences (of judges). Such orderings arise in a variety of domains, including group decision making, consumer marketing, voting and machine learning. Measuring the mutual information and extracting the common patterns in a set of preference orderings are key to these areas. In this paper we deal with the representation of sets of preference orderings, the quantification of the degree to which judges agree on their ordering of the items (i.e. the concordance), and the efficient, meaningful description of such sets. We propose to represent the orderings in a subsequence-based feature space and present a new algorithm to calculate the size of the set of all common subsequences - the basis of a quantification of concordance, not only for pairs of orderings but also for sets of orderings. The new algorithm is fast and storage efficient with a time complexity of only $O(Nn^2)$ for the orderings of $n$ items by $N$ judges and a space complexity of only $O(\min\{Nn,n^2\})$. Also, we propose to represent the set of all $N$ orderings through a smallest set of covering preferences and present an algorithm to construct this smallest covering set. The source code for the algorithms is available at https://github.com/zhiweiuu/secs
Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of contestants, and the test conditions are very difficult to replicate after the main event. We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark (APB). Designed to be reproducible, it consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils. A well-defined evaluation protocol enables the comparison of \emph{complete} robotic systems -- including perception and manipulation -- instead of sub-systems only. Our paper also describes and reports results achieved by an open baseline system based on a Baxter robot.
Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques. For example, they require very large datasets to work effectively, entailing that they are slow to learn even when such datasets are available. Moreover, they lack the ability to reason on an abstract level, which makes it difficult to implement high-level cognitive functions such as transfer learning, analogical reasoning, and hypothesis-based reasoning. Finally, their operation is largely opaque to humans, rendering them unsuitable for domains in which verifiability is important. In this paper, we propose an end-to-end reinforcement learning architecture comprising a neural back end and a symbolic front end with the potential to overcome each of these shortcomings. As proof-of-concept, we present a preliminary implementation of the architecture and apply it to several variants of a simple video game. We show that the resulting system -- though just a prototype -- learns effectively, and, by acquiring a set of symbolic rules that are easily comprehensible to humans, dramatically outperforms a conventional, fully neural DRL system on a stochastic variant of the game.
Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real-world applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose a novel model, named Context-Aware Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN employs adaptive context-specific input matrices and adaptive context-specific transition matrices. The adaptive context-specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context-specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CA-RNN model yields significant improvements over state-of-the-art sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.
In this note we consider the problem of introducing variables in temporal logic programs under the formalism of "Temporal Equilibrium Logic" (TEL), an extension of Answer Set Programming (ASP) for dealing with linear-time modal operators. To this aim, we provide a definition of a first-order version of TEL that shares the syntax of first-order Linear-time Temporal Logic (LTL) but has a different semantics, selecting some LTL models we call "temporal stable models". Then, we consider a subclass of theories (called "splittable temporal logic programs") that are close to usual logic programs but allowing a restricted use of temporal operators. In this setting, we provide a syntactic definition of "safe variables" that suffices to show the property of "domain independence" -- that is, addition of arbitrary elements in the universe does not vary the set of temporal stable models. Finally, we present a method for computing the derivable facts by constructing a non-temporal logic program with variables that is fed to a standard ASP grounder. The information provided by the grounder is then used to generate a subset of ground temporal rules which is equivalent to (and generally smaller than) the full program instantiation.
An optimal data partitioning in parallel & distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster merging. Though partitioning using Kd Tree is being conventionally used in academia, it suffers from performance drenches and bias (non equal distribution) as dimensionality of data increases and hence is not suitable for practical use in industry where dimensionality can be of order of 100s to 1000s. To address these issues we propose two new partitioning techniques using existing mathematical models & study their feasibility, performance (bias and partitioning speed) & possible variants in choosing initial seeds. First method uses an n dimensional hashed grid based approach which is based on mapping the points in space to a set of cubes which hashes the points. Second method uses a tree of voronoi planes where each plane corresponds to a partition. We found that grid based approach was computationally impractical, while using a tree of voronoi planes (using scalable K-Means++ initial seeds) drastically outperformed the Kd-tree tree method as dimensionality increased.
This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input. To this end, we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L1 penalty on the filter activations to further encourage sparsity in the intermediate representations. To the best of our knowledge, this is the first work to propose sparse convolutional layers and L1 regularisation for efficient large-scale processing of 3D data. We demonstrate the efficacy of our approach on the KITTI object detection benchmark and show that Vote3Deep models with as few as three layers outperform the previous state of the art in both laser and laser-vision based approaches by margins of up to 40% while remaining highly competitive in terms of processing time.
High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision avoidance systems exist, these systems require online geometric optimization where tedious parameter tuning and perfect sensing are necessary. We present a novel end-to-end framework to generate reactive collision avoidance policy for efficient distributed multi-agent navigation. Our method formulates an agent's navigation strategy as a deep neural network mapping from the observed noisy sensor measurements to the agent's steering commands in terms of movement velocity. We train the network on a large number of frames of collision avoidance data collected by repeatedly running a multi-agent simulator with different parameter settings. We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations. We also show that our method can be well generalized to scenarios that do not appear in our training data, including scenes with static obstacles and agents with different sizes. Videos are available at https://sites.google.com/view/deepmaca.
Abstractive summarization is an ideal form of summarization since it can synthesize information from multiple documents to create concise informative summaries. In this work, we aim at developing an abstractive summarizer. First, our proposed approach identifies the most important document in the multi-document set. The sentences in the most important document are aligned to sentences in other documents to generate clusters of similar sentences. Second, we generate K-shortest paths from the sentences in each cluster using a word-graph structure. Finally, we select sentences from the set of shortest paths generated from all the clusters employing a novel integer linear programming (ILP) model with the objective of maximizing information content and readability of the final summary. Our ILP model represents the shortest paths as binary variables and considers the length of the path, information score and linguistic quality score in the objective function. Experimental results on the DUC 2004 and 2005 multi-document summarization datasets show that our proposed approach outperforms all the baselines and state-of-the-art extractive summarizers as measured by the ROUGE scores. Our method also outperforms a recent abstractive summarization technique. In manual evaluation, our approach also achieves promising results on informativeness and readability.
In this paper, we deal with two challenges for measuring the similarity of the subject identities in practical video-based face recognition - the variation of the head pose in uncontrolled environments and the computational expense of processing videos. Since the frame-wise feature mean is unable to characterize the pose diversity among frames, we define and preserve the overall pose diversity and closeness in a video. Then, identity will be the only source of variation across videos since the pose varies even within a single video. Instead of simply using all the frames, we select those faces whose pose point is closest to the centroid of the K-means cluster containing that pose point. Then, we represent a video as a bag of frame-wise deep face features while the number of features has been reduced from hundreds to K. Since the video representation can well represent the identity, now we measure the subject similarity between two videos as the max correlation among all possible pairs in the two bags of features. On the official 5,000 video-pairs of the YouTube Face dataset for face verification, our algorithm achieves a comparable performance with VGG-face that averages over deep features of all frames. Other vision tasks can also benefit from the generic idea of employing geometric cues to improve the descriptiveness of deep features.
Recent advances in biosensors technology and mobile electroencephalographic (EEG) interfaces have opened new application fields for cognitive monitoring. A computable biomarker for the assessment of spontaneous aesthetic brain responses during music listening is introduced here. It derives from well-established measures of cross-frequency coupling (CFC) and quantifies the music-induced alterations in the dynamic relationships between brain rhythms. During a stage of exploratory analysis, and using the signals from a suitably designed experiment, we established the biomarker, which acts on brain activations recorded over the left prefrontal cortex and focuses on the functional coupling between high-beta and low-gamma oscillations. Based on data from an additional experimental paradigm, we validated the introduced biomarker and showed its relevance for expressing the subjective aesthetic appreciation of a piece of music. Our approach resulted in an affordable tool that can promote human-machine interaction and, by serving as a personalized music annotation strategy, can be potentially integrated into modern flexible music recommendation systems. Keywords: Cross-frequency coupling; Human-computer interaction; Brain-computer interface
The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the recovery time of football injuries using the UEFA injury recordings: The UEFA recordings is a common standard for recording injuries in professional football. For this investigation, three datasets of UEFA injury recordings were available. Different machine learning algorithms were used in order to build a predictive model. The performance of the machine learning models is then improved by using feature selection conducted through correlation-based subset feature selection and random forests. 2. Predicting injuries in professional football using exposure records: The relationship between exposure (in training hours and match hours) in professional football athletes and injury incidence was studied. A common problem in football is understanding how the training schedule of an athlete can affect the chance of him getting injured. The task was to predict the number of days a player can train before he gets injured. 3. Predicting intrinsic injury incidence using in-training GPS measurements: A significant percentage of football injuries can be attributed to overtraining and fatigue. GPS data collected during training sessions might provide indicators of fatigue, or might be used to detect very intense training sessions which can lead to overtraining. This research used GPS data gathered during training sessions of the first team of THFC, in order to predict whether an injury would take place during a week.
The 'conjunction fallacy' has been extensively debated by scholars in cognitive science and, in recent times, the discussion has been enriched by the proposal of modeling the fallacy using the quantum formalism. Two major quantum approaches have been put forward: the first assumes that respondents use a two-step sequential reasoning and that the fallacy results from the presence of 'question order effects'; the second assumes that respondents evaluate the cognitive situation as a whole and that the fallacy results from the 'emergence of new meanings', as an 'effect of overextension' in the conceptual conjunction. Thus, the question arises as to determine whether and to what extent conjunction fallacies would result from 'order effects' or, instead, from 'emergence effects'. To help clarify this situation, we propose to use the World Wide Web as an 'information space' that can be interrogated both in a sequential and non-sequential way, to test these two quantum approaches. We find that 'emergence effects', and not 'order effects', should be considered the main cognitive mechanism producing the observed conjunction fallacies.
User preference integration is of great importance in multi-objective optimization, in particular in many objective optimization. Preferences have long been considered in traditional multicriteria decision making (MCDM) which is based on mathematical programming. Recently, it is integrated in multi-objective metaheuristics (MOMH), resulting in focus on preferred parts of the Pareto front instead of the whole Pareto front. The number of publications on preference-based multi-objective metaheuristics has increased rapidly over the past decades. There already exist various preference handling methods and MOMH methods, which have been combined in diverse ways. This article proposes to use the Web Ontology Language (OWL) to model and systematize the results developed in this field. A review of the existing work is provided, based on which an ontology is built and instantiated with state-of-the-art results. The OWL ontology is made public and open to future extension. Moreover, the usage of the ontology is exemplified for different use-cases, including querying for methods that match an engineering application, bibliometric analysis, checking existence of combinations of preference models and MOMH techniques, and discovering opportunities for new research and open research questions.
Robust principal component analysis (RPCA) has been widely used for recovering low-rank matrices in many data mining and machine learning problems. It separates a data matrix into a low-rank part and a sparse part. The convex approach has been well studied in the literature. However, state-of-the-art algorithms for the convex approach usually have relatively high complexity due to the need of solving (partial) singular value decompositions of large matrices. A non-convex approach, AltProj, has also been proposed with lighter complexity and better scalability. Given the true rank $r$ of the underlying low rank matrix, AltProj has a complexity of $O(r^2dn)$, where $d\times n$ is the size of data matrix. In this paper, we propose a novel factorization-based model of RPCA, which has a complexity of $O(kdn)$, where $k$ is an upper bound of the true rank. Our method does not need the precise value of the true rank. From extensive experiments, we observe that AltProj can work only when $r$ is precisely known in advance; however, when the needed rank parameter $r$ is specified to a value different from the true rank, AltProj cannot fully separate the two parts while our method succeeds. Even when both work, our method is about 4 times faster than AltProj. Our method can be used as a light-weight, scalable tool for RPCA in the absence of the precise value of the true rank.
Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory. However, memory networks conduct the reasoning on sentence-level memory to output coarse semantic vectors and do not further take any attention mechanism to focus on words, which may lead to the model lose some detail information, especially when the answers are rare or unknown words. In this paper, we propose a novel Hierarchical Memory Networks, dubbed HMN. First, we encode the past facts into sentence-level memory and word-level memory respectively. Then, (k)-max pooling is exploited following reasoning module on the sentence-level memory to sample the (k) most relevant sentences to a question and feed these sentences into attention mechanism on the word-level memory to focus the words in the selected sentences. Finally, the prediction is jointly learned over the outputs of the sentence-level reasoning module and the word-level attention mechanism. The experimental results demonstrate that our approach successfully conducts answer selection on unknown words and achieves a better performance than memory networks.
Model predictive control (MPC) is a popular control method that has proved effective for robotics, among other fields. MPC performs re-planning at every time step. Re-planning is done with a limited horizon per computational and real-time constraints and often also for robustness to potential model errors. However, the limited horizon leads to suboptimal performance. In this work, we consider the iterative learning setting, where the same task can be repeated several times, and propose a policy improvement scheme for MPC. The main idea is that between executions we can, offline, run MPC with a longer horizon, resulting in a hindsight plan. To bring the next real-world execution closer to the hindsight plan, our approach learns to re-shape the original cost function with the goal of satisfying the following property: short horizon planning (as realistic during real executions) with respect to the shaped cost should result in mimicking the hindsight plan. This effectively consolidates long-term reasoning into the short-horizon planning. We empirically evaluate our approach in contact-rich manipulation tasks both in simulated and real environments, such as peg insertion by a real PR2 robot.
With the rapid growth of social media, rumors are also spreading widely on social media and bring harm to people's daily life. Nowadays, information credibility evaluation has drawn attention from academic and industrial communities. Current methods mainly focus on feature engineering and achieve some success. However, feature engineering based methods require a lot of labor and cannot fully reveal the underlying relations among data. In our viewpoint, the key elements of user behaviors for evaluating credibility are concluded as "who", "what", "when", and "how". These existing methods cannot model the correlation among different key elements during the spreading of microblogs. In this paper, we propose a novel representation learning method, Information Credibility Evaluation (ICE), to learn representations of information credibility on social media. In ICE, latent representations are learnt for modeling user credibility, behavior types, temporal properties, and comment attitudes. The aggregation of these factors in the microblog spreading process yields the representation of a user's behavior, and the aggregation of these dynamic representations generates the credibility representation of an event spreading on social media. Moreover, a pairwise learning method is applied to maximize the credibility difference between rumors and non-rumors. To evaluate the performance of ICE, we conduct experiments on a Sina Weibo data set, and the experimental results show that our ICE model outperforms the state-of-the-art methods.
Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia. Special interest is around Convolutional Neural Networks (CNN), which take inspiration from the hierarchical structure of the visual cortex, to form deep layers of convolutional operations, along with fully connected classifiers. Hardware implementations of these deep CNN architectures are challenged with memory bottlenecks that require many convolution and fully-connected layers demanding large amount of communication for parallel computation. Multi-core CPU based solutions have demonstrated their inadequacy for this problem due to the memory wall and low parallelism. Many-core GPU architectures show superior performance but they consume high power and also have memory constraints due to inconsistencies between cache and main memory. FPGA design solutions are also actively being explored, which allow implementing the memory hierarchy using embedded BlockRAM. This boosts the parallel use of shared memory elements between multiple processing units, avoiding data replicability and inconsistencies. This makes FPGAs potentially powerful solutions for real-time classification of CNNs. Both Altera and Xilinx have adopted OpenCL co-design framework from GPU for FPGA designs as a pseudo-automatic development solution. In this paper, a comprehensive evaluation and comparison of Altera and Xilinx OpenCL frameworks for a 5-layer deep CNN is presented. Hardware resources, temporal performance and the OpenCL architecture for CNNs are discussed. Xilinx demonstrates faster synthesis, better FPGA resource utilization and more compact boards. Altera provides multi-platforms tools, mature design community and better execution times.
Structured sparse optimization is an important and challenging problem for analyzing high-dimensional data in a variety of applications such as bioinformatics, medical imaging, social networks, and astronomy. Although a number of structured sparsity models have been explored, such as trees, groups, clusters, and paths, connected subgraphs have been rarely explored in the current literature. One of the main technical challenges is that there is no structured sparsity-inducing norm that can directly model the space of connected subgraphs, and there is no exact implementation of a projection oracle for connected subgraphs due to its NP-hardness. In this paper, we explore efficient approximate projection oracles for connected subgraphs, and propose two new efficient algorithms, namely, Graph-IHT and Graph-GHTP, to optimize a generic nonlinear objective function subject to connectivity constraint on the support of the variables. Our proposed algorithms enjoy strong guarantees analogous to several current methods for sparsity-constrained optimization, such as Projected Gradient Descent (PGD), Approximate Model Iterative Hard Thresholding (AM-IHT), and Gradient Hard Thresholding Pursuit (GHTP) with respect to convergence rate and approximation accuracy. We apply our proposed algorithms to optimize several well-known graph scan statistics in several applications of connected subgraph detection as a case study, and the experimental results demonstrate that our proposed algorithms outperform state-of-the-art methods.
Forecasting the flow of crowds is of great importance to traffic management and public safety, yet a very challenging task affected by many complex factors, such as inter-region traffic, events and weather. In this paper, we propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the in-flow and out-flow of crowds in each and every region through a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the framework of the residual neural networks to model the temporal closeness, period, and trend properties of the crowd traffic, respectively. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of the crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We evaluate ST-ResNet based on two types of crowd flows in Beijing and NYC, finding that its performance exceeds six well-know methods.
Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.
High-Throughput materials discovery involves the rapid synthesis, measurement, and characterization of many different but structurally-related materials. A key problem in materials discovery, the phase map identification problem, involves the determination of the crystal phase diagram from the materials' composition and structural characterization data. We present Phase-Mapper, a novel AI platform to solve the phase map identification problem that allows humans to interact with both the data and products of AI algorithms, including the incorporation of human feedback to constrain or initialize solutions. Phase-Mapper affords incorporation of any spectral demixing algorithm, including our novel solver, AgileFD, which is based on a convolutive non-negative matrix factorization algorithm. AgileFD can incorporate constraints to capture the physics of the materials as well as human feedback. We compare three solver variants with previously proposed methods in a large-scale experiment involving 20 synthetic systems, demonstrating the efficacy of imposing physical constrains using AgileFD. Phase-Mapper has also been used by materials scientists to solve a wide variety of phase diagrams, including the previously unsolved Nb-Mn-V oxide system, which is provided here as an illustrative example.
Networks represent relationships between entities in many complex systems, spanning from online social interactions to biological cell development and brain connectivity. In many cases, relationships between entities are unambiguously known: are two users 'friends' in a social network? Do two researchers collaborate on a published paper? Do two road segments in a transportation system intersect? These are directly observable in the system in question. In most cases, relationship between nodes are not directly observable and must be inferred: does one gene regulate the expression of another? Do two animals who physically co-locate have a social bond? Who infected whom in a disease outbreak in a population? Existing approaches for inferring networks from data are found across many application domains and use specialized knowledge to infer and measure the quality of inferred network for a specific task or hypothesis. However, current research lacks a rigorous methodology which employs standard statistical validation on inferred models. In this survey, we examine (1) how network representations are constructed from underlying data, (2) the variety of questions and tasks on these representations over several domains, and (3) validation strategies for measuring the inferred network's capability of answering questions on the system of interest.
Identity verification based on authenticity assessment of a handwritten signature is an important issue in biometrics. There are many effective methods for signature verification taking into account dynamics of a signing process. Methods based on partitioning take a very important place among them. In this paper we propose a new approach to signature partitioning. Its most important feature is the possibility of selecting and processing of hybrid partitions in order to increase a precision of the test signature analysis. Partitions are formed by a combination of vertical and horizontal sections of the signature. Vertical sections correspond to the initial, middle, and final time moments of the signing process. In turn, horizontal sections correspond to the signature areas associated with high and low pen velocity and high and low pen pressure on the surface of a graphics tablet. Our previous research on vertical and horizontal sections of the dynamic signature (created independently) led us to develop the algorithm presented in this paper. Selection of sections, among others, allows us to define the stability of the signing process in the partitions, promoting signature areas of greater stability (and vice versa). In the test of the proposed method two databases were used: public MCYT-100 and paid BioSecure.
Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction. But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the training set are mostly unrecognized either. We propose a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only. Based on the Deep Q-Network framework, the raw depth image is taken as the only input to estimate the Q values corresponding to all moving commands. The training of the network weights is end-to-end. In arbitrarily constructed simulation environments, we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling. Besides, through analysis of receptive fields of feature representations, deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes. The test results are compared with the exploration strategies separately based on deep learning or reinforcement learning. Even trained only in the simulated environment, experimental results in real-world environment demonstrate that the cognitive ability of robot controller is dramatically improved compared with the supervised method. We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.
Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections. While there are many methods to pseudorandomly generate domains, we focus in this paper on detecting (and generating) domains on a per-domain basis which provides a simple and flexible means to detect known DGA families. Recent machine learning approaches to DGA detection have been successful on fairly simplistic DGAs, many of which produce names of fixed length. However, models trained on limited datasets are somewhat blind to new DGA variants. In this paper, we leverage the concept of generative adversarial networks to construct a deep learning based DGA that is designed to intentionally bypass a deep learning based detector. In a series of adversarial rounds, the generator learns to generate domain names that are increasingly more difficult to detect. In turn, a detector model updates its parameters to compensate for the adversarially generated domains. We test the hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs. We detail solutions to several challenges in training this character-based generative adversarial network (GAN). In particular, our deep learning architecture begins as a domain name auto-encoder (encoder + decoder) trained on domains in the Alexa one million. Then the encoder and decoder are reassembled competitively in a generative adversarial network (detector + generator), with novel neural architectures and training strategies to improve convergence.
Rapid construction of phase diagrams is a central tenet of combinatorial materials science with accelerated materials discovery efforts often hampered by challenges in interpreting combinatorial x-ray diffraction datasets, which we address by developing AgileFD, an artificial intelligence algorithm that enables rapid phase mapping from a combinatorial library of x-ray diffraction patterns. AgileFD models alloying-based peak shifting through a novel expansion of convolutional nonnegative matrix factorization, which not only improves the identification of constituent phases but also maps their concentration and lattice parameter as a function of composition. By incorporating Gibbs phase rule into the algorithm, physically meaningful phase maps are obtained with unsupervised operation, and more refined solutions are attained by injecting expert knowledge of the system. The algorithm is demonstrated through investigation of the V-Mn-Nb oxide system where decomposition of eight oxide phases, including two with substantial alloying, provides the first phase map for this pseudo-ternary system. This phase map enables interpretation of high-throughput band gap data, leading to the discovery of new solar light absorbers and the alloying-based tuning of the direct-allowed band-gap energy of MnV2O6. The open-source family of AgileFD algorithms can be implemented into a broad range of high throughput workflows to accelerate materials discovery.
In big data era, the data continuously generated and its distribution may keep changes overtime. These challenges in online stream of data are known as concept drift. In this paper, we proposed the Adaptive Convolutional ELM method (ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid Extreme Learning Machine (ELM) model plus adaptive capability. This method is aimed for concept drift handling. We enhanced the CNN as convolutional hiererchical features representation learner combined with Elastic ELM (E$^2$LM) as a parallel supervised classifier. We propose an Adaptive OS-ELM (AOS-ELM) for concept drift adaptability in classifier level (named ACNNELM-1) and matrices concatenation ensembles for concept drift adaptability in ensemble level (named ACNNELM-2). Our proposed Adaptive CNNELM is flexible that works well in classifier level and ensemble level while most current methods only proposed to work on either one of the levels. We verified our method in extended MNIST data set and not MNIST data set. We set the experiment to simulate virtual drift, real drift, and hybrid drift event and we demonstrated how our CNNELM adaptability works. Our proposed method works well and gives better accuracy, computation scalability, and concept drifts adaptability compared to the regular ELM and CNN. Further researches are still required to study the optimum parameters and to use more varied image data set.
Neural sequence models are widely used to model time-series data in many fields. Equally ubiquitous is the usage of beam search (BS) as an approximate inference algorithm to decode output sequences from these models. BS explores the search space in a greedy left-right fashion retaining only the top-$B$ candidates -- resulting in sequences that differ only slightly from each other. Producing lists of nearly identical sequences is not only computationally wasteful but also typically fails to capture the inherent ambiguity of complex AI tasks. To overcome this problem, we propose \emph{Diverse Beam Search} (DBS), an alternative to BS that decodes a list of diverse outputs by optimizing for a diversity-augmented objective. We observe that our method finds better top-1 solutions by controlling for the exploration and exploitation of the search space -- implying that DBS is a \emph{better search algorithm}. Moreover, these gains are achieved with minimal computational or memory overhead as compared to beam search. To demonstrate the broad applicability of our method, we present results on image captioning, machine translation and visual question generation using both standard quantitative metrics and qualitative human studies. Our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
This paper presents NetWorks (NW), an interactive music generation system that uses a hierarchically clustered scale free network to generate music that ranges from orderly to chaotic. NW was inspired by the Honing Theory of creativity, according to which human-like creativity hinges on (1) the ability to self-organize and maintain dynamics at the 'edge of chaos' using something akin to 'psychological entropy', and (2) the capacity to shift between analytic and associative processing modes. At the 'edge of chaos', NW generates patterns that exhibit emergent complexity through coherent development at low, mid, and high levels of musical organization, and often suggests goal seeking behaviour. The architecture consists of four 16-node modules: one each for pitch, velocity, duration, and entry delay. The Core allows users to define how nodes are connected, and rules that determine when and how nodes respond to their inputs. The Mapping Layer allows users to map node output values to MIDI data that is routed to software instruments in a digital audio workstation. By shifting between bottom-up and top-down NW shifts between analytic and associative processing modes.
Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great success in supervised learning problems, and multiple instance learning as a typical weakly-supervised learning method is effective for many applications in computer vision, biometrics, nature language processing, etc. In this paper, we revisit the problem of solving multiple instance learning problems using neural networks. Neural networks are appealing for solving multiple instance learning problem. The multiple instance neural networks perform multiple instance learning in an end-to-end way, which take a bag with various number of instances as input and directly output bag label. All of the parameters in a multiple instance network are able to be optimized via back-propagation. We propose a new multiple instance neural network to learn bag representations, which is different from the existing multiple instance neural networks that focus on estimating instance label. In addition, recent tricks developed in deep learning have been studied in multiple instance networks, we find deep supervision is effective for boosting bag classification accuracy. In the experiments, the proposed multiple instance networks achieve state-of-the-art or competitive performance on several MIL benchmarks. Moreover, it is extremely fast for both testing and training, e.g., it takes only 0.0003 second to predict a bag and a few seconds to train on a MIL datasets on a moderate CPU.
It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is often insufficient. Personalized dialogue systems trained on a small dataset can overfit and make it difficult to adapt to different user needs. One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain. By following this idea, we propose "PETAL"(PErsonalized Task-oriented diALogue), a transfer-learning framework based on POMDP to learn a personalized dialogue system. The system first learns common dialogue knowledge from the source domain and then adapts this knowledge to the target user. This framework can avoid the negative transfer problem by considering differences between source and target users. The policy in the personalized POMDP can learn to choose different actions appropriately for different users. Experimental results on a real-world coffee-shopping data and simulation data show that our personalized dialogue system can choose different optimal actions for different users, and thus effectively improve the dialogue quality under the personalized setting.
Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways. Since there are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. Moreover, one must balance between unexpected behavior of other drivers/pedestrians and at the same time not to be too defensive so that normal traffic flow is maintained. In this paper we apply deep reinforcement learning to the problem of forming long term driving strategies. We note that there are two major challenges that make autonomous driving different from other robotic tasks. First, is the necessity for ensuring functional safety - something that machine learning has difficulty with given that performance is optimized at the level of an expectation over many instances. Second, the Markov Decision Process model often used in robotics is problematic in our case because of unpredictable behavior of other agents in this multi-agent scenario. We make three contributions in our work. First, we show how policy gradient iterations can be used without Markovian assumptions. Second, we decompose the problem into a composition of a Policy for Desires (which is to be learned) and trajectory planning with hard constraints (which is not learned). The goal of Desires is to enable comfort of driving, while hard constraints guarantees the safety of driving. Third, we introduce a hierarchical temporal abstraction we call an "Option Graph" with a gating mechanism that significantly reduces the effective horizon and thereby reducing the variance of the gradient estimation even further.
Developing control policies in simulation is often more practical and safer than directly running experiments in the real world. This applies to policies obtained from planning and optimization, and even more so to policies obtained from reinforcement learning, which is often very data demanding. However, a policy that succeeds in simulation often doesn't work when deployed on a real robot. Nevertheless, often the overall gist of what the policy does in simulation remains valid in the real world. In this paper we investigate such settings, where the sequence of states traversed in simulation remains reasonable for the real world, even if the details of the controls are not, as could be the case when the key differences lie in detailed friction, contact, mass and geometry properties. During execution, at each time step our approach computes what the simulation-based control policy would do, but then, rather than executing these controls on the real robot, our approach computes what the simulation expects the resulting next state(s) will be, and then relies on a learned deep inverse dynamics model to decide which real-world action is most suitable to achieve those next states. Deep models are only as good as their training data, and we also propose an approach for data collection to (incrementally) learn the deep inverse dynamics model. Our experiments shows our approach compares favorably with various baselines that have been developed for dealing with simulation to real world model discrepancy, including output error control and Gaussian dynamics adaptation.
We study truthful mechanisms for matching and related problems in a partial information setting, where the agents' true utilities are hidden, and the algorithm only has access to ordinal preference information. Our model is motivated by the fact that in many settings, agents cannot express the numerical values of their utility for different outcomes, but are still able to rank the outcomes in their order of preference. Specifically, we study problems where the ground truth exists in the form of a weighted graph of agent utilities, but the algorithm can only elicit the agents' private information in the form of a preference ordering for each agent induced by the underlying weights. Against this backdrop, we design truthful algorithms to approximate the true optimum solution with respect to the hidden weights. Our techniques yield universally truthful algorithms for a number of graph problems: a 1.76-approximation algorithm for Max-Weight Matching, 2-approximation algorithm for Max k-matching, a 6-approximation algorithm for Densest k-subgraph, and a 2-approximation algorithm for Max Traveling Salesman as long as the hidden weights constitute a metric. We also provide improved approximation algorithms for such problems when the agents are not able to lie about their preferences. Our results are the first non-trivial truthful approximation algorithms for these problems, and indicate that in many situations, we can design robust algorithms even when the agents may lie and only provide ordinal information instead of precise utilities.
This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).
While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly. To increase the practicality of these techniques on real robots, we propose a modular deep reinforcement learning method capable of transferring models trained in simulation to a real-world robotic task. We introduce a bottleneck between perception and control, enabling the networks to be trained independently, but then merged and fine-tuned in an end-to-end manner to further improve hand-eye coordination. On a canonical, planar visually-guided robot reaching task a fine-tuned accuracy of 1.6 pixels is achieved, a significant improvement over naive transfer (17.5 pixels), showing the potential for more complicated and broader applications. Our method provides a technique for more efficient learning and transfer of visuo-motor policies for real robotic systems without relying entirely on large real-world robot datasets.
Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. With potential applications including perception modules and end-to-end controllers for self-driving cars, this raises concerns about their safety. We develop a novel automated verification framework for feed-forward multi-layer neural networks based on Satisfiability Modulo Theory (SMT). We focus on safety of image classification decisions with respect to image manipulations, such as scratches or changes to camera angle or lighting conditions that would result in the same class being assigned by a human, and define safety for an individual decision in terms of invariance of the classification within a small neighbourhood of the original image. We enable exhaustive search of the region by employing discretisation, and propagate the analysis layer by layer. Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations. If found, adversarial examples can be shown to human testers and/or used to fine-tune the network. We implement the techniques using Z3 and evaluate them on state-of-the-art networks, including regularised and deep learning networks. We also compare against existing techniques to search for adversarial examples and estimate network robustness.
We develop a Bayesian model for decision-making under time pressure with endogenous information acquisition. In our model, the decision maker decides when to observe (costly) information by sampling an underlying continuous-time stochastic process (time series) that conveys information about the potential occurrence or non-occurrence of an adverse event which will terminate the decision-making process. In her attempt to predict the occurrence of the adverse event, the decision-maker follows a policy that determines when to acquire information from the time series (continuation), and when to stop acquiring information and make a final prediction (stopping). We show that the optimal policy has a rendezvous structure, i.e. a structure in which whenever a new information sample is gathered from the time series, the optimal "date" for acquiring the next sample becomes computable. The optimal interval between two information samples balances a trade-off between the decision maker's surprise, i.e. the drift in her posterior belief after observing new information, and suspense, i.e. the probability that the adverse event occurs in the time interval between two information samples. Moreover, we characterize the continuation and stopping regions in the decision-maker's state-space, and show that they depend not only on the decision-maker's beliefs, but also on the context, i.e. the current realization of the time series.
Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to unsupervised learning from a massive amount of data, albeit much of it relates to one modality/type of data at a time. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition of utilizing knowledge whenever it is available or can be created purposefully. In this paper, we focus on discussing the indispensable role of knowledge for deeper understanding of complex text and multimodal data in situations where (i) large amounts of training data (labeled/unlabeled) are not available or labor intensive to create, (ii) the objects (particularly text) to be recognized are complex (i.e., beyond simple entity-person/location/organization names), such as implicit entities and highly subjective content, and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create knowledge, varying from comprehensive or cross domain to domain or application specific, and (b) carefully exploit the knowledge to further empower or extend the applications of ML/NLP techniques. Using the early results in several diverse situations - both in data types and applications - we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data.
Food and nutrition occupy an increasingly prevalent space on the web, and dishes and recipes shared online provide an invaluable mirror into culinary cultures and attitudes around the world. More specifically, ingredients, flavors, and nutrition information become strong signals of the taste preferences of individuals and civilizations. However, there is little understanding of these palate varieties. In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world. Using a database of more than 157K recipes from over 200 different cuisines, we analyze ingredients, flavors, and nutritional values which distinguish dishes from different regions, and use this knowledge to assess the predictability of recipes from different cuisines. We then use country health statistics to understand the relation between these factors and health indicators of different nations, such as obesity, diabetes, migration, and health expenditure. Our results confirm the strong effects of geographical and cultural similarities on recipes, health indicators, and culinary preferences across the globe.
In this paper we present a broad overview of the last 40 years of research on cognitive architectures. Although the number of existing architectures is nearing several hundred, most of the existing surveys do not reflect this growth and focus on a handful of well-established architectures. Thus, in this survey we wanted to shift the focus towards a more inclusive and high-level overview of the research on cognitive architectures. Our final set of 84 architectures includes 49 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience. To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, action selection, memory, learning and reasoning. In order to assess the breadth of practical applications of cognitive architectures we gathered information on over 900 practical projects implemented using the cognitive architectures in our list. We use various visualization techniques to highlight overall trends in the development of the field. In addition to summarizing the current state-of-the-art in the cognitive architecture research, this survey describes a variety of methods and ideas that have been tried and their relative success in modeling human cognitive abilities, as well as which aspects of cognitive behavior need more research with respect to their mechanistic counterparts and thus can further inform how cognitive science might progress.
Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e. claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Further, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image datasets establish the advantages of our method over state-of-the-art dictionary learning methods.
Objective: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit (ICU) admissions for clinically deteriorating patients. Methods: The risk scoring system learns a set of latent patient subtypes from the offline electronic health record data, and trains a mixture of Gaussian Process (GP) experts, where each expert models the physiological data streams associated with a specific patient subtype. Transfer learning techniques are used to learn the relationship between a patient's latent subtype and her static admission information (e.g. age, gender, transfer status, ICD-9 codes, etc). Results: Experiments conducted on data from a heterogeneous cohort of 6,321 patients admitted to Ronald Reagan UCLA medical center show that our risk score significantly and consistently outperforms the currently deployed risk scores, such as the Rothman index, MEWS, APACHE and SOFA scores, in terms of timeliness, true positive rate (TPR), and positive predictive value (PPV). Conclusion: Our results reflect the importance of adopting the concepts of personalized medicine in critical care settings; significant accuracy and timeliness gains can be achieved by accounting for the patients' heterogeneity. Significance: The proposed risk scoring methodology can confer huge clinical and social benefits on more than 200,000 critically ill inpatient who exhibit cardiac arrests in the US every year.
In Bayesian statistics probability distributions express beliefs. However, for many problems the beliefs cannot be computed analytically and approximations of beliefs are needed. We seek a loss function that quantifies how "embarrassing" it is to communicate a given approximation. We reproduce and discuss an old proof showing that there is only one ranking under the requirements that (1) the best ranked approximation is the non-approximated belief and (2) that the ranking judges approximations only by their predictions for actual outcomes. The loss function that is obtained in the derivation is equal to the Kullback-Leibler divergence when normalized. This loss function is frequently used in the literature. However, there seems to be confusion about the correct order in which its functional arguments, the approximated and non-approximated beliefs, should be used. The correct order ensures that the recipient of a communication is only deprived of the minimal amount of information. We hope that the elementary derivation settles the apparent confusion. For example when approximating beliefs with Gaussian distributions the optimal approximation is given by moment matching. This is in contrast to many suggested computational schemes.
The research of personalized recommendation techniques today has mostly parted into two mainstream directions, i.e., the factorization-based approaches and topic models. Practically, they aim to benefit from the numerical ratings and textual reviews, correspondingly, which compose two major information sources in various real-world systems. However, although the two approaches are supposed to be correlated for their same goal of accurate recommendation, there still lacks a clear theoretical understanding of how their objective functions can be mathematically bridged to leverage the numerical ratings and textual reviews collectively, and why such a bridge is intuitively reasonable to match up their learning procedures for the rating prediction and top-N recommendation tasks, respectively. In this work, we exposit with mathematical analysis that, the vector-level randomization functions to coordinate the optimization objectives of factorizational and topic models unfortunately do not exist at all, although they are usually pre-assumed and intuitively designed in the literature. Fortunately, we also point out that one can avoid the seeking of such a randomization function by optimizing a Joint Factorizational Topic (JFT) model directly. We apply our JFT model to restaurant recommendation, and study its performance in both normal and cross-city recommendation scenarios, where the latter is an extremely difficult task for its inherent cold-start nature. Experimental results on real-world datasets verified the appealing performance of our approach against previous methods, on both rating prediction and top-N recommendation tasks.
Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). However, it is also a challenging task due to the diversity of traffic scene and the storage, power and computing source limitations of the platforms for traffic scene applications. This paper presents a generalized Haar filter based deep network which is suitable for the object detection tasks in traffic scene. In this approach, we first decompose a object detection task into several easier local regression tasks. Then, we handle the local regression tasks by using several tiny deep networks which simultaneously output the bounding boxes, categories and confidence scores of detected objects. To reduce the consumption of storage and computing resources, the weights of the deep networks are constrained to the form of generalized Haar filter in training phase. Additionally, we introduce the strategy of sparse windows generation to improve the efficiency of the algorithm. Finally, we perform several experiments to validate the performance of our proposed approach. Experimental results demonstrate that the proposed approach is both efficient and effective in traffic scene compared with the state-of-the-art.
Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space. Despite the success of embedding individual nodes for graph analytics, we notice that an important concept of embedding communities (i.e., groups of nodes) is missing. Embedding communities is useful, not only for supporting various community-level applications, but also to help preserve community structure in graph embedding. In fact, we see community embedding as providing a higher-order proximity to define the node closeness, whereas most of the popular graph embedding methods focus on first-order and/or second-order proximities. To learn the community embedding, we hinge upon the insight that community embedding and node embedding reinforce with each other. As a result, we propose ComEmbed, the first community embedding method, which jointly optimizes the community embedding and node embedding together. We evaluate ComEmbed on real-world data sets. We show it outperforms the state-of-the-art baselines in both tasks of node classification and community prediction.
Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN). Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models. To address these problems, we propose a class of probabilistic neural networks, dubbed natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential-family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation before producing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second-order representations for the associated tasks such as link prediction. Experiments on real-world datasets show that NPN can achieve state-of-the-art performance.
Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.
The Neural GPU is a recent model that can learn algorithms such as multi-digit binary addition and binary multiplication in a way that generalizes to inputs of arbitrary length. We show that there are two simple ways of improving the performance of the Neural GPU: by carefully designing a curriculum, and by increasing model size. The latter requires a memory efficient implementation, as a naive implementation of the Neural GPU is memory intensive. We find that these techniques increase the set of algorithmic problems that can be solved by the Neural GPU: we have been able to learn to perform all the arithmetic operations (and generalize to arbitrarily long numbers) when the arguments are given in the decimal representation (which, surprisingly, has not been possible before). We have also been able to train the Neural GPU to evaluate long arithmetic expressions with multiple operands that require respecting the precedence order of the operands, although these have succeeded only in their binary representation, and not with perfect accuracy. In addition, we gain insight into the Neural GPU by investigating its failure modes. We find that Neural GPUs that correctly generalize to arbitrarily long numbers still fail to compute the correct answer on highly-symmetric, atypical inputs: for example, a Neural GPU that achieves near-perfect generalization on decimal multiplication of up to 100-digit long numbers can fail on $000000\dots002 \times 000000\dots002$ while succeeding at $2 \times 2$. These failure modes are reminiscent of adversarial examples.
Various families of malware use domain generation algorithms (DGAs) to generate a large number of pseudo-random domain names to connect to a command and control (C&C) server. In order to block DGA C&C traffic, security organizations must first discover the algorithm by reverse engineering malware samples, then generating a list of domains for a given seed. The domains are then either preregistered or published in a DNS blacklist. This process is not only tedious, but can be readily circumvented by malware authors using a large number of seeds in algorithms with multivariate recurrence properties (e.g., banjori) or by using a dynamic list of seeds (e.g., bedep). Another technique to stop malware from using DGAs is to intercept DNS queries on a network and predict whether domains are DGA generated. Such a technique will alert network administrators to the presence of malware on their networks. In addition, if the predictor can also accurately predict the family of DGAs, then network administrators can also be alerted to the type of malware that is on their networks. This paper presents a DGA classifier that leverages long short-term memory (LSTM) networks to predict DGAs and their respective families without the need for a priori feature extraction. Results are significantly better than state-of-the-art techniques, providing 0.9993 area under the receiver operating characteristic curve for binary classification and a micro-averaged F1 score of 0.9906. In other terms, the LSTM technique can provide a 90% detection rate with a 1:10000 false positive (FP) rate---a twenty times FP improvement over comparable methods. Experiments in this paper are run on open datasets and code snippets are provided to reproduce the results.
Determining the optimal size and orientation of small-scale residential based PV arrays will become increasingly complex in the future smart grid environment with the introduction of smart meters and dynamic tariffs. However consumers can leverage the availability of smart meter data to conduct a more detailed exploration of PV investment options for their particular circumstances. In this paper, an optimization method for PV orientation and sizing is proposed whereby maximizing the PV investment value is set as the defining objective. Solar insolation and PV array models are described to form the basis of the PV array optimization strategy. A constrained particle swarm optimization algorithm is selected due to its strong performance in non-linear applications. The optimization algorithm is applied to real-world metered data to quantify the possible investment value of a PV installation under different energy retailers and tariff structures. The arrangement with the highest value is determined to enable prospective small-scale PV investors to select the most cost-effective system.
Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214.
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence - both semantic and syntactic - but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global underlying semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned end-to-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis on the IMDB movie review dataset and report an error rate of $6.28\%$. This is comparable to the state-of-the-art $5.91\%$ resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation.
Recent years have seen the proposal of a number of neural architectures for the problem of Program Induction. Given a set of input-output examples, these architectures are able to learn mappings that generalize to new test inputs. While achieving impressive results, these approaches have a number of important limitations: (a) they are computationally expensive and hard to train, (b) a model has to be trained for each task (program) separately, and (c) it is hard to interpret or verify the correctness of the learnt mapping (as it is defined by a neural network). In this paper, we propose a novel technique, Neuro-Symbolic Program Synthesis, to overcome the above-mentioned problems. Once trained, our approach can automatically construct computer programs in a domain-specific language that are consistent with a set of input-output examples provided at test time. Our method is based on two novel neural modules. The first module, called the cross correlation I/O network, given a set of input-output examples, produces a continuous representation of the set of I/O examples. The second module, the Recursive-Reverse-Recursive Neural Network (R3NN), given the continuous representation of the examples, synthesizes a program by incrementally expanding partial programs. We demonstrate the effectiveness of our approach by applying it to the rich and complex domain of regular expression based string transformations. Experiments show that the R3NN model is not only able to construct programs from new input-output examples, but it is also able to construct new programs for tasks that it had never observed before during training.
Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge this gap. Rather than designing a "fast" reinforcement learning algorithm, we propose to represent it as a recurrent neural network (RNN) and learn it from data. In our proposed method, RL$^2$, the algorithm is encoded in the weights of the RNN, which are learned slowly through a general-purpose ("slow") RL algorithm. The RNN receives all information a typical RL algorithm would receive, including observations, actions, rewards, and termination flags; and it retains its state across episodes in a given Markov Decision Process (MDP). The activations of the RNN store the state of the "fast" RL algorithm on the current (previously unseen) MDP. We evaluate RL$^2$ experimentally on both small-scale and large-scale problems. On the small-scale side, we train it to solve randomly generated multi-arm bandit problems and finite MDPs. After RL$^2$ is trained, its performance on new MDPs is close to human-designed algorithms with optimality guarantees. On the large-scale side, we test RL$^2$ on a vision-based navigation task and show that it scales up to high-dimensional problems.
Acquiring your first language is an incredible feat and not easily duplicated. Learning to communicate using nothing but a few pictureless books, a corpus, would likely be impossible even for humans. Nevertheless, this is the dominating approach in most natural language processing today. As an alternative, we propose the use of situated interactions between agents as a driving force for communication, and the framework of Deep Recurrent Q-Networks for evolving a shared language grounded in the provided environment. We task the agents with interactive image search in the form of the game Guess Who?. The images from the game provide a non trivial environment for the agents to discuss and a natural grounding for the concepts they decide to encode in their communication. Our experiments show that the agents learn not only to encode physical concepts in their words, i.e. grounding, but also that the agents learn to hold a multi-step dialogue remembering the state of the dialogue from step to step.
Generative adversarial networks (GANs) are a recently proposed class of generative models in which a generator is trained to optimize a cost function that is being simultaneously learned by a discriminator. While the idea of learning cost functions is relatively new to the field of generative modeling, learning costs has long been studied in control and reinforcement learning (RL) domains, typically for imitation learning from demonstrations. In these fields, learning cost function underlying observed behavior is known as inverse reinforcement learning (IRL) or inverse optimal control. While at first the connection between cost learning in RL and cost learning in generative modeling may appear to be a superficial one, we show in this paper that certain IRL methods are in fact mathematically equivalent to GANs. In particular, we demonstrate an equivalence between a sample-based algorithm for maximum entropy IRL and a GAN in which the generator's density can be evaluated and is provided as an additional input to the discriminator. Interestingly, maximum entropy IRL is a special case of an energy-based model. We discuss the interpretation of GANs as an algorithm for training energy-based models, and relate this interpretation to other recent work that seeks to connect GANs and EBMs. By formally highlighting the connection between GANs, IRL, and EBMs, we hope that researchers in all three communities can better identify and apply transferable ideas from one domain to another, particularly for developing more stable and scalable algorithms: a major challenge in all three domains.
Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lingual knowledge alignment will help people in constructing a coherent knowledge base, and assist machines in dealing with different expressions of entity relationships across diverse human languages. Unfortunately, achieving this highly desirable crosslingual alignment by human labor is very costly and errorprone. Thus, we propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution. By encoding entities and relations of each language in a separated embedding space, MTransE provides transitions for each embedding vector to its cross-lingual counterparts in other spaces, while preserving the functionalities of monolingual embeddings. We deploy three different techniques to represent cross-lingual transitions, namely axis calibration, translation vectors, and linear transformations, and derive five variants for MTransE using different loss functions. Our models can be trained on partially aligned graphs, where just a small portion of triples are aligned with their cross-lingual counterparts. The experiments on cross-lingual entity matching and triple-wise alignment verification show promising results, with some variants consistently outperforming others on different tasks. We also explore how MTransE preserves the key properties of its monolingual counterpart TransE.
We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam's razor, we assume that the exogenous variable is simple in the true causal direction. We quantify simplicity using R\'enyi entropy. Our main result is that, under natural assumptions, if the exogenous variable has low $H_0$ entropy (cardinality) in the true direction, it must have high $H_0$ entropy in the wrong direction. We establish several algorithmic hardness results about estimating the minimum entropy exogenous variable. We show that the problem of finding the exogenous variable with minimum entropy is equivalent to the problem of finding minimum joint entropy given $n$ marginal distributions, also known as minimum entropy coupling problem. We propose an efficient greedy algorithm for the minimum entropy coupling problem, that for $n=2$ provably finds a local optimum. This gives a greedy algorithm for finding the exogenous variable with minimum $H_1$ (Shannon Entropy). Our greedy entropy-based causal inference algorithm has similar performance to the state of the art additive noise models in real datasets. One advantage of our approach is that we make no use of the values of random variables but only their distributions. Our method can therefore be used for causal inference for both ordinal and also categorical data, unlike additive noise models.
In this paper, we propose commonsense knowledge enhanced embeddings (KEE) for solving the Pronoun Disambiguation Problems (PDP). The PDP task we investigate in this paper is a complex coreference resolution task which requires the utilization of commonsense knowledge. This task is a standard first round test set in the 2016 Winograd Schema Challenge. In this task, traditional linguistic features that are useful for coreference resolution, e.g. context and gender information, are no longer effective anymore. Therefore, the KEE models are proposed to provide a general framework to make use of commonsense knowledge for solving the PDP problems. Since the PDP task doesn't have training data, the KEE models would be used during the unsupervised feature extraction process. To evaluate the effectiveness of the KEE models, we propose to incorporate various commonsense knowledge bases, including ConceptNet, WordNet, and CauseCom, into the KEE training process. We achieved the best performance by applying the proposed methods to the 2016 Winograd Schema Challenge. In addition, experiments conducted on the standard PDP task indicate that, the proposed KEE models could solve the PDP problems by achieving 66.7% accuracy, which is a new state-of-the-art performance.
The domain of single crossing preference profiles is a widely studied domain in social choice theory. It has been generalized to the domain of single crossing preference profiles with respect to trees which inherits many desirable properties from the single crossing domain, for example, transitivity of majority relation, existence of polynomial time algorithms for finding winners of Kemeny voting rule, etc. In this paper, we consider a further generalization of the domain of single crossing profiles on trees to the domain consisting of all preference profiles which can be extended to single crossing preference profiles with respect to some tree by adding more preferences to it. We call this domain the weakly single crossing domain on trees. We present a polynomial time algorithm for recognizing weakly single crossing profiles on trees. We then move on to develop a polynomial time algorithm with low query complexity for eliciting weakly single crossing profiles on trees even when we do not know any tree with respect to which the closure of the input profile is single crossing and the preferences can be queried only sequentially; moreover, the sequential order is also unknown. We complement the performance of our preference elicitation algorithm by proving that our algorithm makes an optimal number of queries up to constant factors when the number of preferences is large compared to the number of candidates, even if the input profile is known to be single crossing with respect to some given tree and the preferences can be accessed randomly.
We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test based on the maximum mean discrepancy (MMD). This optimized MMD is applied to the setting of unsupervised learning by generative adversarial networks (GAN), in which a model attempts to generate realistic samples, and a discriminator attempts to tell these apart from data samples. In this context, the MMD may be used in two roles: first, as a discriminator, either directly on the samples, or on features of the samples. Second, the MMD can be used to evaluate the performance of a generative model, by testing the model's samples against a reference data set. In the latter role, the optimized MMD is particularly helpful, as it gives an interpretable indication of how the model and data distributions differ, even in cases where individual model samples are not easily distinguished either by eye or by classifier.
We propose an approach to build a neural machine translation system with no supervised resources (i.e., no parallel corpora) using multimodal embedded representation over texts and images. Based on the assumption that text documents are often likely to be described with other multimedia information (e.g., images) somewhat related to the content, we try to indirectly estimate the relevance between two languages. Using multimedia as the "pivot", we project all modalities into one common hidden space where samples belonging to similar semantic concepts should come close to each other, whatever the observed space of each sample is. This modality-agnostic representation is the key to bridging the gap between different modalities. Putting a decoder on top of it, our network can flexibly draw the outputs from any input modality. Notably, in the testing phase, we need only source language texts as the input for translation. In experiments, we tested our method on two benchmarks to show that it can achieve reasonable translation performance. We compared and investigated several possible implementations and found that an end-to-end model that simultaneously optimized both rank loss in multimodal encoders and cross-entropy loss in decoders performed the best.
Max-cut, clustering, and many other partitioning problems that are of significant importance to machine learning and other scientific fields are NP-hard, a reality that has motivated researchers to develop a wealth of approximation algorithms and heuristics. Although the best algorithm to use typically depends on the specific application domain, a worst-case analysis is often used to compare algorithms. This may be misleading if worst-case instances occur infrequently, and thus there is a demand for optimization methods which return the algorithm configuration best suited for the given application's typical inputs. We address this problem for clustering, max-cut, and other partitioning problems, such as integer quadratic programming, by designing computationally efficient and sample efficient learning algorithms which receive samples from an application-specific distribution over problem instances and learn a partitioning algorithm with high expected performance. Our algorithms learn over common integer quadratic programming and clustering algorithm families: SDP rounding algorithms and agglomerative clustering algorithms with dynamic programming. For our sample complexity analysis, we provide tight bounds on the pseudodimension of these algorithm classes, and show that surprisingly, even for classes of algorithms parameterized by a single parameter, the pseudo-dimension is superconstant. In this way, our work both contributes to the foundations of algorithm configuration and pushes the boundaries of learning theory, since the algorithm classes we analyze consist of multi-stage optimization procedures and are significantly more complex than classes typically studied in learning theory.
Our aging population increasingly suffers from multiple chronic diseases simultaneously, necessitating the comprehensive treatment of these conditions. Finding the optimal set of drugs for a combinatorial set of diseases is a combinatorial pattern exploration problem. Association rule mining is a popular tool for such problems, but the requirement of health care for finding causal, rather than associative, patterns renders association rule mining unsuitable. To address this issue, we propose a novel framework based on the Rubin-Neyman causal model for extracting causal rules from observational data, correcting for a number of common biases. Specifically, given a set of interventions and a set of items that define subpopulations (e.g., diseases), we wish to find all subpopulations in which effective intervention combinations exist and in each such subpopulation, we wish to find all intervention combinations such that dropping any intervention from this combination will reduce the efficacy of the treatment. A key aspect of our framework is the concept of closed intervention sets which extend the concept of quantifying the effect of a single intervention to a set of concurrent interventions. We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).
Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept.
In order to have effective human AI collaboration, it is not simply enough to address the question of autonomy; an equally important question is, how the AI's behavior is being perceived by their human counterparts. When AI agent's task plans are generated without such considerations, they may often demonstrate inexplicable behavior from the human's point of view. This problem arises due to the human's partial or inaccurate understanding of the agent's planning process and/or the model. This may have serious implications on human-AI collaboration, from increased cognitive load and reduced trust in the agent, to more serious concerns of safety in interactions with physical agent. In this paper, we address this issue by modeling the notion of plan explicability as a function of the distance between a plan that agent makes and the plan that human expects it to make. To this end, we learn a distance function based on different plan distance measures that can accurately model this notion of plan explicability, and develop an anytime search algorithm that can use this distance as a heuristic to come up with progressively explicable plans. We evaluate the effectiveness of our approach in a simulated autonomous car domain and a physical service robot domain. We provide empirical evaluations that demonstrate the usefulness of our approach in making the planning process of an autonomous agent conform to human expectations.
A game theoretic distributed decision making approach is presented for the problem of control effort allocation in a robotic team based on a novel variant of fictitious play. The proposed learning process allows the robots to accomplish their objectives by coordinating their actions in order to efficiently complete their tasks. In particular, each robot of the team predicts the other robots' planned actions while making decisions to maximise their own expected reward that depends on the reward for joint successful completion of the task. Action selection is interpreted as an $n$-player cooperative game. The approach presented can be seen as part of the \emph{Belief Desire Intention} (BDI) framework, also can address the problem of cooperative, legal, safe, considerate and emphatic decisions by robots if their individual and group rewards are suitably defined. After theoretical analysis the performance of the proposed algorithm is tested on four simulation scenarios. The first one is a coordination game between two material handling robots, the second one is a warehouse patrolling task by a team of robots, the third one presents a coordination mechanism between two robots that carry a heavy object on a corridor and the fourth one is an example of coordination on a sensors network.
Current state of the art in the field of UAV activation relies solely on human operators for the design and adaptation of the drones' flying routes. Furthermore, this is being done today on an individual level (one vehicle per operators), with some exceptions of a handful of new systems, that are comprised of a small number of self-organizing swarms, manually guided by a human operator. Drones-based monitoring is of great importance in variety of civilian domains, such as road safety, homeland security, and even environmental control. In its military aspect, efficiently detecting evading targets by a fleet of unmanned drones has an ever increasing impact on the ability of modern armies to engage in warfare. The latter is true both traditional symmetric conflicts among armies as well as asymmetric ones. Be it a speeding driver, a polluting trailer or a covert convoy, the basic challenge remains the same -- how can its detection probability be maximized using as little number of drones as possible. In this work we propose a novel approach for the optimization of large scale swarms of reconnaissance drones -- capable of producing on-demand optimal coverage strategies for any given search scenario. Given an estimation cost of the threat's potential damages, as well as types of monitoring drones available and their comparative performance, our proposed method generates an analytically provable strategy, stating the optimal number and types of drones to be deployed, in order to cost-efficiently monitor a pre-defined region for targets maneuvering using a given roads networks. We demonstrate our model using a unique dataset of the Israeli transportation network, on which different deployment schemes for drones deployment are evaluated.
In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.
While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models based on inducing variables for big data, little attention is afforded to the other less explored class of low-rank GP approximations that exploit the sparse spectral representation of a GP kernel. This paper presents such an effort to advance the state of the art of sparse spectrum GP models to achieve competitive predictive performance for massive datasets. Our generalized framework of stochastic variational Bayesian sparse spectrum GP (sVBSSGP) models addresses their shortcomings by adopting a Bayesian treatment of the spectral frequencies to avoid overfitting, modeling these frequencies jointly in its variational distribution to enable their interaction a posteriori, and exploiting local data for boosting the predictive performance. However, such structural improvements result in a variational lower bound that is intractable to be optimized. To resolve this, we exploit a variational parameterization trick to make it amenable to stochastic optimization. Interestingly, the resulting stochastic gradient has a linearly decomposable structure that can be exploited to refine our stochastic optimization method to incur constant time per iteration while preserving its property of being an unbiased estimator of the exact gradient of the variational lower bound. Empirical evaluation on real-world datasets shows that sVBSSGP outperforms state-of-the-art stochastic implementations of sparse GP models.
Gaussian processes (GP) provide a prior over functions and allow finding complex regularities in data. Gaussian processes are successfully used for classification/regression problems and dimensionality reduction. In this work we consider the classification problem only. The complexity of standard methods for GP-classification scales cubically with the size of the training dataset. This complexity makes them inapplicable to big data problems. Therefore, a variety of methods were introduced to overcome this limitation. In the paper we focus on methods based on so called inducing inputs. This approach is based on variational inference and proposes a particular lower bound for marginal likelihood (evidence). This bound is then maximized w.r.t. parameters of kernel function of the Gaussian process, thus fitting the model to data. The computational complexity of this method is $O(nm^2)$, where $m$ is the number of inducing inputs used by the model and is assumed to be substantially smaller than the size of the dataset $n$. Recently, a new evidence lower bound for GP-classification problem was introduced. It allows using stochastic optimization, which makes it suitable for big data problems. However, the new lower bound depends on $O(m^2)$ variational parameter, which makes optimization challenging in case of big m. In this work we develop a new approach for training inducing input GP models for classification problems. Here we use quadratic approximation of several terms in the aforementioned evidence lower bound, obtaining analytical expressions for optimal values of most of the parameters in the optimization, thus sufficiently reducing the dimension of optimization space. In our experiments we achieve as well or better results, compared to the existing method. Moreover, our method doesn't require the user to manually set the learning rate, making it more practical, than the existing method.
Structural causal models (SCMs), also known as non-parametric structural equation models (NP-SEMs), are widely used for causal modeling purposes. In this paper, we give a rigorous treatment of structural causal models, dealing with measure-theoretic complications that arise in the presence of cyclic relations. The central question studied in this paper is: given a (possibly cyclic) SCM defined on a large system (consisting of observable endogenous and latent exogenous variables), can we "project it down" to an SCM that describes a subsystem (consisting of a subset of the observed endogenous variables and possibly different latent exogenous variables) in order to obtain a more parsimonious but equivalent representation of the subsystem? We define a marginalization operation that effectively removes a subset of the endogenous variables from the model, and a class of mappings, exogenous reparameterizations, that can be used to reduce the space of exogenous variables. We show that both operations preserve the causal semantics of the model and that under mild conditions they can lead to a significant reduction of the model complexity, at least in terms of the number of variables in the model. We argue that for the task of estimating an SCM from data, the existence of "smooth" reductions would be desirable. We provide several conditions under which the existence of such reductions can be shown, but also provide a counterexample that shows that such reductions do not exist in general. The latter result implies that existing approaches to estimate linear or Markovian SCMs from data cannot be extended to general SCMs.
Credit card plays a very important rule in today's economy. It becomes an unavoidable part of household, business and global activities. Although using credit cards provides enormous benefits when used carefully and responsibly,significant credit and financial damages may be caused by fraudulent activities. Many techniques have been proposed to confront the growth in credit card fraud. However, all of these techniques have the same goal of avoiding the credit card fraud; each one has its own drawbacks, advantages and characteristics. In this paper, after investigating difficulties of credit card fraud detection, we seek to review the state of the art in credit card fraud detection techniques, data sets and evaluation criteria.The advantages and disadvantages of fraud detection methods are enumerated and compared.Furthermore, a classification of mentioned techniques into two main fraud detection approaches, namely, misuses (supervised) and anomaly detection (unsupervised) is presented. Again, a classification of techniques is proposed based on capability to process the numerical and categorical data sets. Different data sets used in literature are then described and grouped into real and synthesized data and the effective and common attributes are extracted for further usage.Moreover, evaluation employed criterions in literature are collected and discussed.Consequently, open issues for credit card fraud detection are explained as guidelines for new researchers.
Reinforcement learning is concerned with identifying reward-maximizing behaviour policies in environments that are initially unknown. State-of-the-art reinforcement learning approaches, such as deep Q-networks, are model-free and learn to act effectively across a wide range of environments such as Atari games, but require huge amounts of data. Model-based techniques are more data-efficient, but need to acquire explicit knowledge about the environment. In this paper, we take a step towards using model-based techniques in environments with a high-dimensional visual state space by demonstrating that it is possible to learn system dynamics and the reward structure jointly. Our contribution is to extend a recently developed deep neural network for video frame prediction in Atari games to enable reward prediction as well. To this end, we phrase a joint optimization problem for minimizing both video frame and reward reconstruction loss, and adapt network parameters accordingly. Empirical evaluations on five Atari games demonstrate accurate cumulative reward prediction of up to 200 frames. We consider these results as opening up important directions for model-based reinforcement learning in complex, initially unknown environments.
In this work, we study the guaranteed delivery model which is widely used in online display advertising. In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers. A crucial problem for the advertising platform is how to fully utilize the valuable user traffic to generate as much as possible revenue. Different from previous works which usually minimize the penalty of unsatisfied contracts and some other cost (e.g. representativeness), we propose the novel consumption minimization model, in which the primary objective is to minimize the user traffic consumed to satisfy all contracts. Under this model, we develop a near optimal method to deliver ads for users. The main advantage of our method lies in that it consumes nearly as least as possible user traffic to satisfy all contracts, therefore more contracts can be accepted to produce more revenue. It also enables the publishers to estimate how much user traffic is redundant or short so that they can sell or buy this part of traffic in bulk in the exchange market. Furthermore, it is robust with regard to priori knowledge of user type distribution. Finally, the simulation shows that our method outperforms the traditional state-of-the-art methods.
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help students learn knowledge concepts efficiently. However, existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing either model knowledge state for each predefined concept separately or fail to pinpoint exactly which concepts a student is good at or unfamiliar with. To solve these problems, this work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept. Unlike standard memory-augmented neural networks that facilitate a single memory matrix or two static memory matrices, our model has one static matrix called key, which stores the knowledge concepts and the other dynamic matrix called value, which stores and updates the mastery levels of corresponding concepts. Experiments show that our model consistently outperforms the state-of-the-art model in a range of KT datasets. Moreover, the DKVMN model can automatically discover underlying concepts of exercises typically performed by human annotations and depict the changing knowledge state of a student.
It is clear that one of the primary tools we can use to mitigate the potential risk from a misbehaving AI system is the ability to turn the system off. As the capabilities of AI systems improve, it is important to ensure that such systems do not adopt subgoals that prevent a human from switching them off. This is a challenge because many formulations of rational agents create strong incentives for self-preservation. This is not caused by a built-in instinct, but because a rational agent will maximize expected utility and cannot achieve whatever objective it has been given if it is dead. Our goal is to study the incentives an agent has to allow itself to be switched off. We analyze a simple game between a human H and a robot R, where H can press R's off switch but R can disable the off switch. A traditional agent takes its reward function for granted: we show that such agents have an incentive to disable the off switch, except in the special case where H is perfectly rational. Our key insight is that for R to want to preserve its off switch, it needs to be uncertain about the utility associated with the outcome, and to treat H's actions as important observations about that utility. (R also has no incentive to switch itself off in this setting.) We conclude that giving machines an appropriate level of uncertainty about their objectives leads to safer designs, and we argue that this setting is a useful generalization of the classical AI paradigm of rational agents.
To enhance developer productivity, all modern integrated development environments (IDEs) include code suggestion functionality that proposes likely next tokens at the cursor. While current IDEs work well for statically-typed languages, their reliance on type annotations means that they do not provide the same level of support for dynamic programming languages as for statically-typed languages. Moreover, suggestion engines in modern IDEs do not propose expressions or multi-statement idiomatic code. Recent work has shown that language models can improve code suggestion systems by learning from software repositories. This paper introduces a neural language model with a sparse pointer network aimed at capturing very long-range dependencies. We release a large-scale code suggestion corpus of 41M lines of Python code crawled from GitHub. On this corpus, we found standard neural language models to perform well at suggesting local phenomena, but struggle to refer to identifiers that are introduced many tokens in the past. By augmenting a neural language model with a pointer network specialized in referring to predefined classes of identifiers, we obtain a much lower perplexity and a 5 percentage points increase in accuracy for code suggestion compared to an LSTM baseline. In fact, this increase in code suggestion accuracy is due to a 13 times more accurate prediction of identifiers. Furthermore, a qualitative analysis shows this model indeed captures interesting long-range dependencies, like referring to a class member defined over 60 tokens in the past.
Decision support systems help decision makers make better decisions in the face of complex decision problems (e.g. investment or policy decisions). Fisheries and Aquaculture is a domain where decision makers face such decisions since they involve factors from many different scientific fields. No systematic overview of literature describing decision support systems and their application in fisheries and aquaculture has been conducted. This paper summarizes scientific literature that describes decision support systems applied to the domain of Fisheries and Aquaculture. We use an established systematic mapping survey method to conduct our literature mapping. Our research questions are: What decision support systems for fisheries and aquaculture exists? What are the most investigated fishery and aquaculture decision support systems topics and how have these changed over time? Do any current DSS for fisheries provide real- time analytics? Do DSSes in Fisheries and Aquaculture build their models using machine learning done on captured and grounded data? The paper then detail how we employ the systematic mapping method in answering these questions. This results in 27 papers being identified as relevant and gives an exposition on the primary methods concluded in the study for designing a decision support system. We provide an analysis of the research done in the studies collected. We discovered that most literature does not consider multiple aspects for multiple stakeholders in their work. In addition we observed that little or no work has been done with real-time analysis in these decision support systems.
Model generation is a problem complementary to theorem proving and is important for fault analysis and debugging of formal specifications of security protocols, programs and terminological definitions. This paper discusses several ways of enhancing the paradigm of bottom-up model generation. The two main contributions are new, generalized blocking techniques and a new range-restriction transformation. The blocking techniques are based on simple transformations of the input set together with standard equality reasoning and redundancy elimination techniques. These provide general methods for finding small, finite models. The range-restriction transformation refines existing transformations to range-restricted clauses by carefully limiting the creation of domain terms. All possible combinations of the introduced techniques and classical range-restriction were tested on the clausal problems of the TPTP Version 6.0.0 with an implementation based on the SPASS theorem prover using a hyperresolution-like refinement. Unrestricted domain blocking gave best results for satisfiable problems showing it is a powerful technique indispensable for bottom-up model generation methods. Both in combination with the new range-restricting transformation, and the classical range-restricting transformation, good results have been obtained. Limiting the creation of terms during the inference process by using the new range restricting transformation has paid off, especially when using it together with a shifting transformation. The experimental results also show that classical range restriction with unrestricted blocking provides a useful complementary method. Overall, the results showed bottom-up model generation methods were good for disproving theorems and generating models for satisfiable problems, but less efficient than SPASS in auto mode for unsatisfiable problems.
Unobserved or unknown confounders complicate even the simplest attempts to estimate the effect of one variable on another using observational data. When cause and effect are both affected by unobserved confounders, methods based on identifying natural experiments have been proposed to eliminate confounds. However, their validity is hard to verify because they depend on assumptions about the independence of variables, that by definition, cannot be measured. In this paper we investigate a particular scenario in time series data that permits causal identification in the presence of unobserved confounders and present an algorithm to automatically find such scenarios. Specifically, we examine what we call the split-door setting, when the effect variable can be split up into two parts: one that is potentially affected by the cause, and another that is independent of it. We show that when both of these variables are caused by the same (unobserved) confounders, the problem of identification reduces to that of testing for independence among observed variables. We discuss various situations in which split-door variables are commonly recorded in both online and offline settings, and demonstrate the method by estimating the causal impact of Amazon's recommender system, obtaining more than 23,000 natural experiments that provide similar---but more precise---estimates than past studies.
With the advent of semantic web, various tools and techniques have been introduced for presenting and organizing knowledge. Concept hierarchies are one such technique which gained significant attention due to its usefulness in creating domain ontologies that are considered as an integral part of semantic web. Automated concept hierarchy learning algorithms focus on extracting relevant concepts from unstructured text corpus and connect them together by identifying some potential relations exist between them. In this paper, we propose a novel approach for identifying relevant concepts from plain text and then learns hierarchy of concepts by exploiting subsumption relation between them. To start with, we model topics using a probabilistic topic model and then make use of some lightweight linguistic process to extract semantically rich concepts. Then we connect concepts by identifying an "is-a" relationship between pair of concepts. The proposed method is completely unsupervised and there is no need for a domain specific training corpus for concept extraction and learning. Experiments on large and real-world text corpora such as BBC News dataset and Reuters News corpus shows that the proposed method outperforms some of the existing methods for concept extraction and efficient concept hierarchy learning is possible if the overall task is guided by a probabilistic topic modeling algorithm.
The applicability of fractional order (FO) automatic generation control (AGC) for power system frequency oscillation damping is investigated in this paper, employing distributed energy generation. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell and aqua electrolyzer along with other energy storage devices like the battery and flywheel. The controller is placed in a remote location while receiving and sending signals over an unreliable communication network with stochastic delay. The controller parameters are tuned using robust optimization techniques employing different variants of Particle Swarm Optimization (PSO) and are compared with the corresponding optimal solutions. An archival based strategy is used for reducing the number of function evaluations for the robust optimization methods. The solutions obtained through the robust optimization are able to handle higher variation in the controller gains and orders without significant decrease in the system performance. This is desirable from the FO controller implementation point of view, as the design is able to accommodate variations in the system parameter which may result due to the approximation of FO operators, using different realization methods and order of accuracy. Also a comparison is made between the FO and the integer order (IO) controllers to highlight the merits and demerits of each scheme.
Humans are remarkably adept at interpreting the gaze direction of other individuals in their surroundings. This skill is at the core of the ability to engage in joint visual attention, which is essential for establishing social interactions. How accurate are humans in determining the gaze direction of others in lifelike scenes, when they can move their heads and eyes freely, and what are the sources of information for the underlying perceptual processes? These questions pose a challenge from both empirical and computational perspectives, due to the complexity of the visual input in real-life situations. Here we measure empirically human accuracy in perceiving the gaze direction of others in lifelike scenes, and study computationally the sources of information and representations underlying this cognitive capacity. We show that humans perform better in face-to-face conditions compared with recorded conditions, and that this advantage is not due to the availability of input dynamics. We further show that humans are still performing well when only the eyes-region is visible, rather than the whole face. We develop a computational model, which replicates the pattern of human performance, including the finding that the eyes-region contains on its own, the required information for estimating both head orientation and direction of gaze. Consistent with neurophysiological findings on task-specific face regions in the brain, the learned computational representations reproduce perceptual effects such as the Wollaston illusion, when trained to estimate direction of gaze, but not when trained to recognize objects or faces.
Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store information about the input history in their units. We show experimentally that all common RNN architectures achieve nearly the same per-task and per-unit capacity bounds with careful training, for a variety of tasks and stacking depths. They can store an amount of task information which is linear in the number of parameters, and is approximately 5 bits per parameter. They can additionally store approximately one real number from their input history per hidden unit. We further find that for several tasks it is the per-task parameter capacity bound that determines performance. These results suggest that many previous results comparing RNN architectures are driven primarily by differences in training effectiveness, rather than differences in capacity. Supporting this observation, we compare training difficulty for several architectures, and show that vanilla RNNs are far more difficult to train, yet have slightly higher capacity. Finally, we propose two novel RNN architectures, one of which is easier to train than the LSTM or GRU for deeply stacked architectures.
The Choquet integral is a powerful aggregation operator which lists many well-known models as its special cases. We look at these special cases and provide their axiomatic analysis. In cases where an axiomatization has been previously given in the literature, we connect the existing results with the framework that we have developed. Next we turn to the question of learning, which is especially important for the practical applications of the model. So far, learning of the Choquet integral has been mostly confined to the learning of the capacity. Such an approach requires making a powerful assumption that all dimensions (e.g. criteria) are evaluated on the same scale, which is rarely justified in practice. Too often categorical data is given arbitrary numerical labels (e.g. AHP), and numerical data is considered cardinally and ordinally commensurate, sometimes after a simple normalization. Such approaches clearly lack scientific rigour, and yet they are commonly seen in all kinds of applications. We discuss the pros and cons of making such an assumption and look at the consequences which axiomatization uniqueness results have for the learning problems. Finally, we review some of the applications of the Choquet integral in decision analysis. Apart from MCDA, which is the main area of interest for our results, we also discuss how the model can be interpreted in the social choice context. We look in detail at the state-dependent utility, and show how comonotonicity, central to the previous axiomatizations, actually implies state-independency in the Choquet integral model. We also discuss the conditions required to have a meaningful state-dependent utility representation and show the novelty of our results compared to the previous methods of building state-dependent models.
The goal of multi-winner elections is to choose a fixed-size committee based on voters' preferences. An important concern in this setting is representation: large groups of voters with cohesive preferences should be adequately represented by the election winners. Recently, Aziz et al. (2015a;2017) proposed two axioms that aim to capture this idea: justified representation (JR) and its strengthening extended justified representation (EJR). In this paper, we extend the work of Aziz et al. in several directions. First, we answer an open question of Aziz et al., by showing that Reweighted Approval Voting satisfies JR for $k=3, 4, 5$, but fails it for $k\ge 6$. Second, we observe that EJR is incompatible with the Perfect Representation criterion, which is important for many applications of multi-winner voting, and propose a relaxation of EJR, which we call Proportional Justified Representation (PJR). PJR is more demanding than JR, but, unlike EJR, it is compatible with perfect representation, and a committee that provides PJR can be computed in polynomial time if the committee size divides the number of voters. Moreover, just like EJR, PJR can be used to characterize the classic PAV rule in the class of weighted PAV rules. On the other hand, we show that EJR provides stronger guarantees with respect to average voter satisfaction than PJR does.
The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations between variables from certain statistical patterns in purely observational data. We introduce Joint Causal Inference (JCI), a novel approach to causal discovery from multiple data sets that elegantly unifies both approaches. JCI is a causal modeling approach rather than a specific algorithm, and it can be used in combination with any causal discovery algorithm that can take into account certain background knowledge. The main idea is to reduce causal discovery from multiple datasets originating from different contexts (e.g., different experimental conditions) to causal discovery from a single pooled dataset by adding a set of auxiliary context variables. JCI offers the following features: it deals with several different types of interventions in a unified fashion, it can learn intervention targets, it pools data across different datasets which improves the statistical power of independence tests, and by exploiting differences in distribution between contexts it improves on the accuracy and identifiability of the predicted causal relations. We evaluate the approach on flow cytometry data.
Although support vector machines (SVMs) are theoretically well understood, their underlying optimization problem becomes very expensive, if, for example, hundreds of thousands of samples and a non-linear kernel are considered. Several approaches have been proposed in the past to address this serious limitation. In this work we investigate a decomposition strategy that learns on small, spatially defined data chunks. Our contributions are two fold: On the theoretical side we establish an oracle inequality for the overall learning method using the hinge loss, and show that the resulting rates match those known for SVMs solving the complete optimization problem with Gaussian kernels. On the practical side we compare our approach to learning SVMs on small, randomly chosen chunks. Here it turns out that for comparable training times our approach is significantly faster during testing and also reduces the test error in most cases significantly. Furthermore, we show that our approach easily scales up to 10 million training samples: including hyper-parameter selection using cross validation, the entire training only takes a few hours on a single machine. Finally, we report an experiment on 32 million training samples. All experiments used liquidSVM (Steinwart and Thomann, 2017).
Recently it has been shown that policy-gradient methods for reinforcement learning can be utilized to train deep end-to-end systems directly on non-differentiable metrics for the task at hand. In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by carefully optimizing our systems using the test metrics of the MSCOCO task, significant gains in performance can be realized. Our systems are built using a new optimization approach that we call self-critical sequence training (SCST). SCST is a form of the popular REINFORCE algorithm that, rather than estimating a "baseline" to normalize the rewards and reduce variance, utilizes the output of its own test-time inference algorithm to normalize the rewards it experiences. Using this approach, estimating the reward signal (as actor-critic methods must do) and estimating normalization (as REINFORCE algorithms typically do) is avoided, while at the same time harmonizing the model with respect to its test-time inference procedure. Empirically we find that directly optimizing the CIDEr metric with SCST and greedy decoding at test-time is highly effective. Our results on the MSCOCO evaluation sever establish a new state-of-the-art on the task, improving the best result in terms of CIDEr from 104.9 to 114.7.
Autonomous systems can be used to search for sparse signals in a large space; e.g., aerial robots can be deployed to localize threats, detect gas leaks, or respond to distress calls. Intuitively, search algorithms may increase efficiency by collecting aggregate measurements summarizing large contiguous regions. However, most existing search methods either ignore the possibility of such region observations (e.g., Bayesian optimization and multi-armed bandits) or make strong assumptions about the sensing mechanism that allow each measurement to arbitrarily encode all signals in the entire environment (e.g., compressive sensing). We propose an algorithm that actively collects data to search for sparse signals using only noisy measurements of the average values on rectangular regions (including single points), based on the greedy maximization of information gain. We analyze our algorithm in 1d and show that it requires $\tilde{O}(\frac{n}{\mu^2}+k^2)$ measurements to recover all of $k$ signal locations with small Bayes error, where $\mu$ and $n$ are the signal strength and the size of the search space, respectively. We also show that active designs can be fundamentally more efficient than passive designs with region sensing, contrasting with the results of Arias-Castro, Candes, and Davenport (2013). We demonstrate the empirical performance of our algorithm on a search problem using satellite image data and in high dimensions.
Machine learning is making substantial progress in diverse applications. The success is mostly due to advances in deep learning. However, deep learning can make mistakes and its generalization abilities to new tasks are questionable. We ask when and how one can combine network outputs, when (i) details of the observations are evaluated by learned deep components and (ii) facts and confirmation rules are available in knowledge based systems. We show that in limited contexts the required number of training samples can be low and self-improvement of pre-trained networks in more general context is possible. We argue that the combination of sparse outlier detection with deep components that can support each other diminish the fragility of deep methods, an important requirement for engineering applications. We argue that supervised learning of labels may be fully eliminated under certain conditions: a component based architecture together with a knowledge based system can train itself and provide high quality answers. We demonstrate these concepts on the State Farm Distracted Driver Detection benchmark. We argue that the view of the Study Panel (2016) may overestimate the requirements on `years of focused research' and `careful, unique construction' for `AI systems'.
We study machine learning formulations of inductive program synthesis; that is, given input-output examples, synthesize source code that maps inputs to corresponding outputs. Our key contribution is TerpreT, a domain-specific language for expressing program synthesis problems. A TerpreT model is composed of a specification of a program representation and an interpreter that describes how programs map inputs to outputs. The inference task is to observe a set of input-output examples and infer the underlying program. From a TerpreT model we automatically perform inference using four different back-ends: gradient descent (thus each TerpreT model can be seen as defining a differentiable interpreter), linear program (LP) relaxations for graphical models, discrete satisfiability solving, and the Sketch program synthesis system. TerpreT has two main benefits. First, it enables rapid exploration of a range of domains, program representations, and interpreter models. Second, it separates the model specification from the inference algorithm, allowing proper comparisons between different approaches to inference. We illustrate the value of TerpreT by developing several interpreter models and performing an extensive empirical comparison between alternative inference algorithms on a variety of program models. To our knowledge, this is the first work to compare gradient-based search over program space to traditional search-based alternatives. Our key empirical finding is that constraint solvers dominate the gradient descent and LP-based formulations. This is a workshop summary of a longer report at arXiv:1608.04428
The concept of uncertainty is posed in almost any complex system including parallel robots as an outstanding instance of dynamical robotics systems. As suggested by the name, uncertainty, is some missing information that is beyond the knowledge of human thus we may tend to handle it properly to minimize the side-effects through the control process. Type-II fuzzy logic has shown its superiority over traditional fuzzy logic when dealing with uncertainty. Type-II fuzzy logic controllers are however newer and more promising approaches that have been recently applied to various fields due to their significant contribution especially when noise (as an important instance of uncertainty) emerges. During the design of Type-I fuzzy logic systems, we presume that we are almost certain about the fuzzy membership functions which is not true in many cases. Thus T2FLS as a more realistic approach dealing with practical applications might have a lot to offer. Type-II fuzzy logic takes into account a higher level of uncertainty, in other words, the membership grade for a type-II fuzzy variable is no longer a crisp number but rather is itself a type-I linguistic term. In this thesis the effects of uncertainty in dynamic control of a parallel robot is considered. More specifically, it is intended to incorporate the Type-II Fuzzy Logic paradigm into a model based controller, the so-called computed torque control method, and apply the result to a 3 degrees of freedom parallel manipulator. ...
The field of connectomics faces unprecedented "big data" challenges. To reconstruct neuronal connectivity, automated pixel-level segmentation is required for petabytes of streaming electron microscopy data. Existing algorithms provide relatively good accuracy but are unacceptably slow, and would require years to extract connectivity graphs from even a single cubic millimeter of neural tissue. Here we present a viable real-time solution, a multi-pass pipeline optimized for shared-memory multicore systems, capable of processing data at near the terabyte-per-hour pace of multi-beam electron microscopes. The pipeline makes an initial fast-pass over the data, and then makes a second slow-pass to iteratively correct errors in the output of the fast-pass. We demonstrate the accuracy of a sparse slow-pass reconstruction algorithm and suggest new methods for detecting morphological errors. Our fast-pass approach provided many algorithmic challenges, including the design and implementation of novel shallow convolutional neural nets and the parallelization of watershed and object-merging techniques. We use it to reconstruct, from image stack to skeletons, the full dataset of Kasthuri et al. (463 GB capturing 120,000 cubic microns) in a matter of hours on a single multicore machine rather than the weeks it has taken in the past on much larger distributed systems.
Model checking of strategic ability under imperfect information is known to be hard. The complexity results range from NP-completeness to undecidability, depending on the precise setup of the problem. No less importantly, fixpoint equivalences do not generally hold for imperfect information strategies, which seriously hampers incremental synthesis of winning strategies. In this paper, we propose translations of ATLir formulae that provide lower and upper bounds for their truth values, and are cheaper to verify than the original specifications. That is, if the expression is verified as true then the corresponding formula of ATLir should also hold in the given model. We begin by showing where the straightforward approach does not work. Then, we propose how it can be modified to obtain guaranteed lower bounds. To this end, we alter the next-step operator in such a way that traversing one's indistinguishability relation is seen as atomic activity. Most interestingly, the lower approximation is provided by a fixpoint expression that uses a nonstandard variant of the next-step ability operator. We show the correctness of the translations, establish their computational complexity, and validate the approach by experiments with a scalable scenario of Bridge play.
Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system. To better understand random backpropagation, we first connect it to the notions of local learning and learning channels. Through this connection, we derive several alternatives to RBP, including skipped RBP (SRPB), adaptive RBP (ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their computational complexity. We then study their behavior through simulations using the MNIST and CIFAR-10 bechnmark datasets. These simulations show that most of these variants work robustly, almost as well as backpropagation, and that multiplication by the derivatives of the activation functions is important. As a follow-up, we study also the low-end of the number of bits required to communicate error information over the learning channel. We then provide partial intuitive explanations for some of the remarkable properties of RBP and its variations. Finally, we prove several mathematical results, including the convergence to fixed points of linear chains of arbitrary length, the convergence to fixed points of linear autoencoders with decorrelated data, the long-term existence of solutions for linear systems with a single hidden layer and convergence in special cases, and the convergence to fixed points of non-linear chains, when the derivative of the activation functions is included.
This paper presents the design of a supervisory algorithm that monitors safety at road intersections and overrides drivers with a safe input when necessary. The design of the supervisor consists of two parts: safety verification and control design. Safety verification is the problem to determine if vehicles will be able to cross the intersection without colliding with current drivers' inputs. We translate this safety verification problem into a jobshop scheduling problem, which minimizes the maximum lateness and evaluates if the optimal cost is zero. The zero optimal cost corresponds to the case in which all vehicles can cross each conflict area without collisions. Computing the optimal cost requires solving a Mixed Integer Nonlinear Programming (MINLP) problem due to the nonlinear second-order dynamics of the vehicles. We therefore estimate this optimal cost by formulating two related Mixed Integer Linear Programming (MILP) problems that assume simpler vehicle dynamics. We prove that these two MILP problems yield lower and upper bounds of the optimal cost. We also quantify the worst case approximation errors of these MILP problems. We design the supervisor to override the vehicles with a safe control input if the MILP problem that computes the upper bound yields a positive optimal cost. We theoretically demonstrate that the supervisor keeps the intersection safe and is non-blocking. Computer simulations further validate that the algorithms can run in real time for problems of realistic size.
In this paper, we study the problem of author identification under double-blind review setting, which is to identify potential authors given information of an anonymized paper. Different from existing approaches that rely heavily on feature engineering, we propose to use network embedding approach to address the problem, which can automatically represent nodes into lower dimensional feature vectors. However, there are two major limitations in recent studies on network embedding: (1) they are usually general-purpose embedding methods, which are independent of the specific tasks; and (2) most of these approaches can only deal with homogeneous networks, where the heterogeneity of the network is ignored. Hence, challenges faced here are two folds: (1) how to embed the network under the guidance of the author identification task, and (2) how to select the best type of information due to the heterogeneity of the network. To address the challenges, we propose a task-guided and path-augmented heterogeneous network embedding model. In our model, nodes are first embedded as vectors in latent feature space. Embeddings are then shared and jointly trained according to task-specific and network-general objectives. We extend the existing unsupervised network embedding to incorporate meta paths in heterogeneous networks, and select paths according to the specific task. The guidance from author identification task for network embedding is provided both explicitly in joint training and implicitly during meta path selection. Our experiments demonstrate that by using path-augmented network embedding with task guidance, our model can obtain significantly better accuracy at identifying the true authors comparing to existing methods.
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256x256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.
Prediction in a small-sized sample with a large number of covariates, the "small n, large p" problem, is challenging. This setting is encountered in multiple applications, such as precision medicine, where obtaining additional samples can be extremely costly or even impossible, and extensive research effort has recently been dedicated to finding principled solutions for accurate prediction. However, a valuable source of additional information, domain experts, has not yet been efficiently exploited. We formulate knowledge elicitation generally as a probabilistic inference process, where expert knowledge is sequentially queried to improve predictions. In the specific case of sparse linear regression, where we assume the expert has knowledge about the values of the regression coefficients or about the relevance of the features, we propose an algorithm and computational approximation for fast and efficient interaction, which sequentially identifies the most informative features on which to query expert knowledge. Evaluations of our method in experiments with simulated and real users show improved prediction accuracy already with a small effort from the expert.
Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.
Consensus formation is investigated for multi-agent systems in which agents' beliefs are both vague and uncertain. Vagueness is represented by a third truth state meaning \emph{borderline}. This is combined with a probabilistic model of uncertainty. A belief combination operator is then proposed which exploits borderline truth values to enable agents with conflicting beliefs to reach a compromise. A number of simulation experiments are carried out in which agents apply this operator in pairwise interactions, under the bounded confidence restriction that the two agents' beliefs must be sufficiently consistent with each other before agreement can be reached. As well as studying the consensus operator in isolation we also investigate scenarios in which agents are influenced either directly or indirectly by the state of the world. For the former we conduct simulations which combine consensus formation with belief updating based on evidence. For the latter we investigate the effect of assuming that the closer an agent's beliefs are to the truth the more visible they are in the consensus building process. In all cases applying the consensus operators results in the population converging to a single shared belief which is both crisp and certain. Furthermore, simulations which combine consensus formation with evidential updating converge faster to a shared opinion which is closer to the actual state of the world than those in which beliefs are only changed as a result of directly receiving new evidence. Finally, if agent interactions are guided by belief quality measured as similarity to the true state of the world, then applying the consensus operator alone results in the population converging to a high quality shared belief.
This paper presents a new method to learn online policies in continuous state, continuous action, model-free Markov decision processes, with two properties that are crucial for practical applications. First, the policies are implementable with a very low computational cost: once the policy is computed, the action corresponding to a given state is obtained in logarithmic time with respect to the number of samples used. Second, our method is versatile: it does not rely on any a priori knowledge of the structure of optimal policies. We build upon the Fitted Q-iteration algorithm which represents the $Q$-value as the average of several regression trees. Our algorithm, the Fitted Policy Forest algorithm (FPF), computes a regression forest representing the Q-value and transforms it into a single tree representing the policy, while keeping control on the size of the policy using resampling and leaf merging. We introduce an adaptation of Multi-Resolution Exploration (MRE) which is particularly suited to FPF. We assess the performance of FPF on three classical benchmarks for reinforcement learning: the "Inverted Pendulum", the "Double Integrator" and "Car on the Hill" and show that FPF equals or outperforms other algorithms, although these algorithms rely on the use of particular representations of the policies, especially chosen in order to fit each of the three problems. Finally, we exhibit that the combination of FPF and MRE allows to find nearly optimal solutions in problems where $\epsilon$-greedy approaches would fail.
Given a knowledge base or KB containing (noisy) facts about common nouns or generics, such as "all trees produce oxygen" or "some animals live in forests", we consider the problem of inferring additional such facts at a precision similar to that of the starting KB. Such KBs capture general knowledge about the world, and are crucial for various applications such as question answering. Different from commonly studied named entity KBs such as Freebase, generics KBs involve quantification, have more complex underlying regularities, tend to be more incomplete, and violate the commonly used locally closed world assumption (LCWA). We show that existing KB completion methods struggle with this new task, and present the first approach that is successful. Our results demonstrate that external information, such as relation schemas and entity taxonomies, if used appropriately, can be a surprisingly powerful tool in this setting. First, our simple yet effective knowledge guided tensor factorization approach achieves state-of-the-art results on two generics KBs (80% precise) for science, doubling their size at 74%-86% precision. Second, our novel taxonomy guided, submodular, active learning method for collecting annotations about rare entities (e.g., oriole, a bird) is 6x more effective at inferring further new facts about them than multiple active learning baselines.
Assumption-Based Argumentation (ABA) is an argumentation framework that has been proposed in the late 20th century. Since then, there was still no solver implemented in a programming language which is easy to setup and no solver have been interfaced to the web, which impedes the interests of the public. This project aims to implement an ABA solver in a modern programming language that performs reasonably well and interface it to the web for easier access by the public. This project has demonstrated the novelty of development of an ABA solver, that computes conflict-free, stable, admissible, grounded, ideal, and complete semantics, in Python programming language which can be used via an easy-to-use web interface for visualization of the argument and dispute trees. Experiments were conducted to determine the project's best configurations and to compare this project with proxdd, a state-of-the-art ABA solver, which has no web interface and computes less number of semantics. From the results of the experiments, this project's best configuration is achieved by utilizing "pickle" technique and tree caching technique. Using this project's best configuration, this project achieved a lower average runtime compared to proxdd. On other aspect, this project encountered more cases with exceptions compared to proxdd, which might be caused by this project computing more semantics and hence requires more resources to do so. Hence, it can be said that this project run comparably well to the state-of-the-art ABA solver proxdd. Future works of this project include computational complexity analysis and efficiency analysis of algorithms implemented, implementation of more semantics in argumentation framework, and usability testing of the web interface.
A heuristic procedure based on novel recursive formulation of sinusoid (RFS) and on regression with predictive least-squares (LS) enables to decompose both uniformly and nonuniformly sampled 1-d signals into a sparse set of sinusoids (SSS). An optimal SSS is found by Levenberg-Marquardt (LM) optimization of RFS parameters of near-optimal sinusoids combined with common criteria for the estimation of the number of sinusoids embedded in noise. The procedure estimates both the cardinality and the parameters of SSS. The proposed algorithm enables to identify the RFS parameters of a sinusoid from a data sequence containing only a fraction of its cycle. In extreme cases when the frequency of a sinusoid approaches zero the algorithm is able to detect a linear trend in data. Also, an irregular sampling pattern enables the algorithm to correctly reconstruct the under-sampled sinusoid. Parsimonious nature of the obtaining models opens the possibilities of using the proposed method in machine learning and in expert and intelligent systems needing analysis and simple representation of 1-d signals. The properties of the proposed algorithm are evaluated on examples of irregularly sampled artificial signals in noise and are compared with high accuracy frequency estimation algorithms based on linear prediction (LP) approach, particularly with respect to Cramer-Rao Bound (CRB).
Deep models are the defacto standard in visual decision models due to their impressive performance on a wide array of visual tasks. However, they are frequently seen as opaque and are unable to explain their decisions. In contrast, humans can justify their decisions with natural language and point to the evidence in the visual world which led to their decisions. We postulate that deep models can do this as well and propose our Pointing and Justification (PJ-X) model which can justify its decision with a sentence and point to the evidence by introspecting its decision and explanation process using an attention mechanism. Unfortunately there is no dataset available with reference explanations for visual decision making. We thus collect two datasets in two domains where it is interesting and challenging to explain decisions. First, we extend the visual question answering task to not only provide an answer but also a natural language explanation for the answer. Second, we focus on explaining human activities which is traditionally more challenging than object classification. We extensively evaluate our PJ-X model, both on the justification and pointing tasks, by comparing it to prior models and ablations using both automatic and human evaluations.
We demonstrate the possibility of classifying causal systems into kinds that share a common structure without first constructing an explicit dynamical model or using prior knowledge of the system dynamics. The algorithmic ability to determine whether arbitrary systems are governed by causal relations of the same form offers significant practical applications in the development and validation of dynamical models. It is also of theoretical interest as an essential stage in the scientific inference of laws from empirical data. The algorithm presented is based on the dynamical symmetry approach to dynamical kinds. A dynamical symmetry with respect to time is an intervention on one or more variables of a system that commutes with the time evolution of the system. A dynamical kind is a class of systems sharing a set of dynamical symmetries. The algorithm presented classifies deterministic, time-dependent causal systems by directly comparing their exhibited symmetries. Using simulated, noisy data from a variety of nonlinear systems, we show that this algorithm correctly sorts systems into dynamical kinds. It is robust under significant sampling error, is immune to violations of normality in sampling error, and fails gracefully with increasing dynamical similarity. The algorithm we demonstrate is the first to address this aspect of automated scientific discovery.
Bayesian inference on structured models typically relies on the ability to infer posterior distributions of underlying hidden variables. However, inference in implicit models or complex posterior distributions is hard. A popular tool for learning implicit models are generative adversarial networks (GANs) which learn parameters of generators by fooling discriminators. Typically, GANs are considered to be models themselves and are not understood in the context of inference. Current techniques rely on inefficient global discrimination of joint distributions to perform learning, or only consider discriminating a single output variable. We overcome these limitations by treating GANs as a basis for likelihood-free inference in generative models and generalize them to Bayesian posterior inference over factor graphs. We propose local learning rules based on message passing minimizing a global divergence criterion involving cooperating local adversaries used to sidestep explicit likelihood evaluations. This allows us to compose models and yields a unified inference and learning framework for adversarial learning. Our framework treats model specification and inference separately and facilitates richly structured models within the family of Directed Acyclic Graphs, including components such as intractable likelihoods, non-differentiable models, simulators and generally cumbersome models. A key result of our treatment is the insight that Bayesian inference on structured models can be performed only with sampling and discrimination when using nonparametric variational families, without access to explicit distributions. As a side-result, we discuss the link to likelihood maximization. These approaches hold promise to be useful in the toolbox of probabilistic modelers and enrich the gamut of current probabilistic programming applications.
Despite widespread interests in reinforcement-learning for task-oriented dialogue systems, several obstacles can frustrate research and development progress. First, reinforcement learners typically require interaction with the environment, so conventional dialogue corpora cannot be used directly. Second, each task presents specific challenges, requiring separate corpus of task-specific annotated data. Third, collecting and annotating human-machine or human-human conversations for task-oriented dialogues requires extensive domain knowledge. Because building an appropriate dataset can be both financially costly and time-consuming, one popular approach is to build a user simulator based upon a corpus of example dialogues. Then, one can train reinforcement learning agents in an online fashion as they interact with the simulator. Dialogue agents trained on these simulators can serve as an effective starting point. Once agents master the simulator, they may be deployed in a real environment to interact with humans, and continue to be trained online. To ease empirical algorithmic comparisons in dialogues, this paper introduces a new, publicly available simulation framework, where our simulator, designed for the movie-booking domain, leverages both rules and collected data. The simulator supports two tasks: movie ticket booking and movie seeking. Finally, we demonstrate several agents and detail the procedure to add and test your own agent in the proposed framework.
We study the TAPF (combined target-assignment and path-finding) problem for teams of agents in known terrain, which generalizes both the anonymous and non-anonymous multi-agent path-finding problems. Each of the teams is given the same number of targets as there are agents in the team. Each agent has to move to exactly one target given to its team such that all targets are visited. The TAPF problem is to first assign agents to targets and then plan collision-free paths for the agents to their targets in a way such that the makespan is minimized. We present the CBM (Conflict-Based Min-Cost-Flow) algorithm, a hierarchical algorithm that solves TAPF instances optimally by combining ideas from anonymous and non-anonymous multi-agent path-finding algorithms. On the low level, CBM uses a min-cost max-flow algorithm on a time-expanded network to assign all agents in a single team to targets and plan their paths. On the high level, CBM uses conflict-based search to resolve collisions among agents in different teams. Theoretically, we prove that CBM is correct, complete and optimal. Experimentally, we show the scalability of CBM to TAPF instances with dozens of teams and hundreds of agents and adapt it to a simulated warehouse system.
In this project we propose a new approach for emotion recognition using web-based similarity (e.g. confidence, PMI and PMING). We aim to extract basic emotions from short sentences with emotional content (e.g. news titles, tweets, captions), performing a web-based quantitative evaluation of semantic proximity between each word of the analyzed sentence and each emotion of a psychological model (e.g. Plutchik, Ekman, Lovheim). The phases of the extraction include: text preprocessing (tokenization, stop words, filtering), search engine automated query, HTML parsing of results (i.e. scraping), estimation of semantic proximity, ranking of emotions according to proximity measures. The main idea is that, since it is possible to generalize semantic similarity under the assumption that similar concepts co-occur in documents indexed in search engines, therefore also emotions can be generalized in the same way, through tags or terms that express them in a particular language, ranking emotions. Training results are compared to human evaluation, then additional comparative tests on results are performed, both for the global ranking correlation (e.g. Kendall, Spearman, Pearson) both for the evaluation of the emotion linked to each single word. Different from sentiment analysis, our approach works at a deeper level of abstraction, aiming at recognizing specific emotions and not only the positive/negative sentiment, in order to predict emotions as semantic data.
In this paper, we consider a realistic and meaningful scenario in the context of smart grids where an electricity retailer serves three different types of customers, i.e., customers with an optimal home energy management system embedded in their smart meters (C-HEMS), customers with only smart meters (C-SM), and customers without smart meters (C-NONE). The main objective of this paper is to support the retailer to make optimal day-ahead dynamic pricing decisions in such a mixed customer pool. To this end, we propose a two-level decision-making framework where the retailer acting as upper-level agent firstly announces its electricity prices of next 24 hours and customers acting as lower-level agents subsequently schedule their energy usages accordingly. For the lower level problem, we model the price responsiveness of different customers according to their unique characteristics. For the upper level problem, we optimize the dynamic prices for the retailer to maximize its profit subject to realistic market constraints. The above two-level model is tackled by genetic algorithms (GA) based distributed optimization methods while its feasibility and effectiveness are confirmed via simulation results.
A dominant paradigm for deep learning based object detection relies on a "bottom-up" approach using "passive" scoring of class agnostic proposals. These approaches are efficient but lack of holistic analysis of scene-level context. In this paper, we present an "action-driven" detection mechanism using our "top-down" visual attention model. We localize an object by taking sequential actions that the attention model provides. The attention model conditioned with an image region provides required actions to get closer toward a target object. An action at each time step is weak itself but an ensemble of the sequential actions makes a bounding-box accurately converge to a target object boundary. This attention model we call AttentionNet is composed of a convolutional neural network. During our whole detection procedure, we only utilize the actions from a single AttentionNet without any modules for object proposals nor post bounding-box regression. We evaluate our top-down detection mechanism over the PASCAL VOC series and ILSVRC CLS-LOC dataset, and achieve state-of-the-art performances compared to the major bottom-up detection methods. In particular, our detection mechanism shows a strong advantage in elaborate localization by outperforming Faster R-CNN with a margin of +7.1% over PASCAL VOC 2007 when we increase the IoU threshold for positive detection to 0.7.
Deep learning techniques have been widely applied, achieving state-of-the-art results in various fields of study. This survey focuses on deep learning solutions that target learning control policies for robotics applications. We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning. For deep reinforcement learning (DRL), we begin from traditional reinforcement learning algorithms, showing how they are extended to the deep context and effective mechanisms that could be added on top of the DRL algorithms. We then introduce representative works that utilize DRL to solve navigation and manipulation tasks in robotics. We continue our discussion on methods addressing the challenge of the reality gap for transferring DRL policies trained in simulation to real-world scenarios, and summarize robotics simulation platforms for conducting DRL research. For imitation leaning, we go through its three main categories, behavior cloning, inverse reinforcement learning and generative adversarial imitation learning, by introducing their formulations and their corresponding robotics applications. Finally, we discuss the open challenges and research frontiers.
Multi-objective evolutionary algorithms (MOEAs) have achieved great progress in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems. In fact, many real-world multi-objective problems usually contain a number of constraints. To promote the research of constrained multi-objective optimization, we first propose three primary types of difficulty, which reflect the challenges in the real-world optimization problems, to characterize the constraint functions in CMOPs, including feasibility-hardness, convergence-hardness and diversity-hardness. We then develop a general toolkit to construct difficulty adjustable and scalable constrained multi-objective optimization problems (CMOPs) with three types of parameterized constraint functions according to the proposed three primary types of difficulty. In fact, combination of the three primary constraint functions with different parameters can lead to construct a large variety of CMOPs, whose difficulty can be uniquely defined by a triplet with each of its parameter specifying the level of each primary difficulty type respectively. Furthermore, the number of objectives in this toolkit are able to scale to more than two. Based on this toolkit, we suggest nine difficulty adjustable and scalable CMOPs named DAS-CMOP1-9. To evaluate the proposed test problems, two popular CMOEAs - MOEA/D-CDP and NSGA-II-CDP are adopted to test their performances on DAS-CMOP1-9 with different difficulty triplets. The experiment results demonstrate that none of them can solve these problems efficiently, which stimulate us to develop new constrained MOEAs to solve the suggested DAS-CMOPs.
The paper proposes an analysis of liquid democracy (or, delegable proxy voting) from the perspective of binary aggregation and of binary diffusion models. We show how liquid democracy on binary issues can be embedded into the framework of binary aggregation with abstentions, enabling the transfer of known results about the latter---such as impossibility theorems---to the former. This embedding also sheds light on the relation between delegation cycles in liquid democracy and the probability of collective abstentions, as well as the issue of individual rationality in a delegable proxy voting setting. We then show how liquid democracy on binary issues can be modeled and analyzed also as a specific process of dynamics of binary opinions on networks. These processes---called Boolean DeGroot processes---are a special case of the DeGroot stochastic model of opinion diffusion. We establish the convergence conditions of such processes and show they provide some novel insights on how the effects of delegation cycles and individual rationality could be mitigated within liquid democracy. The study is a first attempt to provide theoretical foundations to the delgable proxy features of the liquid democracy voting system. Our analysis suggests recommendations on how the system may be modified to make it more resilient with respect to the handling of delegation cycles and of inconsistent majorities.
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.
Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a single-channel blind source separation problem, aims to decompose the mains which records the whole house electricity consumption into appliance-wise readings. This problem is difficult because it is inherently unidentifiable. Recent approaches have shown that the identifiability problem could be reduced by introducing domain knowledge into the model. Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in signal processing problems, which raises issues about how to combine predictions from different sliding windows. In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance. We use convolutional neural networks to train the model. Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem. We applied the proposed neural network approaches to real-world household energy data, and show that the methods achieve state-of-the-art performance, improving two standard error measures by 84% and 92%.
The causal structure of any system can be analyzed at a multitude of spatial and temporal scales. It has long been thought that while higher scale (macro) descriptions of causal structure may be useful to observers, they are at best a compressed description and at worse leave out critical information. However, recent research applying information theory to causal analysis has shown that the causal structure of some systems can actually come into focus (be more informative) at a macroscale (Hoel et al. 2013). That is, a macro model of a system (a map) can be more informative than a fully detailed model of the system (the territory). This has been called causal emergence. While causal emergence may at first glance seem counterintuitive, this paper grounds the phenomenon in a classic concept from information theory: Shannon's discovery of the channel capacity. I argue that systems have a particular causal capacity, and that different causal models of those systems take advantage of that capacity to various degrees. For some systems, only macroscale causal models use the full causal capacity. Such macroscale causal models can either be coarse-grains, or may leave variables and states out of the model (exogenous) in various ways, which can improve the model's efficacy and its informativeness via the same mathematical principles of how error-correcting codes take advantage of an information channel's capacity. As model choice increase, the causal capacity of a system approaches the channel capacity. Ultimately, this provides a general framework for understanding how the causal structure of some systems cannot be fully captured by even the most detailed microscopic model.
We are in the middle of a remarkable rise in the use and capability of artificial intelligence. Much of this growth has been fueled by the success of deep learning architectures: models that map from observables to outputs via multiple layers of latent representations. These deep learning algorithms are effective tools for unstructured prediction, and they can be combined in AI systems to solve complex automated reasoning problems. This paper provides a recipe for combining ML algorithms to solve for causal effects in the presence of instrumental variables -- sources of treatment randomization that are conditionally independent from the response. We show that a flexible IV specification resolves into two prediction tasks that can be solved with deep neural nets: a first-stage network for treatment prediction and a second-stage network whose loss function involves integration over the conditional treatment distribution. This Deep IV framework imposes some specific structure on the stochastic gradient descent routine used for training, but it is general enough that we can take advantage of off-the-shelf ML capabilities and avoid extensive algorithm customization. We outline how to obtain out-of-sample causal validation in order to avoid over-fit. We also introduce schemes for both Bayesian and frequentist inference: the former via a novel adaptation of dropout training, and the latter via a data splitting routine.
Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field, there is a pressing need to increase the depth and speed of the gravitational wave algorithms that have enabled these groundbreaking discoveries. To contribute to this effort, we introduce Deep Filtering, a new highly scalable method for end-to-end time-series signal processing, based on a system of two deep convolutional neural networks, which we designed for classification and regression to rapidly detect and estimate parameters of signals in highly noisy time-series data streams. We demonstrate a novel training scheme with gradually increasing noise levels, and a transfer learning procedure between the two networks. We showcase the application of this method for the detection and parameter estimation of gravitational waves from binary black hole mergers. Our results indicate that Deep Filtering significantly outperforms conventional machine learning techniques, achieves similar performance compared to matched-filtering while being several orders of magnitude faster thus allowing real-time processing of raw big data with minimal resources. More importantly, Deep Filtering extends the range of gravitational wave signals that can be detected with ground-based gravitational wave detectors. This framework leverages recent advances in artificial intelligence algorithms and emerging hardware architectures, such as deep-learning-optimized GPUs, to facilitate real-time searches of gravitational wave sources and their electromagnetic and astro-particle counterparts.
Techniques known as Nonlinear Set Membership prediction, Lipschitz Interpolation or Kinky Inference are approaches to machine learning that utilise presupposed Lipschitz properties to compute inferences over unobserved function values. Provided a bound on the true best Lipschitz constant of the target function is known a priori they offer convergence guarantees as well as bounds around the predictions. Considering a more general setting that builds on Hoelder continuity relative to pseudo-metrics, we propose an online method for estimating the Hoelder constant online from function value observations that possibly are corrupted by bounded observational errors. Utilising this to compute adaptive parameters within a kinky inference rule gives rise to a nonparametric machine learning method, for which we establish strong universal approximation guarantees. That is, we show that our prediction rule can learn any continuous function in the limit of increasingly dense data to within a worst-case error bound that depends on the level of observational uncertainty. We apply our method in the context of nonparametric model-reference adaptive control (MRAC). Across a range of simulated aircraft roll-dynamics and performance metrics our approach outperforms recently proposed alternatives that were based on Gaussian processes and RBF-neural networks. For discrete-time systems, we provide guarantees on the tracking success of our learning-based controllers both for the batch and the online learning setting.
Privacy has become a serious concern for modern Information Societies. The sensitive nature of much of the data that are daily exchanged or released to untrusted parties requires that responsible organizations undertake appropriate privacy protection measures. Nowadays, much of these data are texts (e.g., emails, messages posted in social media, healthcare outcomes, etc.) that, because of their unstructured and semantic nature, constitute a challenge for automatic data protection methods. In fact, textual documents are usually protected manually, in a process known as document redaction or sanitization. To do so, human experts identify sensitive terms (i.e., terms that may reveal identities and/or confidential information) and protect them accordingly (e.g., via removal or, preferably, generalization). To relieve experts from this burdensome task, in a previous work we introduced the theoretical basis of C-sanitization, an inherently semantic privacy model that provides the basis to the development of automatic document redaction/sanitization algorithms and offers clear and a priori privacy guarantees on data protection; even though its potential benefits C-sanitization still presents some limitations when applied to practice (mainly regarding flexibility, efficiency and accuracy). In this paper, we propose a new more flexible model, named (C, g(C))-sanitization, which enables an intuitive configuration of the trade-off between the desired level of protection (i.e., controlled information disclosure) and the preservation of the utility of the protected data (i.e., amount of semantics to be preserved). Moreover, we also present a set of technical solutions and algorithms that provide an efficient and scalable implementation of the model and improve its practical accuracy, as we also illustrate through empirical experiments.
To ease the development of robot learning in industry, two conditions need to be fulfilled. Manipulators must be able to learn high accuracy and precision tasks while being safe for workers in the factory. In this paper, we extend previously submitted work which consists in rapid learning of local high accuracy behaviors. By exploration and regression, linear and quadratic models are learnt for respectively the dynamics and cost function. Iterative Linear Quadratic Gaussian Regulator combined with cost quadratic regression can converge rapidly in the final stages towards high accuracy behavior as the cost function is modelled quite precisely. In this paper, both a different cost function and a second order improvement method are implemented within this framework. We also propose an analysis of the algorithm parameters through simulation for a positioning task. Finally, an experimental validation on a KUKA LBR iiwa robot is carried out. This collaborative robot manipulator can be easily programmed into safety mode, which makes it qualified for the second industry constraint stated above.
Analogy Based Effort Estimation (ABE) is one of the prominent methods for software effort estimation. The fundamental concept of ABE is closer to the mentality of expert estimation but with an automated procedure in which the final estimate is generated by reusing similar historical projects. The main key issue when using ABE is how to adapt the effort of the retrieved nearest neighbors. The adaptation process is an essential part of ABE to generate more successful accurate estimation based on tuning the selected raw solutions, using some adaptation strategy. In this study we show that there are three interrelated decision variables that have great impact on the success of adaptation method: (1) number of nearest analogies (k), (2) optimum feature set needed for adaptation, and (3) adaptation weights. To find the right decision regarding these variables, one need to study all possible combinations and evaluate them individually to select the one that can improve all prediction evaluation measures. The existing evaluation measures usually behave differently, presenting sometimes opposite trends in evaluating prediction methods. This means that changing one decision variable could improve one evaluation measure while it is decreasing the others. Therefore, the main theme of this research is how to come up with best decision variables that improve adaptation strategy and thus, the overall evaluation measures without degrading the others. The impact of these decisions together has not been investigated before, therefore we propose to view the building of adaptation procedure as a multi-objective optimization problem. The Particle Swarm Optimization Algorithm (PSO) is utilized to find the optimum solutions for such decision variables based on optimizing multiple evaluation measures
This paper extends recent work in interactive machine learning (IML) focused on effectively incorporating human feedback. We show how control and feedback signals complement each other in systems which model human reward. We demonstrate that simultaneously incorporating human control and feedback signals can improve interactive robotic systems' performance on a self-mirrored movement control task where an RL-agent controlled right arm attempts to match the preprogrammed movement pattern of the left arm. We illustrate the impact of varying human feedback parameters on task performance by investigating the probability of giving feedback on each time step and the likelihood of given feedback being correct. We further illustrate that varying the temporal decay with which the agent incorporates human feedback has a significant impact on task performance. We found that smearing human feedback over time steps improves performance and we show varying the probability of feedback at each time step, and an increased likelihood of those feedbacks being 'correct' can impact agent performance. We conclude that understanding latent variables in human feedback is crucial for learning algorithms acting in human-machine interaction domains.
Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), and external conditions (e.g., weather and events). We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i.e. inflow and outflow) in each and every region of a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the residual neural network framework to model the temporal closeness, period, and trend properties of crowd traffic. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We have developed a real-time system based on Microsoft Azure Cloud, called UrbanFlow, providing the crowd flow monitoring and forecasting in Guiyang City of China. In addition, we present an extensive experimental evaluation using two types of crowd flows in Beijing and New York City (NYC), where ST-ResNet outperforms nine well-known baselines.
The Internet of Things is arriving to our homes or cities through fields already known like Smart Homes, Smart Cities, or Smart Towns. The monitoring of environmental conditions of cities can help to adapt the indoor locations of the cities in order to be more comfortable for people who stay there. A way to improve the indoor conditions is an efficient temperature control, however, it depends on many factors like the different combinations of outdoor temperature and humidity. Therefore, adjusting the indoor temperature is not setting a value according to other value. There are many more factors to take into consideration, hence the traditional logic based in binary states cannot be used. Many problems cannot be solved with a set of binary solutions and we need a new way of development. Fuzzy logic is able to interpret many states, more than two states, giving to computers the capacity to react in a similar way to people. In this paper we will propose a new approach to control the temperature using the Internet of Things together its platforms and fuzzy logic regarding not only the indoor temperature but also the outdoor temperature and humidity in order to save energy and to set a more comfortable environment for their users. Finally, we will conclude that the fuzzy approach allows us to achieve an energy saving around 40% and thus, save money.
Limited annotated data available for the recognition of facial expression and action units embarrasses the training of deep networks, which can learn disentangled invariant features. However, a linear model with just several parameters normally is not demanding in terms of training data. In this paper, we propose an elegant linear model to untangle confounding factors in challenging realistic multichannel signals such as 2D face videos. The simple yet powerful model does not rely on huge training data and is natural for recognizing facial actions without explicitly disentangling the identity. Base on well-understood intuitive linear models such as Sparse Representation based Classification (SRC), previous attempts require a prepossessing of explicit decoupling which is practically inexact. Instead, we exploit the low-rank property across frames to subtract the underlying neutral faces which are modeled jointly with sparse representation on the action components with group sparsity enforced. On the extended Cohn-Kanade dataset (CK+), our one-shot automatic method on raw face videos performs as competitive as SRC applied on manually prepared action components and performs even better than SRC in terms of true positive rate. We apply the model to the even more challenging task of facial action unit recognition, verified on the MPI Face Video Database (MPI-VDB) achieving a decent performance. All the programs and data have been made publicly available.
In this paper, a novel architecture for a deep recurrent neural network, residual LSTM is introduced. A plain LSTM has an internal memory cell that can learn long term dependencies of sequential data. It also provides a temporal shortcut path to avoid vanishing or exploding gradients in the temporal domain. The residual LSTM provides an additional spatial shortcut path from lower layers for efficient training of deep networks with multiple LSTM layers. Compared with the previous work, highway LSTM, residual LSTM separates a spatial shortcut path with temporal one by using output layers, which can help to avoid a conflict between spatial and temporal-domain gradient flows. Furthermore, residual LSTM reuses the output projection matrix and the output gate of LSTM to control the spatial information flow instead of additional gate networks, which effectively reduces more than 10% of network parameters. An experiment for distant speech recognition on the AMI SDM corpus shows that 10-layer plain and highway LSTM networks presented 13.7% and 6.2% increase in WER over 3-layer aselines, respectively. On the contrary, 10-layer residual LSTM networks provided the lowest WER 41.0%, which corresponds to 3.3% and 2.8% WER reduction over plain and highway LSTM networks, respectively.
The promise of compressive sensing (CS) has been offset by two significant challenges. First, real-world data is not exactly sparse in a fixed basis. Second, current high-performance recovery algorithms are slow to converge, which limits CS to either non-real-time applications or scenarios where massive back-end computing is available. In this paper, we attack both of these challenges head-on by developing a new signal recovery framework we call {\em DeepInverse} that learns the inverse transformation from measurement vectors to signals using a {\em deep convolutional network}. When trained on a set of representative images, the network learns both a representation for the signals (addressing challenge one) and an inverse map approximating a greedy or convex recovery algorithm (addressing challenge two). Our experiments indicate that the DeepInverse network closely approximates the solution produced by state-of-the-art CS recovery algorithms yet is hundreds of times faster in run time. The tradeoff for the ultrafast run time is a computationally intensive, off-line training procedure typical to deep networks. However, the training needs to be completed only once, which makes the approach attractive for a host of sparse recovery problems.
For a social networking service to acquire and retain users, it must find ways to keep them engaged. By accurately gauging their preferences, it is able to serve them with the subset of available content that maximises revenue for the site. Without the constraints of an appropriate regulatory framework, we argue that a sufficiently sophisticated curator algorithm tasked with performing this process may choose to explore curation strategies that are detrimental to users. In particular, we suggest that such an algorithm is capable of learning to manipulate its users, for several qualitative reasons: 1. Access to vast quantities of user data combined with ongoing breakthroughs in the field of machine learning are leading to powerful but uninterpretable strategies for decision making at scale. 2. The availability of an effective feedback mechanism for assessing the short and long term user responses to curation strategies. 3. Techniques from reinforcement learning have allowed machines to learn automated and highly successful strategies at an abstract level, often resulting in non-intuitive yet nonetheless highly appropriate action selection. In this work, we consider the form that these strategies for user manipulation might take and scrutinise the role that regulation should play in the design of such systems.
Stochastic dynamic control systems relate in a prob- abilistic fashion the space of control signals to the space of corresponding future states. Consequently, stochastic dynamic systems can be interpreted as an information channel between the control space and the state space. In this work we study this control-to-state informartion capacity of stochastic dynamic systems in continuous-time, when the states are observed only partially. The control-to-state capacity, known as empowerment, was shown in the past to be useful in solving various Artificial Intelligence & Control benchmarks, and was used to replace problem-specific utilities. The higher the value of empowerment is, the more optional future states an agent may reach by using its controls inside a given time horizon. The contribution of this work is that we derive an efficient solution for computing the control-to-state information capacity for a linear, partially-observed Gaussian dynamic control system in continuous time, and discover new relationships between control-theoretic and information-theoretic properties of dynamic systems. Particularly, using the derived method, we demonstrate that the capacity between the control signal and the system output does not grow without limits with the length of the control signal. This means that only the near-past window of the control signal contributes effectively to the control-to-state capacity, while most of the information beyond this window is irrelevant for the future state of the dynamic system. We show that empowerment depends on a time constant of a dynamic system.
A new, radical CNN design approach is presented in this paper, considering the reduction of the total computational load during inference. This is achieved by a new holistic intervention on both the CNN architecture and the training procedure, which targets to the parsimonious inference by learning to exploit or remove the redundant capacity of a CNN architecture. This is accomplished, by the introduction of a new structural element that can be inserted as an add-on to any contemporary CNN architecture, whilst preserving or even improving its recognition accuracy. Our approach formulates a systematic and data-driven method for developing CNNs that are trained to eventually change size and form in real-time during inference, targeting to the smaller possible computational footprint. Results are provided for the optimal implementation on a few modern, high-end mobile computing platforms indicating a significant speed-up of up to x3 times.
In this work several semantic approaches to concept-based query expansion and reranking schemes are studied and compared with different ontology-based expansion methods in web document search and retrieval. In particular, we focus on concept-based query expansion schemes, where, in order to effectively increase the precision of web document retrieval and to decrease the users browsing time, the main goal is to quickly provide users with the most suitable query expansion. Two key tasks for query expansion in web document retrieval are to find the expansion candidates, as the closest concepts in web document domain, and to rank the expanded queries properly. The approach we propose aims at improving the expansion phase for better web document retrieval and precision. The basic idea is to measure the distance between candidate concepts using the PMING distance, a collaborative semantic proximity measure, i.e. a measure which can be computed by using statistical results from web search engine. Experiments show that the proposed technique can provide users with more satisfying expansion results and improve the quality of web document retrieval.
We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from simple scenes with several isolated objects to very challenging ones with multiple instances of several objects and with a high amount of clutter and occlusion. The images were captured from a systematically sampled view sphere around the object/scene, and are annotated with accurate ground truth 6D poses of all modeled objects. Initial evaluation results indicate that the state of the art in 6D object pose estimation has ample room for improvement, especially in difficult cases with significant occlusion. The T-LESS dataset is available online at cmp.felk.cvut.cz/t-less.
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture. We propose a novel online dictionary-learning (sparse-coding) framework which incorporates the addition and deletion of hidden units (dictionary elements), and is inspired by the adult neurogenesis phenomenon in the dentate gyrus of the hippocampus, known to be associated with improved cognitive function and adaptation to new environments. In the online learning setting, where new input instances arrive sequentially in batches, the neuronal-birth is implemented by adding new units with random initial weights (random dictionary elements); the number of new units is determined by the current performance (representation error) of the dictionary, higher error causing an increase in the birth rate. Neuronal-death is implemented by imposing l1/l2-regularization (group sparsity) on the dictionary within the block-coordinate descent optimization at each iteration of our online alternating minimization scheme, which iterates between the code and dictionary updates. Finally, hidden unit connectivity adaptation is facilitated by introducing sparsity in dictionary elements. Our empirical evaluation on several real-life datasets (images and language) as well as on synthetic data demonstrates that the proposed approach can considerably outperform the state-of-art fixed-size (nonadaptive) online sparse coding of Mairal et al. (2009) in the presence of nonstationary data. Moreover, we identify certain properties of the data (e.g., sparse inputs with nearly non-overlapping supports) and of the model (e.g., dictionary sparsity) associated with such improvements.
The payload of communications satellites must go through a series of tests to assert their ability to survive in space. Each test involves some equipment of the payload to be active, which has an impact on the temperature of the payload. Sequencing these tests in a way that ensures the thermal stability of the payload and minimizes the overall duration of the test campaign is a very important objective for satellite manufacturers. The problem can be decomposed in two sub-problems corresponding to two objectives: First, the number of distinct configurations necessary to run the tests must be minimized. This can be modeled as packing the tests into configurations, and we introduce a set of implied constraints to improve the lower bound of the model. Second, tests must be sequenced so that the number of times an equipment unit has to be switched on or off is minimized. We model this aspect using the constraint Switch, where a buffer with limited capacity represents the currently active equipment units, and we introduce an improvement of the propagation algorithm for this constraint. We then introduce a search strategy in which we sequentially solve the sub-problems (packing and sequencing). Experiments conducted on real and random instances show the respective interest of our contributions.
We consider an online version of the robust Principle Component Analysis (PCA), which arises naturally in time-varying source separations such as video foreground-background separation. This paper proposes a compressive online robust PCA with prior information for recursively separating a sequences of frames into sparse and low-rank components from a small set of measurements. In contrast to conventional batch-based PCA, which processes all the frames directly, the proposed method processes measurements taken from each frame. Moreover, this method can efficiently incorporate multiple prior information, namely previous reconstructed frames, to improve the separation and thereafter, update the prior information for the next frame. We utilize multiple prior information by solving $n\text{-}\ell_{1}$ minimization for incorporating the previous sparse components and using incremental singular value decomposition ($\mathrm{SVD}$) for exploiting the previous low-rank components. We also establish theoretical bounds on the number of measurements required to guarantee successful separation under assumptions of static or slowly-changing low-rank components. Using numerical experiments, we evaluate our bounds and the performance of the proposed algorithm. In addition, we apply the proposed algorithm to online video foreground and background separation from compressive measurements. Experimental results show that the proposed method outperforms the existing methods.
The current trends in next-generation exascale systems go towards integrating a wide range of specialized (co-)processors into traditional supercomputers. Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. However, heterogeneous platforms limit the portability of the applications and increase development complexity due to the programming skills required. Program transformation can help make programming heterogeneous systems easier by defining a step-wise transformation process that translates a given initial code into a semantically equivalent final code, but adapted to a specific platform. Program transformation systems require the definition of efficient transformation strategies to tackle the combinatorial problem that emerges due to the large set of transformations applicable at each step of the process. In this paper we propose a machine learning-based approach to learn heuristics to define program transformation strategies. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of this approach.
Social messages classification is a research domain that has attracted the attention of many researchers in these last years. Indeed, the social message is different from ordinary text because it has some special characteristics like its shortness. Then the development of new approaches for the processing of the social message is now essential to make its classification more efficient. In this paper, we are mainly interested in the classification of social messages based on their spreading on online social networks (OSN). We proposed a new distance metric based on the Dynamic Time Warping distance and we use it with the probabilistic and the evidential k Nearest Neighbors (k-NN) classifiers to classify propagation networks (PrNets) of messages. The propagation network is a directed acyclic graph (DAG) that is used to record propagation traces of the message, the traversed links and their types. We tested the proposed metric with the chosen k-NN classifiers on real world propagation traces that were collected from Twitter social network and we got good classification accuracies.
The Frame Problem (FP) is a puzzle in philosophy of mind and epistemology, articulated by the Stanford Encyclopedia of Philosophy as follows: "How do we account for our apparent ability to make decisions on the basis only of what is relevant to an ongoing situation without having explicitly to consider all that is not relevant?" In this work, we focus on the causal variant of the FP, the Causal Frame Problem (CFP). Assuming that a reasoner's mental causal model can be (implicitly) represented by a causal Bayes net, we first introduce a notion called Potential Level (PL). PL, in essence, encodes the relative position of a node with respect to its neighbors in a causal Bayes net. Drawing on the psychological literature on causal judgment, we substantiate the claim that PL may bear on how time is encoded in the mind. Using PL, we propose an inference framework, called the PL-based Inference Framework (PLIF), which permits a boundedly-rational approach to the CFP to be formally articulated at Marr's algorithmic level of analysis. We show that our proposed framework, PLIF, is consistent with a wide range of findings in causal judgment literature, and that PL and PLIF make a number of predictions, some of which are already supported by existing findings.
We study the problem of identifying the causal relationship between two discrete random variables from observational data. We recently proposed a novel framework called entropic causality that works in a very general functional model but makes the assumption that the unobserved exogenous variable has small entropy in the true causal direction. This framework requires the solution of a minimum entropy coupling problem: Given marginal distributions of m discrete random variables, each on n states, find the joint distribution with minimum entropy, that respects the given marginals. This corresponds to minimizing a concave function of nm variables over a convex polytope defined by nm linear constraints, called a transportation polytope. Unfortunately, it was recently shown that this minimum entropy coupling problem is NP-hard, even for 2 variables with n states. Even representing points (joint distributions) over this space can require exponential complexity (in n, m) if done naively. In our recent work we introduced an efficient greedy algorithm to find an approximate solution for this problem. In this paper we analyze this algorithm and establish two results: that our algorithm always finds a local minimum and also is within an additive approximation error from the unknown global optimum.
Most of researches on image forensics have been mainly focused on detection of artifacts introduced by a single processing tool. They lead in the development of many specialized algorithms looking for one or more particular footprints under specific settings. Naturally, the performance of such algorithms are not perfect, and accordingly the provided output might be noisy, inaccurate and only partially correct. Furthermore, a forged image in practical scenarios is often the result of utilizing several tools available by image-processing software systems. Therefore, reliable tamper detection requires developing more poweful tools to deal with various tempering scenarios. Fusion of forgery detection tools based on Fuzzy Inference System has been used before for addressing this problem. Adjusting the membership functions and defining proper fuzzy rules for attaining to better results are time-consuming processes. This can be accounted as main disadvantage of fuzzy inference systems. In this paper, a Neuro-Fuzzy inference system for fusion of forgery detection tools is developed. The neural network characteristic of these systems provides appropriate tool for automatically adjusting the membership functions. Moreover, initial fuzzy inference system is generated based on fuzzy clustering techniques. The proposed framework is implemented and validated on a benchmark image splicing data set in which three forgery detection tools are fused based on adaptive Neuro-Fuzzy inference system. The outcome of the proposed method reveals that applying Neuro Fuzzy inference systems could be a better approach for fusion of forgery detection tools.
As we know, there is a controversy about the decision making under risk between economists and psychologists. We discuss to build a unified theory of risky choice, which would explain both of compensatory and non-compensatory theories. For risky choice, according to cognition ability, we argue that people could not build a continuous and accurate subjective probability world, but several order concepts, such as small, middle and large probability. People make decisions based on information, experience, imagination and other things. All of these things are so huge that people have to prepare some strategies. That is, people have different strategies when facing to different situations. The distributions of these things have different decision structures. More precisely, decision making is a process of simplifying the decision structure. However, the process of decision structure simplifying is not stuck in a rut, but through different path when facing problems repeatedly. It is why preference reversal always happens when making decisions. The most efficient way to simplify the decision structure is calculating expected value or making decisions based on one or two dimensions. We also argue that the deliberation time at least has four parts, which are consist of substitution time, first order time, second order time and calculation time. Decision structure also can simply explain the phenomenon of paradoxes and anomalies. JEL Codes: C10, D03, D81
A credal network under epistemic irrelevance is a generalised type of Bayesian network that relaxes its two main building blocks. On the one hand, the local probabilities are allowed to be partially specified. On the other hand, the assessments of independence do not have to hold exactly. Conceptually, these two features turn credal networks under epistemic irrelevance into a powerful alternative to Bayesian networks, offering a more flexible approach to graph-based multivariate uncertainty modelling. However, in practice, they have long been perceived as very hard to work with, both theoretically and computationally. The aim of this paper is to demonstrate that this perception is no longer justified. We provide a general introduction to credal networks under epistemic irrelevance, give an overview of the state of the art, and present several new theoretical results. Most importantly, we explain how these results can be combined to allow for the design of recursive inference methods. We provide numerous concrete examples of how this can be achieved, and use these to demonstrate that computing with credal networks under epistemic irrelevance is most definitely feasible, and in some cases even highly efficient. We also discuss several philosophical aspects, including the lack of symmetry, how to deal with probability zero, the interpretation of lower expectations, the axiomatic status of graphoid properties, and the difference between updating and conditioning.
For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function. We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classification tasks, and a set of Atari and Labyrinth reinforcement learning tasks, suggesting PathNets have general applicability for neural network training. Finally, PathNet also significantly improves the robustness to hyperparameter choices of a parallel asynchronous reinforcement learning algorithm (A3C).
Future projection of climate is typically obtained by combining outputs from multiple Earth System Models (ESMs) for several climate variables such as temperature and precipitation. While IPCC has traditionally used a simple model output average, recent work has illustrated potential advantages of using a multitask learning (MTL) framework for projections of individual climate variables. In this paper we introduce a framework for hierarchical multitask learning (HMTL) with two levels of tasks such that each super-task, i.e., task at the top level, is itself a multitask learning problem over sub-tasks. For climate projections, each super-task focuses on projections of specific climate variables spatially using an MTL formulation. For the proposed HMTL approach, a group lasso regularization is added to couple parameters across the super-tasks, which in the climate context helps exploit relationships among the behavior of different climate variables at a given spatial location. We show that some recent works on MTL based on learning task dependency structures can be viewed as special cases of HMTL. Experiments on synthetic and real climate data show that HMTL produces better results than decoupled MTL methods applied separately on the super-tasks and HMTL significantly outperforms baselines for climate projection.
Frequent itemset mining is a popular data mining technique. Apriori, Eclat, and FP-Growth are among the most common algorithms for frequent itemset mining. Considerable research has been performed to compare the relative performance between these three algorithms, by evaluating the scalability of each algorithm as the dataset size increases. While scalability as data size increases is important, previous papers have not examined the performance impact of similarly sized datasets that contain different itemset characteristics. This paper explores the effects that two dataset characteristics can have on the performance of these three frequent itemset algorithms. To perform this empirical analysis, a dataset generator is created to measure the effects of frequent item density and the maximum transaction size on performance. The generated datasets contain the same number of rows. This provides some insight into dataset characteristics that are conducive to each algorithm. The results of this paper's research demonstrate Eclat and FP-Growth both handle increases in maximum transaction size and frequent itemset density considerably better than the Apriori algorithm. This paper explores the effects that two dataset characteristics can have on the performance of these three frequent itemset algorithms. To perform this empirical analysis, a dataset generator is created to measure the effects of frequent item density and the maximum transaction size on performance. The generated datasets contain the same number of rows. This provides some insight into dataset characteristics that are conducive to each algorithm. The results of this paper's research demonstrate Eclat and FP-Growth both handle increases in maximum transaction size and frequent itemset density considerably better than the Apriori algorithm.
This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content such as sprites and sound effects. In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the resulting generated content. Multiple PCGML methods are covered, including neural networks, long short-term memory (LSTM) networks, autoencoders, and deep convolutional networks; Markov models, $n$-grams, and multi-dimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in the application of PCGML, including learning from small datasets, lack of training data, multi-layered learning, style-transfer, parameter tuning, and PCG as a game mechanic.
Optimization of Mixed-Integer Non-Linear Programming (MINLP) supports important decisions in applications such as Chemical Process Engineering. But current solvers have limited ability for deductive reasoning or the use of domain-specific theories, and the management of integrality constraints does not yet exploit automated reasoning tools such as SMT solvers. This seems to limit both scalability and reach of such tools in practice. We therefore present a tool, ManyOpt, for MINLP optimization that enables experimentation with reduction techniques which transform a MINLP problem to feasibility checking realized by an SMT solver. ManyOpt is similar to the SAT solver ManySAT in that it runs a specified number of such reduction techniques in parallel to get the strongest result on a given MINLP problem. The tool is implemented in layers, which we may see as features and where reduction techniques are feature vectors. Some of these features are inspired by known MINLP techniques whereas others are novel and specific to SMT. Our experimental results on standard benchmarks demonstrate the benefits of this approach. The tool supports a variety of SMT solvers and is easily extensible with new features, courtesy of its layered structure. For example, logical formulas for deductive reasoning are easily added to constrain further the optimization of a MINLP problem of interest.
In this paper, we propose a new autonomous braking system based on deep reinforcement learning. The proposed autonomous braking system automatically decides whether to apply the brake at each time step when confronting the risk of collision using the information on the obstacle obtained by the sensors. The problem of designing brake control is formulated as searching for the optimal policy in Markov decision process (MDP) model where the state is given by the relative position of the obstacle and the vehicle's speed, and the action space is defined as whether brake is stepped or not. The policy used for brake control is learned through computer simulations using the deep reinforcement learning method called deep Q-network (DQN). In order to derive desirable braking policy, we propose the reward function which balances the damage imposed to the obstacle in case of accident and the reward achieved when the vehicle runs out of risk as soon as possible. DQN is trained for the scenario where a vehicle is encountered with a pedestrian crossing the urban road. Experiments show that the control agent exhibits desirable control behavior and avoids collision without any mistake in various uncertain environments.
In recent years, machine learning techniques based on neural networks for mobile computing become increasingly popular. Classical multi-layer neural networks require matrix multiplications at each stage. Multiplication operation is not an energy efficient operation and consequently it drains the battery of the mobile device. In this paper, we propose a new energy efficient neural network with the universal approximation property over space of Lebesgue integrable functions. This network, called, additive neural network, is very suitable for mobile computing. The neural structure is based on a novel vector product definition, called ef-operator, that permits a multiplier-free implementation. In ef-operation, the "product" of two real numbers is defined as the sum of their absolute values, with the sign determined by the sign of the product of the numbers. This "product" is used to construct a vector product in $R^N$. The vector product induces the $l_1$ norm. The proposed additive neural network successfully solves the XOR problem. The experiments on MNIST dataset show that the classification performances of the proposed additive neural networks are very similar to the corresponding multi-layer perceptron and convolutional neural networks (LeNet).
The Boolean Satisfiability problem asks if a Boolean formula is satisfiable by some assignment of the variables or not. It belongs to the NP-complete complexity class and hence no algorithm with polynomial time worst-case complexity is known, i.e., the problem is hard. The K-SAT problem is the subset of the Boolean Satisfiability problem, for which the Boolean formula has the conjunctive normal form with K literals per clause. This problem is still NP-complete for $K \ge 3$. Although the worst case complexity of NP-complete problems is conjectured to be exponential, there might be subsets of the realizations where solutions can typically be found in polynomial time. In fact, random $K$-SAT, with the number of clauses to number of variables ratio $\alpha$ as control parameter, shows a phase transition between a satisfiable phase and an unsatisfiable phase, at which the hardest problems are located. We use here several linear programming approaches to reveal further "easy-hard" transition points at which the typical hardness of the problems increases which means that such algorithms can solve the problem on one side efficiently but not beyond this point. For one of these transitions, we observed a coincidence with a structural transition of the literal factor graphs of the problem instances. We also investigated cutting-plane approaches, which often increase the computational efficiency. Also we tried out a mapping to another NP-complete optimization problem using a specific algorithm for that problem. In both cases, no improvement of the performance was observed, i.e., no shift of the easy-hard transition to higher values of $\alpha$.
Observing nearby galaxies would facilitate the search for artificial radio signals by sampling many billions of stars simultaneously, but few efforts have been made to exploit this opportunity. An added attraction is that the Milky Way is the second-largest member of the Local Group, so our galaxy might be a probable target for hypothetical broadcasters in nearby galaxies. We present the first relatively high spectral resolution (<1 kHz) 21 cm band search for intelligent radio signals of complete galaxies in the Local Group with the Jansky VLA, observing the galaxies M31 (Andromeda) and M33 (Triangulum) - the first and third largest members of the group respectively - sampling more stars than any prior search of this kind. We used 122 Hz channels over a 1 MHz spectral window in the target galaxy velocity frame of reference, and 15 Hz channels over a 125 kHz window in our local standard of rest. No narrowband signals were detected above a signal-to-noise ratio of 7, suggesting the absence of continuous narrowband flux greater than approximately 0.24 Jy and 1.33 Jy in the respective spectral windows illuminating our part of the Milky Way during our observations in December 2014 and January 2015. This is also the first study in which the upgraded VLA has been used for SETI.
This paper has three main contributions to our understanding of fixed-depth minimax search: (A) A new formulation for Stockman's SSS* algorithm, based on Alpha-Beta, is presented. It solves all the perceived drawbacks of SSS*, finally transforming it into a practical algorithm. In effect, we show that SSS* = alpha-beta + ransposition tables. The crucial step is the realization that transposition tables contain so-called solution trees, structures that are used in best-first search algorithms like SSS*. Having created a practical version, we present performance measurements with tournament game-playing programs for three different minimax games, yielding results that contradict a number of publications. (B) Based on the insights gained in our attempts at understanding SSS*, we present a framework that facilitates the construction of several best-first fixed- depth game-tree search algorithms, known and new. The framework is based on depth-first null-window Alpha-Beta search, enhanced with storage to allow for the refining of previous search results. It focuses attention on the essential differences between algorithms. (C) We present a new instance of the framework, MTD(f). It is well-suited for use with iterative deepening, and performs better than algorithms that are currently used in most state-of-the-art game-playing programs. We provide experimental evidence to explain why MTD(f) performs better than the other fixed-depth minimax algorithms.
A considerable amount of machine learning algorithms take instance-feature matrices as their inputs. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. This is a great pity since many of these algorithms are effective, robust, efficient, and easy to use. In this paper, we bridge this gap by proposing an efficient representation learning framework that is able to convert a set of time series with equal or unequal lengths to a matrix format. In particular, we guarantee that the pairwise similarities between time series are well preserved after the transformation. The learned feature representation is particularly suitable to the class of learning problems that are sensitive to data similarities. Given a set of $n$ time series, we first construct an $n\times n$ partially observed similarity matrix by randomly sampling $O(n \log n)$ pairs of time series and computing their pairwise similarities. We then propose an extremely efficient algorithm that solves a highly non-convex and NP-hard problem to learn new features based on the partially observed similarity matrix. We use the learned features to conduct experiments on both data classification and clustering tasks. Our extensive experimental results demonstrate that the proposed framework is both effective and efficient.
Researchers in answer set programming and constraint programming have spent significant efforts in the development of hybrid languages and solving algorithms combining the strengths of these traditionally separate fields. These efforts resulted in a new research area: constraint answer set programming. Constraint answer set programming languages and systems proved to be successful at providing declarative, yet efficient solutions to problems involving hybrid reasoning tasks. One of the main contributions of this paper is the first comprehensive account of the constraint answer set language and solver EZCSP, a mainstream representative of this research area that has been used in various successful applications. We also develop an extension of the transition systems proposed by Nieuwenhuis et al. in 2006 to capture Boolean satisfiability solvers. We use this extension to describe the EZCSP algorithm and prove formal claims about it. The design and algorithmic details behind EZCSP clearly demonstrate that the development of the hybrid systems of this kind is challenging. Many questions arise when one faces various design choices in an attempt to maximize system's benefits. One of the key decisions that a developer of a hybrid solver makes is settling on a particular integration schema within its implementation. Thus, another important contribution of this paper is a thorough case study based on EZCSP, focused on the various integration schemas that it provides. Under consideration in Theory and Practice of Logic Programming (TPLP).
In this paper we propose a multi-convex framework for multi-task learning that improves predictions by learning relationships both between tasks and between features. Our framework is a generalization of related methods in multi-task learning, that either learn task relationships, or feature relationships, but not both. We start with a hierarchical Bayesian model, and use the empirical Bayes method to transform the underlying inference problem into a multi-convex optimization problem. We propose a coordinate-wise minimization algorithm that has a closed form solution for each block subproblem. Naively these solutions would be expensive to compute, but by using the theory of doubly stochastic matrices, we are able to reduce the underlying matrix optimization subproblem into a minimum weight perfect matching problem on a complete bipartite graph, and solve it analytically and efficiently. To solve the weight learning subproblem, we propose three different strategies, including a gradient descent method with linear convergence guarantee when the instances are not shared by multiple tasks, and a numerical solution based on Sylvester equation when instances are shared. We demonstrate the efficiency of our method on both synthetic datasets and real-world datasets. Experiments show that the proposed optimization method is orders of magnitude faster than an off-the-shelf projected gradient method, and our model is able to exploit the correlation structures among multiple tasks and features.
Neural language models predict the next token using a latent representation of the immediate token history. Recently, various methods for augmenting neural language models with an attention mechanism over a differentiable memory have been proposed. For predicting the next token, these models query information from a memory of the recent history which can facilitate learning mid- and long-range dependencies. However, conventional attention mechanisms used in memory-augmented neural language models produce a single output vector per time step. This vector is used both for predicting the next token as well as for the key and value of a differentiable memory of a token history. In this paper, we propose a neural language model with a key-value attention mechanism that outputs separate representations for the key and value of a differentiable memory, as well as for encoding the next-word distribution. This model outperforms existing memory-augmented neural language models on two corpora. Yet, we found that our method mainly utilizes a memory of the five most recent output representations. This led to the unexpected main finding that a much simpler model based only on the concatenation of recent output representations from previous time steps is on par with more sophisticated memory-augmented neural language models.
Sparse iterative methods, in particular first-order methods, are known to be among the most effective in solving large-scale two-player zero-sum extensive-form games. The convergence rates of these methods depend heavily on the properties of the distance-generating function that they are based on. We investigate the acceleration of first-order methods for solving extensive-form games through better design of the dilated entropy function---a class of distance-generating functions related to the domains associated with the extensive-form games. By introducing a new weighting scheme for the dilated entropy function, we develop the first distance-generating function for the strategy spaces of sequential games that has no dependence on the branching factor of the player. This result improves the convergence rate of several first-order methods by a factor of $\Omega(b^dd)$, where $b$ is the branching factor of the player, and $d$ is the depth of the game tree. Thus far, counterfactual regret minimization methods have been faster in practice, and more popular, than first-order methods despite their theoretically inferior convergence rates. Using our new weighting scheme and practical tuning we show that, for the first time, the excessive gap technique can be made faster than the fastest counterfactual regret minimization algorithm, CFR+, in practice.
We propose a direct estimation method for R\'{e}nyi and f-divergence measures based on a new graph theoretical interpretation. Suppose that we are given two sample sets $X$ and $Y$, respectively with $N$ and $M$ samples, where $\eta:=M/N$ is a constant value. Considering the $k$-nearest neighbor ($k$-NN) graph of $Y$ in the joint data set $(X,Y)$, we show that the average powered ratio of the number of $X$ points to the number of $Y$ points among all $k$-NN points is proportional to R\'{e}nyi divergence of $X$ and $Y$ densities. A similar method can also be used to estimate f-divergence measures. We derive bias and variance rates, and show that for the class of $\gamma$-H\"{o}lder smooth functions, the estimator achieves the MSE rate of $O(N^{-2\gamma/(\gamma+d)})$. Furthermore, by using a weighted ensemble estimation technique, for density functions with continuous and bounded derivatives of up to the order $d$, and some extra conditions at the support set boundary, we derive an ensemble estimator that achieves the parametric MSE rate of $O(1/N)$. Our estimators are more computationally tractable than other competing estimators, which makes them appealing in many practical applications.
Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their common description, e.g., in binary setting, their shared attributes. In bioinformatics, biclustering is used to find genes, which are active in a subset of situations, thus being candidates for biomarkers. However, the authors of those biclustering techniques that are popular in gene expression analysis, may overlook the existing methods. For instance, BiMax algorithm is aimed at finding biclusters, which are well-known for decades as formal concepts. Moreover, even if bioinformatics classify the biclustering methods according to reasonable domain-driven criteria, their classification taxonomies may be different from survey to survey and not full as well. So, in this paper we propose to use concept lattices as a tool for taxonomy building (in the biclustering domain) and attribute exploration as means for cross-domain taxonomy completion.
The beyond worst-case synthesis problem was introduced recently by Bruy\`ere et al. [BFRR14]: it aims at building system controllers that provide strict worst-case performance guarantees against an antagonistic environment while ensuring higher expected performance against a stochastic model of the environment. Our work extends the framework of [BFRR14] and follow-up papers, which focused on quantitative objectives, by addressing the case of $\omega$-regular conditions encoded as parity objectives, a natural way to represent functional requirements of systems. We build strategies that satisfy a main parity objective on all plays, while ensuring a secondary one with sufficient probability. This setting raises new challenges in comparison to quantitative objectives, as one cannot easily mix different strategies without endangering the functional properties of the system. We establish that, for all variants of this problem, deciding the existence of a strategy lies in ${\sf NP} \cap {\sf coNP}$, the same complexity class as classical parity games. Hence, our framework provides additional modeling power while staying in the same complexity class. [BFRR14] V\'eronique Bruy\`ere, Emmanuel Filiot, Mickael Randour, and Jean-Fran\c{c}ois Raskin. Meet your expectations with guarantees: Beyond worst-case synthesis in quantitative games. In Ernst W. Mayr and Natacha Portier, editors, 31st International Symposium on Theoretical Aspects of Computer Science, STACS 2014, March 5-8, 2014, Lyon, France, volume 25 of LIPIcs, pages 199-213. Schloss Dagstuhl - Leibniz - Zentrum fuer Informatik, 2014.
We study the problem of enumerating the satisfying valuations of a circuit while bounding the delay, i.e., the time needed to compute each successive valuation. We focus on the class of structured d-DNNF circuits originally introduced in knowledge compilation, a sub-area of artificial intelligence. We propose an algorithm for these circuits that enumerates valuations with linear preprocessing and delay linear in the Hamming weight of each valuation. Moreover, valuations of constant Hamming weight can be enumerated with linear preprocessing and constant delay. Our results yield a framework for efficient enumeration that applies to all problems whose solutions can be compiled to structured d-DNNFs. In particular, we use it to recapture classical results in database theory, for factorized database representations and for MSO evaluation. This gives an independent proof of constant-delay enumeration for MSO formulae with first-order free variables on bounded-treewidth structures.
Vertex Separation Minimization Problem (VSMP) consists of finding a layout of a graph G = (V,E) which minimizes the maximum vertex cut or separation of a layout. It is an NP-complete problem in general for which metaheuristic techniques can be applied to find near optimal solution. VSMP has applications in VLSI design, graph drawing and computer language compiler design. VSMP is polynomially solvable for grids, trees, permutation graphs and cographs. Construction heuristics play a very important role in the metaheuristic techniques as they are responsible for generating initial solutions which lead to fast convergence. In this paper, we have proposed three construction heuristics H1, H2 and H3 and performed experiments on Grids, Small graphs, Trees and Harwell Boeing graphs, totaling 248 instances of graphs. Experiments reveal that H1, H2 and H3 are able to achieve best results for 88.71%, 43.5% and 37.1% of the total instances respectively while the best construction heuristic in the literature achieves the best solution for 39.9% of the total instances. We have also compared the results with the state-of-the-art metaheuristic GVNS and observed that the proposed construction heuristics improves the results for some of the input instances. It was found that GVNS obtained best results for 82.9% instances of all input instances and the heuristic H1 obtained best results for 82.3% of all input instances.
Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of a large-scale medical trial or quantitative image analysis. We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions within the predicted liver ROIs of step 1. CFCN models were trained on an abdominal CT dataset comprising 100 hepatic tumor volumes. Validations on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume. We further experimentally demonstrate the robustness of the proposed method on an 38 MRI liver tumor volumes and the public 3DIRCAD dataset.
One of the long-standing challenges in Artificial Intelligence for learning goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential problems has been in the form of distillation based learning wherein a student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks. While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large expert networks which require extensive data and computation time for training. In this work, we propose an efficient multi-task learning framework which solves multiple goal-directed tasks in an on-line setup without the need for expert supervision. Our work uses active learning principles to achieve multi-task learning by sampling the harder tasks more than the easier ones. We propose three distinct models under our active sampling framework. An adaptive method with extremely competitive multi-tasking performance. A UCB-based meta-learner which casts the problem of picking the next task to train on as a multi-armed bandit problem. A meta-learning method that casts the next-task picking problem as a full Reinforcement Learning problem and uses actor critic methods for optimizing the multi-tasking performance directly. We demonstrate results in the Atari 2600 domain on seven multi-tasking instances: three 6-task instances, one 8-task instance, two 12-task instances and one 21-task instance.
Mobile robots are increasingly being employed for performing complex tasks in dynamic environments. Reinforcement learning (RL) methods are recognized to be promising for specifying such tasks in a relatively simple manner. However, the strong dependency between the learning method and the task to learn is a well-known problem that restricts practical implementations of RL in robotics, often requiring major modifications of parameters and adding other techniques for each particular task. In this paper we present a practical core implementation of RL which enables the learning process for multiple robotic tasks with minimal per-task tuning or none. Based on value iteration methods, this implementation includes a novel approach for action selection, called Q-biased softmax regression (QBIASSR), which avoids poor performance of the learning process when the robot reaches new unexplored states. Our approach takes advantage of the structure of the state space by attending the physical variables involved (e.g., distances to obstacles, X,Y,{\theta} pose, etc.), thus experienced sets of states may favor the decision-making process of unexplored or rarely-explored states. This improvement has a relevant role in reducing the tuning of the algorithm for particular tasks. Experiments with real and simulated robots, performed with the software framework also introduced here, show that our implementation is effectively able to learn different robotic tasks without tuning the learning method. Results also suggest that the combination of true online SARSA({\lambda}) with QBIASSR can outperform the existing RL core algorithms in low-dimensional robotic tasks.
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges. State-of-the-art embedding models propose different trade-offs between modeling expressiveness, and time and space complexity. We reconcile both expressiveness and complexity through the use of complex-valued embeddings and explore the link between such complex-valued embeddings and unitary diagonalization. We corroborate our approach theoretically and show that all real square matrices---thus all possible relation/adjacency matrices---are the real part of some unitarily diagonalizable matrix. This results opens the door to a lot of other applications of square matrices factorization. Our approach based on complex embeddings is arguably simple, as it only involves a Hermitian dot product, the complex counterpart of the standard dot product between real vectors, whereas other methods resort to more and more complicated composition functions to increase their expressiveness. The proposed complex embeddings are scalable to large data sets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.
Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is limited by small datasets with labels of patient-reported pain intensities. Fortunately, fine-tuning from a data-extensive pre-trained domain, such as face verification, can alleviate this problem. In this paper, we propose a network that fine-tunes a state-of-the-art face verification network using a regularized regression loss and additional data with expression labels. In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification. The proposed regularized deep regressor is applied to estimate the pain expression intensity and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving the state-of-the-art performance. A weighted evaluation metric is also proposed to address the imbalance issue of different pain intensities.
LTE in unlicensed spectrum (LTE-U) is a promising approach to overcome the wireless spectrum scarcity. However, to reap the benefits of LTE-U, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-U small base stations (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and fractional spectrum access while guaranteeing fairness with existing WiFi networks and other LTE-U operators. Adopting a proactive coexistence mechanism enables future delay-intolerant LTE-U data demands to be served within a given prediction window ahead of their actual arrival time thus avoiding the underutilization of the unlicensed spectrum during off-peak hours while maximizing the total served LTE-U traffic load. To this end, a noncooperative game model is formulated in which SBSs are modeled as Homo Egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with WLAN and other LTE-U operators over a given time horizon. The proposed deep learning algorithm is then shown to reach a mixed-strategy Nash equilibrium (NE), when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-U network.
Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation. A practitioner's goal is typically to maximize a matching market's economic efficiency, possibly subject to some fairness requirements that promote equal access to resources. A natural balancing act exists between fairness and efficiency in matching markets, and has been the subject of much research. In this paper, we study a complementary goal---balancing diversity and efficiency---in a generalization of bipartite matching where agents on one side of the market can be matched to sets of agents on the other. Adapting a classical definition of the diversity of a set, we propose a quadratic programming-based approach to solving a supermodular minimization problem that balances diversity and total weight of the solution. We also provide a scalable greedy algorithm with theoretical performance bounds. We then define the price of diversity, a measure of the efficiency loss due to enforcing diversity, and give a worst-case theoretical bound. Finally, we demonstrate the efficacy of our methods on three real-world datasets, and show that the price of diversity is not bad in practice.
Over the past few years, online aggression and abusive behaviors have occurred in many different forms and on a variety of platforms. In extreme cases, these incidents have evolved into hate, discrimination, and bullying, and even materialized into real-world threats and attacks against individuals or groups. In this paper, we study the Gamergate controversy. Started in August 2014 in the online gaming world, it quickly spread across various social networking platforms, ultimately leading to many incidents of cyberbullying and cyberaggression. We focus on Twitter, presenting a measurement study of a dataset of 340k unique users and 1.6M tweets to study the properties of these users, the content they post, and how they differ from random Twitter users. We find that users involved in this "Twitter war" tend to have more friends and followers, are generally more engaged and post tweets with negative sentiment, less joy, and more hate than random users. We also perform preliminary measurements on how the Twitter suspension mechanism deals with such abusive behaviors. While we focus on Gamergate, our methodology to collect and analyze tweets related to aggressive and bullying activities is of independent interest.
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7 million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future.
Machine learning is essentially the sciences of playing with data. An adaptive data selection strategy, enabling to dynamically choose different data at various training stages, can reach a more effective model in a more efficient way. In this paper, we propose a deep reinforcement learning framework, which we call \emph{\textbf{N}eural \textbf{D}ata \textbf{F}ilter} (\textbf{NDF}), to explore automatic and adaptive data selection in the training process. In particular, NDF takes advantage of a deep neural network to adaptively select and filter important data instances from a sequential stream of training data, such that the future accumulative reward (e.g., the convergence speed) is maximized. In contrast to previous studies in data selection that is mainly based on heuristic strategies, NDF is quite generic and thus can be widely suitable for many machine learning tasks. Taking neural network training with stochastic gradient descent (SGD) as an example, comprehensive experiments with respect to various neural network modeling (e.g., multi-layer perceptron networks, convolutional neural networks and recurrent neural networks) and several applications (e.g., image classification and text understanding) demonstrate that NDF powered SGD can achieve comparable accuracy with standard SGD process by using less data and fewer iterations.
Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In this paper, we introduce a source-target selective joint fine-tuning scheme for improving the performance of deep learning tasks with insufficient training data. In this scheme, a target learning task with insufficient training data is carried out simultaneously with another source learning task with abundant training data. However, the source learning task does not use all existing training data. Our core idea is to identify and use a subset of training images from the original source learning task whose low-level characteristics are similar to those from the target learning task, and jointly fine-tune shared convolutional layers for both tasks. Specifically, we compute descriptors from linear or nonlinear filter bank responses on training images from both tasks, and use such descriptors to search for a desired subset of training samples for the source learning task. Experiments demonstrate that our selective joint fine-tuning scheme achieves state-of-the-art performance on multiple visual classification tasks with insufficient training data for deep learning. Such tasks include Caltech 256, MIT Indoor 67, Oxford Flowers 102 and Stanford Dogs 120. In comparison to fine-tuning without a source domain, the proposed method can improve the classification accuracy by 2% - 10% using a single model.
Congestion problems are omnipresent in today's complex networks and represent a challenge in many research domains. In the context of Multi-agent Reinforcement Learning (MARL), approaches like difference rewards and resource abstraction have shown promising results in tackling such problems. Resource abstraction was shown to be an ideal candidate for solving large-scale resource allocation problems in a fully decentralized manner. However, its performance and applicability strongly depends on some, until now, undocumented assumptions. Two of the main congestion benchmark problems considered in the literature are: the Beach Problem Domain and the Traffic Lane Domain. In both settings the highest system utility is achieved when overcrowding one resource and keeping the rest at optimum capacity. We analyse how abstract grouping can promote this behaviour and how feasible it is to apply this approach in a real-world domain (i.e., what assumptions need to be satisfied and what knowledge is necessary). We introduce a new test problem, the Road Network Domain (RND), where the resources are no longer independent, but rather part of a network (e.g., road network), thus choosing one path will also impact the load on other paths having common road segments. We demonstrate the application of state-of-the-art MARL methods for this new congestion model and analyse their performance. RND allows us to highlight an important limitation of resource abstraction and show that the difference rewards approach manages to better capture and inform the agents about the dynamics of the environment.
We consider a scheduling problem where a cloud service provider has multiple units of a resource available over time. Selfish clients submit jobs, each with an arrival time, deadline, length, and value. The service provider's goal is to implement a truthful online mechanism for scheduling jobs so as to maximize the social welfare of the schedule. Recent work shows that under a stochastic assumption on job arrivals, there is a single-parameter family of mechanisms that achieves near-optimal social welfare. We show that given any such family of near-optimal online mechanisms, there exists an online mechanism that in the worst case performs nearly as well as the best of the given mechanisms. Our mechanism is truthful whenever the mechanisms in the given family are truthful and prompt, and achieves optimal (within constant factors) regret. We model the problem of competing against a family of online scheduling mechanisms as one of learning from expert advice. A primary challenge is that any scheduling decisions we make affect not only the payoff at the current step, but also the resource availability and payoffs in future steps. Furthermore, switching from one algorithm (a.k.a. expert) to another in an online fashion is challenging both because it requires synchronization with the state of the latter algorithm as well as because it affects the incentive structure of the algorithms. We further show how to adapt our algorithm to a non-clairvoyant setting where job lengths are unknown until jobs are run to completion. Once again, in this setting, we obtain truthfulness along with asymptotically optimal regret (within poly-logarithmic factors).
Conversion optimization means designing a web interface so that as many users as possible take a desired action on it, such as register or purchase. Such design is usually done by hand, testing one change at a time through A/B testing, or a limited number of combinations through multivariate testing, making it possible to evaluate only a small fraction of designs in a vast design space. This paper describes Sentient Ascend, an automatic conversion optimization system that uses evolutionary optimization to create effective web interface designs. Ascend makes it possible to discover and utilize interactions between the design elements that are difficult to identify otherwise. Moreover, evaluation of design candidates is done in parallel online, i.e. with a large number of real users interacting with the system. A case study on an existing media site shows that significant improvements (i.e. over 43%) are possible beyond human design. Ascend can therefore be seen as an approach to massively multivariate conversion optimization, based on a massively parallel interactive evolution.
Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Our goal is to minimize human participation, so we employ evolutionary algorithms to discover such networks automatically. Despite significant computational requirements, we show that it is now possible to evolve models with accuracies within the range of those published in the last year. Specifically, we employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions and reaching accuracies of 94.6% (95.6% for ensemble) and 77.0%, respectively. To do this, we use novel and intuitive mutation operators that navigate large search spaces; we stress that no human participation is required once evolution starts and that the output is a fully-trained model. Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements.
Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from a density model, to generalize count-based exploration to non-tabular reinforcement learning. This pseudo-count was used to generate an exploration bonus for a DQN agent and combined with a mixed Monte Carlo update was sufficient to achieve state of the art on the Atari 2600 game Montezuma's Revenge. We consider two questions left open by their work: First, how important is the quality of the density model for exploration? Second, what role does the Monte Carlo update play in exploration? We answer the first question by demonstrating the use of PixelCNN, an advanced neural density model for images, to supply a pseudo-count. In particular, we examine the intrinsic difficulties in adapting Bellemare et al.'s approach when assumptions about the model are violated. The result is a more practical and general algorithm requiring no special apparatus. We combine PixelCNN pseudo-counts with different agent architectures to dramatically improve the state of the art on several hard Atari games. One surprising finding is that the mixed Monte Carlo update is a powerful facilitator of exploration in the sparsest of settings, including Montezuma's Revenge.
Unifying seemingly disparate algorithmic ideas to produce better performing algorithms has been a longstanding goal in reinforcement learning. As a primary example, TD($\lambda$) elegantly unifies one-step TD prediction with Monte Carlo methods through the use of eligibility traces and the trace-decay parameter $\lambda$. Currently, there are a multitude of algorithms that can be used to perform TD control, including Sarsa, $Q$-learning, and Expected Sarsa. These methods are often studied in the one-step case, but they can be extended across multiple time steps to achieve better performance. Each of these algorithms is seemingly distinct, and no one dominates the others for all problems. In this paper, we study a new multi-step action-value algorithm called $Q(\sigma)$ which unifies and generalizes these existing algorithms, while subsuming them as special cases. A new parameter, $\sigma$, is introduced to allow the degree of sampling performed by the algorithm at each step during its backup to be continuously varied, with Sarsa existing at one extreme (full sampling), and Expected Sarsa existing at the other (pure expectation). $Q(\sigma)$ is generally applicable to both on- and off-policy learning, but in this work we focus on experiments in the on-policy case. Our results show that an intermediate value of $\sigma$, which results in a mixture of the existing algorithms, performs better than either extreme. The mixture can also be varied dynamically which can result in even greater performance.
Simulation-based training (SBT) is gaining popularity as a low-cost and convenient training technique in a vast range of applications. However, for a SBT platform to be fully utilized as an effective training tool, it is essential that feedback on performance is provided automatically in real-time during training. It is the aim of this paper to develop an efficient and effective feedback generation method for the provision of real-time feedback in SBT. Existing methods either have low effectiveness in improving novice skills or suffer from low efficiency, resulting in their inability to be used in real-time. In this paper, we propose a neural network based method to generate feedback using the adversarial technique. The proposed method utilizes a bounded adversarial update to minimize a L1 regularized loss via back-propagation. We empirically show that the proposed method can be used to generate simple, yet effective feedback. Also, it was observed to have high effectiveness and efficiency when compared to existing methods, thus making it a promising option for real-time feedback generation in SBT.
Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the current variants of GANs that make use of class label information. With class aware gradient and cross-entropy decomposition, we reveal how class labels and associated losses influence GAN's training. Based on that, we propose Activation Maximization Generative Adversarial Networks (AM-GAN) as an advanced solution. Comprehensive experiments have been conducted to validate our analysis and evaluate the effectiveness of our solution, where AM-GAN outperforms other strong baselines and achieves state-of-the-art Inception Score (8.91) on CIFAR-10. In addition, we demonstrate that, with the Inception ImageNet classifier, Inception Score mainly tracks the diversity of the generator, and there is, however, no reliable evidence that it can reflect the true sample quality. We thus propose a new metric, called AM Score, to provide more accurate estimation on the sample quality. Our proposed model also outperforms the baseline methods in the new metric.
Being able to fall safely is a necessary motor skill for humanoids performing highly dynamic tasks, such as running and jumping. We propose a new method to learn a policy that minimizes the maximal impulse during the fall. The optimization solves for both a discrete contact planning problem and a continuous optimal control problem. Once trained, the policy can compute the optimal next contacting body part (e.g. left foot, right foot, or hands), contact location and timing, and the required joint actuation. We represent the policy as a mixture of actor-critic neural network, which consists of n control policies and the corresponding value functions. Each pair of actor-critic is associated with one of the n possible contacting body parts. During execution, the policy corresponding to the highest value function will be executed while the associated body part will be the next contact with the ground. With this mixture of actor-critic architecture, the discrete contact sequence planning is solved through the selection of the best critics while the continuous control problem is solved by the optimization of actors. We show that our policy can achieve comparable, sometimes even higher, rewards than a recursive search of the action space using dynamic programming, while enjoying 50 to 400 times of speed gain during online execution.
Despite progress in visual perception tasks such as image classification and detection, computers still struggle to understand the interdependency of objects in the scene as a whole, e.g., relations between objects or their attributes. Existing methods often ignore global context cues capturing the interactions among different object instances, and can only recognize a handful of types by exhaustively training individual detectors for all possible relationships. To capture such global interdependency, we propose a deep Variation-structured Reinforcement Learning (VRL) framework to sequentially discover object relationships and attributes in the whole image. First, a directed semantic action graph is built using language priors to provide a rich and compact representation of semantic correlations between object categories, predicates, and attributes. Next, we use a variation-structured traversal over the action graph to construct a small, adaptive action set for each step based on the current state and historical actions. In particular, an ambiguity-aware object mining scheme is used to resolve semantic ambiguity among object categories that the object detector fails to distinguish. We then make sequential predictions using a deep RL framework, incorporating global context cues and semantic embeddings of previously extracted phrases in the state vector. Our experiments on the Visual Relationship Detection (VRD) dataset and the large-scale Visual Genome dataset validate the superiority of VRL, which can achieve significantly better detection results on datasets involving thousands of relationship and attribute types. We also demonstrate that VRL is able to predict unseen types embedded in our action graph by learning correlations on shared graph nodes.
This paper develops a general framework for learning interpretable data representation via Long Short-Term Memory (LSTM) recurrent neural networks over hierarchal graph structures. Instead of learning LSTM models over the pre-fixed structures, we propose to further learn the intermediate interpretable multi-level graph structures in a progressive and stochastic way from data during the LSTM network optimization. We thus call this model the structure-evolving LSTM. In particular, starting with an initial element-level graph representation where each node is a small data element, the structure-evolving LSTM gradually evolves the multi-level graph representations by stochastically merging the graph nodes with high compatibilities along the stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two connected nodes from their corresponding LSTM gate outputs, which is used to generate a merging probability. The candidate graph structures are accordingly generated where the nodes are grouped into cliques with their merging probabilities. We then produce the new graph structure with a Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in local optimums by stochastic sampling with an acceptance probability. Once a graph structure is accepted, a higher-level graph is then constructed by taking the partitioned cliques as its nodes. During the evolving process, representation becomes more abstracted in higher-levels where redundant information is filtered out, allowing more efficient propagation of long-range data dependencies. We evaluate the effectiveness of structure-evolving LSTM in the application of semantic object parsing and demonstrate its advantage over state-of-the-art LSTM models on standard benchmarks.
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.
Recently, DNN model compression based on network architecture design, e.g., SqueezeNet, attracted a lot attention. No accuracy drop on image classification is observed on these extremely compact networks, compared to well-known models. An emerging question, however, is whether these model compression techniques hurt DNN's learning ability other than classifying images on a single dataset. Our preliminary experiment shows that these compression methods could degrade domain adaptation (DA) ability, though the classification performance is preserved. Therefore, we propose a new compact network architecture and unsupervised DA method in this paper. The DNN is built on a new basic module Conv-M which provides more diverse feature extractors without significantly increasing parameters. The unified framework of our DA method will simultaneously learn invariance across domains, reduce divergence of feature representations, and adapt label prediction. Our DNN has 4.1M parameters, which is only 6.7% of AlexNet or 59% of GoogLeNet. Experiments show that our DNN obtains GoogLeNet-level accuracy both on classification and DA, and our DA method slightly outperforms previous competitive ones. Put all together, our DA strategy based on our DNN achieves state-of-the-art on sixteen of total eighteen DA tasks on popular Office-31 and Office-Caltech datasets.
In this paper we look into the problem of planning over hybrid domains, where change can be both discrete and instantaneous, or continuous over time. In addition, it is required that each state on the trajectory induced by the execution of plans complies with a given set of global constraints. We approach the computation of plans for such domains as the problem of searching over a deterministic state model. In this model, some of the successor states are obtained by solving numerically the so-called initial value problem over a set of ordinary differential equations (ODE) given by the current plan prefix. These equations hold over time intervals whose duration is determined dynamically, according to whether zero crossing events take place for a set of invariant conditions. The resulting planner, FS+, incorporates these features together with effective heuristic guidance. FS+ does not impose any of the syntactic restrictions on process effects often found on the existing literature on Hybrid Planning. A key concept of our approach is that a clear separation is struck between planning and simulation time steps. The former is the time allowed to observe the evolution of a given dynamical system before committing to a future course of action, whilst the later is part of the model of the environment. FS+ is shown to be a robust planner over a diverse set of hybrid domains, taken from the existing literature on hybrid planning and systems.
A new model of symbol grounding is presented, in which the structures of natural language, logical semantics, perception and action are represented categorically, and symbol grounding is modeled via the composition of morphisms between the relevant categories. This model gives conceptual insight into the fundamentally systematic nature of symbol grounding, and also connects naturally to practical real-world AI systems in current research and commercial use. Specifically, it is argued that the structure of linguistic syntax can be modeled as a certain asymmetric monoidal category, as e.g. implicit in the link grammar formalism; the structure of spatiotemporal relationships and action plans can be modeled similarly using "image grammars" and "action grammars"; and common-sense logical semantic structure can be modeled using dependently-typed lambda calculus with uncertain truth values. Given these formalisms, the grounding of linguistic descriptions in spatiotemporal perceptions and coordinated actions consists of following morphisms from language to logic through to spacetime and body (for comprehension), and vice versa (for generation). The mapping is indicated between the spatial relationships in the Region Connection Calculus and Allen Interval Algebra and corresponding entries in the link grammar syntax parsing dictionary. Further, the abstractions introduced here are shown to naturally model the structures and systems currently being deployed in the context of using the OpenCog cognitive architecture to control Hanson Robotics humanoid robots.
Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimization typically does not use derivative information. In this paper we show how Bayesian optimization can exploit derivative information to decrease the number of objective function evaluations required for good performance. In particular, we develop a novel Bayesian optimization algorithm, the derivative-enabled knowledge-gradient (dKG), for which we show one-step Bayes-optimality, asymptotic consistency, and greater one-step value of information than is possible in the derivative-free setting. Our procedure accommodates noisy and incomplete derivative information, comes in both sequential and batch forms, and can optionally reduce the computational cost of inference through automatically selected retention of a single directional derivative. We also compute the d-KG acquisition function and its gradient using a novel fast discretization-free technique. We show d-KG provides state-of-the-art performance compared to a wide range of optimization procedures with and without gradients, on benchmarks including logistic regression, deep learning, kernel learning, and k-nearest neighbors.
The Entity Disambiguation and Linking (EDL) task matches entity mentions in text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase id. It plays a critical role in the construction of a high quality information network, and can be further leveraged for a variety of information retrieval and NLP tasks such as text categorization and document tagging. EDL is a complex and challenging problem due to ambiguity of the mentions and real world text being multi-lingual. Moreover, EDL systems need to have high throughput and should be lightweight in order to scale to large datasets and run on off-the-shelf machines. More importantly, these systems need to be able to extract and disambiguate dense annotations from the data in order to enable an Information Retrieval or Extraction task running on the data to be more efficient and accurate. In order to address all these challenges, we present the Lithium EDL system and algorithm - a high-throughput, lightweight, language-agnostic EDL system that extracts and correctly disambiguates 75% more entities than state-of-the-art EDL systems and is significantly faster than them.
We present a computational evaluation of three hypotheses about sources of deficit in sentence comprehension in aphasia: slowed processing, intermittent deficiency, and resource reduction. The ACT-R based Lewis and Vasishth (2005) model is used to implement these three proposals. Slowed processing is implemented as slowed default production-rule firing time; intermittent deficiency as increased random noise in activation of chunks in memory; and resource reduction as reduced goal activation. As data, we considered subject vs. object rela- tives whose matrix clause contained either an NP or a reflexive, presented in a self-paced listening modality to 56 individuals with aphasia (IWA) and 46 matched controls. The participants heard the sentences and carried out a picture verification task to decide on an interpretation of the sentence. These response accuracies are used to identify the best parameters (for each participant) that correspond to the three hypotheses mentioned above. We show that controls have more tightly clustered (less variable) parameter values than IWA; specifically, compared to controls, among IWA there are more individuals with low goal activations, high noise, and slow default action times. This suggests that (i) individual patients show differential amounts of deficit along the three dimensions of slowed processing, intermittent deficient, and resource reduction, (ii) overall, there is evidence for all three sources of deficit playing a role, and (iii) IWA have a more variable range of parameter values than controls. In sum, this study contributes a proof of concept of a quantitative implementation of, and evidence for, these three accounts of comprehension deficits in aphasia.
Both the ethics of autonomous systems and the problems of their technical implementation have by now been studied in some detail. Less attention has been given to the areas in which these two separate concerns meet. This paper, written by both philosophers and engineers of autonomous systems, addresses a number of issues in machine ethics that are located at precisely the intersection between ethics and engineering. We first discuss the main challenges which, in our view, machine ethics posses to moral philosophy. We them consider different approaches towards the conceptual design of autonomous systems and their implications on the ethics implementation in such systems. Then we examine problematic areas regarding the specification and verification of ethical behavior in autonomous systems, particularly with a view towards the requirements of future legislation. We discuss transparency and accountability issues that will be crucial for any future wide deployment of autonomous systems in society. Finally we consider the, often overlooked, possibility of intentional misuse of AI systems and the possible dangers arising out of deliberately unethical design, implementation, and use of autonomous robots.
Learning to learn has emerged as an important direction for achieving artificial intelligence. Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks. We introduce a learned gradient descent optimizer that generalizes well to new tasks, and which has significantly reduced memory and computation overhead. We achieve this by introducing a novel hierarchical RNN architecture, with minimal per-parameter overhead, augmented with additional architectural features that mirror the known structure of optimization tasks. We also develop a meta-training ensemble of small, diverse optimization tasks capturing common properties of loss landscapes. The optimizer learns to outperform RMSProp/ADAM on problems in this corpus. More importantly, it performs comparably or better when applied to small convolutional neural networks, despite seeing no neural networks in its meta-training set. Finally, it generalizes to train Inception V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps, optimization problems that are of a vastly different scale than those it was trained on. We release an open source implementation of the meta-training algorithm.
Human parsing has recently attracted a lot of research interests due to its huge application potentials. However existing datasets have limited number of images and annotations, and lack the variety of human appearances and the coverage of challenging cases in unconstrained environment. In this paper, we introduce a new benchmark "Look into Person (LIP)" that makes a significant advance in terms of scalability, diversity and difficulty, a contribution that we feel is crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels, which are captured from a wider range of viewpoints, occlusions and background complexity. Given these rich annotations we perform detailed analyses of the leading human parsing approaches, gaining insights into the success and failures of these methods. Furthermore, in contrast to the existing efforts on improving the feature discriminative capability, we solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision (i.e., no need for specifically labeling human joints in model training). Our self-supervised learning framework can be injected into any advanced neural networks to help incorporate rich high-level knowledge regarding human joints from a global perspective and improve the parsing results. Extensive evaluations on our LIP and the public PASCAL-Person-Part dataset demonstrate the superiority of our method.
In today's databases, previous query answers rarely benefit answering future queries. For the first time, to the best of our knowledge, we change this paradigm in an approximate query processing (AQP) context. We make the following observation: the answer to each query reveals some degree of knowledge about the answer to another query because their answers stem from the same underlying distribution that has produced the entire dataset. Exploiting and refining this knowledge should allow us to answer queries more analytically, rather than by reading enormous amounts of raw data. Also, processing more queries should continuously enhance our knowledge of the underlying distribution, and hence lead to increasingly faster response times for future queries. We call this novel idea---learning from past query answers---Database Learning. We exploit the principle of maximum entropy to produce answers, which are in expectation guaranteed to be more accurate than existing sample-based approximations. Empowered by this idea, we build a query engine on top of Spark SQL, called Verdict. We conduct extensive experiments on real-world query traces from a large customer of a major database vendor. Our results demonstrate that Verdict supports 73.7% of these queries, speeding them up by up to 23.0x for the same accuracy level compared to existing AQP systems.
We recommend that the search for exoplanets around binary stars be extended to include X-ray binaries in which the accretor is a white dwarf, neutron star, or black hole. We present a novel idea for detecting planets bound to such mass transfer binaries: we propose that the X-ray light curves of these binaries be inspected for signatures of transiting planets. X-ray transits may be the only way to detect planets around some systems, while providing a complementary approach to optical and/or radio observations in others. Any planets associated with X-ray binaries must be in stable orbits. We consider the range of allowable separations and find that orbital periods can be hours or longer, while transit durations extend upward from about a minute for Earth-radius planets in very close orbits, to hours for Jupiter-radius planets in wider orbits. The search for planets around mass transfer binaries could begin at once with existing X-ray observations of these systems. If and when a planet is detected around an X-ray binary, the size and mass of the planet may be readily measured, and it may also be possible to study the transmission and absorption of X-rays through its atmosphere. Finally, a noteworthy application of our proposal is that the same technique could be used to search for signals from extraterrestrial intelligence. If an advanced exocivilization placed a Dyson sphere or similar structure in orbit around the accretor of an X-ray binary in order to capture energy, such an artificial structure might cause detectable transits in the X-ray light curve.
We discuss the computational complexity of approximating maximum a posteriori inference in sum-product networks. We first show NP-hardness in trees of height two by a reduction from maximum independent set; this implies non-approximability within a sublinear factor. We show that this is a tight bound, as we can find an approximation within a linear factor in networks of height two. We then show that, in trees of height three, it is NP-hard to approximate the problem within a factor $2^{f(n)}$ for any sublinear function $f$ of the size of the input $n$. Again, this bound is tight, as we prove that the usual max-product algorithm finds (in any network) approximations within factor $2^{c \cdot n}$ for some constant $c < 1$. Last, we present a simple algorithm, and show that it provably produces solutions at least as good as, and potentially much better than, the max-product algorithm. We empirically analyze the proposed algorithm against max-product using synthetic and realistic networks.
Most games have, or can be generalised to have, a number of parameters that may be varied in order to provide instances of games that lead to very different player experiences. The space of possible parameter settings can be seen as a search space, and we can therefore use a Random Mutation Hill Climbing algorithm or other search methods to find the parameter settings that induce the best games. One of the hardest parts of this approach is defining a suitable fitness function. In this paper we explore the possibility of using one of a growing set of General Video Game AI agents to perform automatic play-testing. This enables a very general approach to game evaluation based on estimating the skill-depth of a game. Agent-based play-testing is computationally expensive, so we compare two simple but efficient optimisation algorithms: the Random Mutation Hill-Climber and the Multi-Armed Bandit Random Mutation Hill-Climber. For the test game we use a space-battle game in order to provide a suitable balance between simulation speed and potential skill-depth. Results show that both algorithms are able to rapidly evolve game versions with significant skill-depth, but that choosing a suitable resampling number is essential in order to combat the effects of noise.
As autonomous vehicles become an every-day reality, high-accuracy pedestrian detection is of paramount practical importance. Pedestrian detection is a highly researched topic with mature methods, but most datasets focus on common scenes of people engaged in typical walking poses on sidewalks. But performance is most crucial for dangerous scenarios, such as children playing in the street or people using bicycles/skateboards in unexpected ways. Such "in-the-tail" data is notoriously hard to observe, making both training and testing difficult. To analyze this problem, we have collected a novel annotated dataset of dangerous scenarios called the Precarious Pedestrian dataset. Even given a dedicated collection effort, it is relatively small by contemporary standards (around 1000 images). To allow for large-scale data-driven learning, we explore the use of synthetic data generated by a game engine. A significant challenge is selected the right "priors" or parameters for synthesis: we would like realistic data with poses and object configurations that mimic true Precarious Pedestrians. Inspired by Generative Adversarial Networks (GANs), we generate a massive amount of synthetic data and train a discriminative classifier to select a realistic subset, which we deem the Adversarial Imposters. We demonstrate that this simple pipeline allows one to synthesize realistic training data by making use of rendering/animation engines within a GAN framework. Interestingly, we also demonstrate that such data can be used to rank algorithms, suggesting that Adversarial Imposters can also be used for "in-the-tail" validation at test-time, a notoriously difficult challenge for real-world deployment.
We introduce the first goal-driven training for visual question answering and dialog agents. Specifically, we pose a cooperative 'image guessing' game between two agents -- Qbot and Abot -- who communicate in natural language dialog so that Qbot can select an unseen image from a lineup of images. We use deep reinforcement learning (RL) to learn the policies of these agents end-to-end -- from pixels to multi-agent multi-round dialog to game reward. We demonstrate two experimental results. First, as a 'sanity check' demonstration of pure RL (from scratch), we show results on a synthetic world, where the agents communicate in ungrounded vocabulary, i.e., symbols with no pre-specified meanings (X, Y, Z). We find that two bots invent their own communication protocol and start using certain symbols to ask/answer about certain visual attributes (shape/color/style). Thus, we demonstrate the emergence of grounded language and communication among 'visual' dialog agents with no human supervision. Second, we conduct large-scale real-image experiments on the VisDial dataset, where we pretrain with supervised dialog data and show that the RL 'fine-tuned' agents significantly outperform SL agents. Interestingly, the RL Qbot learns to ask questions that Abot is good at, ultimately resulting in more informative dialog and a better team.
Translating information between text and image is a fundamental problem in artificial intelligence that connects natural language processing and computer vision. In the past few years, performance in image caption generation has seen significant improvement through the adoption of recurrent neural networks (RNN). Meanwhile, text-to-image generation begun to generate plausible images using datasets of specific categories like birds and flowers. We've even seen image generation from multi-category datasets such as the Microsoft Common Objects in Context (MSCOCO) through the use of generative adversarial networks (GANs). Synthesizing objects with a complex shape, however, is still challenging. For example, animals and humans have many degrees of freedom, which means that they can take on many complex shapes. We propose a new training method called Image-Text-Image (I2T2I) which integrates text-to-image and image-to-text (image captioning) synthesis to improve the performance of text-to-image synthesis. We demonstrate that %the capability of our method to understand the sentence descriptions, so as to I2T2I can generate better multi-categories images using MSCOCO than the state-of-the-art. We also demonstrate that I2T2I can achieve transfer learning by using a pre-trained image captioning module to generate human images on the MPII Human Pose
In the modern era, each Internet user leaves enormous amounts of auxiliary digital residuals (footprints) by using a variety of on-line services. All this data is already collected and stored for many years. In recent works, it was demonstrated that it's possible to apply simple machine learning methods to analyze collected digital footprints and to create psycho-demographic profiles of individuals. However, while these works clearly demonstrated the applicability of machine learning methods for such an analysis, created simple prediction models still lacks accuracy necessary to be successfully applied for practical needs. We have assumed that using advanced deep machine learning methods may considerably increase the accuracy of predictions. We started with simple machine learning methods to estimate basic prediction performance and moved further by applying advanced methods based on shallow and deep neural networks. Then we compared prediction power of studied models and made conclusions about its performance. Finally, we made hypotheses how prediction accuracy can be further improved. As result of this work, we provide full source code used in the experiments for all interested researchers and practitioners in corresponding GitHub repository. We believe that applying deep machine learning for psycho-demographic profiling may have an enormous impact on the society (for good or worse) and provides means for Artificial Intelligence (AI) systems to better understand humans by creating their psychological profiles. Thus AI agents may achieve the human-like ability to participate in conversation (communication) flow by anticipating human opponents' reactions, expectations, and behavior.
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global constraint-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Finally, we also extend our approach by introducing a multi-structure scheme, which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair, so as to handle the spatial misalignments between individual images in a more precise way. Experimental results on various datasets demonstrate the effectiveness of our approach.
A natural image usually conveys rich semantic content and can be viewed from different angles. Existing image description methods are largely restricted by small sets of biased visual paragraph annotations, and fail to cover rich underlying semantics. In this paper, we investigate a semi-supervised paragraph generative framework that is able to synthesize diverse and semantically coherent paragraph descriptions by reasoning over local semantic regions and exploiting linguistic knowledge. The proposed Recurrent Topic-Transition Generative Adversarial Network (RTT-GAN) builds an adversarial framework between a structured paragraph generator and multi-level paragraph discriminators. The paragraph generator generates sentences recurrently by incorporating region-based visual and language attention mechanisms at each step. The quality of generated paragraph sentences is assessed by multi-level adversarial discriminators from two aspects, namely, plausibility at sentence level and topic-transition coherence at paragraph level. The joint adversarial training of RTT-GAN drives the model to generate realistic paragraphs with smooth logical transition between sentence topics. Extensive quantitative experiments on image and video paragraph datasets demonstrate the effectiveness of our RTT-GAN in both supervised and semi-supervised settings. Qualitative results on telling diverse stories for an image also verify the interpretability of RTT-GAN.
Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image with any personalized signals (even signals unseen during training), such as diverse paintings and arbitrary descriptive attributes. However, existing methods are either inefficient to simultaneously process multiple signals (let alone generalize to unseen signals), or unable to handle signals from other modalities. In this paper, we make the first attempt to address the zero-shot image manipulation task. We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a fully-differentiable architecture that jointly optimizes an image-transformation network (TNet) and a parameter network (PNet). The PNet learns to generate key transformation parameters for the TNet given any guiding signal while the TNet performs fast zero-shot image manipulation according to both signal-dependent parameters from the PNet and signal-invariant parameters from the TNet itself. Extensive experiments show that our ZM-Net can perform high-quality image manipulation conditioned on different forms of guiding signals (e.g. style images and attributes) in real-time (tens of milliseconds per image) even for unseen signals. Moreover, a large-scale style dataset with over 20,000 style images is also constructed to promote further research.
The problem of automatically generating a computer program from some specification has been studied since the early days of AI. Recently, two competing approaches for automatic program learning have received significant attention: (1) neural program synthesis, where a neural network is conditioned on input/output (I/O) examples and learns to generate a program, and (2) neural program induction, where a neural network generates new outputs directly using a latent program representation. Here, for the first time, we directly compare both approaches on a large-scale, real-world learning task. We additionally contrast to rule-based program synthesis, which uses hand-crafted semantics to guide the program generation. Our neural models use a modified attention RNN to allow encoding of variable-sized sets of I/O pairs. Our best synthesis model achieves 92% accuracy on a real-world test set, compared to the 34% accuracy of the previous best neural synthesis approach. The synthesis model also outperforms a comparable induction model on this task, but we more importantly demonstrate that the strength of each approach is highly dependent on the evaluation metric and end-user application. Finally, we show that we can train our neural models to remain very robust to the type of noise expected in real-world data (e.g., typos), while a highly-engineered rule-based system fails entirely.
We provide new results for noise-tolerant and sample-efficient learning algorithms under $s$-concave distributions. The new class of $s$-concave distributions is a broad and natural generalization of log-concavity, and includes many important additional distributions, e.g., the Pareto distribution and $t$-distribution. This class has been studied in the context of efficient sampling, integration, and optimization, but much remains unknown about the geometry of this class of distributions and their applications in the context of learning. The challenge is that unlike the commonly used distributions in learning (uniform or more generally log-concave distributions), this broader class is not closed under the marginalization operator and many such distributions are fat-tailed. In this work, we introduce new convex geometry tools to study the properties of $s$-concave distributions and use these properties to provide bounds on quantities of interest to learning including the probability of disagreement between two halfspaces, disagreement outside a band, and the disagreement coefficient. We use these results to significantly generalize prior results for margin-based active learning, disagreement-based active learning, and passive learning of intersections of halfspaces. Our analysis of geometric properties of $s$-concave distributions might be of independent interest to optimization more broadly.
When using reinforcement learning (RL) algorithms to evaluate a policy it is common, given a large state space, to introduce some form of approximation architecture for the value function (VF). The exact form of this architecture can have a significant effect on the accuracy of the VF estimate, however, and determining a suitable approximation architecture can often be a highly complex task. Consequently there is a large amount of interest in the potential for allowing RL algorithms to adaptively generate (i.e. to learn) approximation architectures. We investigate a method of adapting approximation architectures which uses feedback regarding the frequency with which an agent has visited certain states to guide which areas of the state space to approximate with greater detail. We introduce an algorithm based upon this idea which adapts a state aggregation approximation architecture on-line. Assuming $S$ states, we demonstrate theoretically that - provided the following relatively non-restrictive assumptions are satisfied: (a) the number of cells $X$ in the state aggregation architecture is of order $\sqrt{S}\ln{S}\log_2{S}$ or greater, (b) the policy and transition function are close to deterministic, and (c) the prior for the transition function is uniformly distributed - our algorithm can guarantee, assuming we use an appropriate scoring function to measure VF error, error which is arbitrarily close to zero as $S$ becomes large. It is able to do this despite having only $O(X\log_2{S})$ space complexity (and negligible time complexity). We conclude by generating a set of empirical results which support the theoretical results.
Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-designs, being proposed in academia and industry. The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.
In visual question answering (VQA), an algorithm must answer text-based questions about images. While multiple datasets for VQA have been created since late 2014, they all have flaws in both their content and the way algorithms are evaluated on them. As a result, evaluation scores are inflated and predominantly determined by answering easier questions, making it difficult to compare different methods. In this paper, we analyze existing VQA algorithms using a new dataset. It contains over 1.6 million questions organized into 12 different categories. We also introduce questions that are meaningless for a given image to force a VQA system to reason about image content. We propose new evaluation schemes that compensate for over-represented question-types and make it easier to study the strengths and weaknesses of algorithms. We analyze the performance of both baseline and state-of-the-art VQA models, including multi-modal compact bilinear pooling (MCB), neural module networks, and recurrent answering units. Our experiments establish how attention helps certain categories more than others, determine which models work better than others, and explain how simple models (e.g. MLP) can surpass more complex models (MCB) by simply learning to answer large, easy question categories.
In one perspective, the central problem pursued in this research is that of the inverse problem in the context of general rough sets. The problem is about the existence of rough basis for given approximations in a context. Granular operator spaces were recently introduced by the present author as an optimal framework for anti-chain based algebraic semantics of general rough sets and the inverse problem. In the framework, various subtypes of crisp and non crisp objects are identifiable that may be missed in more restrictive formalism. This is also because in the latter cases the concept of complementation and negation are taken for granted. This opens the door for a general approach to dialectical rough sets building on previous work of the present author and figures of opposition. In this paper dialectical rough logics are developed from a semantic perspective, concept of dialectical predicates is formalized, connection with dialethias and glutty negation established, parthood analyzed and studied from the point of view of classical and dialectical figures of opposition. Potential semantics through dialectical counting based on these figures are proposed building on earlier work by the present author. Her methods become more geometrical and encompass parthood as a primary relation (as opposed to roughly equivalent objects) for algebraic semantics. Dialectical counting strategies over anti chains (a specific form of dialectical structure) for semantics are also proposed.
Rating platforms enable large-scale collection of user opinion about items (products, other users, etc.). However, many untrustworthy users give fraudulent ratings for excessive monetary gains. In the paper, we present FairJudge, a system to identify such fraudulent users. We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product. Intuitively, a user is fair if it provides reliable ratings that are close to the goodness of the product. We formulate a mutually recursive definition of these metrics, and further address cold start problems and incorporate behavioral properties of users and products in the formulation. We propose an iterative algorithm, FairJudge, to predict the values of the three metrics. We prove that FairJudge is guaranteed to converge in a bounded number of iterations, with linear time complexity. By conducting five different experiments on five rating platforms, we show that FairJudge significantly outperforms nine existing algorithms in predicting fair and unfair users. We reported the 100 most unfair users in the Flipkart network to their review fraud investigators, and 80 users were correctly identified (80% accuracy). The FairJudge algorithm is already being deployed at Flipkart.
Most existing neural network models for music generation use recurrent neural networks. However, the recent WaveNet model proposed by DeepMind shows that convolutional neural networks (CNNs) can also generate realistic musical waveforms in the audio domain. Following this light, we investigate using CNNs for generating melody (a series of MIDI notes) one bar after another in the symbolic domain. In addition to the generator, we use a discriminator to learn the distributions of melodies, making it a generative adversarial network (GAN). Moreover, we propose a novel conditional mechanism to exploit available prior knowledge, so that the model can generate melodies either from scratch, by following a chord sequence, or by conditioning on the melody of previous bars (e.g. a priming melody), among other possibilities. The resulting model, named MidiNet, can be expanded to generate music with multiple MIDI channels (i.e. tracks). We conduct a user study to compare the melody of eight-bar long generated by MidiNet and by Google's MelodyRNN models, each time using the same priming melody. Result shows that MidiNet performs comparably with MelodyRNN models in being realistic and pleasant to listen to, yet MidiNet's melodies are reported to be much more interesting.
The Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detection avoidance techniques in order to hide their malicious activities from analysis tools. These include a wide range of anti-emulator techniques, where the malware programs attempt to hide their malicious activities by detecting the emulator. For this reason, countermeasures against antiemulation are becoming increasingly important in Android malware detection. Analysis and detection based on real devices can alleviate the problems of anti-emulation as well as improve the effectiveness of dynamic analysis. Hence, in this paper we present an investigation of machine learning based malware detection using dynamic analysis on real devices. A tool is implemented to automatically extract dynamic features from Android phones and through several experiments, a comparative analysis of emulator based vs. device based detection by means of several machine learning algorithms is undertaken. Our study shows that several features could be extracted more effectively from the on-device dynamic analysis compared to emulators. It was also found that approximately 24% more apps were successfully analysed on the phone. Furthermore, all of the studied machine learning based detection performed better when applied to features extracted from the on-device dynamic analysis.
One of the defining properties of deep learning is that models are chosen to have many more parameters than available training data. In light of this capacity for overfitting, it is remarkable that simple algorithms like SGD reliably return solutions with low test error. One roadblock to explaining these phenomena in terms of implicit regularization, structural properties of the solution, and/or easiness of the data is that many learning bounds are quantitatively vacuous when applied to networks learned by SGD in this "deep learning" regime. Logically, in order to explain generalization, we need nonvacuous bounds. We return to an idea by Langford and Caruana (2001), who used PAC-Bayes bounds to compute nonvacuous numerical bounds on generalization error for stochastic two-layer two-hidden-unit neural networks via a sensitivity analysis. By optimizing the PAC-Bayes bound directly, we are able to extend their approach and obtain nonvacuous generalization bounds for deep stochastic neural network classifiers with millions of parameters trained on only tens of thousands of examples. We connect our findings to recent and old work on flat minima and MDL-based explanations of generalization.
Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to detect drifts and be able to adapt to them, over time. Detecting drifts has traditionally been approached as a supervised task, with labeled data constantly being used for validating the learned model. Although effective in detecting drifts, these techniques are impractical, as labeling is a difficult, costly and time consuming activity. On the other hand, unsupervised change detection techniques are unreliable, as they produce a large number of false alarms. The inefficacy of the unsupervised techniques stems from the exclusion of the characteristics of the learned classifier, from the detection process. In this paper, we propose the Margin Density Drift Detection (MD3) algorithm, which tracks the number of samples in the uncertainty region of a classifier, as a metric to detect drift. The MD3 algorithm is a distribution independent, application independent, model independent, unsupervised and incremental algorithm for reliably detecting drifts from data streams. Experimental evaluation on 6 drift induced datasets and 4 additional datasets from the cybersecurity domain demonstrates that the MD3 approach can reliably detect drifts, with significantly fewer false alarms compared to unsupervised feature based drift detectors. The reduced false alarms enables the signaling of drifts only when they are most likely to affect classification performance. As such, the MD3 approach leads to a detection scheme which is credible, label efficient and general in its applicability.
As part of Smart Cities initiatives, national, regional and local governments all over the globe are under the mandate of being more open regarding how they share their data. Under this mandate, many of these governments are publishing data under the umbrella of open government data, which includes measurement data from city-wide sensor networks. Furthermore, many of these data are published in so-called data portals as documents that may be spreadsheets, comma-separated value (CSV) data files, or plain documents in PDF or Word documents. The sharing of these documents may be a convenient way for the data provider to convey and publish data but it is not the ideal way for data consumers to reuse the data. For example, the problems of reusing the data may range from difficulty opening a document that is provided in any format that is not plain text, to the actual problem of understanding the meaning of each piece of knowledge inside of the document. Our proposal tackles those challenges by identifying metadata that has been regarded to be relevant for measurement data and providing a schema for this metadata. We further leverage the Human-Aware Sensor Network Ontology (HASNetO) to build an architecture for data collected in urban environments. We discuss the use of HASNetO and the supporting infrastructure to manage both data and metadata in support of the City of Fortaleza, a large metropolitan area in Brazil.
Significant efforts have been made to understand and document knowledge related to scientific measurements. Many of those efforts resulted in one or more high-quality ontologies that describe some aspects of scientific measurements, but not in a comprehensive and coherently integrated manner. For instance, we note that many of these high-quality ontologies are not properly aligned, and more challenging, that they have different and often conflicting concepts and approaches for encoding knowledge about empirical measurements. As a result of this lack of an integrated view, it is often challenging for scientists to determine whether any two scientific measurements were taken in semantically compatible manners, thus making it difficult to decide whether measurements should be analyzed in combination or not. In this paper, we present the Human-Aware Sensor Network Ontology that is a comprehensive alignment and integration of a sensing infrastructure ontology and a provenance ontology. HASNetO has been under development for more than one year, and has been reviewed, shared and used by multiple scientific communities. The ontology has been in use to support the data management of a number of large-scale ecological monitoring activities (observations) and empirical experiments.
The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an extension of the well-known Vehicle Routing Problem (VRP), which takes into account the dynamic nature of the problem. This aspect requires the vehicle routes to be updated in an ongoing manner as new customer requests arrive in the system and must be incorporated into an evolving schedule during the working day. Besides the vehicle capacity constraint involved in the classical VRP, DVRPTW considers in addition time windows, which are able to better capture real-world situations. Despite this, so far, few studies have focused on tackling this problem of greater practical importance. To this end, this study devises for the resolution of DVRPTW, an ant colony optimization based algorithm, which resorts to a joint solution construction mechanism, able to construct in parallel the vehicle routes. This method is coupled with a local search procedure, aimed to further improve the solutions built by ants, and with an insertion heuristics, which tries to reduce the number of vehicles used to service the available customers. The experiments indicate that the proposed algorithm is competitive and effective, and on DVRPTW instances with a higher dynamicity level, it is able to yield better results compared to existing ant-based approaches.
An overview of current debates and contemporary research devoted to the modeling of decision making processes and their facilitation directs attention to the Analytic Hierarchy Process (AHP). At the core of the AHP are various prioritization procedures (PPs) and consistency measures (CMs) for a Pairwise Comparison Matrix (PCM) which, in a sense, reflects preferences of decision makers. Certainly, when judgments about these preferences are perfectly consistent (cardinally transitive), all PPs coincide and the quality of the priority ratios (PRs) estimation is exemplary. However, human judgments are very rarely consistent, thus the quality of PRs estimation may significantly vary. The scale of these variations depends on the applied PP and utilized CM for a PCM. This is why it is important to find out which PPs and which CMs for a PCM lead directly to an improvement of the PRs estimation accuracy. The main goal of this research is realized through the properly designed, coded and executed seminal and sophisticated simulation algorithms in Wolfram Mathematica 8.0. These research results convince that the embedded in the AHP and commonly applied, both genuine PP and CM for PCM may significantly deteriorate the quality of PRs estimation; however, solutions proposed in this paper can significantly improve the methodology.
A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackle using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any supervised learning problem, whereas others are specifically designed to tackle a particular task, using complex and computational expensive processes such as lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we propose a minimalistic and wide system able to tackle text classification tasks independent of domain and language, namely microTC. It is composed by some easy to implement text transformations, text representations, and a supervised learning algorithm. These pieces produce a competitive classifier even in the domain of informally written text. We provide a detailed description of microTC along with an extensive experimental comparison with relevant state-of-the-art methods. mircoTC was compared on 30 different datasets. Regarding accuracy, microTC obtained the best performance in 20 datasets while achieves competitive results in the remaining 10. The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution. Furthermore, it is important to state that our approach allows the usage of the technology even without knowledge of machine learning and natural language processing.
Major advances have recently been made in merging language and vision representations. But most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw data to perform certain types of higher-level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like 'few', 'some' and 'all'. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in 'most fish are red', most encodes the proportion of fish which are red fish. In this paper, we study how well current language and vision strategies model such relations. We show that state-of-the-art attention mechanisms coupled with a traditional linguistic formalisation of quantifiers gives best performance on the task. Additionally, we provide insights on the role of 'gist' representations in quantification. A 'logical' strategy to tackle the task would be to first obtain a numerosity estimation for the two involved sets and then compare their cardinalities. We however argue that precisely identifying the composition of the sets is not only beyond current state-of-the-art models but perhaps even detrimental to a task that is most efficiently performed by refining the approximate numerosity estimator of the system.
The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input attention mechanism to adaptively extract relevant driving series (a.k.a., input features) at each time step by referring to the previous encoder hidden state. In the second stage, we use a temporal attention mechanism to select relevant encoder hidden states across all time steps. With this dual-stage attention scheme, our model can not only make predictions effectively, but can also be easily interpreted. Thorough empirical studies based upon the SML 2010 dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can outperform state-of-the-art methods for time series prediction.
Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep reinforcement learning approach to learning a dialogue manager that operates at different temporal scales. The dialogue manager consists of: (1) a top-level dialogue policy that selects among subtasks or options, (2) a low-level dialogue policy that selects primitive actions to complete the subtask given by the top-level policy, and (3) a global state tracker that helps ensure all cross-subtask constraints be satisfied. Experiments on a travel planning task with simulated and real users show that our approach leads to significant improvements over three baselines, two based on handcrafted rules and the other based on flat deep reinforcement learning.
This paper targets on the problem of set to set recognition, which learns the metric between two image sets. Images in each set belong to the same identity. Since images in a set can be complementary, they hopefully lead to higher accuracy in practical applications. However, the quality of each sample cannot be guaranteed, and samples with poor quality will hurt the metric. In this paper, the quality aware network (QAN) is proposed to confront this problem, where the quality of each sample can be automatically learned although such information is not explicitly provided in the training stage. The network has two branches, where the first branch extracts appearance feature embedding for each sample and the other branch predicts quality score for each sample. Features and quality scores of all samples in a set are then aggregated to generate the final feature embedding. We show that the two branches can be trained in an end-to-end manner given only the set-level identity annotation. Analysis on gradient spread of this mechanism indicates that the quality learned by the network is beneficial to set-to-set recognition and simplifies the distribution that the network needs to fit. Experiments on both face verification and person re-identification show advantages of the proposed QAN. The source code and network structure can be downloaded at https://github.com/sciencefans/Quality-Aware-Network.
We consider off-policy temporal-difference (TD) learning in discounted Markov decision processes, where the goal is to evaluate a policy in a model-free way by using observations of a state process generated without executing the policy. To curb the high variance issue in off-policy TD learning, we propose a new scheme of setting the $\lambda$-parameters of TD, based on generalized Bellman equations. Our scheme is to set $\lambda$ according to the eligibility trace iterates calculated in TD, thereby easily keeping these traces in a desired bounded range. Compared to prior works, this scheme is more direct and flexible, and allows much larger $\lambda$ values for off-policy TD learning with bounded traces. Using Markov chain theory, we prove the ergodicity of the joint state-trace process under nonrestrictive conditions, and we show that associated with our scheme is a generalized Bellman equation (for the policy to be evaluated) that depends on both the evolution of $\lambda$ and the unique invariant probability measure of the state-trace process. These results not only lead immediately to a characterization of the convergence behavior of least-squares based implementation of our scheme, but also prepare the ground for further analysis of gradient-based implementations.
High Energy Physics (HEP) distributed computing infrastructures require automatic tools to monitor, analyze and react to potential security incidents. These tools should collect and inspect data such as resource consumption, logs and sequence of system calls for detecting anomalies that indicate the presence of a malicious agent. They should also be able to perform automated reactions to attacks without administrator intervention. We describe a novel framework that accomplishes these requirements, with a proof of concept implementation for the ALICE experiment at CERN. We show how we achieve a fully virtualized environment that improves the security by isolating services and Jobs without a significant performance impact. We also describe a collected dataset for Machine Learning based Intrusion Prevention and Detection Systems on Grid computing. This dataset is composed of resource consumption measurements (such as CPU, RAM and network traffic), logfiles from operating system services, and system call data collected from production Jobs running in an ALICE Grid test site and a big set of malware. This malware was collected from security research sites. Based on this dataset, we will proceed to develop Machine Learning algorithms able to detect malicious Jobs.
Online reinforcement learning (RL) is increasingly popular for the personalized mobile health (mHealth) intervention. It is able to personalize the type and dose of interventions according to user's ongoing statuses and changing needs. However, at the beginning of online learning, there are usually too few samples to support the RL updating, which leads to poor performances. A delay in good performance of the online learning algorithms can be especially detrimental in the mHealth, where users tend to quickly disengage with the mHealth app. To address this problem, we propose a new online RL methodology that focuses on an effective warm start. The main idea is to make full use of the data accumulated and the decision rule achieved in a former study. As a result, we can greatly enrich the data size at the beginning of online learning in our method. Such case accelerates the online learning process for new users to achieve good performances not only at the beginning of online learning but also through the whole online learning process. Besides, we use the decision rules achieved in a previous study to initialize the parameter in our online RL model for new users. It provides a good initialization for the proposed online RL algorithm. Experiment results show that promising improvements have been achieved by our method compared with the state-of-the-art method.
In this paper, we propose a simple variant of the original stochastic variance reduction gradient (SVRG), where hereafter we refer to as the variance reduced stochastic gradient descent (VR-SGD). Different from the choices of the snapshot point and starting point in SVRG and its proximal variant, Prox-SVRG, the two vectors of each epoch in VR-SGD are set to the average and last iterate of the previous epoch, respectively. This setting allows us to use much larger learning rates or step sizes than SVRG, e.g., 3/(7L) for VR-SGD vs 1/(10L) for SVRG, and also makes our convergence analysis more challenging. In fact, a larger learning rate enjoyed by VR-SGD means that the variance of its stochastic gradient estimator asymptotically approaches zero more rapidly. Unlike common stochastic methods such as SVRG and proximal stochastic methods such as Prox-SVRG, we design two different update rules for smooth and non-smooth objective functions, respectively. In other words, VR-SGD can tackle non-smooth and/or non-strongly convex problems directly without using any reduction techniques such as quadratic regularizers. Moreover, we analyze the convergence properties of VR-SGD for strongly convex problems, which show that VR-SGD attains a linear convergence rate. We also provide the convergence guarantees of VR-SGD for non-strongly convex problems. Experimental results show that the performance of VR-SGD is significantly better than its counterparts, SVRG and Prox-SVRG, and it is also much better than the best known stochastic method, Katyusha.
Progress in science has advanced the development of human society across history, with dramatic revolutions shaped by information theory, genetic cloning, and artificial intelligence, among the many scientific achievements produced in the 20th century. However, the way that science advances itself is much less well-understood. In this work, we study the evolution of scientific development over the past century by presenting an anatomy of 89 million digitalized papers published between 1900 and 2015. We find that science has benefited from the shift from individual work to collaborative effort, with over 90% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s. We discover that rather than the frequent myopic- and self-referencing that was common in the early 20th century, modern scientists instead tend to look for literature further back and farther around. Finally, we also observe the globalization of scientific development from 1900 to 2015, including 25-fold and 7-fold increases in international collaborations and citations, respectively, as well as a dramatic decline in the dominant accumulation of citations by the US, the UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are meant to serve as a starter for exploring the visionary ways in which science has developed throughout the past century, generating insight into and an impact upon the current scientific innovations and funding policies.
Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can execute arbitrary code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware.
Humans can ground natural language commands to tasks at both abstract and fine-grained levels of specificity. For instance, a human forklift operator can be instructed to perform a high-level action, like "grab a pallet" or a lowlevel action like "tilt back a little bit." While robots are also capable of grounding language commands to tasks, previous methods implicitly assume that all commands and tasks reside at a single, fixed level of abstraction. Additionally, those approaches that do not use abstraction experience inefficient planning and execution times due to the large, intractable state-action spaces, which closely resemble real world complexity. In this work, by grounding commands to all the tasks or subtasks available in a hierarchical planning framework, we arrive at a model capable of interpreting language at multiple levels of specificity ranging from coarse to more granular. We show that the accuracy of the grounding procedure is improved when simultaneously inferring the degree of abstraction in language used to communicate the task. Leveraging hierarchy also improves efficiency: our proposed approach enables a robot to respond to a command within one second on 90% of our tasks, while baselines take over twenty seconds on half the tasks. Finally, we demonstrate that a real, physical robot can ground commands at multiple levels of abstraction allowing it to efficiently plan different subtasks within the same planning hierarchy.
Making high-quality decisions in strategic spatial planning is heavily dependent on extracting knowledge from vast amounts of data. Although many decision-making problems like developing urban areas require such perception and reasoning, existing methods in this field usually neglect the deep knowledge mined from geographic databases and are based on pure statistical methods. Due to the large volume of data gathered in spatial databases, and the uncertainty of spatial objects, mining association rules for high-level knowledge representation is a challenging task. Few algorithms manage geographical and non-geographical data using topological relations. In this paper, a novel approach for spatial data mining based on the MOSES evolutionary framework is presented which improves the classic genetic programming approach. A hybrid architecture called GGeo is proposed to apply the MOSES mining rules considering fuzzy topological relations from spatial data. The uncertainty and fuzziness aspects are addressed using an enriched model of topological relations by fuzzy region connection calculus. Moreover, to overcome the problem of time-consuming fuzzy topological relationships calculations, this a novel data pre-processing method is offered. GGeo analyses and learns from geographical and non-geographical data and uses topological and distance parameters, and returns a series of arithmetic-spatial formulas as classification rules. The proposed approach is resistant to noisy data, and all its stages run in parallel to increase speed. This approach may be used in different spatial data classification problems as well as representing an appropriate method of data analysis and economic policy making.
We consider the problem of online learning in misspecified linear stochastic multi-armed bandit problems. Regret guarantees for state-of-the-art linear bandit algorithms such as Optimism in the Face of Uncertainty Linear bandit (OFUL) hold under the assumption that the arms expected rewards are perfectly linear in their features. It is, however, of interest to investigate the impact of potential misspecification in linear bandit models, where the expected rewards are perturbed away from the linear subspace determined by the arms features. Although OFUL has recently been shown to be robust to relatively small deviations from linearity, we show that any linear bandit algorithm that enjoys optimal regret performance in the perfectly linear setting (e.g., OFUL) must suffer linear regret under a sparse additive perturbation of the linear model. In an attempt to overcome this negative result, we define a natural class of bandit models characterized by a non-sparse deviation from linearity. We argue that the OFUL algorithm can fail to achieve sublinear regret even under models that have non-sparse deviation.We finally develop a novel bandit algorithm, comprising a hypothesis test for linearity followed by a decision to use either the OFUL or Upper Confidence Bound (UCB) algorithm. For perfectly linear bandit models, the algorithm provably exhibits OFULs favorable regret performance, while for misspecified models satisfying the non-sparse deviation property, the algorithm avoids the linear regret phenomenon and falls back on UCBs sublinear regret scaling. Numerical experiments on synthetic data, and on recommendation data from the public Yahoo! Learning to Rank Challenge dataset, empirically support our findings.
Though the deep learning is pushing the machine learning to a new stage, basic theories of machine learning are still limited. The principle of learning, the role of the a prior knowledge, the role of neuron bias, and the basis for choosing neural transfer function and cost function, etc., are still far from clear. In this paper, we present a general theoretical framework for machine learning. We classify the prior knowledge into common and problem-dependent parts, and consider that the aim of learning is to maximally incorporate them. The principle we suggested for maximizing the former is the design risk minimization principle, while the neural transfer function, the cost function, as well as pretreatment of samples, are endowed with the role for maximizing the latter. The role of the neuron bias is explained from a different angle. We develop a Monte Carlo algorithm to establish the input-output responses, and we control the input-output sensitivity of a learning machine by controlling that of individual neurons. Applications of function approaching and smoothing, pattern recognition and classification, are provided to illustrate how to train general learning machines based on our theory and algorithm. Our method may in addition induce new applications, such as the transductive inference.
This manuscript introduces the problem of prominent object detection and recognition inspired by the fact that human seems to priorities perception of scene elements. The problem deals with finding the most important region of interest, segmenting the relevant item/object in that area, and assigning it an object class label. In other words, we are solving the three problems of saliency modeling, saliency detection, and object recognition under one umbrella. The motivation behind such a problem formulation is (1) the benefits to the knowledge representation-based vision pipelines, and (2) the potential improvements in emulating bio-inspired vision systems by solving these three problems together. We are foreseeing extending this problem formulation to fully semantically segmented scenes with instance object priority for high-level inferences in various applications including assistive vision. Along with a new problem definition, we also propose a method to achieve such a task. The proposed model predicts the most important area in the image, segments the associated objects, and labels them. The proposed problem and method are evaluated against human fixations, annotated segmentation masks, and object class categories. We define a chance level for each of the evaluation criterion to compare the proposed algorithm with. Despite the good performance of the proposed baseline, the overall evaluations indicate that the problem of prominent object detection and recognition is a challenging task that is still worth investigating further.
Patient time series classification faces challenges in high degrees of dimensionality and missingness. In light of patient similarity theory, this study explores effective temporal feature engineering and reduction, missing value imputation, and change point detection methods that can afford similarity-based classification models with desirable accuracy enhancement. We select a piecewise aggregation approximation method to extract fine-grain temporal features and propose a minimalist method to impute missing values in temporal features. For dimensionality reduction, we adopt a gradient descent search method for feature weight assignment. We propose new patient status and directional change definitions based on medical knowledge or clinical guidelines about the value ranges for different patient status levels, and develop a method to detect change points indicating positive or negative patient status changes. We evaluate the effectiveness of the proposed methods in the context of early Intensive Care Unit mortality prediction. The evaluation results show that the k-Nearest Neighbor algorithm that incorporates methods we select and propose significantly outperform the relevant benchmarks for early ICU mortality prediction. This study makes contributions to time series classification and early ICU mortality prediction via identifying and enhancing temporal feature engineering and reduction methods for similarity-based time series classification.
Decoding human brain activities via functional magnetic resonance imaging (fMRI) has gained increasing attention in recent years. While encouraging results have been reported in brain states classification tasks, reconstructing the details of human visual experience still remains difficult. Two main challenges that hinder the development of effective models are the perplexing fMRI measurement noise and the high dimensionality of limited data instances. Existing methods generally suffer from one or both of these issues and yield dissatisfactory results. In this paper, we tackle this problem by casting the reconstruction of visual stimulus as the Bayesian inference of missing view in a multiview latent variable model. Sharing a common latent representation, our joint generative model of external stimulus and brain response is not only "deep" in extracting nonlinear features from visual images, but also powerful in capturing correlations among voxel activities of fMRI recordings. The nonlinearity and deep structure endow our model with strong representation ability, while the correlations of voxel activities are critical for suppressing noise and improving prediction. We devise an efficient variational Bayesian method to infer the latent variables and the model parameters. To further improve the reconstruction accuracy, the latent representations of testing instances are enforced to be close to that of their neighbours from the training set via posterior regularization. Experiments on three fMRI recording datasets demonstrate that our approach can more accurately reconstruct visual stimuli.
The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining tool-kit ProM (http://promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.
A ranking is an ordered sequence of items, in which an item with higher ranking score is more preferred than the items with lower ranking scores. In many information systems, rankings are widely used to represent the preferences over a set of items or candidates. The consensus measure of rankings is the problem of how to evaluate the degree to which the rankings agree. The consensus measure can be used to evaluate rankings in many information systems, as quite often there is not ground truth available for evaluation. This paper introduces a novel approach for consensus measure of rankings by using graph representation, in which the vertices or nodes are the items and the edges are the relationship of items in the rankings. Such representation leads to various algorithms for consensus measure in terms of different aspects of rankings, including the number of common patterns, the number of common patterns with fixed length and the length of the longest common patterns. The proposed measure can be adopted for various types of rankings, such as full rankings, partial rankings and rankings with ties. This paper demonstrates how the proposed approaches can be used to evaluate the quality of rank aggregation and the quality of top-$k$ rankings from Google and Bing search engines.
A central goal in cancer genomics is to identify the somatic alterations that underpin tumor initiation and progression. This task is challenging as the mutational profiles of cancer genomes exhibit vast heterogeneity, with many alterations observed within each individual, few shared somatically mutated genes across individuals, and important roles in cancer for both frequently and infrequently mutated genes. While commonly mutated cancer genes are readily identifiable, those that are rarely mutated across samples are difficult to distinguish from the large numbers of other infrequently mutated genes. Here, we introduce a method that considers per-individual mutational profiles within the context of protein-protein interaction networks in order to identify small connected subnetworks of genes that, while not individually frequently mutated, comprise pathways that are perturbed across (i.e., "cover") a large fraction of the individuals. We devise a simple yet intuitive objective function that balances identifying a small subset of genes with covering a large fraction of individuals. We show how to solve this problem optimally using integer linear programming and also give a fast heuristic algorithm that works well in practice. We perform a large-scale evaluation of our resulting method, nCOP, on 6,038 TCGA tumor samples across 24 different cancer types. We demonstrate that our approach nCOP is more effective in identifying cancer genes than both methods that do not utilize any network information as well as state-of-the-art network-based methods that aggregate mutational information across individuals. Overall, our work demonstrates the power of combining per-individual mutational information with interaction networks in order to uncover genes functionally relevant in cancers, and in particular those genes that are less frequently mutated.
Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose LatPlan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), and a pair of images representing the initial and the goal states (planning inputs), LatPlan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. The contribution of this paper is twofold: (1) State Autoencoder, which finds a propositional state representation of the environment using a Variational Autoencoder. It generates a discrete latent vector from the images, based on which a PDDL model can be constructed and then solved by an off-the-shelf planner. (2) Action Autoencoder / Discriminator, a neural architecture which jointly finds the action symbols and the implicit action models (preconditions/effects), and provides a successor function for the implicit graph search. We evaluate LatPlan using image-based versions of 3 planning domains: 8-puzzle, Towers of Hanoi and LightsOut.
A logical theory of regular double or multiple recurrence of eventualities, which are regular patterns of occurrences that are repeated, in time, has been developed within the context of temporal reasoning that enabled reasoning about the problem of coincidence. i.e. if two complex eventualities, or eventuality sequences consisting respectively of component eventualities x0, x1,....,xr and y0, y1, ..,ys both recur over an interval k and all eventualities are of fixed durations, is there a subinterval of k over which the occurrence xp and yq for p between 1 and r and q between 1 and s coincide. We present the ideas behind a new algorithm for detecting the coincidence of eventualities xp and yq within a cycle of the double recurrence of x and y. The algorithm is based on the novel concept of gcd partitions that requires the partitioning of each of the incidences of both x and y into eventuality sequences each of which components have a duration that is equal to the greatest common divisor of the durations of x and y. The worst case running time of the partitioning algorithm is linear in the maximum of the duration of x and that of y, while the worst case running time of an algorithm exploring a complete cycle is quadratic in the durations of x and y. Hence the partitioning algorithm works faster than the cyclical exploration in the worst case.
Different from other sequential data, sentences in natural language are structured by linguistic grammars. Previous generative conversational models with chain-structured decoder ignore this structure in human language and might generate plausible responses with less satisfactory relevance and fluency. In this study, we aim to incorporate the results from linguistic analysis into the process of sentence generation for high-quality conversation generation. Specifically, we use a dependency parser to transform each response sentence into a dependency tree and construct a training corpus of sentence-tree pairs. A tree-structured decoder is developed to learn the mapping from a sentence to its tree, where different types of hidden states are used to depict the local dependencies from an internal tree node to its children. For training acceleration, we propose a tree canonicalization method, which transforms trees into equivalent ternary trees. Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are finally flattened into sequences as the system output. Experimental results demonstrate that the proposed X2Tree framework outperforms baseline methods over 11.15% increase of acceptance ratio.
Mental illnesses adversely affect a significant proportion of the population worldwide. However, the methods traditionally used for estimating and characterizing the prevalence of mental health conditions are time-consuming and expensive. Consequently, best-available estimates concerning the prevalence of mental health conditions are often years out of date. Automated approaches to supplement these survey methods with broad, aggregated information derived from social media content provides a potential means for near real-time estimates at scale. These may, in turn, provide grist for supporting, evaluating and iteratively improving upon public health programs and interventions. We propose a novel model for automated mental health status quantification that incorporates user embeddings. This builds upon recent work exploring representation learning methods that induce embeddings by leveraging social media post histories. Such embeddings capture latent characteristics of individuals (e.g., political leanings) and encode a soft notion of homophily. In this paper, we investigate whether user embeddings learned from twitter post histories encode information that correlates with mental health statuses. To this end, we estimated user embeddings for a set of users known to be affected by depression and post-traumatic stress disorder (PTSD), and for a set of demographically matched `control' users. We then evaluated these embeddings with respect to: (i) their ability to capture homophilic relations with respect to mental health status; and (ii) the performance of downstream mental health prediction models based on these features. Our experimental results demonstrate that the user embeddings capture similarities between users with respect to mental conditions, and are predictive of mental health.
Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel adversarial training procedure to leverage unpaired data in the target domain. Two critic networks are introduced to guide the captioner, namely domain critic and multi-modal critic. The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain. The multi-modal critic assesses whether an image and its generated sentence are a valid pair. During training, the critics and captioner act as adversaries -- captioner aims to generate indistinguishable sentences, whereas critics aim at distinguishing them. The assessment improves the captioner through policy gradient updates. During inference, we further propose a novel critic-based planning method to select high-quality sentences without additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k) as the target domains. Our method consistently performs well on all datasets. In particular, on CUB-200-2011, we achieve 21.8% CIDEr-D improvement after adaptation. Utilizing critics during inference further gives another 4.5% boost.
This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design. The approach adopted in this paper is to use observations of AI agents playing the game to estimate the game's quality. Some of best agents for this purpose are General Video Game AI agents, since they can be deployed directly on a new game without game-specific tuning; these agents tend to be based on stochastic algorithms which give robust but noisy results and tend to be expensive to run. This motivates the main contribution of the paper: the development of the novel N-Tuple Bandit Evolutionary Algorithm, where a model is used to estimate the fitness of unsampled points and a bandit approach is used to balance exploration and exploitation of the search space. Initial results on optimising a Space Battle game variant suggest that the algorithm offers far more robust results than the Random Mutation Hill Climber and a Biased Mutation variant, which are themselves known to offer competitive performance across a range of problems. Subjective observations are also given by human players on the nature of the evolved games, which indicate a preference towards games generated by the N-Tuple algorithm.
In the era of big data, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high as the data size and the cluster number are large. It is well known that the processing bottleneck of k-means lies in the operation of seeking closest centroid in each iteration. In this paper, a novel solution towards the scalability issue of k-means is presented. In the proposal, k-means is supported by an approximate k-nearest neighbors graph. In the k-means iteration, each data sample is only compared to clusters that its nearest neighbors reside. Since the number of nearest neighbors we consider is much less than k, the processing cost in this step becomes minor and irrelevant to k. The processing bottleneck is therefore overcome. The most interesting thing is that k-nearest neighbor graph is constructed by iteratively calling the fast $k$-means itself. Comparing with existing fast k-means variants, the proposed algorithm achieves hundreds to thousands times speed-up while maintaining high clustering quality. As it is tested on 10 million 512-dimensional data, it takes only 5.2 hours to produce 1 million clusters. In contrast, to fulfill the same scale of clustering, it would take 3 years for traditional k-means.
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverages "instance-level explanations", measures of local feature relevance that explain single instances, and uses them to build a set of visual representations that guide the users in their investigation. The workflow is based on three main visual representations and steps: one based on aggregate statistics to see how data distributes across correct / incorrect decisions; one based on explanations to understand which features are used to make these decisions; and one based on raw data, to derive insights on potential root causes for the observed patterns. The workflow is derived from a long-term collaboration with a group of machine learning and healthcare professionals who used our method to make sense of machine learning models they developed. The case study from this collaboration demonstrates that the proposed workflow helps experts derive useful knowledge about the model and the phenomena it describes, thus experts can generate useful hypotheses on how a model can be improved.
Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews.
Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user's experience level and how this is expressed in the user's writing in a review community. In this paper, we model the joint evolution of user experience, interest in specific item facets, writing style, and rating behavior. This way we can generate individual recommendations that take into account the user's maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the user's experience and interests in a latent model learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time -- with solely user reviews and ratings as observables over time. The facets of a user's interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with five real-world datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. We also show, in a use-case study, that our model performs well in the assessment of user experience levels.
Machine Learning (ML) has found it particularly useful in malware detection. However, as the malware evolves very fast, the stability of the feature extracted from malware serves as a critical issue in malware detection. Recent success of deep learning in image recognition, natural language processing, and machine translation indicate a potential solution for stabilizing the malware detection effectiveness. We present a coloR-inspired convolutional neuRal network-based AndroiD malware Detection (R2-D2), which can detect malware without extracting pre-selected features (e.g., the control-flow of op-code, classes, methods of functions and the timing they are invoked etc.) from Android apps. In particular, we develop a color representation for translating Android apps into RGB color code and transform them to a fixed-sized encoded image. After that, the encoded image is fed to convolutional neural network for automatic feature extraction and learning, reducing the expert's intervention. We have collected over 1 million malware samples and 1 million benign samples according to the data provided by Leopard Mobile Inc. from its core product Security Master (which has 623 million monthly active users and 10k new malware samples per day). It is shown that R2-D2 can effectively detect the malware. Furthermore, we keep our research results and release experiment material on http://R2D2.TWMAN.ORG if there is any update.
Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.
We present the third generation of the constraint answer set system clingcon, combining Answer Set Programming (ASP) with finite domain constraint processing (CP). While its predecessors rely on a black-box approach to hybrid solving by integrating the CP solver gecode, the new clingcon system pursues a lazy approach using dedicated constraint propagators to extend propagation in the underlying ASP solver clasp. No extension is needed for parsing and grounding clingcon's hybrid modeling language since both can be accommodated by the new generic theory handling capabilities of the ASP grounder gringo. As a whole, clingcon 3 is thus an extension of the ASP system clingo 5, which itself relies on the grounder gringo and the solver clasp. The new approach of clingcon offers a seamless integration of CP propagation into ASP solving that benefits from the whole spectrum of clasp's reasoning modes, including for instance multi-shot solving and advanced optimization techniques. This is accomplished by a lazy approach that unfolds the representation of constraints and adds it to that of the logic program only when needed. Although the unfolding is usually dictated by the constraint propagators during solving, it can already be partially (or even totally) done during preprocessing. Moreover, clingcon's constraint preprocessing and propagation incorporate several well established CP techniques that greatly improve its performance. We demonstrate this via an extensive empirical evaluation contrasting, first, the various techniques in the context of CSP solving and, second, the new clingcon system with other hybrid ASP systems. Under consideration in Theory and Practice of Logic Programming (TPLP)
We propose an algorithm to separate simultaneously speaking persons from each other, the "cocktail party problem", using a single microphone. Our approach involves a deep recurrent neural networks regression to a vector space that is descriptive of independent speakers. Such a vector space can embed empirically determined speaker characteristics and is optimized by distinguishing between speaker masks. We call this technique source-contrastive estimation. The methodology is inspired by negative sampling, which has seen success in natural language processing, where an embedding is learned by correlating and de-correlating a given input vector with output weights. Although the matrix determined by the output weights is dependent on a set of known speakers, we only use the input vectors during inference. Doing so will ensure that source separation is explicitly speaker-independent. Our approach is similar to recent deep neural network clustering and permutation-invariant training research; we use weighted spectral features and masks to augment individual speaker frequencies while filtering out other speakers. We avoid, however, the severe computational burden of other approaches with our technique. Furthermore, by training a vector space rather than combinations of different speakers or differences thereof, we avoid the so-called permutation problem during training. Our algorithm offers an intuitive, computationally efficient response to the cocktail party problem, and most importantly boasts better empirical performance than other current techniques.
We consider an extension of the set covering problem (SCP) introducing (i)~multicover and (ii)~generalized upper bound (GUB)~constraints. For the conventional SCP, the pricing method has been introduced to reduce the size of instances, and several efficient heuristic algorithms based on such reduction techniques have been developed to solve large-scale instances. However, GUB constraints often make the pricing method less effective, because they often prevent solutions from containing highly evaluated variables together. To overcome this problem, we develop heuristic algorithms to reduce the size of instances, in which new evaluation schemes of variables are introduced taking account of GUB constraints. We also develop an efficient implementation of a 2-flip neighborhood local search algorithm that reduces the number of candidates in the neighborhood without sacrificing the solution quality. In order to guide the search to visit a wide variety of good solutions, we also introduce a path relinking method that generates new solutions by combining two or more solutions obtained so far. According to computational comparison on benchmark instances, the proposed method succeeds in selecting a small number of promising variables properly and performs quite effectively even for large-scale instances having hard GUB constraints.
A broad range of on-line behaviors are mediated by interfaces in which people make choices among sets of options. A rich and growing line of work in the behavioral sciences indicate that human choices follow not only from the utility of alternatives, but also from the choice set in which alternatives are presented. In this work we study comparison-based choice functions, a simple but surprisingly rich class of functions capable of exhibiting so-called choice-set effects. Motivated by the challenge of predicting complex choices, we study the query complexity of these functions in a variety of settings. We consider settings that allow for active queries or passive observation of a stream of queries, and give analyses both at the granularity of individuals or populations that might exhibit heterogeneous choice behavior. Our main result is that any comparison-based choice function in one dimension can be inferred as efficiently as a basic maximum or minimum choice function across many query contexts, suggesting that choice-set effects need not entail any fundamental algorithmic barriers to inference. We also introduce a class of choice functions we call distance-comparison-based functions, and briefly discuss the analysis of such functions. The framework we outline provides intriguing connections between human choice behavior and a range of questions in the theory of sorting.
The content ranking problem in a social news website, is typically a function that maximizes a scalar metric of interest like dwell-time. However, like in most real-world applications we are interested in more than one metric---for instance simultaneously maximizing click-through rate, monetization metrics, dwell-time---and also satisfy the traffic requirements promised to different publishers. All this needs to be done on online data and under the settings where the objective function and the constraints can dynamically change; this could happen if for instance new publishers are added, some contracts are adjusted, or if some contracts are over. In this paper, we formulate this problem as a constrained, dynamic, multi-objective optimization problem. We propose a novel framework that extends a successful genetic optimization algorithm, NSGA-II, to solve this online, data-driven problem. We design the modules of NSGA-II to suit our problem. We evaluate optimization performance using Hypervolume and introduce a confidence interval metric for assessing the practicality of a solution. We demonstrate the application of this framework on a real-world Article Ranking problem. We observe that we make considerable improvements in both time and performance over a brute-force baseline technique that is currently in production.
This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an optimum treelike structure, i.e., a natural hierarchical structure that accommodates simplicity by combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure provides a high degree of approximation accuracy. The construction of HFIT takes place in two phases. Firstly, a nondominated sorting based multiobjective genetic programming (MOGP) is applied to obtain a simple tree structure (a low complexity model) with a high accuracy. Secondly, the differential evolution algorithm is applied to optimize the obtained tree's parameters. In the derived tree, each node acquires a different input's combination, where the evolutionary process governs the input's combination. Hence, HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP for the tree's structural optimization that accepts inputs only relevant to the knowledge contained in data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared both theoretically and empirically with recently proposed FISs methods from the literature, such as McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the obtained results, it was found that the HFIT provided less complex and highly accurate models compared to the models produced by the most of other methods. Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selection.
Outlier detection is the identification of points in a dataset that do not conform to the norm. Outlier detection is highly sensitive to the choice of the detection algorithm and the feature subspace used by the algorithm. Extracting domain-relevant insights from outliers needs systematic exploration of these choices since diverse outlier sets could lead to complementary insights. This challenge is especially acute in an interactive setting, where the choices must be explored in a time-constrained manner. In this work, we present REMIX, the first system to address the problem of outlier detection in an interactive setting. REMIX uses a novel mixed integer programming (MIP) formulation for automatically selecting and executing a diverse set of outlier detectors within a time limit. This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors. REMIX provides two distinct ways for the analyst to consume its results: (i) a partitioning of the detectors explored by REMIX into perspectives through low-rank non-negative matrix factorization; each perspective can be easily visualized as an intuitive heatmap of experiments versus outliers, and (ii) an ensembled set of outliers which combines outlier scores from all detectors. We demonstrate the benefits of REMIX through extensive empirical validation on real-world data.
The main idea of this paper is to represent shopping items through vectors because these vectors act as the base for building em- beddings for customers and shopping carts. Also, these vectors are input to the mathematical models that act as either a recommendation engine or help in targeting potential customers. We have used exponential family embeddings as the tool to construct two basic vectors - product embeddings and context vectors. Using the basic vectors, we build combined embeddings, trip embeddings and customer embeddings. Combined embeddings mix linguistic properties of product names with their shopping patterns. The customer embeddings establish an understand- ing of the buying pattern of customers in a group and help in building customer profile. For example a customer profile can represent customers frequently buying pet-food. Identifying such profiles can help us bring out offers and discounts. Similarly, trip embeddings are used to build trip profiles. People happen to buy similar set of products in a trip and hence their trip embeddings can be used to predict the next product they would like to buy. This is a novel technique and the first of its kind to make recommendation using product, trip and customer embeddings.
The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.
By utilizing different communication channels, such as verbal language, gestures or facial expressions, virtually embodied interactive humans hold a unique potential to bridge the gap between human-computer interaction and actual interhuman communication. The use of virtual humans is consequently becoming increasingly popular in a wide range of areas where such a natural communication might be beneficial, including entertainment, education, mental health research and beyond. Behind this development lies a series of technological advances in a multitude of disciplines, most notably natural language processing, computer vision, and speech synthesis. In this paper we discuss a Virtual Human Journalist, a project employing a number of novel solutions from these disciplines with the goal to demonstrate their viability by producing a humanoid conversational agent capable of naturally eliciting and reacting to information from a human user. A set of qualitative and quantitative evaluation sessions demonstrated the technical feasibility of the system whilst uncovering a number of deficits in its capacity to engage users in a way that would be perceived as natural and emotionally engaging. We argue that naturalness should not always be seen as a desirable goal and suggest that deliberately suppressing the naturalness of virtual human interactions, such as by altering its personality cues, might in some cases yield more desirable results.
Infrared (IR) imaging has the potential to enable more robust action recognition systems compared to visible spectrum cameras due to lower sensitivity to lighting conditions and appearance variability. While the action recognition task on videos collected from visible spectrum imaging has received much attention, action recognition in IR videos is significantly less explored. Our objective is to exploit imaging data in this modality for the action recognition task. In this work, we propose a novel two-stream 3D convolutional neural network (CNN) architecture by introducing the discriminative code layer and the corresponding discriminative code loss function. The proposed network processes IR image and the IR-based optical flow field sequences. We pretrain the 3D CNN model on the visible spectrum Sports-1M action dataset and finetune it on the Infrared Action Recognition (InfAR) dataset. To our best knowledge, this is the first application of the 3D CNN to action recognition in the IR domain. We conduct an elaborate analysis of different fusion schemes (weighted average, single and double-layer neural nets) applied to different 3D CNN outputs. Experimental results demonstrate that our approach can achieve state-of-the-art average precision (AP) performances on the InfAR dataset: (1) the proposed two-stream 3D CNN achieves the best reported 77.5% AP, and (2) our 3D CNN model applied to the optical flow fields achieves the best reported single stream 75.42% AP.
The problem of sparse rewards is one of the hardest challenges in contemporary reinforcement learning. Hierarchical reinforcement learning (HRL) tackles this problem by using a set of temporally-extended actions, or options, each of which has its own subgoal. These subgoals are normally handcrafted for specific tasks. Here, though, we introduce a generic class of subgoals with broad applicability in the visual domain. Underlying our approach (in common with work using "auxiliary tasks") is the hypothesis that the ability to control aspects of the environment is an inherently useful skill to have. We incorporate such subgoals in an end-to-end hierarchical reinforcement learning system and test two variants of our algorithm on a number of games from the Atari suite. We highlight the advantage of our approach in one of the hardest games -- Montezuma's revenge -- for which the ability to handle sparse rewards is key. Our agent learns several times faster than the current state-of-the-art HRL agent in this game, reaching a similar level of performance. UPDATE 22/11/17: We found that a standard A3C agent with a simple shaped reward, i.e. extrinsic reward + feature control intrinsic reward, has comparable performance to our agent in Montezuma Revenge. In light of the new experiments performed, the advantage of our HRL approach can be attributed more to its ability to learn useful features from intrinsic rewards rather than its ability to explore and reuse abstracted skills with hierarchical components. This has led us to a new conclusion about the result.
Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for modeling images is similar to traditional computer vision tasks, such as object recognition and image classification, visual question answering raises a different need for textual representation as compared to other natural language processing tasks. In this work, we perform a detailed analysis on natural language questions in visual question answering. Based on the analysis, we propose to rely on convolutional neural networks for learning textual representations. By exploring the various properties of convolutional neural networks specialized for text data, such as width and depth, we present our "CNN Inception + Gate" model. We show that our model improves question representations and thus the overall accuracy of visual question answering models. We also show that the text representation requirement in visual question answering is more complicated and comprehensive than that in conventional natural language processing tasks, making it a better task to evaluate textual representation methods. Shallow models like fastText, which can obtain comparable results with deep learning models in tasks like text classification, are not suitable in visual question answering.
Generality is one of the main advantages of heuristic algorithms, as such, multiple parameters are exposed to the user with the objective of allowing them to shape the algorithms to their specific needs. Parameter selection, therefore, becomes an intrinsic problem of every heuristic algorithm. Selecting good parameter values relies not only on knowledge related to the problem at hand, but to the algorithms themselves. This research explores the usage of self-organized criticality to reduce user interaction in the process of selecting suitable parameters for particle swarm optimization (PSO) heuristics. A particle swarm variant (named Adaptive PSO) with self-organized criticality is developed and benchmarked against the standard PSO. Criticality is observed in the dynamic behaviour of this swarm and excellent results are observed in the long run. In contrast with the standard PSO, the Adaptive PSO does not stagnate at any point in time, balancing the concepts of exploration and exploitation better. A software platform for experimenting with particle swarms, called PSO Laboratory, is also developed. This software is used to test the standard PSO as well as all other PSO variants developed in the process of creating the Adaptive PSO. As the software is intended to be of aid to future and related research, special attention has been put in the development of a friendly graphical user interface. Particle swarms are executed in real time, allowing users to experiment by changing parameters on-the-fly.
A goal of cloud service management is to design self-adaptable auto-scaler to react to workload fluctuations and changing the resources assigned. The key problem is how and when to add/remove resources in order to meet agreed service-level agreements. Reducing application cost and guaranteeing service-level agreements (SLAs) are two critical factors of dynamic controller design. In this paper, we compare two dynamic learning strategies based on a fuzzy logic system, which learns and modifies fuzzy scaling rules at runtime. A self-adaptive fuzzy logic controller is combined with two reinforcement learning (RL) approaches: (i) Fuzzy SARSA learning (FSL) and (ii) Fuzzy Q-learning (FQL). As an off-policy approach, Q-learning learns independent of the policy currently followed, whereas SARSA as an on-policy always incorporates the actual agent's behavior and leads to faster learning. Both approaches are implemented and compared in their advantages and disadvantages, here in the OpenStack cloud platform. We demonstrate that both auto-scaling approaches can handle various load traffic situations, sudden and periodic, and delivering resources on demand while reducing operating costs and preventing SLA violations. The experimental results demonstrate that FSL and FQL have acceptable performance in terms of adjusted number of virtual machine targeted to optimize SLA compliance and response time.
The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting. To further investigate the properties and feasibility on real-world applications, this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a novel reinforcement learning (RL) benchmark that aims at being realistic by including a variety of aspects found in industrial applications, like continuous state and action spaces, a high dimensional, partially observable state space, delayed effects, and complex stochasticity. The experimental results of PSO-P on IB are compared to results of closed-form control policies derived from the model-based Recurrent Control Neural Network (RCNN) and the model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not only of interest for academic benchmarks, but also for real-world industrial applications, since it also yielded the best performing policy in our IB setting. Compared to other well established RL techniques, PSO-P produced outstanding results in performance and robustness, requiring only a relatively low amount of effort in finding adequate parameters or making complex design decisions.
Deep Reinforcement Learning (DRL) methods have performed well in an increasing numbering of high-dimensional visual decision making domains. Among all such visual decision making problems, those with discrete action spaces often tend to have underlying compositional structure in the said action space. Such action spaces often contain actions such as go left, go up as well as go diagonally up and left (which is a composition of the former two actions). The representations of control policies in such domains have traditionally been modeled without exploiting this inherent compositional structure in the action spaces. We propose a new learning paradigm, Factored Action space Representations (FAR) wherein we decompose a control policy learned using a Deep Reinforcement Learning Algorithm into independent components, analogous to decomposing a vector in terms of some orthogonal basis vectors. This architectural modification of the control policy representation allows the agent to learn about multiple actions simultaneously, while executing only one of them. We demonstrate that FAR yields considerable improvements on top of two DRL algorithms in Atari 2600: FARA3C outperforms A3C (Asynchronous Advantage Actor Critic) in 9 out of 14 tasks and FARAQL outperforms AQL (Asynchronous n-step Q-Learning) in 9 out of 13 tasks.
Reinforcement Learning (RL) can model complex behavior policies for goal-directed sequential decision making tasks. A hallmark of RL algorithms is Temporal Difference (TD) learning: value function for the current state is moved towards a bootstrapped target that is estimated using next state's value function. $\lambda$-returns generalize beyond 1-step returns and strike a balance between Monte Carlo and TD learning methods. While lambda-returns have been extensively studied in RL, they haven't been explored a lot in Deep RL. This paper's first contribution is an exhaustive benchmarking of lambda-returns. Although mathematically tractable, the use of exponentially decaying weighting of n-step returns based targets in lambda-returns is a rather ad-hoc design choice. Our second major contribution is that we propose a generalization of lambda-returns called Confidence-based Autodidactic Returns (CAR), wherein the RL agent learns the weighting of the n-step returns in an end-to-end manner. This allows the agent to learn to decide how much it wants to weigh the n-step returns based targets. In contrast, lambda-returns restrict RL agents to use an exponentially decaying weighting scheme. Autodidactic returns can be used for improving any RL algorithm which uses TD learning. We empirically demonstrate that using sophisticated weighted mixtures of multi-step returns (like CAR and lambda-returns) considerably outperforms the use of n-step returns. We perform our experiments on the Asynchronous Advantage Actor Critic (A3C) algorithm in the Atari 2600 domain.
Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN) have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This success is mainly attributed to the power of deep neural networks to learn rich domain representations for approximating the value function or policy. Batch reinforcement learning methods with linear representations, on the other hand, are more stable and require less hyper parameter tuning. Yet, substantial feature engineering is necessary to achieve good results. In this work we propose a hybrid approach -- the Least Squares Deep Q-Network (LS-DQN), which combines rich feature representations learned by a DRL algorithm with the stability of a linear least squares method. We do this by periodically re-training the last hidden layer of a DRL network with a batch least squares update. Key to our approach is a Bayesian regularization term for the least squares update, which prevents over-fitting to the more recent data. We tested LS-DQN on five Atari games and demonstrate significant improvement over vanilla DQN and Double-DQN. We also investigated the reasons for the superior performance of our method. Interestingly, we found that the performance improvement can be attributed to the large batch size used by the LS method when optimizing the last layer.
This paper examines two-sided matching with budget constraints where one side (a firm or hospital) can make monetary transfers (offer wages) to the other (a worker or doctor). In a standard model, while multiple doctors can be matched to a single hospital, a hospital has a {\em maximum quota}, thus, the number of doctors assigned to that hospital cannot exceed a certain limit. In our model, in contrast, a hospital instead has a {\em fixed budget}, that is, the total amount of wages allocated by each hospital to the doctors is constrained. With budget constraints, stable matchings may fail to exist and checking for the existence is hard. To deal with the nonexistence of stable matchings, we extend the "matching with contracts" model of Hatfield and Milgrom, so that it deals with \textit{near-feasible} matchings that exceed each hospital budget by a certain amount. We then propose two novel mechanisms that efficiently return such a near-feasible matching that is stable with respect to the actual amount of wages allocated by each hospital. Specifically, by sacrificing strategy-proofness, our second mechanism achieves the best possible bound of budget excess.
Sequential decision making problems, such as structured prediction, robotic control, and game playing, require a combination of planning policies and generalisation of those plans. In this paper, we present Expert Iteration (ExIt), a novel reinforcement learning algorithm which decomposes the problem into separate planning and generalisation tasks. Planning new policies is performed by tree search, while a deep neural network generalises those plans. Subsequently, tree search is improved by using the neural network policy to guide search, increasing the strength of new plans. In contrast, standard deep Reinforcement Learning algorithms rely on a neural network not only to generalise plans, but to discover them too. We show that ExIt outperforms REINFORCE for training a neural network to play the board game Hex, and our final tree search agent, trained tabula rasa, defeats MoHex 1.0, the most recent Olympiad Champion player to be publicly released.
Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD). Although some theoretical guarantees of MMD have been studied, the empirical performance of GMMN is still not as competitive as that of GAN on challenging and large benchmark datasets. The computational efficiency of GMMN is also less desirable in comparison with GAN, partially due to its requirement for a rather large batch size during the training. In this paper, we propose to improve both the model expressiveness of GMMN and its computational efficiency by introducing adversarial kernel learning techniques, as the replacement of a fixed Gaussian kernel in the original GMMN. The new approach combines the key ideas in both GMMN and GAN, hence we name it MMD GAN. The new distance measure in MMD GAN is a meaningful loss that enjoys the advantage of weak topology and can be optimized via gradient descent with relatively small batch sizes. In our evaluation on multiple benchmark datasets, including MNIST, CIFAR- 10, CelebA and LSUN, the performance of MMD-GAN significantly outperforms GMMN, and is competitive with other representative GAN works.
The knowledge representation community has built general-purpose ontologies which contain large amounts of commonsense knowledge over relevant aspects of the world, including useful visual information, e.g.: "a ball is used by a football player", "a tennis player is located at a tennis court". Current state-of-the-art approaches for visual recognition do not exploit these rule-based knowledge sources. Instead, they learn recognition models directly from training examples. In this paper, we study how general-purpose ontologies---specifically, MIT's ConceptNet ontology---can improve the performance of state-of-the-art vision systems. As a testbed, we tackle the problem of sentence-based image retrieval. Our retrieval approach incorporates knowledge from ConceptNet on top of a large pool of object detectors derived from a deep learning technique. In our experiments, we show that ConceptNet can improve performance on a common benchmark dataset. Key to our performance is the use of the ESPGAME dataset to select visually relevant relations from ConceptNet. Consequently, a main conclusion of this work is that general-purpose commonsense ontologies improve performance on visual reasoning tasks when properly filtered to select meaningful visual relations.
To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits must be compiled to take into account constraints on the hardware. For near-term hardware, with only limited means to mitigate decoherence, it is critical to minimize the duration of the circuit. We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus on compiling to superconducting hardware architectures with nearest neighbor constraints. Our initial experiments focus on compiling Quantum Alternating Operator Ansatz (QAOA) circuits whose high number of commuting gates allow great flexibility in the order in which the gates can be applied. That freedom makes it more challenging to find optimal compilations but also means there is a greater potential win from more optimized compilation than for less flexible circuits. We map this quantum circuit compilation problem to a temporal planning problem, and generated a test suite of compilation problems for QAOA circuits of various sizes to a realistic hardware architecture. We report compilation results from several state-of-the-art temporal planners on this test set. This early empirical evaluation demonstrates that temporal planning is a viable approach to quantum circuit compilation.
Semantic Image Interpretation (SII) is the task of extracting structured semantic descriptions from images. It is widely agreed that the combined use of visual data and background knowledge is of great importance for SII. Recently, Statistical Relational Learning (SRL) approaches have been developed for reasoning under uncertainty and learning in the presence of data and rich knowledge. Logic Tensor Networks (LTNs) are an SRL framework which integrates neural networks with first-order fuzzy logic to allow (i) efficient learning from noisy data in the presence of logical constraints, and (ii) reasoning with logical formulas describing general properties of the data. In this paper, we develop and apply LTNs to two of the main tasks of SII, namely, the classification of an image's bounding boxes and the detection of the relevant part-of relations between objects. To the best of our knowledge, this is the first successful application of SRL to such SII tasks. The proposed approach is evaluated on a standard image processing benchmark. Experiments show that the use of background knowledge in the form of logical constraints can improve the performance of purely data-driven approaches, including the state-of-the-art Fast Region-based Convolutional Neural Networks (Fast R-CNN). Moreover, we show that the use of logical background knowledge adds robustness to the learning system when errors are present in the labels of the training data.
A kidney exchange is a centrally-administered barter market where patients swap their willing yet incompatible donors. Modern kidney exchanges use 2-cycles, 3-cycles, and chains initiated by non-directed donors (altruists who are willing to give a kidney to anyone) as the means for swapping. We propose significant generalizations to kidney exchange. We allow more than one donor to donate in exchange for their desired patient receiving a kidney. We also allow for the possibility of a donor willing to donate if any of a number of patients receive kidneys. Furthermore, we combine these notions and generalize them. The generalization is to exchange among organ clubs, where a club is willing to donate organs outside the club if and only if the club receives organs from outside the club according to given specifications. We prove that unlike in the standard model, the uncapped clearing problem is NP-complete. We also present the notion of operation frames that can be used to sequence the operations across batches, and present integer programming formulations for the market clearing problems for these new types of organ exchanges. Experiments show that in the single-donation setting, operation frames improve planning by 34%--51%. Allowing up to two donors to donate in exchange for one kidney donated to their designated patient yields a further increase in social welfare.
Process mining is a research field focused on the analysis of event data with the aim of extracting insights related to dynamic behavior. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. Finding the right event labels to enable the application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (overgeneralizing). Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models. However, there exists no automated approach to generate refinements of event labels in the context of process mining. In this paper we propose a framework for the automated generation of label refinements based on the time attribute of events, allowing us to distinguish behaviourally different instances of the same event type based on their time attribute. We show on a case study with real life smart home event data that using automatically generated refined labels in process discovery, we can find more specific, and therefore more insightful, process models. We observe that one label refinement could have an effect on the usefulness of other label refinements when used together. Therefore, we explore four strategies to generate useful combinations of multiple label refinements and evaluate those on three real life smart home event logs.
While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. Naive application of such algorithms on multiple source domain adaptation problem may lead to suboptimal solutions. As a step toward bridging the gap, we propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances. Compared with existing bounds, the new bound does not require expert knowledge about the target distribution, nor the optimal combination rule for multisource domains. Interestingly, our theory also leads to an efficient learning strategy using adversarial neural networks: we show how to interpret it as learning feature representations that are invariant to the multiple domain shifts while still being discriminative for the learning task. To this end, we propose two models, both of which we call multisource domain adversarial networks (MDANs): the first model optimizes directly our bound, while the second model is a smoothed approximation of the first one, leading to a more data-efficient and task-adaptive model. The optimization tasks of both models are minimax saddle point problems that can be optimized by adversarial training. To demonstrate the effectiveness of MDANs, we conduct extensive experiments showing superior adaptation performance on three real-world datasets: sentiment analysis, digit classification, and vehicle counting.
New types of machine learning hardware in development and entering the market hold the promise of revolutionizing deep learning in a manner as profound as GPUs. However, existing software frameworks and training algorithms for deep learning have yet to evolve to fully leverage the capability of the new wave of silicon. We already see the limitations of existing algorithms for models that exploit structured input via complex and instance-dependent control flow, which prohibits minibatching. We present an asynchronous model-parallel (AMP) training algorithm that is specifically motivated by training on networks of interconnected devices. Through an implementation on multi-core CPUs, we show that AMP training converges to the same accuracy as conventional synchronous training algorithms in a similar number of epochs, but utilizes the available hardware more efficiently even for small minibatch sizes, resulting in significantly shorter overall training times. Our framework opens the door for scaling up a new class of deep learning models that cannot be efficiently trained today.
We introduce a new flexible paradigm of grounding and solving in Answer Set Programming (ASP), which we refer to as multi-shot ASP solving, and present its implementation in the ASP system clingo. Multi-shot ASP solving features grounding and solving processes that deal with continuously changing logic programs. In doing so, they remain operative and accommodate changes in a seamless way. For instance, such processes allow for advanced forms of search, as in optimization or theory solving, or interaction with an environment, as in robotics or query-answering. Common to them is that the problem specification evolves during the reasoning process, either because data or constraints are added, deleted, or replaced. This evolutionary aspect adds another dimension to ASP since it brings about state changing operations. We address this issue by providing an operational semantics that characterizes grounding and solving processes in multi-shot ASP solving. This characterization provides a semantic account of grounder and solver states along with the operations manipulating them. The operative nature of multi-shot solving avoids redundancies in relaunching grounder and solver programs and benefits from the solver's learning capacities. clingo accomplishes this by complementing ASP's declarative input language with control capacities. On the declarative side, a new directive allows for structuring logic programs into named and parameterizable subprograms. The grounding and integration of these subprograms into the solving process is completely modular and fully controllable from the procedural side. To this end, clingo offers a new application programming interface that is conveniently accessible via scripting languages.
Bandit based optimisation has a remarkable advantage over gradient based approaches due to their global perspective, which eliminates the danger of getting stuck at local optima. However, for continuous optimisation problems or problems with a large number of actions, bandit based approaches can be hindered by slow learning. Gradient based approaches, on the other hand, navigate quickly in high-dimensional continuous spaces through local optimisation, following the gradient in fine grained steps. Yet, apart from being susceptible to local optima, these schemes are less suited for online learning due to their reliance on extensive trial-and-error before the optimum can be identified. In this paper, we propose a Bayesian approach that unifies the above two paradigms in one single framework, with the aim of combining their advantages. At the heart of our approach we find a stochastic linear approximation of the function to be optimised, where both the gradient and values of the function are explicitly captured. This allows us to learn from both noisy function and gradient observations, and predict these properties across the action space to support optimisation. We further propose an accompanying bandit driven exploration scheme that uses Bayesian credible bounds to trade off exploration against exploitation. Our empirical results demonstrate that by unifying bandit and gradient based learning, one obtains consistently improved performance across a wide spectrum of problem environments. Furthermore, even when gradient feedback is unavailable, the flexibility of our model, including gradient prediction, still allows us outperform competing approaches, although with a smaller margin. Due to the pervasiveness of bandit based optimisation, our scheme opens up for improved performance both in meta-optimisation and in applications where gradient related information is readily available.
This paper proposes a new approach to a novel value network architecture for the game Go, called a multi-labelled (ML) value network. In the ML value network, different values (win rates) are trained simultaneously for different settings of komi, a compensation given to balance the initiative of playing first. The ML value network has three advantages, (a) it outputs values for different komi, (b) it supports dynamic komi, and (c) it lowers the mean squared error (MSE). This paper also proposes a new dynamic komi method to improve game-playing strength. This paper also performs experiments to demonstrate the merits of the architecture. First, the MSE of the ML value network is generally lower than the value network alone. Second, the program based on the ML value network wins by a rate of 67.6% against the program based on the value network alone. Third, the program with the proposed dynamic komi method significantly improves the playing strength over the baseline that does not use dynamic komi, especially for handicap games. To our knowledge, up to date, no handicap games have been played openly by programs using value networks. This paper provides these programs with a useful approach to playing handicap games.
We present a new system S for handling uncertainty in a quantified modal logic (first-order modal logic). The system is based on both probability theory and proof theory. The system is derived from Chisholm's epistemology. We concretize Chisholm's system by grounding his undefined and primitive (i.e. foundational) concept of reasonablenes in probability and proof theory. S can be useful in systems that have to interact with humans and provide justifications for their uncertainty. As a demonstration of the system, we apply the system to provide a solution to the lottery paradox. Another advantage of the system is that it can be used to provide uncertainty values for counterfactual statements. Counterfactuals are statements that an agent knows for sure are false. Among other cases, counterfactuals are useful when systems have to explain their actions to users. Uncertainties for counterfactuals fall out naturally from our system. Efficient reasoning in just simple first-order logic is a hard problem. Resolution-based first-order reasoning systems have made significant progress over the last several decades in building systems that have solved non-trivial tasks (even unsolved conjectures in mathematics). We present a sketch of a novel algorithm for reasoning that extends first-order resolution. Finally, while there have been many systems of uncertainty for propositional logics, first-order logics and propositional modal logics, there has been very little work in building systems of uncertainty for first-order modal logics. The work described below is in progress; and once finished will address this lack.
The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In this paper, we therefore study a lifelong version of the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting. One agent has to be assigned to each delivery task. This agent has to first move to a given pickup location and then to a given delivery location while avoiding collisions with other agents. We present two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS). Theoretically, we show that they solve all well-formed MAPD instances, a realistic subclass of MAPD instances. Experimentally, we compare them against a centralized strawman MAPD algorithm without this guarantee in a simulated warehouse system. TP can easily be extended to a fully distributed MAPD algorithm and is the best choice when real-time computation is of primary concern since it remains efficient for MAPD instances with hundreds of agents and tasks. TPTS requires limited communication among agents and balances well between TP and the centralized MAPD algorithm.
We consider the online one-class collaborative filtering (CF) problem that consists of recommending items to users over time in an online fashion based on positive ratings only. This problem arises when users respond only occasionally to a recommendation with a positive rating, and never with a negative one. We study the impact of the probability of a user responding to a recommendation, p_f, on the sample complexity, i.e., the number of ratings required to make `good' recommendations, and ask whether receiving positive and negative ratings, instead of positive ratings only, improves the sample complexity. Both questions arise in the design of recommender systems. We introduce a simple probabilistic user model, and analyze the performance of an online user-based CF algorithm. We prove that after an initial cold start phase, where recommendations are invested in exploring the user's preferences, this algorithm makes---up to a fraction of the recommendations required for updating the user's preferences---perfect recommendations. The number of ratings required for the cold start phase is nearly proportional to 1/p_f, and that for updating the user's preferences is essentially independent of p_f. As a consequence we find that, receiving positive and negative ratings instead of only positive ones improves the number of ratings required for initial exploration by a factor of 1/p_f, which can be significant.
DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross-modal training data. In single-modal scenario, similar problem is usually relieved by transferring knowledge from large-scale auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is also very useful for cross-modal retrieval, which can provide rich general semantic information that can be shared across different modalities. However, it is challenging to transfer useful knowledge from single-modal (as image) source domain to cross-modal (as image/text) target domain. Knowledge in source domain cannot be directly transferred to both two different modalities in target domain, and the inherent cross-modal correlation contained in target domain provides key hints for cross-modal retrieval which should be preserved during transfer process. This paper proposes Cross-modal Hybrid Transfer Network (CHTN) with two subnetworks: Modal-sharing transfer subnetwork utilizes the modality in both source and target domains as a bridge, for transferring knowledge to both two modalities simultaneously; Layer-sharing correlation subnetwork preserves the inherent cross-modal semantic correlation to further adapt to cross-modal retrieval task. Cross-modal data can be converted to common representation by CHTN for retrieval, and comprehensive experiment on 3 datasets shows its effectiveness.
Off-policy model-free deep reinforcement learning methods using previously collected data can improve sample efficiency over on-policy policy gradient techniques. On the other hand, on-policy algorithms are often more stable and easier to use. This paper examines, both theoretically and empirically, approaches to merging on- and off-policy updates for deep reinforcement learning. Theoretical results show that off-policy updates with a value function estimator can be interpolated with on-policy policy gradient updates whilst still satisfying performance bounds. Our analysis uses control variate methods to produce a family of policy gradient algorithms, with several recently proposed algorithms being special cases of this family. We then provide an empirical comparison of these techniques with the remaining algorithmic details fixed, and show how different mixing of off-policy gradient estimates with on-policy samples contribute to improvements in empirical performance. The final algorithm provides a generalization and unification of existing deep policy gradient techniques, has theoretical guarantees on the bias introduced by off-policy updates, and improves on the state-of-the-art model-free deep RL methods on a number of OpenAI Gym continuous control benchmarks.
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder. This allows us to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks to learn representations for the remaining variables. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. We evaluate our framework's ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets.
We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters. The algorithm --- an iterative application of compressed sensing techniques for orthogonal polynomials --- requires only uniform sampling of the hyperparameters and is thus easily parallelizable. Experiments for training deep neural networks on Cifar-10 show that compared to state-of-the-art tools (e.g., Hyperband and Spearmint), our algorithm finds significantly improved solutions, in some cases better than what is attainable by hand-tuning. In terms of overall running time (i.e., time required to sample various settings of hyperparameters plus additional computation time), we are at least an order of magnitude faster than Hyperband and Bayesian Optimization. We also outperform Random Search 8x. Additionally, our method comes with provable guarantees and yields the first improvements on the sample complexity of learning decision trees in over two decades. In particular, we obtain the first quasi-polynomial time algorithm for learning noisy decision trees with polynomial sample complexity.
A novel skill learning approach is proposed that allows a robot to acquire human-like visuospatial skills for object manipulation tasks. Visuospatial skills are attained by observing spatial relationships among objects through demonstrations. The proposed Visuospatial Skill Learning (VSL) is a goal-based approach that focuses on achieving a desired goal configuration of objects relative to one another while maintaining the sequence of operations. VSL is capable of learning and generalizing multi-operation skills from a single demonstration, while requiring minimum prior knowledge about the objects and the environment. In contrast to many existing approaches, VSL offers simplicity, efficiency and user-friendly human-robot interaction. We also show that VSL can be easily extended towards 3D object manipulation tasks, simply by employing point cloud processing techniques. In addition, a robot learning framework, VSL-SP, is proposed by integrating VSL, Imitation Learning, and a conventional planning method. In VSL-SP, the sequence of performed actions are learned using VSL, while the sensorimotor skills are learned using a conventional trajectory-based learning approach. such integration easily extends robot capabilities to novel situations, even by users without programming ability. In VSL-SP the internal planner of VSL is integrated with an existing action-level symbolic planner. Using the underlying constraints of the task and extracted symbolic predicates, identified by VSL, symbolic representation of the task is updated. Therefore the planner maintains a generalized representation of each skill as a reusable action, which can be used in planning and performed independently during the learning phase. The proposed approach is validated through several real-world experiments.
Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories. We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity. We present a technique for preprocessing textual story data into event sequences. We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence). We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.
Using established principles from Information Theory and Statistics, we show that in a deep neural network invariance to nuisance factors is equivalent to information minimality of the learned representation, and that stacking layers and injecting noise during training naturally bias the network towards learning invariant representations. We then show that, in order to avoid memorization, we need to limit the quantity of information stored in the weights, which leads to a novel usage of the Information Bottleneck Lagrangian on the weights as a learning criterion. This also has an alternative interpretation as minimizing a PAC-Bayesian bound on the test error. Finally, we exploit a duality between weights and activations induced by the architecture, to show that the information in the weights bounds the minimality and Total Correlation of the layers, therefore showing that regularizing the weights explicitly or implicitly, using SGD, not only helps avoid overfitting, but also fosters invariance and disentangling of the learned representation. The theory also enables predicting sharp phase transitions between underfitting and overfitting random labels at precise information values, and sheds light on the relation between the geometry of the loss function, in particular so-called "flat minima," and generalization.
This study investigates how adequate coordination among the different cognitive processes of a humanoid robot can be developed through end-to-end learning of direct perception of visuomotor stream. We propose a deep dynamic neural network model built on a dynamic vision network, a motor generation network, and a higher-level network. The proposed model was designed to process and to integrate direct perception of dynamic visuomotor patterns in a hierarchical model characterized by different spatial and temporal constraints imposed on each level. We conducted synthetic robotic experiments in which a robot learned to read human's intention through observing the gestures and then to generate the corresponding goal-directed actions. Results verify that the proposed model is able to learn the tutored skills and to generalize them to novel situations. The model showed synergic coordination of perception, action and decision making, and it integrated and coordinated a set of cognitive skills including visual perception, intention reading, attention switching, working memory, action preparation and execution in a seamless manner. Analysis reveals that coherent internal representations emerged at each level of the hierarchy. Higher-level representation reflecting actional intention developed by means of continuous integration of the lower-level visuo-proprioceptive stream.
This study presents a dynamic neural network model based on the predictive coding framework for perceiving and predicting the dynamic visuo-proprioceptive patterns. In our previous study [1], we have shown that the deep dynamic neural network model was able to coordinate visual perception and action generation in a seamless manner. In the current study, we extended the previous model under the predictive coding framework to endow the model with a capability of perceiving and predicting dynamic visuo-proprioceptive patterns as well as a capability of inferring intention behind the perceived visuomotor information through minimizing prediction error. A set of synthetic experiments were conducted in which a robot learned to imitate the gestures of another robot in a simulation environment. The experimental results showed that with given intention states, the model was able to mentally simulate the possible incoming dynamic visuo-proprioceptive patterns in a top-down process without the inputs from the external environment. Moreover, the results highlighted the role of minimizing prediction error in inferring underlying intention of the perceived visuo-proprioceptive patterns, supporting the predictive coding account of the mirror neuron systems. The results also revealed that minimizing prediction error in one modality induced the recall of the corresponding representation of another modality acquired during the consolidative learning of raw-level visuo-proprioceptive patterns.
Humans and animals are constantly exposed to a continuous stream of sensory information from different modalities. At the same time, they form more compressed representations like concepts or symbols. In species that use language, this process is further structured by this interaction, where a mapping between the sensorimotor concepts and linguistic elements needs to be established. There is evidence that children might be learning language by simply disambiguating potential meanings based on multiple exposures to utterances in different contexts (cross-situational learning). In existing models, the mapping between modalities is usually found in a single step by directly using frequencies of referent and meaning co-occurrences. In this paper, we present an extension of this one-step mapping and introduce a newly proposed sequential mapping algorithm together with a publicly available Matlab implementation. For demonstration, we have chosen a less typical scenario: instead of learning to associate objects with their names, we focus on body representations. A humanoid robot is receiving tactile stimulations on its body, while at the same time listening to utterances of the body part names (e.g., hand, forearm and torso). With the goal at arriving at the correct "body categories", we demonstrate how a sequential mapping algorithm outperforms one-step mapping. In addition, the effect of data set size and noise in the linguistic input are studied.
Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like Trip.com and Amazon; whereas the latter refers to social networking services (SNSs) that have rich user-user connections, such as Facebook and Twitter. Despite their heterogeneity, these two domains can be bridged by a few overlapping users, dubbed as bridge users. In this work, we address the problem of cross-domain social recommendation, i.e., recommending relevant items of information domains to potential users of social networks. To our knowledge, this is a new problem that has rarely been studied before. Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped. Towards this end, we present a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs. In the information domain part, the attributes of users and items are leveraged to strengthen the embedding learning of users and items. In the SNS part, the embeddings of bridge users are propagated to learn the embeddings of other non-bridge users. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our NSCR method.
Over 13 months in 2016-17 the FCC conducted an "incentive auction" to repurpose radio spectrum from broadcast television to wireless internet. In the end, the auction yielded $19.8 billion, $10.05 billion of which was paid to 175 broadcasters for voluntarily relinquishing their licenses across 14 UHF channels. Stations that continued broadcasting were assigned potentially new channels to fit as densely as possible into the channels that remained. The government netted more than $7 billion (used to pay down the national debt) after covering costs. A crucial element of the auction design was the construction of a solver, dubbed SATFC, that determined whether sets of stations could be "repacked" in this way; it needed to run every time a station was given a price quote. This paper describes the process by which we built SATFC. We adopted an approach we dub "deep optimization", taking a data-driven, highly parametric, and computationally intensive approach to solver design. More specifically, to build SATFC we designed software that could pair both complete and local-search SAT-encoded feasibility checking with a wide range of domain-specific techniques. We then used automatic algorithm configuration techniques to construct a portfolio of eight complementary algorithms to be run in parallel, aiming to achieve good performance on instances that arose in proprietary auction simulations. To evaluate the impact of our solver in this paper, we built an open-source reverse auction simulator. We found that within the short time budget required in practice, SATFC solved more than 95% of the problems it encountered. Furthermore, the incentive auction paired with SATFC produced nearly optimal allocations in a restricted setting and substantially outperformed other alternatives at national scale.
In order to perform complex actions in human environments, an autonomous robot needs the ability to understand the environment, that is, to gather and maintain spatial knowledge. Topological map is commonly used for representing large scale, global maps such as floor plans. Although much work has been done in topological map extraction, we have found little previous work on the problem of learning the topological map using a probabilistic model. Learning a topological map means learning the structure of the large-scale space and dependency between places, for example, how the evidence of a group of places influence the attributes of other places. This is an important step towards planning complex actions in the environment. In this thesis, we consider the problem of using probabilistic deep learning model to learn the topological map, which is essentially a sparse undirected graph where nodes represent places annotated with their semantic attributes (e.g. place category). We propose to use a novel probabilistic deep model, Sum-Product Networks (SPNs), due to their unique properties. We present two methods for learning topological maps using SPNs: the place grid method and the template-based method. We contribute an algorithm that builds SPNs for graphs using template models. Our experiments evaluate the ability of our models to enable robots to infer semantic attributes and detect maps with novel semantic attribute arrangements. Our results demonstrate their understanding of the topological map structure and spatial relations between places.
Designing an auction that maximizes expected revenue is an intricate task. Indeed, as of today--despite major efforts and impressive progress over the past few years--only the single-item case is fully understood. In this work, we initiate the exploration of the use of tools from deep learning on this topic. The design objective is revenue optimal, dominant-strategy incentive compatible auctions. We show that multi-layer neural networks can learn almost-optimal auctions for settings for which there are analytical solutions, such as Myerson's auction for a single item, Manelli and Vincent's mechanism for a single bidder with additive preferences over two items, or Yao's auction for two additive bidders with binary support distributions and multiple items, even if no prior knowledge about the form of optimal auctions is encoded in the network and the only feedback during training is revenue and regret. We further show how characterization results, even rather implicit ones such as Rochet's characterization through induced utilities and their gradients, can be leveraged to obtain more precise fits to the optimal design. We conclude by demonstrating the potential of deep learning for deriving optimal auctions with high revenue for poorly understood problems.
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the-art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.
Verification determines whether two samples belong to the same class or not, and has important applications such as face and fingerprint verification, where thousands or millions of categories are present but each category has scarce labeled examples, presenting two major challenges for existing deep learning models. We propose a deep semi-supervised model named SEmi-supervised VErification Network (SEVEN) to address these challenges. The model consists of two complementary components. The generative component addresses the lack of supervision within each category by learning general salient structures from a large amount of data across categories. The discriminative component exploits the learned general features to mitigate the lack of supervision within categories, and also directs the generative component to find more informative structures of the whole data manifold. The two components are tied together in SEVEN to allow an end-to-end training of the two components. Extensive experiments on four verification tasks demonstrate that SEVEN significantly outperforms other state-of-the-art deep semi-supervised techniques when labeled data are in short supply. Furthermore, SEVEN is competitive with fully supervised baselines trained with a larger amount of labeled data. It indicates the importance of the generative component in SEVEN.
Recommendation algorithms that incorporate techniques from deep learning are becoming increasingly popular. Due to the structure of the data coming from recommendation domains (i.e., one-hot-encoded vectors of item preferences), these algorithms tend to have large input and output dimensionalities that dominate their overall size. This makes them difficult to train, due to the limited memory of graphical processing units, and difficult to deploy on mobile devices with limited hardware. To address these difficulties, we propose Bloom embeddings, a compression technique that can be applied to the input and output of neural network models dealing with sparse high-dimensional binary-coded instances. Bloom embeddings are computationally efficient, and do not seriously compromise the accuracy of the model up to 1/5 compression ratios. In some cases, they even improve over the original accuracy, with relative increases up to 12%. We evaluate Bloom embeddings on 7 data sets and compare it against 4 alternative methods, obtaining favorable results. We also discuss a number of further advantages of Bloom embeddings, such as 'on-the-fly' constant-time operation, zero or marginal space requirements, training time speedups, or the fact that they do not require any change to the core model architecture or training configuration.
The success of automated driving deployment is highly depending on the ability to develop an efficient and safe driving policy. The problem is well formulated under the framework of optimal control as a cost optimization problem. Model based solutions using traditional planning are efficient, but require the knowledge of the environment model. On the other hand, model free solutions suffer sample inefficiency and require too many interactions with the environment, which is infeasible in practice. Methods under the Reinforcement Learning framework usually require the notion of a reward function, which is not available in the real world. Imitation learning helps in improving sample efficiency by introducing prior knowledge obtained from the demonstrated behavior, on the risk of exact behavior cloning without generalizing to unseen environments. In this paper we propose a Meta learning framework, based on data set aggregation, to improve generalization of imitation learning algorithms. Under the proposed framework, we propose MetaDAgger, a novel algorithm which tackles the generalization issues in traditional imitation learning. We use The Open Race Car Simulator (TORCS) to test our algorithm. Results on unseen test tracks show significant improvement over traditional imitation learning algorithms, improving the learning time and sample efficiency in the same time. The results are also supported by visualization of the learnt features to prove generalization of the captured details.
Monte Carlo tree search (MCTS) is extremely popular in computer Go which determines each action by enormous simulations in a broad and deep search tree. However, human experts select most actions by pattern analysis and careful evaluation rather than brute search of millions of future nteractions. In this paper, we propose a computer Go system that follows experts way of thinking and playing. Our system consists of two parts. The first part is a novel deep alternative neural network (DANN) used to generate candidates of next move. Compared with existing deep convolutional neural network (DCNN), DANN inserts recurrent layer after each convolutional layer and stacks them in an alternative manner. We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction. The second part is a long-term evaluation (LTE) module used to provide a reliable evaluation of candidates rather than a single probability from move predictor. This is consistent with human experts nature of playing since they can foresee tens of steps to give an accurate estimation of candidates. In our system, for each candidate, LTE calculates a cumulative reward after several future interactions when local variations are settled. Combining criteria from the two parts, our system determines the optimal choice of next move. For more comprehensive experiments, we introduce a new professional Go dataset (PGD), consisting of 253233 professional records. Experiments on GoGoD and PGD datasets show the DANN can substantially improve performance of move prediction over pure DCNN. When combining LTE, our system outperforms most relevant approaches and open engines based on MCTS.
An Autonomous Underwater Vehicle (AUV) should carry out complex tasks in a limited time interval. Since existing AUVs have limited battery capacity and restricted endurance, they should autonomously manage mission time and the resources to perform effective persistent deployment in longer missions. Task assignment requires making decisions subject to resource constraints, while tasks are assigned with costs and/or values that are budgeted in advance. Tasks are distributed in a particular operation zone and mapped by a waypoint covered network. Thus, design an efficient routing-task priority assign framework considering vehicle's availabilities and properties is essential for increasing mission productivity and on-time mission completion. This depends strongly on the order and priority of the tasks that are located between node-like waypoints in an operation network. On the other hand, autonomous operation of AUVs in an unfamiliar dynamic underwater and performing quick response to sudden environmental changes is a complicated process. Water current instabilities can deflect the vehicle to an undesired direction and perturb AUVs safety. The vehicle's robustness to strong environmental variations is extremely crucial for its safe and optimum operations in an uncertain and dynamic environment. To this end, the AUV needs to have a general overview of the environment in top level to perform an autonomous action selection (task selection) and a lower level local motion planner to operate successfully in dealing with continuously changing situations. This research deals with developing a novel reactive control architecture to provide a higher level of decision autonomy for the AUV operation that enables a single vehicle to accomplish multiple tasks in a single mission in the face of periodic disturbances in a turbulent and highly uncertain environment.
Traditional approaches to building a large scale knowledge graph have usually relied on extracting information (entities, their properties, and relations between them) from unstructured text (e.g. Dbpedia). Recent advances in Convolutional Neural Networks (CNN) allow us to shift our focus to learning entities and relations from images, as they build robust models that require little or no pre-processing of the images. In this paper, we present an approach to identify and extract spatial relations (e.g., The girl is standing behind the table) from images using CNNs. Our research addresses two specific challenges: providing insight into how spatial relations are learned by the network and which parts of the image are used to predict these relations. We use the pre-trained network VGGNet to extract features from an image and train a Multi-layer Perceptron (MLP) on a set of synthetic images and the sun09 dataset to extract spatial relations. The MLP predicts spatial relations without a bounding box around the objects or the space in the image depicting the relation. To understand how the spatial relations are represented in the network, a heatmap is overlayed on the image to show the regions that are deemed important by the network. Also, we analyze the MLP to show the relationship between the activation of consistent groups of nodes and the prediction of a spatial relation. We show how the loss of these groups affects the networks ability to identify relations.
Backdoors and backbones of Boolean formulas are hidden structural properties. A natural goal, already in part realized, is that solver algorithms seek to obtain substantially better performance by exploiting these structures. However, the present paper is not intended to improve the performance of SAT solvers, but rather is a cautionary paper. In particular, the theme of this paper is that there is a potential chasm between the existence of such structures in the Boolean formula and being able to effectively exploit them. This does not mean that these structures are not useful to solvers. It does mean that one must be very careful not to assume that it is computationally easy to go from the existence of a structure to being able to get one's hands on it and/or being able to exploit the structure. For example, in this paper we show that, under the assumption that P $\neq$ NP, there are easily recognizable sets of Boolean formulas for which it is hard to determine whether they have a large backbone. We also show that, also under the assumption P $\neq$ NP, there are easily recognizable families of Boolean formulas with strong backdoors that are easy to find, yet for which it is hard to determine whether they are satisfiable.
We want to build robots that are useful in unstructured real world applications, such as doing work in the household. Grasping in particular is an important skill in this domain, yet it remains a challenge. One of the key hurdles is handling unexpected changes or motion in the objects being grasped and kinematic noise or other errors in the robot. This paper proposes an approach to learning a closed-loop controller for robotic grasping that dynamically guides the gripper to the object. We use a wrist-mounted sensor to acquire depth images in front of the gripper and train a convolutional neural network to learn a distance function to true grasps for grasp configurations over an image. The training sensor data is generated in simulation, a major advantage over previous work that uses real robot experience, which is costly to obtain. Despite being trained in simulation, our approach works well on real noisy sensor images. We compare our controller in simulated and real robot experiments to a strong baseline for grasp pose detection, and find that our approach significantly outperforms the baseline in the presence of kinematic noise, perceptual errors and disturbances of the object during grasping.
We consider the effect of introducing a curriculum of targets when training Boolean models on supervised Multi Label Classification (MLC) problems. In particular, we consider how to order targets in the absence of prior knowledge, and how such a curriculum may be enforced when using meta-heuristics to train discrete non-linear models. We show that hierarchical dependencies between targets can be exploited by enforcing an appropriate curriculum using hierarchical loss functions. On several multi output circuit-inference problems with known target difficulties, Feedforward Boolean Networks (FBNs) trained with such a loss function achieve significantly lower out-of-sample error, up to $10\%$ in some cases. This improvement increases as the loss places more emphasis on target order and is strongly correlated with an easy-to-hard curricula. We also demonstrate the same improvements on three real-world models and two Gene Regulatory Network (GRN) inference problems. We posit a simple a-priori method for identifying an appropriate target order and estimating the strength of target relationships in Boolean MLCs. These methods use intrinsic dimension as a proxy for target difficulty, which is estimated using optimal solutions to a combinatorial optimisation problem known as the Minimum-Feature-Set (minFS) problem. We also demonstrate that the same generalisation gains can be achieved without providing any knowledge of target difficulty.
The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these requirements is to use a heterogeneous distributed environment with a mixture of hardware devices such as CPUs and GPUs. Importantly, the decision of placing parts of the neural models on devices is often made by human experts based on simple heuristics and intuitions. In this paper, we propose a method which learns to optimize device placement for TensorFlow computational graphs. Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices. The execution time of the predicted placements is then used as the reward signal to optimize the parameters of the sequence-to-sequence model. Our main result is that on Inception-V3 for ImageNet classification, and on RNN LSTM, for language modeling and neural machine translation, our model finds non-trivial device placements that outperform hand-crafted heuristics and traditional algorithmic methods.
A key part of any evolutionary algorithm is fitness evaluation. When fitness evaluations are corrupted by noise, as happens in many real-world problems as a consequence of various types of uncertainty, a strategy is needed in order to cope with this. Resampling is one of the most common strategies, whereby each solution is evaluated many times in order to reduce the variance of the fitness estimates. When evaluating the performance of a noisy optimisation algorithm, a key consideration is the stopping condition for the algorithm. A frequently used stopping condition in runtime analysis, known as "First Hitting Time", is to stop the algorithm as soon as it encounters the optimal solution. However, this is unrealistic for real-world problems, as if the optimal solution were already known, there would be no need to search for it. This paper argues that the use of First Hitting Time, despite being a commonly used approach, is significantly flawed and overestimates the quality of many algorithms in real-world cases, where the optimum is not known in advance and has to be genuinely searched for. A better alternative is to measure the quality of the solution an algorithm returns after a fixed evaluation budget, i.e., to focus on final solution quality. This paper argues that focussing on final solution quality is more realistic and demonstrates cases where the results produced by each algorithm evaluation method lead to very different conclusions regarding the quality of each noisy optimisation algorithm.
The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.
Note that a newer expanded version of this paper is now available at: arXiv:1802.03888 It is critical in many applications to understand what features are important for a model, and why individual predictions were made. For tree ensemble methods these questions are usually answered by attributing importance values to input features, either globally or for a single prediction. Here we show that current feature attribution methods are inconsistent, which means changing the model to rely more on a given feature can actually decrease the importance assigned to that feature. To address this problem we develop fast exact solutions for SHAP (SHapley Additive exPlanation) values, which were recently shown to be the unique additive feature attribution method based on conditional expectations that is both consistent and locally accurate. We integrate these improvements into the latest version of XGBoost, demonstrate the inconsistencies of current methods, and show how using SHAP values results in significantly improved supervised clustering performance. Feature importance values are a key part of understanding widely used models such as gradient boosting trees and random forests, so improvements to them have broad practical implications.
Evolution sculpts both the body plans and nervous systems of agents together over time. In contrast, in AI and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behavior arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioral performance. Here, we further examine this hypothesis and demonstrate a technique for "morphological innovation protection", which temporarily reduces selection pressure on recently morphologically-changed individuals, thus enabling evolution some time to "readapt" to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioral training -- while simultaneously providing a testbed to investigate the theory of embodied cognition.
In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features that are the outcome of an optimization algorithm. I-vectors are the result of a modeling technique that usually takes engineered features as input. It has been shown that standard MFCCs extracted from monaural audio signals lead to i-vectors that exhibit poor performance, especially on indoor acoustic scenes. At the same time, Convolutional Neural Networks (CNNs) are well known for their ability to learn features by optimizing their filters. They have been applied on ASC and have shown promising results. In this paper, we first propose a novel multi-channel i-vector extraction and scoring scheme for ASC, improving their performance on indoor and outdoor scenes. Second, we propose a CNN architecture that achieves promising ASC results. Further, we show that i-vectors and CNNs capture complementary information from acoustic scenes. Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique. Using our method, we participated in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1 st rank among 49 submissions, substantially improving the previous state of the art.
This paper introduces a new framework for real-time decision making in video games. An Ensemble agent is a compound agent composed of multiple agents, each with its own tasks or goals to achieve. Usually when dealing with real-time decision making, reactive agents are used; that is agents that return a decision based on the current state. While reactive agents are very fast, most games require more than just a rule-based agent to achieve good results. Deliberative agents---agents that use a forward model to search future states---are very useful in games with no hard time limit, such as Go or Backgammon, but generally take too long for real-time games. The Ensemble framework addresses this issue by allowing the agent to be both deliberative and reactive at the same time. This is achieved by breaking up the game-play into logical roles and having highly focused components for each role, with each component disregarding anything outwith its own role. Reactive agents can be used where a reactive agent is suited to the role, and where a deliberative approach is required, branching is kept to a minimum by the removal of all extraneous factors, enabling an informed decision to be made within a much smaller time-frame. An Arbiter is used to combine the component results, allowing high performing agents to be created from simple, efficient components.
In recent years, research has been done on applying Recurrent Neural Networks (RNNs) as recommender systems. Results have been promising, especially in the session-based setting where RNNs have been shown to outperform state-of-the-art models. In many of these experiments, the RNN could potentially improve the recommendations by utilizing information about the user's past sessions, in addition to its own interactions in the current session. A problem for session-based recommendation, is how to produce accurate recommendations at the start of a session, before the system has learned much about the user's current interests. We propose a novel approach that extends a RNN recommender to be able to process the user's recent sessions, in order to improve recommendations. This is done by using a second RNN to learn from recent sessions, and predict the user's interest in the current session. By feeding this information to the original RNN, it is able to improve its recommendations. Our experiments on two different datasets show that the proposed approach can significantly improve recommendations throughout the sessions, compared to a single RNN working only on the current session. The proposed model especially improves recommendations at the start of sessions, and is therefore able to deal with the cold start problem within sessions.
Advances in image processing and computer vision in the latest years have brought about the use of visual features in artwork recommendation. Recent works have shown that visual features obtained from pre-trained deep neural networks (DNNs) perform very well for recommending digital art. Other recent works have shown that explicit visual features (EVF) based on attractiveness can perform well in preference prediction tasks, but no previous work has compared DNN features versus specific attractiveness-based visual features (e.g. brightness, texture) in terms of recommendation performance. In this work, we study and compare the performance of DNN and EVF features for the purpose of physical artwork recommendation using transactional data from UGallery, an online store of physical paintings. In addition, we perform an exploratory analysis to understand if DNN embedded features have some relation with certain EVF. Our results show that DNN features outperform EVF, that certain EVF features are more suited for physical artwork recommendation and, finally, we show evidence that certain neurons in the DNN might be partially encoding visual features such as brightness, providing an opportunity for explaining recommendations based on visual neural models.
Hierarchical models are utilized in a wide variety of problems which are characterized by task hierarchies, where predictions on smaller subtasks are useful for trying to predict a final task. Typically, neural networks are first trained for the subtasks, and the predictions of these networks are subsequently used as additional features when training a model and doing inference for a final task. In this work, we focus on improving learning for such hierarchical models and demonstrate our method on the task of speaker trait prediction. Speaker trait prediction aims to computationally identify which personality traits a speaker might be perceived to have, and has been of great interest to both the Artificial Intelligence and Social Science communities. Persuasiveness prediction in particular has been of interest, as persuasive speakers have a large amount of influence on our thoughts, opinions and beliefs. In this work, we examine how leveraging the relationship between related speaker traits in a hierarchical structure can help improve our ability to predict how persuasive a speaker is. We present a novel algorithm that allows us to backpropagate through this hierarchy. This hierarchical model achieves a 25% relative error reduction in classification accuracy over current state-of-the art methods on the publicly available POM dataset.
We present a straightforward source-to-source transformation that introduces justifications for user-defined constraints into the CHR programming language. Then a scheme of two rules suffices to allow for logical retraction (deletion, removal) of constraints during computation. Without the need to recompute from scratch, these rules remove not only the constraint but also undo all consequences of the rule applications that involved the constraint. We prove a confluence result concerning the rule scheme and show its correctness. When algorithms are written in CHR, constraints represent both data and operations. CHR is already incremental by nature, i.e. constraints can be added at runtime. Logical retraction adds decrementality. Hence any algorithm written in CHR with justifications will become fully dynamic. Operations can be undone and data can be removed at any point in the computation without compromising the correctness of the result. We present two classical examples of dynamic algorithms, written in our prototype implementation of CHR with justifications that is available online: maintaining the minimum of a changing set of numbers and shortest paths in a graph whose edges change.
In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning. On the one side, the generation of a high rate of structured data on the Web led to the creation and publication of the so-called knowledge graphs. On the other side, deep learning emerged as one of the most promising approaches in the generation and training of models that can be applied to a wide variety of application fields. More recently, autoencoders have proven their strength in various scenarios, playing a fundamental role in unsupervised learning. In this paper, we instigate how to exploit the semantic information encoded in a knowledge graph to build connections between units in a Neural Network, thus leading to a new method, SEM-AUTO, to extract and weigh semantic features that can eventually be used to build a recommender system. As adding content-based side information may mitigate the cold user problems, we tested how our approach behave in the presence of a few rating from a user on the Movielens 1M dataset and compare results with BPRSLIM.
Online advertising and product recommendation are important domains of applications for multi-armed bandit methods. In these fields, the reward that is immediately available is most often only a proxy for the actual outcome of interest, which we refer to as a conversion. For instance, in web advertising, clicks can be observed within a few seconds after an ad display but the corresponding sale --if any-- will take hours, if not days to happen. This paper proposes and investigates a new stochas-tic multi-armed bandit model in the framework proposed by Chapelle (2014) --based on empirical studies in the field of web advertising-- in which each action may trigger a future reward that will then happen with a stochas-tic delay. We assume that the probability of conversion associated with each action is unknown while the distribution of the conversion delay is known, distinguishing between the (idealized) case where the conversion events may be observed whatever their delay and the more realistic setting in which late conversions are censored. We provide performance lower bounds as well as two simple but efficient algorithms based on the UCB and KLUCB frameworks. The latter algorithm, which is preferable when conversion rates are low, is based on a Poissonization argument, of independent interest in other settings where aggregation of Bernoulli observations with different success probabilities is required.
Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. The framework consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. This framework is realized in three instants in this work with a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. All three instances of the framework monopolize the top three positions in all experiments, outdistancing other compared trading algorithms. Although with a high commission rate of 0.25% in the backtests, the framework is able to achieve at least 4-fold returns in 50 days.
Markov decision processes (MDPs) are the standard formalism for modelling sequential decision making in stochastic environments. Policy synthesis addresses the problem of how to control or limit the decisions an agent makes so that a given specification is met. In this paper we consider PCTL*, the probabilistic counterpart of CTL*, as the specification language. Because in general the policy synthesis problem for PCTL* is undecidable, we restrict to policies whose execution history memory is finitely bounded a priori. Surprisingly, no algorithm for policy synthesis for this natural and expressive framework has been developed so far. We close this gap and describe a tableau-based algorithm that, given an MDP and a PCTL* specification, derives in a non-deterministic way a system of (possibly nonlinear) equalities and inequalities. The solutions of this system, if any, describe the desired (stochastic) policies. Our main result in this paper is the correctness of our method, i.e., soundness, completeness and termination.
In reinforcement learning (RL) tasks, an efficient exploration mechanism should be able to encourage an agent to take actions that lead to less frequent states which may yield higher accumulative future return. However, both knowing about the future and evaluating the frequentness of states are non-trivial tasks, especially for deep RL domains, where a state is represented by high-dimensional image frames. In this paper, we propose a novel informed exploration framework for deep RL tasks, where we build the capability for a RL agent to predict over the future transitions and evaluate the frequentness for the predicted future frames in a meaningful manner. To this end, we train a deep prediction model to generate future frames given a state-action pair, and a convolutional autoencoder model to generate deep features for conducting hashing over the seen frames. In addition, to utilize the counts derived from the seen frames to evaluate the frequentness for the predicted frames, we tackle the challenge of making the hash codes for the predicted future frames to match with their corresponding seen frames. In this way, we could derive a reliable metric for evaluating the novelty of the future direction pointed by each action, and hence inform the agent to explore the least frequent one. We use Atari 2600 games as the testing environment and demonstrate that the proposed framework achieves significant performance gain over a state-of-the-art informed exploration approach in most of the domains.
Regression or classification? This is perhaps the most basic question faced when tackling a new supervised learning problem. We present an Evolutionary Deep Learning (EDL) algorithm that automatically solves this by identifying the question type with high accuracy, along with a proposed deep architecture. Typically, a significant amount of human insight and preparation is required prior to executing machine learning algorithms. For example, when creating deep neural networks, the number of parameters must be selected in advance and furthermore, a lot of these choices are made based upon pre-existing knowledge of the data such as the use of a categorical cross entropy loss function. Humans are able to study a dataset and decide whether it represents a classification or a regression problem, and consequently make decisions which will be applied to the execution of the neural network. We propose the Automated Problem Identification (API) algorithm, which uses an evolutionary algorithm interface to TensorFlow to manipulate a deep neural network to decide if a dataset represents a classification or a regression problem. We test API on 16 different classification, regression and sentiment analysis datasets with up to 10,000 features and up to 17,000 unique target values. API achieves an average accuracy of $96.3\%$ in identifying the problem type without hardcoding any insights about the general characteristics of regression or classification problems. For example, API successfully identifies classification problems even with 1000 target values. Furthermore, the algorithm recommends which loss function to use and also recommends a neural network architecture. Our work is therefore a step towards fully automated machine learning.
In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical methods exist for determining high-confidence policy performance bounds in the inverse reinforcement learning setting---where the true reward function is unknown and only samples of expert behavior are given. We propose a sampling method based on Bayesian inverse reinforcement learning that uses demonstrations to determine practical high-confidence upper bounds on the $\alpha$-worst-case difference in expected return between any evaluation policy and the optimal policy under the expert's unknown reward function. We evaluate our proposed bound on both a standard grid navigation task and a simulated driving task and achieve tighter and more accurate bounds than a feature count-based baseline. We also give examples of how our proposed bound can be utilized to perform risk-aware policy selection and risk-aware policy improvement. Because our proposed bound requires several orders of magnitude fewer demonstrations than existing high-confidence bounds, it is the first practical method that allows agents that learn from demonstration to express confidence in the quality of their learned policy.
In this paper, we propose ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research. Using ELF, we implement a highly customizable real-time strategy (RTS) engine with three game environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a miniature version of StarCraft, captures key game dynamics and runs at 40K frame-per-second (FPS) per core on a Macbook Pro notebook. When coupled with modern reinforcement learning methods, the system can train a full-game bot against built-in AIs end-to-end in one day with 6 CPUs and 1 GPU. In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like Arcade Learning Environment. Using ELF, we thoroughly explore training parameters and show that a network with Leaky ReLU and Batch Normalization coupled with long-horizon training and progressive curriculum beats the rule-based built-in AI more than $70\%$ of the time in the full game of Mini-RTS. Strong performance is also achieved on the other two games. In game replays, we show our agents learn interesting strategies. ELF, along with its RL platform, is open-sourced at https://github.com/facebookresearch/ELF.
Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned representations should also be discriminative in prediction. To learn such representations, domain adaptation frameworks usually include a domain invariant representation learning approach to measure and reduce the domain discrepancy, as well as a discriminator for classification. Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by the domain critic, to estimate empirical Wasserstein distance between the source and target samples and optimizes the feature extractor network to minimize the estimated Wasserstein distance in an adversarial manner. The theoretical advantages of Wasserstein distance for domain adaptation lie in its gradient property and promising generalization bound. Empirical studies on common sentiment and image classification adaptation datasets demonstrate that our proposed WDGRL outperforms the state-of-the-art domain invariant representation learning approaches.
Despite large incentives, ecorrectness in software remains an elusive goal. Declarative programming techniques, where algorithms are derived from a specification of the desired behavior, offer hope to address this problem, since there is a combinatorial reduction in complexity in programming in terms of specifications instead of algorithms, and arbitrary desired properties can be expressed and enforced in specifications directly. However, limitations on performance have prevented programming with declarative specifications from becoming a mainstream technique for general-purpose programming. To address the performance bottleneck in deriving an algorithm from a specification, I propose information-gain computation, a framework where an adaptive evaluation strategy is used to efficiently perform a search which derives algorithms that provide information about a query most directly. Within this framework, opportunities to compress the search space present themselves, which suggest that information-theoretic bounds on the performance of such a system might be articulated and a system designed to achieve them. In a preliminary empirical study of adaptive evaluation for a simple test program, the evaluation strategy adapts successfully to evaluate a query efficiently.
Recognizing seismic waves immediately is very important for the realization of efficient disaster prevention. Generally these systems consist of a network of seismic detectors that send real time data to a central server. The server elaborates the data and attempts to recognize the first signs of an earthquake. The current problem with this approach is that it is subject to false alarms. A critical trade-off exists between sensitivity of the system and error rate. To overcame this problems, an artificial neural network based intelligent learning systems can be used. However, conventional supervised ANN systems are difficult to train, CPU intensive and prone to false alarms. To surpass these problems, here we attempt to use a next-generation unsupervised cortical algorithm HTM. This novel approach does not learn particular waveforms, but adapts to continuously fed data reaching the ability to discriminate between normality (seismic sensor background noise in no-earthquake conditions) and anomaly (sensor response to a jitter or an earthquake). Main goal of this study is test the ability of the HTM algorithm to be used to signal earthquakes automatically in a feasible disaster prevention system. We describe the methodology used and give the first qualitative assessments of the recognition ability of the system. Our preliminary results show that the cortical algorithm used is very robust to noise and that can successfully recognize synthetic earthquake-like signals efficiently and reliably.
This paper proposes a new fuzzy assessing procedure with application in management decision making. The proposed fuzzy approach build the membership functions for system characteristics of a standby repairable system. This method is used to extract a family of conventional crisp intervals from the fuzzy repairable system for the desired system characteristics. This can be determined with a set of nonlinear parametric programing using the membership functions. When system characteristics are governed by the membership functions, more information is provided for use by management, and because the redundant system is extended to the fuzzy environment, general repairable systems are represented more accurately and the analytic results are more useful for designers and practitioners. Also beside standby, active redundancy systems are used in many cases so this article has many practical instances. Different from other studies, our model provides, a good estimated value based on uncertain environments, a comparison discussion of using fuzzy theory and conventional method and also a comparison between parallel (active redundancy) and series system in fuzzy world when we have standby redundancy. When the membership function intervals cannot be inverted explicitly, system management or designers can specify the system characteristics of interest, perform numerical calculations, examine the corresponding {\alpha}-cuts, and use this information to develop or improve system processes.
The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals. In practice, however, it is common to carefully hand-design the reward function to encourage a particular solution, or to derive it from demonstration data. In this paper explore how a rich environment can help to promote the learning of complex behavior. Specifically, we train agents in diverse environmental contexts, and find that this encourages the emergence of robust behaviours that perform well across a suite of tasks. We demonstrate this principle for locomotion -- behaviours that are known for their sensitivity to the choice of reward. We train several simulated bodies on a diverse set of challenging terrains and obstacles, using a simple reward function based on forward progress. Using a novel scalable variant of policy gradient reinforcement learning, our agents learn to run, jump, crouch and turn as required by the environment without explicit reward-based guidance. A visual depiction of highlights of the learned behavior can be viewed following https://youtu.be/hx_bgoTF7bs .
The enormous amount of texts published daily by Internet users has fostered the development of methods to analyze this content in several natural language processing areas, such as sentiment analysis. The main goal of this task is to classify the polarity of a message. Even though many approaches have been proposed for sentiment analysis, some of the most successful ones rely on the availability of large annotated corpus, which is an expensive and time-consuming process. In recent years, distant supervision has been used to obtain larger datasets. So, inspired by these techniques, in this paper we extend such approaches to incorporate popular graphic symbols used in electronic messages, the emojis, in order to create a large sentiment corpus for Portuguese. Trained on almost one million tweets, several models were tested in both same domain and cross-domain corpora. Our methods obtained very competitive results in five annotated corpora from mixed domains (Twitter and product reviews), which proves the domain-independent property of such approach. In addition, our results suggest that the combination of emoticons and emojis is able to properly capture the sentiment of a message.
We describe the Inspire system which participated in the first competition on Inductive Logic Programming (ILP). Inspire is based on Answer Set Programming (ASP). The distinguishing feature of Inspire is an ASP encoding for hypothesis space generation: given a set of facts representing the mode bias, and a set of cost configuration parameters, each answer set of this encoding represents a single rule that is considered for finding a hypothesis that entails the given examples. Compared with state-of-the-art methods that use the length of the rule body as a metric for rule complexity, our approach permits a much more fine-grained specification of the shape of hypothesis candidate rules. The Inspire system iteratively increases the rule cost limit and thereby increases the search space until it finds a suitable hypothesis. The system searches for a hypothesis that entails a single example at a time, utilizing an ASP encoding derived from the encoding used in XHAIL. We perform experiments with the development and test set of the ILP competition. For comparison we also adapted the ILASP system to process competition instances. Experimental results show that the cost parameters for the hypothesis search space are an important factor for finding hypotheses to competition instances within tight resource bounds.
The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10x or 100x? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between `enormous data' and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pre-training) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-the-art results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets.
Robotic motion planning problems are typically solved by constructing a search tree of valid maneuvers from a start to a goal configuration. Limited onboard computation and real-time planning constraints impose a limit on how large this search tree can grow. Heuristics play a crucial role in such situations by guiding the search towards potentially good directions and consequently minimizing search effort. Moreover, it must infer such directions in an efficient manner using only the information uncovered by the search up until that time. However, state of the art methods do not address the problem of computing a heuristic that explicitly minimizes search effort. In this paper, we do so by training a heuristic policy that maps the partial information from the search to decide which node of the search tree to expand. Unfortunately, naively training such policies leads to slow convergence and poor local minima. We present SaIL, an efficient algorithm that trains heuristic policies by imitating "clairvoyant oracles" - oracles that have full information about the world and demonstrate decisions that minimize search effort. We leverage the fact that such oracles can be efficiently computed using dynamic programming and derive performance guarantees for the learnt heuristic. We validate the approach on a spectrum of environments which show that SaIL consistently outperforms state of the art algorithms. Our approach paves the way forward for learning heuristics that demonstrate an anytime nature - finding feasible solutions quickly and incrementally refining it over time.
A number of intriguing decision scenarios revolve around partitioning a collection of objects to optimize some application specific objective function. This problem is generally referred to as the Object Partitioning Problem (OPP) and is known to be NP-hard. We here consider a particularly challenging version of OPP, namely, the Stochastic On-line Equi-Partitioning Problem (SO-EPP). In SO-EPP, the target partitioning is unknown and has to be inferred purely from observing an on-line sequence of object pairs. The paired objects belong to the same partition with probability $p$ and to different partitions with probability $1-p$, with $p$ also being unknown. As an additional complication, the partitions are required to be of equal cardinality. Previously, only sub-optimal solution strategies have been proposed for SO- EPP. In this paper, we propose the first optimal solution strategy. In brief, the scheme that we propose, BN-EPP, is founded on a Bayesian network representation of SO-EPP problems. Based on probabilistic reasoning, we are not only able to infer the underlying object partitioning with optimal accuracy. We are also able to simultaneously infer $p$, allowing us to accelerate learning as object pairs arrive. Furthermore, our scheme is the first to support arbitrary constraints on the partitioning (Constrained SO-EPP). Being optimal, BN-EPP provides superior performance compared to existing solution schemes. We additionally introduce Walk-BN-EPP, a novel WalkSAT inspired algorithm for solving large scale BN-EPP problems. Finally, we provide a BN-EPP based solution to the problem of order picking, a representative real-life application of BN-EPP.
Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task. We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information. In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins.
Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, and embodiment. We term this kind of imitation learning as imitation-from-observation and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations and actions in the same environment, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show that our approach can perform imitation-from-observation for a variety of real-world robotic tasks modeled on common household chores, acquiring skills such as sweeping from videos of a human demonstrator. Videos can be found at https://sites.google.com/site/imitationfromobservation
It has been shown that most machine learning algorithms are susceptible to adversarial perturbations. Slightly perturbing an image in a carefully chosen direction in the image space may cause a trained neural network model to misclassify it. Recently, it was shown that physical adversarial examples exist: printing perturbed images then taking pictures of them would still result in misclassification. This raises security and safety concerns. However, these experiments ignore a crucial property of physical objects: the camera can view objects from different distances and at different angles. In this paper, we show experiments that suggest that current constructions of physical adversarial examples do not disrupt object detection from a moving platform. Instead, a trained neural network classifies most of the pictures taken from different distances and angles of a perturbed image correctly. We believe this is because the adversarial property of the perturbation is sensitive to the scale at which the perturbed picture is viewed, so (for example) an autonomous car will misclassify a stop sign only from a small range of distances. Our work raises an important question: can one construct examples that are adversarial for many or most viewing conditions? If so, the construction should offer very significant insights into the internal representation of patterns by deep networks. If not, there is a good prospect that adversarial examples can be reduced to a curiosity with little practical impact.
Two fundamental problems for extraterrestrial intelligences (ETIs) attempting to establish interstellar communication are timing and energy consumption. Humanity's study of exoplanets via their transit across the host star highlights a means of solving both problems. An ETI 'A' can communicate with ETI 'B' if B is observing transiting planets in A's star system, either by building structures to produce artificial transits observable by B, or by emitting signals at B during transit, at significantly lower energy consumption than typical electromagnetic transmission schemes. This can produce a network of interconnected civilisations, establishing contact via observing each other's transits. Assuming that civilisations reside in a Galactic Habitable Zone (GHZ), I conduct Monte Carlo Realisation simulations of the establishment and growth of this network, and analyse its properties in the context of graph theory. I find that at any instant, only a few civilisations are correctly aligned to communicate via transits. However, we should expect the true network to be cumulative, where a "handshake" connection at any time guarantees connection in the future via e.g. electromagnetic signals. In all our simulations, the cumulative network connects all civilisations together in a complete network. If civilisations share knowledge of their network connections, the network can be fully complete on timescales of order a hundred thousand years. Once established, this network can connect any two civilisations either directly, or via intermediate civilisations, with a path much less than the dimensions of the GHZ.
Neural networks are generally built by interleaving (adaptable) linear layers with (fixed) nonlinear activation functions. To increase their flexibility, several authors have proposed methods for adapting the activation functions themselves, endowing them with varying degrees of flexibility. None of these approaches, however, have gained wide acceptance in practice, and research in this topic remains open. In this paper, we introduce a novel family of flexible activation functions that are based on an inexpensive kernel expansion at every neuron. Leveraging over several properties of kernel-based models, we propose multiple variations for designing and initializing these kernel activation functions (KAFs), including a multidimensional scheme allowing to nonlinearly combine information from different paths in the network. The resulting KAFs can approximate any mapping defined over a subset of the real line, either convex or nonconvex. Furthermore, they are smooth over their entire domain, linear in their parameters, and they can be regularized using any known scheme, including the use of $\ell_1$ penalties to enforce sparseness. To the best of our knowledge, no other known model satisfies all these properties simultaneously. In addition, we provide a relatively complete overview on alternative techniques for adapting the activation functions, which is currently lacking in the literature. A large set of experiments validates our proposal.
This paper introduces an unsupervised framework to extract semantically rich features for video representation. Inspired by how the human visual system groups objects based on motion cues, we propose a deep convolutional neural network that disentangles motion, foreground and background information. The proposed architecture consists of a 3D convolutional feature encoder for blocks of 16 frames, which is trained for reconstruction tasks over the first and last frames of the sequence. A preliminary supervised experiment was conducted to verify the feasibility of proposed method by training the model with a fraction of videos from the UCF-101 dataset taking as ground truth the bounding boxes around the activity regions. Qualitative results indicate that the network can successfully segment foreground and background in videos as well as update the foreground appearance based on disentangled motion features. The benefits of these learned features are shown in a discriminative classification task, where initializing the network with the proposed pretraining method outperforms both random initialization and autoencoder pretraining. Our model and source code are publicly available at https://imatge-upc.github.io/unsupervised-2017-cvprw/ .
Text-dependent speaker verification is becoming popular in the speaker recognition society. However, the conventional i-vector framework which has been successful for speaker identification and other similar tasks works relatively poorly in this task. Researchers have proposed several new methods to improve performance, but it is still unclear that which model is the best choice, especially when the pass-phrases are prompted during enrollment and test. In this paper, we introduce four modeling methods and compare their performance on the newly published RedDots dataset. To further explore the influence of different frame alignments, Viterbi and forward-backward algorithms are both used in the HMM-based models. Several bottleneck features are also investigated. Our experiments show that, by explicitly modeling the lexical content, the HMM-based modeling achieves good results in the fixed-phrase condition. In the prompted-phrase condition, GMM-HMM and i-vector/HMM are not as successful. In both conditions, the forward-backward algorithm brings more benefits to the i-vector/HMM system. Additionally, we also find that even though bottleneck features perform well for text-independent speaker verification, they do not outperform MFCCs on the most challenging Imposter-Correct trials on RedDots.
Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling stored state transitions. However, care is required when using ERMs for multi-agent deep reinforcement learning (MA-DRL), as stored transitions can become outdated because agents update their policies in parallel [11]. In this work we apply leniency [23] to MA-DRL. Lenient agents map state-action pairs to decaying temperature values that control the amount of leniency applied towards negative policy updates that are sampled from the ERM. This introduces optimism in the value-function update, and has been shown to facilitate cooperation in tabular fully-cooperative multi-agent reinforcement learning problems. We evaluate our Lenient-DQN (LDQN) empirically against the related Hysteretic-DQN (HDQN) algorithm [22] as well as a modified version we call scheduled-HDQN, that uses average reward learning near terminal states. Evaluations take place in extended variations of the Coordinated Multi-Agent Object Transportation Problem (CMOTP) [8] which include fully-cooperative sub-tasks and stochastic rewards. We find that LDQN agents are more likely to converge to the optimal policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN agents.
We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. For this setting, first, we propose a novel algorithm that never decreases offered prices and has a tight strategic regret bound in $\Theta(\log\log T)$ under some mild assumptions on the buyer surplus discounting. This result closes the open research question on the existence of a no-regret horizon-independent weakly consistent pricing. The proposed algorithm is inspired by our observation that a double decrease of offered prices in a weakly consistent algorithm is enough to cause a linear regret. This motivates us to construct a novel transformation that maps a right-consistent algorithm to a weakly consistent one that never decreases offered prices. Second, we outperform the previously known strategic regret upper bound of the algorithm PRRFES, where the improvement is achieved by means of a finer constant factor $C$ of the principal term $C\log\log T$ in this upper bound. Finally, we generalize results on strategic regret previously known for geometric discounting of the buyer's surplus to discounting of other types, namely: the optimality of the pricing PRRFES to the case of geometrically concave decreasing discounting; and linear lower bound on the strategic regret of a wide range of horizon-independent weakly consistent algorithms to the case of arbitrary discounts.
Among the myriad of desirable properties discussed in the context of forgetting in Answer Set Programming (ASP), strong persistence naturally captures its essence. Recently, it has been shown that it is not always possible to forget a set of atoms from a program while obeying this property, and a precise criterion regarding what can be forgotten has been presented, accompanied by a class of forgetting operators that return the correct result when forgetting is possible. However, it is an open question what to do when we have to forget a set of atoms, but cannot without violating this property. In this paper, we address this issue and investigate three natural alternatives to forget when forgetting without violating strong persistence is not possible, which turn out to correspond to the different possible relaxations of the characterization of strong persistence. Additionally, we discuss their preferable usage, shed light on the relation between forgetting and notions of relativized equivalence established earlier in the context of ASP, and present a detailed study on their computational complexity.
AI systems are increasingly applied to complex tasks that involve interaction with humans. During training, such systems are potentially dangerous, as they haven't yet learned to avoid actions that could cause serious harm. How can an AI system explore and learn without making a single mistake that harms humans or otherwise causes serious damage? For model-free reinforcement learning, having a human "in the loop" and ready to intervene is currently the only way to prevent all catastrophes. We formalize human intervention for RL and show how to reduce the human labor required by training a supervised learner to imitate the human's intervention decisions. We evaluate this scheme on Atari games, with a Deep RL agent being overseen by a human for four hours. When the class of catastrophes is simple, we are able to prevent all catastrophes without affecting the agent's learning (whereas an RL baseline fails due to catastrophic forgetting). However, this scheme is less successful when catastrophes are more complex: it reduces but does not eliminate catastrophes and the supervised learner fails on adversarial examples found by the agent. Extrapolating to more challenging environments, we show that our implementation would not scale (due to the infeasible amount of human labor required). We outline extensions of the scheme that are necessary if we are to train model-free agents without a single catastrophe.
This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning}) is a novel metric learning approach for recommendation. More specifically, instead of simple push-pull mechanisms between user and item pairs, we propose to learn latent relations that describe each user item interaction. This helps to alleviate the potential geometric inflexibility of existing metric learing approaches. This enables not only better performance but also a greater extent of modeling capability, allowing our model to scale to a larger number of interactions. In order to do so, we employ a augmented memory module and learn to attend over these memory blocks to construct latent relations. The memory-based attention module is controlled by the user-item interaction, making the learned relation vector specific to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. The proposed architecture demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by $6\%-7.5\%$ in terms of Hits@10 and nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.
In this paper, we address the basic problem of recognizing moving objects in video images using Visual Vocabulary model and Bag of Words and track our object of interest in the subsequent video frames using species inspired PSO. Initially, the shadow free images are obtained by background modelling followed by foreground modeling to extract the blobs of our object of interest. Subsequently, we train a cubic SVM with human body datasets in accordance with our domain of interest for recognition and tracking. During training, using the principle of Bag of Words we extract necessary features of certain domains and objects for classification. Subsequently, matching these feature sets with those of the extracted object blobs that are obtained by subtracting the shadow free background from the foreground, we detect successfully our object of interest from the test domain. The performance of the classification by cubic SVM is satisfactorily represented by confusion matrix and ROC curve reflecting the accuracy of each module. After classification, our object of interest is tracked in the test domain using species inspired PSO. By combining the adaptive learning tools with the efficient classification of description, we achieve optimum accuracy in recognition of the moving objects. We evaluate our algorithm benchmark datasets: iLIDS, VIVID, Walking2, Woman. Comparative analysis of our algorithm against the existing state-of-the-art trackers shows very satisfactory and competitive results.
In Computer Vision domain, moving Object Tracking considered as one of the toughest problem.As there so many factors associated like illumination of light, noise, occlusion, sudden start and stop of moving object, shading which makes tracking even harder problem not only for dynamic background but also for static background.In this paper we present a new object tracking algorithm based on Dominant points on tracked object using Quantum particle swarm optimization (QPSO) which is a new different version of PSO based on Quantum theory. The novelty in our approach is that it can be successfully applicable in variable background as well as static background and application of quantum PSO makes the algorithm runs lot faster where other basic PSO algorithm failed to do so due to heavy computation.In our approach firstly dominants points of tracked objects detected, then a group of particles form a swarm are initialized randomly over the image search space and then start searching the curvature connected between two consecutive dominant points until they satisfy fitness criteria. Obviously it is a Multi-Swarm approach as there are multiple dominant points, as they moves, the curvature moves and the curvature movement is tracked by the swarm throughout the video and eventually when the swarm reaches optimal solution , a bounding box drawn based on particles final position.Experimental results demonstrate this proposed QPSO based method work efficiently and effectively in visual object tracking in both dynamic and static environments and run time shows that it runs closely 90% faster than basic PSO.in our approach we also apply parallelism using MatLab Parfor command to show how very less number of iteration and swarm size will enable us to successfully track object.
Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gear onto an axle or insert and turn a key in a lock. These goal-oriented tasks present a considerable challenge for reinforcement learning, since their natural reward function is sparse and prohibitive amounts of exploration are required to reach the goal and receive some learning signal. Past approaches tackle these problems by exploiting expert demonstrations or by manually designing a task-specific reward shaping function to guide the learning agent. Instead, we propose a method to learn these tasks without requiring any prior knowledge other than obtaining a single state in which the task is achieved. The robot is trained in reverse, gradually learning to reach the goal from a set of start states increasingly far from the goal. Our method automatically generates a curriculum of start states that adapts to the agent's performance, leading to efficient training on goal-oriented tasks. We demonstrate our approach on difficult simulated navigation and fine-grained manipulation problems, not solvable by state-of-the-art reinforcement learning methods.
One promising trend in digital system integration consists of boosting on-chip communication performance by means of silicon photonics, thus materializing the so-called Optical Networks-on-Chip (ONoCs). Among them, wavelength routing can be used to route a signal to destination by univocally associating a routing path to the wavelength of the optical carrier. Such wavelengths should be chosen so to minimize interferences among optical channels and to avoid routing faults. As a result, physical parameter selection of such networks requires the solution of complex constrained optimization problems. In previous work, published in the proceedings of the International Conference on Computer-Aided Design, we proposed and solved the problem of computing the maximum parallelism obtainable in the communication between any two endpoints while avoiding misrouting of optical signals. The underlying technology, only quickly mentioned in that paper, is Answer Set Programming (ASP). In this work, we detail the ASP approach we used to solve such problem. Another important design issue is to select the wavelengths of optical carriers such that they are spread across the available spectrum, in order to reduce the likelihood that, due to imperfections in the manufacturing process, unintended routing faults arise. We show how to address such problem in Constraint Logic Programming on Finite Domains (CLP(FD)). This paper is under consideration for possible publication on Theory and Practice of Logic Programming.
We introduce a parallel offline algorithm for computing hybrid conditional plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies on modeling actuation actions and sensing actions in an expressive nonmonotonic language of answer set programming (ASP), and computation of the branches of a conditional plan in parallel using an ASP solver. In particular, thanks to external atoms, continuous feasibility checks (like collision checks) are embedded into formal representations of actuation actions and sensing actions in ASP; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing nonmonotonic constructs and nondeterministic choices, partial knowledge about states and nondeterministic effects of sensing actions can be explicitly formalized in ASP; and thus each branch of a conditional plan can be computed by an ASP solver without necessitating a conformant planner and an ordering of sensing actions in advance. We apply our method in a service robotics domain and report experimental evaluations. Furthermore, we present performance comparisons with other compilation based conditional planners on standardized benchmark domains. This paper is under consideration for acceptance in TPLP.
Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model can be used to evaluate a plan, it does not prescribe how to construct a plan. Here we introduce the "Imagination-based Planner", the first model-based, sequential decision-making agent that can learn to construct, evaluate, and execute plans. Before any action, it can perform a variable number of imagination steps, which involve proposing an imagined action and evaluating it with its model-based imagination. All imagined actions and outcomes are aggregated, iteratively, into a "plan context" which conditions future real and imagined actions. The agent can even decide how to imagine: testing out alternative imagined actions, chaining sequences of actions together, or building a more complex "imagination tree" by navigating flexibly among the previously imagined states using a learned policy. And our agent can learn to plan economically, jointly optimizing for external rewards and computational costs associated with using its imagination. We show that our architecture can learn to solve a challenging continuous control problem, and also learn elaborate planning strategies in a discrete maze-solving task. Our work opens a new direction toward learning the components of a model-based planning system and how to use them.
Both hybrid automata and action languages are formalisms for describing the evolution of dynamic systems. This paper establishes a formal relationship between them. We show how to succinctly represent hybrid automata in an action language which in turn is defined as a high-level notation for answer set programming modulo theories (ASPMT) --- an extension of answer set programs to the first-order level similar to the way satisfiability modulo theories (SMT) extends propositional satisfiability (SAT). We first show how to represent linear hybrid automata with convex invariants by an action language modulo theories. A further translation into SMT allows for computing them using SMT solvers that support arithmetic over reals. Next, we extend the representation to the general class of non-linear hybrid automata allowing even non-convex invariants. We represent them by an action language modulo ODE (Ordinary Differential Equations), which can be compiled into satisfiability modulo ODE. We developed a prototype system cplus2aspmt based on these translations, which allows for a succinct representation of hybrid transition systems that can be computed effectively by the state-of-the-art SMT solver dReal.
This paper studies Bayesian ranking and selection (R&S) problems with correlated prior beliefs and continuous domains, i.e. Bayesian optimization (BO). Knowledge gradient methods [Frazier et al., 2008, 2009] have been widely studied for discrete R&S problems, which sample the one-step Bayes-optimal point. When used over continuous domains, previous work on the knowledge gradient [Scott et al., 2011, Wu and Frazier, 2016, Wu et al., 2017] often rely on a discretized finite approximation. However, the discretization introduces error and scales poorly as the dimension of domain grows. In this paper, we develop a fast discretization-free knowledge gradient method for Bayesian optimization. Our method is not restricted to the fully sequential setting, but useful in all settings where knowledge gradient can be used over continuous domains. We show how our method can be generalized to handle (i) batch of points suggestion (parallel knowledge gradient); (ii) the setting where derivative information is available in the optimization process (derivative-enabled knowledge gradient). In numerical experiments, we demonstrate that the discretization-free knowledge gradient method finds global optima significantly faster than previous Bayesian optimization algorithms on both synthetic test functions and real-world applications, especially when function evaluations are noisy; and derivative-enabled knowledge gradient can further improve the performances, even outperforming the gradient-based optimizer such as BFGS when derivative information is available.
Imitation learning algorithms learn viable policies by imitating an expert's behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies when the expert's behavior is available as a fixed set of trajectories. We evaluate in terms of the expert's cost function and observe that the distribution of trajectory-costs is often more heavy-tailed for GAIL-agents than the expert at a number of benchmark continuous-control tasks. Thus, high-cost trajectories, corresponding to tail-end events of catastrophic failure, are more likely to be encountered by the GAIL-agents than the expert. This makes the reliability of GAIL-agents questionable when it comes to deployment in risk-sensitive applications like robotic surgery and autonomous driving. In this work, we aim to minimize the occurrence of tail-end events by minimizing tail risk within the GAIL framework. We quantify tail risk by the Conditional-Value-at-Risk (CVaR) of trajectories and develop the Risk-Averse Imitation Learning (RAIL) algorithm. We observe that the policies learned with RAIL show lower tail-end risk than those of vanilla GAIL. Thus the proposed RAIL algorithm appears as a potent alternative to GAIL for improved reliability in risk-sensitive applications.
Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of fine-grained event data about business process executions, the problem of predictive process monitoring has received substantial attention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcome-oriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible outcomes - e.g. Will the customer complain or not? Will an order be delivered, cancelled or withdrawn? Unfortunately, different authors have used different datasets, experimental settings, evaluation measures and baselines to assess their proposals, resulting in poor comparability and an unclear picture of the relative merits and applicability of different methods. To address this gap, this article presents a systematic review and taxonomy of outcome-oriented predictive process monitoring methods, and a comparative experimental evaluation of eleven representative methods using a benchmark covering twelve predictive process monitoring tasks based on four real-life event logs.
In recent work, we proved that the domain recursion inference rule makes domain-lifted inference possible on several relational probability models (RPMs) for which the best known time complexity used to be exponential. We also identified two classes of RPMs for which inference becomes domain lifted when using domain recursion. These two classes subsume the largest lifted classes that were previously known. In this paper, we show that domain recursion can also be applied to models with existential quantifiers. Currently, all lifted inference algorithms assume that existential quantifiers have been removed in pre-processing by Skolemization. We show that besides introducing potentially inconvenient negative weights, Skolemization may increase the time complexity of inference. We give two example models where domain recursion can replace Skolemization, avoids the need for dealing with negative numbers, and reduces the time complexity of inference. These two examples may be interesting from three theoretical aspects: 1- they provide a better and deeper understanding of domain recursion and, in general, (lifted) inference, 2- they may serve as evidence that there are larger classes of models for which domain recursion can satisfyingly replace Skolemization, and 3- they may serve as evidence that better Skolemization techniques exist.
Many real world systems need to operate on heterogeneous information networks that consist of numerous interacting components of different types. Examples include systems that perform data analysis on biological information networks; social networks; and information extraction systems processing unstructured data to convert raw text to knowledge graphs. Many previous works describe specialized approaches to perform specific types of analysis, mining and learning on such networks. In this work, we propose a unified framework consisting of a data model -a graph with a first order schema along with a declarative language for constructing, querying and manipulating such networks in ways that facilitate relational and structured machine learning. In particular, we provide an initial prototype for a relational and graph traversal query language where queries are directly used as relational features for structured machine learning models. Feature extraction is performed by making declarative graph traversal queries. Learning and inference models can directly operate on this relational representation and augment it with new data and knowledge that, in turn, is integrated seamlessly into the relational structure to support new predictions. We demonstrate this system's capabilities by showcasing tasks in natural language processing and computational biology domains.
The theory of belief functions is an effective tool to deal with the multiple uncertain information. In recent years, many evidence combination rules have been proposed in this framework, such as the conjunctive rule, the cautious rule, the PCR (Proportional Conflict Redistribution) rules and so on. These rules can be adopted for different types of sources. However, most of these rules are not applicable when the number of sources is large. This is due to either the complexity or the existence of an absorbing element (such as the total conflict mass function for the conjunctive-based rules when applied on unreliable evidence). In this paper, based on the assumption that the majority of sources are reliable, a combination rule for a large number of sources, named LNS (stands for Large Number of Sources), is proposed on the basis of a simple idea: the more common ideas one source shares with others, the morereliable the source is. This rule is adaptable for aggregating a large number of sources among which some are unreliable. It will keep the spirit of the conjunctive rule to reinforce the belief on the focal elements with which the sources are in agreement. The mass on the empty set will be kept as an indicator of the conflict. Moreover, it can be used to elicit the major opinion among the experts. The experimental results on synthetic mass functionsverify that the rule can be effectively used to combine a large number of mass functions and to elicit the major opinion.
One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.
In this paper we introduce {\em global and local announcement logic} (GLAL), a dynamic epistemic logic with two distinct announcement operators -- $[\phi]^+_A$ and $[\phi]^-_A$ indexed to a subset $A$ of the set $Ag$ of all agents -- for global and local announcements respectively. The boundary case $[\phi]^+_{Ag}$ corresponds to the public announcement of $\phi$, as known from the literature. Unlike standard public announcements, which are {\em model transformers}, the global and local announcements are {\em pointed model transformers}. In particular, the update induced by the announcement may be different in different states of the model. Therefore, the resulting computations are trees of models, rather than the typical sequences. A consequence of our semantics is that modally bisimilar states may be distinguished in our logic. Then, we provide a stronger notion of bisimilarity and we show that it preserves modal equivalence in GLAL. Additionally, we show that GLAL is strictly more expressive than public announcement logic with common knowledge. We prove a wide range of validities for GLAL involving the interaction between dynamics and knowledge, and show that the satisfiability problem for GLAL is decidable. We illustrate the formal machinery by means of detailed epistemic scenarios.
In the typical framework for boolean games (BG) each player can change the truth value of some propositional atoms, while attempting to make her goal true. In standard BG goals are propositional formulas, whereas in iterated BG goals are formulas of Linear Temporal Logic. Both notions of BG are characterised by the fact that agents have exclusive control over their set of atoms, meaning that no two agents can control the same atom. In the present contribution we drop the exclusivity assumption and explore structures where an atom can be controlled by multiple agents. We introduce Concurrent Game Structures with Shared Propositional Control (CGS-SPC) and show that they ac- count for several classes of repeated games, including iterated boolean games, influence games, and aggregation games. Our main result shows that, as far as verification is concerned, CGS-SPC can be reduced to concurrent game structures with exclusive control. This result provides a polynomial reduction for the model checking problem of specifications in Alternating-time Temporal Logic on CGS-SPC.
Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel classification and visual semantic role labeling, respectively.
Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better empirical performance. However, the resulting optimization problem is much more challenging. Recent state-of-the-art requires an expensive full SVD in each iteration. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, a cutoff can be derived to automatically threshold the singular values obtained from the proximal operator. This allows such operator being efficiently approximated by power method. Based on it, we develop a proximal gradient algorithm (and its accelerated variant) with inexact proximal splitting and prove that a convergence rate of O(1/T) where T is the number of iterations is guaranteed. Furthermore, we show the proposed algorithm can be well parallelized, which achieves nearly linear speedup w.r.t the number of threads. Extensive experiments are performed on matrix completion and robust principal component analysis, which shows a significant speedup over the state-of-the-art. Moreover, the matrix solution obtained is more accurate and has a lower rank than that of the nuclear norm regularizer.
The past decade has seen a revolution in genomic technologies that enable a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation by predicting gene expression from large-scale chromatin measurements. Two fundamental challenges exist for such learning tasks: (1) genome-wide chromatin signals are spatially structured, high-dimensional and highly modular; and (2) the core aim is to understand what are the relevant factors and how they work together? Previous studies either failed to model complex dependencies among input signals or relied on separate feature analysis to explain the decisions. This paper presents an attention-based deep learning approach; we call AttentiveChrome, that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation. AttentiveChrome uses a hierarchy of multiple Long short-term memory (LSTM) modules to encode the input signals and to model how various chromatin marks cooperate automatically. AttentiveChrome trains two levels of attention jointly with the target prediction, enabling it to attend differentially to relevant marks and to locate important positions per mark. We evaluate the model across 56 different cell types (tasks) in human. Not only is the proposed architecture more accurate, but its attention scores also provide a better interpretation than state-of-the-art feature visualization methods such as saliency map. Code and data are shared at www.deepchrome.org
Human aware planning requires an agent to be aware of the intentions, capabilities and mental model of the human in the loop during its decision process. This can involve generating plans that are explicable to a human observer as well as the ability to provide explanations when such plans cannot be generated. This has led to the notion "multi-model planning" which aim to incorporate effects of human expectation in the deliberative process of a planner - either in the form of explicable task planning or explanations produced thereof. In this paper, we bring these two concepts together and show how a planner can account for both these needs and achieve a trade-off during the plan generation process itself by means of a model-space search method MEGA. This in effect provides a comprehensive perspective of what it means for a decision making agent to be "human-aware" by bringing together existing principles of planning under the umbrella of a single plan generation process. We situate our discussion specifically keeping in mind the recent work on explicable planning and explanation generation, and illustrate these concepts in modified versions of two well known planning domains, as well as a demonstration on a robot involved in a typical search and reconnaissance task with an external supervisor.
With computers to handle more and more complicated things in variable environments, it becomes an urgent requirement that the artificial intelligence has the ability of automatic judging and deciding according to numerous specific conditions so as to deal with the complicated and variable cases. ANNs inspired by brain is a good candidate. However, most of current numeric ANNs are not good at representing logical relations because these models still try to represent logical relations in the form of ratio based on functional approximation. On the other hand, researchers have been trying to design novel neural network models to make neural network model represent logical relations. In this work, a novel neural network model specified for representing logical relations is proposed and applied. New neurons and multiple kinds of links are defined. Inhibitory links are introduced besides exciting links. Different from current numeric ANNs, one end of an inhibitory link connects an exciting link rather than a neuron. Inhibitory links inhibit the connected exciting links conditionally to make this neural network model represent logical relations correctly. This model can simulate the operations of Boolean logic gates, and construct complex logical relations with the advantages of simpler neural network structures than recent works in this area. This work provides some ideas to make neural networks represent logical relations more directly and efficiently, and the model could be used as the complement to current numeric ANN to deal with logical issues and expand the application areas of ANN.
While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from {\em small} data. In particular, we introduce \emph{hidden physics models}, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schr\"odinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.
Deep neural networks have become ubiquitous for applications related to visual recognition and language understanding tasks. However, it is often prohibitive to use typical neural networks on devices like mobile phones or smart watches since the model sizes are huge and cannot fit in the limited memory available on such devices. While these devices could make use of machine learning models running on high-performance data centers with CPUs or GPUs, this is not feasible for many applications because data can be privacy sensitive and inference needs to be performed directly "on" device. We introduce a new architecture for training compact neural networks using a joint optimization framework. At its core lies a novel objective that jointly trains using two different types of networks--a full trainer neural network (using existing architectures like Feed-forward NNs or LSTM RNNs) combined with a simpler "projection" network that leverages random projections to transform inputs or intermediate representations into bits. The simpler network encodes lightweight and efficient-to-compute operations in bit space with a low memory footprint. The two networks are trained jointly using backpropagation, where the projection network learns from the full network similar to apprenticeship learning. Once trained, the smaller network can be used directly for inference at low memory and computation cost. We demonstrate the effectiveness of the new approach at significantly shrinking the memory requirements of different types of neural networks while preserving good accuracy on visual recognition and text classification tasks. We also study the question "how many neural bits are required to solve a given task?" using the new framework and show empirical results contrasting model predictive capacity (in bits) versus accuracy on several datasets.
Recent studies have shown that attackers can force deep learning models to misclassify so-called "adversarial examples": maliciously generated images formed by making imperceptible modifications to pixel values. With growing interest in deep learning for security applications, it is important for security experts and users of machine learning to recognize how learning systems may be attacked. Due to the complex nature of deep learning, it is challenging to understand how deep models can be fooled by adversarial examples. Thus, we present a web-based visualization tool, Adversarial-Playground, to demonstrate the efficacy of common adversarial methods against a convolutional neural network (CNN) system. Adversarial-Playground is educational, modular and interactive. (1) It enables non-experts to compare examples visually and to understand why an adversarial example can fool a CNN-based image classifier. (2) It can help security experts explore more vulnerability of deep learning as a software module. (3) Building an interactive visualization is challenging in this domain due to the large feature space of image classification (generating adversarial examples is slow in general and visualizing images are costly). Through multiple novel design choices, our tool can provide fast and accurate responses to user requests. Empirically, we find that our client-server division strategy reduced the response time by an average of 1.5 seconds per sample. Our other innovation, a faster variant of JSMA evasion algorithm, empirically performed twice as fast as JSMA and yet maintains a comparable evasion rate. Project source code and data from our experiments available at: https://github.com/QData/AdversarialDNN-Playground
Serious games are beneficial for education in various computer science areas. Numerous works have reported the experiences of using games (not only playing but also development) in teaching and learning. Considering it could be difficult for teachers/students to prepare/develop a game from scratch during one semester, assistant educational materials would be crucial in the corresponding courses. Unfortunately, the literature shows that not many materials from educational game projects are shared. To help different educators identify suitable courseware and help students implement game development, it is worth further investigating and accumulating the educational resources from individual game projects. Following such an idea, this paper proposes a game development project of an object-oriented Sokoban solver, and exposes relevant educational materials. The documented system design can be viewed as a ready-to-use resource for education in object-oriented analysis and design (OOAD), while the Sokoban solver itself may be used as an assignment platform for teaching artificial intelligence (AI). Further documentation, platform, and APIs will be realized and shared in the future to facilitate others' educational activities. Overall, this work is supposed to inspire and encourage other researchers and educators to post available materials of more game projects for the purpose of sharing and reuse.
Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.
In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks. The proposed method modifies a family of activation functions as nonlinearity generators (NGs). NGs make the activation functions linear symmetric for their inputs to lower model capacity, and automatically introduce nonlinearity to enhance the capacity of the model during training. The proposed method can be considered an unusual form of regularization: the model parameters are obtained by training a relatively low-capacity model, that is relatively easy to optimize at the beginning, with only a few iterations, and these parameters are reused for the initialization of a higher-capacity model. We derive the upper and lower bounds of variance of the weight variation, and show that the initial symmetric structure of NGs helps stabilize training. We evaluate the proposed method on different frameworks of convolutional neural networks over two object recognition benchmark tasks (CIFAR-10 and CIFAR-100). Experimental results showed that the proposed method allows us to (1) speed up the convergence of training, (2) allow for less careful weight initialization, (3) improve or at least maintain the performance of the model at negligible extra computational cost, and (4) easily train a very deep model.
The multi-armed bandit problem forms the foundation for solving a wide range of on-line stochastic optimization problems through a simple, yet effective mechanism. One simply casts the problem as a gambler that repeatedly pulls one out of N slot machine arms, eliciting random rewards. Learning of reward probabilities is then combined with reward maximization, by carefully balancing reward exploration against reward exploitation. In this paper, we address a particularly intriguing variant of the multi-armed bandit problem, referred to as the {\it Stochastic Point Location (SPL) Problem}. The gambler is here only told whether the optimal arm (point) lies to the "left" or to the "right" of the arm pulled, with the feedback being erroneous with probability $1-\pi$. This formulation thus captures optimization in continuous action spaces with both {\it informative} and {\it deceptive} feedback. To tackle this class of problems, we formulate a compact and scalable Bayesian representation of the solution space that simultaneously captures both the location of the optimal arm as well as the probability of receiving correct feedback. We further introduce the accompanying Thompson Sampling guided Stochastic Point Location (TS-SPL) scheme for balancing exploration against exploitation. By learning $\pi$, TS-SPL also supports {\it deceptive} environments that are lying about the direction of the optimal arm. This, in turn, allows us to solve the fundamental Stochastic Root Finding (SRF) Problem. Empirical results demonstrate that our scheme deals with both deceptive and informative environments, significantly outperforming competing algorithms both for SRF and SPL.
Background Road collisions and casualties pose a serious threat to commuters around the globe. Autonomous Vehicles (AVs) aim to make the use of technology to reduce the road accidents. However, the most of research work in the context of collision avoidance has been performed to address, separately, the rear end, front end and lateral collisions in less congested and with high inter-vehicular distances. Purpose The goal of this paper is to introduce the concept of a social agent, which interact with other AVs in social manners like humans are social having the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. The proposed social agent is based on a human-brain inspired mentalizing and mirroring capabilities and has been modelled for collision detection and avoidance under congested urban road traffic. Method We designed our social agent having the capabilities of mentalizing and mirroring and for this purpose we utilized Exploratory Agent Based Modeling (EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by Niazi and Hussain. Results Our simulation and practical experiments reveal that by embedding Richardson's arms race model within AVs, collisions can be avoided while travelling on congested urban roads in a flock like topologies. The performance of the proposed social agent has been compared at two different levels.
Deep neural networks are used in many state-of-the-art systems for machine perception. Once a network is trained to do a specific task, e.g., bird classification, it cannot easily be trained to do new tasks, e.g., incrementally learning to recognize additional bird species or learning an entirely different task such as flower recognition. When new tasks are added, typical deep neural networks are prone to catastrophically forgetting previous tasks. Networks that are capable of assimilating new information incrementally, much like how humans form new memories over time, will be more efficient than re-training the model from scratch each time a new task needs to be learned. There have been multiple attempts to develop schemes that mitigate catastrophic forgetting, but these methods have not been directly compared, the tests used to evaluate them vary considerably, and these methods have only been evaluated on small-scale problems (e.g., MNIST). In this paper, we introduce new metrics and benchmarks for directly comparing five different mechanisms designed to mitigate catastrophic forgetting in neural networks: regularization, ensembling, rehearsal, dual-memory, and sparse-coding. Our experiments on real-world images and sounds show that the mechanism(s) that are critical for optimal performance vary based on the incremental training paradigm and type of data being used, but they all demonstrate that the catastrophic forgetting problem has yet to be solved.
Highly automated robot ecologies (HARE), or societies of independent autonomous robots or agents, are rapidly becoming an important part of much of the world's critical infrastructure. As with human societies, regulation, wherein a governing body designs rules and processes for the society, plays an important role in ensuring that HARE meet societal objectives. However, to date, a careful study of interactions between a regulator and HARE is lacking. In this paper, we report on three user studies which give insights into how to design systems that allow people, acting as the regulatory authority, to effectively interact with HARE. As in the study of political systems in which governments regulate human societies, our studies analyze how interactions between HARE and regulators are impacted by regulatory power and individual (robot or agent) autonomy. Our results show that regulator power, decision support, and adaptive autonomy can each diminish the social welfare of HARE, and hint at how these seemingly desirable mechanisms can be designed so that they become part of successful HARE.
As deep neural networks become more complex and input datasets grow larger, it can take days or even weeks to train a deep neural network to the desired accuracy. Therefore, distributed Deep Learning at a massive scale is a critical capability, since it offers the potential to reduce the training time from weeks to hours. In this paper, we present a software-hardware co-optimized distributed Deep Learning system that can achieve near-linear scaling up to hundreds of GPUs. The core algorithm is a multi-ring communication pattern that provides a good tradeoff between latency and bandwidth and adapts to a variety of system configurations. The communication algorithm is implemented as a library for easy use. This library has been integrated into Tensorflow, Caffe, and Torch. We train Resnet-101 on Imagenet 22K with 64 IBM Power8 S822LC servers (256 GPUs) in about 7 hours to an accuracy of 33.8 % validation accuracy. Microsoft's ADAM and Google's DistBelief results did not reach 30 % validation accuracy for Imagenet 22K. Compared to Facebook AI Research's recent paper on 256 GPU training, we use a different communication algorithm, and our combined software and hardware system offers better communication overhead for Resnet-50. A PowerAI DDL enabled version of Torch completed 90 epochs of training on Resnet 50 for 1K classes in 50 minutes using 64 IBM Power8 S822LC servers (256 GPUs).
Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets. This paper addresses both of those challenges, through an image to video feature-level domain adaptation approach, to learn discriminative video frame representations. The framework utilizes large-scale unlabeled video data to reduce the gap between different domains while transferring discriminative knowledge from large-scale labeled still images. Given a face recognition network that is pretrained in the image domain, the adaptation is achieved by (i) distilling knowledge from the network to a video adaptation network through feature matching, (ii) performing feature restoration through synthetic data augmentation and (iii) learning a domain-invariant feature through a domain adversarial discriminator. We further improve performance through a discriminator-guided feature fusion that boosts high-quality frames while eliminating those degraded by video domain-specific factors. Experiments on the YouTube Faces and IJB-A datasets demonstrate that each module contributes to our feature-level domain adaptation framework and substantially improves video face recognition performance to achieve state-of-the-art accuracy. We demonstrate qualitatively that the network learns to suppress diverse artifacts in videos such as pose, illumination or occlusion without being explicitly trained for them.
The Computational Algebraic Geometry applied in Algebraic Statistics; are beginning to exploring new branches and applications; in artificial intelligence and others areas. Currently, the development of the mathematics is very extensive and it is difficult to see the immediate application of few theorems in different areas, such as is the case of the Theorem 3.9 given in [10] and proved in part of here. Also this work has the intention to show the Hilbert basis as a powerful tool in data science; and for that reason we compile important results proved in works by, S. Watanabe [27], D. Cox, J. Little and H. Schenck [8], B. Sturmfels [16] and G. Ewald [10]. In this work we study, first, the fundamental concepts in Toric Algebraic Geometry. The principal contribution of this work is the application of Hilbert basis (as one realization of Theorem 3.9) for the resolution of singularities with toric varieties, and a background in Lattice Polytope. In the second part we apply this theorem to problems in statistical learning, principally in a recent area as is the Singular Learning Theory. We define the singular machines and the problem of Singular Learning through the computing of learning curves on these statistical machines. We review and compile results on the work of S. Watanabe in Singular Learning Theory, ref.; [17], [20], [21], also revising the important result in [26], about almost the machines are singular, we formalize this theory withtoric resolution morphism in a theorem proved here (Theorem 5.4), characterizing these Learning Machines as toric varieties, and we reproduce results previously published in Singular Statistical Learning seen in [19], [20], [23].
Cross-modal hashing is usually regarded as an effective technique for large-scale textual-visual cross retrieval, where data from different modalities are mapped into a shared Hamming space for matching. Most of the traditional textual-visual binary encoding methods only consider holistic image representations and fail to model descriptive sentences. This renders existing methods inappropriate to handle the rich semantics of informative cross-modal data for quality textual-visual search tasks. To address the problem of hashing cross-modal data with semantic-rich cues, in this paper, a novel integrated deep architecture is developed to effectively encode the detailed semantics of informative images and long descriptive sentences, named as Textual-Visual Deep Binaries (TVDB). In particular, region-based convolutional networks with long short-term memory units are introduced to fully explore image regional details while semantic cues of sentences are modeled by a text convolutional network. Additionally, we propose a stochastic batch-wise training routine, where high-quality binary codes and deep encoding functions are efficiently optimized in an alternating manner. Experiments are conducted on three multimedia datasets, i.e. Microsoft COCO, IAPR TC-12, and INRIA Web Queries, where the proposed TVDB model significantly outperforms state-of-the-art binary coding methods in the task of cross-modal retrieval.
The study of causality or causal inference - how much a given treatment causally affects a given outcome in a population - goes way beyond correlation or association analysis of variables, and is critical in making sound data driven decisions and policies in a multitude of applications. The gold standard in causal inference is performing "controlled experiments", which often is not possible due to logistical or ethical reasons. As an alternative, inferring causality on "observational data" based on the "Neyman-Rubin potential outcome model" has been extensively used in statistics, economics, and social sciences over several decades. In this paper, we present a formal framework for sound causal analysis on observational datasets that are given as multiple relations and where the population under study is obtained by joining these base relations. We study a crucial condition for inferring causality from observational data, called the "strong ignorability assumption" (the treatment and outcome variables should be independent in the joined relation given the observed covariates), using known conditional independences that hold in the base relations. We also discuss how the structure of the conditional independences in base relations given as graphical models help infer new conditional independences in the joined relation. The proposed framework combines concepts from databases, statistics, and graphical models, and aims to initiate new research directions spanning these fields to facilitate powerful data-driven decisions in today's big data world.
In the context of superintelligent AI systems, the term "oracle" has two meanings. One refers to modular systems queried for domain-specific tasks. Another usage, referring to a class of systems which may be useful for addressing the value alignment and AI control problems, is a superintelligent AI system that only answers questions. The aim of this manuscript is to survey contemporary research problems related to oracles which align with long-term research goals of AI safety. We examine existing question answering systems and argue that their high degree of architectural heterogeneity makes them poor candidates for rigorous analysis as oracles. On the other hand, we identify computer algebra systems (CASs) as being primitive examples of domain-specific oracles for mathematics and argue that efforts to integrate computer algebra systems with theorem provers, systems which have largely been developed independent of one another, provide a concrete set of problems related to the notion of provable safety that has emerged in the AI safety community. We review approaches to interfacing CASs with theorem provers, describe well-defined architectural deficiencies that have been identified with CASs, and suggest possible lines of research and practical software projects for scientists interested in AI safety.
Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance. Model-based algorithms, in principle, can provide for much more efficient learning, but have proven difficult to extend to expressive, high-capacity models such as deep neural networks. In this work, we demonstrate that medium-sized neural network models can in fact be combined with model predictive control (MPC) to achieve excellent sample complexity in a model-based reinforcement learning algorithm, producing stable and plausible gaits to accomplish various complex locomotion tasks. We also propose using deep neural network dynamics models to initialize a model-free learner, in order to combine the sample efficiency of model-based approaches with the high task-specific performance of model-free methods. We empirically demonstrate on MuJoCo locomotion tasks that our pure model-based approach trained on just random action data can follow arbitrary trajectories with excellent sample efficiency, and that our hybrid algorithm can accelerate model-free learning on high-speed benchmark tasks, achieving sample efficiency gains of 3-5x on swimmer, cheetah, hopper, and ant agents. Videos can be found at https://sites.google.com/view/mbmf
Detection of surface water in natural environment via multi-spectral imagery has been widely utilized in many fields, such land cover identification. However, due to the similarity of the spectra of water bodies, built-up areas, approaches based on high-resolution satellites sometimes confuse these features. A popular direction to detect water is spectral index, often requiring the ground truth to find appropriate thresholds manually. As for traditional machine learning methods, they identify water merely via differences of spectra of various land covers, without taking specific properties of spectral reflection into account. In this paper, we propose an automatic approach to detect water bodies based on Dempster-Shafer theory, combining supervised learning with specific property of water in spectral band in a fully unsupervised context. The benefits of our approach are twofold. On the one hand, it performs well in mapping principle water bodies, including little streams and branches. On the other hand, it labels all objects usually confused with water as `ignorance', including half-dry watery areas, built-up areas and semi-transparent clouds and shadows. `Ignorance' indicates not only limitations of the spectral properties of water and supervised learning itself but insufficiency of information from multi-spectral bands as well, providing valuable information for further land cover classification.
The simulation of pedestrian crowd that reflects reality is a major challenge for researches. Several crowd simulation models have been proposed such as cellular automata model, agent-based model, fluid dynamic model, etc. It is important to note that agent-based model is able, over others approaches, to provide a natural description of the system and then to capture complex human behaviors. In this paper, we propose a multi-agent simulation model in which pedestrian positions are updated at discrete time intervals. It takes into account the major normal conditions of a simple pedestrian situated in a crowd such as preferences, realistic perception of environment, etc. Our objective is to simulate the pedestrian crowd realistically towards a simulation of believable pedestrian behaviors. Typical pedestrian phenomena, including the unidirectional and bidirectional movement in a corridor as well as the flow through bottleneck, are simulated. The conducted simulations show that our model is able to produce realistic pedestrian behaviors. The obtained fundamental diagram and flow rate at bottleneck agree very well with classic conclusions and empirical study results. It is hoped that the idea of this study may be helpful in promoting the modeling and simulation of pedestrian crowd in a simple way.
In recent years supervised representation learning has provided state of the art or close to the state of the art results in semantic analysis tasks including ranking and information retrieval. The core idea is to learn how to embed items into a latent space such that they optimize a supervised objective in that latent space. The dimensions of the latent space have no clear semantics, and this reduces the interpretability of the system. For example, in personalization models, it is hard to explain why a particular item is ranked high for a given user profile. We propose a novel model of representation learning called Supervised Explicit Semantic Analysis (SESA) that is trained in a supervised fashion to embed items to a set of dimensions with explicit semantics. The model learns to compare two objects by representing them in this explicit space, where each dimension corresponds to a concept from a knowledge base. This work extends Explicit Semantic Analysis (ESA) with a supervised model for ranking problems. We apply this model to the task of Job-Profile relevance in LinkedIn in which a set of skills defines our explicit dimensions of the space. Every profile and job are encoded to this set of skills their similarity is calculated in this space. We use RNNs to embed text input into this space. In addition to interpretability, our model makes use of the web-scale collaborative skills data that is provided by users for each LinkedIn profile. Our model provides state of the art result while it remains interpretable.
The ability to learn new tasks and generalize performance to others is one of the most remarkable characteristics of the human brain and of recent AI systems. The ability to perform multiple tasks simultaneously is also a signature characteristic of large-scale parallel architectures, that is evident in the human brain, and has been exploited effectively more traditional, massively parallel computational architectures. Here, we show that these two characteristics are in tension, reflecting a fundamental tradeoff between interactive parallelism that supports learning and generalization, and independent parallelism that supports processing efficiency through concurrent multitasking. We formally show that, while the maximum number of tasks that can be performed simultaneously grows linearly with network size, under realistic scenarios (e.g. in an unpredictable environment), the expected number that can be performed concurrently grows radically sub-linearly with network size. Hence, even modest reliance on shared representation strictly constrains the number of tasks that can be performed simultaneously, implying profound consequences for the development of artificial intelligence that optimally manages the tradeoff between learning and processing, and for understanding the human brains remarkably puzzling mix of sequential and parallel capabilities.
Data-driven techniques are used in cyber-physical systems (CPS) for controlling autonomous vehicles, handling demand responses for energy management, and modeling human physiology for medical devices. These data-driven techniques extract models from training data, where their performance is often analyzed with respect to random errors in the training data. However, if the training data is maliciously altered by attackers, the effect of these attacks on the learning algorithms underpinning data-driven CPS have yet to be considered. In this paper, we analyze the resilience of classification algorithms to training data attacks. Specifically, a generic metric is proposed that is tailored to measure resilience of classification algorithms with respect to worst-case tampering of the training data. Using the metric, we show that traditional linear classification algorithms are resilient under restricted conditions. To overcome these limitations, we propose a linear classification algorithm with a majority constraint and prove that it is strictly more resilient than the traditional algorithms. Evaluations on both synthetic data and a real-world retrospective arrhythmia medical case-study show that the traditional algorithms are vulnerable to tampered training data, whereas the proposed algorithm is more resilient (as measured by worst-case tampering).
Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more effective, search engines provide related queries to help users in specifying the information need in their search process. In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion. In our model, we employ a query-aware attention mechanism to capture the structure of the session context. is enables us to control the scope of the session from which we infer the suggested next query, which helps not only handle the noisy data but also automatically detect session boundaries. Furthermore, we observe that, based on the user query reformulation behavior, within a single session a large portion of query terms is retained from the previously submitted queries and consists of mostly infrequent or unseen terms that are usually not included in the vocabulary. We therefore empower the decoder of our model to access the source words from the session context during decoding by incorporating a copy mechanism. Moreover, we propose evaluation metrics to assess the quality of the generative models for query suggestion. We conduct an extensive set of experiments and analysis. e results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion.
Active Object Recognition (AOR) has been approached as an unsupervised learning problem, in which optimal trajectories for object inspection are not known and are to be discovered by reducing label uncertainty measures or training with reinforcement learning. Such approaches have no guarantees of the quality of their solution. In this paper, we treat AOR as a Partially Observable Markov Decision Process (POMDP) and find near-optimal policies on training data using Belief Tree Search (BTS) on the corresponding belief Markov Decision Process (MDP). AOR then reduces to the problem of knowledge transfer from near-optimal policies on training set to the test set. We train a Long Short Term Memory (LSTM) network to predict the best next action on the training set rollouts. We sho that the proposed AOR method generalizes well to novel views of familiar objects and also to novel objects. We compare this supervised scheme against guided policy search, and find that the LSTM network reaches higher recognition accuracy compared to the guided policy method. We further look into optimizing the observation function to increase the total collected reward of optimal policy. In AOR, the observation function is known only approximately. We propose a gradient-based method update to this approximate observation function to increase the total reward of any policy. We show that by optimizing the observation function and retraining the supervised LSTM network, the AOR performance on the test set improves significantly.
Over 150,000 new people in the United States are diagnosed with colorectal cancer each year. Nearly a third die from it (American Cancer Society). The only approved noninvasive diagnosis tools currently involve fecal blood count tests (FOBTs) or stool DNA tests. Fecal blood count tests take only five minutes and are available over the counter for as low as \$15. They are highly specific, yet not nearly as sensitive, yielding a high percentage (25%) of false negatives (Colon Cancer Alliance). Moreover, FOBT results are far too generalized, meaning that a positive result could mean much more than just colorectal cancer, and could just as easily mean hemorrhoids, anal fissure, proctitis, Crohn's disease, diverticulosis, ulcerative colitis, rectal ulcer, rectal prolapse, ischemic colitis, angiodysplasia, rectal trauma, proctitis from radiation therapy, and others. Stool DNA tests, the modern benchmark for CRC screening, have a much higher sensitivity and specificity, but also cost \$600, take two weeks to process, and are not for high-risk individuals or people with a history of polyps. To yield a cheap and effective CRC screening alternative, a unique ensemble-based classification algorithm is put in place that considers the FIT result, BMI, smoking history, and diabetic status of patients. This method is tested under ten-fold cross validation to have a .95 AUC, 92% specificity, 89% sensitivity, .88 F1, and 90% precision. Once clinically validated, this test promises to be cheaper, faster, and potentially more accurate when compared to a stool DNA test.
Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multi-core neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.
From medicines to materials, small organic molecules are indispensable for human well-being. To plan their syntheses, chemists employ a problem solving technique called retrosynthesis. In retrosynthesis, target molecules are recursively transformed into increasingly simpler precursor compounds until a set of readily available starting materials is obtained. Computer-aided retrosynthesis would be a highly valuable tool, however, past approaches were slow and provided results of unsatisfactory quality. Here, we employ Monte Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS was combined with an expansion policy network that guides the search, and an "in-scope" filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on 12 million reactions, which represents essentially all reactions ever published in organic chemistry. Our system solves almost twice as many molecules and is 30 times faster in comparison to the traditional search method based on extracted rules and hand-coded heuristics. Finally after a 60 year history of computer-aided synthesis planning, chemists can no longer distinguish between routes generated by a computer system and real routes taken from the scientific literature. We anticipate that our method will accelerate drug and materials discovery by assisting chemists to plan better syntheses faster, and by enabling fully automated robot synthesis.
The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested example and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures? This review attempts to answer the previous question through evaluating the performance (measured by accuracy, precision and recall) of the KNN using a large number of distance measures, tested on a number of real world datasets, with and without adding different levels of noise. The experimental results show that the performance of KNN classifier depends significantly on the distance used, the results showed large gaps between the performances of different distances. We found that a recently proposed non-convex distance performed the best when applied on most datasets comparing to the other tested distances. In addition, the performance of the KNN degraded only about $20\%$ while the noise level reaches $90\%$, this is true for all the distances used. This means that the KNN classifier using any of the top $10$ distances tolerate noise to a certain degree. Moreover, the results show that some distances are less affected by the added noise comparing to other distances.
Previous research on automatic pain estimation from facial expressions has focused primarily on "one-size-fits-all" metrics (such as PSPI). In this work, we focus on directly estimating each individual's self-reported visual-analog scale (VAS) pain metric, as this is considered the gold standard for pain measurement. The VAS pain score is highly subjective and context-dependent, and its range can vary significantly among different persons. To tackle these issues, we propose a novel two-stage personalized model, named DeepFaceLIFT, for automatic estimation of VAS. This model is based on (1) Neural Network and (2) Gaussian process regression models, and is used to personalize the estimation of self-reported pain via a set of hand-crafted personal features and multi-task learning. We show on the benchmark dataset for pain analysis (The UNBC-McMaster Shoulder Pain Expression Archive) that the proposed personalized model largely outperforms the traditional, unpersonalized models: the intra-class correlation improves from a baseline performance of 19\% to a personalized performance of 35\% while also providing confidence in the model\textquotesingle s estimates -- in contrast to existing models for the target task. Additionally, DeepFaceLIFT automatically discovers the pain-relevant facial regions for each person, allowing for an easy interpretation of the pain-related facial cues.
Training model to generate data has increasingly attracted research attention and become important in modern world applications. We propose in this paper a new geometry-based optimization approach to address this problem. Orthogonal to current state-of-the-art density-based approaches, most notably VAE and GAN, we present a fresh new idea that borrows the principle of minimal enclosing ball to train a generator G\left(\bz\right) in such a way that both training and generated data, after being mapped to the feature space, are enclosed in the same sphere. We develop theory to guarantee that the mapping is bijective so that its inverse from feature space to data space results in expressive nonlinear contours to describe the data manifold, hence ensuring data generated are also lying on the data manifold learned from training data. Our model enjoys a nice geometric interpretation, hence termed Geometric Enclosing Networks (GEN), and possesses some key advantages over its rivals, namely simple and easy-to-control optimization formulation, avoidance of mode collapsing and efficiently learn data manifold representation in a completely unsupervised manner. We conducted extensive experiments on synthesis and real-world datasets to illustrate the behaviors, strength and weakness of our proposed GEN, in particular its ability to handle multi-modal data and quality of generated data.
Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete attributes for each entity. Intuitively, these attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and attributes that encode information about both tasks. Moreover, such attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and attribute value prediction.
As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. In this work, we design a cooperative game - GuessWhich - to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend - that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.
Music is usually highly structured and it is still an open question how to design models which can successfully learn to recognize and represent musical structure. A fundamental problem is that structurally related patterns can have very distinct appearances, because the structural relationships are often based on transformations of musical material, like chromatic or diatonic transposition, inversion, retrograde, or rhythm change. In this preliminary work, we study the potential of two unsupervised learning techniques - Restricted Boltzmann Machines (RBMs) and Gated Autoencoders (GAEs) - to capture pre-defined transformations from constructed data pairs. We evaluate the models by using the learned representations as inputs in a discriminative task where for a given type of transformation (e.g. diatonic transposition), the specific relation between two musical patterns must be recognized (e.g. an upward transposition of diatonic steps). Furthermore, we measure the reconstruction error of models when reconstructing musical transformed patterns. Lastly, we test the models in an analogy-making task. We find that it is difficult to learn musical transformations with the RBM and that the GAE is much more adequate for this task, since it is able to learn representations of specific transformations that are largely content-invariant. We believe these results show that models such as GAEs may provide the basis for more encompassing music analysis systems, by endowing them with a better understanding of the structures underlying music.
Training deep networks is expensive and time-consuming with the training period increasing with data size and growth in model parameters. In this paper, we provide a framework for distributed training of deep networks over a cluster of CPUs in Apache Spark. The framework implements both Data Parallelism and Model Parallelism making it suitable to use for deep networks which require huge training data and model parameters which are too big to fit into the memory of a single machine. It can be scaled easily over a cluster of cheap commodity hardware to attain significant speedup and obtain better results making it quite economical as compared to farm of GPUs and supercomputers. We have proposed a new algorithm for training of deep networks for the case when the network is partitioned across the machines (Model Parallelism) along with detailed cost analysis and proof of convergence of the same. We have developed implementations for Fully-Connected Feedforward Networks, Convolutional Neural Networks, Recurrent Neural Networks and Long Short-Term Memory architectures. We present the results of extensive simulations demonstrating the speedup and accuracy obtained by our framework for different sizes of the data and model parameters with variation in the number of worker cores/partitions; thereby showing that our proposed framework can achieve significant speedup (upto 11X for CNN) and is also quite scalable.
We consider a wide range of regularized stochastic minimization problems with two regularization terms, one of which is composed with a linear function. This optimization model abstracts a number of important applications in artificial intelligence and machine learning, such as fused Lasso, fused logistic regression, and a class of graph-guided regularized minimization. The computational challenges of this model are in two folds. On one hand, the closed-form solution of the proximal mapping associated with the composed regularization term or the expected objective function is not available. On the other hand, the calculation of the full gradient of the expectation in the objective is very expensive when the number of input data samples is considerably large. To address these issues, we propose a stochastic variant of extra-gradient type methods, namely \textsf{Stochastic Primal-Dual Proximal ExtraGradient descent (SPDPEG)}, and analyze its convergence property for both convex and strongly convex objectives. For general convex objectives, the uniformly average iterates generated by \textsf{SPDPEG} converge in expectation with $O(1/\sqrt{t})$ rate. While for strongly convex objectives, the uniformly and non-uniformly average iterates generated by \textsf{SPDPEG} converge with $O(\log(t)/t)$ and $O(1/t)$ rates, respectively. The order of the rate of the proposed algorithm is known to match the best convergence rate for first-order stochastic algorithms. Experiments on fused logistic regression and graph-guided regularized logistic regression problems show that the proposed algorithm performs very efficiently and consistently outperforms other competing algorithms.
The Recurrent Chinese Restaurant Process (RCRP) is a powerful statistical method for modeling evolving clusters in large scale social media data. With the RCRP, one can allow both the number of clusters and the cluster parameters in a model to change over time. However, application of the RCRP has largely been limited due to the non-conjugacy between the cluster evolutionary priors and the Multinomial likelihood. This non-conjugacy makes inference di cult and restricts the scalability of models which use the RCRP, leading to the RCRP being applied only in simple problems, such as those that can be approximated by a single Gaussian emission. In this paper, we provide a novel solution for the non-conjugacy issues for the RCRP and an example of how to leverage our solution for one speci c problem - the social event discovery problem. By utilizing Sequential Monte Carlo methods in inference, our approach can be massively paralleled and is highly scalable, to the extent it can work on tens of millions of documents. We are able to generate high quality topical and location distributions of the clusters that can be directly interpreted as real social events, and our experimental results suggest that the approaches proposed achieve much better predictive performance than techniques reported in prior work. We also demonstrate how the techniques we develop can be used in a much more general ways toward similar problems.
We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors' past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while remaining flexible to model re-specification. Optimized strategies allow agents to correctly identify most false claims, when all agents receive unbiased private signals. However, an adversary's attempt to spread fake news by targeting a subset of agents with a biased private signal can be successful. Even more so when the adversary has information about agents' network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary's attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users' private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected.
Actual causation is concerned with the question "what caused what?". Consider a transition between two subsequent observations within a system of elements. Even under perfect knowledge of the system, a straightforward answer to this question may not be available. Counterfactual accounts of actual causation based on graphical models, paired with system interventions, have demonstrated initial success in addressing specific problem cases. We present a formal account of actual causation, applicable to discrete dynamical systems of interacting elements, that considers all counterfactual states of a state transition from t-1 to t. Within such a transition, causal links are considered from two complementary points of view: we can ask if any occurrence at time t has an actual cause at t-1, but also if any occurrence at time t-1 has an actual effect at t. We address the problem of identifying such actual causes and actual effects in a principled manner by starting from a set of basic requirements for causation (existence, composition, information, integration, and exclusion). We present a formal framework to implement these requirements based on system manipulations and partitions. This framework is used to provide a complete causal account of the transition by identifying and quantifying the strength of all actual causes and effects linking two occurrences. Finally, we examine several exemplary cases and paradoxes of causation and show that they can be illuminated by the proposed framework for quantifying actual causation.
Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.
The volume and velocity of information that gets generated online limits current journalistic practices to fact-check claims at the same rate. Computational approaches for fact checking may be the key to help mitigate the risks of massive misinformation spread. Such approaches can be designed to not only be scalable and effective at assessing veracity of dubious claims, but also to boost a human fact checker's productivity by surfacing relevant facts and patterns to aid their analysis. To this end, we present a novel, unsupervised network-flow based approach to determine the truthfulness of a statement of fact expressed in the form of a (subject, predicate, object) triple. We view a knowledge graph of background information about real-world entities as a flow network, and knowledge as a fluid, abstract commodity. We show that computational fact checking of such a triple then amounts to finding a "knowledge stream" that emanates from the subject node and flows toward the object node through paths connecting them. Evaluation on a range of real-world and hand-crafted datasets of facts related to entertainment, business, sports, geography and more reveals that this network-flow model can be very effective in discerning true statements from false ones, outperforming existing algorithms on many test cases. Moreover, the model is expressive in its ability to automatically discover several useful path patterns and surface relevant facts that may help a human fact checker corroborate or refute a claim.
This paper provides a theoretical justification of the superior classification performance of deep rectifier networks over shallow rectifier networks from the geometrical perspective of piecewise linear (PWL) classifier boundaries. We show that, for a given threshold on the approximation error, the required number of boundary facets to approximate a general smooth boundary grows exponentially with the dimension of the data, and thus the number of boundary facets, referred to as boundary resolution, of a PWL classifier is an important quality measure that can be used to estimate a lower bound on the classification errors. However, learning naively an exponentially large number of boundary facets requires the determination of an exponentially large number of parameters and also requires an exponentially large number of training patterns. To overcome this issue of "curse of dimensionality", compressive representations of high resolution classifier boundaries are required. To show the superior compressive power of deep rectifier networks over shallow rectifier networks, we prove that the maximum boundary resolution of a single hidden layer rectifier network classifier grows exponentially with the number of units when this number is smaller than the dimension of the patterns. When the number of units is larger than the dimension of the patterns, the growth rate is reduced to a polynomial order. Consequently, the capacity of generating a high resolution boundary will increase if the same large number of units are arranged in multiple layers instead of a single hidden layer. Taking high dimensional spherical boundaries as examples, we show how deep rectifier networks can utilize geometric symmetries to approximate a boundary with the same accuracy but with a significantly fewer number of parameters than single hidden layer nets.
We address a problem of area protection in graph-based scenarios with multiple agents. The problem consists of two adversarial teams of agents that move in an undirected graph shared by both teams. Agents are placed in vertices of the graph; at most one agent can occupy a vertex; and they can move into adjacent vertices in a conflict free way. Teams have asymmetric goals: the aim of one team - attackers - is to invade into given area while the aim of the opponent team - defenders - is to protect the area from being entered by attackers by occupying selected vertices. We study strategies for allocating vertices to be occupied by the team of defenders to block attacking agents. We show that the decision version of the problem of area protection is PSPACE-hard under the assumption that agents can allocate their target vertices multiple times. Further we develop various on-line vertex-allocation strategies for the defender team in a simplified variant of the problem with single stage vertex allocation and evaluated their performance in multiple benchmarks. The success of a strategy is heavily dependent on the type of the instance, and so one of the contributions of this work is that we identify suitable vertex-allocation strategies for diverse instance types. In particular, we introduce a simulation-based method that identifies and tries to capture bottlenecks in the graph, that are frequently used by the attackers. Our experimental evaluation suggests that this method often allows a successful defense even in instances where the attackers significantly outnumber the defenders.
This paper focuses on the problem of learning 6-DOF grasping with a parallel jaw gripper in simulation. We propose the notion of a geometry-aware representation in grasping based on the assumption that knowledge of 3D geometry is at the heart of interaction. Our key idea is constraining and regularizing grasping interaction learning through 3D geometry prediction. Specifically, we formulate the learning of deep geometry-aware grasping model in two steps: First, we learn to build mental geometry-aware representation by reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via generative 3D shape modeling. Second, we learn to predict grasping outcome with its internal geometry-aware representation. The learned outcome prediction model is used to sequentially propose grasping solutions via analysis-by-synthesis optimization. Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations. This dataset includes 101 everyday objects spread across 7 categories, additionally, we propose a data augmentation strategy for effective learning; (3) We demonstrate that the learned geometry-aware representation leads to about 10 percent relative performance improvement over the baseline CNN on grasping objects from our dataset. (4) We further demonstrate that the model generalizes to novel viewpoints and object instances.
Information theory is a mathematical theory of learning with deep connections with topics as diverse as artificial intelligence, statistical physics, and biological evolution. Many primers on the topic paint a broad picture with relatively little mathematical sophistication, while many others develop specific application areas in detail. In contrast, these informal notes aim to outline some elements of the information-theoretic "way of thinking," by cutting a rapid and interesting path through some of the theory's foundational concepts and theorems. We take the Kullback-Leibler divergence as our foundational concept, and then proceed to develop the entropy and mutual information. We discuss some of the main foundational results, including the Chernoff bounds as a characterization of the divergence; Gibbs' Theorem; and the Data Processing Inequality. A recurring theme is that the definitions of information theory support natural theorems that sound "obvious" when translated into English. More pithily, "information theory makes common sense precise." Since the focus of the notes is not primarily on technical details, proofs are provided only where the relevant techniques are illustrative of broader themes. Otherwise, proofs and intriguing tangents are referenced in liberally-sprinkled footnotes. The notes close with a highly nonexhaustive list of references to resources and other perspectives on the field.
We look at the unbiased Maker-Breaker Hamiltonicity game played on the edge set of a complete graph $K_n$, where Maker's goal is to claim a Hamiltonian cycle. First, we prove that, independent of who starts, Maker can win the game for $n = 8$ and $n = 9$. Then we use an inductive argument to show that, independent of who starts, Maker can win the game if and only if $n \geq 8$. This, in particular, resolves in the affirmative the long-standing conjecture of Papaioannou. We also study two standard positional games related to Hamiltonicity game. For Hamiltonian Path game, we show that Maker can claim a Hamiltonian path if and only if $n \geq 5$, independent of who starts. Next, we look at Fixed Hamiltonian Path game, where the goal of Maker is to claim a Hamiltonian path between two predetermined vertices. We prove that if Maker starts the game, he wins if and only if $n \geq 7$, and if Breaker starts, Maker wins if and only if $n \geq 8$. Using this result, we are able to improve the previously best upper bound on the smallest number of edges a graph on $n$ vertices can have, knowing that Maker can win the Maker-Breaker Hamiltonicity game played on its edges. To resolve the outcomes of the mentioned games on small (finite) boards, we devise algorithms for efficiently searching game trees and then obtain our results with the help of a computer.
We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators. For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization. The mechanism maintains a directed graph alongside the running program that evolves dynamically as operations are triggered upon it. Nodes of the graph represent random variables, edges the analytically-tractable relationships between them. Random variables remain in the graph for as long as possible, to be sampled only when they are used by the program in a way that cannot be resolved analytically. In the meantime, they are conditioned on as many observations as possible. We demonstrate the mechanism with a few pedagogical examples, as well as a linear-nonlinear state-space model with simulated data, and an epidemiological model with real data of a dengue outbreak in Micronesia. In all cases one or more variables are automatically marginalized out to significantly reduce variance in estimates of the marginal likelihood, in the final case facilitating a random-weight or pseudo-marginal-type importance sampler for parameter estimation. We have implemented the approach in Anglican and a new probabilistic programming language called Birch.
Dependency graph, as a heterogeneous graph representing the intrinsic relationships between different pairs of system entities, is essential to many data analysis applications, such as root cause diagnosis, intrusion detection, etc. Given a well-trained dependency graph from a source domain and an immature dependency graph from a target domain, how can we extract the entity and dependency knowledge from the source to enhance the target? One way is to directly apply a mature dependency graph learned from a source domain to the target domain. But due to the domain variety problem, directly using the source dependency graph often can not achieve good performance. Traditional transfer learning methods mainly focus on numerical data and are not applicable. In this paper, we propose ACRET, a knowledge transfer based model for accelerating dependency graph learning from heterogeneous categorical event streams. In particular, we first propose an entity estimation model to filter out irrelevant entities from the source domain based on entity embedding and manifold learning. Only the entities with statistically high correlations are transferred to the target domain. On the surviving entities, we propose a dependency construction model for constructing the unbiased dependency relationships by solving a two-constraint optimization problem. The experimental results on synthetic and real-world datasets demonstrate the effectiveness and efficiency of ACRET. We also apply ACRET to a real enterprise security system for intrusion detection. Our method is able to achieve superior detection performance at least 20 days lead lag time in advance with more than 70% accuracy.
We investigate task clustering for deep-learning based multi-task and few-shot learning in a many-task setting. We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario. Although this matrix provides us critical information regarding similarity between tasks, its asymmetric property and unreliable performance scores can affect conventional clustering methods adversely. Additionally, the uncertain task-pairs, i.e., the ones with extremely asymmetric transfer scores, may collectively mislead clustering algorithms to output an inaccurate task-partition. To overcome these limitations, we propose a novel task-clustering algorithm by using the matrix completion technique. The proposed algorithm constructs a partially-observed similarity matrix based on the certainty of cluster membership of the task-pairs. We then use a matrix completion algorithm to complete the similarity matrix. Our theoretical analysis shows that under mild constraints, the proposed algorithm will perfectly recover the underlying "true" similarity matrix with a high probability. Our results show that the new task clustering method can discover task clusters for training flexible and superior neural network models in a multi-task learning setup for sentiment classification and dialog intent classification tasks. Our task clustering approach also extends metric-based few-shot learning methods to adapt multiple metrics, which demonstrates empirical advantages when the tasks are diverse.
Traffic speed is a key indicator for the efficiency of an urban transportation system. Accurate modeling of the spatiotemporally varying traffic speed thus plays a crucial role in urban planning and development. This paper addresses the problem of efficient fine-grained traffic speed prediction using big traffic data obtained from static sensors. Gaussian processes (GPs) have been previously used to model various traffic phenomena, including flow and speed. However, GPs do not scale with big traffic data due to their cubic time complexity. In this work, we address their efficiency issues by proposing local GPs to learn from and make predictions for correlated subsets of data. The main idea is to quickly group speed variables in both spatial and temporal dimensions into a finite number of clusters, so that future and unobserved traffic speed queries can be heuristically mapped to one of such clusters. A local GP corresponding to that cluster can then be trained on the fly to make predictions in real-time. We call this method localization. We use non-negative matrix factorization for localization and propose simple heuristics for cluster mapping. We additionally leverage on the expressiveness of GP kernel functions to model road network topology and incorporate side information. Extensive experiments using real-world traffic data collected in the two U.S. cities of Pittsburgh and Washington, D.C., show that our proposed local GPs significantly improve both runtime performances and prediction accuracies compared to the baseline global and local GPs.
Active appearance models (AAMs) are a class of generative models that have seen tremendous success in face analysis. However, model learning depends on the availability of detailed annotation of canonical landmark points. As a result, when accurate AAM fitting is required on a different set of variations (expression, pose, identity), a new dataset is collected and annotated. To overcome the need for time consuming data collection and annotation, transfer learning approaches have received recent attention. The goal is to transfer knowledge from previously available datasets (source) to a new dataset (target). We propose a subspace transfer learning method, in which we select a subspace from the source that best describes the target space. We propose a metric to compute the directional similarity between the source eigenvectors and the target subspace. We show an equivalence between this metric and the variance of target data when projected onto source eigenvectors. Using this equivalence, we select a subset of source principal directions that capture the variance in target data. To define our model, we augment the selected source subspace with the target subspace learned from a handful of target examples. In experiments done on six publicly available datasets, we show that our approach outperforms the state of the art in terms of the RMS fitting error as well as the percentage of test examples for which AAM fitting converges to the ground truth.
Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention. Most major manufacturers including Tesla, GM, Ford, BMW, and Waymo/Google are working on building and testing different types of autonomous vehicles. The lawmakers of several US states including California, Texas, and New York have passed new legislation to fast-track the process of testing and deployment of autonomous vehicles on their roads. However, despite their spectacular progress, DNNs, just like traditional software, often demonstrate incorrect or unexpected corner case behaviors that can lead to potentially fatal collisions. Several such real-world accidents involving autonomous cars have already happened including one which resulted in a fatality. Most existing testing techniques for DNN-driven vehicles are heavily dependent on the manual collection of test data under different driving conditions which become prohibitively expensive as the number of test conditions increases. In this paper, we design, implement and evaluate DeepTest, a systematic testing tool for automatically detecting erroneous behaviors of DNN-driven vehicles that can potentially lead to fatal crashes. First, our tool is designed to automatically generated test cases leveraging real-world changes in driving conditions like rain, fog, lighting conditions, etc. DeepTest systematically explores different parts of the DNN logic by generating test inputs that maximize the numbers of activated neurons. DeepTest found thousands of erroneous behaviors under different realistic driving conditions (e.g., blurring, rain, fog, etc.) many of which lead to potentially fatal crashes in three top performing DNNs in the Udacity self-driving car challenge.
Knowing where people live is a fundamental component of many decision making processes such as urban development, infectious disease containment, evacuation planning, risk management, conservation planning, and more. While bottom-up, survey driven censuses can provide a comprehensive view into the population landscape of a country, they are expensive to realize, are infrequently performed, and only provide population counts over broad areas. Population disaggregation techniques and population projection methods individually address these shortcomings, but also have shortcomings of their own. To jointly answer the questions of "where do people live" and "how many people live there," we propose a deep learning model for creating high-resolution population estimations from satellite imagery. Specifically, we train convolutional neural networks to predict population in the USA at a $0.01^{\circ} \times 0.01^{\circ}$ resolution grid from 1-year composite Landsat imagery. We validate these models in two ways: quantitatively, by comparing our model's grid cell estimates aggregated at a county-level to several US Census county-level population projections, and qualitatively, by directly interpreting the model's predictions in terms of the satellite image inputs. We find that aggregating our model's estimates gives comparable results to the Census county-level population projections and that the predictions made by our model can be directly interpreted, which give it advantages over traditional population disaggregation methods. In general, our model is an example of how machine learning techniques can be an effective tool for extracting information from inherently unstructured, remotely sensed data to provide effective solutions to social problems.
Chemical multisensor devices need calibration algorithms to estimate gas concentrations. Their possible adoption as indicative air quality measurements devices poses new challenges due to the need to operate in continuous monitoring modes in uncontrolled environments. Several issues, including slow dynamics, continue to affect their real world performances. At the same time, the need for estimating pollutant concentrations on board the devices, espe- cially for wearables and IoT deployments, is becoming highly desirable. In this framework, several calibration approaches have been proposed and tested on a variety of proprietary devices and datasets; still, no thorough comparison is available to researchers. This work attempts a benchmarking of the most promising calibration algorithms according to recent literature with a focus on machine learning approaches. We test the techniques against absolute and dynamic performances, generalization capabilities and computational/storage needs using three different datasets sharing continuous monitoring operation methodology. Our results can guide researchers and engineers in the choice of optimal strategy. They show that non-linear multivariate techniques yield reproducible results, outperforming lin- ear approaches. Specifically, the Support Vector Regression method consistently shows good performances in all the considered scenarios. We highlight the enhanced suitability of shallow neural networks in a trade-off between performance and computational/storage needs. We confirm, on a much wider basis, the advantages of dynamic approaches with respect to static ones that only rely on instantaneous sensor array response. The latter have been shown to be best choice whenever prompt and precise response is needed.
Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective. In this paper, we aim to make the analyst's job easier by allowing for analyst feedback during the investigation process. Ideally, the feedback influences the ranking of the anomaly detector in a way that reduces the number of false positives that must be examined before discovering the anomalies of interest. In particular, we introduce a novel technique for incorporating simple binary feedback into tree-based anomaly detectors. We focus on the Isolation Forest algorithm as a representative tree-based anomaly detector, and show that we can significantly improve its performance by incorporating feedback, when compared with the baseline algorithm that does not incorporate feedback. Our technique is simple and scales well as the size of the data increases, which makes it suitable for interactive discovery of anomalies in large datasets.
In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al., 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that "reads" the passages to generate an answer to the question. Performance in this setting lags considerably behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader $(R^3)$, based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of generating the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning. We report extensive experimental results showing that our method significantly improves on the state of the art for multiple open-domain QA datasets.
A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion.
A significant amount of the world's knowledge is stored in relational databases. However, the ability for users to retrieve facts from a database is limited due to a lack of understanding of query languages such as SQL. We propose Seq2SQL, a deep neural network for translating natural language questions to corresponding SQL queries. Our model leverages the structure of SQL queries to significantly reduce the output space of generated queries. Moreover, we use rewards from in-the-loop query execution over the database to learn a policy to generate unordered parts of the query, which we show are less suitable for optimization via cross entropy loss. In addition, we will publish WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables from Wikipedia. This dataset is required to train our model and is an order of magnitude larger than comparable datasets. By applying policy-based reinforcement learning with a query execution environment to WikiSQL, our model Seq2SQL outperforms attentional sequence to sequence models, improving execution accuracy from 35.9% to 59.4% and logical form accuracy from 23.4% to 48.3%.
The introduction of artificial intelligence (AI) on visual images for emotional analysis obliterates the natural subjectivity and contextual dependence of our facial displays. Emotion AI places itself as an algorithmic lens on our digital artifacts and real-time interactions, creating the illusion of a new, objective class of data: our emotional and mental states. Building upon a rich network of existing public photographs--as well as fresh feeds from surveillance footage or smart phone cameras--these emotion algorithms require no additional infrastructure or improvements on image quality. In order to examine the potential policy and legal remedies for emotion AI as an emerging technology, we first establish a framework of actors, collection motivations, time scales, and space considerations that differentiates emotion AI from other algorithmic lenses. Each of these elements influences available policy remedies, and should shape continuing discussions on the antecedent conditions that make emotional AI acceptable or not in particular contexts. Based on our framework of unique elements, we examine potential available policy remedies to prevent or remediate harm. Specifically, our paper looks toward the regulatory role of the Federal Trade Commission in the US, gaps in the EU's General Data Protection Regulation (GDPR) allowing for emotion data collection, and precedent set by polygraph technologies in evidentiary and use restrictions set by law. We also examine the way social norms and adaptations could grow to also modulate broader use. Given the challenges in controlling the flow of these data, we call for further research and attention as emotion AI technology remains poised for adoption.
One key challenge in talent search is to translate complex criteria of a hiring position into a search query, while it is relatively easy for a searcher to list examples of suitable candidates for a given position. To improve search efficiency, we propose the next generation of talent search at LinkedIn, also referred to as Search By Ideal Candidates. In this system, a searcher provides one or several ideal candidates as the input to hire for a given position. The system then generates a query based on the ideal candidates and uses it to retrieve and rank results. Shifting from the traditional Query-By-Keyword to this new Query-By-Example system poses a number of challenges: How to generate a query that best describes the candidates? When moving to a completely different paradigm, how does one leverage previous product logs to learn ranking models and/or evaluate the new system with no existing usage logs? Finally, given the different nature between the two search paradigms, the ranking features typically used for Query-By-Keyword systems might not be optimal for Query-By-Example. This paper describes our approach to solving these challenges. We present experimental results confirming the effectiveness of the proposed solution, particularly on query building and search ranking tasks. As of writing this paper, the new system has been available to all LinkedIn members.
More and more of the information available on the web is dialogic, and a significant portion of it takes place in online forum conversations about current social and political topics. We aim to develop tools to summarize what these conversations are about. What are the CENTRAL PROPOSITIONS associated with different stances on an issue, what are the abstract objects under discussion that are central to a speaker's argument? How can we recognize that two CENTRAL PROPOSITIONS realize the same FACET of the argument? We hypothesize that the CENTRAL PROPOSITIONS are exactly those arguments that people find most salient, and use human summarization as a probe for discovering them. We describe our corpus of human summaries of opinionated dialogs, then show how we can identify similar repeated arguments, and group them into FACETS across many discussions of a topic. We define a new task, ARGUMENT FACET SIMILARITY (AFS), and show that we can predict AFS with a .54 correlation score, versus an ngram system baseline of .39 and a semantic textual similarity system baseline of .45.
We address a problem of area protection in graph-based scenarios with multiple mobile agents where connectivity is maintained among agents to ensure they can communicate. The problem consists of two adversarial teams of agents that move in an undirected graph shared by both teams. Agents are placed in vertices of the graph; at most one agent can occupy a vertex; and they can move into adjacent vertices in a conflict free way. Teams have asymmetric goals: the aim of one team - attackers - is to invade into given area while the aim of the opponent team - defenders - is to protect the area from being entered by attackers by occupying selected vertices. The team of defenders need to maintain connectivity of vertices occupied by its own agents in a visibility graph. The visibility graph models possibility of communication between pairs of vertices. We study strategies for allocating vertices to be occupied by the team of defenders to block attacking agents where connectivity is maintained at the same time. To do this we reserve a subset of defending agents that do not try to block the attackers but instead are placed to support connectivity of the team. The performance of strategies is tested in multiple benchmarks. The success of a strategy is heavily dependent on the type of the instance, and so one of the contributions of this work is that we identify suitable strategies for diverse instance types.
We introduce a novel method to train agents of reinforcement learning (RL) by sharing knowledge in a way similar to the concept of using a book. The recorded information in the form of a book is the main means by which humans learn knowledge. Nevertheless, the conventional deep RL methods have mainly focused either on experiential learning where the agent learns through interactions with the environment from the start or on imitation learning that tries to mimic the teacher. Contrary to these, our proposed book learning shares key information among different agents in a book-like manner by delving into the following two characteristic features: (1) By defining the linguistic function, input states can be clustered semantically into a relatively small number of core clusters, which are forwarded to other RL agents in a prescribed manner. (2) By defining state priorities and the contents for recording, core experiences can be selected and stored in a small container. We call this container as `BOOK'. Our method learns hundreds to thousand times faster than the conventional methods by learning only a handful of core cluster information, which shows that deep RL agents can effectively learn through the shared knowledge from other agents.
Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.
Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criticized as uninterpretable "black-boxes" which cannot explain their decision making processes. This is a major shortcoming that prevents the widespread application of deep learning to domains with regulatory processes such as finance. As such, industries such as finance have to rely on traditional models like decision trees that are much more interpretable but less effective than deep learning for complex problems. In this paper, we propose CLEAR-Trade, a novel financial AI visualization framework for deep learning-driven stock market prediction that mitigates the interpretability issue of deep learning methods. In particular, CLEAR-Trade provides a effective way to visualize and explain decisions made by deep stock market prediction models. We show the efficacy of CLEAR-Trade in enhancing the interpretability of stock market prediction by conducting experiments based on S&P 500 stock index prediction. The results demonstrate that CLEAR-Trade can provide significant insight into the decision-making process of deep learning-driven financial models, particularly for regulatory processes, thus improving their potential uptake in the financial industry.
Generative modeling, which learns joint probability distribution from training data and generates samples according to it, is an important task in machine learning and artificial intelligence. Inspired by probabilistic interpretation of quantum physics, we propose a generative model using matrix product states, which is a tensor network originally proposed for describing (particularly one-dimensional) entangled quantum states. Our model enjoys efficient learning by utilizing the density matrix renormalization group method which allows dynamic adjusting dimensions of the tensors, and offers an efficient direct sampling approach, Zipper, for generative tasks. We apply our method to generative modeling of several standard datasets including the principled Bars and Stripes, random binary patterns and the MNIST handwritten digits, to illustrate ability of our model, and discuss features as well as drawbacks of our model over popular generative models such as Hopfield model, Boltzmann machines and generative adversarial networks. Our work shed light on many interesting directions for future exploration on the development of quantum-inspired algorithms for unsupervised machine learning, which is of possibility of being realized by a quantum device.
When people converse about social or political topics, similar arguments are often paraphrased by different speakers, across many different conversations. Debate websites produce curated summaries of arguments on such topics; these summaries typically consist of lists of sentences that represent frequently paraphrased propositions, or labels capturing the essence of one particular aspect of an argument, e.g. Morality or Second Amendment. We call these frequently paraphrased propositions ARGUMENT FACETS. Like these curated sites, our goal is to induce and identify argument facets across multiple conversations, and produce summaries. However, we aim to do this automatically. We frame the problem as consisting of two steps: we first extract sentences that express an argument from raw social media dialogs, and then rank the extracted arguments in terms of their similarity to one another. Sets of similar arguments are used to represent argument facets. We show here that we can predict ARGUMENT FACET SIMILARITY with a correlation averaging 0.63 compared to a human topline averaging 0.68 over three debate topics, easily beating several reasonable baselines.
We propose an adversarial training procedure for learning a causal implicit generative model for a given causal graph. We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal graph. We consider the application of generating faces based on given binary labels where the dependency structure between the labels is preserved with a causal graph. This problem can be seen as learning a causal implicit generative model for the image and labels. We devise a two-stage procedure for this problem. First we train a causal implicit generative model over binary labels using a neural network consistent with a causal graph as the generator. We empirically show that WassersteinGAN can be used to output discrete labels. Later, we propose two new conditional GAN architectures, which we call CausalGAN and CausalBEGAN. We show that the optimal generator of the CausalGAN, given the labels, samples from the image distributions conditioned on these labels. The conditional GAN combined with a trained causal implicit generative model for the labels is then a causal implicit generative model over the labels and the generated image. We show that the proposed architectures can be used to sample from observational and interventional image distributions, even for interventions which do not naturally occur in the dataset.
Multiple automakers have in development or in production automated driving systems (ADS) that offer freeway-pilot functions. This type of ADS is typically limited to restricted-access freeways only, that is, the transition from manual to automated modes takes place only after the ramp merging process is completed manually. One major challenge to extend the automation to ramp merging is that the automated vehicle needs to incorporate and optimize long-term objectives (e.g. successful and smooth merge) when near-term actions must be safely executed. Moreover, the merging process involves interactions with other vehicles whose behaviors are sometimes hard to predict but may influence the merging vehicle optimal actions. To tackle such a complicated control problem, we propose to apply Deep Reinforcement Learning (DRL) techniques for finding an optimal driving policy by maximizing the long-term reward in an interactive environment. Specifically, we apply a Long Short-Term Memory (LSTM) architecture to model the interactive environment, from which an internal state containing historical driving information is conveyed to a Deep Q-Network (DQN). The DQN is used to approximate the Q-function, which takes the internal state as input and generates Q-values as output for action selection. With this DRL architecture, the historical impact of interactive environment on the long-term reward can be captured and taken into account for deciding the optimal control policy. The proposed architecture has the potential to be extended and applied to other autonomous driving scenarios such as driving through a complex intersection or changing lanes under varying traffic flow conditions.
A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images. We introduce ImageGraph, a KG with 1,330 relation types, 14,870 entities, and 829,931 images. Visual-relational KGs lead to novel probabilistic query types where images are treated as first-class citizens. Both the prediction of relations between unseen images and multi-relational image retrieval can be formulated as query types in a visual-relational KG. We approach the problem of answering such queries with a novel combination of deep convolutional networks and models for learning knowledge graph embeddings. The resulting models can answer queries such as "How are these two unseen images related to each other?" We also explore a zero-shot learning scenario where an image of an entirely new entity is linked with multiple relations to entities of an existing KG. The multi-relational grounding of unseen entity images into a knowledge graph serves as the description of such an entity. We conduct experiments to demonstrate that the proposed deep architectures in combination with KG embedding objectives can answer the visual-relational queries efficiently and accurately.
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including template-based models, bag-of-words models, sequence-to-sequence neural network and latent variable neural network models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than many competing systems. Due to its machine learning architecture, the system is likely to improve with additional data.
Deep reinforcement learning has become an important paradigm for constructing agents that can enter complex multi-agent situations and improve their policies through experience. One commonly used technique is reactive training - applying standard RL methods while treating other agents as a part of the learner's environment. It is known that in general-sum games reactive training can lead groups of agents to converge to inefficient outcomes. We focus on one such class of environments: Stag Hunt games. Here agents either choose a risky cooperative policy (which leads to high payoffs if both choose it but low payoffs to an agent who attempts it alone) or a safe one (which leads to a safe payoff no matter what). We ask how we can change the learning rule of a single agent to improve its outcomes in Stag Hunts that include other reactive learners. We extend existing work on reward-shaping in multi-agent reinforcement learning and show that that making a single agent prosocial, that is, making them care about the rewards of their partners can increase the probability that groups converge to good outcomes. Thus, even if we control a single agent in a group making that agent prosocial can increase our agent's long-run payoff. We show experimentally that this result carries over to a variety of more complex environments with Stag Hunt-like dynamics including ones where agents must learn from raw input pixels.
Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data due to the latter's propensity to suggest a large number of unnecessary inspections. In this paper, we propose a novel system that combines automated statistical decision making with expert knowledge. First, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data. The methodology used is specifically tailored to the level of noise in the data. Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. This work has resulted in appreciable results on a real-world data set of 3.6M customers. Our system is being deployed in a commercial NTL detection software.
Reinforcement Learning is divided in two main paradigms: model-free and model-based. Each of these two paradigms has strengths and limitations, and has been successfully applied to real world domains that are appropriate to its corresponding strengths. In this paper, we present a new approach aimed at bridging the gap between these two paradigms. We aim to take the best of the two paradigms and combine them in an approach that is at the same time data-efficient and cost-savvy. We do so by learning a probabilistic dynamics model and leveraging it as a prior for the intertwined model-free optimization. As a result, our approach can exploit the generality and structure of the dynamics model, but is also capable of ignoring its inevitable inaccuracies, by directly incorporating the evidence provided by the direct observation of the cost. Preliminary results demonstrate that our approach outperforms purely model-based and model-free approaches, as well as the approach of simply switching from a model-based to a model-free setting.
This paper defines software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates, focusing on causality in discriminatory behavior. Evidence of software discrimination has been found in modern software systems that recommend criminal sentences, grant access to financial products, and determine who is allowed to participate in promotions. Our approach, Themis, generates efficient test suites to measure discrimination. Given a schema describing valid system inputs, Themis generates discrimination tests automatically and does not require an oracle. We evaluate Themis on 20 software systems, 12 of which come from prior work with explicit focus on avoiding discrimination. We find that (1) Themis is effective at discovering software discrimination, (2) state-of-the-art techniques for removing discrimination from algorithms fail in many situations, at times discriminating against as much as 98% of an input subdomain, (3) Themis optimizations are effective at producing efficient test suites for measuring discrimination, and (4) Themis is more efficient on systems that exhibit more discrimination. We thus demonstrate that fairness testing is a critical aspect of the software development cycle in domains with possible discrimination and provide initial tools for measuring software discrimination.
The infinite restricted Boltzmann machine (iRBM) is an extension of the classic RBM. It enjoys a good property of automatically deciding the size of the hidden layer according to specific training data. With sufficient training, the iRBM can achieve a competitive performance with that of the classic RBM. However, the convergence of learning the iRBM is slow, due to the fact that the iRBM is sensitive to the ordering of its hidden units, the learned filters change slowly from the left-most hidden unit to right. To break this dependency between neighboring hidden units and speed up the convergence of training, a novel training strategy is proposed. The key idea of the proposed training strategy is randomly regrouping the hidden units before each gradient descent step. Potentially, a mixing of infinite many iRBMs with different permutations of the hidden units can be achieved by this learning method, which has a similar effect of preventing the model from over-fitting as the dropout. The original iRBM is also modified to be capable of carrying out discriminative training. To evaluate the impact of our method on convergence speed of learning and the model's generalization ability, several experiments have been performed on the binarized MNIST and CalTech101 Silhouettes datasets. Experimental results indicate that the proposed training strategy can greatly accelerate learning and enhance generalization ability of iRBMs.
Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. Previous attempts mostly focused on the analysis of hand-crafted geometric features and the use of external sensors in order to allow the vehicle to approach the land-pad. In this article, we propose a method based on deep reinforcement learning that only requires low-resolution images taken from a down-looking camera in order to identify the position of the marker and land the UAV on it. The proposed approach is based on a hierarchy of Deep Q-Networks (DQNs) used as high-level control policy for the navigation toward the marker. We implemented different technical solutions, such as the combination of vanilla and double DQNs, and a partitioned buffer replay. Using domain randomization we trained the vehicle on uniform textures and we tested it on a large variety of simulated and real-world environments. The overall performance is comparable with a state-of-the-art algorithm and human pilots.
A commonly used technique for managing AI complexity in real-time strategy (RTS) games is to use action and/or state abstractions. High-level abstractions can often lead to good strategic decision making, but tactical decision quality may suffer due to lost details. A competing method is to sample the search space which often leads to good tactical performance in simple scenarios, but poor high-level planning. We propose to use a deep convolutional neural network (CNN) to select among a limited set of abstract action choices, and to utilize the remaining computation time for game tree search to improve low level tactics. The CNN is trained by supervised learning on game states labelled by Puppet Search, a strategic search algorithm that uses action abstractions. The network is then used to select a script --- an abstract action --- to produce low level actions for all units. Subsequently, the game tree search algorithm improves the tactical actions of a subset of units using a limited view of the game state only considering units close to opponent units. Experiments in the microRTS game show that the combined algorithm results in higher win-rates than either of its two independent components and other state-of-the-art microRTS agents. To the best of our knowledge, this is the first successful application of a convolutional network to play a full RTS game on standard game maps, as previous work has focused on sub-problems, such as combat, or on very small maps.
We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The standard QSAR learning problem is: given a target (usually a protein) and a set of chemical compounds (small molecules) with associated bioactivities (e.g. inhibition of the target), learn a predictive mapping from molecular representation to activity. Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs, and therefore the problem area is well-suited to meta-learning. We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 6 molecular representations, applied to more than 2,700 QSAR problems. (These results have been made publicly available on OpenML and represent a valuable resource for testing novel meta-learning methods.) We then investigated the utility of algorithm selection for QSAR problems. We found that this meta-learning approach outperformed the best individual QSAR learning method (random forests using a molecular fingerprint representation) by up to 13%, on average. We conclude that meta-learning outperforms base-learning methods for QSAR learning, and as this investigation is one of the most extensive ever comparisons of base and meta-learning methods ever made, it provides evidence for the general effectiveness of meta-learning over base-learning.
The rapid advances in e-commerce and Web 2.0 technologies have greatly increased the impact of commercial advertisements on the general public. As a key enabling technology, a multitude of recommender systems exists which analyzes user features and browsing patterns to recommend appealing advertisements to users. In this work, we seek to study the characteristics or attributes that characterize an effective advertisement and recommend a useful set of features to aid the designing and production processes of commercial advertisements. We analyze the temporal patterns from multimedia content of advertisement videos including auditory, visual and textual components, and study their individual roles and synergies in the success of an advertisement. The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users. Our proposed framework employs the signal processing technique of cross modality feature learning where data streams from different components are employed to train separate neural network models and are then fused together to learn a shared representation. Subsequently, a neural network model trained on this joint feature embedding representation is utilized as a classifier to predict advertisement effectiveness. We validate our approach using subjective ratings from a dedicated user study, the sentiment strength of online viewer comments, and a viewer opinion metric of the ratio of the Likes and Views received by each advertisement from an online platform.
Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts. However, it is challenging to build QA systems which can learn to reason over knowledge graphs based on question-answer pairs alone. First, when people ask questions, their expressions are noisy (for example, typos in texts, or variations in pronunciations), which is non-trivial for the QA system to match those mentioned entities to the knowledge graph. Second, many questions require multi-hop logic reasoning over the knowledge graph to retrieve the answers. To address these challenges, we propose a novel and unified deep learning architecture, and an end-to-end variational learning algorithm which can handle noise in questions, and learn multi-hop reasoning simultaneously. Our method achieves state-of-the-art performance on a recent benchmark dataset in the literature. We also derive a series of new benchmark datasets, including questions for multi-hop reasoning, questions paraphrased by neural translation model, and questions in human voice. Our method yields very promising results on all these challenging datasets.
In allocation problems, a given set of goods are assigned to agents in such a way that the social welfare is maximised, that is, the largest possible global worth is achieved. When goods are indivisible, it is possible to use money compensation to perform a fair allocation taking into account the actual contribution of all agents to the social welfare. Coalitional games provide a formal mathematical framework to model such problems, in particular the Shapley value is a solution concept widely used for assigning worths to agents in a fair way. Unfortunately, computing this value is a $\#{\rm P}$-hard problem, so that applying this good theoretical notion is often quite difficult in real-world problems. We describe useful properties that allow us to greatly simplify the instances of allocation problems, without affecting the Shapley value of any player. Moreover, we propose algorithms for computing lower bounds and upper bounds of the Shapley value, which in some cases provide the exact result and that can be combined with approximation algorithms. The proposed techniques have been implemented and tested on a real-world application of allocation problems, namely, the Italian research assessment program, known as VQR. For the large university considered in the experiments, the problem involves thousands of agents and goods (here, researchers and their research products). The algorithms described in the paper are able to compute the Shapley value for most of those agents, and to get a good approximation of the Shapley value for all of them.
There has been a good amount of progress in sentiment analysis over the past 10 years, including the proposal of new methods and the creation of benchmark datasets. In some papers, however, there is a tendency to compare models only on one or two datasets, either because of time restraints or because the model is tailored to a specific task. Accordingly, it is hard to understand how well a certain model generalizes across different tasks and datasets. In this paper, we contribute to this situation by comparing several models on six different benchmarks, which belong to different domains and additionally have different levels of granularity (binary, 3-class, 4-class and 5-class). We show that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are particularly good at fine-grained sentiment tasks (i. e., with more than two classes). Incorporating sentiment information into word embeddings during training gives good results for datasets that are lexically similar to the training data. With our experiments, we contribute to a better understanding of the performance of different model architectures on different data sets. Consequently, we detect novel state-of-the-art results on the SenTube datasets.
Reinforcement Learning AI commonly uses reward/penalty signals that are objective and explicit in an environment -- e.g. game score, completion time, etc. -- in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additional reinforcement that is implicit and subjective -- e.g. human preferences for certain AI behavior -- in order to adapt the AI behavior to idiosyncratic human preferences. Such adaptations would mirror naturally occurring processes that increase trust and comfort during social interactions. Here, we show how a hybrid brain-computer-interface (hBCI), which detects an individual's level of interest in objects/events in a virtual environment, can be used to adapt the behavior of a Deep Reinforcement Learning AI agent that is controlling a virtual autonomous vehicle. Specifically, we show that the AI learns a driving strategy that maintains a safe distance from a lead vehicle, and most novelly, preferentially slows the vehicle when the human passengers of the vehicle encounter objects of interest. This adaptation affords an additional 20\% viewing time for subjectively interesting objects. This is the first demonstration of how an hBCI can be used to provide implicit reinforcement to an AI agent in a way that incorporates user preferences into the control system.
Visual Question Answering (VQA) models should have both high robustness and accuracy. Unfortunately, most of the current VQA research only focuses on accuracy because there is a lack of proper methods to measure the robustness of VQA models. There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the ranked basic questions, with similarity scores, of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the text-based answer of the main question about the given image. We claim that a robust VQA model is one, whose performance is not changed much when related basic questions as also made available to it as input. We formulate the basic questions generation problem as a LASSO optimization, and also propose a large scale Basic Question Dataset (BQD) and Rscore (novel robustness measure), for analyzing the robustness of VQA models. We hope our BQD will be used as a benchmark for to evaluate the robustness of VQA models, so as to help the community build more robust and accurate VQA models.
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively. Attention mechanisms have recently attracted enormous interest due to their highly parallelizable computation, significantly less training time, and flexibility in modeling dependencies. We propose a novel attention mechanism in which the attention between elements from input sequence(s) is directional and multi-dimensional (i.e., feature-wise). A light-weight neural net, "Directional Self-Attention Network (DiSAN)", is then proposed to learn sentence embedding, based solely on the proposed attention without any RNN/CNN structure. DiSAN is only composed of a directional self-attention with temporal order encoded, followed by a multi-dimensional attention that compresses the sequence into a vector representation. Despite its simple form, DiSAN outperforms complicated RNN models on both prediction quality and time efficiency. It achieves the best test accuracy among all sentence encoding methods and improves the most recent best result by 1.02% on the Stanford Natural Language Inference (SNLI) dataset, and shows state-of-the-art test accuracy on the Stanford Sentiment Treebank (SST), Multi-Genre natural language inference (MultiNLI), Sentences Involving Compositional Knowledge (SICK), Customer Review, MPQA, TREC question-type classification and Subjectivity (SUBJ) datasets.
Time series prediction is of great significance in many applications and has attracted extensive attention from the data mining community. Existing work suggests that for many problems, the shape in the current time series may correlate an upcoming shape in the same or another series. Therefore, it is a promising strategy to associate two recurring patterns as a rule's antecedent and consequent: the occurrence of the antecedent can foretell the occurrence of the consequent, and the learned shape of consequent will give accurate predictions. Earlier work employs symbolization methods, but the symbolized representation maintains too little information of the original series to mine valid rules. The state-of-the-art work, though directly manipulating the series, fails to segment the series precisely for seeking antecedents/consequents, resulting in inaccurate rules in common scenarios. In this paper, we propose a novel motif-based rule discovery method, which utilizes motif discovery to accurately extract frequently occurring consecutive subsequences, i.e. motifs, as antecedents/consequents. It then investigates the underlying relationships between motifs by matching motifs as rule candidates and ranking them based on the similarities. Experimental results on real open datasets show that the proposed approach outperforms the baseline method by 23.9%. Furthermore, it extends the applicability from single time series to multiple ones.
Our understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve problems. We reproduce this simplification process using a neural network to build a low dimensional state representation of the world from images acquired by a robot. As in Jonschkowski et al. 2015, we learn in an unsupervised way using prior knowledge about the world as loss functions called robotic priors and extend this approach to high dimension richer images to learn a 3D representation of the hand position of a robot from RGB images. We propose a quantitative evaluation of the learned representation using nearest neighbors in the state space that allows to assess its quality and show both the potential and limitations of robotic priors in realistic environments. We augment image size, add distractors and domain randomization, all crucial components to achieve transfer learning to real robots. Finally, we also contribute a new prior to improve the robustness of the representation. The applications of such low dimensional state representation range from easing reinforcement learning (RL) and knowledge transfer across tasks, to facilitating learning from raw data with more efficient and compact high level representations. The results show that the robotic prior approach is able to extract high level representation as the 3D position of an arm and organize it into a compact and coherent space of states in a challenging dataset.
Tracking humans that are interacting with the other subjects or environment remains unsolved in visual tracking, because the visibility of the human of interests in videos is unknown and might vary over time. In particular, it is still difficult for state-of-the-art human trackers to recover complete human trajectories in crowded scenes with frequent human interactions. In this work, we consider the visibility status of a subject as a fluent variable, whose change is mostly attributed to the subject's interaction with the surrounding, e.g., crossing behind another object, entering a building, or getting into a vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e.g., from visible to invisible) and track humans in videos. We formulate this joint task as an iterative search of a feasible causal graph structure that enables fast search algorithm, e.g., dynamic programming method. We apply the proposed method on challenging video sequences to evaluate its capabilities of estimating visibility fluent changes of subjects and tracking subjects of interests over time. Results with comparisons demonstrate that our method outperforms the alternative trackers and can recover complete trajectories of humans in complicated scenarios with frequent human interactions.
To capture the inherent geometric features of many community detection problems, we propose to use a new random graph model of communities that we call a Geometric Block Model. The geometric block model generalizes the random geometric graphs in the same way that the well-studied stochastic block model generalizes the Erdos-Renyi random graphs. It is also a natural extension of random community models inspired by the recent theoretical and practical advancement in community detection. While being a topic of fundamental theoretical interest, our main contribution is to show that many practical community structures are better explained by the geometric block model. We also show that a simple triangle-counting algorithm to detect communities in the geometric block model is near-optimal. Indeed, even in the regime where the average degree of the graph grows only logarithmically with the number of vertices (sparse-graph), we show that this algorithm performs extremely well, both theoretically and practically. In contrast, the triangle-counting algorithm is far from being optimum for the stochastic block model. We simulate our results on both real and synthetic datasets to show superior performance of both the new model as well as our algorithm.
The vehicle to represent Knowledge Organization Systems (KOSs) in the environment of the Semantic Web and linked data is the Simple Knowledge Organization System (SKOS). SKOS provides a way to assign a URI to each concept, and this URI functions as a surrogate for the concept. This fact makes of main concern the need to clarify the URIs' ontological meaning. The aim of this study is to investigate the relation between the ontological substance of KOS concepts and concepts revealed through the grammatical and syntactic formalisms of natural language. For this purpose, we examined the dividableness of concepts in specific KOSs (i.e. a thesaurus, a subject headings system and a classification scheme) by applying Natural Language Processing (NLP) techniques (i.e. morphosyntactic analysis) to the lexical representations (i.e. RDF literals) of SKOS concepts. The results of the comparative analysis reveal that, despite the use of multi-word units, thesauri tend to represent concepts in a way that can hardly be further divided conceptually, while Subject Headings and Classification Schemes - to a certain extent - comprise terms that can be decomposed into more conceptual constituents. Consequently, SKOS concepts deriving from thesauri are more likely to represent atomic conceptual units and thus be more appropriate tools for inference and reasoning. Since identifiers represent the meaning of a concept, complex concepts are neither the most appropriate nor the most efficient way of modelling a KOS for the Semantic Web.
Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However, recent studies demonstrated that DNNs are vulnerable to adversarial manipulations at testing time. Specifically, suppose we have a testing example, whose label can be correctly predicted by a DNN classifier. An attacker can add a small carefully crafted noise to the testing example such that the DNN classifier predicts an incorrect label, where the crafted testing example is called adversarial example. Such attacks are called evasion attacks. Evasion attacks are one of the biggest challenges for deploying DNNs in safety and security critical applications such as self-driving cars. In this work, we develop new methods to defend against evasion attacks. Our key observation is that adversarial examples are close to the classification boundary. Therefore, we propose region-based classification to be robust to adversarial examples. For a benign/adversarial testing example, we ensemble information in a hypercube centered at the example to predict its label. In contrast, traditional classifiers are point-based classification, i.e., given a testing example, the classifier predicts its label based on the testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets demonstrate that our region-based classification can significantly mitigate evasion attacks without sacrificing classification accuracy on benign examples. Specifically, our region-based classification achieves the same classification accuracy on testing benign examples as point-based classification, but our region-based classification is significantly more robust than point-based classification to various evasion attacks.
This paper proposes a push and pull search (PPS) framework for solving constrained multi-objective optimization problems (CMOPs). To be more specific, the proposed PPS divides the search process into two different stages, including the push and pull search stages. In the push stage, a multi-objective evolutionary algorithm (MOEA) is adopted to explore the search space without considering any constraints, which can help to get across infeasible regions very fast and approach the unconstrained Pareto front. Furthermore, the landscape of CMOPs with constraints can be probed and estimated in the push stage, which can be utilized to conduct the parameters setting for constraint-handling approaches applied in the pull stage. Then, a constrained multi-objective evolutionary algorithm (CMOEA) equipped with an improved epsilon constraint-handling is applied to pull the infeasible individuals achieved in the push stage to the feasible and non-dominated regions. Compared with other CMOEAs, the proposed PPS method can more efficiently get across infeasible regions and converge to the feasible and non-dominated regions by applying push and pull search strategies at different stages. To evaluate the performance regarding convergence and diversity, a set of benchmark CMOPs is used to test the proposed PPS and compare with other five CMOEAs, including MOEA/D-CDP, MOEA/D-SR, C-MOEA/D, MOEA/D-Epsilon and MOEA/D-IEpsilon. The comprehensive experimental results demonstrate that the proposed PPS achieves significantly better or competitive performance than the other five CMOEAs on most of the benchmark set.
Developing useful interfaces between brains and machines is a grand challenge of neuroengineering. An effective interface has the capacity to not only interpret neural signals, but predict the intentions of the human to perform an action in the near future; prediction is made even more challenging outside well-controlled laboratory experiments. This paper describes our approach to detect and to predict natural human arm movements in the future, a key challenge in brain computer interfacing that has never before been attempted. We introduce the novel Annotated Joints in Long-term ECoG (AJILE) dataset; AJILE includes automatically annotated poses of 7 upper body joints for four human subjects over 670 total hours (more than 72 million frames), along with the corresponding simultaneously acquired intracranial neural recordings. The size and scope of AJILE greatly exceeds all previous datasets with movements and electrocorticography (ECoG), making it possible to take a deep learning approach to movement prediction. We propose a multimodal model that combines deep convolutional neural networks (CNN) with long short-term memory (LSTM) blocks, leveraging both ECoG and video modalities. We demonstrate that our models are able to detect movements and predict future movements up to 800 msec before movement initiation. Further, our multimodal movement prediction models exhibit resilience to simulated ablation of input neural signals. We believe a multimodal approach to natural neural decoding that takes context into account is critical in advancing bioelectronic technologies and human neuroscience.
As computational power has continued to increase, and sensors have become more accurate, the corresponding advent of systems that are cognitive-and-immersive (CAI) has come to pass. CAI systems fall squarely into the intersection of AI with HCI/HRI: such systems interact with and assist the human agents that enter them, in no small part because such systems are infused with AI able to understand and reason about these humans and their beliefs, goals, and plans. We herein explain our approach to engineering CAI systems. We emphasize the capacity of a CAI system to develop and reason over a "theory of the mind" of its humans partners. This capacity means that the AI in question has a sophisticated model of the beliefs, knowledge, goals, desires, emotions, etc. of these humans. To accomplish this engineering, a formal framework of very high expressivity is needed. In our case, this framework is a \textit{cognitive event calculus}, a partciular kind of quantified multi-modal logic, and a matching high-expressivity planner. To explain, advance, and to a degree validate our approach, we show that a calculus of this type can enable a CAI system to understand a psychologically tricky scenario couched in what we call the \textit{cognitive blockworld framework} (CBF). CBF includes machinery able to represent and plan over not merely blocks and actions, but also agents and their mental attitudes about other agents.
We present a novel and scalable label embedding framework for large-scale multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using Distributional Semantics). Our approach draws inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings for natural language processing tasks. Learning such embeddings can be reduced to a certain matrix factorization. Our approach is novel in that it highlights interesting connections between label embedding methods used for multi-label learning and paragraph/document embedding methods commonly used for learning representations of text data. The framework can also be easily extended to incorporate auxiliary information such as label-label correlations; this is crucial especially when there are a lot of missing labels in the training data. We demonstrate the effectiveness of our approach through an extensive set of experiments on a variety of benchmark datasets, and show that the proposed learning methods perform favorably compared to several baselines and state-of-the-art methods for large-scale multi-label learning. To facilitate end-to-end learning, we develop a joint learning algorithm that can learn the embeddings as well as a regression model that predicts these embeddings given input features, via efficient gradient-based methods.
We consider the problem of non-parametric Conditional Independence testing (CI testing) for continuous random variables. Given i.i.d samples from the joint distribution $f(x,y,z)$ of continuous random vectors $X,Y$ and $Z,$ we determine whether $X \perp Y | Z$. We approach this by converting the conditional independence test into a classification problem. This allows us to harness very powerful classifiers like gradient-boosted trees and deep neural networks. These models can handle complex probability distributions and allow us to perform significantly better compared to the prior state of the art, for high-dimensional CI testing. The main technical challenge in the classification problem is the need for samples from the conditional product distribution $f^{CI}(x,y,z) = f(x|z)f(y|z)f(z)$ -- the joint distribution if and only if $X \perp Y | Z.$ -- when given access only to i.i.d. samples from the true joint distribution $f(x,y,z)$. To tackle this problem we propose a novel nearest neighbor bootstrap procedure and theoretically show that our generated samples are indeed close to $f^{CI}$ in terms of total variational distance. We then develop theoretical results regarding the generalization bounds for classification for our problem, which translate into error bounds for CI testing. We provide a novel analysis of Rademacher type classification bounds in the presence of non-i.i.d near-independent samples. We empirically validate the performance of our algorithm on simulated and real datasets and show performance gains over previous methods.
Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments/tracks with their own temporal dynamics, but collectively they unfold over time interdependently. Lastly, musical notes are often grouped into chords, arpeggios or melodies in polyphonic music, and thereby introducing a chronological ordering of notes is not naturally suitable. In this paper, we propose three models for symbolic multi-track music generation under the framework of generative adversarial networks (GANs). The three models, which differ in the underlying assumptions and accordingly the network architectures, are referred to as the jamming model, the composer model and the hybrid model. We trained the proposed models on a dataset of over one hundred thousand bars of rock music and applied them to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings. A few intra-track and inter-track objective metrics are also proposed to evaluate the generative results, in addition to a subjective user study. We show that our models can generate coherent music of four bars right from scratch (i.e. without human inputs). We also extend our models to human-AI cooperative music generation: given a specific track composed by human, we can generate four additional tracks to accompany it. All code, the dataset and the rendered audio samples are available at https://salu133445.github.io/musegan/ .
While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.
In knowledge bases such as Wikidata, it is possible to assert a large set of properties for entities, ranging from generic ones such as name and place of birth to highly profession-specific or background-specific ones such as doctoral advisor or medical condition. Determining a preference or ranking in this large set is a challenge in tasks such as prioritisation of edits or natural-language generation. Most previous approaches to ranking knowledge base properties are purely data-driven, that is, as we show, mistake frequency for interestingness. In this work, we have developed a human-annotated dataset of 350 preference judgments among pairs of knowledge base properties for fixed entities. From this set, we isolate a subset of pairs for which humans show a high level of agreement (87.5% on average). We show, however, that baseline and state-of-the-art techniques achieve only 61.3% precision in predicting human preferences for this subset. We then analyze what contributes to one property being rated as more important than another one, and identify that at least three factors play a role, namely (i) general frequency, (ii) applicability to similar entities and (iii) semantic similarity between property and entity. We experimentally analyze the contribution of each factor and show that a combination of techniques addressing all the three factors achieves 74% precision on the task. The dataset is available at www.kaggle.com/srazniewski/wikidatapropertyranking.
Objective: Electronic medical records (EMRs) contain an amount of medical knowledge which can be used for clinical decision support (CDS). Our objective is a general system that can extract and represent these knowledge contained in EMRs to support three CDS tasks: test recommendation, initial diagnosis, and treatment plan recommendation, with the given condition of one patient. Methods: We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a single record. Three bipartite subgraphs (bi-graphs) were extracted from the EMKN to support each task. One part of the bi-graph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bi-graph was regarded as a Markov random field to support the inference. Three lazy energy functions and one parameter-based energy function were proposed, as well as two knowledge representation learning-based energy functions, which can provide a distributed representation of medical entities. Three measures were utilized for performance evaluation. Results: On the initial diagnosis task, 80.11% of the test records identified at least one correct disease from top 10 candidates. Test and treatment recommendation results were 87.88% and 92.55%, respectively. These results altogether indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships in regards to knowledge level. Conclusion: Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require designing their energy functions individually.
The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. Among the few proposed approaches, the recently introduced Black-DROPS algorithm exploits a black-box optimization algorithm to achieve both high data-efficiency and good computation times when several cores are used; nevertheless, like all model-based policy search approaches, Black-DROPS does not scale to high dimensional state/action spaces. In this paper, we introduce a new model learning procedure in Black-DROPS that leverages parameterized black-box priors to (1) scale up to high-dimensional systems, and (2) be robust to large inaccuracies of the prior information. We demonstrate the effectiveness of our approach with the "pendubot" swing-up task in simulation and with a physical hexapod robot (48D state space, 18D action space) that has to walk forward as fast as possible. The results show that our new algorithm is more data-efficient than previous model-based policy search algorithms (with and without priors) and that it can allow a physical 6-legged robot to learn new gaits in only 16 to 30 seconds of interaction time.
Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. Although there have been recent advances of deep RL algorithms applied to multi-agent systems, learning communication protocols while simultaneously learning the behavior of the agents is still beyond the reach of deep RL algorithms. However, while it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building, building a communication link, and pushing an intruder. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols.
Suzuki and Niida (Ann. Pure. Appl. Logic, 2015) showed the following results on independent distributions (IDs) on an AND-OR tree, where they took only depth-first algorithms into consideration. (1) Among IDs such that probability of the root having value 0 is fixed as a given r such that 0 < r < 1, if d is a maximizer of cost of the best algorithm then d is an independent and identical distribution (IID). (2) Among all IDs, if d is a maximizer of cost of the best algorithm then d is an IID. In the case where non-depth-first algorithms are taken into consideration, the counter parts of (1) and (2) are left open in the above work. Peng et al. (Inform. Process. Lett., 2017) extended (1) and (2) to multi-branching trees, where in (2) they put an additional hypothesis on IDs that probability of the root having value 0 is neither 0 nor 1. We give positive answers for the two questions of Suzuki-Niida. A key to the proof is that if ID d achieves the equilibrium among IDs then we can chose an algorithm of the best cost against d from depth-first algorithms. In addition, we extend the result of Peng et al. to the case where non-depth-first algorithms are taken into consideration.
We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We then rank these attributes according to their importance for making predictions. We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.
We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image. Since depth estimation from monocular images alone is inherently ambiguous and unreliable, to attain a higher level of robustness and accuracy, we introduce additional sparse depth samples, which are either acquired with a low-resolution depth sensor or computed via visual Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of a single deep regression network to learn directly from the RGB-D raw data, and explore the impact of number of depth samples on prediction accuracy. Our experiments show that, compared to using only RGB images, the addition of 100 spatially random depth samples reduces the prediction root-mean-square error by 50% on the NYU-Depth-v2 indoor dataset. It also boosts the percentage of reliable prediction from 59% to 92% on the KITTI dataset. We demonstrate two applications of the proposed algorithm: a plug-in module in SLAM to convert sparse maps to dense maps, and super-resolution for LiDARs. Software and video demonstration are publicly available.
E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell billions of products. Machine learning (ML) algorithms involving products are often used to improve the customer experience and increase revenue, e.g., product similarity, recommendation, and price estimation. The products are required to be represented as features before training an ML algorithm. In this paper, we propose an approach called MRNet-Product2Vec for creating generic embeddings of products within an e-commerce ecosystem. We learn a dense and low-dimensional embedding where a diverse set of signals related to a product are explicitly injected into its representation. We train a Discriminative Multi-task Bidirectional Recurrent Neural Network (RNN), where the input is a product title fed through a Bidirectional RNN and at the output, product labels corresponding to fifteen different tasks are predicted. The task set includes several intrinsic characteristics about a product such as price, weight, size, color, popularity, and material. We evaluate the proposed embedding quantitatively and qualitatively. We demonstrate that they are almost as good as sparse and extremely high-dimensional TF-IDF representation in spite of having less than 3% of the TF-IDF dimension. We also use a multimodal autoencoder for comparing products from different language-regions and show preliminary yet promising qualitative results.
Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximally preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.
Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which ground-truth annotations are generated automatically. Unfortunately, models trained purely on simulated data often fail to generalize to the real world. We study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images. We extensively evaluate our approaches with a total of more than 25,000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN. We show that, by using synthetic data and domain adaptation, we are able to reduce the number of real-world samples needed to achieve a given level of performance by up to 50 times, using only randomly generated simulated objects. We also show that by using only unlabeled real-world data and our GraspGAN methodology, we obtain real-world grasping performance without any real-world labels that is similar to that achieved with 939,777 labeled real-world samples.
Authoring of OWL-DL ontologies is intellectually challenging and to make this process simpler, many systems accept natural language text as input. A text-based ontology authoring approach can be successful only when it is combined with an effective method for extracting ontological axioms from text. Extracting axioms from unrestricted English input is a substantially challenging task due to the richness of the language. Controlled natural languages (CNLs) have been proposed in this context and these tend to be highly restrictive. In this paper, we propose a new CNL called TEDEI (TExtual DEscription Identifier) whose grammar is inspired by the different ways OWL-DL constructs are expressed in English. We built a system that transforms TEDEI sentences into corresponding OWL-DL axioms. Now, ambiguity due to different possible lexicalizations of sentences and semantic ambiguity present in sentences are challenges in this context. We find that the best way to handle these challenges is to construct axioms corresponding to alternative formalizations of the sentence so that the end-user can make an appropriate choice. The output is compared against human-authored axioms and in substantial number of cases, human-authored axiom is indeed one of the alternatives given by the system. The proposed system substantially enhances the types of sentence structures that can be used for ontology authoring.
Biclustering techniques have been widely used to identify homogeneous subgroups within large data matrices, such as subsets of genes similarly expressed across subsets of patients. Mining a max-sum sub-matrix is a related but distinct problem for which one looks for a (non-necessarily contiguous) rectangular sub-matrix with a maximal sum of its entries. Le Van et al. (Ranked Tiling, 2014) already illustrated its applicability to gene expression analysis and addressed it with a constraint programming (CP) approach combined with large neighborhood search (CP-LNS). In this work, we exhibit some key properties of this NP-hard problem and define a bounding function such that larger problems can be solved in reasonable time. Two different algorithms are proposed in order to exploit the highlighted characteristics of the problem: a CP approach with a global constraint (CPGC) and mixed integer linear programming (MILP). Practical experiments conducted both on synthetic and real gene expression data exhibit the characteristics of these approaches and their relative benefits over the original CP-LNS method. Overall, the CPGC approach tends to be the fastest to produce a good solution. Yet, the MILP formulation is arguably the easiest to formulate and can also be competitive.
Recently there has been increasing interest in probabilistic solvers for ordinary differential equations (ODEs) that return full probability measures, instead of point estimates, over the solution and can incorporate uncertainty over the ODE at hand, e.g. if the vector field or the initial value is only approximately known or evaluable. The ODE filter proposed in recent work models the solution of the ODE by a Gauss-Markov process which serves as a prior in the sense of Bayesian statistics. While previous work employed a Wiener process prior on the (possibly multiple times) differentiated solution of the ODE and established equivalence of the corresponding solver with classical numerical methods, this paper raises the question whether other priors also yield practically useful solvers. To this end, we discuss a range of possible priors which enable fast filtering and propose a new prior--the Integrated Ornstein Uhlenbeck Process (IOUP)--that complements the existing Integrated Wiener process (IWP) filter by encoding the property that a derivative in time of the solution is bounded in the sense that it tends to drift back to zero. We provide experiments comparing IWP and IOUP filters which support the belief that IWP approximates better divergent ODE's solutions whereas IOUP is a better prior for trajectories with bounded derivatives.
A new prior is proposed for representation learning, which can be combined with other priors in order to help disentangling abstract factors from each other. It is inspired by the phenomenon of consciousness seen as the formation of a low-dimensional combination of a few concepts constituting a conscious thought, i.e., consciousness as awareness at a particular time instant. This provides a powerful constraint on the representation in that such low-dimensional thought vectors can correspond to statements about reality which are true, highly probable, or very useful for taking decisions. The fact that a few elements of the current state can be combined into such a predictive or useful statement is a strong constraint and deviates considerably from the maximum likelihood approaches to modelling data and how states unfold in the future based on an agent's actions. Instead of making predictions in the sensory (e.g. pixel) space, the consciousness prior allows the agent to make predictions in the abstract space, with only a few dimensions of that space being involved in each of these predictions. The consciousness prior also makes it natural to map conscious states to natural language utterances or to express classical AI knowledge in the form of facts and rules, although the conscious states may be richer than what can be expressed easily in the form of a sentence, a fact or a rule.
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining with policy gradient, Generative Adversarial Nets (GAN) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation. However, the scalar guiding signal is only available after the entire text has been generated and lacks intermediate information about text structure during the generative process. As such, it limits its success when the length of the generated text samples is long (more than 20 words). In this paper, we propose a new framework, called LeakGAN, to address the problem for long text generation. We allow the discriminative net to leak its own high-level extracted features to the generative net to further help the guidance. The generator incorporates such informative signals into all generation steps through an additional Manager module, which takes the extracted features of current generated words and outputs a latent vector to guide the Worker module for next-word generation. Our extensive experiments on synthetic data and various real-world tasks with Turing test demonstrate that LeakGAN is highly effective in long text generation and also improves the performance in short text generation scenarios. More importantly, without any supervision, LeakGAN would be able to implicitly learn sentence structures only through the interaction between Manager and Worker.
Ultra-dense heterogeneous networks (Ud-HetNets) have been put forward to improve the network capacity for next-generation wireless networks. However, counter to the 5G vision, ultra-dense deployment of networks would significantly increase energy consumption and thus decrease network energy efficiency suffering from the conventional worst-case network design philosophy. This problem becomes particularly severe when Ud-HetNets meet big data because of the traditional reactive request-transmit service mode. In view of these, this article first develops a big-data-aware artificial intelligent based framework for energy-efficient operations of Ud-HetNets. Based on the framework, we then identify four promising techniques, namely big data analysis, adaptive base station operation, proactive caching, and interference-aware resource allocation, to reduce energy cost on both large and small scales. We further develop a load-aware stochastic optimization approach to show the potential of our proposed framework and techniques in energy conservation. In a nutshell, we devote to constructing green Ud-HetNets of big data with the abilities of learning and inferring by improving the flexibility of control from worst-case to adaptive design and shifting the manner of services from reactive to proactive modes.
We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.
This paper is concerned with the problem of exact MAP inference in general higher-order graphical models by means of a traditional linear programming relaxation approach. In fact, the proof that we have developed in this paper is a rather simple algebraic proof being made straightforward, above all, by the introduction of two novel algebraic tools. Indeed, on the one hand, we introduce the notion of delta-distribution which merely stands for the difference of two arbitrary probability distributions, and which mainly serves to alleviate the sign constraint inherent to a traditional probability distribution. On the other hand, we develop an approximation framework of general discrete functions by means of an orthogonal projection expressing in terms of linear combinations of function margins with respect to a given collection of point subsets, though, we rather exploit the latter approach for the purpose of modeling locally consistent sets of discrete functions from a global perspective. After that, as a first step, we develop from scratch the expectation optimization framework which is nothing else than a reformulation, on stochastic grounds, of the convex-hull approach, as a second step, we develop the traditional LP relaxation of such an expectation optimization approach, and we show that it enables to solve the MAP inference problem in graphical models under rather general assumptions. Last but not least, we describe an algorithm which allows to compute an exact MAP solution from a perhaps fractional optimal (probability) solution of the proposed LP relaxation.
Scholars and practitioners across domains are increasingly concerned with algorithmic transparency and opacity, interrogating the values and assumptions embedded in automated, black-boxed systems, particularly in user-generated content platforms. I report from an ethnography of infrastructure in Wikipedia to discuss an often understudied aspect of this topic: the local, contextual, learned expertise involved in participating in a highly automated social-technical environment. Today, the organizational culture of Wikipedia is deeply intertwined with various data-driven algorithmic systems, which Wikipedians rely on to help manage and govern the "anyone can edit" encyclopedia at a massive scale. These bots, scripts, tools, plugins, and dashboards make Wikipedia more efficient for those who know how to work with them, but like all organizational culture, newcomers must learn them if they want to fully participate. I illustrate how cultural and organizational expertise is enacted around algorithmic agents by discussing two autoethnographic vignettes, which relate my personal experience as a veteran in Wikipedia. I present thick descriptions of how governance and gatekeeping practices are articulated through and in alignment with these automated infrastructures. Over the past 15 years, Wikipedian veterans and administrators have made specific decisions to support administrative and editorial workflows with automation in particular ways and not others. I use these cases of Wikipedia's bot-supported bureaucracy to discuss several issues in the fields of critical algorithms studies, critical data studies, and fairness, accountability, and transparency in machine learning -- most principally arguing that scholarship and practice must go beyond trying to "open up the black box" of such systems and also examine sociocultural processes like newcomer socialization.
In the context of fitness coaching or for rehabilitation purposes, the motor actions of a human participant must be observed and analyzed for errors in order to provide effective feedback. This task is normally carried out by human coaches, and it needs to be solved automatically in technical applications that are to provide automatic coaching (e.g. training environments in VR). However, most coaching systems only provide coarse information on movement quality, such as a scalar value per body part that describes the overall deviation from the correct movement. Further, they are often limited to static body postures or rather simple movements of single body parts. While there are many approaches to distinguish between different types of movements (e.g., between walking and jumping), the detection of more subtle errors in a motor performance is less investigated. We propose a novel approach to classify errors in sports or rehabilitation exercises such that feedback can be delivered in a rapid and detailed manner: Homogeneous sub-sequences of exercises are first temporally aligned via Dynamic Time Warping. Next, we extract a feature vector from the aligned sequences, which serves as a basis for feature selection using Random Forests. The selected features are used as input for Support Vector Machines, which finally classify the movement errors. We compare our algorithm to a well established state-of-the-art approach in time series classification, 1-Nearest Neighbor combined with Dynamic Time Warping, and show our algorithm's superiority regarding classification quality as well as computational cost.
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours. We show that, as an example, we are able to accurately predict the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer patients, where the average dice similarity coefficient is 0.91 when comparing the predicted vs. true isodose volumes between 0% and 100% of the prescription dose. The average value of the absolute differences in [max, mean] dose is found to be under 5% of the prescription dose, specifically for each structure is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%, 1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%, 1.62%](Rectum) of the prescription dose. We thus managed to map a desired radiation dose distribution from a patient's PTV and OAR contours. As an additional advantage, relatively little data was used in the techniques and models described in this paper.
This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.
In this dissertation the practical speech emotion recognition technology is studied, including several cognitive related emotion types, namely fidgetiness, confidence and tiredness. The high quality of naturalistic emotional speech data is the basis of this research. The following techniques are used for inducing practical emotional speech: cognitive task, computer game, noise stimulation, sleep deprivation and movie clips. A practical speech emotion recognition system is studied based on Gaussian mixture model. A two-class classifier set is adopted for performance improvement under the small sample case. Considering the context information in continuous emotional speech, a Gaussian mixture model embedded with Markov networks is proposed. A further study is carried out for system robustness analysis. First, noise reduction algorithm based on auditory masking properties is fist introduced to the practical speech emotion recognition. Second, to deal with the complicated unknown emotion types under real situation, an emotion recognition method with rejection ability is proposed, which enhanced the system compatibility against unknown emotion samples. Third, coping with the difficulties brought by a large number of unknown speakers, an emotional feature normalization method based on speaker-sensitive feature clustering is proposed. Fourth, by adding the electrocardiogram channel, a bi-modal emotion recognition system based on speech signals and electrocardiogram signals is first introduced. The speech emotion recognition methods studied in this dissertation may be extended into the cross-language speech emotion recognition and the whispered speech emotion recognition.
The increasing interconnectivity of industrial networks is one of the central current hot topics. It is adressed by research institutes, as well as industry. In order to perform the fourth industrial revolution, a full connectivity between production facilities is necessary. Due to this connectivity, however, an abundance of new attack vectors emerges. In the National Reference Project for Industrial IT-Security (IUNO), these risks and threats are addressed and solutions are developed. These solutions are especially applicable for small and medium sized enterprises that have not as much means in staff as well as money as larger companies. These enterprises should be able to implement the solutions without much effort. The security solutions are derived from four use cases and implemented prototypically. A further topic of this work are the research areas of the German Research Center for Artificial Intelligence that address the given challenges, as well as the solutions developed in the context of IUNO. Aside from the project itself, a method for distributed network data collection aggregation is presented, as a prerequisite for anomaly detection for network security.
We address the problem of assisting human dispatchers in operating power grids in today's changing context using machine learning, with theaim of increasing security and reducing costs. Power networks are highly regulated systems, which at all times must meet varying demands of electricity with a complex production system, including conventional power plants, less predictable renewable energies (such as wind or solar power), and the possibility of buying/selling electricity on the international market with more and more actors involved at a Europeanscale. This problem is becoming ever more challenging in an aging network infrastructure. One of the primary goals of dispatchers is to protect equipment (e.g. avoid that transmission lines overheat) with few degrees of freedom: we are considering in this paper solely modifications in network topology, i.e. re-configuring the way in which lines, transformers, productions and loads are connected in sub-stations. Using years of historical data collected by the French Transmission Service Operator (TSO) "R\'eseau de Transport d'Electricit\'e" (RTE), we develop novel machine learning techniques (drawing on "deep learning") to mimic human decisions to devise "remedial actions" to prevent any line to violate power flow limits (so-called "thermal limits"). The proposed technique is hybrid. It does not rely purely on machine learning: every action will be tested with actual simulators before being proposed to the dispatchers or implemented on the grid.
This paper focuses on two commonly used path assignment policies for agents traversing a congested network: self-interested routing, and system-optimum routing. In the self-interested routing policy each agent selects a path that optimizes its own utility, while the system-optimum routing agents are assigned paths with the goal of maximizing system performance. This paper considers a scenario where a centralized network manager wishes to optimize utilities over all agents, i.e., implement a system-optimum routing policy. In many real-life scenarios, however, the system manager is unable to influence the route assignment of all agents due to limited influence on route choice decisions. Motivated by such scenarios, a computationally tractable method is presented that computes the minimal amount of agents that the system manager needs to influence (compliant agents) in order to achieve system optimal performance. Moreover, this methodology can also determine whether a given set of compliant agents is sufficient to achieve system optimum and compute the optimal route assignment for the compliant agents to do so. Experimental results are presented showing that in several large-scale, realistic traffic networks optimal flow can be achieved with as low as 13% of the agent being compliant and up to 54%.
We present Edina, the University of Edinburgh's social bot for the Amazon Alexa Prize competition. Edina is a conversational agent whose responses utilize data harvested from Amazon Mechanical Turk (AMT) through an innovative new technique we call self-dialogues. These are conversations in which a single AMT Worker plays both participants in a dialogue. Such dialogues are surprisingly natural, efficient to collect and reflective of relevant and/or trending topics. These self-dialogues provide training data for a generative neural network as well as a basis for soft rules used by a matching score component. Each match of a soft rule against a user utterance is associated with a confidence score which we show is strongly indicative of reply quality, allowing this component to self-censor and be effectively integrated with other components. Edina's full architecture features a rule-based system backing off to a matching score, backing off to a generative neural network. Our hybrid data-driven methodology thus addresses both coverage limitations of a strictly rule-based approach and the lack of guarantees of a strictly machine-learning approach.
Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmaca
While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data. One way to increase the speed at which agents are able to learn to perform tasks is by leveraging the input of human trainers. Although such input can take many forms, real-time, scalar-valued feedback is especially useful in situations where it proves difficult or impossible for humans to provide expert demonstrations. Previous approaches have shown the usefulness of human input provided in this fashion (e.g., the TAMER framework), but they have thus far not considered high-dimensional state spaces or employed the use of deep learning. In this paper, we do both: we propose Deep TAMER, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer. We demonstrate Deep TAMER's success by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of Bowling - a task that has proven difficult for even state-of-the-art reinforcement learning methods.
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware designed to mimic the dynamics and architecture of biological neural networks. However, neuromorphic implementations of embedded learning at large scales that are both flexible and efficient have been hindered by a lack of a suitable algorithmic framework. As a result, most neuromorphic hardware are trained off-line on large clusters of dedicated processors or GPUs and transferred post hoc to the device. We address this by introducing the neural and synaptic array transceiver (NSAT), a neuromorphic computational framework facilitating flexible and efficient embedded learning. NSAT supports event-driven supervised, unsupervised and reinforcement learning algorithms including deep learning. We demonstrate the NSAT in a wide range of tasks, including the simulation of Mihalas-Niebur neuron, dynamic neural fields, event-driven random back-propagation for event-based deep learning, event-based contrastive divergence for unsupervised learning, and voltage-based learning rules for sequence learning. We anticipate that this contribution will establish the foundation for a new generation of devices enabling adaptive mobile systems, wearable devices, and robots with data-driven autonomy.
We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.
Enabling robots to autonomously navigate complex environments is essential for real-world deployment. Prior methods approach this problem by having the robot maintain an internal map of the world, and then use a localization and planning method to navigate through the internal map. However, these approaches often include a variety of assumptions, are computationally intensive, and do not learn from failures. In contrast, learning-based methods improve as the robot acts in the environment, but are difficult to deploy in the real-world due to their high sample complexity. To address the need to learn complex policies with few samples, we propose a generalized computation graph that subsumes value-based model-free methods and model-based methods, with specific instantiations interpolating between model-free and model-based. We then instantiate this graph to form a navigation model that learns from raw images and is sample efficient. Our simulated car experiments explore the design decisions of our navigation model, and show our approach outperforms single-step and $N$-step double Q-learning. We also evaluate our approach on a real-world RC car and show it can learn to navigate through a complex indoor environment with a few hours of fully autonomous, self-supervised training. Videos of the experiments and code can be found at github.com/gkahn13/gcg
In this work, we present an approach to deep visuomotor control using structured deep dynamics models. Our deep dynamics model, a variant of SE3-Nets, learns a low-dimensional pose embedding for visuomotor control via an encoder-decoder structure. Unlike prior work, our dynamics model is structured: given an input scene, our network explicitly learns to segment salient parts and predict their pose-embedding along with their motion modeled as a change in the pose space due to the applied actions. We train our model using a pair of point clouds separated by an action and show that given supervision only in the form of point-wise data associations between the frames our network is able to learn a meaningful segmentation of the scene along with consistent poses. We further show that our model can be used for closed-loop control directly in the learned low-dimensional pose space, where the actions are computed by minimizing error in the pose space using gradient-based methods, similar to traditional model-based control. We present results on controlling a Baxter robot from raw depth data in simulation and in the real world and compare against two baseline deep networks. Our method runs in real-time, achieves good prediction of scene dynamics and outperforms the baseline methods on multiple control runs. Video results can be found at: https://rse-lab.cs.washington.edu/se3-structured-deep-ctrl/
Deep neural networks are increasingly being used in a variety of machine learning applications applied to rich user data on the cloud. However, this approach introduces a number of privacy and efficiency challenges, as the cloud operator can perform secondary inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep models for cooperative, privacy-preserving analytics. We do this by breaking down the popular deep architectures and fine-tune them in a particular way. We then evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also asses the local inference cost of different layers on a modern handset for mobile applications. Our evaluations show that by using certain kind of fine-tuning and embedding techniques and at a small processing costs, we can greatly reduce the level of information available to unintended tasks applied to the data feature on the cloud, and hence achieving the desired tradeoff between privacy and performance.
Mobile Ad hoc Network (MANET) is an infrastructure-less network formed between a set of mobile nodes. The discovery of services in MANET is a challenging job due to the unique properties of network. In this paper, a novel service discovery framework called Hybrid Association Rules Based Network Layer Discovery of Services for Ad hoc Networks (HANDY) has been proposed. HANDY provides three major research contributions. At first, it adopts a cross-layer optimized design for discovery of services that is based on simultaneous discovery of services and corresponding routes. Secondly, it provides a multi-level ontology-based approach to describe the services. This resolves the issue of semantic interoperability among the service consumers in a scalable fashion. Finally, to further optimize the performance of the discovery process, HANDY recommends exploiting the inherent associations present among the services. These associations are used in two ways. First, periodic service advertisements are performed based on these associations. In addition, when a response of a service discovery request is generated, correlated services are also attached with the response. The proposed service discovery scheme has been implemented in JIST/SWANS simulator. The results demonstrate that the proposed modifications give rise to improvement in hit ratio of the service consumers and latency of discovery process.
The infamous exploration-exploitation dilemma is one of the oldest and most important problems in reinforcement learning (RL). Deliberate and effective exploration is necessary for RL agents to succeed in most environments. However, until very recently even very sophisticated RL algorithms employed simple, undirected exploration strategies in large-scale RL tasks. We introduce a new optimistic count-based exploration algorithm for RL that is feasible in high-dimensional MDPs. The success of RL algorithms in these domains depends crucially on generalization from limited training experience. Function approximation techniques enable RL agents to generalize in order to estimate the value of unvisited states, but at present few methods have achieved generalization about the agent's uncertainty regarding unvisited states. We present a new method for computing a generalized state visit-count, which allows the agent to estimate the uncertainty associated with any state. In contrast to existing exploration techniques, our $\phi$-$\textit{pseudocount}$ achieves generalization by exploiting the feature representation of the state space that is used for value function approximation. States that have less frequently observed features are deemed more uncertain. The resulting $\phi$-$\textit{Exploration-Bonus}$ algorithm rewards the agent for exploring in feature space rather than in the original state space. This method is simpler and less computationally expensive than some previous proposals, and achieves near state-of-the-art results on high-dimensional RL benchmarks. In particular, we report world-class results on several notoriously difficult Atari 2600 video games, including Montezuma's Revenge.
The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representation learning of deep neural networks. In the chemistry domain, recent advances have also led to the development of similar CNN models, such as Chemception, that is trained to predict chemical properties using images of molecular drawings. In this work, we investigate the effects of systematically removing and adding localized domain-specific information to the image channels of the training data. By augmenting images with only 3 additional basic information, and without introducing any architectural changes, we demonstrate that an augmented Chemception (AugChemception) outperforms the original model in the prediction of toxicity, activity, and solvation free energy. Then, by altering the information content in the images, and examining the resulting model's performance, we also identify two distinct learning patterns in predicting toxicity/activity as compared to solvation free energy. These patterns suggest that Chemception is learning about its tasks in the manner that is consistent with established knowledge. Thus, our work demonstrates that advanced chemical knowledge is not a pre-requisite for deep learning models to accurately predict complex chemical properties.
Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks. The results show that our method is able to improve the performance of DNNs with different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.
Clustering samples according to an effective metric and/or vector space representation is a challenging unsupervised learning task with a wide spectrum of applications. Among several clustering algorithms, k-means and its kernelized version have still a wide audience because of their conceptual simplicity and efficacy. However, the systematic application of the kernelized version of k-means is hampered by its inherent square scaling in memory with the number of samples. In this contribution, we devise an approximate strategy to minimize the kernel k-means cost function in which the trade-off between accuracy and velocity is automatically ruled by the available system memory. Moreover, we define an ad-hoc parallelization scheme well suited for hybrid cpu-gpu state-of-the-art parallel architectures. We proved the effectiveness both of the approximation scheme and of the parallelization method on standard UCI datasets and on molecular dynamics (MD) data in the realm of computational chemistry. In this applicative domain, clustering can play a key role for both quantitively estimating kinetics rates via Markov State Models or to give qualitatively a human compatible summarization of the underlying chemical phenomenon under study. For these reasons, we selected it as a valuable real-world application scenario.
A smart grid can be considered as a complex network where each node represents a generation unit or a consumer. Whereas links can be used to represent transmission lines. One way to study complex systems is by using the agent-based modeling (ABM) paradigm. An ABM is a way of representing a complex system of autonomous agents interacting with each other. Previously, a number of studies have been presented in the smart grid domain making use of the ABM paradigm. However, to the best of our knowledge, none of these studies have focused on the specification aspect of ABM. An ABM specification is important not only for understanding but also for replication of the model. In this study, we focus on development as well as specification of ABM for smart grid. We propose an ABM by using a combination of agent-based and complex network-based approaches. For ABM specification, we use ODD and DREAM specification approaches. We analyze these two specification approaches qualitatively as well as quantitatively. Extensive experiments demonstrate that DREAM is a most useful approach as compared with ODD for modeling as well as for replication of models for smart grid.
One of the key challenges when looking for the causes of a complex event is to determine the causal status of factors that are neither individually necessary nor individually sufficient to produce that event. In order to reason about how such factors should be taken into account, we need a vocabulary to distinguish different cases. In philosophy, the concept of overdetermination and the concept of preemption serve an important purpose in this regard, although their exact meaning tends to remain elusive. In this paper, I provide theory-neutral definitions of these concepts using structural equations in the Halpern-Pearl tradition. While my definitions do not presuppose any particular causal theory, they take such a theory as a variable parameter. This enables us to specify formal constraints on theories of causality, in terms of a pre-theoretic understanding of what preemption and overdetermination actually mean. I demonstrate the usefulness of this by presenting and arguing for what I call the principle of presumption. Roughly speaking, this principle states that a possible cause can only be regarded as having been preempted if there is independent evidence to support such an inference. I conclude by showing that the principle of presumption is violated by the two main theories of causality formulated in the Halpern-Pearl tradition. The paper concludes by defining the class of empirical causal theories, characterised in terms of a fixed-point of counterfactual reasoning about difference-making. It is argued that theories of actual causality ought to be empirical.
The goal of this paper is to advance an extensible theory of living systems using an approach to biomathematics and biocomputation that suitably addresses self-organized, self-referential and anticipatory systems with multi-temporal multi-agents. Our first step is to provide foundations for modelling of emergent and evolving dynamic multi-level organic complexes and their sustentative processes in artificial and natural life systems. Main applications are in life sciences, medicine, ecology and astrobiology, as well as robotics, industrial automation and man-machine interface. Since 2011 over 100 scientists from a number of disciplines have been exploring a substantial set of theoretical frameworks for a comprehensive theory of life known as Integral Biomathics. That effort identified the need for a robust core model of organisms as dynamic wholes, using advanced and adequately computable mathematics. The work described here for that core combines the advantages of a situation and context aware multivalent computational logic for active self-organizing networks, Wandering Logic Intelligence (WLI), and a multi-scale dynamic category theory, Memory Evolutive Systems (MES), hence WLIMES. This is presented to the modeller via a formal augmented reality language as a first step towards practical modelling and simulation of multi-level living systems. Initial work focuses on the design and implementation of this visual language and calculus (VLC) and its graphical user interface. The results will be integrated within the current methodology and practices of theoretical biology and (personalized) medicine to deepen and to enhance the holistic understanding of life.
Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As model sizes grow, the memory and compute requirements for training these models also increases. We introduce a technique to train deep neural networks using half precision floating point numbers. In our technique, weights, activations and gradients are stored in IEEE half-precision format. Half-precision floating numbers have limited numerical range compared to single-precision numbers. We propose two techniques to handle this loss of information. Firstly, we recommend maintaining a single-precision copy of the weights that accumulates the gradients after each optimizer step. This single-precision copy is rounded to half-precision format during training. Secondly, we propose scaling the loss appropriately to handle the loss of information with half-precision gradients. We demonstrate that this approach works for a wide variety of models including convolution neural networks, recurrent neural networks and generative adversarial networks. This technique works for large scale models with more than 100 million parameters trained on large datasets. Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x. In future processors, we can also expect a significant computation speedup using half-precision hardware units.
This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM) neurons. LSTM RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, making this approach ungeneralizable across multiple engines. In initial work, multiple LSTM RNN architectures were proposed, evaluated and compared. This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network. A parallelized version of the ACO neuroevolution algorithm has been developed and the evolved LSTM RNNs were compared to the previously used fixed topology. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, evolving 1000 different LSTM cell structures using 168 cores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%, reducing prediction error from 5.51% to 4.17% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of weights from 21,170 to 11,810.
We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling-based path planning with reinforcement learning (RL) agents. The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology, while the sampling-based planners provide an approximate map of the space of possible configurations of the robot from which collision-free trajectories feasible for the RL agents can be identified. The same RL agents are used to control the robot under the direction of the planning, enabling long-range navigation. We use the Probabilistic Roadmaps (PRMs) for the sampling-based planner. The RL agents are constructed using feature-based and deep neural net policies in continuous state and action spaces. We evaluate PRM-RL on two navigation tasks with non-trivial robot dynamics: end-to-end differential drive indoor navigation in office environments, and aerial cargo delivery in urban environments with load displacement constraints. These evaluations included both simulated environments and on-robot tests. Our results show improvement in navigation task completion over both RL agents on their own and traditional sampling-based planners. In the indoor navigation task, PRM-RL successfully completes up to 215 meters long trajectories under noisy sensor conditions, and the aerial cargo delivery completes flights over 1000 meters without violating the task constraints in an environment 63 million times larger than used in training.
Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output mapping. This paper aims to address the problem of data and computation efficiency of program induction by leveraging information from related tasks. Specifically, we propose two approaches for cross-task knowledge transfer to improve program induction in limited-data scenarios. In our first proposal, portfolio adaptation, a set of induction models is pretrained on a set of related tasks, and the best model is adapted towards the new task using transfer learning. In our second approach, meta program induction, a $k$-shot learning approach is used to make a model generalize to new tasks without additional training. To test the efficacy of our methods, we constructed a new benchmark of programs written in the Karel programming language. Using an extensive experimental evaluation on the Karel benchmark, we demonstrate that our proposals dramatically outperform the baseline induction method that does not use knowledge transfer. We also analyze the relative performance of the two approaches and study conditions in which they perform best. In particular, meta induction outperforms all existing approaches under extreme data sparsity (when a very small number of examples are available), i.e., fewer than ten. As the number of available I/O examples increase (i.e. a thousand or more), portfolio adapted program induction becomes the best approach. For intermediate data sizes, we demonstrate that the combined method of adapted meta program induction has the strongest performance.
The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is the main computational bottleneck in spectral clustering. In this work, we introduce a highly-scalable, spectrum-preserving graph sparsification algorithm that enables to build ultra-sparse NN (u-NN) graphs with guaranteed preservation of the original graph spectrums, such as the first few eigenvectors of the original graph Laplacian. Our approach can immediately lead to scalable spectral clustering of large data networks without sacrificing solution quality. The proposed method starts from constructing low-stretch spanning trees (LSSTs) from the original graphs, which is followed by iteratively recovering small portions of "spectrally critical" off-tree edges to the LSSTs by leveraging a spectral off-tree embedding scheme. To determine the suitable amount of off-tree edges to be recovered to the LSSTs, an eigenvalue stability checking scheme is proposed, which enables to robustly preserve the first few Laplacian eigenvectors within the sparsified graph. Additionally, an incremental graph densification scheme is proposed for identifying extra edges that have been missing in the original NN graphs but can still play important roles in spectral clustering tasks. Our experimental results for a variety of well-known data sets show that the proposed method can dramatically reduce the complexity of NN graphs, leading to significant speedups in spectral clustering.
Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of scalable tools to perform analysis over a large number of events. In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated with the safety incident. Precursors are valuable to systems health and safety monitoring and in explaining and forecasting safety incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal behavior. We propose an approach by combining multiple-instance learning (MIL) and deep recurrent neural networks (DRNN) to take advantage of MIL's ability to learn using weakly supervised data and DRNN's ability to model temporal behavior. We describe the algorithm, the data, the intuition behind taking a MIL approach, and a comparative analysis of the proposed algorithm with baseline models. We also discuss the application to a real-world aviation safety problem using data from a commercial airline company and discuss the model's abilities and shortcomings, with some final remarks about possible deployment directions.
Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction. This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input. The encoder of the autoencoder allows us to do comparisons within the latent space, while the decoder allows us to visualize the learned prototypes. The training objective has four terms: an accuracy term, a term that encourages every prototype to be similar to at least one encoded input, a term that encourages every encoded input to be close to at least one prototype, and a term that encourages faithful reconstruction by the autoencoder. The distances computed in the prototype layer are used as part of the classification process. Since the prototypes are learned during training, the learned network naturally comes with explanations for each prediction, and the explanations are loyal to what the network actually computes.
Social network analysis provides meaningful information about behavior of network members that can be used for diverse applications such as classification, link prediction. However, network analysis is computationally expensive because of feature learning for different applications. In recent years, many researches have focused on feature learning methods in social networks. Network embedding represents the network in a lower dimensional representation space with the same properties which presents a compressed representation of the network. In this paper, we introduce a novel algorithm named "CARE" for network embedding that can be used for different types of networks including weighted, directed and complex. Current methods try to preserve local neighborhood information of nodes, whereas the proposed method utilizes local neighborhood and community information of network nodes to cover both local and global structure of social networks. CARE builds customized paths, which are consisted of local and global structure of network nodes, as a basis for network embedding and uses the Skip-gram model to learn representation vector of nodes. Subsequently, stochastic gradient descent is applied to optimize our objective function and learn the final representation of nodes. Our method can be scalable when new nodes are appended to network without information loss. Parallelize generation of customized random walks is also used for speeding up CARE. We evaluate the performance of CARE on multi label classification and link prediction tasks. Experimental results on various networks indicate that the proposed method outperforms others in both Micro and Macro-f1 measures for different size of training data.
Both resources in the natural environment and concepts in a semantic space are distributed "patchily", with large gaps in between the patches. To describe people's internal and external foraging behavior, various random walk models have been proposed. In particular, internal foraging has been modeled as sampling: in order to gather relevant information for making a decision, people draw samples from a mental representation using random-walk algorithms such as Markov chain Monte Carlo (MCMC). However, two common empirical observations argue against simple sampling algorithms such as MCMC. First, the spatial structure is often best described by a L\'evy flight distribution: the probability of the distance between two successive locations follows a power-law on the distances. Second, the temporal structure of the sampling that humans and other animals produce have long-range, slowly decaying serial correlations characterized as $1/f$-like fluctuations. We propose that mental sampling is not done by simple MCMC, but is instead adapted to multimodal representations and is implemented by Metropolis-coupled Markov chain Monte Carlo (MC$^3$), one of the first algorithms developed for sampling from multimodal distributions. MC$^3$ involves running multiple Markov chains in parallel but with target distributions of different temperatures, and it swaps the states of the chains whenever a better location is found. Heated chains more readily traverse valleys in the probability landscape to propose moves to far-away peaks, while the colder chains make the local steps that explore the current peak or patch. We show that MC$^3$ generates distances between successive samples that follow a L\'evy flight distribution and $1/f$-like serial correlations, providing a single mechanistic account of these two puzzling empirical phenomena.
In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning signal that is always available for autonomously collected data is prediction: if a robot can learn to predict the future, it can use this predictive model to take actions to produce desired outcomes, such as moving an object to a particular location. However, in complex open-world scenarios, designing a representation for prediction is difficult. In this work, we instead aim to enable self-supervised robotic learning through direct video prediction: instead of attempting to design a good representation, we directly predict what the robot will see next, and then use this model to achieve desired goals. A key challenge in video prediction for robotic manipulation is handling complex spatial arrangements such as occlusions. To that end, we introduce a video prediction model that can keep track of objects through occlusion by incorporating temporal skip-connections. Together with a novel planning criterion and action space formulation, we demonstrate that this model substantially outperforms prior work on video prediction-based control. Our results show manipulation of objects not seen during training, handling multiple objects, and pushing objects around obstructions. These results represent a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robotic learning.
In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i) the effect of class imbalance on classification performance is detrimental; (ii) the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii) oversampling should be applied to the level that totally eliminates the imbalance, whereas undersampling can perform better when the imbalance is only removed to some extent; (iv) as opposed to some classical machine learning models, oversampling does not necessarily cause overfitting of CNNs; (v) thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.
Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. Recent research aims at finding distributed vector representations (embeddings) for words, such that semantically similar words are relatively close within the vector-space. Encoding the "meaning" of text into vectors is a current trend, and text can range from words, phrases and documents to actual human-to-human conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the vector representation of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system.
Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in the systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. To our best knowledge, SMR is the first to learn embeddings of a patient-disease-medicine graph for medicine recommendation in the world. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework.
Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.
We propose a framework to understand the unprecedented performance and robustness of deep neural networks using field theory. Correlations between the weights within the same layer can be described by symmetries in that layer, and networks generalize better if such symmetries are broken to reduce the redundancies of the weights. Using a two parameter field theory, we find that the network can break such symmetries itself towards the end of training in a process commonly known in physics as spontaneous symmetry breaking. This corresponds to a network generalizing itself without any user input layers to break the symmetry, but by communication with adjacent layers. In the layer decoupling limit applicable to residual networks (He et al., 2015), we show that the remnant symmetries that survive the non-linear layers are spontaneously broken. The Lagrangian for the non-linear and weight layers together has striking similarities with the one in quantum field theory of a scalar. Using results from quantum field theory we show that our framework is able to explain many experimentally observed phenomena,such as training on random labels with zero error (Zhang et al., 2017), the information bottleneck, the phase transition out of it and gradient variance explosion (Shwartz-Ziv & Tishby, 2017), shattered gradients (Balduzzi et al., 2017), and many more.
In order for robots to perform mission-critical tasks, it is essential that they are able to quickly adapt to changes in their environment as well as to injuries and or other bodily changes. Deep reinforcement learning has been shown to be successful in training robot control policies for operation in complex environments. However, existing methods typically employ only a single policy. This can limit the adaptability since a large environmental modification might require a completely different behavior compared to the learning environment. To solve this problem, we propose Map-based Multi-Policy Reinforcement Learning (MMPRL), which aims to search and store multiple policies that encode different behavioral features while maximizing the expected reward in advance of the environment change. Thanks to these policies, which are stored into a multi-dimensional discrete map according to its behavioral feature, adaptation can be performed within reasonable time without retraining the robot. An appropriate pre-trained policy from the map can be recalled using Bayesian optimization. Our experiments show that MMPRL enables robots to quickly adapt to large changes without requiring any prior knowledge on the type of injuries that could occur. A highlight of the learned behaviors can be found here: https://youtu.be/QwInbilXNOE .
Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose a transparent model distillation approach to audit such models. Model distillation was first introduced to transfer knowledge from a large, complex teacher model to a faster, simpler student model without significant loss in prediction accuracy. To this we add a third criterion - transparency. To gain insight into black-box models, we treat them as teachers, training transparent student models to mimic the risk scores assigned by the teacher. Moreover, we use side information in the form of the actual outcomes the teacher scoring model was intended to predict in the first place. By training a second transparent model on the outcomes, we can compare the two models to each other. When comparing models trained on risk scores to models trained on outcomes, we show that it is necessary to calibrate the risk-scoring model's predictions to remove distortion that may have been added to the black-box risk-scoring model during or after its training process. We also show how to compute confidence intervals for the particular class of transparent student models we use - tree-based additive models with pairwise interactions (GA2Ms) - to support comparison of the two transparent models. We demonstrate the methods on four public datasets: COMPAS, Lending Club, Stop-and-Frisk, and Chicago Police.
We describe the adaptation and refinement of a graphical user interface designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot dialogue data. The data collected will be used to develop a dialogue system for robot navigation. Building on an interface previously used in the development of dialogue systems for virtual agents and video playback, we add templates with open parameters which allow the wizard to quickly produce a wide variety of utterances. Our research demonstrates that this approach to data collection is viable as an intermediate step in developing a dialogue system for physical robots in remote locations from their users - a domain in which the human and robot need to regularly verify and update a shared understanding of the physical environment. We show that our WoZ interface and the fixed set of utterances and templates therein provide for a natural pace of dialogue with good coverage of the navigation domain.
We consider two questions at the heart of machine learning; how can we predict if a minimum will generalize to the test set, and why does stochastic gradient descent find minima that generalize well? Our work responds to Zhang et al. (2016), who showed deep neural networks can easily memorize randomly labeled training data, despite generalizing well on real labels of the same inputs. We show that the same phenomenon occurs in small linear models. These observations are explained by the Bayesian evidence, which penalizes sharp minima but is invariant to model parameterization. We also demonstrate that, when one holds the learning rate fixed, there is an optimum batch size which maximizes the test set accuracy. We propose that the noise introduced by small mini-batches drives the parameters towards minima whose evidence is large. Interpreting stochastic gradient descent as a stochastic differential equation, we identify the "noise scale" $g = \epsilon (\frac{N}{B} - 1) \approx \epsilon N/B$, where $\epsilon$ is the learning rate, $N$ the training set size and $B$ the batch size. Consequently the optimum batch size is proportional to both the learning rate and the size of the training set, $B_{opt} \propto \epsilon N$. We verify these predictions empirically.
In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation. Our model directly takes 2D pose as input and learns a generalized 2D-3D mapping function. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNN) on the top to explicitly incorporate a set of knowledge regarding human body configuration (i.e., kinematics, symmetry, motor coordination). The proposed model thus enforces high-level constraints over human poses. In learning, we develop a pose sample simulator to augment training samples in virtual camera views, which further improves our model generalizability. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization capability of different methods. We empirically observe that most state-of-the-art methods encounter difficulty under such setting while our method can well handle such challenges.
Sentence representation at the semantic level is a challenging task for Natural Language Processing and Artificial Intelligence. Despite the advances in word embeddings (i.e. word vector representations), capturing sentence meaning is an open question due to complexities of semantic interactions among words. In this paper, we present an embedding method, which is aimed at learning unsupervised sentence representations from unlabeled text. We propose an unsupervised method that models a sentence as a weighted series of word embeddings. The weights of the word embeddings are fitted by using Shannon's word entropies provided by the Term Frequency--Inverse Document Frequency (TF--IDF) transform. The hyperparameters of the model can be selected according to the properties of data (e.g. sentence length and textual gender). Hyperparameter selection involves word embedding methods and dimensionalities, as well as weighting schemata. Our method offers advantages over existing methods: identifiable modules, short-term training, online inference of (unseen) sentence representations, as well as independence from domain, external knowledge and language resources. Results showed that our model outperformed the state of the art in well-known Semantic Textual Similarity (STS) benchmarks. Moreover, our model reached state-of-the-art performance when compared to supervised and knowledge-based STS systems.
This paper presents a novel differential evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence and, therefore, when it is trapped into local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino-acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our algorithm and the proposed algorithm is superior to other state-of-the-art algorithms. It obtained a hit ratio of 100 % for sequences up to 18 monomers within a budget of $10^{11}$ solution evaluations. New best-known solutions were obtained for most of the sequences. The existence of the symmetric best-known solutions is also demonstrated in the paper.
Convolutional neural networks rely on image texture and structure to serve as discriminative features to classify the image content. Image enhancement techniques can be used as preprocessing steps to help improve the overall image quality and in turn improve the overall effectiveness of a CNN. Existing image enhancement methods, however, are designed to improve the perceptual quality of an image for a human observer. In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception. To this end, we present a unified CNN architecture that uses a range of enhancement filters that can enhance image-specific details via end-to-end dynamic filter learning. We demonstrate the effectiveness of this strategy on four challenging benchmark datasets for fine-grained, object, scene, and texture classification: CUB-200-2011, PASCAL-VOC2007, MIT-Indoor, and DTD. Experiments using our proposed enhancement show promising results on all the datasets. In addition, our approach is capable of improving the performance of all generic CNN architectures.
SGD (Stochastic Gradient Descent) is a popular algorithm for large scale optimization problems due to its low iterative cost. However, SGD can not achieve linear convergence rate as FGD (Full Gradient Descent) because of the inherent gradient variance. To attack the problem, mini-batch SGD was proposed to get a trade-off in terms of convergence rate and iteration cost. In this paper, a general CVI (Convergence-Variance Inequality) equation is presented to state formally the interaction of convergence rate and gradient variance. Then a novel algorithm named SSAG (Stochastic Stratified Average Gradient) is introduced to reduce gradient variance based on two techniques, stratified sampling and averaging over iterations that is a key idea in SAG (Stochastic Average Gradient). Furthermore, SSAG can achieve linear convergence rate of $\mathcal {O}((1-\frac{\mu}{8CL})^k)$ at smaller storage and iterative costs, where $C\geq 2$ is the category number of training data. This convergence rate depends mainly on the variance between classes, but not on the variance within the classes. In the case of $C\ll N$ ($N$ is the training data size), SSAG's convergence rate is much better than SAG's convergence rate of $\mathcal {O}((1-\frac{\mu}{8NL})^k)$. Our experimental results show SSAG outperforms SAG and many other algorithms.
Due to the lack of enough generalization in the state-space, common methods in Reinforcement Learning (RL) suffer from slow learning speed especially in the early learning trials. This paper introduces a model-based method in discrete state-spaces for increasing learning speed in terms of required experience (but not required computational time) by exploiting generalization in the experiences of the subspaces. A subspace is formed by choosing a subset of features in the original state representation (full-space). Generalization and faster learning in a subspace are due to many-to-one mapping of experiences from the full-space to each state in the subspace. Nevertheless, due to inherent perceptual aliasing in the subspaces, the policy suggested by each subspace does not generally converge to the optimal policy. Our approach, called Model Based Learning with Subspaces (MoBLeS), calculates confidence intervals of the estimated Q-values in the full-space and in the subspaces. These confidence intervals are used in the decision making, such that the agent benefits the most from the possible generalization while avoiding from detriment of the perceptual aliasing in the subspaces. Convergence of MoBLeS to the optimal policy is theoretically investigated. Additionally, we show through several experiments that MoBLeS improves the learning speed in the early trials.
High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.
Memristors have recently received significant attention as ubiquitous device-level components for building a novel generation of computing systems. These devices have many promising features, such as non-volatility, low power consumption, high density, and excellent scalability. The ability to control and modify biasing voltages at the two terminals of memristors make them promising candidates to perform matrix-vector multiplications and solve systems of linear equations. In this article, we discuss how networks of memristors arranged in crossbar arrays can be used for efficiently solving optimization and machine learning problems. We introduce a new memristor-based optimization framework that combines the computational merit of memristor crossbars with the advantages of an operator splitting method, alternating direction method of multipliers (ADMM). Here, ADMM helps in splitting a complex optimization problem into subproblems that involve the solution of systems of linear equations. The capability of this framework is shown by applying it to linear programming, quadratic programming, and sparse optimization. In addition to ADMM, implementation of a customized power iteration (PI) method for eigenvalue/eigenvector computation using memristor crossbars is discussed. The memristor-based PI method can further be applied to principal component analysis (PCA). The use of memristor crossbars yields a significant speed-up in computation, and thus, we believe, has the potential to advance optimization and machine learning research in artificial intelligence (AI).
This paper presents a practical approach for identifying unknown mechanical parameters, such as mass and friction models of manipulated rigid objects or actuated robotic links, in a succinct manner that aims to improve the performance of policy search algorithms. Key features of this approach are the use of off-the-shelf physics engines and the adaptation of a black-box Bayesian optimization framework for this purpose. The physics engine is used to reproduce in simulation experiments that are performed on a real robot, and the mechanical parameters of the simulated system are automatically fine-tuned so that the simulated trajectories match with the real ones. The optimized model is then used for learning a policy in simulation, before safely deploying it on the real robot. Given the well-known limitations of physics engines in modeling real-world objects, it is generally not possible to find a mechanical model that reproduces in simulation the real trajectories exactly. Moreover, there are many scenarios where a near-optimal policy can be found without having a perfect knowledge of the system. Therefore, searching for a perfect model may not be worth the computational effort in practice. The proposed approach aims then to identify a model that is good enough to approximate the value of a locally optimal policy with a certain confidence, instead of spending all the computational resources on searching for the most accurate model. Empirical evaluations, performed in simulation and on a real robotic manipulation task, show that model identification via physics engines can significantly boost the performance of policy search algorithms that are popular in robotics, such as TRPO, PoWER and PILCO, with no additional real-world data.
Markov decision processes (MDPs) are a popular model for performance analysis and optimization of stochastic systems. The parameters of stochastic behavior of MDPs are estimates from empirical observations of a system; their values are not known precisely. Different types of MDPs with uncertain, imprecise or bounded transition rates or probabilities and rewards exist in the literature. Commonly, analysis of models with uncertainties amounts to searching for the most robust policy which means that the goal is to generate a policy with the greatest lower bound on performance (or, symmetrically, the lowest upper bound on costs). However, hedging against an unlikely worst case may lead to losses in other situations. In general, one is interested in policies that behave well in all situations which results in a multi-objective view on decision making. In this paper, we consider policies for the expected discounted reward measure of MDPs with uncertain parameters. In particular, the approach is defined for bounded-parameter MDPs (BMDPs) [8]. In this setting the worst, best and average case performances of a policy are analyzed simultaneously, which yields a multi-scenario multi-objective optimization problem. The paper presents and evaluates approaches to compute the pure Pareto optimal policies in the value vector space.
Graphs (networks) are ubiquitous and allow us to model entities (nodes) and the dependencies (edges) between them. Learning a useful feature representation from graph data lies at the heart and success of many machine learning tasks such as classification, anomaly detection, link prediction, among many others. Many existing techniques use random walks as a basis for learning features or estimating the parameters of a graph model for a downstream prediction task. Examples include recent node embedding methods such as DeepWalk, node2vec, as well as graph-based deep learning algorithms. However, the simple random walk used by these methods is fundamentally tied to the identity of the node. This has three main disadvantages. First, these approaches are inherently transductive and do not generalize to unseen nodes and other graphs. Second, they are not space-efficient as a feature vector is learned for each node which is impractical for large graphs. Third, most of these approaches lack support for attributed graphs. To make these methods more generally applicable, we propose a framework for inductive network representation learning based on the notion of attributed random walk that is not tied to node identity and is instead based on learning a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute vector $\mathrm{\rm \bf x}$ to a type $w$. This framework serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many other previous methods that leverage traditional random walks.
The Advent of the Internet-of-Things (IoT) paradigm has brought opportunities to solve many real-world problems. Energy management, for example, has attracted huge interest from academia, industries, governments and regulatory bodies. It involves collecting energy usage data, analyzing it, and optimizing the energy consumption by applying control strategies. However, in industrial environments, performing such optimization is not trivial. The changes in business rules, process control, and customer requirements make it much more challenging. In this paper, a Semantic Rules Engine (SRE) for industrial gateways is presented that allows implementing dynamic and flexible rule-based control strategies. It is simple, expressive, and allows managing rules on-the-fly without causing any service interruption. Additionally, it can handle semantic queries and provide results by inferring additional knowledge from previously defined concepts in ontologies. SRE has been validated and tested on different hardware platforms and in commercial products. Performance evaluations are also presented to validate its conformance to the customer requirements.
Vagueness and uncertainty management is counted among one of the challenges that remain unresolved in systems that generate texts from non-linguistic data, known as data-to-text systems. In the last decade, work in fuzzy linguistic summarization and description of data has raised the interest of using fuzzy sets to model and manage the imprecision of human language in data-to-text systems. However, despite some research in this direction, there has not been an actual clear discussion and justification on how fuzzy sets can contribute to data-to-text for modeling vagueness and uncertainty in words and expressions. This paper intends to bridge this gap by answering the following questions: What does vagueness mean in fuzzy sets theory? What does vagueness mean in data-to-text contexts? In what ways can fuzzy sets theory contribute to improve data-to-text systems? What are the challenges that researchers from both disciplines need to address for a successful integration of fuzzy sets into data-to-text systems? In what cases should the use of fuzzy sets be avoided in D2T? For this, we review and discuss the state of the art of vagueness modeling in natural language generation and data-to-text, describe potential and actual usages of fuzzy sets in data-to-text contexts, and provide some additional insights about the engineering of data-to-text systems that make use of fuzzy set-based techniques.
We consider the problem of performing inverse reinforcement learning when the trajectory of the expert is not perfectly observed by the learner. Instead, a noisy continuous-time observation of the trajectory is provided to the learner. This problem exhibits wide-ranging applications and the specific application we consider here is the scenario in which the learner seeks to penetrate a perimeter patrolled by a robot. The learner's field of view is limited due to which it cannot observe the patroller's complete trajectory. Instead, we allow the learner to listen to the expert's movement sound, which it can also use to estimate the expert's state and action using an observation model. We treat the expert's state and action as hidden data and present an algorithm based on expectation maximization and maximum entropy principle to solve the non-linear, non-convex problem. Related work considers discrete-time observations and an observation model that does not include actions. In contrast, our technique takes expectations over both state and action of the expert, enabling learning even in the presence of extreme noise and broader applications.
Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown considerable promise in recent years as a potential tool for improving clinical decision support in medical oncology, particularly those based around the concept of Discovery Radiomics, where radiomic sequencers are discovered through the analysis of medical imaging data. One of the main limitations with current CAD approaches is that it is very difficult to gain insight or rationale as to how decisions are made, thus limiting their utility to clinicians. Methods: In this study, we propose CLEAR-DR, a novel interpretable CAD system based on the notion of CLass-Enhanced Attentive Response Discovery Radiomics for the purpose of clinical decision support for diabetic retinopathy. Results: In addition to disease grading via the discovered deep radiomic sequencer, the CLEAR-DR system also produces a visual interpretation of the decision-making process to provide better insight and understanding into the decision-making process of the system. Conclusion: We demonstrate the effectiveness and utility of the proposed CLEAR-DR system of enhancing the interpretability of diagnostic grading results for the application of diabetic retinopathy grading. Significance: CLEAR-DR can act as a potential powerful tool to address the uninterpretability issue of current CAD systems, thus improving their utility to clinicians.
Deep learning models require extensive architecture design exploration and hyperparameter optimization to perform well on a given task. The exploration of the model design space is often made by a human expert, and optimized using a combination of grid search and search heuristics over a large space of possible choices. Neural Architecture Search (NAS) is a Reinforcement Learning approach that has been proposed to automate architecture design. NAS has been successfully applied to generate Neural Networks that rival the best human-designed architectures. However, NAS requires sampling, constructing, and training hundreds to thousands of models to achieve well-performing architectures. This procedure needs to be executed from scratch for each new task. The application of NAS to a wide set of tasks currently lacks a way to transfer generalizable knowledge across tasks. In this paper, we present the Multitask Neural Model Search (MNMS) controller. Our goal is to learn a generalizable framework that can condition model construction on successful model searches for previously seen tasks, thus significantly speeding up the search for new tasks. We demonstrate that MNMS can conduct an automated architecture search for multiple tasks simultaneously while still learning well-performing, specialized models for each task. We then show that pre-trained MNMS controllers can transfer learning to new tasks. By leveraging knowledge from previous searches, we find that pre-trained MNMS models start from a better location in the search space and reduce search time on unseen tasks, while still discovering models that outperform published human-designed models.
Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and discriminators in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.
Endowing robots with the capability of assessing risk and making risk-aware decisions is widely considered a key step toward ensuring safety for robots operating under uncertainty. But, how should a robot quantify risk? A natural and common approach is to consider the framework whereby costs are assigned to stochastic outcomes - an assignment captured by a cost random variable. Quantifying risk then corresponds to evaluating a risk metric, i.e., a mapping from the cost random variable to a real number. Yet, the question of what constitutes a "good" risk metric has received little attention within the robotics community. The goal of this paper is to explore and partially address this question by advocating axioms that risk metrics in robotics applications should satisfy in order to be employed as rational assessments of risk. We discuss general representation theorems that precisely characterize the class of metrics that satisfy these axioms (referred to as distortion risk metrics), and provide instantiations that can be used in applications. We further discuss pitfalls of commonly used risk metrics in robotics, and discuss additional properties that one must consider in sequential decision making tasks. Our hope is that the ideas presented here will lead to a foundational framework for quantifying risk (and hence safety) in robotics applications.
We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code. The majority of past work in semantic code repair assumed access to unit tests against which candidate repairs could be validated. In contrast, the goal here is to develop a strong statistical model to accurately predict both bug locations and exact fixes without access to information about the intended correct behavior of the program. Achieving such a goal requires a robust contextual repair model, which we train on a large corpus of real-world source code that has been augmented with synthetically injected bugs. Our framework adopts a two-stage approach where first a large set of repair candidates are generated by rule-based processors, and then these candidates are scored by a statistical model using a novel neural network architecture which we refer to as Share, Specialize, and Compete. Specifically, the architecture (1) generates a shared encoding of the source code using an RNN over the abstract syntax tree, (2) scores each candidate repair using specialized network modules, and (3) then normalizes these scores together so they can compete against one another in comparable probability space. We evaluate our model on a real-world test set gathered from GitHub containing four common categories of bugs. Our model is able to predict the exact correct repair 41\% of the time with a single guess, compared to 13\% accuracy for an attentional sequence-to-sequence model.
Recent work on quantum machine learning has demonstrated that quantum computers can offer dramatic improvements over classical devices for data mining, prediction and classification. However, less is known about the advantages using quantum computers may bring in the more general setting of reinforcement learning, where learning is achieved via interaction with a task environment that provides occasional rewards. Reinforcement learning can incorporate data-analysis-oriented learning settings as special cases, but also includes more complex situations where, e.g., reinforcing feedback is delayed. In a few recent works, Grover-type amplification has been utilized to construct quantum agents that achieve up-to-quadratic improvements in learning efficiency. These encouraging results have left open the key question of whether super-polynomial improvements in learning times are possible for genuine reinforcement learning problems, that is problems that go beyond the other more restricted learning paradigms. In this work, we provide a family of such genuine reinforcement learning tasks. We construct quantum-enhanced learners which learn super-polynomially, and even exponentially faster than any classical reinforcement learning model, and we discuss the potential impact our results may have on future technologies.
In this project, we aimed to improve the runtime of Minisat, a Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional Boolean Satisfiability (SAT) problem. We first used a logistic regression model to predict the satisfiability of propositional boolean formulae after fixing the values of a certain fraction of the variables in each formula. We then applied the logistic model and added a preprocessing period to Minisat to determine the preferable initial value (either true or false) of each boolean variable using a Monte-Carlo approach. Concretely, for each Monte-Carlo trial, we fixed the values of a certain ratio of randomly selected variables, and calculated the confidence that the resulting sub-formula is satisfiable with our logistic regression model. The initial value of each variable was set based on the mean confidence scores of the trials that started from the literals of that variable. We were particularly interested in setting the initial values of the backbone variables correctly, which are variables that have the same value in all solutions of a SAT formula. Our Monte-Carlo method was able to set 78% of the backbones correctly. Excluding the preprocessing time, compared with the default setting of Minisat, the runtime of Minisat for satisfiable formulae decreased by 23%. However, our method did not outperform vanilla Minisat in runtime, as the decrease in the conflicts was outweighed by the long runtime of the preprocessing period.
One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging multi-label classification task. There are two major biological mechanisms for TF binding: (1) sequence-specific binding patterns on genomes known as "motifs" and (2) interactions among TFs known as co-binding effects. In this paper, we propose a novel deep architecture, the Prototype Matching Network (PMN) to mimic the TF binding mechanisms. Our PMN model automatically extracts prototypes ("motif"-like features) for each TF through a novel prototype-matching loss. Borrowing ideas from few-shot matching models, we use the notion of support set of prototypes and an LSTM to learn how TFs interact and bind to genomic sequences. On a reference TFBS dataset with $2.1$ $million$ genomic sequences, PMN significantly outperforms baselines and validates our design choices empirically. To our knowledge, this is the first deep learning architecture that introduces prototype learning and considers TF-TF interactions for large-scale TFBS prediction. Not only is the proposed architecture accurate, but it also models the underlying biology.
Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the tree. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al. 2017) on multiple Atari games. Furthermore, we present ablation studies that demonstrate the effect of different auxiliary losses on learning transition models.
Border crossing delays cause problems like huge economics loss and heavy environmental pollutions. To understand more about the nature of border crossing delay, this study applies a dictionary-based compression algorithm to process the historical Niagara Frontier border wait times data. It can identify the abnormal spatial-temporal patterns for both passenger vehicles and trucks at three bridges connecting US and Canada. Furthermore, it provides a quantitate anomaly score to rank the wait times patterns across the three bridges for each vehicle type and each direction. By analyzing the top three most abnormal patterns, we find that there are at least two factors contributing the anomaly of the patterns. The weekends and holidays may cause unusual heave congestions at the three bridges at the same time, and the freight transportation demand may be uneven from Canada to the USA at Peace Bridge and Lewiston-Queenston Bridge, which may lead to a high anomaly score. By calculating the frequency of the top 5% abnormal patterns by hour of the day, the results show that for cars from the USA to Canada, the frequency of abnormal waiting time patterns is the highest during noon while for trucks in the same direction, it is the highest during the afternoon peak hours. For Canada to US direction, the frequency of abnormal border wait time patterns for both cars and trucks reaches to the peak during the afternoon. The analysis of abnormal spatial-temporal wait times patterns is promising to improve the border crossing management
Recurrent neural networks (RNNs) are important class of architectures among neural networks useful for language modeling and sequential prediction. However, optimizing RNNs is known to be harder compared to feed-forward neural networks. A number of techniques have been proposed in literature to address this problem. In this paper we propose a simple technique called fraternal dropout that takes advantage of dropout to achieve this goal. Specifically, we propose to train two identical copies of an RNN (that share parameters) with different dropout masks while minimizing the difference between their (pre-softmax) predictions. In this way our regularization encourages the representations of RNNs to be invariant to dropout mask, thus being robust. We show that our regularization term is upper bounded by the expectation-linear dropout objective which has been shown to address the gap due to the difference between the train and inference phases of dropout. We evaluate our model and achieve state-of-the-art results in sequence modeling tasks on two benchmark datasets - Penn Treebank and Wikitext-2. We also show that our approach leads to performance improvement by a significant margin in image captioning (Microsoft COCO) and semi-supervised (CIFAR-10) tasks.
Purpose: A new method for magnetic resonance (MR) imaging water-fat separation using a convolutional neural network (ConvNet) and deep learning (DL) is presented. Feasibility of the method with complex and magnitude images is demonstrated with a series of patient studies and accuracy of predicted quantitative values is analyzed. Methods: Water-fat separation of 1200 gradient-echo acquisitions from 90 imaging sessions (normal, acute and chronic myocardial infarction) was performed using a conventional model based method with modeling of R2* and off-resonance and a multi-peak fat spectrum. A U-Net convolutional neural network for calculation of water-only, fat-only, R2* and off-resonance images was trained with 900 gradient-echo Multiple and single-echo complex and magnitude input data algorithms were studied and compared to conventional extended echo modeling. Results: The U-Net ConvNet was easily trained and provided water-fat separation results visually comparable to conventional methods. Myocardial fat deposition in chronic myocardial infarction and intramyocardial hemorrhage in acute myocardial infarction were well visualized in the DL results. Predicted values for R2*, off-resonance, water and fat signal intensities were well correlated with conventional model based water fat separation (R2>=0.97, p<0.001). DL images had a 14% higher signal-to-noise ratio (p<0.001) when compared to the conventional method. Conclusion: Deep learning utilizing ConvNets is a feasible method for MR water-fat separationimaging with complex, magnitude and single echo image data. A trained U-Net can be efficiently used for MR water-fat separation, providing results comparable to conventional model based methods.
Background: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc., and do not perform satisfactorily when such information is unavailable. We propose a new, alternative method for DTI prediction that makes use of only network topology information attempting to solve this problem. Results: We compare our method for DTI prediction against the well-known RBM approach. We show that when applied to the MATADOR database, our approach based on node neighborhoods yield higher precision for high-ranking predictions than RBM when no information regarding DTI types is available. Conclusion: This demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions.
The success of Deep Learning and its potential use in many important safety- critical applications has motivated research on formal verification of Neural Network (NN) models. Despite the reputation of learned NN models to behave as black boxes and the theoretical hardness of proving their properties, researchers have been successful in verifying some classes of models by exploiting their piecewise linear structure. Unfortunately, most of these approaches test their algorithms without comparison with other approaches. As a result, the pros and cons of the different algorithms are not well understood. Motivated by the need to accelerate progress in this very important area, we investigate the trade-offs of a number of different approaches based on Mixed Integer Programming, Satisfiability Modulo Theory, as well as a novel method based on the Branch-and-Bound framework. We also propose a new data set of benchmarks, in addition to a collection of pre- viously released testcases that can be used to compare existing methods. Our analysis not only allows a comparison to be made between different strategies, the comparison of results from different solvers also revealed implementation bugs in published methods. We expect that the availability of our benchmark and the analysis of the different approaches will allow researchers to develop and evaluate promising approaches for making progress on this important topic.
School bus planning is usually divided into routing and scheduling due to the complexity of solving them concurrently. However, the separation between these two steps may lead to worse solutions with higher overall costs than that from solving them together. When finding the minimal number of trips in the routing problem, neglecting the importance of trip compatibility may increase the number of buses actually needed in the scheduling problem. This paper proposes a new formulation for the multi-school homogeneous fleet routing problem that maximizes trip compatibility while minimizing total travel time. This incorporates the trip compatibility for the scheduling problem in the routing problem. Since the problem is inherently just a routing problem, finding a good solution is not cumbersome. To compare the performance of the model with traditional routing problems, we generate eight mid-size data sets. Through importing the generated trips of the routing problems into the bus scheduling (blocking) problem, it is shown that the proposed model uses up to 13% fewer buses than the common traditional routing models.
Safely serving the school transportation demand with the minimum number of buses is one of the highest financial goals of school transportation directors. To achieve that objective, a good and efficient way to solve the routing and scheduling problem is required. Due to the growth of the computing power, the spotlight has been shed on solving the combined problem of the school bus routing and scheduling problem. We show that an integrated multi-school bus routing and scheduling can be formulated with the help of trip compatibility. A novel decomposition algorithm is proposed to solve the integrated model. The merit of this integrated model and the decomposition method is that with the consideration of the trip compatibility, the interrelationship between the routing and scheduling sub-problems will not be lost in the process of decomposition. Results show the proposed decomposed problem could provide the solutions using the same number of buses as the integrated model in much shorter time (as little as 0.6%) and that the proposed method can save up to 26% number of buses from existing research.
User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.
Accumulating evidence suggest that human behavior in trial-and-error learning tasks based on decisions between discrete actions may involve a combination of reinforcement learning (RL) and working-memory (WM). While the understanding of brain activity at stake in this type of tasks often involve the comparison with non-human primate neurophysiological results, it is not clear whether monkeys use similar combined RL and WM processes to solve these tasks. Here we analyzed the behavior of five monkeys with computational models combining RL and WM. Our model-based analysis approach enables to not only fit trial-by-trial choices but also transient slowdowns in reaction times, indicative of WM use. We found that the behavior of the five monkeys was better explained in terms of a combination of RL and WM despite inter-individual differences. The same coordination dynamics we used in a previous study in humans best explained the behavior of some monkeys while the behavior of others showed the opposite pattern, revealing a possible different dynamics of WM process. We further analyzed different variants of the tested models to open a discussion on how the long pretraining in these tasks may have favored particular coordination dynamics between RL and WM. This points towards either inter-species differences or protocol differences which could be further tested in humans.
Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax. For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered. We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures. In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs. We evaluate our method on two tasks: VarNaming, in which a network attempts to predict the name of a variable given its usage, and VarMisuse, in which the network learns to reason about selecting the correct variable that should be used at a given program location. Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VarMisuse task in many cases. Additionally, our testing showed that VarMisuse identifies a number of bugs in mature open-source projects.
The largest source of sound events is web videos. Most videos lack sound event labels at segment level, however, a significant number of them do respond to text queries, from a match found using metadata by search engines. In this paper we explore the extent to which a search query can be used as the true label for detection of sound events in videos. We present a framework for large-scale sound event recognition on web videos. The framework crawls videos using search queries corresponding to 78 sound event labels drawn from three datasets. The datasets are used to train three classifiers, and we obtain a prediction on 3.7 million web video segments. We evaluated performance using the search query as true label and compare it with human labeling. Both types of ground truth exhibited close performance, to within 10%, and similar performance trend with increasing number of evaluated segments. Hence, our experiments show potential for using search query as a preliminary true label for sound event recognition in web videos.
We propose a method to learn deep ReLU-based classifiers that are provably robust against norm-bounded adversarial perturbations on the training data. For previously unseen examples, the approach is guaranteed to detect all adversarial examples, though it may flag some non-adversarial examples as well. The basic idea is to consider a convex outer approximation of the set of activations reachable through a norm-bounded perturbation, and we develop a robust optimization procedure that minimizes the worst case loss over this outer region (via a linear program). Crucially, we show that the dual problem to this linear program can be represented itself as a deep network similar to the backpropagation network, leading to very efficient optimization approaches that produce guaranteed bounds on the robust loss. The end result is that by executing a few more forward and backward passes through a slightly modified version of the original network (though possibly with much larger batch sizes), we can learn a classifier that is provably robust to any norm-bounded adversarial attack. We illustrate the approach on a number of tasks to train classifiers with robust adversarial guarantees (e.g. for MNIST, we produce a convolutional classifier that provably has less than 5.8% test error for any adversarial attack with bounded $\ell_\infty$ norm less than $\epsilon = 0.1$). Code for all experiments in the paper is available at https://github.com/locuslab/convex_adversarial.
In many real-world optimization problems, the objective function evaluation is subject to noise, and we cannot obtain the exact objective value. Evolutionary algorithms (EAs), a type of general-purpose randomized optimization algorithm, have shown able to solve noisy optimization problems well. However, previous theoretical analyses of EAs mainly focused on noise-free optimization, which makes the theoretical understanding largely insufficient. Meanwhile, the few existing theoretical studies under noise often considered the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation; while in many realistic applications, several bits of a solution can be changed simultaneously. In this paper, we study a natural extension of one-bit noise, the bit-wise noise model, which independently flips each bit of a solution with some probability. We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds. The analysis on LeadingOnes under bit-wise noise can be easily transferred to one-bit noise, and improves the previously known results. Since our analysis discloses that the (1+1)-EA can be efficient only under low noise levels, we also study whether the sampling strategy can bring robustness to noise. We prove that using sampling can significantly increase the largest noise level allowing a polynomial running time, that is, sampling is robust to noise.
Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs can contain chaotic activities. These activities are independent of the state of the process and can, therefore, happen at rather arbitrary points in time. We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques. The current modus operandi for filtering activities from event logs is to simply filter out infrequent activities. We show that frequency-based filtering of activities does not solve the problems that are caused by chaotic activities. Moreover, we propose a novel technique to filter out chaotic activities from event logs. We evaluate this technique on a collection of seventeen real-life event logs that originate from both the business process management domain and the smart home environment domain. As demonstrated, the developed activity filtering methods enable the discovery of process models that are more behaviorally specific compared to process models that are discovered using standard frequency-based filtering.
In robotics, it is essential to be able to plan efficiently in high-dimensional continuous state-action spaces for long horizons. For such complex planning problems, unguided uniform sampling of actions until a path to a goal is found is hopelessly inefficient, and gradient-based approaches often fall short when the optimization manifold of a given problem is not smooth. In this paper we present an approach that guides the search of a state-space planner, such as A*, by learning an action-sampling distribution that can generalize across different instances of a planning problem. The motivation is that, unlike typical learning approaches for planning for continuous action space that estimate a policy, an estimated action sampler is more robust to error since it has a planner to fall back on. We use a Generative Adversarial Network (GAN), and address an important issue: search experience consists of a relatively large number of actions that are not on a solution path and a relatively small number of actions that actually are on a solution path. We introduce a new technique, based on an importance-ratio estimation method, for using samples from a non-target distribution to make GAN learning more data-efficient. We provide theoretical guarantees and empirical evaluation in three challenging continuous robot planning problems to illustrate the effectiveness of our algorithm.
Rapid growth of modern technologies such as internet and mobile computing are bringing dramatically increased e-commerce payments, as well as the explosion in transaction fraud. Meanwhile, fraudsters are continually refining their tricks, making rule-based fraud detection systems difficult to handle the ever-changing fraud patterns. Many data mining and artificial intelligence methods have been proposed for identifying small anomalies in large transaction data sets, increasing detecting efficiency to some extent. Nevertheless, there is always a contradiction that most methods are irrelevant to transaction sequence, yet sequence-related methods usually cannot learn information at single-transaction level well. In this paper, a new "within->between->within" sandwich-structured sequence learning architecture has been proposed by stacking an ensemble method, a deep sequential learning method and another top-layer ensemble classifier in proper order. Moreover, attention mechanism has also been introduced in to further improve performance. Models in this structure have been manifested to be very efficient in scenarios like fraud detection, where the information sequence is made up of vectors with complex interconnected features.
The performance of many parallel applications depends on loop-level parallelism. However, manually parallelizing all loops may result in degrading parallel performance, as some of them cannot scale desirably to a large number of threads. In addition, the overheads of manually tuning loop parameters might prevent an application from reaching its maximum parallel performance. We illustrate how machine learning techniques can be applied to address these challenges. In this research, we develop a framework that is able to automatically capture the static and dynamic information of a loop. Moreover, we advocate a novel method by introducing HPX smart executors for determining the execution policy, chunk size, and prefetching distance of an HPX loop to achieve higher possible performance by feeding static information captured during compilation and runtime-based dynamic information to our learning model. Our evaluated execution results show that using these smart executors can speed up the HPX execution process by around 12%-35% for the Matrix Multiplication, Stream and $2D$ Stencil benchmarks compared to setting their HPX loop's execution policy/parameters manually or using HPX auto-parallelization techniques.
Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.
Building effective recommender systems for domains like fashion is challenging due to the high level of subjectivity and the semantic complexity of the features involved (i.e., fashion styles). Recent work has shown that approaches to `visual' recommendation (e.g.~clothing, art, etc.) can be made more accurate by incorporating visual signals directly into the recommendation objective, using `off-the-shelf' feature representations derived from deep networks. Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning `fashion aware' image representations directly, i.e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pre-trained visual features. Furthermore, we show that our model can be used \emph{generatively}, i.e., given a user and a product category, we can generate new images (i.e., clothing items) that are most consistent with their personal taste. This represents a first step towards building systems that go beyond recommending existing items from a product corpus, but which can be used to suggest styles and aid the design of new products.
DR-submodular continuous functions are important objectives with wide real-world applications spanning MAP inference in determinantal point processes (DPPs), and mean-field inference for probabilistic submodular models, amongst others. DR-submodularity captures a subclass of non-convex functions that enables both exact minimization and approximate maximization in polynomial time. In this work we study the problem of maximizing non-monotone DR-submodular continuous functions under general down-closed convex constraints. We start by investigating geometric properties that underlie such objectives, e.g., a strong relation between (approximately) stationary points and global optimum is proved. These properties are then used to devise two optimization algorithms with provable guarantees. Concretely, we first devise a "two-phase" algorithm with $1/4$ approximation guarantee. This algorithm allows the use of existing methods for finding (approximately) stationary points as a subroutine, thus, harnessing recent progress in non-convex optimization. Then we present a non-monotone Frank-Wolfe variant with $1/e$ approximation guarantee and sublinear convergence rate. Finally, we extend our approach to a broader class of generalized DR-submodular continuous functions, which captures a wider spectrum of applications. Our theoretical findings are validated on synthetic and real-world problem instances.
Embedding methods such as word embedding have become pillars for many applications containing discrete structures. Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying linear transformation based on "one-hot" encoding of the discrete symbols. Despite its simplicity, such approach yields number of parameters that grows linearly with the vocabulary size and can lead to overfitting. In this work we propose a much more compact K-way D-dimensional discrete encoding scheme to replace the "one-hot" encoding. In "KD encoding", each symbol is represented by a $D$-dimensional code, and each of its dimension has a cardinality of $K$. The final symbol embedding vector can be generated by composing the code embedding vectors. To learn the semantically meaningful code, we derive a relaxed discrete optimization technique based on stochastic gradient descent. By adopting the new coding system, the efficiency of parameterization can be significantly improved (from linear to logarithmic), and this can also mitigate the over-fitting problem. In our experiments with language modeling, the number of embedding parameters can be reduced by 97\% while achieving similar or better performance.
We describe a novel spiking neural network (SNN) for automated, real-time handwritten digit classification and its implementation on a GP-GPU platform. Information processing within the network, from feature extraction to classification is implemented by mimicking the basic aspects of neuronal spike initiation and propagation in the brain. The feature extraction layer of the SNN uses fixed synaptic weight maps to extract the key features of the image and the classifier layer uses the recently developed NormAD approximate gradient descent based supervised learning algorithm for spiking neural networks to adjust the synaptic weights. On the standard MNIST database images of handwritten digits, our network achieves an accuracy of 99.80% on the training set and 98.06% on the test set, with nearly 7x fewer parameters compared to the state-of-the-art spiking networks. We further use this network in a GPU based user-interface system demonstrating real-time SNN simulation to infer digits written by different users. On a test set of 500 such images, this real-time platform achieves an accuracy exceeding 97% while making a prediction within an SNN emulation time of less than 100ms.
As technology becomes more advanced, those who design, use and are otherwise affected by it want to know that it will perform correctly, and understand why it does what it does, and how to use it appropriately. In essence they want to be able to trust the systems that are being designed. In this survey we present assurances that are the method by which users can understand how to trust autonomous systems. Trust between humans and autonomy is reviewed, and the implications for the design of assurances are highlighted. A survey of existing research related to assurances is presented. Much of the surveyed research originates from fields such as interpretable, comprehensible, transparent, and explainable machine learning, as well as human-computer interaction, human-robot interaction, and e-commerce. Several key ideas are extracted from this work in order to refine the definition of assurances. The design of assurances is found to be highly dependent not only on the capabilities of the autonomous system, but on the characteristics of the human user, and the appropriate trust-related behaviors. Several directions for future research are identified and discussed.
We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a non-trivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts, but the majority of the generated negative facts can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TransE and TransD, each with assistance from one of the two probability-based models, DistMult and ComplEx. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings.
Differential performance debugging is a technique to find performance problems. It applies in situations where the performance of a program is (unexpectedly) different for different classes of inputs. The task is to explain the differences in asymptotic performance among various input classes in terms of program internals. We propose a data-driven technique based on discriminant regression tree (DRT) learning problem where the goal is to discriminate among different classes of inputs. We propose a new algorithm for DRT learning that first clusters the data into functional clusters, capturing different asymptotic performance classes, and then invokes off-the-shelf decision tree learning algorithms to explain these clusters. We focus on linear functional clusters and adapt classical clustering algorithms (K-means and spectral) to produce them. For the K-means algorithm, we generalize the notion of the cluster centroid from a point to a linear function. We adapt spectral clustering by defining a novel kernel function to capture the notion of linear similarity between two data points. We evaluate our approach on benchmarks consisting of Java programs where we are interested in debugging performance. We show that our algorithm significantly outperforms other well-known regression tree learning algorithms in terms of running time and accuracy of classification.
Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential flaws, which may lead to severe information loss and performance degradation. First, predefined similarity graph might not be optimal for subsequent clustering. It is well-accepted that similarity graph highly affects the clustering results. To this end, we propose to automatically learn similarity information from data and simultaneously consider the constraint that the similarity matrix has exact c connected components if there are c clusters. Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers. In this work, we transform the candidate solution into a new one that better approximates the discrete one. Finally, those three subtasks are integrated into a unified framework, with each subtask iteratively boosted by using the results of the others towards an overall optimal solution. It is known that the performance of a kernel method is largely determined by the choice of kernels. To tackle this practical problem of how to select the most suitable kernel for a particular data set, we further extend our model to incorporate multiple kernel learning ability. Extensive experiments demonstrate the superiority of our proposed method as compared to existing clustering approaches.
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FILE: data/arxiv/artificial intelligence_10047_15000_15_title.txt
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Impact of Artificial Intelligence on Economic Theory
Artificial Intelligence in Humans
Three IQs of AI Systems and their Testing Methods
Philosophy in the Face of Artificial Intelligence
A Study on Artificial Intelligence IQ and Standard Intelligent Model
Hybrid Systems Knowledge Representation Using Modelling Environment System Techniques Artificial Intelligence
Tests of Machine Intelligence
An Approximation of the Universal Intelligence Measure
Decision under Uncertainty
Action Selection Properties in a Software Simulated Agent
A Novel Method for Developing Robotics via Artificial Intelligence and Internet of Things
Modular Belief Updates and Confusion about Measures of Certainty in Artificial Intelligence Research
Quantifying Natural and Artificial Intelligence in Robots and Natural Systems with an Algorithmic Behavioural Test
A Formal Measure of Machine Intelligence
Computational Narrative Intelligence: A Human-Centered Goal for Artificial Intelligence
Artificial Intelligence and Economic Theories
A Definition of Artificial Intelligence
Guidelines for Artificial Intelligence Containment
Improving content marketing processes with the approaches by artificial intelligence
Ethical Considerations in Artificial Intelligence Courses
A Framework for Searching for General Artificial Intelligence
Brain Intelligence: Go Beyond Artificial Intelligence
Intelligence Quotient and Intelligence Grade of Artificial Intelligence
Swarm Intelligence
On the influence of intelligence in (social) intelligence testing environments
Hybrid Reasoning and the Future of Iconic Representations
Diverse Consequences of Algorithmic Probability
A Backwards View for Assessment
Foundations of Probability Theory for AI - The Application of Algorithmic Probability to Problems in Artificial Intelligence
Towards Verified Artificial Intelligence
Multilayered Model of Speech
Artificial and Biological Intelligence
Man and Machine: Questions of Risk, Trust and Accountability in Today's AI Technology
Human-in-the-loop Artificial Intelligence
Measurements of collective machine intelligence
The Role of Artificial Intelligence Technologies in Crisis Response
A Systematic Approach to Artificial Agents
Comparison between the two definitions of AI
A Python Engine for Teaching Artificial Intelligence in Games
Probability Judgement in Artificial Intelligence
AI Methods in Algorithmic Composition: A Comprehensive Survey
Rational Choice and Artificial Intelligence
Institutional Metaphors for Designing Large-Scale Distributed AI versus AI Techniques for Running Institutions
Intelligible Artificial Intelligence
How Intelligent is your Intelligent Robot?
Quantization of Games: Towards Quantum Artificial Intelligence
Identifying Independencies in Causal Graphs with Feedback
Constructing Lower Probabilities
The Assumptions Behind Dempster's Rule
The Nature of the Unnormalized Beliefs Encountered in the Transferable Belief Model
Coefficients of Relations for Probabilistic Reasoning
Evolution towards Smart Optical Networking: Where Artificial Intelligence (AI) meets the World of Photonics
One Decade of Universal Artificial Intelligence
Realizing Intelligence
Artificial Intelligence and Asymmetric Information Theory
Explanation in Artificial Intelligence: Insights from the Social Sciences
Memory Based Machine Intelligence Techniques in VLSI hardware
Quantitative Analysis of Whether Machine Intelligence Can Surpass Human Intelligence
Agent Models of Political Interactions
Introduction to intelligent computing unit 1
A primer on Answer Set Programming
Universal Intelligence: A Definition of Machine Intelligence
Measuring Intelligence through Games
Avoiding Undesired Choices Using Intelligent Adaptive Systems
Detecting Qualia in Natural and Artificial Agents
Design of the Artificial: lessons from the biological roots of general intelligence
A Cyber Science Based Ontology for Artificial General Intelligence Containment
Considerations upon the Machine Learning Technologies
Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence
Hybrid Systems for Knowledge Representation in Artificial Intelligence
Types of Cognition and its Implications for future High-Level Cognitive Machines
Ambiente de Planejamento Ipê
Artificial Intelligence Approaches To UCAV Autonomy
Responsible Autonomy
Artificial Intelligence and its Role in Near Future
Open Problems in Universal Induction & Intelligence
A Collection of Definitions of Intelligence
A framework: Cluster detection and multidimensional visualization of automated data mining using intelligent agents
The Lovelace 2.0 Test of Artificial Creativity and Intelligence
Intelligent Biohybrid Neurotechnologies: Are They Really What They Claim?
Design and development of a software system for swarm intelligence based research studies
A Survey of Question Answering for Math and Science Problem
Challenges and Characteristics of Intelligent Autonomy for Internet of Battle Things in Highly Adversarial Environments
The Computational Theory of Intelligence: Information Entropy
A Model for Combination of External and Internal Stimuli in the Action Selection of an Autonomous Agent
Artificial Intelligence Techniques for Steam Generator Modelling
Analysis of Microarray Data using Artificial Intelligence Based Techniques
Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program
Artificial Intelligence Based Malware Analysis
Knowledge Transfer Between Artificial Intelligence Systems
Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agents Location Using Hidden Markov Models
Intelligence in Artificial Intelligence
The SP Theory of Intelligence as a Foundation for the Development of a General, Human-Level Thinking Machine
Towards an Intelligent Database System Founded on the SP Theory of Computing and Cognition
Faith in the Algorithm, Part 1: Beyond the Turing Test
An existing, ecologically-successful genus of collectively intelligent artificial creatures
Death and Suicide in Universal Artificial Intelligence
Open Ended Intelligence: The individuation of Intelligent Agents
Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop
Universal Algorithmic Intelligence: A mathematical top->down approach
Is Intelligence Artificial?
Turing: Then, Now and Still Key
Non-Evolutionary Superintelligences Do Nothing, Eventually
Cluster-based Specification Techniques in Dempster-Shafer Theory for an Evidential Intelligence Analysis of MultipleTarget Tracks (Thesis Abstract)
Evidential Force Aggregation
Effect of noise in intelligent cellular decision making
Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition
Analysis of first prototype universal intelligence tests: evaluating and comparing AI algorithms and humans
OPUS: An Efficient Admissible Algorithm for Unordered Search
A Resolution Calculus for Dynamic Semantics
A Logic for Reasoning about Evidence
The Road to Quantum Artificial Intelligence
Heterogeneous knowledge representation using a finite automaton and first order logic: a case study in electromyography
Approximate Counting of Graphical Models Via MCMC Revisited
The Complexity of Plan Existence and Evaluation in Probabilistic Domains
Probabilistic Conceptual Network: A Belief Representation Scheme for Utility-Based Categorization
Discounting and Combination Operations in Evidential Reasoning
Towards a Simulation-Based Programming Paradigm for AI applications
Decision Under Uncertainty in Diagnosis
Relative Entropy, Probabilistic Inference and AI
An Evaluation of Two Alternatives to Minimax
Research Priorities for Robust and Beneficial Artificial Intelligence
The Computational Power of Dynamic Bayesian Networks
Don't Fear the Reaper: Refuting Bostrom's Superintelligence Argument
General Video Game AI: Learning from Screen Capture
Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments
Self-Regulating Artificial General Intelligence
Viewpoint: Artificial Intelligence and Labour
An architecture for the evaluation of intelligent systems
Quantitative Results Comparing Three Intelligent Interfaces for Information Capture: A Case Study Adding Name Information into an Electronic Personal Organizer
Avoiding Wireheading with Value Reinforcement Learning
A proposal for ethically traceable artificial intelligence
Elementary epistemological features of machine intelligence
Formal Definition of AI
Stream Computing
An Analysis of General Fuzzy Logic and Fuzzy Reasoning Method
Human-Level Intelligence or Animal-Like Abilities?
Design of a P System based Artificial Graph Chemistry
Goal Conflict in Designing an Autonomous Artificial System
Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review
On the idea of a new artificial intelligence based optimization algorithm inspired from the nature of vortex
Characterizations of Decomposable Dependency Models
Conditional Plausibility Measures and Bayesian Networks
Instantaneously Trained Neural Networks
A Rational Decision Maker with Ordinal Utility under Uncertainty: Optimism and Pessimism
Artificial Brain Based on Credible Neural Circuits in a Human Brain
Finding a Path is Harder than Finding a Tree
SHOP2: An HTN Planning System
New Polynomial Classes for Logic-Based Abduction
Asymptotically Optimal Agents
Engineering a Conformant Probabilistic Planner
Maximum likelihood fitting of acyclic directed mixed graphs to binary data
On Measurement Bias in Causal Inference
Symmetry Breaking Constraints: Recent Results
Complexity Analysis and Variational Inference for Interpretation-based Probabilistic Description Logic
Identifying Dynamic Sequential Plans
Reading Dependencies from Polytree-Like Bayesian Networks
A Criterion for Parameter Identification in Structural Equation Models
Sufficient conditions for convergence of Loopy Belief Propagation
Description Logics with Fuzzy Concrete Domains
Optimistic Agents are Asymptotically Optimal
Factorization of Discrete Probability Distributions
Approximate Planning for Factored POMDPs using Belief State Simplification
Bayesian Control for Concentrating Mixed Nuclear Waste
A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability Distributions
Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method
Exploiting Uncertain and Temporal Information in Correlation
A Scheme for Approximating Probabilistic Inference
Limitations of Skeptical Default Reasoning
On Stable Multi-Agent Behavior in Face of Uncertainty
Region-Based Approximations for Planning in Stochastic Domains
Propagation of 2-Monotone Lower Probabilities on an Undirected Graph
Topological Parameters for Time-Space Tradeoff
Computing Upper and Lower Bounds on Likelihoods in Intractable Networks
Binary Join Trees
Real Time Estimation of Bayesian Networks
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks
Toward a Characterization of Uncertainty Measure for the Dempster-Shafer Theory
Causal Inference and Causal Explanation with Background Knowledge
Strong Completeness and Faithfulness in Bayesian Networks
Defaults and Infinitesimals: Defeasible Inference by Nonarchimedean Entropy-Maximization
A Bayesian Method Reexamined
Possibility and Necessity Functions over Non-classical Logics
From Influence Diagrams to Junction Trees
Belief Induced by the Partial Knowledge of the Probabilities
On Axiomatization of Probabilistic Conditional Independencies
Normative Engineering Risk Management Systems
Two Procedures for Compiling Influence Diagrams
Deciding Morality of Graphs is NP-complete
Qualitative Measures of Ambiguity
Jeffrey's rule of conditioning generalized to belief functions
Inference with Possibilistic Evidence
Entropy and Belief Networks
Objection-Based Causal Networks
A Note on the Measure of Discord
Bayesian Networks Aplied to Therapy Monitoring
A Probabilistic Analysis of Marker-Passing Techniques for Plan-Recognition
A Reason Maintenace System Dealing with Vague Data
Representation Requirements for Supporting Decision Model Formulation
A Fusion Algorithm for Solving Bayesian Decision Problems
Algorithms for Irrelevance-Based Partial MAPs
From Relational Databases to Belief Networks
Conditional Plausibility Measures and Bayesian Networks
Generalized Qualitative Probability: Savage Revisited
Quantum Annealing for Clustering
From Ordinary Differential Equations to Structural Causal Models: the deterministic case
A Counter Example to Theorems of Cox and Fine
Imperfect Match: PDDL 2.1 and Real Applications
PDDL 2.1: Representation vs. Computation
Context-Dependent Similarity
Similarity Networks for the Construction of Multiple-Faults Belief Networks
On Some Equivalence Relations between Incidence Calculus and Dempster-Shafer Theory of Evidence
Analysis in HUGIN of Data Conflict
d-Separation: From Theorems to Algorithms
Maximum Uncertainty Procedures for Interval-Valued Probability Distributions
Directed Cycles in Belief Networks
Normalization and the Representation of Nonmonotonic Knowledge in the Theory of Evidence
A Method for Using Belief Networks as Influence Diagrams
Modeling uncertain and vague knowledge in possibility and evidence theories
Truth Maintenance Under Uncertainty
The Optimality of Satisficing Solutions
Probabilistic Inference and Probabilistic Reasoning
A Linear Approximation Method for Probabilistic Inference
Handling uncertainty in a system for text-symbol context analysis
Do We Need Higher-Order Probabilities and, If So, What Do They Mean?
Using the Dempster-Shafer Scheme in a Diagnostic Expert System Shell
Comparisons of Reasoning Mechanisms for Computer Vision
Evidential Reasoning in Image Understanding
Explanation of Probabilistic Inference for Decision Support Systems
Efficient Inference on Generalized Fault Diagrams
Learning Link-Probabilities in Causal Trees
Generalizing Fuzzy Logic Probabilistic Inferences
Qualitative Probabilistic Networks for Planning Under Uncertainty
On Implementing Usual Values
On the Combinality of Evidence in the Dempster-Shafer Theory
A Constraint Propagation Approach to Probabilistic Reasoning
Implementing Probabilistic Reasoning
A factorization criterion for acyclic directed mixed graphs
Rule reasoning for legal norm validation of FSTP facts
Decidability, Introduction Rules and Automata
On Quantum Decision Trees
The MacGyver Test - A Framework for Evaluating Machine Resourcefulness and Creative Problem Solving
AI Safety and Reproducibility: Establishing Robust Foundations for the Neuroscience of Human Values
Dimensions of Neural-symbolic Integration - A Structured Survey
Can Intelligence Explode?
A Heuristic Search Algorithm Using the Stability of Learning Algorithms in Certain Scenarios as the Fitness Function: An Artificial General Intelligence Engineering Approach
A Theory of Universal Artificial Intelligence based on Algorithmic Complexity
Towards an Intelligent Tutor for Mathematical Proofs
Subjective Reality and Strong Artificial Intelligence
On the Compatibility Between Physics and Intelligent Organisms
Intelligent encoding and economical communication in the visual stream
Automatic Synthesis of Geometry Problems for an Intelligent Tutoring System
The Computational Theory of Intelligence: Data Aggregation
Intelligent User Interfaces - A Tutorial
Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like
Conscious Intelligent Systems - Part II - Mind, Thought, Language and Understanding
Ultimate Intelligence Part III: Measures of Intelligence, Perception and Intelligent Agents
Landau Theory of Adaptive Integration in Computational Intelligence
Affect Control Processes: Intelligent Affective Interaction using a Partially Observable Markov Decision Process
A Model for Web-Intelligence Index to Evaluate the Web Intelligence Capacity of Government Web Sites of Sri Lanka
Visual Character Recognition using Artificial Neural Networks
Artificial Learning in Artificial Memories
Modeling Belief in Dynamic Systems, Part II: Revision and Update
A note on Darwiche and Pearl
Matrix Games, Linear Programming, and Linear Approximation
Quality Classifiers for Open Source Software Repositories
Fact Sheet on Semantic Web
Approximated Structured Prediction for Learning Large Scale Graphical Models
Artificial Intelligence in Reverse Supply Chain Management: The State of the Art
Design of Automatically Adaptable Web Wrappers
Artificial Decision Making Under Uncertainty in Intelligent Buildings
A Misanthropic Reinterpretation of the Chinese Room Problem
Experimental Realization of Quantum Artificial Intelligence
Two Gaussian Approaches to Black-Box Optomization
SAT as a game
Are Minds Computable?
Quantifying Morphological Computation based on an Information Decomposition of the Sensorimotor Loop
A Survey on Artificial Intelligence and Data Mining for MOOCs
Unethical Research: How to Create a Malevolent Artificial Intelligence
A Comment on Argumentation
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
Entropy Non-increasing Games for the Improvement of Dataflow Programming
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
How linguistic descriptions of data can help to the teaching-learning process in higher education, case of study: artificial intelligence
Innateness, AlphaZero, and Artificial Intelligence
Blockchain and Artificial Intelligence
Artificial intelligence and pediatrics: A synthetic mini review
The AGINAO Self-Programming Engine
Perspectives for Strong Artificial Life
Thoughts on an Unified Framework for Artificial Chemistries
Towards a Conceptual Framework for Innate Immunity
Chemlambda, universality and self-multiplication
AI Researchers, Video Games Are Your Friends!
Realizing an optimization approach inspired from Piagets theory on cognitive development
Artificial Intelligence and Legal Liability
An Integrated Framework for Learning and Reasoning
Variations of the Turing Test in the Age of Internet and Virtual Reality
Modeling and Verification of a Multi-Agent Argumentation System using NuSMV
A Case Study in Knowledge Discovery and Elicitation in an Intelligent Tutoring Application
Risk Agoras: Dialectical Argumentation for Scientific Reasoning
A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters
Network Engineering for Complex Belief Networks
On Non-monotonic Conditional Reasoning
Bayesian Inference in Model-Based Machine Vision
An Application of Non-Monotonic Probabilistic Reasoning to Air Force Threat Correlation
Energetics of the brain and AI
Using artificial intelligence for data reduction in mechanical engineering
On the Use of Skeletons when Learning in Bayesian Networks
Algorithms and Complexity Results for Persuasive Argumentation
Iris Codes Classification Using Discriminant and Witness Directions
Introduction to the SP theory of intelligence
Managing Inconsistent Intelligence
Analysis of Algorithms and Partial Algorithms
Emotional Responses in Artificial Agent-Based Systems: Reflexivity and Adaptation in Artificial Life
Artificial Immune Systems Tutorial
Enhancing a Search Algorithm to Perform Intelligent Backtracking
Modeling of Social Transitions Using Intelligent Systems
A theory of intelligence: networked problem solving in animal societies
Intelligent Semantic Web Search Engines: A Brief Survey
Creating Intelligent Linking for Information Threading in Knowledge Networks
An Intelligent Location Management approaches in GSM Mobile Network
Are there intelligent Turing machines?
A New Theoretical and Technological System of Imprecise-Information Processing
IQ of Neural Networks
Artificial Intelligence and Systems Theory: Applied to Cooperative Robots
On-line Planning and Scheduling: An Application to Controlling Modular Printers
Mathematical Foundations for Designing and Development of Intelligent Systems of Information Analysis
Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification
Using Thought-Provoking Children's Questions to Drive Artificial Intelligence Research
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Designing a Safe Autonomous Artificial Intelligence Agent based on Human Self-Regulation
SenseNet: 3D Objects Database and Tactile Simulator
Self-Regulated Artificial Ant Colonies on Digital Image Habitats
Structured Learning Modulo Theories
Design of an Alarm System for Isfahan Ozone Level based on Artificial Intelligence Predictor Models
A Model of Pathways to Artificial Superintelligence Catastrophe for Risk and Decision Analysis
Decoupling Learning Rules from Representations
Minimally Naturalistic Artificial Intelligence
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks
A World of Views: A World of Interacting Post-human Intelligences
Autonomous robots and the SP theory of intelligence
Intelligent Systems: Architectures and Perspectives
Intelligent location of simultaneously active acoustic emission sources: Part I
Does intelligence imply contradiction?
Polyethism in a colony of artificial ants
Digital Genesis: Computers, Evolution and Artificial Life
Nanoscale artificial intelligence: creating artificial neural networks using autocatalytic reactions
Toward Formalizing Teleportation of Pedagogical Artificial Agents
Intelligent User Interface in Fuzzy Environment
Applying Artificial Intelligence and Internet Techniques in Rural Tourism Domain
Intelligent Human Machine Interface Design for Advanced Product Life Cycle Management Systems
8-Valent Fuzzy Logic for Iris Recognition and Biometry
Cognitive Bias for Universal Algorithmic Intelligence
Extending Universal Intelligence Models with Formal Notion of Representation
Self-Modification of Policy and Utility Function in Rational Agents
Human vs. Computer Go: Review and Prospect
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network
Simple Cortex: A Model of Cells in the Sensory Nervous System
Neural Networks
A Dynamical Systems Approach for Static Evaluation in Go
The Search for Computational Intelligence
From Seed AI to Technological Singularity via Recursively Self-Improving Software
Fuzzy Clustering Data Given in the Ordinal Scale
Transferrable Plausibility Model - A Probabilistic Interpretation of Mathematical Theory of Evidence
Can Machines Think in Radio Language?
Forced Evolution in Silico by Artificial Transposons and their Genetic Operators: The John Muir Ant Problem
Do Artificial Reinforcement-Learning Agents Matter Morally?
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks
Quantum Artificial Life in an IBM Quantum Computer
The structure of evolved representations across different substrates for artificial intelligence
Classifying Signals with Local Classifiers
The Future of Scientific Simulations: from Artificial Life to Artificial Cosmogenesis
I'm sorry to say, but your understanding of image processing fundamentals is absolutely wrong
Computational Understanding and Manipulation of Symmetries
Definition and properties to assess multi-agent environments as social intelligence tests
Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence
Linguistics Computation, Automatic Model Generation, and Intensions
Dynamic Backtracking
Applying GSAT to Non-Clausal Formulas
On Planning while Learning
Decision-Theoretic Foundations for Causal Reasoning
Statistical Feature Combination for the Evaluation of Game Positions
Rule-based Machine Learning Methods for Functional Prediction
Well-Founded Semantics for Extended Logic Programs with Dynamic Preferences
Quantum Computing and Phase Transitions in Combinatorial Search
Mean Field Theory for Sigmoid Belief Networks
Improved Use of Continuous Attributes in C4.5
Active Learning with Statistical Models
A Divergence Critic for Inductive Proof
Learning First-Order Definitions of Functions
Lifeworld Analysis
A Complete Classification of Tractability in RCC-5
Eight Maximal Tractable Subclasses of Allen's Algebra with Metric Time
Defining Relative Likelihood in Partially-Ordered Preferential Structures
Representation Theory for Default Logic
Mixing Metaphors
Fuzzy Approaches to Abductive Inference
Exact Phase Transitions in Random Constraint Satisfaction Problems
Entrenchment Relations: A Uniform Approach to Nonmonotonicity
A Tableau Calculus for Pronoun Resolution
Generalized Qualitative Probability: Savage revisited
Bayesian Information Extraction Network
Information Compression by Multiple Alignment, Unification and Search as a Unifying Principle in Computing and Cognition
Using Artificial Intelligence for Model Selection
Memory As A Monadic Control Construct In Problem-Solving
Defensive forecasting
Relation Variables in Qualitative Spatial Reasoning
Adaptation Knowledge Discovery from a Case Base
Dependency Parsing with Dynamic Bayesian Network
Neural Networks with c-NOT Gated Nodes
Arabic Speech Recognition System using CMU-Sphinx4
Symbolic sensors
Artificial Intelligence for Conflict Management
A Novel Model of Working Set Selection for SMO Decomposition Methods
Feature Dynamic Bayesian Networks
Breaking Value Symmetry
Symmetry Breaking Using Value Precedence
Stochastic Constraint Programming
Decompositions of All Different, Global Cardinality and Related Constraints
Reasoning about soft constraints and conditional preferences: complexity results and approximation techniques
Multiset Ordering Constraints
Pattern Recognition Theory of Mind
How to Complete an Interactive Configuration Process?
A fuzzified BRAIN algorithm for learning DNF from incomplete data
Symmetry within Solutions
Integrating multiple sources to answer questions in Algebraic Topology
Faithfulness in Chain Graphs: The Gaussian Case
AI 3D Cybug Gaming
Use of Python and Phoenix-M Interface in Robotics
Solving the Satisfiability Problem Through Boolean Networks
Teraflop-scale Incremental Machine Learning
Evolutionary Algorithms for Reinforcement Learning
Reasoning about Minimal Belief and Negation as Failure
Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan
On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note)
Nonapproximability Results for Partially Observable Markov Decision Processes
Mean Field Methods for a Special Class of Belief Networks
Planning by Rewriting
Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes
Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System
ATTac-2000: An Adaptive Autonomous Bidding Agent
Efficient Methods for Qualitative Spatial Reasoning
Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs
Expert-Guided Subgroup Discovery: Methodology and Application
An Architectural Approach to Ensuring Consistency in Hierarchical Execution
Learning to Coordinate Efficiently: A Model-based Approach
Temporal Decision Trees: Model-based Diagnosis of Dynamic Systems On-Board
A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence
Can We Learn to Beat the Best Stock
Restricted Value Iteration: Theory and Algorithms
Towards a Reliable Framework of Uncertainty-Based Group Decision Support System
An Information Theoretic Representation of Agent Dynamics as Set Intersections
Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves
On the Practical use of Variable Elimination in Constraint Optimization Problems: 'Still-life' as a Case Study
Reasoning about Action: An Argumentation - Theoretic Approach
Logical Hidden Markov Models
mGPT: A Probabilistic Planner Based on Heuristic Search
Optiplan: Unifying IP-based and Graph-based Planning
PDDL2.1 - The Art of the Possible? Commentary on Fox and Long
The Case for Durative Actions: A Commentary on PDDL2.1
Generative Prior Knowledge for Discriminative Classification
Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting
The Complexity of Planning Problems With Simple Causal Graphs
A Bayesian Model for Plan Recognition in RTS Games applied to StarCraft
Reasoning about Unreliable Actions
Three new sensitivity analysis methods for influence diagrams
Distribution over Beliefs for Memory Bounded Dec-POMDP Planning
BEEM : Bucket Elimination with External Memory
Solving Hybrid Influence Diagrams with Deterministic Variables
A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks
Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker
Confounding Equivalence in Causal Inference
Characterizing the Set of Coherent Lower Previsions with a Finite Number of Constraints or Vertices
Modeling Events with Cascades of Poisson Processes
Bayesian Model Averaging Using the k-best Bayesian Network Structures
Truthful Feedback for Sanctioning Reputation Mechanisms
Solving Multistage Influence Diagrams using Branch-and-Bound Search
Multiple faults diagnosis using causal graph
Development of knowledge Base Expert System for Natural treatment of Diabetes disease
Constraint Processing in Lifted Probabilistic Inference
Deterministic POMDPs Revisited
AND/OR Importance Sampling
Identifying reasoning patterns in games
Complexity of Inference in Graphical Models
Identifying Optimal Sequential Decisions
Sampling First Order Logical Particles
Improving Gradient Estimation by Incorporating Sensor Data
Explanation Trees for Causal Bayesian Networks
Model-Based Bayesian Reinforcement Learning in Large Structured Domains
Bounding Search Space Size via (Hyper)tree Decompositions
New Techniques for Algorithm Portfolio Design
Refractor Importance Sampling
Inference for Multiplicative Models
Large-Flip Importance Sampling
Causal Reasoning in Graphical Time Series Models
Minimax regret based elicitation of generalized additive utilities
Polynomial Constraints in Causal Bayesian Networks
Accuracy Bounds for Belief Propagation
What Counterfactuals Can Be Tested
Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs
On the Robustness of Most Probable Explanations
Inequality Constraints in Causal Models with Hidden Variables
A new axiomatization for likelihood gambles
From influence diagrams to multi-operator cluster DAGs
Approximate Separability for Weak Interaction in Dynamic Systems
Identifying the Relevant Nodes Without Learning the Model
Belief Update in CLG Bayesian Networks With Lazy Propagation
Reasoning about Uncertainty in Metric Spaces
Stratified Analysis of `Probabilities of Causation'
Bayesian Inference for Gaussian Mixed Graph Models
Identification of Conditional Interventional Distributions
Rule Based Expert System for Cerebral Palsy Diagnosis
Cost Sensitive Reachability Heuristics for Handling State Uncertainty
Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service
Belief Updating and Learning in Semi-Qualitative Probabilistic Networks
Common Voting Rules as Maximum Likelihood Estimators
On Bayesian Network Approximation by Edge Deletion
Exploiting Evidence in Probabilistic Inference
Local Markov Property for Models Satisfying Composition Axiom
Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints
Metrics for Markov Decision Processes with Infinite State Spaces
Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation
The Graphical Identification for Total Effects by using Surrogate Variables
The Relationship Between AND/OR Search and Variable Elimination
Point-Based POMDP Algorithms: Improved Analysis and Implementation
Qualitative Decision Making Under Possibilistic Uncertainty: Toward more discriminating criteria
Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement
Reasoning about Agent Programs using ATL-like Logics
Metrics for Finite Markov Decision Processes
Dynamic Programming for Structured Continuous Markov Decision Problems
Region-Based Incremental Pruning for POMDPs
A Unified framework for order-of-magnitude confidence relations
Mixtures of Deterministic-Probabilistic Networks and their AND/OR Search Space
Compact Value-Function Representations for Qualitative Preferences
Selection of Identifiability Criteria for Total Effects by using Path Diagrams
Identifying Conditional Causal Effects
Heuristic Search Value Iteration for POMDPs
Robustness of Causal Claims
An improvement direction for filter selection techniques using information theory measures and quadratic optimization
Join-graph based cost-shifting schemes
A Maximum Likelihood Approach For Selecting Sets of Alternatives
A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound over Graphical Models
The Complexity of Approximately Solving Influence Diagrams
Belief Propagation for Structured Decision Making
An Approximate Solution Method for Large Risk-Averse Markov Decision Processes
From imprecise probability assessments to conditional probabilities with quasi additive classes of conditioning events
Multi-objective Influence Diagrams
Verbalizing Ontologies in Controlled Baltic Languages
Quantum Consciousness Soccer Simulator
Gliders2012: Development and Competition Results
A Dataset for StarCraft AI \& an Example of Armies Clustering
A possibilistic handling of partially ordered information
Structure-Based Causes and Explanations in the Independent Choice Logic
Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories
A Linear Belief Function Approach to Portfolio Evaluation
Policy-contingent abstraction for robust robot control
An Axiomatic Approach to Robustness in Search Problems with Multiple Scenarios
A constraint satisfaction approach to the robust spanning tree problem with interval data
Qualitative MDPs and POMDPs: An Order-Of-Magnitude Approximation
Generalized Instrumental Variables
Causes and Explanations in the Structural-Model Approach: Tractable Cases
Statistical Decisions Using Likelihood Information Without Prior Probabilities
Reduction of Maximum Entropy Models to Hidden Markov Models
Expectation Propogation for approximate inference in dynamic Bayesian networks
Coordinates: Probabilistic Forecasting of Presence and Availability
Efficient Nash Computation in Large Population Games with Bounded Influence
Formalizing Scenario Analysis
Factored Particles for Scalable Monitoring
Inference with Seperately Specified Sets of Probabilities in Credal Networks
Asymptotic Model Selection for Naive Bayesian Networks
Loopy Belief Propogation and Gibbs Measures
Particle Filters in Robotics (Invited Talk)
On the Testable Implications of Causal Models with Hidden Variables
Markov Chain Monte Carlo using Tree-Based Priors on Model Structure
A Calculus for Causal Relevance
Instrumentality Tests Revisited
Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models
Causes and Explanations: A Structural-Model Approach --- Part 1: Causes
Plausible reasoning from spatial observations
Probabilistic Logic Programming under Inheritance with Overriding
Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy Propagation
Direct and Indirect Effects
Vector-space Analysis of Belief-state Approximation for POMDPs
A Mixed Graphical Model for Rhythmic Parsing
A Tractable POMDP for a Class of Sequencing Problems
Causal Discovery from Changes
Using Temporal Data for Making Recommendations
Perfect Tree-Like Markovian Distributions
A Principled Analysis of Merging Operations in Possibilistic Logic
Approximately Optimal Monitoring of Plan Preconditions
Stochastic Logic Programs: Sampling, Inference and Applications
A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic Beliefs
Causal Mechanism-based Model Construction
Evaluating Influence Diagrams using LIMIDs
Conversation as Action Under Uncertainty
Pivotal Pruning of Trade-offs in QPNs
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
Probabilities of Causation: Bounds and Identification
An Application of Uncertain Reasoning to Requirements Engineering
Loglinear models for first-order probabilistic reasoning
Learning Polytrees
Quantifier Elimination for Statistical Problems
Faithful Approximations of Belief Functions
Estimating the Value of Computation in Flexible Information Refinement
Representing and Combining Partially Specified CPTs
On the Complexity of Policy Iteration
A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables
Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms
Enhancing QPNs for Trade-off Resolution
A Possibilistic Model for Qualitative Sequential Decision Problems under Uncertainty in Partially Observable Environments
Efficient Value of Information Computation
Multiplicative Factorization of Noisy-Max
A Method for Speeding Up Value Iteration in Partially Observable Markov Decision Processes
On the Acceptability of Arguments in Preference-Based Argumentation
Merging Uncertain Knowledge Bases in a Possibilistic Logic Framework
Marginalizing in Undirected Graph and Hypergraph Models
Irrelevance and Independence Relations in Quasi-Bayesian Networks
On the Semi-Markov Equivalence of Causal Models
Comparative Uncertainty, Belief Functions and Accepted Beliefs
Qualitative Decision Theory with Sugeno Integrals
Learning the Structure of Dynamic Probabilistic Networks
Solving POMDPs by Searching in Policy Space
Measure Selection: Notions of Rationality and Representation Independence
Exact Inference of Hidden Structure from Sample Data in Noisy-OR Networks
Using Qualitative Relationships for Bounding Probability Distributions
Constructing Situation Specific Belief Networks
Logarithmic Time Parallel Bayesian Inference
Probabilistic Inference in Influence Diagrams
Correlated Action Effects in Decision Theoretic Regression
Algorithms for Learning Decomposable Models and Chordal Graphs
Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes
Defining Explanation in Probabilistic Systems
Efficient Induction of Finite State Automata
A Standard Approach for Optimizing Belief Network Inference using Query DAGs
Algorithm Portfolio Design: Theory vs. Practice
Inference with Idempotent Valuations
Time-Critical Reasoning: Representations and Application
Relational Bayesian Networks
Probabilistic Acceptance
Incremental Map Generation by Low Cost Robots Based on Possibility/Necessity Grids
Structure and Parameter Learning for Causal Independence and Causal Interaction Models
Learning Bayesian Networks from Incomplete Databases
Independence of Causal Influence and Clique Tree Propagation
Fast Value Iteration for Goal-Directed Markov Decision Processes
Approximations for Decision Making in the Dempster-Shafer Theory of Evidence
Arguing for Decisions: A Qualitative Model of Decision Making
Some Experiments with Real-Time Decision Algorithms
Bucket Elimination: A Unifying Framework for Several Probabilistic Inference
Flexible Policy Construction by Information Refinement
Efficient Search-Based Inference for Noisy-OR Belief Networks: TopEpsilon
MIDAS - An Influence Diagram for Management of Mildew in Winter Wheat
Toward a Market Model for Bayesian Inference
A Graph-Theoretic Analysis of Information Value
Optimal Monte Carlo Estimation of Belief Network Inference
Coherent Knowledge Processing at Maximum Entropy by SPIRIT
A Measure of Decision Flexibility
Testing Implication of Probabilistic Dependencies
In Love With a Robot: the Dawn of Machine-To-Machine Marketing
Counterfactuals and Policy Analysis in Structural Models
Belief Functions and Default Reasoning
Chain Graphs for Learning
A Transformational Characterization of Equivalent Bayesian Network Structures
Conditioning Methods for Exact and Approximate Inference in Causal Networks
Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians
Testing Identifiability of Causal Effects
Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains
Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem Proving under Limited Resources
Improved Sampling for Diagnostic Reasoning in Bayesian Networks
Cautious Propagation in Bayesian Networks
HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees
Is There a Role for Qualitative Risk Assessment?
On the Complexity of Solving Markov Decision Problems
A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection
Refining Reasoning in Qualitative Probabilistic Networks
On the Testability of Causal Models with Latent and Instrumental Variables
Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables
Causal Inference in the Presence of Latent Variables and Selection Bias
An Order of Magnitude Calculus
A Method for Implementing a Probabilistic Model as a Relational Database
Generating Explanations for Evidential Reasoning
Inference with Causal Independence in the CPSC Network
Modus Ponens Generating Function in the Class of ^-valuations of Plausibility
Approximation Algorithms for the Loop Cutset Problem
Exploratory Model Building
A Stratified Simulation Scheme for Inference in Bayesian Belief Networks
Symbolic Probabilitistic Inference in Large BN2O Networks
Integrating Planning and Execution in Stochastic Domains
Penalty logic and its Link with Dempster-Shafer Theory
Conditional Independence in Possibility Theory
Backward Simulation in Bayesian Networks
Abstracting Probabilistic Actions
On Modal Logics for Qualitative Possibility in a Fuzzy Setting
A Logic for Default Reasoning About Probabilities
Optimal Junction Trees
Constructing Belief Networks to Evaluate Plans
Anytime Decision Making with Imprecise Probabilities
Solving Asymmetric Decision Problems with Influence Diagrams
A Probabilistic Approach to Hierarchical Model-based Diagnosis
Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models
Exceptional Subclasses in Qualitative Probability
A Defect in Dempster-Shafer Theory
State-space Abstraction for Anytime Evaluation of Probabilistic Networks
Generating Graphoids from Generalised Conditional Probability
Evidential Reasoning with Conditional Belief Functions
Inter-causal Independence and Heterogeneous Factorization
Causality in Bayesian Belief Networks
From Conditional Oughts to Qualitative Decision Theory
Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate
Causal Independence for Knowledge Acquisition and Inference
Sensitivity Analysis for Probability Assessments in Bayesian Networks
Causal Modeling
Reasoning about the Value of Decision-Model Refinement: Methods and Application
Valuation Networks and Conditional Independence
A Generalization of the Noisy-Or Model
Graph-Grammar Assistance for Automated Generation of Influence Diagrams
An Algorithm for the Construction of Bayesian Network Structures from Data
A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging
Incremental Probabilistic Inference
Intercausal Reasoning with Uninstantiated Ancestor Nodes
Inference Algorithms for Similarity Networks
Using Tree-Decomposable Structures to Approximate Belief Networks
Using Potential Influence Diagrams for Probabilistic Inference and Decision Making
Incremental computation of the value of perfect information in stepwise-decomposable influence diagrams
Argument Calculus and Networks
On reasoning in networks with qualitative uncertainty
Probabilistic Assumption-Based Reasoning
Partially Specified Belief Functions
Belief Revision in Probability Theory
A Belief-Function Based Decision Support System
RES - a Relative Method for Evidential Reasoning
Optimizing Causal Orderings for Generating DAGs from Data
Modal Logics for Qualitative Possibility and Beliefs
Lattice-Based Graded Logic: a Multimodal Approach
Dynamic Network Models for Forecasting
Parallelizing Probabilistic Inference: Some Early Explorations
An Entropy-based Learning Algorithm of Bayesian Conditional Trees
Knowledge Integration for Conditional Probability Assessments
A computational scheme for Reasoning in Dynamic Probabilistic Networks
The Dynamic of Belief in the Transferable Belief Model and Specialization-Generalization Matrices
Some Problems for Convex Bayesians
Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small World
The Bounded Bayesian
Representing Context-Sensitive Knowledge in a Network Formalism: A Preliminary Report
A Probabilistic Network of Predicates
Empirical Probabilities in Monadic Deductive Databases
aHUGIN: A System Creating Adaptive Causal Probabilistic Networks
MESA: Maximum Entropy by Simulated Annealing
Guess-And-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems
Decision Making Using Probabilistic Inference Methods
Conditional Independence in Uncertainty Theories
A Fuzzy Logic Approach to Target Tracking
Towards Precision of Probabilistic Bounds Propagation
Generalizing Jeffrey Conditionalization
Interval Structure: A Framework for Representing Uncertain Information
"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models
Combination of Upper and Lower Probabilities
A Bayesian Method for Constructing Bayesian Belief Networks from Databases
Advances in Probabilistic Reasoning
Time-Dependent Utility and Action Under Uncertainty
Non-monotonic Reasoning and the Reversibility of Belief Change
Reasoning with Mass Distributions
Conflict and Surprise: Heuristics for Model Revision
Reasoning under Uncertainty: Some Monte Carlo Results
A Modification to Evidential Probability
Non-monotonic Negation in Probabilistic Deductive Databases
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm
Representing Bayesian Networks within Probabilistic Horn Abduction
Pulcinella: A General Tool for Propagating Uncertainty in Valuation Networks
On the Generation of Alternative Explanations with Implications for Belief Revision
About Updating
Compressed Constraints in Probabilistic Logic and Their Revision
Detecting Causal Relations in the Presence of Unmeasured Variables
An Efficient Implementation of Belief Function Propagation
Why Do We Need Foundations for Modelling Uncertainties?
How to minimize the energy consumption in mobile ad-hoc networks
Probabilistic Conditional Preference Networks
Advances in Bayesian Network Learning using Integer Programming
A Sound and Complete Algorithm for Learning Causal Models from Relational Data
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
Case Adaptation with Qualitative Algebras
Planning based on classification by induction graph
Giving the AI definition a form suitable for the engineer
Transductive Rademacher Complexity and its Applications
The Role of Macros in Tractable Planning
Fast Set Bounds Propagation Using a BDD-SAT Hybrid
On the Intertranslatability of Argumentation Semantics
Dr.Fill: Crosswords and an Implemented Solver for Singly Weighted CSPs
Interactions between Knowledge and Time in a First-Order Logic for Multi-Agent Systems: Completeness Results
Narrative Planning: Compilations to Classical Planning
Rational Counterfactuals
Model revision inference for extensions of first order logic
Flow for Meta Control
A Logic for Reasoning about Evidence
A Logic for Reasoning about Upper Probabilities
A Heuristic Search Algorithm for Solving First-Order MDPs
Markov Chains on Orbits of Permutation Groups
Some Reflections on the Set-based and the Conditional-based Interpretations of Statements in Syllogistic Reasoning
Qsmodels: ASP Planning in Interactive Gaming Environment
On the Computability of AIXI
Welfare of Sequential Allocation Mechanisms for Indivisible Goods
Bounded Optimal Exploration in MDP
Normative Multiagent Systems: A Dynamic Generalization
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users
Procedural Generation of Angry Birds Levels using Building Constructive Grammar with Chinese-Style and/or Japanese-Style Models
Propositional Abduction with Implicit Hitting Sets
Teaching natural language to computers
Learning to Rank for Synthesizing Planning Heuristics
Smart Policies for Artificial Intelligence
Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language
Variational Cumulant Expansions for Intractable Distributions
Solving Highly Constrained Search Problems with Quantum Computers
Properties and Applications of Programs with Monotone and Convex Constraints
Solving Factored MDPs with Hybrid State and Action Variables
Learning Symbolic Models of Stochastic Domains
Combining Spatial and Temporal Logics: Expressiveness vs. Complexity
The Power of Modeling - a Response to PDDL2.1
Auctions with Severely Bounded Communication
Marvin: A Heuristic Search Planner with Online Macro-Action Learning
Discovering Classes of Strongly Equivalent Logic Programs
Phase Transition for Random Quantified XOR-Formulas
Exploiting Functional Dependencies in Qualitative Probabilistic Reasoning
Managing Uncertainty in Rule Based Cognitive Models
Problem Formulation as the Reduction of a Decision Model
Dynamic Construction of Belief Networks
Ergo: A Graphical Environment for Constructing Bayesian
A Dynamic Approach to Probabilistic Inference
Robust Inference Policies
Minimum Error Tree Decomposition
IDEAL: A Software Package for Analysis of Influence Diagrams
Optimal Decomposition of Belief Networks
On Heuristics for Finding Loop Cutsets in Multiply-Connected Belief Networks
A Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System
Using Dempster-Shafer Theory in Knowledge Representation
Amplitude-Based Approach to Evidence Accumulation
A Probabilistic Reasoning Environment
Decisions with Limited Observations over a Finite Product Space: the Klir Effect
Rules, Belief Functions and Default Logic
Computing Probability Intervals Under Independency Constraints
An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network
Towards a Normative Theory of Scientific Evidence
Plan Recognition in Stories and in Life
Decision Making "Biases" and Support for Assumption-Based Higher-Order Reasoning
Deciding Consistency of Databases Containing Defeasible and Strict Information
Heuristic Search as Evidential Reasoning
Inference Policies
Strategies for Generating Micro Explanations for Bayesian Belief Networks
Evidence Absorption and Propagation through Evidence Reversals
Freedom: A Measure of Second-order Uncertainty for Intervalic Probability Schemes
Efficient Parallel Estimation for Markov Random Fields
Can Uncertainty Management be Realized in a Finite Totally Ordered Probability Algebra?
Summary of A New Normative Theory of Probabilistic Logic
Process, Structure, and Modularity in Reasoning with Uncertainty
A Temporal Logic for Uncertain Events and An Outline of A Possible Implementation in An Extension of PROLOG
Probability as a Modal Operator
On the Logic of Causal Models
An Empirical Comparison of Three Inference Methods
Parallel Belief Revision
Rational Nonmonotonic Reasoning
Epistemological Relevance and Statistical Knowledge
Justifying the Principle of Interval Constraints
An Axiomatic Framework for Bayesian and Belief-function Propagation
Updating Probabilities in Multiply-Connected Belief Networks
Causal Networks: Semantics and Expressiveness
MCE Reasoning in Recursive Causal Networks
Nonmonotonic Reasoning via Possibility Theory
Is Shafer General Bayes?
Modifiable Combining Functions
Dempster-Shafer vs. Probabilistic Logic
Belief in Belief Functions: An Examination of Shafer's Canonical Examples
Can Evidence Be Combined in the Dempster-Shafer Theory
Temporal Reasoning About Uncertain Worlds
A Perspective on Confidence and Its Use in Focusing Attention During Knowledge Acquisition
Practical Issues in Constructing a Bayes' Belief Network
Objective Probability
Decision Tree Induction Systems: A Bayesian Analysis
The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations
The Inductive Logic of Information Systems
The Recovery of Causal Poly-Trees from Statistical Data
A Heuristic Bayesian Approach to Knowledge Acquisition: Application to Analysis of Tissue-Type Plasminogen Activator
A Study of Associative Evidential Reasoning
Convergent Deduction for Probabilistic Logic
A Knowledge Engineer's Comparison of Three Evidence Aggregation Methods
Problem Structure and Evidential Reasoning
The Role of Tuning Uncertain Inference Systems
Integrating Logical and Probabilistic Reasoning for Decision Making
An Algorithm for Computing Probabilistic Propositions
Taxonomy, Structure, and Implementation of Evidential Reasoning
Towards The Inductive Acquisition of Temporal Knowledge
Reasoning With Uncertain Knowledge
Deriving And Combining Continuous Possibility Functions in the Framework of Evidential Reasoning
Non-Monotonicity in Probabilistic Reasoning
Flexible Interpretations: A Computational Model for Dynamic Uncertainty Assessment
Evidence as Opinions of Experts
Bayesian Inference for Radar Imagery Based Surveillance
Evidential Reasoning in Parallel Hierarchical Vision Programs
Computing Reference Classes
An Uncertainty Management Calculus for Ordering Searches in Distributed Dynamic Databases
Estimating Uncertain Spatial Relationships in Robotics
Evaluation of Uncertain Inference Models I: PROSPECTOR
A Framework for Non-Monotonic Reasoning About Probabilistic Assumptions
Induction, of and by Probability
Combining Uncertain Estimates
Incidence Calculus: A Mechanism for Probabilistic Reasoning
Exact Reasoning Under Uncertainty
Strong & Weak Methods: A Logical View of Uncertainty
Statistical Mechanics Algorithm for Response to Targets (SMART)
Knowledge Structures and Evidential Reasoning in Decision Analysis
A Social Welfare Optimal Sequential Allocation Procedure
Statistical Constraints
What Is It Like to Be a Brain Simulation?
Evolutionary solving of the debts' clearing problem
An eigenvector-based hotspot detection
Probabilistic Selection in AgentSpeak(L)
Quantum computing for pattern classification
Using the Mean Absolute Percentage Error for Regression Models
Search Strategies for Binary Feature Selection for a Naive Bayes Classifier
Learning from Pairwise Marginal Independencies
Turing's Imitation Game has been Improved
An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments
Composing inference algorithms as program transformations
Review of state-of-the-arts in artificial intelligence with application to AI safety problem
Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey
Proceedings Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge
Robust Natural Language Processing - Combining Reasoning, Cognitive Semantics and Construction Grammar for Spatial Language
Latent Dependency Forest Models
An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm
Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
Criticality & Deep Learning I: Generally Weighted Nets
Minimax density estimation for growing dimension
BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning
Source-Sensitive Belief Change
MOBA: a New Arena for Game AI
Low Impact Artificial Intelligences
A Tutor Agent for MOBA Games
Bandit Models of Human Behavior: Reward Processing in Mental Disorders
A New Probabilistic Algorithm for Approximate Model Counting
AI-Powered Social Bots
Armstrong's Axioms and Navigation Strategies
Strategic Coalitions with Perfect Recall
Proceedings Sixteenth Conference on Theoretical Aspects of Rationality and Knowledge
Declarative Sequential Pattern Mining of Care Pathways
Exact Inference for Relational Graphical Models with Interpreted Functions: Lifted Probabilistic Inference Modulo Theories
Commonsense Scene Semantics for Cognitive Robotics: Towards Grounding Embodied Visuo-Locomotive Interactions
An enhanced method to compute the similarity between concepts of ontology
The Promise and Peril of Human Evaluation for Model Interpretability
The mind as a computational system
A Slow Read attack Using Cloud
Simulated Autonomous Driving on Realistic Road Networks using Deep Reinforcement Learning
Deep Learning: A Critical Appraisal
Trading the Twitter Sentiment with Reinforcement Learning
Quantified Degrees of Group Responsibility (Extended Abstract)
Etymo: A New Discovery Engine for AI Research
Morphologic for knowledge dynamics: revision, fusion, abduction
Bernoulli Embeddings for Graphs
Generative Design in Minecraft (GDMC), Settlement Generation Competition
Application of Grey Numbers to Assessment Processes
Visual Analytics for Explainable Deep Learning
Order to Disorder Transitions in Hybrid Intelligent Systems: a Hatch to the Interactions of Nations -Governments
Hybrid technique for effective knowledge representation & a comparative study
A short note on estimating intelligence from user profiles in the context of universal psychometrics: prospects and caveats
Advice from the Oracle: Really Intelligent Information Retrieval
Intelligent Identification of Two-Dimensional Structure by Machine-Learning Optical Microscopy
Textbook examples of recursion
To Preference via Entrenchment
The Logic Programming Paradigm and Prolog
A theory of experiment
Value Based Argumentation Frameworks
Knowledge Representation
Toward the Implementation of Functions in the DLV System (Preliminary Technical Report)
Quantum Computers
Self-organizing neural networks in classification and image recognition
A Note on the PAC Bayesian Theorem
Inferring knowledge from a large semantic network
Self-Organizing Multilayered Neural Networks of Optimal Complexity
Redundancy in Logic III: Non-Mononotonic Reasoning
Yet Another Efficient Unification Algorithm
Islands for SAT
Solving planning domains with polytree causal graphs is NP-complete
Quantum Artificial Intelligence
Calculating Valid Domains for BDD-Based Interactive Configuration
How to realize "a sense of humour" in computers ?
Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis (book review)
Temporized Equilibria
Identification of parameters underlying emotions and a classification of emotions
A remark on higher order RUE-resolution with EXTRUE
Fuzzy Mnesors
The Soft Cumulative Constraint
Modelling Concurrent Behaviors in the Process Specification Language
Beyond Turing Machines
ABC-LogitBoost for Multi-class Classification
Algorithms for finding dispensable variables
Dominion -- A constraint solver generator
A Formalization of the Turing Test
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments
Predicting growth fluctuation in network economy
Instantiation Schemes for Nested Theories
'Just Enough' Ontology Engineering
Kernel diff-hash
Principles of Solomonoff Induction and AIXI
Efficient Methods for Unsupervised Learning of Probabilistic Models
An example illustrating the imprecision of the efficient approach for diagnosis of Petri nets via integer linear programming
Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence
Modification of conceptual clustering algorithm Cobweb for numerical data using fuzzy membership function
The Doxastic Interpretation of Team Semantics
Introduction to Judea Pearl's Do-Calculus
Lambda Dependency-Based Compositional Semantics
A short note on the axiomatic requirements of uncertainty measure
Logic in the Lab
Free-configuration Biased Sampling for Motion Planning: Errata
Cortex simulation system proposal using distributed computer network environments
Dynamic Sweep Filtering Algorithm for FlexC
AI Evaluation: past, present and future
Neurocontrol methods review
Expressibility of norms in temporal logic
Xapagy: a cognitive architecture for narrative reasoning
Fuzzy Inference Systems Optimization
Deontic modality based on preference
Qualitative shape representation based on the qualitative relative direction and distance calculus eOPRAm
About Tau-Chain
Norm-Based Capacity Control in Neural Networks
A Note on Information-Directed Sampling and Thompson Sampling
Why Bother With Syntax?
An Application of the Generalized Rectangular Fuzzy Model to Critical Thinking Assessment
Concept Generation in Language Evolution
Negative Learning Rates and P-Learning
Obstacle evasion using fuzzy logic in a sliding blades problem environment
A note on adjusting $R^2$ for using with cross-validation
How to avoid ethically relevant Machine Consciousness
How to advance general game playing artificial intelligence by player modelling
Simplified Boardgames
Lattice Structure of Variable Precision Rough Sets
Divisive-agglomerative algorithm and complexity of automatic classification problems
On Seeking Consensus Between Document Similarity Measures
Resolving the Complexity of Some Fundamental Problems in Computational Social Choice
Brief Notes on Hard Takeoff, Value Alignment, and Coherent Extrapolated Volition
A History of Metaheuristics
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
The Causality/Repair Connection in Databases: Causality-Programs
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines
The Complex Negotiation Dialogue Game
Scientists in silico?
MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples
Network Analysis for Explanation
Paranom: A Parallel Anomaly Dataset Generator
Computing as compression: the SP theory of intelligence
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology
Reputation in M2M Economy
Monotonicity and Persistence in Preferential Logics
On the accuracy and running time of GSAT
Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decision Theory
Annotated revision programs
Robust Feature Selection by Mutual Information Distributions
The New AI: General & Sound & Relevant for Physics
Anusaaraka: Machine Translation in Stages
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence
Default reasoning over domains and concept hierarchies
Artificial Neural Networks and Support Vector Machines for Water Demand Time Series Forecasting
2006: Celebrating 75 years of AI - History and Outlook: the Next 25 Years
Local search heuristics: Fitness Cloud versus Fitness Landscape
A Reactive Tabu Search Algorithm for Stimuli Generation in Psycholinguistics
Reasoning about Cardinal Directions between Extended Objects
Life, the Universe, and almost Everything: Signs of Cosmic Design?
A Monte Carlo Algorithm for Universally Optimal Bayesian Sequence Prediction and Planning
Feature Importance in Bayesian Assessment of Newborn Brain Maturity from EEG
Symmetry within and between solutions
Hybrid tractability of soft constraint problems
Quantum Interaction Approach in Cognition, Artificial Intelligence and Robotics
The Complexity of Reasoning about Spatial Congruence
Decentralized Supply Chain Formation: A Market Protocol and Competitive Equilibrium Analysis
A Maximal Tractable Class of Soft Constraints
Evaluation of a Simple, Scalable, Parallel Best-First Search Strategy
Classification of artificial intelligence ids for smurf attack
Making life better one large system at a time: Challenges for UAI research
A unified setting for inference and decision: An argumentation-based approach
Existence and Finiteness Conditions for Risk-Sensitive Planning: Results and Conjectures
Bayes' Bluff: Opponent Modelling in Poker
FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs
Understanding (dis)similarity measures
Efficient Approximation for Triangulation of Minimum Treewidth
Choosing Among Interpretations of Probability
Expected Utility Networks
Inference Using Message Propagation and Topology Transformation in Vector Gaussian Continuous Networks
Probabilistic Exploration in Planning while Learning
Some Properties of Joint Probability Distributions
Three Approaches to Probability Model Selection
Expressing Relational and Temporal Knowledge in Visual Probabilistic Networks
A Sensitivity Analysis of Pathfinder: A Follow-up Study
Projective simulation for classical learning agents: a comprehensive investigation
Second Order Swarm Intelligence
A brief network analysis of Artificial Intelligence publication
The Complexity of Integer Bound Propagation
Combining Evaluation Metrics via the Unanimous Improvement Ratio and its Application to Clustering Tasks
Real Time Strategy Language
E-Generalization Using Grammars
Robust Feature Selection by Mutual Information Distributions
Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments
Strategic Dialogue Management via Deep Reinforcement Learning
Limits to Verification and Validation of Agentic Behavior
Pilot Testing an Artificial Intelligence Algorithm That Selects Homeless Youth Peer Leaders Who Promote HIV Testing
A Randomized Approximation Algorithm of Logic Sampling
Integrating Case-Based and Rule-Based Reasoning: the Possibilistic Connection
The Effects of Perfect and Sample Information on Fuzzy Utilities in Decision-Making
Bayesian Prediction for Artificial Intelligence
Knowledge Engineering Within A Generalized Bayesian Framework
The Rational and Computational Scope of Probabilistic Rule-Based Expert Systems
Machine Learning, Clustering, and Polymorphy
Probabilistic Conflict Resolution in Hierarchical Hypothesis Spaces
Pattern recognition issues on anisotropic smoothed particle hydrodynamics
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
Friendly Artificial Intelligence: the Physics Challenge
A Quantum Production Model
Ascribing Consciousness to Artificial Intelligence
Artificial general intelligence through recursive data compression and grounded reasoning: a position paper
Automated Assignment of Backbone NMR Data using Artificial Intelligence
A Minimal Architecture for General Cognition
A Topological Approach to Meta-heuristics: Analytical Results on the BFS vs. DFS Algorithm Selection Problem
Philosophical Fictionalism and Problem of Artificial Intelligence
Category theoretic foundation of single-photon-based decision making
Automatic Bridge Bidding Using Deep Reinforcement Learning
Long-Term Trends in the Public Perception of Artificial Intelligence
Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures
Self-Correcting Models for Model-Based Reinforcement Learning
Basic protocols in quantum reinforcement learning with superconducting circuits
Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features
Who Will Win Practical Artificial Intelligence? AI Engineerings in China
A System for Accessible Artificial Intelligence
Ethical Artificial Intelligence - An Open Question
OPEB: Open Physical Environment Benchmark for Artificial Intelligence
Learning Photography Aesthetics with Deep CNNs
Discriminant chronicles mining: Application to care pathways analytics
Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems
What Automated Planning can do for Business Process Management
Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo
Artificial Intelligence and Statistics
In folly ripe. In reason rotten. Putting machine theology to rest
Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey
Can Computers Create Art?
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning
Semantic Vector Spaces for Broadening Consideration of Consequences
Categorizing Variants of Goodhart's Law
Neural Network Quine
Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview
Applications of Artificial Intelligence to Network Security
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis
Review of Deep Learning
An electronic-game framework for evaluating coevolutionary algorithms
Intelligent Anticipated Exploration of Web Sites
Alleviating Media Bias Through Intelligent Agent Blogging
State of the Art Review for Applying Computational Intelligence and Machine Learning Techniques to Portfolio Optimisation
Semantic Oriented Agent based Approach towards Engineering Data Management, Web Information Retrieval and User System Communication Problems
Building Smart Communities with Cyber-Physical Systems
Intelligent Search Heuristics for Cost Based Scheduling
Research on the mobile robots intelligent path planning based on ant colony algorithm application in manufacturing logistics
A Roadmap towards Machine Intelligence
Making Math Searchable in Wikipedia
Gap Analysis of Natural Language Processing Systems with respect to Linguistic Modality
Is swarm intelligence able to create mazes?
Emotional Metaheuristics For in-situ Foraging Using Sensor Constrained Robot Swarms
On the Development of Intelligent Agents for MOBA Games
Un résultat intrigant en commande sans modèle
Between collective intelligence and semantic web : hypermediating sites. Contribution to technologies of intelligence
Order Effects for Queries in Intelligent Systems
Decision Support Systems Using Intelligent Paradigms
A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems
Faith in the Algorithm, Part 2: Computational Eudaemonics
Contribution of Case Based Reasoning (CBR) in the Exploitation of Return of Experience. Application to Accident Scenarii in Railroad Transport
Expert PC Troubleshooter With Fuzzy-Logic And Self-Learning Support
Applications of Algorithmic Probability to the Philosophy of Mind
Only T3-AI can reach human-level intelligence: A variety argument
Emotional control - conditio sine qua non for advanced artificial intelligences?
The Anatomy of a Modular System for Media Content Analysis
Advances in Artificial Intelligence: Are you sure, we are on the right track?
The Singularity May Never Be Near
A Tutorial on Deep Neural Networks for Intelligent Systems
Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research
Can Turing machine be curious about its Turing test results? Three informal lectures on physics of intelligence
The Danger Theory and Its Application to Artificial Immune Systems
An Artificial Immune System as a Recommender System for Web Sites
Next Challenges in Bringing Artificial Immune Systems to Production in Network Security
An Immune Inspired Approach to Anomaly Detection
Artificial Hormone Reaction Networks: Towards Higher Evolvability in Evolutionary Multi-Modular Robotics
Estimating Continuous Distributions in Bayesian Classifiers
Empirical Study of Artificial Fish Swarm Algorithm
The Information-theoretic and Algorithmic Approach to Human, Animal and Artificial Cognition
Niépce-Bell or Turing: How to Test Odor Reproduction?
Learning Solving Procedure for Artificial Neural Network
Parrondo Strategies for Artificial Traders
Learning and discrimination through STDP in a top-down modulated associative memory
Movie Recommendation Systems Using An Artificial Immune System
Articulation and Clarification of the Dendritic Cell Algorithm
Dendritic Cells for Real-Time Anomaly Detection
Application of PSO, Artificial Bee Colony and Bacterial Foraging Optimization algorithms to economic load dispatch: An analysis
Motion Planning Of an Autonomous Mobile Robot Using Artificial Neural Network
Obesity Heuristic, New Way On Artificial Immune Systems
Artificial Neuron Modelling Based on Wave Shape
Toward Idealized Decision Theory
Training artificial neural networks to learn a nondeterministic game
Stoic Ethics for Artificial Agents
Self-Organization and Artificial Life: A Review
Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human--like learning
Machine learning \& artificial intelligence in the quantum domain
Artificial Ant Colonies in Digital Image Habitats - A Mass Behaviour Effect Study on Pattern Recognition
Conscious Intelligent Systems - Part 1 : I X I
Automatic Vehicle Checking Agent (VCA)
From Cognitive Binary Logic to Cognitive Intelligent Agents
Perception Lie Paradox: Mathematically Proved Uncertainty about Humans Perception Similarity
Information Retrieval in Intelligent Systems: Current Scenario & Issues
Applicability of Crisp and Fuzzy Logic in Intelligent Response Generation
Review of intelligent tutoring systems using bayesian approach
Social and Business Intelligence Analysis Using PSO
Probabilistic Graphical Models on Multi-Core CPUs using Java 8
Measuring Machine Intelligence Through Visual Question Answering
Simplified firefly algorithm for 2D image key-points search
Interactive Restless Multi-armed Bandit Game and Swarm Intelligence Effect
Ultimate Intelligence Part II: Physical Measure and Complexity of Intelligence
Networked Intelligence: Towards Autonomous Cyber Physical Systems
You want to survive the data deluge: Be careful, Computational Intelligence will not serve you as a rescue boat
Commonsense reasoning, commonsense knowledge, and the SP theory of intelligence
Provably Bounded-Optimal Agents
POMDPs Make Better Hackers: Accounting for Uncertainty in Penetration Testing
Artificial Intelligence Based Cognitive Routing for Cognitive Radio Networks
Active learning machine learns to create new quantum experiments
An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning
Intelligent Fault Analysis in Electrical Power Grids
Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities
Artificial Immune Systems
Quantum Structure in Cognition: Fundamentals and Applications
Elementos de ingeniería de explotación de la información aplicados a la investigación tributaria fiscal
Adaptive Parallel Iterative Deepening Search
BENCHIP: Benchmarking Intelligence Processors
Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test
The Hyper-Cortex of Human Collective-Intelligence Systems
A Distributed AI Aided 3D Domino Game
A study on non-destructive method for detecting Toxin in pepper using Neural networks
The SP theory of intelligence: benefits and applications
What is Learning? A primary discussion about information and Representation
AIXIjs: A Software Demo for General Reinforcement Learning
Meta-Learning Evolutionary Artificial Neural Networks
Biological Inspiration for Artificial Immune Systems
Experimenting with Innate Immunity
Recognition of cDNA microarray image Using Feedforward artificial neural network
Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data
How the symbol grounding of living organisms can be realized in artificial agents
Evolutionary Training of Sparse Artificial Neural Networks: A Network Science Perspective
Brainstorm/J: a Java Framework for Intelligent Agents
Information Integration and Computational Logic
From Alife Agents to a Kingdom of N Queens
Modeling Belief in Dynamic Systems, Part II: Revisions and Update
Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms
Adaptation of Mamdani Fuzzy Inference System Using Neuro - Genetic Approach for Tactical Air Combat Decision Support System
New Millennium AI and the Convergence of History
On Granular Knowledge Structures
New parallel programming language design: a bridge between brain models and multi-core/many-core computers?
Feature Markov Decision Processes
Feature Reinforcement Learning: Part I: Unstructured MDPs
Automated Reasoning and Presentation Support for Formalizing Mathematics in Mizar
Detecting Danger: The Dendritic Cell Algorithm
A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems
Accelerating Reinforcement Learning through Implicit Imitation
Intelligent Self-Repairable Web Wrappers
Informledge System: A Modified Knowledge Network with Autonomous Nodes using Multi-lateral Links
A Combinatorial Optimisation Approach to Designing Dual-Parented Long-Reach Passive Optical Networks
Reasoning with Very Expressive Fuzzy Description Logics
MIVAR: Transition from Productions to Bipartite Graphs MIVAR Nets and Practical Realization of Automated Constructor of Algorithms Handling More than Three Million Production Rules
Modelling Social Structures and Hierarchies in Language Evolution
Toward Experiential Utility Elicitation for Interface Customization
Of Starships and Klingons: Bayesian Logic for the 23rd Century
MOB-ESP and other Improvements in Probability Estimation
Application of Fuzzy Mathematics to Speech-to-Text Conversion by Elimination of Paralinguistic Content
Toward Large-Scale Agent Guidance in an Urban Taxi Service
Hypothesis Management in Situation-Specific Network Construction
Building a Stochastic Dynamic Model of Application Use
Probabilistic Models for Agents' Beliefs and Decisions
Model Criticism of Bayesian Networks with Latent Variables
A New Model of Plan Recognition
The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users
Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems
WNtags: A Web-Based Tool For Image Labeling And Retrieval With Lexical Ontologies
Plan Development using Local Probabilistic Models
A Decision-Based View of Causality
A Construction of Bayesian Networks from Databases Based on an MDL Principle
R&D Analyst: An Interactive Approach to Normative Decision System Model Construction
Semi-bounded Rationality: A model for decision making
Flexibly-bounded Rationality and Marginalization of Irrationality Theories for Decision Making
Conceptive Artificial Intelligence: Insights from design theory
Advances in Artificial Intelligence: Deep Intentions, Shallow Achievements
Multi-Context Systems for Reactive Reasoning in Dynamic Environments
The AGI Containment Problem
Time, Chance, and Action
BaRT: A Bayesian Reasoning Tool for Knowledge Based Systems
Planning, Scheduling, and Uncertainty in the Sequence of Future Events
Information and Multi-Sensor Coordination
Machine Generalization and Human Categorization: An Information-Theoretic View
Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents
Universal Empathy and Ethical Bias for Artificial General Intelligence
Universal Psychometrics Tasks: difficulty, composition and decomposition
Projective simulation with generalization
Emotion Analysis of Songs Based on Lyrical and Audio Features
Measuring an Artificial Intelligence System's Performance on a Verbal IQ Test For Young Children
Introspective Agents: Confidence Measures for General Value Functions
Resource Planning For Rescue Operations
Handwriting Profiling using Generative Adversarial Networks
Interaction Networks for Learning about Objects, Relations and Physics
Message Passing Multi-Agent GANs
Machine Reading with Background Knowledge
Artificial Intelligence Probes for Interstellar Exploration and Colonization
Artificial Intelligence as an Enabler for Cognitive Self-Organizing Future Networks
Learning A Physical Long-term Predictor
Learning Macromanagement in StarCraft from Replays using Deep Learning
Toward the Starting Line: A Systems Engineering Approach to Strong AI
General AI Challenge - Round One: Gradual Learning
Artificial Intelligence and Data Science in the Automotive Industry
Abstractions for AI-Based User Interfaces and Systems
Deep Reinforcement Learning for Conversational AI
Good and safe uses of AI Oracles
Cooperative Multi-Agent Planning: A Survey
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
Recent Advances in Neural Program Synthesis
Value Alignment, Fair Play, and the Rights of Service Robots
The 2017 AIBIRDS Competition
Evidence Feed Forward Hidden Markov Model: A New Type of Hidden Markov Model
Design and implementation of computational platform for social-humanoid robot Lumen as an exhibition guide in Electrical Engineering Days 2015
A Market-Oriented Programming Environment and its Application to Distributed Multicommodity Flow Problems
Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models
Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction
A System for Induction of Oblique Decision Trees
Integrative Windowing
Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms
On the Implicit and on the Artificial - Morphogenesis and Emergent Aesthetics in Autonomous Collective Systems
Artificial Immune Systems (AIS) - A New Paradigm for Heuristic Decision Making
Extended Mixture of MLP Experts by Hybrid of Conjugate Gradient Method and Modified Cuckoo Search
Feature Selection for Generator Excitation Neurocontroller Development Using Filter Technique
A Sampling-Based Approach to Computing Equilibria in Succinct Extensive-Form Games
Improved Local Search in Artificial Bee Colony using Golden Section Search
Towards the Evolution of Novel Vertical-Axis Wind Turbines
Estimating Well-Performing Bayesian Networks using Bernoulli Mixtures
Optimization of Inter-Subnet Belief Updating in Multiply Sectioned Bayesian Networks
Structured Message Passing
Treedy: A Heuristic for Counting and Sampling Subsets
ABC-SG: A New Artificial Bee Colony Algorithm-Based Distance of Sequential Data Using Sigma Grams
A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem
Memory shapes time perception and intertemporal choices
Bridging LSTM Architecture and the Neural Dynamics during Reading
The Gn,m Phase Transition is Not Hard for the Hamiltonian Cycle Problem
AntNet: Distributed Stigmergetic Control for Communications Networks
Using Artificial Bee Colony Algorithm for MLP Training on Earthquake Time Series Data Prediction
Evolutionary Search in the Space of Rules for Creation of New Two-Player Board Games
DeepMind Lab
Intelligent information extraction based on artificial neural network
Experience Replay Using Transition Sequences
Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living
A Genetic Programming Framework for 2D Platform AI
A dataset and architecture for visual reasoning with a working memory
Ontology Based Information Extraction for Disease Intelligence
Towards a New Science of a Clinical Data Intelligence
Next Generation Business Intelligence and Analytics: A Survey
FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go
Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning
Initial Reference Architecture of an Intelligent Autonomous Agent for Cyber Defense
On the semantics of merging
Problem solving in ID-logic with aggregates: some experiments
Semantic Parsing based on Verbal Subcategorization
On Nonspecific Evidence
Beslutstödssystemet Dezzy - en översikt
A Flexible Rule Compiler for Speech Synthesis
Cooperative Game Theory within Multi-Agent Systems for Systems Scheduling
Belief Calculus
Is there an Elegant Universal Theory of Prediction?
A Foundation to Perception Computing, Logic and Automata
Non-Computability of Consciousness
Towards Physarum robots: computing and manipulating on water surface
Swarm-Based Spatial Sorting
Proposition of the Interactive Pareto Iterated Local Search Procedure - Elements and Initial Experiments
ECOLANG - Communications Language for Ecological Simulations Network
I, Quantum Robot: Quantum Mind control on a Quantum Computer
Considerations on Construction Ontologies
Back analysis based on SOM-RST system
The Application of Mamdani Fuzzy Model for Auto Zoom Function of a Digital Camera
On Building a Knowledge Base for Stability Theory
Brain-Like Stochastic Search: A Research Challenge and Funding Opportunity
Modélisation d'une analyse pragma-linguistique d'un forum de discussion
MiBoard: Multiplayer Interactive Board Game
Querying Biomedical Ontologies in Natural Language using Answer Set
Automatic Estimation of the Exposure to Lateral Collision in Signalized Intersections using Video Sensors
Leo Breiman
Extraction of handwritten areas from colored image of bank checks by an hybrid method
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction
The Harmonic Theory; A mathematical framework to build intelligent contextual and adaptive computing, cognition and sensory system
Detecting lateral genetic material transfer
When majority voting fails: Comparing quality assurance methods for noisy human computation environment
A Mixed Observability Markov Decision Process Model for Musical Pitch
Clustering of Local Optima in Combinatorial Fitness Landscapes
Changing the Environment based on Intrinsic Motivation
Dealing with the Fuzziness of Human Reasoning
Semantic information and artificial intelligence
Implementing Anti-Unification Modulo Equational Theory
Investigation of A Collective Decision Making System of Different Neighbourhood-Size Based on Hyper-Geometric Distribution
Robotics Technology in Mental Health Care
Enacting textual entailment and ontologies for automated essay grading in chemical domain
Information retrieval in folktales using natural language processing
Backward-Forward Search for Manipulation Planning
Moving Beyond the Turing Test with the Allen AI Science Challenge
Semantic Reasoning for Context-aware Internet of Things Applications
Towards Visual Type Theory as a Mathematical Tool and Mathematical User Interface
Event Selection Rules to Compute Explanations
Predicting User Actions in Software Processes
Simulated Car Racing Championship: Competition Software Manual
Measuring the Directional Distance Between Fuzzy Sets
A Fuzzy Directional Distance Measure
Using Answer Set Programming for pattern mining
Different Types of Conflicting Knowledge in AmI Environments
Emergence of synchrony in an Adaptive Interaction Model
Evaluating Go Game Records for Prediction of Player Attributes
Evolving Non-linear Stacking Ensembles for Prediction of Go Player Attributes
SimpleDS: A Simple Deep Reinforcement Learning Dialogue System
Towards Machine Intelligence
CITlab ARGUS for historical handwritten documents
Ms. Pac-Man Versus Ghost Team CIG 2016 Competition
Application of Ontologies in Cloud Computing: The State-Of-The-Art
An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes
Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics
The formal-logical characterisation of lies, deception, and associated notions
Fuzzy Constraints Linear Discriminant Analysis
Imitating Driver Behavior with Generative Adversarial Networks
C3A: A Cognitive Collaborative Control Architecture For an Intelligent Wheelchair
Universal Reasoning, Rational Argumentation and Human-Machine Interaction
Geracao Automatica de Paineis de Controle para Analise de Mobilidade Urbana Utilizando Redes Complexas
Static Gesture Recognition using Leap Motion
Unsupervised Neural-Symbolic Integration
P-Tree Programming
Autoencoder-augmented Neuroevolution for Visual Doom Playing
Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments
Intelligent Subset Selection of Power Generators for Economic Dispatch
Generating OWA weights using truncated distributions
An Ontology to support automated negotiation
Bypass Fraud Detection: Artificial Intelligence Approach
AI2-THOR: An Interactive 3D Environment for Visual AI
Winograd Schema - Knowledge Extraction Using Narrative Chains
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Human-Machine Inference Networks For Smart Decision Making: Opportunities and Challenges
Bridging Cognitive Programs and Machine Learning
The problem of the development ontology-driven architecture of intellectual software systems
MIRIAM: A Multimodal Chat-Based Interface for Autonomous Systems
On Chatbots Exhibiting Goal-Directed Autonomy in Dynamic Environments
FutureMapping: The Computational Structure of Spatial AI Systems
Emotion Orientated Recommendation System for Hiroshima Tourist by Fuzzy Petri Net
Centralized reward system gives rise to fast and efficient work sharing for intelligent Internet agents lacking direct communication
Intelligent search strategies based on adaptive Constraint Handling Rules
Protocol Requirements for Self-organizing Artifacts: Towards an Ambient Intelligence
Learning to Bluff
Phase transition in SONFIS&SORST
Intuitive visualization of the intelligence for the run-down of terrorist wire-pullers
Development of Hybrid Intelligent Systems and their Applications from Engineering Systems to Complex Systems
Design of Intelligent layer for flexible querying in databases
Knowledge Embedding and Retrieval Strategies in an Informledge System
An Intelligent Approach for Negotiating between chains in Supply Chain Management Systems
Evaluation of Distributed Intelligence on the Smart Card
Principles of modal and vector theory of formal intelligence systems
Using the quaternion's representation of individuals in swarm intelligence and evolutionary computation
A Novel Approach for Intelligent Robot Path Planning
Intelligent City Traffic Management and Public Transportation System
An Argumentation-Based Framework to Address the Attribution Problem in Cyber-Warfare
Towards Bayesian Deep Learning: A Survey
A Hybrid, PDE-ODE Control Strategy for Intercepting an Intelligent, well-informed Target in a Stationary, Cluttered Environment
Discovering patterns of correlation and similarities in software project data with the Circos visualization tool
On Generalized Bayesian Data Fusion with Complex Models in Large Scale Networks
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System
The Computational Principles of Learning Ability
Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach
Feynman Machine: The Universal Dynamical Systems Computer
Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments
Interactive, Intelligent Tutoring for Auxiliary Constructions in Geometry Proofs
A Bi-population Particle Swarm Optimizer for Learning Automata based Slow Intelligent System
Random Worlds and Maximum Entropy
A Uniform Framework for Concept Definitions in Description Logics
Synthesizing Customized Planners from Specifications
An Average Analysis of Backtracking on Random Constraint Satisfaction Problems
NetNeg: A Connectionist-Agent Integrated System for Representing Musical Knowledge
Multi-Instance Multi-Label Learning
Adaptive Branching for Constraint Satisfaction Problems
The tractability of CSP classes defined by forbidden patterns
Implementing Human-like Intuition Mechanism in Artificial Intelligence
Use of Markov Chains to Design an Agent Bidding Strategy for Continuous Double Auctions
Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation
Computational Aspects of Nearly Single-Peaked Electorates
A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification
A Bayesian Variant of Shafer's Commonalities For Modelling Unforeseen Events
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
GOTCHA Password Hackers!
Prime Implicates and Prime Implicants: From Propositional to Modal Logic
Case-Based Subgoaling in Real-Time Heuristic Search for Video Game Pathfinding
Online Speedup Learning for Optimal Planning
Ethical Artificial Intelligence
The Limitations of Standardized Science Tests as Benchmarks for Artificial Intelligence Research: Position Paper
Using Automated Theorem Provers to Teach Knowledge Representation in First-Order Logic
Some Epistemological Problems with the Knowledge Level in Cognitive Architectures
An Empirical Evaluation of a Randomized Algorithm for Probabilistic Inference
A Decision-Theoretic Model for Using Scientific Data
Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Material
Efficiency and complexity of price competition among single-product vendors
Meta-learning within Projective Simulation
Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups
The Morphospace of Consciousness
Receptor uptake arrays for vitamin B12, siderophores and glycans shape bacterial communities
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Towards a Deep Reinforcement Learning Approach for Tower Line Wars
Null Dynamical State Models of Human Cognitive Dysfunction
Augmented Artificial Intelligence: a Conceptual Framework
Generating retinal flow maps from structural optical coherence tomography with artificial intelligence
Can Autism be Catered with Artificial Intelligence-Assisted Intervention Technology? A Literature Review
Evolving a Stigmergic Self-Organized Data-Mining
An Intelligent System For Effective Forest Fire Detection Using Spatial Data
Simplification and integration in computing and cognition: the SP theory and the multiple alignment concept
Multi-objects association in perception of dynamical situation
Fighting Sample Degeneracy and Impoverishment in Particle Filters: A Review of Intelligent Approaches
Scientific Discovery by Machine Intelligence: A New Avenue for Drug Research
Designing Intelligent Instruments
Metaheuristic Algorithms for Convolution Neural Network
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces
The Essence of Constraint Propagation
Cox's Theorem Revisited
Extending Classical Logic with Inductive Definitions
QUIP - A Tool for Computing Nonmonotonic Reasoning Tasks
Coherence, Belief Expansion and Bayesian Networks
BDD-based reasoning in the fluent calculus - first results
PAL: Pertinence Action Language
Local Diagnosis
XNMR: A tool for knowledge bases exploration
Constraint compiling into rules formalism constraint compiling into rules formalism for dynamic CSPs computing
Knowledge Theoretic Properties of Topological Spaces
Modal Logics for Topological Spaces
On the relationship between fuzzy logic and four-valued relevance logic
The alldifferent Constraint: A Survey
Optimization Over Zonotopes and Training Support Vector Machines
The Representation of Legal Contracts
The logical meaning of Expansion
The Traits of the Personable
Two Representations for Iterative Non-prioritized Change
Collective Argumentation
XCB, the Last of the Shortest Single Axioms for the Classical Equivalential Calculus
Unsupervised Learning in a Framework of Information Compression by Multiple Alignment, Unification and Search
Universal Sequential Decisions in Unknown Environments
Implementing an Agent Trade Server
Transient Diversity in Multi-Agent Systems
WSAT(cc) - a fast local-search ASP solver
Great Expectations. Part II: Generalized Expected Utility as a Universal Decision Rule
Demolishing Searle's Chinese Room
Where Fail-Safe Default Logics Fail
Parametric external predicates for the DLV System
Propositional Defeasible Logic has Linear Complexity
Generalized Evolutionary Algorithm based on Tsallis Statistics
Augmenting ALC(D) (atemporal) roles and (aspatial) concrete domain with temporal roles and a spatial concrete domain -first results
A TCSP-like decidable constraint language generalising existing cardinal direction relations
Issues in Exploiting GermaNet as a Resource in Real Applications
Transforming Business Rules Into Natural Language Text
Self-Organization of the Neuron Collective of Optimal Complexity
Metalinguistic Information Extraction for Terminology
A Study for the Feature Core of Dynamic Reduct
Universal Learning of Repeated Matrix Games
Evolutionary Computing
Using Domain Knowledge in Evolutionary System Identification
UniCalc.LIN: a linear constraint solver for the UniCalc system
Belief Conditioning Rules (BCRs)
Semantic Description of Parameters in Web Service Annotations
Modular self-organization
Une expérience de sémantique inférentielle
On Geometric Algebra representation of Binary Spatter Codes
Constant for associative patterns ensemble
Attribute Value Weighting in K-Modes Clustering
On the Complexity of the Numerically Definite Syllogistic and Related Fragments
Mathematical model of interest matchmaking in electronic social networks
Remarks on Inheritance Systems
Mathematics as an Exact and Precise Language of Nature
Incompleteness, Complexity, Randomness and Beyond
Preconditioned Temporal Difference Learning
Can the Internet cope with stress?
Compositional Semantics Grounded in Commonsense Metaphysics
HORPO with Computability Closure : A Reconstruction
Effective Generation of Subjectively Random Binary Sequences
Measuring the Evolvability Landscape to study Neutrality
From vectors to mnesors
About Algorithm for Transformation of Logic Functions (ATLF)
Numerical Sensitivity and Efficiency in the Treatment of Epistemic and Aleatory Uncertainty
The Choquet integral for the aggregation of interval scales in multicriteria decision making
Data-Complexity of the Two-Variable Fragment with Counting Quantifiers
On Introspection, Metacognitive Control and Augmented Data Mining Live Cycles
Comparison between CPBPV, ESC/Java, CBMC, Blast, EUREKA and Why for Bounded Program Verification
Generalized Prediction Intervals for Arbitrary Distributed High-Dimensional Data
On-the-fly Macros
Multi-Agent Reinforcement Learning and Genetic Policy Sharing
Artificial intelligence for Bidding Hex
N-norm and N-conorm in Neutrosophic Logic and Set, and the Neutrosophic Topologies
XML Representation of Constraint Networks: Format XCSP 2.1
Writing Positive/Negative-Conditional Equations Conveniently
Combining Symmetry Breaking and Global Constraints
Optimistic Simulated Exploration as an Incentive for Real Exploration
Guarded resolution for answer set programming
Feasibility of random basis function approximators for modeling and control
Learning Nonlinear Dynamic Models
Automating Quantified Multimodal Logics in Simple Type Theory -- A Case Study
On Defining 'I' "I logy"
Toward a Category Theory Design of Ontological Knowledge Bases
Mnesors for automatic control
A Class of DSm Conditional Rules
An improved axiomatic definition of information granulation
Logic with Verbs
The Weighted CFG Constraint
Proceedings 6th International Workshop on Local Search Techniques in Constraint Satisfaction
A Decision-Optimization Approach to Quantum Mechanics and Game Theory
Similarité en intension vs en extension : à la croisée de l'informatique et du théâtre
Exponential Family Hybrid Semi-Supervised Learning
Release ZERO.0.1 of package RefereeToolbox
Importance of Sources using the Repeated Fusion Method and the Proportional Conflict Redistribution Rules #5 and #6
Predictive Gain Estimation - A mathematical analysis
The Socceral Force
Computing by Means of Physics-Based Optical Neural Networks
Where are the hard manipulation problems?
Parameterized Complexity Results in Symmetry Breaking
Border Algorithms for Computing Hasse Diagrams of Arbitrary Lattices
Using Semantic Wikis for Structured Argument in Medical Domain
Scientific Collaborations: principles of WikiBridge Design
On the CNF encoding of cardinality constraints and beyond
BoolVar/PB v1.0, a java library for translating pseudo-Boolean constraints into CNF formulae
Real Islamic Logic
Translation-based Constraint Answer Set Solving
Generating Schemata of Resolution Proofs
ALPprolog --- A New Logic Programming Method for Dynamic Domains
Conscious Machines and Consciousness Oriented Programming
Self-Organizing Mixture Networks for Representation of Grayscale Digital Images
Detection and emergence
dynPARTIX - A Dynamic Programming Reasoner for Abstract Argumentation
Une analyse basée sur la S-DRT pour la modélisation de dialogues pathologiques
aspcud: A Linux Package Configuration Tool Based on Answer Set Programming
Technical Note: Exploring Σ^P_2 / Π^P_2-hardness for Argumentation Problems with fixed distance to tractable classes
Abstract Representations and Frequent Pattern Discovery
EDML: A Method for Learning Parameters in Bayesian Networks
(weak) Calibration is Computationally Hard
Relational Reinforcement Learning in Infinite Mario
The Equational Approach to CF2 Semantics
Generalisation of language and knowledge models for corpus analysis
Applications of fuzzy logic to Case-Based Reasoning
Learning in Riemannian Orbifolds
EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect
Learning AMP Chain Graphs under Faithfulness
Quantified Conditional Logics are Fragments of HOL
Dissimilarity Clustering by Hierarchical Multi-Level Refinement
A Simplified Description of Fuzzy TOPSIS
The Causal Topography of Cognition
The hardest logic puzzle ever becomes even tougher
Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing
Challenges for Distributional Compositional Semantics
Introduction of the weight edition errors in the Levenshtein distance
New results of ant algorithms for the Linear Ordering Problem
A Linguistic Model for Terminology Extraction based Conditional Random Fields
Learning Riemannian Metrics
A Study on Fuzzy Systems
Knowledge Sharing: A Model
Automated Variational Inference in Probabilistic Programming
Experiments with Random Projection
Phoneme discrimination using KS algebra I
Update report: LEO-II version 1.5
Stochastic gradient descent algorithms for strongly convex functions at O(1/T) convergence rates
Fast Collision Checking: From Single Robots to Multi-Robot Teams
Exponentiated Gradient LINUCB for Contextual Multi-Armed Bandits
Normalized Online Learning
Using Genetic Programming to Model Software
Syntactic sensitive complexity for symbol-free sequence
A fully automatic problem solver with human-style output
A finite axiomatization of conditional independence and inclusion dependencies
Distributed Reinforcement Learning via Gossip
Q-learning optimization in a multi-agents system for image segmentation
Strategic Argumentation is NP-Complete
The DIAMOND System for Argumentation: Preliminary Report
Does Syntactic Knowledge help English-Hindi SMT?
A Microkernel Architecture for Constraint Programming
Propagators and Violation Functions for Geometric and Workload Constraints Arising in Airspace Sectorisation
A proof challenge: multiple alignment and information compression
On a correlational clustering of integers
Thou Shalt is not You Will
Ontology as a Source for Rule Generation
TurKPF: TurKontrol as a Particle Filter
Do we need Asimov's Laws?
Some thoughts about benchmarks for NMR
Dialogues for proof search
A Self-Adaptive Network Protection System
Vicious Circle Principle and Logic Programs with Aggregates
Lexpresso: a Controlled Natural Language
Possibility neutrosophic soft sets with applications in decision making and similarity measure
Imparo is complete by inverse subsumption
Normalized Online Learning
The Universe of Minds
Domain-Independent Optimistic Initialization for Reinforcement Learning
The probatilistic Quantifier Fuzzification Mechanism FA: A theoretical analysis
Scalable Parallel Numerical CSP Solver
Introduction to ROSS: A New Representational Scheme
Cognitive Systems and Question Answering
On Generalized Rectangular Fuzzy Model for Assessment
Automatic Observer Script for StarCraft: Brood War Bot Games (technical report)
A Definition of Happiness for Reinforcement Learning Agents
Shedding Light on the Asymmetric Learning Capability of AdaBoost
Lazy Explanation-Based Approximation for Probabilistic Logic Programming
On the Computability of Solomonoff Induction and Knowledge-Seeking
YARBUS : Yet Another Rule Based belief Update System
A genetic algorithm for autonomous navigation in partially observable domain
Using Ontology-Based Context in the Portuguese-English Translation of Homographs in Textual Dialogues
Asymptotic Logical Uncertainty and The Benford Test
System Descriptions of the First International Competition on Computational Models of Argumentation (ICCMA'15)
My Reflections on the First Man vs. Machine No-Limit Texas Hold 'em Competition
Turing's Red Flag
Z Specification for the W3C Editor's Draft Core SHACL Semantics
Abstract Attribute Exploration with Partial Object Descriptions
RHOG: A Refinement-Operator Library for Directed Labeled Graphs
Quantifier Scope in Categorical Compositional Distributional Semantics
Holophrasm: a neural Automated Theorem Prover for higher-order logic
Five dimensions of reasoning in the wild
The Movie Graph Argument Revisited
Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity
Logical Fuzzy Optimization
Modèle flou d'expression des préférences basé sur les CP-Nets
Symmetry-Aware Marginal Density Estimation
Developing and Analyzing Boundary Detection Operators Using Probabilistic Models
Solving WCSP by Extraction of Minimal Unsatisfiable Cores
OntoRich - A Support Tool for Semi-Automatic Ontology Enrichment and Evaluation
A Markov Model for Ontology Alignment
A novice looks at emotional cognition
Three Generalizations of the FOCUS Constraint
From Ordinary Differential Equations to Structural Causal Models: the deterministic case
Stratified Labelings for Abstract Argumentation
Recommandation mobile, sensible au contexte de contenus évolutifs: Contextuel-E-Greedy
Relations on FP-Soft Sets Applied to Decision Making Problems
A Powerful Genetic Algorithm for Traveling Salesman Problem
Cascading A*: a Parallel Approach to Approximate Heuristic Search
Initial Experiments with TPTP-style Automated Theorem Provers on ACL2 Problems
Belief revision by examples
Semantic HMC for Big Data Analysis
A tool for implementation of a domain model based on fuzzy relationships
Bach in 2014: Music Composition with Recurrent Neural Network
Neutrosophic information in the framework of multi-valued representation
Workshop Notes of the 6th International Workshop on Acquisition, Representation and Reasoning about Context with Logic (ARCOE-Logic 2014)
A Generalization of Gustafson-Kessel Algorithm Using a New Constraint Parameter
A New Penta-valued Logic Based Knowledge Representation
Development of a VO Registry Subject Ontology using Automated Methods
Tensor SimRank for Heterogeneous Information Networks
Knowledge reduction of dynamic covering decision information systems with immigration of more objects
Temporal ordering of clinical events
Bridging belief function theory to modern machine learning
Controlled Query Evaluation for Datalog and OWL 2 Profile Ontologies
Can Machines Truly Think
Similarity, Cardinality and Entropy for Bipolar Fuzzy Set in the Framework of Penta-valued Representation
Entropy and Syntropy in the Context of Five-Valued Logics
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
Decomposition and Identification of Linear Structural Equation Models
Why is GDP growth linear?
Constructing Abstraction Hierarchies Using a Skill-Symbol Loop
Solving a Mathematical Problem in Square War: a Go-like Board Game
Thinking Required
Convolutional Monte Carlo Rollouts in Go
Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition
A Predictive Model using the Markov Property
Inference rules for RDF(S) and OWL in N3Logic
Pricing Vehicle Sharing with Proximity Information
Probabilistic Models for Computerized Adaptive Testing: Experiments
Applying Boolean discrete methods in the production of a real-valued probabilistic programming model
Range-based argumentation semantics as 2-valued models
GeoGebra Tools with Proof Capabilities
COCO: The Experimental Procedure
Building the Signature of Set Theory Using the MathSem Program
A Step from Probabilistic Programming to Cognitive Architectures
Improving abcdSAT by At-Least-One Recently Used Clause Management Strategy
Differences between Industrial Models of Autonomy and Systemic Models of Autonomy
OpenAI Gym
Relating Strong Spatial Cognition to Symbolic Problem Solving --- An Example
X575: writing rengas with web services
Using Recurrent Neural Network for Learning Expressive Ontologies
Adaptive Artificial Intelligence in Games: Issues, Requirements, and a Solution through Behavlets-based General Player Modelling
Assisting Drivers During Overtaking Using Car-2-Car Communication and Multi-Agent Systems
Modeling selectional restrictions in a relational type system
Reprowd: Crowdsourced Data Processing Made Reproducible
First-Order Bayesian Network Specifications Capture the Complexity Class PP
Micro-Data Learning: The Other End of the Spectrum
DeepAlgebra - an outline of a program
Fairness as a Program Property
Introduction: Cognitive Issues in Natural Language Processing
A Projective Simulation Scheme for a Partially-Observable Multi-Agent Game
Quantile Reinforcement Learning
Dependence and Relevance: A probabilistic view
Bayesian Non-parametric model to Target Gamification Notifications Using Big Data
Generalized LR parsing and the shuffle operator
Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)
Bipolar Weighted Argumentation Graphs
Comparing Apples and Oranges: Two Examples of the Limits of Statistical Inference, With an Application to Google Advertising Markets
Algorithmic Songwriting with ALYSIA
Efficient iterative policy optimization
Towards Smart Proof Search for Isabelle
Multiclass MinMax Rank Aggregation
Interaction Information for Causal Inference: The Case of Directed Triangle
ASHACL: Alternative Shapes Constraint Language
Solving the Brachistochrone Problem by an Influence Diagram
T-SKIRT: Online Estimation of Student Proficiency in an Adaptive Learning System
Monte Carlo Action Programming
Exchangeable choice functions
Segmentation of skin lesions based on fuzzy classification of pixels and histogram thresholding
Solving the Goddard problem by an influence diagram
Pseudorehearsal in value function approximation
Synergy of all-purpose static solver and temporal reasoning tools in dynamic integrated expert systems
Knowledge Fusion via Embeddings from Text, Knowledge Graphs, and Images
Structured Production System (extended abstract)
Composition of Credal Sets via Polyhedral Geometry
A note on the uniqueness of models in social abstract argumentation
A Survey of Distant Supervision Methods using PGMs
A rational analysis of curiosity
A Method for Determining Weights of Criterias and Alternative of Fuzzy Group Decision Making Problem
Experience enrichment based task independent reward model
Compatible extensions and consistent closures: a fuzzy approach
The Singularity May Be Near
Regular Boardgames
Evidence Against Evidence Theory (?!)
Generative Models for Learning from Crowds
Technical Report: Implementation and Validation of a Smart Health Application
Learning and Evaluating Musical Features with Deep Autoencoders
On the enumeration of sentences by compactness
Deductive and Analogical Reasoning on a Semantically Embedded Knowledge Graph
Mathematical aspect of the combinatorial game "Mahjong"
Detection of Abnormal Input-Output Associations
Investigating Reinforcement Learning Agents for Continuous State Space Environments
Non-FPT lower bounds for structural restrictions of decision DNNF
Plausibility and probability in deductive reasoning
Linking Generative Adversarial Learning and Binary Classification
LoIDE: a web-based IDE for Logic Programming - Preliminary Technical Report
A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
Assumption-Based Approaches to Reasoning with Priorities
Tensors Come of Age: Why the AI Revolution will help HPC
Creating a Social Brain for Cooperative Connected Autonomous Vehicles: Issues and Challenges
Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency
Sufficient and necessary causation are dual
Topological characteristics of oil and gas reservoirs and their applications
Note on Representing attribute reduction and concepts in concepts lattice using graphs
The destiny of constant structure discrete time closed semantic systems
A Study on Modeling of Inputting Electrical Power of Ultra High Power Electric Furnace by using Fuzzy Rule and Regression Model
Variational Deep Q Network
Nintendo Super Smash Bros. Melee: An "Untouchable" Agent
Sentiment Predictability for Stocks
Pseudorehearsal in actor-critic agents with neural network function approximation
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection
Precision and Recall for Range-Based Anomaly Detection
Reasoning about multiple aspects in DLs: Semantics and Closure Construction
Multi-optional Many-sorted Past Present Future structures and its description
Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
A Scheme-Driven Approach to Learning Programs from Input/Output Equations
Average Size of Implicational Bases
Reasoning in a Hierarchical System with Missing Group Size Information
Detecting truth, just on parts
Technique for designing a domain ontology
Integrated Tools for Engineering Ontologies
Principles of design and software development models of ontological-driven computer systems
SufiSent - Universal Sentence Representations Using Suffix Encodings
On the scaling of polynomial features for representation matching
Decision-making processes in the Cognitive Theory of True Conditions
An Application of HodgeRank to Online Peer Assessment
Learning and analyzing vector encoding of symbolic representations
On the Algebra in Boole's Laws of Thought
Information Theoretic Interpretation of Deep learning
Weakly Aggregative Modal Logic: Characterization and Interpolation
A Rule for Committee Selection with Soft Diversity Constraints
The Logical Essentials of Bayesian Reasoning
Linguistic Structure as Composition and Perturbation
Quantitative Neural Network Model of the Tip-of-the-Tongue Phenomenon Based on Synthesized Memory-Psycholinguistic-Metacognitive Approach
Market-Based Reinforcement Learning in Partially Observable Worlds
Behaviour-based Knowledge Systems: An Epigenetic Path from Behaviour to Knowledge
Multidimensional data classification with artificial neural networks
Applying Evolutionary Optimisation to Robot Obstacle Avoidance
Artificial Agents and Speculative Bubbles
A Data-Parallel Version of Aleph
Optimising the topology of complex neural networks
A System for Predicting Subcellular Localization of Yeast Genome Using Neural Network
Characterization of the convergence of stationary Fokker-Planck learning
An introduction to DSmT
Cooperative Automated Worm Response and Detection Immune Algorithm
Evolving Genes to Balance a Pole
Building a Chaotic Proved Neural Network
Negotiating Socially Optimal Allocations of Resources
Control Neuronal por Modelo Inverso de un Servosistema Usando Algoritmos de Aprendizaje Levenberg-Marquardt y Bayesiano
Unsupervised Classification Using Immune Algorithm
Dynamic consistency and decision making under vacuous belief
Discovering causal structures in binary exclusive-or skew acyclic models
A Comparative Study of State Transition Algorithm with Harmony Search and Artificial Bee Colony
Constraints on the search space of argumentation
Log-Optimal Portfolio Selection Using the Blackwell Approachability Theorem
Towards Learning Object Affordance Priors from Technical Texts
Hypotheses of neural code and the information model of the neuron-detector
The Effect of Social Learning on Individual Learning and Evolution
Chases and Escapes, and Optimization Problems
An Evolutionary Algorithm for Error-Driven Learning via Reinforcement
Robot Dream
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
Optimal Binary Autoencoding with Pairwise Correlations
Machine Learning for Dental Image Analysis
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
Deep learning evaluation using deep linguistic processing
Technical Problems With "Programmable self-assembly in a thousand-robot swarm"
Explaining Trained Neural Networks with Semantic Web Technologies: First Steps
An Artificial Neural Network Architecture Based on Context Transformations in Cortical Minicolumns
Reasons and Means to Model Preferences as Incomplete
Machine learning and evolutionary techniques in interplanetary trajectory design
A Model of Free Will for Artificial Entities
A Bayesian Model for Activities Recommendation and Event Structure Optimization Using Visitors Tracking
Big Data Analytics, Machine Learning and Artificial Intelligence in Next-Generation Wireless Networks
English Sentence Recognition using Artificial Neural Network through Mouse-based Gestures
On Affinity Measures for Artificial Immune System Movie Recommenders
An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots
Artificial Immune Tissue using Self-Orgamizing Networks
Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomoly Detection
An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network
Optimization of artificial flockings by means of anisotropy measurements
Using Belief Theory to Diagnose Control Knowledge Quality. Application to cartographic generalisation
Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method
Implementation of a Vision System for a Landmine Detecting Robot Using Artificial Neural Network
The method of artificial systems
Using Artificial Neural Network Techniques for Prediction of Electric Energy Consumption
A Self-Taught Artificial Agent for Multi-Physics Computational Model Personalization
Intelligent decision: towards interpreting the Pe Algorithm
Design of a GIS-based Assistant Software Agent for the Incident Commander to Coordinate Emergency Response Operations
Cognitive Development of the Web
Fault Detection Engine in Intelligent Predictive Analytics Platform for DCIM
Artificial Immune Systems (INTROS 2)
A symbolic description of punning riddles and its computer implementation
An implemented model of punning riddles
Morphology with a Null-Interface
Natural Language Interfaces to Databases - An Introduction
A Variant of Earley Parsing
Nature's Way of Optimizing
An Empirical Analysis of Search in GSAT
The Difficulties of Learning Logic Programs with Cut
Software Agents: Completing Patterns and Constructing User Interfaces
Decidable Reasoning in Terminological Knowledge Representation Systems
Teleo-Reactive Programs for Agent Control
Substructure Discovery Using Minimum Description Length and Background Knowledge
Bias-Driven Revision of Logical Domain Theories
A Semantics and Complete Algorithm for Subsumption in the CLASSIC Description Logic
Pattern Matching and Discourse Processing in Information Extraction from Japanese Text
Wrap-Up: a Trainable Discourse Module for Information Extraction
Operations for Learning with Graphical Models
Total-Order and Partial-Order Planning: A Comparative Analysis
A Domain-Independent Algorithm for Plan Adaptation
Truncating Temporal Differences: On the Efficient Implementation of TD(lambda) for Reinforcement Learning
On the Informativeness of the DNA Promoter Sequences Domain Theory
Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
Using Pivot Consistency to Decompose and Solve Functional CSPs
Adaptive Load Balancing: A Study in Multi-Agent Learning
Pac-Learning Recursive Logic Programs: Efficient Algorithms
Pac-learning Recursive Logic Programs: Negative Results
Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs
Building and Refining Abstract Planning Cases by Change of Representation Language
Using Qualitative Hypotheses to Identify Inaccurate Data
Diffusion of Context and Credit Information in Markovian Models
Learning Membership Functions in a Function-Based Object Recognition System
Flexibly Instructable Agents
Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach
Generalization of Clauses under Implication
Translating between Horn Representations and their Characteristic Models
The Design and Experimental Analysis of Algorithms for Temporal Reasoning
Logarithmic-Time Updates and Queries in Probabilistic Networks
Practical Methods for Proving Termination of General Logic Programs
Further Experimental Evidence against the Utility of Occam's Razor
Least Generalizations and Greatest Specializations of Sets of Clauses
Reinforcement Learning: A Survey
Adaptive Problem-solving for Large-scale Scheduling Problems: A Case Study
A Formal Framework for Speedup Learning from Problems and Solutions
2Planning for Contingencies: A Decision-based Approach
A Principled Approach Towards Symbolic Geometric Constraint Satisfaction
On Partially Controlled Multi-Agent Systems
Cue Phrase Classification Using Machine Learning
Exploiting Causal Independence in Bayesian Network Inference
Improved Heterogeneous Distance Functions
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
Flaw Selection Strategies for Partial-Order Planning
A New Look at the Easy-Hard-Easy Pattern of Combinatorial Search Difficulty
Identifying Hierarchical Structure in Sequences: A linear-time algorithm
Analysis of Three-Dimensional Protein Images
A Model Approximation Scheme for Planning in Partially Observable Stochastic Domains
When Gravity Fails: Local Search Topology
Bidirectional Heuristic Search Reconsidered
Tractability of Theory Patching
Model-Based Diagnosis using Structured System Descriptions
A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle
Set-Theoretic Completeness for Epistemic and Conditional Logic
The Computational Complexity of Probabilistic Planning
Semantics and Conversations for an Agent Communication Language
Learning Nested Agent Models in an Information Economy
Fixpoint 3-valued semantics for autoepistemic logic
Resolving Part-of-Speech Ambiguity in the Greek Language Using Learning Techniques
ACLP: Integrating Abduction and Constraint Solving
Some Remarks on Boolean Constraint Propagation
Naive Bayes and Exemplar-Based approaches to Word Sense Disambiguation Revisited
Ordering-based Representations of Rational Inference
Contextual Inference in Computational Semantics
Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison
A Constraint-Driven System for Contract Assembly
Another perspective on Default Reasoning
Preferred well-founded semantics for logic programming by alternating fixpoints: Preliminary report
Question answering: from partitions to Prolog
Geometric Aspects of Multiagent Systems
A uniform approach to logic programming semantics
Formal Concept Analysis and Resolution in Algebraic Domains
Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection
Definition and Complexity of Some Basic Metareasoning Problems
Modeling Object Oriented Constraint Programs in Z
Polyhierarchical Classifications Induced by Criteria Polyhierarchies, and Taxonomy Algebra
NLML--a Markup Language to Describe the Unlimited English Grammar
Exploiting Cross-Document Relations for Multi-document Evolving Summarization
A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria
Data Mining Approach for Analyzing Call Center Performance
Deductive Algorithmic Knowledge
Pruning Search Space in Defeasible Argumentation
Sub-Structural Niching in Non-Stationary Environments
Explorations in engagement for humans and robots
Stochastic Process Semantics for Dynamical Grammar Syntax: An Overview
Robust Inference of Trees
Approximate Discrete Probability Distribution Representation using a Multi-Resolution Binary Tree
Imagination as Holographic Processor for Text Animation
Representing Knowledge about Norms
Case Base Mining for Adaptation Knowledge Acquisition
Open-Ended Artificial Evolution
A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network
SANA - Network Protection through artificial Immunity
A Network Protection Framework through Artificial Immunity
Neural networks in 3D medical scan visualization
Extension of Inagaki General Weighted Operators and A New Fusion Rule Class of Proportional Redistribution of Intersection Masses
A comparison of the notions of optimality in soft constraints and graphical games
On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach
The Expressive Power of Binary Submodular Functions
The Latent Relation Mapping Engine: Algorithm and Experiments
Emotions, diffusive emotional control and the motivational problem for autonomous cognitive systems
The Semantics of Kalah Game
Granularity-Adaptive Proof Presentation
Filtering Algorithms for the Multiset Ordering Constraint
The Parameterized Complexity of Global Constraints
Decompositions of Grammar Constraints
SLIDE: A Useful Special Case of the CARDPATH Constraint
Tagging multimedia stimuli with ontologies
Building the information kernel and the problem of recognition
Where are the really hard manipulation problems? The phase transition in manipulating the veto rule
Circuit Complexity and Decompositions of Global Constraints
Scenario-based Stochastic Constraint Programming
On Maximum a Posteriori Estimation of Hidden Markov Processes
A Novel Two-Staged Decision Support based Threat Evaluation and Weapon Assignment Algorithm, Asset-based Dynamic Weapon Scheduling using Artificial Intelligence Techinques
Scheme of thinking quantum systems
Higher coordination with less control - A result of information maximization in the sensorimotor loop
Neural Networks for Dynamic Shortest Path Routing Problems - A Survey
Closing the Learning-Planning Loop with Predictive State Representations
A Survey of Paraphrasing and Textual Entailment Methods
Computational and Biological Analogies for Understanding Fine-Tuned Parameters in Physics
Propagating Conjunctions of AllDifferent Constraints
PCA 4 DCA: The Application Of Principal Component Analysis To The Dendritic Cell Algorithm
The Production of Probabilistic Entropy in Structure/Action Contingency Relations
How to correctly prune tropical trees
An Empirical Study of the Manipulability of Single Transferable Voting
Symmetries of Symmetry Breaking Constraints
Memristor Crossbar-based Hardware Implementation of Fuzzy Membership Functions
Text Classification using Artificial Intelligence
Efficient Knowledge Base Management in DCSP
Steepest Ascent Hill Climbing For A Mathematical Problem
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Learning Planar Ising Models
To study the phenomenon of the Moravec's Paradox
DD-EbA: An algorithm for determining the number of neighbors in cost estimation by analogy using distance distributions
Planning with Partial Preference Models
A Factorial Experiment on Scalability of Search Based Software Testing
Context Capture in Software Development
Meaning Negotiation as Inference
Artificial Immune Privileged Sites as an Enhancement to Immuno-Computing Paradigm
GRASP and path-relinking for Coalition Structure Generation
On Minimal Constraint Networks
Planning Graph Heuristics for Belief Space Search
Tractable Set Constraints
The Good Old Davis-Putnam Procedure Helps Counting Models
Identifying Mislabeled Training Data
Markov Localization for Mobile Robots in Dynamic Environments
Decentralized Markets versus Central Control: A Comparative Study
Randomized Algorithms for the Loop Cutset Problem
Space Efficiency of Propositional Knowledge Representation Formalisms
Value-Function Approximations for Partially Observable Markov Decision Processes
Robust Agent Teams via Socially-Attentive Monitoring
What's in an Attribute? Consequences for the Least Common Subsumer
The Complexity of Reasoning with Cardinality Restrictions and Nominals in Expressive Description Logics
An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email
Asimovian Adaptive Agents
A Model of Inductive Bias Learning
On the Compilability and Expressive Power of Propositional Planning Formalisms
Partial-Order Planning with Concurrent Interacting Actions
Grounding the Lexical Semantics of Verbs in Visual Perception using Force Dynamics and Event Logic
The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning
Experiments with Infinite-Horizon, Policy-Gradient Estimation
Reasoning within Fuzzy Description Logics
GIB: Imperfect Information in a Computationally Challenging Game
Domain Filtering Consistencies
The FF Planning System: Fast Plan Generation Through Heuristic Search
Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap
A Sequence of Relaxations Constraining Hidden Variable Models
Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
Extensions of Simple Conceptual Graphs: the Complexity of Rules and Constraints
Fusions of Description Logics and Abstract Description Systems
When do Numbers Really Matter?
Automatically Training a Problematic Dialogue Predictor for a Spoken Dialogue System
A Knowledge Compilation Map
Inferring Strategies for Sentence Ordering in Multidocument News Summarization
Machine Learning Markets
Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video
An Analysis of Phase Transition in NK Landscapes
Propositional Independence - Formula-Variable Independence and Forgetting
Translation of Pronominal Anaphora between English and Spanish: Discrepancies and Evaluation
Monte Carlo Methods for Tempo Tracking and Rhythm Quantization
Exploiting Contextual Independence In Probabilistic Inference
Bound Propagation
On Polynomial Sized MDP Succinct Policies
Compiling Causal Theories to Successor State Axioms and STRIPS-Like Systems
VHPOP: Versatile Heuristic Partial Order Planner
Answer Set Planning Under Action Costs
SAPA: A Multi-objective Metric Temporal Planner
AltAltp: Online Parallelization of Plans with Heuristic State Search
Planning Through Stochastic Local Search and Temporal Action Graphs in LPG
TALplanner in IPC-2002: Extensions and Control Rules
Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions
The Metric-FF Planning System: Translating "Ignoring Delete Lists" to Numeric State Variables
The 3rd International Planning Competition: Results and Analysis
CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements
IDL-Expressions: A Formalism for Representing and Parsing Finite Languages in Natural Language Processing
Effective Dimensions of Hierarchical Latent Class Models
A Personalized System for Conversational Recommendations
Coherent Integration of Databases by Abductive Logic Programming
Grounded Semantic Composition for Visual Scenes
Price Prediction in a Trading Agent Competition
Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences
Competitive Coevolution through Evolutionary Complexification
Dual Modelling of Permutation and Injection Problems
Generalizing Boolean Satisfiability I: Background and Survey of Existing Work
Graduality in Argumentation
Decentralized Control of Cooperative Systems: Categorization and Complexity Analysis
On Prediction Using Variable Order Markov Models
Ordered Landmarks in Planning
A Comprehensive Trainable Error Model for Sung Music Queries
Phase Transitions and Backbones of the Asymmetric Traveling Salesman Problem
Linear Latent Force Models using Gaussian Processes
Information, Utility & Bounded Rationality
Undithering using linear filtering and non-linear diffusion techniques
A KIF Formalization for the IFF Category Theory Ontology
ATP and Presentation Service for Mizar Formalizations
Structured Knowledge Representation for Image Retrieval
Generalizing Boolean Satisfiability II: Theory
Relational Dynamic Bayesian Networks
Solving Set Constraint Satisfaction Problems using ROBDDs
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis
Generalizing Boolean Satisfiability III: Implementation
Perseus: Randomized Point-based Value Iteration for POMDPs
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators
Approximate Policy Iteration with a Policy Language Bias: Solving Relational Markov Decision Processes
The Deterministic Part of IPC-4: An Overview
Binary Encodings of Non-binary Constraint Satisfaction Problems: Algorithms and Experimental Results
Dynamic Local Search for the Maximum Clique Problem
Representing Conversations for Scalable Overhearing
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems
Admissible and Restrained Revision
On Graphical Modeling of Preference and Importance
Fault Tolerant Boolean Satisfiability
Cognitive Principles in Robust Multimodal Interpretation
Multiple-Goal Heuristic Search
FluCaP: A Heuristic Search Planner for First-Order MDPs
New Inference Rules for Max-SAT
Obtaining Reliable Feedback for Sanctioning Reputation Mechanisms
Conjunctive Query Answering for the Description Logic SHIQ
Exploiting Subgraph Structure in Multi-Robot Path Planning
CTL Model Update for System Modifications
Extended RDF as a Semantic Foundation of Rule Markup Languages
Loosely Coupled Formulations for Automated Planning: An Integer Programming Perspective
A Constraint Programming Approach for Solving a Queueing Control Problem
First Order Decision Diagrams for Relational MDPs
Revisiting Numerical Pattern Mining with Formal Concept Analysis
A Well-typed Lightweight Situation Calculus
Belief change with noisy sensing in the situation calculus
An MLP based Approach for Recognition of Handwritten `Bangla' Numerals
Gibbs Sampling in Open-Universe Stochastic Languages
Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference
Possibilistic Answer Set Programming Revisited
Probabilistic Similarity Logic
An Online Learning-based Framework for Tracking
Lifted Inference for Relational Continuous Models
A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation
Playing games against nature: optimal policies for renewable resource allocation
Learning Game Representations from Data Using Rationality Constraints
Real-Time Scheduling via Reinforcement Learning
Formula-Based Probabilistic Inference
Intracluster Moves for Constrained Discrete-Space MCMC
Causal Conclusions that Flip Repeatedly and Their Justification
Anytime Planning for Decentralized POMDPs using Expectation Maximization
The Cost of Troubleshooting Cost Clusters with Inside Information
On a Class of Bias-Amplifying Variables that Endanger Effect Estimates
Irregular-Time Bayesian Networks
On the Validity of Covariate Adjustment for Estimating Causal Effects
Bayesian Inference in Monte-Carlo Tree Search
Learning Why Things Change: The Difference-Based Causality Learner
Primal View on Belief Propagation
Rollout Sampling Policy Iteration for Decentralized POMDPs
Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario
Multi-Domain Collaborative Filtering
A Convex Formulation for Learning Task Relationships in Multi-Task Learning
RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains
Understanding Sampling Style Adversarial Search Methods
Eliminating the Weakest Link: Making Manipulation Intractable?
Quantum Interference in Cognition: Structural Aspects of the Brain
The Network of French Legal Codes
Most Relevant Explanation: Properties, Algorithms, and Evaluations
Exploring compact reinforcement-learning representations with linear regression
Measuring Inconsistency in Probabilistic Knowledge Bases
Effects of Treatment on the Treated: Identification and Generalization
Bisimulation-based Approximate Lifted Inference
Regret-based Reward Elicitation for Markov Decision Processes
Exact Structure Discovery in Bayesian Networks with Less Space
Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence
Convexifying the Bethe Free Energy
Convergent message passing algorithms - a unifying view
MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts
Monolingual Probabilistic Programming Using Generalized Coroutines
Counting Belief Propagation
Temporal Action-Graph Games: A New Representation for Dynamic Games
MAP Estimation, Message Passing, and Perfect Graphs
Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling
First-Order Mixed Integer Linear Programming
Distributed Parallel Inference on Large Factor Graphs
Generating Optimal Plans in Highly-Dynamic Domains
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds on Probability Distributions in Bayesian Networks
Softening Fuzzy Knowledge Representation Tool with the Learning of New Words in Natural Language
Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstractions
Adaptive Inference on General Graphical Models
On Identifying Total Effects in the Presence of Latent Variables and Selection bias
Bayesian network learning by compiling to weighted MAX-SAT
Strategy Selection in Influence Diagrams using Imprecise Probabilities
Knowledge Combination in Graphical Multiagent Model
Almost Optimal Intervention Sets for Causal Discovery
Gibbs Sampling in Factorized Continuous-Time Markov Processes
Learning and Solving Many-Player Games through a Cluster-Based Representation
Causal discovery of linear acyclic models with arbitrary distributions
Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium
Sparse Stochastic Finite-State Controllers for POMDPs
The Computational Complexity of Sensitivity Analysis and Parameter Tuning
Partitioned Linear Programming Approximations for MDPs
The Evaluation of Causal Effects in Studies with an Unobserved Exposure/Outcome Variable: Bounds and Identification
Learning Arithmetic Circuits
Discovering Cyclic Causal Models by Independent Components Analysis
CT-NOR: Representing and Reasoning About Events in Continuous Time
Improving the Accuracy and Efficiency of MAP Inference for Markov Logic
Observation Subset Selection as Local Compilation of Performance Profiles
Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping
Tightening LP Relaxations for MAP using Message Passing
Efficient inference in persistent Dynamic Bayesian Networks
Hierarchical POMDP Controller Optimization by Likelihood Maximization
Propagation using Chain Event Graphs
Sensitivity analysis in decision circuits
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality
Search for Choquet-optimal paths under uncertainty
A new parameter Learning Method for Bayesian Networks with Qualitative Influences
Learning Probabilistic Relational Dynamics for Multiple Tasks
Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams
Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks
Reachability Under Uncertainty
Evaluating influence diagrams with decision circuits
Optimizing Memory-Bounded Controllers for Decentralized POMDPs
Mixture-of-Parents Maximum Entropy Markov Models
Consensus ranking under the exponential model
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Weighted Graphical Models
Best-First AND/OR Search for Most Probable Explanations
Learning Bayesian Network Structure from Correlation-Immune Data
Evaluation of the Causal Effect of Control Plans in Nonrecursive Structural Equation Models
Survey Propagation Revisited
Ranking Under Uncertainty
More-or-Less CP-Networks
Importance Sampling via Variational Optimization
Policy Iteration for Relational MDPs
Constrained Automated Mechanism Design for Infinite Games of Incomplete Information
Improved Dynamic Schedules for Belief Propagation
Markov Logic in Infinite Domains
Predicting the behavior of interacting humans by fusing data from multiple sources
An Empirical Comparison of Algorithms for Aggregating Expert Predictions
MAIES: A Tool for DNA Mixture Analysis
A Variational Approach for Approximating Bayesian Networks by Edge Deletion
Sensitivity Analysis for Threshold Decision Making with Dynamic Networks
Optimal Coordinated Planning Amongst Self-Interested Agents with Private State
Graphical Condition for Identification in recursive SEM
Cutset Sampling with Likelihood Weighting
An Efficient Triplet-based Algorithm for Evidential Reasoning
Non-Minimal Triangulations for Mixed Stochastic/Deterministic Graphical Models
Linear Algebra Approach to Separable Bayesian Networks
Advances in exact Bayesian structure discovery in Bayesian networks
The AI&M Procedure for Learning from Incomplete Data
Pearl's Calculus of Intervention Is Complete
Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks
Methods for computing state similarity in Markov Decision Processes
Asymmetric separation for local independence graphs
General-Purpose MCMC Inference over Relational Structures
Visualization of Collaborative Data
A compact, hierarchical Q-function decomposition
Structured Priors for Structure Learning
A theoretical study of Y structures for causal discovery
On the Number of Samples Needed to Learn the Correct Structure of a Bayesian Network
A Non-Parametric Bayesian Method for Inferring Hidden Causes
Axiomatic Foundations for a Class of Generalized Expected Utility: Algebraic Expected Utility
Recognizing Activities and Spatial Context Using Wearable Sensors
Incremental Model-based Learners With Formal Learning-Time Guarantees
A simple approach for finding the globally optimal Bayesian network structure
Inference in Hybrid Bayesian Networks Using Mixtures of Gaussians
Practical Linear Value-approximation Techniques for First-order MDPs
Stable Independence in Perfect Maps
'Say EM' for Selecting Probabilistic Models for Logical Sequences
A Differential Semantics of Lazy AR Propagation
Modifying Bayesian Networks by Probability Constraints
Exploiting Evidence-dependent Sensitivity Bounds
MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
Local Utility Elicitation in GAI Models
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
Hybrid Bayesian Networks with Linear Deterministic Variables
Counterexample-guided Planning
Nonparametric Bayesian Logic
Counterfactual Reasoning in Linear Structural Equation Models
Efficient algorithm for estimation of qualitative expected utility in possibilistic case-based reasoning
Unsupervised Activity Discovery and Characterization From Event-Streams
Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences
Planning in POMDPs Using Multiplicity Automata
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables
Near-optimal Nonmyopic Value of Information in Graphical Models
On the optimality of tree-reweighted max-product message-passing
A Revision-Based Approach to Resolving Conflicting Information
Asynchronous Dynamic Bayesian Networks
Robotic Mapping with Polygonal Random Fields
Expectation Propagation for Continuous Time Bayesian Networks
A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance
Representation Policy Iteration
Approximate Linear Programming for First-order MDPs
Predictive Linear-Gaussian Models of Stochastic Dynamical Systems
Efficient Test Selection in Active Diagnosis via Entropy Approximation
A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables
Importance Sampling in Bayesian Networks: An Influence-Based Approximation Strategy for Importance Functions
Structured Region Graphs: Morphing EP into GBP
Arabic CALL system based on pedagogically indexed text
Qualitative Approximate Behavior Composition
Exploiting First-Order Regression in Inductive Policy Selection
A Complete Anytime Algorithm for Treewidth
Decision Making for Symbolic Probability
Stable Independance and Complexity of Representation
Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments
Bayesian Biosurveillance of Disease Outbreaks
A Logic Programming Framework for Possibilistic Argumentation with Vague Knowledge
Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters
On finding minimal w-cutset
Using arguments for making decisions: A possibilistic logic approach
Case-Factor Diagrams for Structured Probabilistic Modeling
Convolutional Factor Graphs as Probabilistic Models
An Empirical Evaluation of Possible Variations of Lazy Propagation
Pre-Selection of Independent Binary Features: An Application to Diagnosing Scrapie in Sheep
Solving Factored MDPs with Continuous and Discrete Variables
Annealed MAP
Discretized Approximations for POMDP with Average Cost
On the Choice of Regions for Generalized Belief Propagation
Monotonicity in Bayesian Networks
Predictive State Representations: A New Theory for Modeling Dynamical Systems
A New Characterization of Probabilities in Bayesian Networks
Evidence-invariant Sensitivity Bounds
Robust Probabilistic Inference in Distributed Systems
On Modeling Profiles instead of Values
Learning Diagnostic Policies from Examples by Systematic Search
Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials
The Arcade Learning Environment: An Evaluation Platform for General Agents
Improving multivariate Horner schemes with Monte Carlo tree search
A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems
Modeling and Control of CSTR using Model based Neural Network Predictive Control
A hybrid ACO approach to the Matrix Bandwidth Minimization Problem
Soft Computing approaches on the Bandwidth Problem
Conflict Anticipation in the Search for Graph Automorphisms
Automated Marble Plate Classification System Based On Different Neural Network Input Training Sets and PLC Implementation
Qualitative Modelling via Constraint Programming: Past, Present and Future
Scoring and Searching over Bayesian Networks with Causal and Associative Priors
Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference
An Efficient Message-Passing Algorithm for the M-Best MAP Problem
Causal Inference by Surrogate Experiments: z-Identifiability
Exploiting Uniform Assignments in First-Order MPE
The Do-Calculus Revisited
Weighted Sets of Probabilities and MinimaxWeighted Expected Regret: New Approaches for Representing Uncertainty and Making Decisions
Semantic Understanding of Professional Soccer Commentaries
Generalized Belief Propagation on Tree Robust Structured Region Graphs
Uniform Solution Sampling Using a Constraint Solver As an Oracle
Scaling Up Decentralized MDPs Through Heuristic Search
A Bayesian Approach to Constraint Based Causal Inference
Dynamic Stochastic Orienteering Problems for Risk-Aware Applications
A Theory of Goal-Oriented MDPs with Dead Ends
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables
Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games
Exploiting Structure in Cooperative Bayesian Games
Hilbert Space Embeddings of POMDPs
Local Structure Discovery in Bayesian Networks
Learning STRIPS Operators from Noisy and Incomplete Observations
Closed-Form Learning of Markov Networks from Dependency Networks
Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing
New Advances and Theoretical Insights into EDML
An Improved Admissible Heuristic for Learning Optimal Bayesian Networks
Dynamic Teaching in Sequential Decision Making Environments
Tracking Group Evolution in Social Networks
Full Object Boundary Detection by Applying Scale Invariant Features in a Region Merging Segmentation Algorithm
Influence of Context on Decision Making during Requirements Elicitation
Adaptive Bee Colony in an Artificial Bee Colony for Solving Engineering Design Problems
Dynamic Decision Support System Based on Bayesian Networks Application to fight against the Nosocomial Infections
An ontology-based approach to relax traffic regulation for autonomous vehicle assistance
On revising fuzzy belief bases
Upgrading Ambiguous Signs in QPNs
An Empirical Study of w-Cutset Sampling for Bayesian Networks
Value Elimination: Bayesian Inference via Backtracking Search
New Advances in Inference by Recursive Conditioning
Symbolic Generalization for On-line Planning
A Simple Insight into Iterative Belief Propagation's Success
A Robust Independence Test for Constraint-Based Learning of Causal Structure
Large-Sample Learning of Bayesian Networks is NP-Hard
Using the structure of d-connecting paths as a qualitative measure of the strength of dependence
Reasoning about Bayesian Network Classifiers
Monte Carlo Matrix Inversion Policy Evaluation
Approximate Decomposition: A Method for Bounding and Estimating Probabilistic and Deterministic Queries
LAYERWIDTH: Analysis of a New Metric for Directed Acyclic Graphs
Approximate Inference and Constrained Optimization
Monte-Carlo optimizations for resource allocation problems in stochastic network systems
Implementation and Comparison of Solution Methods for Decision Processes with Non-Markovian Rewards
Decision Making with Partially Consonant Belief Functions
Phase Transition of Tractability in Constraint Satisfaction and Bayesian Network Inference
Decentralized Sensor Fusion With Distributed Particle Filters
Solving MAP Exactly using Systematic Search
Marginalizing Out Future Passengers in Group Elevator Control
On Local Optima in Learning Bayesian Networks
Optimal Limited Contingency Planning
Practically Perfect
Systematic vs. Non-systematic Algorithms for Solving the MPE Task
An Importance Sampling Algorithm Based on Evidence Pre-propagation
Exploiting Locality in Searching the Web
The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach
Efficient Inference in Large Discrete Domains
Markov Equivalence Classes for Maximal Ancestral Graphs
On the Construction of the Inclusion Boundary Neighbourhood for Markov Equivalence Classes of Bayesian Network Structures
Introducing Variable Importance Tradeoffs into CP-Nets
Iterative Join-Graph Propagation
The Thing That We Tried Didn't Work Very Well : Deictic Representation in Reinforcement Learning
Distributed Planning in Hierarchical Factored MDPs
Unconstrained Influence Diagrams
CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence
A Bayesian Network Scoring Metric That Is Based On Globally Uniform Parameter Priors
Value Function Approximation in Zero-Sum Markov Games
Polynomial Value Iteration Algorithms for Detrerminstic MDPs
Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes
Continuous Time Bayesian Networks
Modelling Information Incorporation in Markets, with Application to Detecting and Explaining Events
From Qualitative to Quantitative Probabilistic Networks
An MDP-based Recommender System
Discriminative Probabilistic Models for Relational Data
Anytime State-Based Solution Methods for Decision Processes with non-Markovian Rewards
Exploiting Functional Dependence in Bayesian Network Inference
Decision Principles to justify Carnap's Updating Method and to Suggest Corrections of Probability Judgments (Invited Talks)
IPF for Discrete Chain Factor Graphs
Inductive Policy Selection for First-Order MDPs
Graphical readings of possibilistic logic bases
Pre-processing for Triangulation of Probabilistic Networks
Confidence Inference in Bayesian Networks
Semi-Instrumental Variables: A Test for Instrument Admissibility
Using Bayesian Networks to Identify the Causal Effect of Speeding in Individual Vehicle/Pedestrian Collisions
Efficient Stepwise Selection in Decomposable Models
Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables
Learning the Dimensionality of Hidden Variables
Multivariate Information Bottleneck
A Comparison of Axiomatic Approaches to Qualitative Decision Making Using Possibility Theory
Robust Combination of Local Controllers
A Clustering Approach to Solving Large Stochastic Matching Problems
A Bayesian Approach to Tackling Hard Computational Problems
On characterizing Inclusion of Bayesian Networks
Improved learning of Bayesian networks
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms
A Bayesian Multiresolution Independence Test for Continuous Variables
Aggregating Learned Probabilistic Beliefs
Expectation Propagation for approximate Bayesian inference
The Factored Frontier Algorithm for Approximate Inference in DBNs
Lattice Particle Filters
Approximating MAP using Local Search
Sufficiency, Separability and Temporal Probabilistic Models
Toward General Analysis of Recursive Probability Models
Value-Directed Sampling Methods for POMDPs
Decision-Theoretic Planning with Concurrent Temporally Extended Actions
Policy Improvement for POMDPs Using Normalized Importance Sampling
Maximum Likelihood Bounded Tree-Width Markov Networks
Bayesian Error-Bars for Belief Net Inference
Analysing Sensitivity Data from Probabilistic Networks
The Optimal Reward Baseline for Gradient-Based Reinforcement Learning
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation
Statistical Modeling in Continuous Speech Recognition (CSR)(Invited Talk)
Planning and Acting under Uncertainty: A New Model for Spoken Dialogue Systems
A Complete Calculus for Possibilistic Logic Programming with Fuzzy Propositional Variables
The Complexity of Decentralized Control of Markov Decision Processes
Dynamic Bayesian Multinets
Utilities as Random Variables: Density Estimation and Structure Discovery
Computational Investigation of Low-Discrepancy Sequences in Simulation Algorithms for Bayesian Networks
A Decision Theoretic Approach to Targeted Advertising
A Bayesian Method for Causal Modeling and Discovery Under Selection
Separation Properties of Sets of Probability Measures
A Differential Approach to Inference in Bayesian Networks
Any-Space Probabilistic Inference
Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks With Mixed Continuous And Discrete Variables
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Likelihood Computations Using Value Abstractions
Inference for Belief Networks Using Coupling From the Past
Dependency Networks for Collaborative Filtering and Data Visualization
YGGDRASIL - A Statistical Package for Learning Split Models
Marginalization in Composed Probabilistic Models
Fast Planning in Stochastic Games
Making Sensitivity Analysis Computationally Efficient
Game Networks
Combinatorial Optimization by Learning and Simulation of Bayesian Networks
Credal Networks under Maximum Entropy
Tractable Bayesian Learning of Tree Belief Networks
Representing and Solving Asymmetric Bayesian Decision Problems
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example
Adaptive Importance Sampling for Estimation in Structured Domains
Probabilistic Models for Query Approximation with Large Sparse Binary Datasets
Value-Directed Belief State Approximation for POMDPs
Probabilistic State-Dependent Grammars for Plan Recognition
Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules
Model-Based Hierarchical Clustering
Conditional Independence and Markov Properties in Possibility Theory
Variational Approximations between Mean Field Theory and the Junction Tree Algorithm
Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks
User Interface Tools for Navigation in Conditional Probability Tables and Elicitation of Probabilities in Bayesian Networks
Computer Poker Research at LIACC
A Temporal Bayesian Network for Diagnosis and Prediction
Possibilistic logic bases and possibilistic graphs
Reasoning With Conditional Ceteris Paribus Preference Statem
Discovering the Hidden Structure of Complex Dynamic Systems
Comparing Bayesian Network Classifiers
A Hybrid Anytime Algorithm for the Constructiion of Causal Models From Sparse Data
Model-Based Bayesian Exploration
Hybrid Probabilistic Programs: Algorithms and Complexity
Assessing the value of a candidate. Comparing belief function and possibility theories
Qualitative Models for Decision Under Uncertainty without the Commensurability Assumption
Data Analysis with Bayesian Networks: A Bootstrap Approach
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm
On Transformations between Probability and Spohnian Disbelief Functions
A Hybrid Approach to Reasoning with Partially Elicited Preference Models
SPUDD: Stochastic Planning using Decision Diagrams
Attention-Sensitive Alerting
Mini-Bucket Heuristics for Improved Search
A General Algorithm for Approximate Inference and its Application to Hybrid Bayes Nets
Bayesian Poker
Lazy Evaluation of Symmetric Bayesian Decision Problems
Solving POMDPs by Searching the Space of Finite Policies
Learning Finite-State Controllers for Partially Observable Environments
Bayes Nets in Educational Assessment: Where Do the Numbers Come From?
A Bayesian Network Classifier that Combines a Finite Mixture Model and a Naive Bayes Model
Loopy Belief Propagation for Approximate Inference: An Empirical Study
Learning Bayesian Networks with Restricted Causal Interactions
The Decision-Theoretic Interactive Video Advisor
Graphical Representations of Consensus Belief
Bayesian Networks for Dependability Analysis: an Application to Digital Control Reliability
Inference Networks and the Evaluation of Evidence: Alternative Analyses
Learning Hidden Markov Models with Geometrical Constraints
An Update Semantics for Defeasible Obligations
How to Elicit Many Probabilities
Probabilistic Belief Change: Expansion, Conditioning and Constraining
Contextual Weak Independence in Bayesian Networks
Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation
Time-Critical Dynamic Decision Making
Towards a Logic-Based Unifying Framework for Computing
On the Semantics and Automated Deduction for PLFC, a Logic of Possibilistic Uncertainty and Fuzziness
A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian Networks and Its Complexity
Structured Reachability Analysis for Markov Decision Processes
Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus
Dynamic Jointrees
The Bayesian Structural EM Algorithm
Psychological and Normative Theories of Causal Power and the Probabilities of Causes
Towards Case-Based Preference Elicitation: Similarity Measures on Preference Structures
Hierarchical Solution of Markov Decision Processes using Macro-actions
Inferring Informational Goals from Free-Text Queries: A Bayesian Approach
Evaluating Las Vegas Algorithms - Pitfalls and Remedies
An Anytime Algorithm for Decision Making under Uncertainty
Implementing Resolute Choice Under Uncertainty
Dealing with Uncertainty on the Initial State of a Petri Net
Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge
Lazy Propagation in Junction Trees
From Likelihood to Plausibility
A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data
Resolving Conflicting Arguments under Uncertainties
Learning From What You Don't Observe
Context-Specific Approximation in Probabilistic Inference
Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding
Decision Theoretic Foundations of Graphical Model Selection
Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)
Switching Portfolios
Bayesian Networks from the Point of View of Chain Graphs
Learning Mixtures of DAG Models
Flexible and Approximate Computation through State-Space Reduction
Learning to Rank for Expert Search in Digital Libraries of Academic Publications
The SETI Episode in the 1967 Discovery of Pulsars
Bayes Networks for Sonar Sensor Fusion
Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks
A Bayesian Approach to Learning Bayesian Networks with Local Structure
Exploring Parallelism in Learning Belief Networks
Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions
Myopic Value of Information in Influence Diagrams
Decision-making Under Ordinal Preferences and Comparative Uncertainty
Sequential Update of Bayesian Network Structure
Image Segmentation in Video Sequences: A Probabilistic Approach
Learning Bayesian Nets that Perform Well
Probability Update: Conditioning vs. Cross-Entropy
Problem-Focused Incremental Elicitation of Multi-Attribute Utility Models
Perception, Attention, and Resources: A Decision-Theoretic Approach to Graphics Rendering
Learning Belief Networks in Domains with Recursively Embedded Pseudo Independent Submodels
Composition of Probability Measures on Finite Spaces
Nested Junction Trees
Nonuniform Dynamic Discretization in Hybrid Networks
Network Fragments: Representing Knowledge for Constructing Probabilistic Models
Computational Advantages of Relevance Reasoning in Bayesian Belief Networks
A Target Classification Decision Aid
Support and Plausibility Degrees in Generalized Functional Models
The Cognitive Processing of Causal Knowledge
Cost-Sharing in Bayesian Knowledge Bases
Conditional Utility, Utility Independence, and Utility Networks
Sequential Thresholds: Context Sensitive Default Extensions
Score and Information for Recursive Exponential Models with Incomplete Data
An Algorithm for Finding Minimum d-Separating Sets in Belief Networks
Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response
A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modeling Techniques
An Alternative Markov Property for Chain Graphs
Entailment in Probability of Thresholded Generalizations
Object Recognition with Imperfect Perception and Redundant Description
A Sufficiently Fast Algorithm for Finding Close to Optimal Junction Trees
Context-Specific Independence in Bayesian Networks
Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment
Tail Sensitivity Analysis in Bayesian Networks
Decision-Analytic Approaches to Operational Decision Making: Application and Observation
Learning Equivalence Classes of Bayesian Networks Structures
Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
Independence with Lower and Upper Probabilities
Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem
Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework
Belief Revision with Uncertain Inputs in the Possibilistic Setting
An Evaluation of Structural Parameters for Probabilistic Reasoning: Results on Benchmark Circuits
Learning Bayesian Networks with Local Structure
A Qualitative Markov Assumption and its Implications for Belief Change
Asymptotic Model Selection for Directed Networks with Hidden Variables
Theoretical Foundations for Abstraction-Based Probabilistic Planning
Why Is Diagnosis Using Belief Networks Insensitive to Imprecision In Probabilities?
A Probabilistic Model For Sensor Validation
Uncertain Inferences and Uncertain Conclusions
Probabilistic Disjunctive Logic Programming
Geometric Implications of the Naive Bayes Assumption
A Discovery Algorithm for Directed Cyclis Graphs
A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models
Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks
Efficient Enumeration of Instantiations in Bayesian Networks
On Separation Criterion and Recovery Algorithm for Chain Graphs
Possible World Partition Sequences: A Unifying Framework for Uncertain Reasoning
Supply Restoration in Power Distribution Systems - A Case Study in Integrating Model-Based Diagnosis and Repair Planning
Optimal Factory Scheduling using Stochastic Dominance A*
Critical Remarks on Single Link Search in Learning Belief Networks
Graphical Models for Preference and Utility
An Algebraic Semantics for Possibilistic Logic
Automating Computer Bottleneck Detection with Belief Nets
Error Estimation in Approximate Bayesian Belief Network Inference
Practical Model-Based Diagnosis with Qualitative Possibilistic Uncertainty
Independence Concepts for Convex Sets of Probabilities
Clustering Without (Thinking About) Triangulation
Elicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information
Numerical Representations of Acceptance
Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures
A Constraint Satisfaction Approach to Decision under Uncertainty
Plausibility Measures: A User's Guide
Fast Belief Update Using Order-of-Magnitude Probabilities
A Definition and Graphical Representation for Causality
Display of Information for Time-Critical Decision Making
Information/Relevance Influence Diagrams
On the Detection of Conflicts in Diagnostic Bayesian Networks Using Abstraction
Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks
Exploiting the Rule Structure for Decision Making within the Independent Choice Logic
Abstraction in Belief Networks: The Role of Intermediate States in Diagnostic Reasoning
Accounting for Context in Plan Recognition, with Application to Traffic Monitoring
A New Pruning Method for Solving Decision Trees and Game Trees
Directed Cyclic Graphical Representations of Feedback Models
Modeling Failure Priors and Persistence in Model-Based Diagnosis
A Polynomial Algorithm for Computing the Optimal Repair Strategy in a System with Independent Component Failures
Exploiting System Hierarchy to Compute Repair Plans in Probabilistic Model-based Diagnosis
Path Planning under Time-Dependent Uncertainty
Development of Yes/No Arabic Question Answering System
An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief Networks Usin
Probabilistic Constraint Satisfaction with Non-Gaussian Noise
Generating New Beliefs From Old
Counterfactual Probabilities: Computational Methods, Bounds and Applications
Planning with External Events
Properties of Bayesian Belief Network Learning Algorithms
Efficient Estimation of the Value of Information in Monte Carlo Models
Action Networks: A Framework for Reasoning about Actions and Change under Uncertainty
On the Relation between Kappa Calculus and Probabilistic Reasoning
A Structured, Probabilistic Representation of Action
Localized Partial Evaluation of Belief Networks
A Probabilistic Model of Action for Least-Commitment Planning with Information Gather
An Ordinal View of Independence with Application to Plausible Reasoning
Value of Evidence on Influence Diagrams
Learning Gaussian Networks
On Testing Whether an Embedded Bayesian Network Represents a Probability Model
Epsilon-Safe Planning
Generating Bayesian Networks from Probability Logic Knowledge Bases
A New Look at Causal Independence
Probabilistic Description Logics
An Experimental Comparison of Numerical and Qualitative Probabilistic Reasoning
An Alternative Proof Method for Possibilistic Logic and its Application to Terminological Logics
Possibilistic Conditioning and Propagation
The Automated Mapping of Plans for Plan Recognition
Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependencies
Using New Data to Refine a Bayesian Network
Syntax-based Default Reasoning as Probabilistic Model-based Diagnosis
Fuzzy Geometric Relations to Represent Hierarchical Spatial Information
Operator Selection While Planning Under Uncertainty
Model-Based Diagnosis with Qualitative Temporal Uncertainty
Incremental Dynamic Construction of Layered Polytree Networks
A Probabilistic Calculus of Actions
Robust Planning in Uncertain Environments
Knowledge Engineering for Large Belief Networks
Belief Maintenance in Bayesian Networks
Global Conditioning for Probabilistic Inference in Belief Networks
Ignorance and the Expressiveness of Single- and Set-Valued Probability Models of Belief
General Belief Measures
A Probabilistic Algorithm for Calculating Structure: Borrowing from Simulated Annealing
Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams
End-User Construction of Influence Diagrams for Bayesian Statistics
On Considering Uncertainty and Alternatives in Low-Level Vision
Forecasting Sleep Apnea with Dynamic Network Models
Diagnosis of Multiple Faults: A Sensitivity Analysis
Additive Belief-Network Models
Dialectic Reasoning with Inconsistent Information
Utility-Based Abstraction and Categorization
Some Complexity Considerations in the Combination of Belief Networks
Deriving a Minimal I-map of a Belief Network Relative to a Target Ordering of its Nodes
Mixtures of Gaussians and Minimum Relative Entropy Techniques for Modeling Continuous Uncertainties
Relevant Explanations: Allowing Disjunctive Assignments
Using First-Order Probability Logic for the Construction of Bayesian Networks
Representing and Reasoning With Probabilistic Knowledge: A Bayesian Approach
Using Causal Information and Local Measures to Learn Bayesian Networks
Minimal Assumption Distribution Propagation in Belief Networks
Knowledge-Based Decision Model Construction for Hierarchical Diagnosis: A Preliminary Report
An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks
Deliberation Scheduling for Time-Critical Sequential Decision Making
An efficient approach for finding the MPE in belief networks
A Method for Planning Given Uncertain and Incomplete Information
The use of conflicts in searching Bayesian networks
GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks
Argumentative inference in uncertain and inconsistent knowledge bases
Argumentation as a General Framework for Uncertain Reasoning
The Probability of a Possibility: Adding Uncertainty to Default Rules
Possibilistic decreasing persistence
Security Assessment of Software Design using Neural Network
Structural Controllability and Observability in Influence Diagrams
Reformulating Inference Problems Through Selective Conditioning
A Symbolic Approach to Reasoning with Linguistic Quantifiers
Possibilistic Assumption based Truth Maintenance System, Validation in a Data Fusion Application
Integrating Model Construction and Evaluation
Reasoning With Qualitative Probabilities Can Be Tractable
Semantics for Probabilistic Inference
Representing Heuristic Knowledge in D-S Theory
The Topological Fusion of Bayes Nets
Calculating Uncertainty Intervals From Conditional Convex Sets of Probabilities
Sensor Validation Using Dynamic Belief Networks
Decision Methods for Adaptive Task-Sharing in Associate Systems
Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis
Possibilistic Constraint Satisfaction Problems or "How to handle soft constraints?"
Intuitions about Ordered Beliefs Leading to Probabilistic Models
An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation
Exploring Localization in Bayesian Networks for Large Expert Systems
A Decision Calculus for Belief Functions in Valuation-Based Systems
Sidestepping the Triangulation Problem in Bayesian Net Computations
ARCO1: An Application of Belief Networks to the Oil Market
Combining Multiple-Valued Logics in Modular Expert Systems
Constraint Propagation with Imprecise Conditional Probabilities
Some Properties of Plausible Reasoning
Theory Refinement on Bayesian Networks
Symbolic Probabilistic Inference with Continuous Variables
Probability Estimation in Face of Irrelevant Information
An Approximate Nonmyopic Computation for Value of Information
Search-based Methods to Bound Diagnostic Probabilities in Very Large Belief Nets
Belief and Surprise - A Belief-Function Formulation
Evidential Reasoning in a Categorial Perspective: Conjunction and Disjunction of Belief Functions
A Logic of Graded Possibility and Certainty Coping with Partial Inconsistency
A Language for Planning with Statistics
Management of Uncertainty in the Multi-Level Monitoring and Diagnosis of the Time of Flight Scintillation Array
Dynamic Network Updating Techniques For Diagnostic Reasoning
High Level Path Planning with Uncertainty
Deliberation and its Role in the Formation of Intentions
Truth as Utility: A Conceptual Synthesis
Structuring Bodies of Evidence
A Method for Integrating Utility Analysis into an Expert System for Design Evaluation
Compatibility of Quantitative and Qualitative Representations of Belief
A Non-Numeric Approach to Multi-Criteria/Multi-Expert Aggregation Based on Approximate Reasoning
Universal Induction with Varying Sets of Combinators
Artificial Intelligence MArkup Language: A Brief Tutorial
Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases
Reasoning about Probabilities in Dynamic Systems using Goal Regression
SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure
Automorphism Groups of Graphical Models and Lifted Variational Inference
Learning Sparse Causal Models is not NP-hard
Qualitative Possibilistic Mixed-Observable MDPs
Optimization With Parity Constraints: From Binary Codes to Discrete Integration
Monte-Carlo Planning: Theoretically Fast Convergence Meets Practical Efficiency
Bethe-ADMM for Tree Decomposition based Parallel MAP Inference
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure
Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search
Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks
On the Complexity of Strong and Epistemic Credal Networks
Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries
Tighter Linear Program Relaxations for High Order Graphical Models
Cyclic Causal Discovery from Continuous Equilibrium Data
Evaluating computational models of explanation using human judgments
Approximation of Lorenz-Optimal Solutions in Multiobjective Markov Decision Processes
Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation
Sparse Nested Markov models with Log-linear Parameters
Preference Elicitation For General Random Utility Models
Dynamic Blocking and Collapsing for Gibbs Sampling
Bounded Approximate Symbolic Dynamic Programming for Hybrid MDPs
On MAP Inference by MWSS on Perfect Graphs
Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models
Double four-bar crank-slider mechanism dynamic balancing by meta-heuristic algorithms
Studying a Chaotic Spiking Neural Model
A Big Data Approach to Computational Creativity
Planning by case-based reasoning based on fuzzy logic
Abstraction in decision-makers with limited information processing capabilities
Bounded Recursive Self-Improvement
Decision Making under Uncertainty: A Quasimetric Approach
Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference
Networks of Influence Diagrams: A Formalism for Representing Agents' Beliefs and Decision-Making Processes
Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learning
A Heuristic Search Approach to Planning with Continuous Resources in Stochastic Domains
Online Planning Algorithms for POMDPs
The Ultrametric Constraint and its Application to Phylogenetics
Interactive Policy Learning through Confidence-Based Autonomy
Asynchronous Forward Bounding for Distributed COPs
Completeness and Performance Of The APO Algorithm
The Computational Complexity of Dominance and Consistency in CP-Nets
Monte Carlo Sampling Methods for Approximating Interactive POMDPs
Solving #SAT and Bayesian Inference with Backtracking Search
Generic Preferences over Subsets of Structured Objects
A Bilinear Programming Approach for Multiagent Planning
Learning Bayesian Network Equivalence Classes with Ant Colony Optimization
Planning over Chain Causal Graphs for Variables with Domains of Size 5 Is NP-Hard
Compiling Uncertainty Away in Conformant Planning Problems with Bounded Width
Exploiting Single-Cycle Symmetries in Continuous Constraint Problems
Conservative Inference Rule for Uncertain Reasoning under Incompleteness
The Complexity of Circumscription in DLs
Relaxed Survey Propagation for The Weighted Maximum Satisfiability Problem
Hypertableau Reasoning for Description Logics
The DL-Lite Family and Relations
Join-Graph Propagation Algorithms
ParamILS: An Automatic Algorithm Configuration Framework
Predicting the Performance of IDA* using Conditional Distributions
Efficient Planning under Uncertainty with Macro-actions
Active Tuples-based Scheme for Bounding Posterior Beliefs
Change in Abstract Argumentation Frameworks: Adding an Argument
Developing Approaches for Solving a Telecommunications Feature Subscription Problem
Multiattribute Auctions Based on Generalized Additive Independence
Automatic Induction of Bellman-Error Features for Probabilistic Planning
Approximate Model-Based Diagnosis Using Greedy Stochastic Search
Nominals, Inverses, Counting, and Conjunctive Queries or: Why Infinity is your Friend!
Algorithms for Closed Under Rational Behavior (CURB) Sets
Logical Foundations of RDF(S) with Datatypes
Planning with Noisy Probabilistic Relational Rules
Best-First Heuristic Search for Multicore Machines
An Effective Algorithm for and Phase Transitions of the Directed Hamiltonian Cycle Problem
A Logical Study of Partial Entailment
Iterated Belief Change Due to Actions and Observations
Clause-Learning Algorithms with Many Restarts and Bounded-Width Resolution
Learning to Make Predictions In Partially Observable Environments Without a Generative Model
Second-Order Consistencies
Properties of Bethe Free Energies and Message Passing in Gaussian Models
Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition
Probabilistic Relational Planning with First Order Decision Diagrams
Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference
Analyzing Search Topology Without Running Any Search: On the Connection Between Causal Graphs and h+
Interpolable Formulas in Equilibrium Logic and Answer Set Programming
First-Order Stable Model Semantics and First-Order Loop Formulas
Decidability and Undecidability Results for Propositional Schemata
Defeasible Inclusions in Low-Complexity DLs
Making Decisions Using Sets of Probabilities: Updating, Time Consistency, and Calibration
Proximity-Based Non-uniform Abstractions for Approximate Planning
SAS+ Planning as Satisfiability
Learning and Reasoning with Action-Related Places for Robust Mobile Manipulation
Counting-Based Search: Branching Heuristics for Constraint Satisfaction Problems
Semantic Similarity Measures Applied to an Ontology for Human-Like Interaction
Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming
Local Consistency and SAT-Solvers
Algorithms and Limits for Compact Plan Representations
The Logical Difference for the Lightweight Description Logic EL
Algorithms for Generating Ordered Solutions for Explicit AND/OR Structures
Reasoning over Ontologies with Hidden Content: The Import-by-Query Approach
Quality of Geographic Information: Ontological approach and Artificial Intelligence Tools
Dynamic Move Chains -- a Forward Pruning Approach to Tree Search in Computer Chess
Approximation Models of Combat in StarCraft 2
Axiomatization of Finite Algebras
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing
Projective simulation applied to the grid-world and the mountain-car problem
A Comparison of Monte Carlo Tree Search and Mathematical Optimization for Large Scale Dynamic Resource Allocation
Generating Natural Language Descriptions from OWL Ontologies: the NaturalOWL System
A Multi-threshold Segmentation Approach Based on Artificial Bee Colony Optimization
Analogy-Based and Case-Based Reasoning: Two sides of the same coin
Inferring latent structures via information inequalities
Allocation in Practice
Context Aware Dynamic Traffic Signal Optimization
Fuzzy inference system for integrated VVC in isolated power systems
Efficient Bayesian Nonparametric Modelling of Structured Point Processes
Defining Relative Likelihood in Partially-Ordered Preferential Structures
Updating Probabilities
Reasoning about Expectation
When Ignorance is Bliss
Evidence with Uncertain Likelihoods
A Game-Theoretic Analysis of Updating Sets of Probabilities
MDPs with Unawareness
Market Making with Decreasing Utility for Information
Logarithmic-Time Updates and Queries in Probabilistic Networks
Axiomatizing Causal Reasoning
Learning to Cooperate via Policy Search
Updating with incomplete observations
When do Numbers Really Matter?
Sensitivity analysis for finite Markov chains in discrete time
On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach
Approximate inference on planar graphs using Loop Calculus and Belief Propagation
Efficient Clustering with Limited Distance Information
Selecting Computations: Theory and Applications
Predicting the behavior of interacting humans by fusing data from multiple sources
Active Sensing as Bayes-Optimal Sequential Decision Making
Scoring and Searching over Bayesian Networks with Causal and Associative Priors
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits
Stochastic Discriminative EM
An improved multimodal PSO method based on electrostatic interaction using n- nearest-neighbor local search
Consensus Message Passing for Layered Graphical Models
Logical Limitations to Machine Ethics with Consequences to Lethal Autonomous Weapons
ROSS User's Guide and Reference Manual (Version 1.0)
Genetic Algorithms in Wireless Networking: Techniques, Applications, and Issues
A Fuzzy Syllogistic Reasoning Schema for Generalized Quantifiers
On the analysis of set-based fuzzy quantified reasoning using classical syllogistics
Solving Games with Functional Regret Estimation
Belief Approach for Social Networks
Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs
A Distance-Based Decision in the Credal Level
An approach to multi-agent planning with incomplete information
Reactive Reasoning with the Event Calculus
Towards Ideal Semantics for Analyzing Stream Reasoning
Asynchronous Multi-Context Systems
On Minimal Change in Evolving Multi-Context Systems (Preliminary Report)
Towards Large-scale Inconsistency Measurement
Towards Efficient Evolving Multi-Context Systems (Preliminary Report)
Probabilistic Constraint Programming for Parameters Optimisation of Generative Models
Fast Cross-Validation for Incremental Learning
A New Approach to Probabilistic Programming Inference
Black-Box Policy Search with Probabilistic Programs
Budget Constraints in Prediction Markets
Review-Level Sentiment Classification with Sentence-Level Polarity Correction
Taxonomy of Pathways to Dangerous AI
Introduzione all'Intelligenza Artificiale
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond
Near-Optimal Active Learning of Multi-Output Gaussian Processes
Solving Transition-Independent Multi-agent MDPs with Sparse Interactions (Extended version)
Learning to Generate Posters of Scientific Papers
Landmark-Based Plan Recognition
A system of serial computation for classified rules prediction in non-regular ontology trees
Learning Social Affordance for Human-Robot Interaction
Visual Storytelling
Tasks for agent-based negotiation teams: Analysis, review, and challenges
Managing Overstaying Electric Vehicles in Park-and-Charge Facilities
Constructive Preference Elicitation by Setwise Max-margin Learning
Bayesian Inference of Recursive Sequences of Group Activities from Tracks
Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games
Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks
The Power of Arc Consistency for CSPs Defined by Partially-Ordered Forbidden Patterns
Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables
Essentials of an Integrated Crowd Management Support System Based on Collective Artificial Intelligence
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
Typical models: minimizing false beliefs
The Ariadne's Clew Algorithm
Computational Aspects of Reordering Plans
The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic Inference
A Temporal Description Logic for Reasoning about Actions and Plans
Order of Magnitude Comparisons of Distance
The Automatic Inference of State Invariants in TIM
Complexity of Prioritized Default Logics
Squeaky Wheel Optimization
Efficient Implementation of the Plan Graph in STAN
Cooperation between Top-Down and Bottom-Up Theorem Provers
Probabilistic Deduction with Conditional Constraints over Basic Events
Variational Probabilistic Inference and the QMR-DT Network
Extensible Knowledge Representation: the Case of Description Reasoners
Learning to Order Things
Constructing Conditional Plans by a Theorem-Prover
Issues in Stacked Generalization
Reasoning on Interval and Point-based Disjunctive Metric Constraints in Temporal Contexts
Causes of Ineradicable Spurious Predictions in Qualitative Simulation
How the Landscape of Random Job Shop Scheduling Instances Depends on the Ratio of Jobs to Machines
Preference-based Search using Example-Critiquing with Suggestions
Anytime Point-Based Approximations for Large POMDPs
Learning Sentence-internal Temporal Relations
Confidence-based Reasoning in Stochastic Constraint Programming
Modelling Mixed Discrete-Continuous Domains for Planning
Set Intersection and Consistency in Constraint Networks
Consistency and Random Constraint Satisfaction Models
Uncertainty in Soft Temporal Constraint Problems:A General Framework and Controllability Algorithms forThe Fuzzy Case
The Generalized A* Architecture
An Approach to Temporal Planning and Scheduling in Domains with Predictable Exogenous Events
Proactive Algorithms for Job Shop Scheduling with Probabilistic Durations
The Language of Search
Understanding Algorithm Performance on an Oversubscribed Scheduling Application
Anytime Heuristic Search
Cutset Sampling for Bayesian Networks
Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic Approach
Solution-Guided Multi-Point Constructive Search for Job Shop Scheduling
Resource Allocation Among Agents with MDP-Induced Preferences
A Continuation Method for Nash Equilibria in Structured Games
Extending Object-Oriented Languages by Declarative Specifications of Complex Objects using Answer-Set Programming
Performance Evaluation of Road Traffic Control Using a Fuzzy Cellular Model
Qualitative Propagation and Scenario-based Explanation of Probabilistic Reasoning
Integrating Probabilistic, Taxonomic and Causal Knowledge in Abductive Diagnosis
What is an Optimal Diagnosis?
Kutato: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases
Computationally-Optimal Real-Resource Strategies
Reducing Uncertainty in Navigation and Exploration
Decision Making with Interval Influence Diagrams
Approximations in Bayesian Belief Universe for Knowledge Based Systems
A Polynomial Time Algorithm for Finding Bayesian Probabilities from Marginal Constraints
Computation of Variances in Causal Networks
A Sensitivity Analysis of Pathfinder
On the Equivalence of Causal Models
Directed Reduction Algorithms and Decomposable Graphs
Possibility as Similarity: the Semantics of Fuzzy Logic
Credibility Discounting in the Theory of Approximate Reasoning
Updating with Belief Functions, Ordinal Conditioning Functions and Possibility Measures
Valuation-Based Systems for Discrete Optimization
Computational Aspects of the Mobius Transform
A Hierarchical Approach to Designing Approximate Reasoning-Based Controllers for Dynamic Physical Systems
Evidence Combination and Reasoning and Its Application to Real-World Problem-Solving
Using Belief Functions for Uncertainty Management and Knowledge Acquisition: An Expert Application
An Architecture for Probabilistic Concept-Based Information Retrieval
Fine-Grained Decision-Theoretic Search Control
Combination of Evidence Using the Principle of Minimum Information Gain
Probabilistic Evaluation of Candidates and Symptom Clustering for Multidisorder Diagnosis
Extending Term Subsumption systems for Uncertainty Management
Refinement and Coarsening of Bayesian Networks
Second Order Probabilities for Uncertain and Conflicting Evidence
A Model for Non-Monotonic Reasoning Using Dempster's Rule
Default Reasoning and the Transferable Belief Model
Separable and transitive graphoids
Map Learning with Indistinguishable Locations
Temporal Reasoning with Probabilities
Now that I Have a Good Theory of Uncertainty, What Else Do I Need?
Uncertainty and Incompleteness
Automated Reasoning Using Possibilistic Logic: Semantics, Belief Revision and Variable Certainty Weights
How Much More Probable is "Much More Probable"? Verbal Expressions for Probability Updates
Interval Influence Diagrams
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
The Relationship between Knowledge, Belief and Certainty
The Compilation of Decision Models
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources
Hierarchical Evidence Accumulation in the Pseiki System and Experiments in Model-Driven Mobile Robot Navigation
When Should a Decision Maker Ignore the Advice of a Decision Aid?
Model-based Influence Diagrams for Machine Vision
Defeasible Decisions: What the Proposal is and isn't
Experiments Using Belief Functions and Weights of Evidence incorporating Statistical Data and Expert Opinions
Conditioning on Disjunctive Knowledge: Defaults and Probabilities
A Logical Interpretation of Dempster-Shafer Theory, with Application to Visual Recognition
Simulation Approaches to General Probabilistic Inference on Belief Networks
Assessment, Criticism and Improvement of Imprecise Subjective Probabilities for a Medical Expert System
Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information
Making Decisions with Belief Functions
Comparing Expert Systems Built Using Different Uncertain Inference Systems
A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior
The structure of Bayes nets for vision recognition
Probability Distributions Over Possible Worlds
Hierarchical Evidence and Belief Functions
Induction and Uncertainty Management Techniques Applied to Veterinary Medical Diagnosis
KNET: Integrating Hypermedia and Bayesian Modeling
Probabilistic Causal Reasoning
Uncertainty Management for Fuzzy Decision Support Systems
Stochastic Sensitivity Analysis Using Fuzzy Influence Diagrams
A Representation of Uncertainty to Aid Insight into Decision Models
A Comparison of Decision Analysis and Expert Rules for Sequential Diagnosis
Multiple decision trees
Probabilistic Semantics and Defaults
Decision Making with Linear Constraints on Probabilities
Maintenance in Probabilistic Knowledge-Based Systems
Generating Decision Structures and Causal Explanations for Decision Making
Generalizing the Dempster-Shafer Theory to Fuzzy Sets
Higher Order Probabilities
An Interesting Uncertainty-Based Combinatoric Problem in Spare Parts Forecasting: The FRED System
Stochastic Simulation of Bayesian Belief Networks
NAIVE: A Method for Representing Uncertainty and Temporal Relationships in an Automated Reasoner
Satisfaction of Assumptions is a Weak Predictor of Performance
Structuring Causal Tree Models with Continuous Variables
Implementing Evidential Reasoning in Expert Systems
Automated Generation of Connectionist Expert Systems for Problems Involving Noise and Redundancy
Towards Solving the Multiple Extension Problem: Combining Defaults and Probabilities
The Role of Calculi in Uncertain Inference Systems
Implementing a Bayesian Scheme for Revising Belief Commitments
Compiling Fuzzy Logic Control Rules to Hardware Implementations
Steps Towards Programs that Manage Uncertainty
Combining Symbolic and Numeric Approaches to Uncertainty Management
Estimation Procedures for Robust Sensor Control
Reasoning About Beliefs and Actions Under Computational Resource Constraints
Advantages and a Limitation of Using LEG Nets in a Real-TIme Problem
Application of Evidential Reasoning to Helicopter Flight Path Control
Probabilistic Reasoning About Ship Images
Predicting The Performance of Minimax and Product in Game-Tree
The Myth of Modularity in Rule-Based Systems
An Axiomatic Framework for Belief Updates
Imprecise Meanings as a Cause of Uncertainty in Medical Knowledge-Based Systems
An Explanation Mechanism for Bayesian Inferencing Systems
Distributed Revision of Belief Commitment in Multi-Hypothesis Interpretations
Approximate Deduction in Single Evidential Bodies
A Causal Bayesian Model for the Diagnosis of Appendicitis
Experimentally Comparing Uncertain Inference Systems to Probability
An Inequality Paradigm for Probabilistic Knowledge
Probabilistic Interpretations for MYCIN's Certainty Factors
Uncertain Reasoning Using Maximum Entropy Inference
Independence and Bayesian Updating Methods
A Framework for Comparing Uncertain Inference Systems to Probability
Inductive Inference and the Representation of Uncertainty
An Odds Ratio Based Inference Engine
A Framework for Control Strategies in Uncertain Inference Networks
Confidence Factors, Empiricism and the Dempster-Shafer Theory of Evidence
Evidential Confirmation as Transformed Probability
Backdoors to Abduction
A Hybrid Rule Based Fuzzy-Neural Expert System For Passive Network Monitoring
An n-ary Constraint for the Stable Marriage Problem
Space as an invention of biological organisms
History Based Coalition Formation in Hedonic Context Using Trust
Formalization, Mechanization and Automation of Gödel's Proof of God's Existence
David Poole's Specificity Revised
Optimal Rectangle Packing: An Absolute Placement Approach
Safe Exploration of State and Action Spaces in Reinforcement Learning
Irrelevant and independent natural extension for sets of desirable gambles
Lifted Variable Elimination: Decoupling the Operators from the Constraint Language
Boolean Equi-propagation for Concise and Efficient SAT Encodings of Combinatorial Problems
Description Logic Knowledge and Action Bases
Learning to Predict from Textual Data
Reasoning about Explanations for Negative Query Answers in DL-Lite
A Survey of Multi-Objective Sequential Decision-Making
Extended Breadth-First Search Algorithm
Near Optimal Bayesian Active Learning for Decision Making
A self-organizing system for urban traffic control based on predictive interval microscopic model
Automated Generation of Geometric Theorems from Images of Diagrams
Generic construction of scale-invariantly coarse grained memory
Event and Anomaly Detection Using Tucker3 Decomposition
Lifted Tree-Reweighted Variational Inference
How good is the Shapley value-based approach to the influence maximization problem?
Non-myopic learning in repeated stochastic games
Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar
GraATP: A Graph Theoretic Approach for Automated Theorem Proving in Plane Geometry
Intelligent Indoor Mobile Robot Navigation Using Stereo Vision
Unified vector space mapping for knowledge representation systems
Game-theoretic Approach for Non-Cooperative Planning
A Minimal Active Inference Agent
The concept of free will as an infinite metatheoretic recursion
Using Latent Semantic Analysis to Identify Quality in Use (QU) Indicators from User Reviews
Evaluation Evaluation a Monte Carlo study
An Optimized Hybrid Approach for Path Finding
Discovery of the $D$-basis in binary tables based on hypergraph dualization
Grid-based angle-constrained path planning
Arguments for the Effectiveness of Human Problem Solving
Reducing offline evaluation bias of collaborative filtering algorithms
Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks
SNA-based reasoning for multiagent team composition
A Survey of Current Datasets for Vision and Language Research
On Design Mining: Coevolution and Surrogate Models
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning
A note on the complexity of the causal ordering problem
Semi-described and semi-supervised learning with Gaussian processes
Ensemble UCT Needs High Exploitation
Tuned and GPU-accelerated parallel data mining from comparable corpora
Reasoning in Infinitely Valued G-IALCQ
Toward a Taxonomy and Computational Models of Abnormalities in Images
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation
Subsumptive reflection in SNOMED CT: a large description logic-based terminology for diagnosis
Sentence Entailment in Compositional Distributional Semantics
Increasing the Action Gap: New Operators for Reinforcement Learning
Constrained Sampling and Counting: Universal Hashing Meets SAT Solving
Information-Theoretic Bounded Rationality
Complexity of Shift Bribery in Committee Elections
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)
A Comparative Study of Ranking-based Semantics for Abstract Argumentation
Reinforcement Learning approach for Real Time Strategy Games Battle city and S3
Authorship Attribution Using a Neural Network Language Model
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning
Distributed Constraint Optimization Problems and Applications: A Survey
Harnessing disordered quantum dynamics for machine learning
Bounded Rational Decision-Making in Feedforward Neural Networks
An Online Mechanism for Ridesharing in Autonomous Mobility-on-Demand Systems
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint
Learning Purposeful Behaviour in the Absence of Rewards
Small Representations of Big Kidney Exchange Graphs
A PAC RL Algorithm for Episodic POMDPs
Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization
The Dark Side of Ethical Robots
Towards Anthropo-inspired Computational Systems: the $P^3$ Model
Concrete Problems in AI Safety
On Gaussian Markov models for conditional independence
On the Semantic Relationship between Probabilistic Soft Logic and Markov Logic
Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests
Learning Relational Dependency Networks for Relation Extraction
Situated Structure Learning of a Bayesian Logic Network for Commonsense Reasoning
Visualizing Natural Language Descriptions: A Survey
Path planning with Inventory-driven Jump-Point-Search
Learning opening books in partially observable games: using random seeds in Phantom Go
Global Continuous Optimization with Error Bound and Fast Convergence
Generating Images Part by Part with Composite Generative Adversarial Networks
Automatically Reinforcing a Game AI
Psychologically inspired planning method for smart relocation task
Finite LTL Synthesis is EXPTIME-complete
The Option-Critic Architecture
Structured Inference Networks for Nonlinear State Space Models
Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity
Quantum-enhanced machine learning
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Recursive Decomposition for Nonconvex Optimization
Double-quantitative $γ^{\ast}-$fuzzy coverings approximation operators
Quantum Enhanced Inference in Markov Logic Networks
Local Discriminant Hyperalignment for multi-subject fMRI data alignment
Accelerated Gradient Temporal Difference Learning
Overcoming catastrophic forgetting in neural networks
Using Discourse Signals for Robust Instructor Intervention Prediction
Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks
Technical Report: A Generalized Matching Pursuit Approach for Graph-Structured Sparsity
Computing Human-Understandable Strategies
Solving Set Optimization Problems by Cardinality Optimization via Weak Constraints with an Application to Argumentation
The Predictron: End-To-End Learning and Planning
Accelerated Convolutions for Efficient Multi-Scale Time to Contact Computation in Julia
Curiosity-Aware Bargaining
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning
The Absent-Minded Driver Problem Redux
Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning
Neural Programming by Example
Probabilistic Models for Computerized Adaptive Testing
The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study
Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions
Approximating the Backbone in the Weighted Maximum Satisfiability Problem
Tweeting AI: Perceptions of AI-Tweeters (AIT) vs Expert AI-Tweeters (EAIT)
Tramp Ship Scheduling Problem with Berth Allocation Considerations and Time-dependent Constraints
A Reasoning System for a First-Order Logic of Limited Belief
An Anthropic Argument against the Future Existence of Superintelligent Artificial Intelligence
Pitfalls and Best Practices in Algorithm Configuration
Why You Should Charge Your Friends for Borrowing Your Stuff
XOR-Sampling for Network Design with Correlated Stochastic Events
Continual Learning with Deep Generative Replay
When Will AI Exceed Human Performance? Evidence from AI Experts
Efficient, Safe, and Probably Approximately Complete Learning of Action Models
An Empirical Analysis of Approximation Algorithms for the Euclidean Traveling Salesman Problem
Universal Reinforcement Learning Algorithms: Survey and Experiments
Learning to Represent Mechanics via Long-term Extrapolation and Interpolation
Rapid Randomized Restarts for Multi-Agent Path Finding Solvers
Dynamic Difficulty Adjustment on MOBA Games
Structured Best Arm Identification with Fixed Confidence
Expert and Non-Expert Opinion about Technological Unemployment
A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional Visual Environment
Count-Based Exploration in Feature Space for Reinforcement Learning
The Relationship Between Emotion Models and Artificial Intelligence
Causal Consistency of Structural Equation Models
Maintaining cooperation in complex social dilemmas using deep reinforcement learning
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously
Robust Bayesian Optimization with Student-t Likelihood
Towards learning domain-independent planning heuristics
Learning Sparse Representations in Reinforcement Learning with Sparse Coding
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models
Deep Style Match for Complementary Recommendation
Visual art inspired by the collective feeding behavior of sand-bubbler crabs
Gigamachine: incremental machine learning on desktop computers
Probability Reversal and the Disjunction Effect in Reasoning Systems
Perspectives for Evaluating Conversational AI
A Streaming Accelerator for Deep Convolutional Neural Networks with Image and Feature Decomposition for Resource-limited System Applications
Algorithms and Architecture for Real-time Recommendations at News UK
Augmenting End-to-End Dialog Systems with Commonsense Knowledge
On Inductive Abilities of Latent Factor Models for Relational Learning
Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization
Tweeting AI: Perceptions of Lay vs Expert Twitterati
Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF
Specification Inference from Demonstrations
Arguing Machines: Perception-Control System Redundancy and Edge Case Discovery in Real-World Autonomous Driving
Combinatorial Multi-armed Bandits for Real-Time Strategy Games
Fast Top-k Area Topics Extraction with Knowledge Base
SemTK: An Ontology-first, Open Source Semantic Toolkit for Managing and Querying Knowledge Graphs
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning
Distributed Bayesian Piecewise Sparse Linear Models
From Algorithmic Black Boxes to Adaptive White Boxes: Declarative Decision-Theoretic Ethical Programs as Codes of Ethics
Modeling Epistemological Principles for Bias Mitigation in AI Systems: An Illustration in Hiring Decisions
Teaching a Machine to Read Maps with Deep Reinforcement Learning
JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs
Multiagent Simple Temporal Problem: The Arc-Consistency Approach
Improvised Comedy as a Turing Test
Interactive Robot Learning of Gestures, Language and Affordances
Learning to Rank based on Analogical Reasoning
Happiness Pursuit: Personality Learning in a Society of Agents
Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects
Deep Learning Can Reverse Photon Migration for Diffuse Optical Tomography
Discriminant Projection Representation-based Classification for Vision Recognition
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Cogniculture: Towards a Better Human-Machine Co-evolution
A Bayesian Clearing Mechanism for Combinatorial Auctions
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning
Practical Challenges in Explicit Ethical Machine Reasoning
Overcoming catastrophic forgetting with hard attention to the task
EBIC: an artificial intelligence-based parallel biclustering algorithm for pattern discovery
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication
A Quantitative Approach in Heuristic Evaluation of E-commerce Websites
The Role of Conditional Independence in the Evolution of Intelligent Systems
Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling
Probabilistic Planning by Probabilistic Programming
Deceptive Games
Short-term Memory of Deep RNN
Multi-attention Recurrent Network for Human Communication Comprehension
Ways of Applying Artificial Intelligence in Software Engineering
Answerer in Questioner's Mind for Goal-Oriented Visual Dialogue
An Ontology Based Modeling Framework for Design of Educational Technologies
Learning to Search with MCTSnets
Quantitative Predictions in Quantum Decision Theory
Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples
Vector Field Based Neural Networks
Meta Multi-Task Learning for Sequence Modeling
Shaping Influence and Influencing Shaping: A Computational Red Teaming Trust-based Swarm Intelligence Model
Selective Experience Replay for Lifelong Learning
Computational Theories of Curiosity-Driven Learning
Inferencing Based on Unsupervised Learning of Disentangled Representations
Predicting Crime Using Spatial Features
A New Result on the Complexity of Heuristic Estimates for the A* Algorithm
Learning State Representations for Query Optimization with Deep Reinforcement Learning
Scalable photonic reinforcement learning by time-division multiplexing of laser chaos
A Distributed Extension of the Turing Machine
Artificial Intelligence and Robotics
An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power
Learning to Reason with HOL4 tactics
Designing Autonomous Vehicles: Evaluating the Role of Human Emotions and Social Norms
From Statistical Knowledge Bases to Degrees of Belief
Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming
Computational Chemotaxis in Ants and Bacteria over Dynamic Environments
A Computational Study on Emotions and Temperament in Multi-Agent Systems
A survey on independence-based Markov networks learning
Use of Fuzzy Sets in Semantic Nets for Providing On-Line Assistance to User of Technological Systems
Analysis of Statistical Hypothesis based Learning Mechanism for Faster Crawling
A Framework for Intelligent Medical Diagnosis using Rough Set with Formal Concept Analysis
Using Artificial Intelligence Models in System Identification
Improving circuit miniaturization and its efficiency using Rough Set Theory
New Ideas for Brain Modelling
Building Machines That Learn and Think Like People
Ideal Reformulation of Belief Networks
A VLSI Design and Implementation for a Real-Time Approximate Reasoning
Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)
Automated Big Text Security Classification
Morphognosis: the shape of knowledge in space and time
Analysis of Agent Expertise in Ms. Pac-Man using Value-of-Information-based Policies
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Always Lurking: Understanding and Mitigating Bias in Online Human Trafficking Detection
A Berkeley View of Systems Challenges for AI
Solutions to problems with deep learning
Game of Sketches: Deep Recurrent Models of Pictionary-style Word Guessing
Fractal AI: A fragile theory of intelligence
Computational Power and the Social Impact of Artificial Intelligence
Empirical Analysis of Foundational Distinctions in the Web of Data
Learning to Navigate in Cities Without a Map
StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
A Mathematical Framework for Superintelligent Machines
PCT and Beyond: Towards a Computational Framework for `Intelligent' Communicative Systems
Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis
Data mining and Privacy in Public Sector using Intelligent Agents (discussion paper)
A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems
Can an Organism Adapt Itself to Unforeseen Circumstances?
Intelligent systems in the context of surrounding environment
Intelligent location of simultaneously active acoustic emission sources: Part II
Computational Intelligence Characterization Method of Semiconductor Device
An Intelligent Multi-Agent Recommender System for Human Capacity Building
An Agent Based Classification Model
Learning Better Context Characterizations: An Intelligent Information Retrieval Approach
STORM - A Novel Information Fusion and Cluster Interpretation Technique
System Dynamics Modelling of the Processes Involving the Maintenance of the Naive T Cell Repertoire
Informal Concepts in Machines
The DCA:SOMe Comparison A comparative study between two biologically-inspired algorithms
Not only a lack of right definitions: Arguments for a shift in information-processing paradigm
A Novel Approach for Cardiac Disease Prediction and Classification Using Intelligent Agents
Automatic Wrapper Adaptation by Tree Edit Distance Matching
Finding Shortest Path for Developed Cognitive Map Using Medial Axis
A Proposed Decision Support System/Expert System for Guiding Fresh Students in Selecting a Faculty in Gomal University, Pakistan
Towards Maximum Spanning Tree Model in Web 3.0 Design and Development for Students using Discriminant Analysis
Intelligent Automated Diagnosis of Client Device Bottlenecks in Private Clouds
Diagnosing client faults using SVM-based intelligent inference from TCP packet traces
System identification and modeling for interacting and non-interacting tank systems using intelligent techniques
The state-of-the-art in web-scale semantic information processing for cloud computing
Penetration Testing == POMDP Solving?
Smart machines and the SP theory of intelligence
Quantum speedup for active learning agents
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction
Proposal of a multiagent-based smart environment for the IoT
Bad Universal Priors and Notions of Optimality
IBMMS Decision Support Tool For Management of Bank Telemarketing Campaigns
Optimal Route Planning with Prioritized Task Scheduling for AUV Missions
AGI and Reflexivity
Multiple ant-bee colony optimization for load balancing in packet-switched networks
Firefly Algorithm: Recent Advances and Applications
Learning-Based Procedural Content Generation
Software & Systems Engineering Process and Tools for the Development of Autonomous Driving Intelligence
Implementing an intelligent version of the classical sliding-puzzle game for unix terminals using Golang's concurrency primitives
Maintaining prediction quality under the condition of a growing knowledge space
A different perspective on a scale for pairwise comparisons
A Neuro-Fuzzy Method to Improving Backfiring Conversion Ratios
An intelligent extension of Variable Neighbourhood Search for labelling graph problems
Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving
Decision Aids for Adversarial Planning in Military Operations: Algorithms, Tools, and Turing-test-like Experimental Validation
On Reward Function for Survival
Superintelligence cannot be contained: Lessons from Computability Theory
MIST: Missing Person Intelligence Synthesis Toolkit
Constrained Cohort Intelligence using Static and Dynamic Penalty Function Approach for Mechanical Components Design
Applying Chatbots to the Internet of Things: Opportunities and Architectural Elements
Iterative Multi-document Neural Attention for Multiple Answer Prediction
Ontology based Scene Creation for the Development of Automated Vehicles
Intelligent Personal Assistant with Knowledge Navigation
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Rise of the humanbot
Software engineering and the SP theory of intelligence
What's up with Privacy?: User Preferences and Privacy Concerns in Intelligent Personal Assistants
Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies
AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience
Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence
Antifragility for Intelligent Autonomous Systems
Analyzing Business Process Anomalies Using Autoencoders
Towards Intelligent Vehicular Networks: A Machine Learning Framework
Not just about size - A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications
Clustering and Retrieval Method of Immunological Memory Cell in Clonal Selection Algorithm
Combating catastrophic forgetting with developmental compression
Affective Recommendation System for Tourists by Using Emotion Generating Calculations
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
Spontaneous organization leads to robustness in evolutionary algorithms
Schema Redescription in Cellular Automata: Revisiting Emergence in Complex Systems
Acquiring Word-Meaning Mappings for Natural Language Interfaces
On the Formal Semantics of Speech-Act Based Communication in an Agent-Oriented Programming Language
Software Aging Analysis of Web Server Using Neural Networks
Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance
An Extensive Report on Cellular Automata Based Artificial Immune System for Strengthening Automated Protein Prediction
Theta*: Any-Angle Path Planning on Grids
Using Learned Predictions as Feedback to Improve Control and Communication with an Artificial Limb: Preliminary Findings
Knowledge and Uncertainty
Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm
Grounded Language Learning in a Simulated 3D World
Machine Learning, Deepest Learning: Statistical Data Assimilation Problems
Learning Visual Reasoning Without Strong Priors
Determining Positive Cancer Rescue Mutations in p53 Based Cancers by using Artificial Intelligence
A Benchmark Environment Motivated by Industrial Control Problems
Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer
User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living
Deep Reinforcement Learning using Capsules in Advanced Game Environments
A New Multi Criteria Decision Making Method: Approach of Logarithmic Concept (APLOCO)
Predicting Natural Hazards with Neuronal Networks
Exploration of RNA Editing and Design of Robust Genetic Algorithms
On the possible Computational Power of the Human Mind
Application of Artificial Neural Network in Jitter Analysis of Dispersion-Managed Communication System
Response Prediction of Structural System Subject to Earthquake Motions using Artificial Neural Network
On the Effects of Idiotypic Interactions for Recommendation Communities in Artificial Immune Systems
Danger Theory: The Link between AIS and IDS?
A Recommender System based on Idiotypic Artificial Immune Networks
AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies
Distributed Self Management for Distributed Security Systems
Artificial Dendritic Cells: Multi-faceted Perspectives
Further Exploration of the Dendritic Cell Algorithm: Antigen Multiplier and Time Windows
libtissue - implementing innate immunity
Artificial Immune Systems Metaphor for Agent Based Modeling of Crisis Response Operations
Oil Price Trackers Inspired by Immune Memory
Price Trackers Inspired by Immune Memory
The Application of a Dendritic Cell Algorithm to a Robotic Classifier
The Deterministic Dendritic Cell Algorithm
Detecting Anomalous Process Behaviour using Second Generation Artificial Immune Systems
Artificial Immune Systems (2010)
Modeling Spammer Behavior: Naïve Bayes vs. Artificial Neural Networks
An Artificial Immune System Model for Multi-Agents Resource Sharing in Distributed Environments
Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
Design of Emergent and Adaptive Virtual Players in a War RTS Game
A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring
Memetic Artificial Bee Colony Algorithm for Large-Scale Global Optimization
Neuro-Fuzzy Computing System with the Capacity of Implementation on Memristor-Crossbar and Optimization-Free Hardware Training
Artificial Neural Network Fuzzy Inference System (ANFIS) For Brain Tumor Detection
Termite-hill: From natural to artificial termites in sensor networks
Estimation of soil moisture in paddy field using Artificial Neural Networks
Real-world Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms
English Character Recognition using Artificial Neural Network
Evaluation the efficiency of artificial bee colony and the firefly algorithm in solving the continuous optimization problem
Assessment of Customer Credit through Combined Clustering of Artificial Neural Networks, Genetics Algorithm and Bayesian Probabilities
An ANN Based Call Handoff Management Scheme for Mobile Cellular Network
Comparison of Selection Methods in On-line Distributed Evolutionary Robotics
Emergence-focused design in complex system simulation
Discovering Latent States for Model Learning: Applying Sensorimotor Contingencies Theory and Predictive Processing to Model Context
Toward the Coevolution of Novel Vertical-Axis Wind Turbines
Quantum Cybernetics and Complex Quantum Systems Science - A Quantum Connectionist Exploration
Does the D.C. Response of Memristors Allow Robotic Short-Term Memory and a Possible Route to Artificial Time Perception?
A Novel Energy Aware Node Clustering Algorithm for Wireless Sensor Networks Using a Modified Artificial Fish Swarm Algorithm
Artificial Prediction Markets for Online Prediction of Continuous Variables-A Preliminary Report
Comparing Human and Automated Evaluation of Open-Ended Student Responses to Questions of Evolution
On the Performance of Network Parallel Training in Artificial Neural Networks
Learning an attention model in an artificial visual system
Maximum Resilience of Artificial Neural Networks
Mind the Gap: A Well Log Data Analysis
HoME: a Household Multimodal Environment
Human Perception of Performance
Comparing heterogeneous entities using artificial neural networks of trainable weighted structural components and machine-learned activation functions
Galactic Gradients, Postbiological Evolution and the Apparent Failure of SETI
Business Intelligence from Web Usage Mining
Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks
Cognitive Internet of Things: A New Paradigm beyond Connection
Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions
Towards an intelligent VNS heuristic for the k-labelled spanning forest problem
Darknet and Deepnet Mining for Proactive Cybersecurity Threat Intelligence
Detection, Recognition and Tracking of Moving Objects from Real-time Video via SP Theory of Intelligence and Species Inspired PSO
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture
JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services
The Ethics of Robotics
Belief and Truth in Hypothesised Behaviours
Shootout-89: A Comparative Evaluation of Knowledge-based Systems that Forecast Severe Weather
Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner
Bionic Humans Using EAP as Artificial Muscles Reality and Challenges
The Self-Organization of Speech Sounds
Exploration Of The Dendritic Cell Algorithm Using The Duration Calculus
Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications
Collective Intelligence, Data Routing and Braess' Paradox
The SP theory of intelligence: an overview
Information Compression, Intelligence, Computing, and Mathematics
A Market-Inspired Approach for Intersection Management in Urban Road Traffic Networks
Proposal for the creation of a research facility for the development of the SP machine
AI Gamma-Ray Burst Classification: Methodology/Preliminary Results
Self Control of Chaotic Dynamics using LTI Filters
An Empirically Motivated Reinterpretation of Dependency Grammar
The Acquisition of a Lexicon from Paired Phoneme Sequences and Semantic Representations
Measuring semantic complexity
Constraint Categorial Grammars
Using Information Content to Evaluate Semantic Similarity in a Taxonomy
A Chart Generator for Shake and Bake Machine Translation
A Conceptual Reasoning Approach to Textual Ellipsis
Rationality, Cooperation and Conversational Implicature
Chess Pure Strategies are Probably Chaotic
A Proof Theoretic View of Constraint Programming
An Adaptive Agent Oriented Software Architecture
Inducing a Semantically Annotated Lexicon via EM-Based Clustering
Predicate Logic with Definitions
Automatically Selecting Useful Phrases for Dialogue Act Tagging
Events in Property Patterns
Extending the Stable Model Semantics with More Expressive Rules
The Rough Guide to Constraint Propagation
Deduction over Mixed-Level Logic Representations for Text Passage Retrieval
Mixed-Level Knowledge Representation and Variable-Depth Inference in Natural Language Processing
Safe cooperative robot dynamics on graphs
Prospects for in-depth story understanding by computer
A database and lexicon of scripts for ThoughtTreasure
Conditional indifference and conditional preservation
Description of GADEL
Hypothetical revision and matter-of-fact supposition
Probabilistic Default Reasoning with Conditional Constraints
A Compiler for Ordered Logic Programs
SLDNFA-system
Declarative Representation of Revision Strategies
DLV - A System for Declarative Problem Solving
Fages' Theorem and Answer Set Programming
A note on the Declarative reading(s) of Logic Programming
SATEN: An Object-Oriented Web-Based Revision and Extraction Engine
dcs: An Implementation of DATALOG with Constraints
Representation results for defeasible logic
Programming in Alma-0, or Imperative and Declarative Programming Reconciled
Practical Reasoning for Expressive Description Logics
Reasoning with Individuals for the Description Logic SHIQ
Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies
Modeling the Uncertainty in Complex Engineering Systems
The SAT Phase Transition
Multiagent Control of Self-reconfigurable Robots
Knowledge on Treelike Spaces
Computing Presuppositions by Contextual Reasoning
Tree-gram Parsing: Lexical Dependencies and Structural Relations
Causes and Explanations: A Structural-Model Approach, Part I: Causes
Order-consistent programs are cautiously monotonic
Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking
Solving Composed First-Order Constraints from Discrete-Time Robust Control
Soft Scheduling
File mapping Rule-based DBMS and Natural Language Processing
Stacking classifiers for anti-spam filtering of e-mail
A Sequential Model for Multi-Class Classification
Enhancing Constraint Propagation with Composition Operators
aspps --- an implementation of answer-set programming with propositional schemata
Integrating Multiple Knowledge Sources for Robust Semantic Parsing
A logic-based approach to data integration
Gradient-based Reinforcement Planning in Policy-Search Methods
A Tight Upper Bound on the Number of Candidate Patterns
Representation of Uncertainty for Limit Processes
Interactive Constrained Association Rule Mining
Rational Competitive Analysis
The Deductive Database System LDL++
Distance Semantics for Belief Revision
Preferred History Semantics for Iterated Updates
Optimal Solutions for Multi-Unit Combinatorial Auctions: Branch and Bound Heuristics
Covariance Plasticity and Regulated Criticality
Stereotypical Reasoning: Logical Properties
Two results for proiritized logic programming
The Algorithms of Updating Sequential Patterns
Fast Hands-free Writing by Gaze Direction
Belief Revision and Rational Inference
Computing stable models: worst-case performance estimates
Relational Association Rules: getting WARMeR
On Concise Encodings of Preferred Extensions
Alternative Characterizations for Strong Equivalence of Logic Programs
Some logics of belief and disbelief
Well-Founded Argumentation Semantics for Extended Logic Programming
Logic Programming with Ordered Disjunction
The Rise and Fall of the Church-Turing Thesis
Embedding Default Logic in Propositional Argumentation Systems
Revising Partially Ordered Beliefs
Compilability of Abduction
Adaptive Development of Koncepts in Virtual Animats: Insights into the Development of Knowledge
Thinking Adaptive: Towards a Behaviours Virtual Laboratory
Redundancy in Logic I: CNF Propositional Formulae
Monadic Style Control Constructs for Inference Systems
Dynamic Adjustment of the Motivation Degree in an Action Selection Mechanism
A Theory of Cross-Validation Error
Merging Locally Correct Knowledge Bases: A Preliminary Report
Kalman filter control in the reinforcement learning framework
A semantic framework for preference handling in answer set programming
Constraint-based analysis of composite solvers
Tight Logic Programs
Kalman-filtering using local interactions
On the Notion of Cognition
Time-scales, Meaning, and Availability of Information in a Global Brain
Clustering belief functions based on attracting and conflicting metalevel evidence
Techniques for effective vocabulary selection
Lexicographic probability, conditional probability, and nonstandard probability
Reinforcement Learning with Linear Function Approximation and LQ control Converges
An Alternative to RDF-Based Languages for the Representation and Processing of Ontologies in the Semantic Web
A logic for reasoning about upper probabilities
Learning in Multiagent Systems: An Introduction from a Game-Theoretic Perspective
Model-Based Debugging using Multiple Abstract Models
A Hierarchical Situation Calculus
Application of Kullback-Leibler Metric to Speech Recognition
The Algebra of Utility Inference
An information theory for preferences
Great Expectations. Part I: On the Customizability of Generalized Expected Utility
Using Counterfactuals in Knowledge-Based Programming
Responsibility and blame: a structural-model approach
Diagnostic reasoning with A-Prolog
Dialogue as Discourse: Controlling Global Properties of Scripted Dialogue
Acquiring Lexical Paraphrases from a Single Corpus
Unifying Computing and Cognition: The SP Theory and its Applications
Self-Organising Networks for Classification: developing Applications to Science Analysis for Astroparticle Physics
The Complexity of Modified Instances
The role of behavior modifiers in representation development
A Comparative Study of Arithmetic Constraints on Integer Intervals
XML framework for concept description and knowledge representation
Knowledge And The Action Description Language A
"In vivo" spam filtering: A challenge problem for data mining
Multi-agent coordination using nearest neighbor rules: revisiting the Vicsek model
On the Complexity of Case-Based Planning
A Sequent Calculus and a Theorem Prover for Standard Conditional Logics
An Algorithm for Quasi-Associative and Quasi-Markovian Rules of Combination in Information Fusion
On Global Warming (Softening Global Constraints)
FLUX: A Logic Programming Method for Reasoning Agents
The Generalized Pignistic Transformation
Applying Policy Iteration for Training Recurrent Neural Networks
L1 regularization is better than L2 for learning and predicting chaotic systems
Intransitivity and Vagueness
Sleeping Beauty Reconsidered: Conditioning and Reflection in Asynchronous Systems
Robust Dialogue Understanding in HERALD
Bounded Input Bounded Predefined Control Bounded Output
Topological Navigation of Simulated Robots using Occupancy Grid
A Link Clustering Based Approach for Clustering Categorical Data
Finite Domain Bounds Consistency Revisited
Clever Search: A WordNet Based Wrapper for Internet Search Engines
Corpus based Enrichment of GermaNet Verb Frames
Context Related Derivation of Word Senses
Transforming and Enriching Documents for the Semantic Web
Estimating mutual information and multi--information in large networks
Decomposable Problems, Niching, and Scalability of Multiobjective Estimation of Distribution Algorithms
Towards a Systematic Account of Different Semantics for Logic Programs
Property analysis of symmetric travelling salesman problem instances acquired through evolution
Complexity Issues in Finding Succinct Solutions of PSPACE-Complete Problems
An Optimization Model for Outlier Detection in Categorical Data
Monotonic and Nonmonotonic Preference Revision
Constraint-Based Qualitative Simulation
Learning Polynomial Networks for Classification of Clinical Electroencephalograms
A Learning Algorithm for Evolving Cascade Neural Networks
Polynomial Neural Networks Learnt to Classify EEG Signals
An Evolving Cascade Neural Network Technique for Cleaning Sleep Electroencephalograms
The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns
Single-solution Random 3-SAT Instances
Beyond Hypertree Width: Decomposition Methods Without Decompositions
Wavelet Time Shift Properties Integration with Support Vector Machines
Redundancy in Logic II: 2CNF and Horn Propositional Formulae
Two-dimensional cellular automata and the analysis of correlated time series
In the beginning was game semantics
Data complexity of answering conjunctive queries over SHIQ knowledge bases
Temporal Phylogenetic Networks and Logic Programming
Planning with Preferences using Logic Programming
Transitive Text Mining for Information Extraction and Hypothesis Generation
A formally verified proof of the prime number theorem
K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset
Authoring case based training by document data extraction
Interactive Unawareness Revisited
Automatic extraction of paraphrastic phrases from medium size corpora
Sur le statut référentiel des entités nommées
K-ANMI: A Mutual Information Based Clustering Algorithm for Categorical Data
Integration of Declarative and Constraint Programming
Processing Uncertainty and Indeterminacy in Information Systems success mapping
Mathematical Models in Schema Theory
The logic of interactive Turing reduction
Distributed Kernel Regression: An Algorithm for Training Collaboratively
The intuitionistic fragment of computability logic at the propositional level
Avoiding the Bloat with Stochastic Grammar-based Genetic Programming
Explaining Constraint Programming
Metatheory of actions: beyond consistency
Convergence of Min-Sum Message Passing for Quadratic Optimization
Consensus Propagation
Application of Support Vector Regression to Interpolation of Sparse Shock Physics Data Sets
Approximation Algorithms for K-Modes Clustering
Nearly optimal exploration-exploitation decision thresholds
Adaptative combination rule and proportional conflict redistribution rule for information fusion
Perspective alignment in spatial language
A framework of reusable structures for mobile agent development
Mobile Agent Based Solutions for Knowledge Assessment in elearning Environments
Classification of Ordinal Data
A Decision-Making Support System Based on Know-How
Building a logical model in the machining domain for CAPP expert systems
The Cumulative Rule for Belief Fusion
Database Querying under Changing Preferences
An Analysis of Arithmetic Constraints on Integer Intervals
Dealing with Metonymic Readings of Named Entities
Linguistically Grounded Models of Language Change
Reasoning with Intervals on Granules
About Norms and Causes
Towards "Propagation = Logic + Control"
Infinite Qualitative Simulations by Means of Constraint Programming
Cascade hash tables: a series of multilevel double hashing schemes with O(1) worst case lookup time
Logic programs with monotone abstract constraint atoms
The role of time in considering collections
An application-oriented terminology evaluation: the case of back-of-the book indexes
Ontologies and Information Extraction
Rapport technique du projet OGRE
Why did the accident happen? A norm-based reasoning approach
Norm Based Causal Reasoning in Textual Corpus
Farthest-Point Heuristic based Initialization Methods for K-Modes Clustering
Analytic Tableaux Calculi for KLM Logics of Nonmonotonic Reasoning
Evolutionary Optimization in an Algorithmic Setting
Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization
On Measuring the Impact of Human Actions in the Machine Learning of a Board Game's Playing Policies
Player co-modelling in a strategy board game: discovering how to play fast
Lossless fitness inheritance in genetic algorithms for decision trees
Propositional theories are strongly equivalent to logic programs
A novel set of rotationally and translationally invariant features for images based on the non-commutative bispectrum
Dealing With Logical Omniscience: Expressiveness and Pragmatics
Uniform and Partially Uniform Redistribution Rules
Logic Programming with Satisfiability
Redesigning Decision Matrix Method with an indeterminacy-based inference process
Copula Component Analysis
Modelling Complexity in Musical Rhythm
Reinforcement Learning for Adaptive Routing
Recursion relations for two-loop self-energy diagrams on-shell
Application of the Worldline Path Integral Method to the Calculation of Inverse Mass Expansions
Some Local Measures of Complexity of Convex Hulls and Generalization Bounds
Learning a Machine for the Decision in a Partially Observable Markov Universe
Fast Non-Parametric Bayesian Inference on Infinite Trees
Strong Asymptotic Assertions for Discrete MDL in Regression and Classification
Statistical Modeling of Nuclear Systematics
Deterministic Chaos: A signature of Quantumlike Mechanics in Self-Organized Adaptive Network
Semiclassical Neural Network
Quantum Aspects of Semantic Analysis and Symbolic Artificial Intelligence
A genetic algorithm for finding pulse sequences for NMR quantum computing
Introduction to Arabic Speech Recognition Using CMUSphinx System
Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition
A Note on Ontology and Ordinary Language
Fault Classification in Cylinders Using Multilayer Perceptrons, Support Vector Machines and Guassian Mixture Models
The Parameter-Less Self-Organizing Map algorithm
Bayesian Approach to Neuro-Rough Models
Evolving Symbolic Controllers
A first-order Temporal Logic for Actions
Fuzzy and Multilayer Perceptron for Evaluation of HV Bushings
On the monotonization of the training set
Loop corrections for message passing algorithms in continuous variable models
Epistemic Analysis of Strategic Games with Arbitrary Strategy Sets
Automatically Restructuring Practice Guidelines using the GEM DTD
Bijective Faithful Translations among Default Logics
Learning Probabilistic Models of Word Sense Disambiguation
A structure from motion inequality
On Ullman's theorem in computer vision
Raising a Hardness Result
On Ultrametric Algorithmic Information
Qualitative Belief Conditioning Rules (QBCR)
Multi-Sensor Fusion Method using Dynamic Bayesian Network for Precise Vehicle Localization and Road Matching
Autoencoder, Principal Component Analysis and Support Vector Regression for Data Imputation
From Texts to Structured Documents: The Case of Health Practice Guidelines
Quantum Causal Networks
What's in a Name?
Stationary probability density of stochastic search processes in global optimization
Analyzing covert social network foundation behind terrorism disaster
Node discovery problem for a social network
Computer Model of a "Sense of Humour". I. General Algorithm
Computer Model of a "Sense of Humour". II. Realization in Neural Networks
Derivative of functions over lattices as a basis for the notion of interaction between attributes
Can a Computer Laugh ?
The Second Law as a Cause of the Evolution
A Spectral Approach to Analyzing Belief Propagation for 3-Coloring
Decomposition During Search for Propagation-Based Constraint Solvers
Common knowledge logic in a higher order proof assistant?
Judgment
iBOA: The Incremental Bayesian Optimization Algorithm
A review of the Statistical Mechanics approach to Random Optimization Problems
Brain architecture: A design for natural computation
Dempster-Shafer for Anomaly Detection
Improved evolutionary generation of XSLT stylesheets
Tableau-based decision procedures for logics of strategic ability in multi-agent systems
Towards a human eye behavior model by applying Data Mining Techniques on Gaze Information from IEC
Eye-Tracking Evolutionary Algorithm to minimize user's fatigue in IEC applied to Interactive One-Max problem
Universality in Globally Coupled Maps and Flows
Combinatorial Explorations in Su-Doku
Support Vector Machine Classification with Indefinite Kernels
Graphical Estimation of Permeability Using RST&NFIS
Application of Rough Set Theory to Analysis of Hydrocyclone Operation
A Unified Semi-Supervised Dimensionality Reduction Framework for Manifold Learning
From Qualitative to Quantitative Proofs of Security Properties Using First-Order Conditional Logic
Causal models have no complete axiomatic characterization
Grainy Numbers
SimDialog: A visual game dialog editor
Contact state analysis using NFIS and SOM
A Fast Algorithm and Datalog Inexpressibility for Temporal Reasoning
Toward Fuzzy block theory
Analysis of hydrocyclone performance based on information granulation theory
Cognitive Architecture for Direction of Attention Founded on Subliminal Memory Searches, Pseudorandom and Nonstop
Compressing Binary Decision Diagrams
Logic programming with social features
An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data
Fusion for Evaluation of Image Classification in Uncertain Environments
Fusion de classifieurs pour la classification d'images sonar
Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification
Defaults and Normality in Causal Structures
On Sequences with Non-Learnable Subsequences
Prediction with Expert Advice in Games with Unbounded One-Step Gains
On empirical meaning of randomness with respect to a real parameter
A new Hedging algorithm and its application to inferring latent random variables
Belief decision support and reject for textured images characterization
A new probabilistic transformation of belief mass assignment
AceWiki: Collaborative Ontology Management in Controlled Natural Language
Initial Results on the F-logic to OWL Bi-directional Translation on a Tabled Prolog Engine
n-ary Fuzzy Logic and Neutrosophic Logic Operators
Improving Local Search for Fuzzy Scheduling Problems
Bin Packing Under Multiple Objectives - a Heuristic Approximation Approach
An application of the Threshold Accepting metaheuristic for curriculum based course timetabling
Peek Arc Consistency
Predicting Abnormal Returns From News Using Text Classification
Learning Hidden Markov Models using Non-Negative Matrix Factorization
Determining the Unithood of Word Sequences using a Probabilistic Approach
Three New Complexity Results for Resource Allocation Problems
A global physician-oriented medical information system
On combinations of local theory extensions
Combining Semantic Wikis and Controlled Natural Language
The many faces of optimism - Extended version
The use of entropy to measure structural diversity
A computational model of affects
Probabilistic reasoning with answer sets
Logic programs with propositional connectives and aggregates
A New Trend in Optimization on Multi Overcomplete Dictionary toward Inpainting
Pattern Recognition and Memory Mapping using Mirroring Neural Networks
Analyse et structuration automatique des guides de bonnes pratiques cliniques : essai d'évaluation
A Computational Model to Disentangle Semantic Information Embedded in Word Association Norms
A New Method for Knowledge Representation in Expert System's (XMLKR)
On the Optimal Convergence Probability of Univariate Estimation of Distribution Algorithms
Transitivity vs. Intransitivity in decision making process. (An example in quantum game theory)
Hiding Quiet Solutions in Random Constraint Satisfaction Problems
Resource Adaptive Agents in Interactive Theorem Proving
Geospatial semantics: beyond ontologies, towards an enactive approach
Cut-Simulation and Impredicativity
Back analysis of microplane model parameters using soft computing methods
Topological Centrality and Its Applications
Feature Hashing for Large Scale Multitask Learning
Error-Correcting Tournaments
Progress in Computer-Assisted Inductive Theorem Proving by Human-Orientedness and Descente Infinie?
Full First-Order Sequent and Tableau Calculi With Preservation of Solutions and the Liberalized delta-Rule but Without Skolemization
Why Would You Trust B?
Range and Roots: Two Common Patterns for Specifying and Propagating Counting and Occurrence Constraints
Impact of Cognitive Radio on Future Management of Spectrum
Online Estimation of SAT Solving Runtime
Breaking Value Symmetry
Efficiently Learning a Detection Cascade with Sparse Eigenvectors
Designing a GUI for Proofs - Evaluation of an HCI Experiment
Conditional Probability Tree Estimation Analysis and Algorithms
Time manipulation technique for speeding up reinforcement learning in simulations
Learning for Dynamic subsumption
Safe Reasoning Over Ontologies
Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning
Optimal Tableau Decision Procedures for PDL
Dependency Pairs and Polynomial Path Orders
Agent-Based Decision Support System to Prevent and Manage Risk Situations
HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach
Semantic Social Network Analysis
Adaptive Learning with Binary Neurons
Quantified Multimodal Logics in Simple Type Theory
Quantum Annealing for Clustering
Quantum Annealing for Variational Bayes Inference
Information Modeling for a Dynamic Representation of an Emergency Situation
The CIFF Proof Procedure for Abductive Logic Programming with Constraints: Theory, Implementation and Experiments
Towards Improving Validation, Verification, Crash Investigations, and Event Reconstruction of Flight-Critical Systems with Self-Forensics
Two-Dimensional ARMA Modeling for Breast Cancer Detection and Classification
Soft Constraints for Quality Aspects in Service Oriented Architectures
Concept-based Recommendations for Internet Advertisement
General combination rules for qualitative and quantitative beliefs
Restricted Global Grammar Constraints
Agent-Oriented Approach for Detecting and Managing Risks in Emergency Situations
Computational Scenario-based Capability Planning
Credit Assignment in Adaptive Evolutionary Algorithms
How Controlled English can Improve Semantic Wikis
Improvements for multi-objective flow shop scheduling by Pareto Iterated Local Search
Graph Theory and Optimization Problems for Very Large Networks
Restart Strategy Selection using Machine Learning Techniques
On Classification from Outlier View
Convergence of Expected Utility for Universal AI
Knowledge Discovery of Hydrocyclone s Circuit Based on SONFIS and SORST
Practical approach to programmable analog circuits with memristors
Quantifying Rational Belief
Maximizing profit using recommender systems
Assessing the Impact of Informedness on a Consultant's Profit
n-Opposition theory to structure debates
Stochastic Optimization of Linear Dynamic Systems with Parametric Uncertainties
Building upon Fast Multipole Methods to Detect and Model Organizations
A Local Search Modeling for Constrained Optimum Paths Problems (Extended Abstract)
Integrating Conflict Driven Clause Learning to Local Search
Sonet Network Design Problems
Toward an automaton Constraint for Local Search
Ludics and its Applications to natural Language Semantics
Machine Learning: When and Where the Horses Went Astray?
Manipulating Tournaments in Cup and Round Robin Competitions
Active Learning for Mention Detection: A Comparison of Sentence Selection Strategies
Emotion: Appraisal-coping model for the "Cascades" problem
Emotion : modèle d'appraisal-coping pour le problème des Cascades
Apply Ant Colony Algorithm to Search All Extreme Points of Function
A Semantic Similarity Measure for Expressive Description Logics
Opportunistic Adaptation Knowledge Discovery
A Multi-stage Probabilistic Algorithm for Dynamic Path-Planning
Data management in Systems biology II - Outlook towards the semantic web
Multi-valued Action Languages in CLP(FD)
New Generalization Bounds for Learning Kernels
Elkan's k-Means for Graphs
Complexity of stochastic branch and bound methods for belief tree search in Bayesian reinforcement learning
Abstract Answer Set Solvers with Learning
Document Clustering with K-tree
K-tree: Large Scale Document Clustering
Graph Quantization
A betting interpretation for probabilities and Dempster-Shafer degrees of belief
Detecting Botnets Through Log Correlation
Classifying Network Data with Deep Kernel Machines
Janus: Automatic Ontology Builder from XSD Files
Genetic algorithm for robotic telescope scheduling
Constraint solvers: An empirical evaluation of design decisions
Logical Evaluation of Consciousness: For Incorporating Consciousness into Machine Architecture
Using CODEQ to Train Feed-forward Neural Networks
Dire n'est pas concevoir
A Generalization of the Chow-Liu Algorithm and its Application to Statistical Learning
Using ATL to define advanced and flexible constraint model transformations
Convergence of Bayesian Control Rule
Nonparametric Estimation and On-Line Prediction for General Stationary Ergodic Sources
Less Regret via Online Conditioning
Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition
Proceedings FM-09 Workshop on Formal Methods for Aerospace
Geometric Algebra Model of Distributed Representations
Ontology-supported processing of clinical text using medical knowledge integration for multi-label classification of diagnosis coding
Node inspection and analysis thereof in the light of area estimation and curve fitting
Spatio-Temporal Graphical Model Selection
Causality and the semantics of provenance
Publishing Math Lecture Notes as Linked Data
Ontology-based inference for causal explanation
Designing neural networks that process mean values of random variables
Simple Type Theory as Framework for Combining Logics
The Exact Closest String Problem as a Constraint Satisfaction Problem
An introduction to spectral distances in networks (extended version)
Adaptive Bases for Reinforcement Learning
Using machine learning to make constraint solver implementation decisions
A Soft Computing Model for Physicians' Decision Process
Genetic algorithms and the art of Zen
Evidence Algorithm and System for Automated Deduction: A Retrospective View
Proofs, proofs, proofs, and proofs
Failover in cellular automata
Building Computer Network Attacks
MDPs with Unawareness
Mirrored Language Structure and Innate Logic of the Human Brain as a Computable Model of the Oracle Turing Machine
Online Cake Cutting
A Brief Introduction to Temporality and Causality
Testing and Debugging Techniques for Answer Set Solver Development
A decidable subclass of finitary programs
Identifying Causal Effects with Computer Algebra
Threat assessment of a possible Vehicle-Born Improvised Explosive Device using DSmT
Co-evolution is Incompatible with the Markov Assumption in Phylogenetics
Associative control processor with a rigid structure
Towards arrow-theoretic semantics of ontologies: conceptories
A Learning Algorithm based on High School Teaching Wisdom
Role of Ontology in Semantic Web Development
A formalism for causal explanations with an Answer Set Programming translation
Learning from Profession Knowledge: Application on Knitting
Distributed solving through model splitting
Prediction by Compression
Optimizing Selective Search in Chess
Ontology Temporal Evolution for Multi-Entity Bayesian Networks under Exogenous and Endogenous Semantic Updating
Measuring Similarity of Graphs and their Nodes by Neighbor Matching
A Cost-Minimizing Algorithm for School Choice
Introduction to the iDian
A Partial Taxonomy of Substitutability and Interchangeability
Qualitative Reasoning about Relative Direction on Adjustable Levels of Granularity
Prunnig Algorithm of Generation a Minimal Set of Rule Reducts Based on Rough Set Theory
Reasoning about Cardinal Directions between Extended Objects: The Hardness Result
Probabilistic Inferences in Bayesian Networks
Target tracking in the recommender space: Toward a new recommender system based on Kalman filtering
Reified unit resolution and the failed literal rule
New Methods of Analysis of Narrative and Semantics in Support of Interactivity
Optimizing real-time RDF data streams
Bayesian Modeling of a Human MMORPG Player
Are SNOMED CT Browsers Ready for Institutions? Introducing MySNOM
First steps in the logic-based assessment of post-composed phenotypic descriptions
Nondeterministic fuzzy automata
Phase Transitions of Plan Modification in Conformant Planning
A new Recommender system based on target tracking: a Kalman Filter approach
Descriptive-complexity based distance for fuzzy sets
Interpolation in Equilibrium Logic and Answer Set Programming: the Propositional Case
Extending Binary Qualitative Direction Calculi with a Granular Distance Concept: Hidden Feature Attachment
Adaptive Submodular Optimization under Matroid Constraints
Restructuring in Combinatorial Optimization
Hybrid Model for Solving Multi-Objective Problems Using Evolutionary Algorithm and Tabu Search
Automated Complexity Analysis Based on the Dependency Pair Method
Evolved preambles for MAX-SAT heuristics
Counting Solutions of Constraint Satisfiability Problems:Exact Phase Transitions and Approximate Algorithm
New Worst-Case Upper Bound for #XSAT
Worst-Case Upper Bound for (1, 2)-QSAT
Practical inventory routing: A problem definition and an optimization method
An Agent Based Architecture (Using Planning) for Dynamic and Semantic Web Services Composition in an EBXML Context
Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks
The AllDifferent Constraint with Precedences
Informed Heuristics for Guiding Stem-and-Cycle Ejection Chains
Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs
Representing First-Order Causal Theories by Logic Programs
On Understanding and Machine Understanding
Foundations for Uniform Interpolation and Forgetting in Expressive Description Logics
Understanding Exhaustive Pattern Learning
Boolean Equi-propagation for Optimized SAT Encoding
Hybrid Tractable Classes of Binary Quantified Constraint Satisfaction Problems
Arc Consistency and Friends
Limits of Preprocessing
Mean-Variance Optimization in Markov Decision Processes
Proposal of Pattern Recognition as a necessary and sufficient Principle to Cognitive Science
A Linear Time Natural Evolution Strategy for Non-Separable Functions
Actual causation and the art of modeling
Extensional Higher-Order Logic Programming
On the expressive power of unit resolution
Embedding and Automating Conditional Logics in Classical Higher-Order Logic
Coincidences and the encounter problem: A formal account
Symmetry-Based Search Space Reduction For Grid Maps
Understanding opinions. A cognitive and formal account
Exploiting Reputation in Distributed Virtual Environments
Pose Estimation from a Single Depth Image for Arbitrary Kinematic Skeletons
Class-based Rough Approximation with Dominance Principle
A case of combination of evidence in the Dempster-Shafer theory inconsistent with evaluation of probabilities
A Probabilistic Attack on NP-complete Problems
Law of Connectivity in Machine Learning
Task swapping networks in distributed systems
Current State and Challenges of Automatic Planning in Web Service Composition
Rule-Based Semantic Sensing
On the Undecidability of Fuzzy Description Logics with GCIs with Lukasiewicz t-norm
An end-to-end machine learning system for harmonic analysis of music
A theory of multiclass boosting
Origins of Answer-Set Programming - Some Background And Two Personal Accounts
Solving puzzles described in English by automated translation to answer set programming and learning how to do that translation
Convergence of a Recombination-Based Elitist Evolutionary Algorithm on the Royal Roads Test Function
Computing with Logic as Operator Elimination: The ToyElim System
Event in Compositional Dynamic Semantics
Encoding Phases using Commutativity and Non-commutativity in a Logical Framework
FdConfig: A Constraint-Based Interactive Product Configurator
A prototype of a knowledge-based programming environment
A Constraint Logic Programming Approach for Computing Ordinal Conditional Functions
Confidentiality-Preserving Data Publishing for Credulous Users by Extended Abduction
Proof System for Plan Verification under 0-Approximation Semantics
Domain-specific Languages in a Finite Domain Constraint Programming System
Coprocessor - a Standalone SAT Preprocessor
Transfer from Multiple MDPs
Visual Inference Specification Methods for Modularized Rulebases. Overview and Integration Proposal
Application of the Modified 2-opt and Jumping Gene Operators in Multi-Objective Genetic Algorithm to solve MOTSP
Conceptual Knowledge Markup Language: The central core
Conjure Revisited: Towards Automated Constraint Modelling
Digital Libraries, Conceptual Knowledge Systems, and the Nebula Interface
On the use of reference points for the biobjective Inventory Routing Problem
A Characterization of the Combined Effects of Overlap and Imbalance on the SVM Classifier
Social choice rules driven by propositional logic
Explicit Approximations of the Gaussian Kernel
New Candidates Welcome! Possible Winners with respect to the Addition of New Candidates
Unbiased Statistics of a CSP - A Controlled-Bias Generator
Constraining the Size Growth of the Task Space with Socially Guided Intrinsic Motivation using Demonstrations
Continuity in Information Algebras
Tacit knowledge mining algorithm based on linguistic truth-valued concept lattice
The computation of first order moments on junction trees
A Dichotomy for 2-Constraint Forbidden CSP Patterns
Progress in animation of an EMA-controlled tongue model for acoustic-visual speech synthesis
A Description Logic Primer
Cognitive Memory Network
Improving feature selection algorithms using normalised feature histograms
Recommender System Based on Algorithm of Bicluster Analysis RecBi
Towards quantitative measures in applied ontology
A temporally abstracted Viterbi algorithm
Strictly Proper Mechanisms with Cooperating Players
Bayesian network learning with cutting planes
Efficient Inference in Markov Control Problems
Reasoning about RoboCup Soccer Narratives
Suboptimality Bounds for Stochastic Shortest Path Problems
Noisy Search with Comparative Feedback
Variational Algorithms for Marginal MAP
Order-of-Magnitude Influence Diagrams
Iterated risk measures for risk-sensitive Markov decision processes with discounted cost
The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information
Graphical Models for Bandit Problems
MAV Stabilization using Machine Learning and Onboard Sensors
Elitism Levels Traverse Mechanism For The Derivation of Upper Bounds on Unimodal Functions
Marginality: a numerical mapping for enhanced treatment of nominal and hierarchical attributes
(Dual) Hoops Have Unique Halving
A Probabilistic Transmission Expansion Planning Methodology based on Roulette Wheel Selection and Social Welfare
Combining Voting Rules Together
Gaussian Process Topic Models
Super-Samples from Kernel Herding
Regularized Maximum Likelihood for Intrinsic Dimension Estimation
Approximating Higher-Order Distances Using Random Projections
Dirichlet Process Mixtures of Generalized Mallows Models
A Bayesian Matrix Factorization Model for Relational Data
Learning networks determined by the ratio of prior and data
The Abzooba Smart Health Informatics Platform (SHIP) TM - From Patient Experiences to Big Data to Insights
Learning Feature Hierarchies with Centered Deep Boltzmann Machines
On Training Deep Boltzmann Machines
Global preferential consistency for the topological sorting-based maximal spanning tree problem
Unit contradiction versus unit propagation
Characterization of Dynamic Bayesian Network
Skin-color based videos categorization
Publishing Identifiable Experiment Code And Configuration Is Important, Good and Easy
Derivation of Upper Bounds on Optimization Time of Population-Based Evolutionary Algorithm on a Function with Fitness Plateaus Using Elitism Levels Traverse Mechanism
Simultaneous Object Detection, Tracking, and Event Recognition
Solution Representations and Local Search for the bi-objective Inventory Routing Problem
Automatic Sampling of Geographic objects
Objective Function Designing Led by User Preferences Acquisition
On the Complexity of Finding Second-Best Abductive Explanations
A Fuzzy Model for Analogical Problem Solving
Poultry Diseases Expert System using Dempster-Shafer Theory
Document summarization using positive pointwise mutual information
Publishing and linking transport data on the Web
Modularity-Based Clustering for Network-Constrained Trajectories
The Infinite Latent Events Model
Herding Dynamic Weights for Partially Observed Random Field Models
Temporal-Difference Networks for Dynamical Systems with Continuous Observations and Actions
Probabilistic Structured Predictors
Domain Knowledge Uncertainty and Probabilistic Parameter Constraints
Interpretation and Generalization of Score Matching
Correlated Non-Parametric Latent Feature Models
REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs
Operations on soft sets revisited
Unfair items detection in educational measurement
The Good, the Bad, and the Odd: Cycles in Answer-Set Programs
Neural Networks for Handwritten English Alphabet Recognition
A Mixed Integer Programming Model Formulation for Solving the Lot-Sizing Problem
Feature Weighting for Improving Document Image Retrieval System Performance
Online open neuroimaging mass meta-analysis
Approximating the Partition Function by Deleting and then Correcting for Model Edges
Multi-View Learning in the Presence of View Disagreement
Learning Convex Inference of Marginals
Estimation and Clustering with Infinite Rankings
Learning Hidden Markov Models for Regression using Path Aggregation
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
Improving the Asymmetric TSP by Considering Graph Structure
Identifying Independence in Relational Models
TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing Multiple Ratings
Near-Optimal BRL using Optimistic Local Transitions
Continuous Inverse Optimal Control with Locally Optimal Examples
Active Learning for Matching Problems
Machine Learning that Matters
Statistical Translation, Heat Kernels and Expected Distances
Generalized Polya Urn for Time-varying Dirichlet Process Mixtures
On Discarding, Caching, and Recalling Samples in Active Learning
Markov Chains on Orbits of Permutation Groups
Bounded Planning in Passive POMDPs
Extension of Three-Variable Counterfactual Casual Graphic Model: from Two-Value to Three-Value Random Variable
A concentration theorem for projections
Direct and Indirect Effects of Sequential Treatments
Sequential Document Representations and Simplicial Curves
Predicting Conditional Quantiles via Reduction to Classification
Bayesian Multicategory Support Vector Machines
Alternative Restart Strategies for CMA-ES
Relational Data Mining Through Extraction of Representative Exemplars
Unsupervised spectral learning
On Privacy-Preserving Histograms
Two-Way Latent Grouping Model for User Preference Prediction
Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
An Algorithm for Computing Stochastically Stable Distributions with Applications to Multiagent Learning in Repeated Games
Super-Mixed Multiple Attribute Group Decision Making Method Based on Hybrid Fuzzy Grey Relation Approach Degree
Generalized Hybrid Grey Relation Method for Multiple Attribute Mixed Type Decision Making
The SeqBin Constraint Revisited
Rule Based Expert System for Diagnosis of Neuromuscular Disorders
On Formal Specification of Maple Programs
On-line Prediction with Kernels and the Complexity Approximation Principle
Applying Discrete PCA in Data Analysis
Exponential Families for Conditional Random Fields
An Extended Cencov-Campbell Characterization of Conditional Information Geometry
Maximum Entropy for Collaborative Filtering
Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values
Computing Best-Response Strategies in Infinite Games of Incomplete Information
Towards Understanding Triangle Construction Problems
Probability Bracket Notation, Multivariable Systems and Static Bayesian Networks
VOI-aware MCTS
Redundant Sudoku Rules
Earthquake Scenario Reduction by Symmetry Reasoning
Diversity in Ranking using Negative Reinforcement
FMLtoHOL (version 1.0): Automating First-order Modal Logics with LEO-II and Friends
Free Lunch or No Free Lunch: That is not Just a Question?
Credal nets under epistemic irrelevance
Algorithmic Simplicity and Relevance
Experiments with Game Tree Search in Real-Time Strategy Games
Elimination of ISI Using Improved LMS Based Decision Feedback Equalizer
Lifted Variable Elimination: A Novel Operator and Completeness Results
A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based Approaches for E-GTSP
Parallel ACO with a Ring Neighborhood for Dynamic TSP
Design of Low Noise Amplifiers Using Particle Swarm Optimization
A matrix approach for computing extensions of argumentation frameworks
Multimodal diffusion geometry by joint diagonalization of Laplacians
Pattern Detection with Rare Item-set Mining
Tractable Optimization Problems through Hypergraph-Based Structural Restrictions
Theorem Proving in Large Formal Mathematics as an Emerging AI Field
Speech Signal Filters based on Soft Computing Techniques: A Comparison
On Move Pattern Trends in a Large Go Games Corpus
Efficient Natural Evolution Strategies
Relative Expressiveness of Defeasible Logics
Information fusion in multi-task Gaussian processes
Simulated Tom Thumb, the Rule Of Thumb for Autonomous Robots
AI in arbitrary world
Distributional Framework for Emergent Knowledge Acquisition and its Application to Automated Document Annotation
Multi-threaded ASP Solving with clasp
Artex is AnotheR TEXt summarizer
Introduction to the 28th International Conference on Logic Programming Special Issue
Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance
Relational Theories with Null Values and Non-Herbrand Stable Models
Budget Optimization for Sponsored Search: Censored Learning in MDPs
Combining local search techniques and path following for bimatrix games
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits
Hokusai - Sketching Streams in Real Time
Hidden Trends in 90 Years of Harvard Business Review
Attractor networks and memory replay of phase coded spike patterns
A Biomimetic Approach Based on Immune Systems for Classification of Unstructured Data
Get my pizza right: Repairing missing is-a relations in ALC ontologies (extended version)
Hierarchical Learning Algorithm for the Beta Basis Function Neural Network
Temporal Autoencoding Restricted Boltzmann Machine
An Experiment on the Connection between the DLs' Family DL and the Real World
A hybrid cross entropy algorithm for solving dynamic transit network design problem
Shadows and headless shadows: a worlds-based, autobiographical approach to reasoning
Modeling problems of identity in Little Red Riding Hood
Shadows and Headless Shadows: an Autobiographical Approach to Narrative Reasoning
New Hoopoe Heuristic Optimization
New Heuristics for Interfacing Human Motor System using Brain Waves
Provocative radio transients and base rate bias: a Bayesian argument for conservatism
Compositional Stochastic Modeling and Probabilistic Programming
Compiling Relational Database Schemata into Probabilistic Graphical Models
A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence
Markov Random Walk Representations with Continuous Distributions
Efficient Parametric Projection Pursuit Density Estimation
Boltzmann Machine Learning with the Latent Maximum Entropy Principle
Accelerating Inference: towards a full Language, Compiler and Hardware stack
Product/Brand extraction from WikiPedia
Keyword Extraction for Identifying Social Actors
A trust-based security mechanism for nomadic users in pervasive systems
Improving problem solving by exploiting the concept of symmetry
General Lower Bounds based on Computer Generated Higher Order Expansions
Staged Mixture Modelling and Boosting
Mechanism Design with Execution Uncertainty
Unsupervised Active Learning in Large Domains
Adaptive Foreground and Shadow Detection inImage Sequences
Translating NP-SPEC into ASP
Language ASP{f} with Arithmetic Expressions and Consistency-Restoring Rules
Answer Set Programming for Stream Reasoning
Two New Definitions of Stable Models of Logic Programs with Generalized Quantifiers
Lloyd-Topor Completion and General Stable Models
Fuzzy Soft Set Based Classification for Gene Expression Data
A Forgetting-based Approach to Merging Knowledge Bases
Proceedings of Answer Set Programming and Other Computing Paradigms (ASPOCP 2012), 5th International Workshop, September 4, 2012, Budapest, Hungary
Discovering Multiple Constraints that are Frequently Approximately Satisfied
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk
Cutting Recursive Autoencoder Trees
Monte Carlo Inference via Greedy Importance Sampling
An Uncertainty Framework for Classification
A formalization of re-identification in terms of compatible probabilities
Recycling Proof Patterns in Coq: Case Studies
Approximation of Classification and Measures of Uncertainty in Rough Set on Two Universal Sets
Learning by Transduction
On the Geometry of Bayesian Graphical Models with Hidden Variables
Efficient Partial Order CDCL Using Assertion Level Choice Heuristics
Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs
Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks
Complexity distribution of agent policies
Reasoning about Independence in Probabilistic Models of Relational Data
Preference-Based Unawareness
Learning in Multi-level Stochastic games with Delayed Information
The Semantic Web takes Wing: Programming Ontologies with Tawny-OWL
Reducing Validity in Epistemic ATL to Validity in Epistemic CTL
Gene-Machine, a new search heuristic algorithm
Quantum and Concept Combination, Entangled Measurements and Prototype Theory
Towards Automated Proof Strategy Generalisation
Separating Topology and Geometry in Space Planning
Generating extrema approximation of analytically incomputable functions through usage of parallel computer aided genetic algorithms
Discovering Semantic Spatial and Spatio-Temporal Outliers from Moving Object Trajectories
Model Based Framework for Estimating Mutation Rate of Hepatitis C Virus in Egypt
Bipolar Fuzzy Soft sets and its applications in decision making problem
Discrete Optimization of Statistical Sample Sizes in Simulation by Using the Hierarchical Bootstrap Method
Design for a Darwinian Brain: Part 1. Philosophy and Neuroscience
Symmetries in Modal Logics
Testing Hypotheses by Regularized Maximum Mean Discrepancy
Extending Modern SAT Solvers for Enumerating All Models
An Improved EM algorithm
Generalized Neutrosophic Soft Set
A Mining-Based Compression Approach for Constraint Satisfaction Problems
Aplicacion de las Redes Neuronales al Reconocimiento de Sistemas Operativos
Robust Logistic Regression using Shift Parameters (Long Version)
Semantic Web Search based on Ontology Modeling using Protege Reasoner
Improved Branch-and-Bound for Low Autocorrelation Binary Sequences
A Cooperative Coevolutionary Genetic Algorithm for Learning Bayesian Network Structures
Collaborative ontology sharing and editing
Algebraic Properties of Qualitative Spatio-Temporal Calculi
Modelling Electricity Consumption in Office Buildings: An Agent Based Approach
Sensitive Ants for Denial Jamming Attack on Wireless Sensor Network
Towards Detection of Bottlenecks in Modular Systems
LLAMA: Leveraging Learning to Automatically Manage Algorithms
Direct Uncertainty Estimation in Reinforcement Learning
Accomplishable Tasks in Knowledge Representation
Sparse Auto-Regressive: Robust Estimation of AR Parameters
Verifying the Steane code with Quantomatic
Solution to Quadratic Equation Using Genetic Algorithm
Parallel Algorithm for Longest Common Subsequence in a String
Distributed Heuristic Forward Search for Multi-Agent Systems
Simulating Ability: Representing Skills in Games
READ-EVAL-PRINT in Parallel and Asynchronous Proof-checking
Decision Making for Inconsistent Expert Judgments Using Negative Probabilities
A novel approach of solving the CNF-SAT problem
Learning to Understand by Evolving Theories
Reasoning for Moving Blocks Problem: Formal Representation and Implementation
Integration of 3D Object Recognition and Planning for Robotic Manipulation: A Preliminary Report
Extracting Information-rich Part of Texts using Text Denoising
Sigma Point Belief Propagation
Analysing Quality of English-Hindi Machine Translation Engine Outputs Using Bayesian Classification
Graded Causation and Defaults
Compact Representations of Extended Causal Models
Approximate Counting CSP Solutions Using Partition Function
Beyond the quantum formalism: consequences of a neural-oscillator model to quantum cognition
A new look at reweighted message passing
An evolutionary approach to Function
Generating Explanations for Biomedical Queries
Boosting in the presence of label noise
Gaussian Processes for Big Data
Structured Convex Optimization under Submodular Constraints
Beyond Log-Supermodularity: Lower Bounds and the Bethe Partition Function
Speedy Model Selection (SMS) for Copula Models
Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs
An upper bound on prototype set size for condensed nearest neighbor
Learning Chordal Markov Networks by Constraint Satisfaction
A necessary and sufficient condition for two relations to induce the same definable set family
A Sparse and Adaptive Prior for Time-Dependent Model Parameters
Activity date estimation in timestamped interaction networks
When is an Example a Counterexample?
Technical Report: Distribution Temporal Logic: Combining Correctness with Quality of Estimation
Information, Computation, Cognition. Agency-based Hierarchies of Levels
A novel local search based on variable-focusing for random K-SAT
A generalized evidence distance
Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing
A hybrid decision support system : application on healthcare
A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database
Introduction to Neutrosophic Measure, Neutrosophic Integral, and Neutrosophic Probability
Case-Based Merging Techniques in OAKPLAN
A state vector algebra for algorithmic implementation of second-order logic
OntoVerbal: a Generic Tool and Practical Application to SNOMED CT
Representing Knowledge Base into Database for WAP and Web-based Expert System
Path Based Mapping Technique for Robots
Parkinson's Disease Motor Symptoms in Machine Learning: A Review
Sharpening independence results for Huntington's affine geometry
Semantic Annotation: The Mainstay of Semantic Web
Conservative, Proportional and Optimistic Contextual Discounting in the Belief Functions Theory
Generating Shortest Synchronizing Sequences using Answer Set Programming
Abstract Modular Systems and Solvers
Volumetric Spanners: an Efficient Exploration Basis for Learning
The Value Iteration Algorithm is Not Strongly Polynomial for Discounted Dynamic Programming
Description Logics based Formalization of Wh-Queries
A regression model with a hidden logistic process for signal parametrization
Functional Mixture Discriminant Analysis with hidden process regression for curve classification
Proceedings of Answer Set Programming and Other Computing Paradigms (ASPOCP 2013), 6th International Workshop, August 25, 2013, Istanbul, Turkey
A Review: Expert System for Diagnosis of Myocardial Infarction
A stochastic model for Case-Based Reasoning
Antipodal Interval-Valued Fuzzy Graphs
Cortical prediction markets
A logic for reasoning about ambiguity
Latent Tree Models and Approximate Inference in Bayesian Networks
RoxyBot-06: Stochastic Prediction and Optimization in TAC Travel
Mechanisms for Multi-Unit Auctions
Policy Invariance under Reward Transformations for General-Sum Stochastic Games
Solving the Minimum Common String Partition Problem with the Help of Ants
Skill Analysis with Time Series Image Data
Sentence Compression as Tree Transduction
Cross-lingual Annotation Projection for Semantic Roles
An Enhanced Branch-and-bound Algorithm for the Talent Scheduling Problem
Hypergraph Acyclicity and Propositional Model Counting
Tractable Epistemic Reasoning with Functional Fluents, Static Causal Laws and Postdiction
Design a Persian Automated Plagiarism Detector (AMZPPD)
Defuzzify firstly or finally: Dose it matter in fuzzy DEMATEL under uncertain environment?
Non-characterizability of belief revision: an application of finite model theory
Difficulty Rating of Sudoku Puzzles: An Overview and Evaluation
Self-protection and self-healing in the context of cognitive radio
Reasoning about Knowledge and Strategies: Epistemic Strategy Logic
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
MTD(f), A Minimax Algorithm Faster Than NegaScout
Verification of confliction and unreachability in rule-based expert systems with model checking
An Integer Programming Model for the Dynamic Location and Relocation of Emergency Vehicles: A Case Study
A new combination approach based on improved evidence distance
Generalized Evidence Theory
Causal Interfaces
Graph Kernels via Functional Embedding
Modeling multi-stage decision optimization problems
Gradual Classical Logic for Attributed Objects
A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm
Rough Clustering Based Unsupervised Image Change Detection
Unsupervised Text Extraction from G-Maps
Credulous and Skeptical Argument Games for Complete Semantics in Conflict Resolution based Argumentation
Deontic Logic for Human Reasoning
Exchangeable Variable Models
On the Relative Expressiveness of Argumentation Frameworks, Normal Logic Programs and Abstract Dialectical Frameworks
The Multi-engine ASP Solver ME-ASP: Progress Report
Gabor Filter and Rough Clustering Based Edge Detection
Towards a Benchmark of Natural Language Arguments
An expert system for recommending suitable ornamental fish addition to an aquarium based on aquarium condition
Transalg: a Tool for Translating Procedural Descriptions of Discrete Functions to SAT
Adaptive Monte Carlo via Bandit Allocation
Developing Corpus-based Translation Methods between Informal and Formal Mathematics: Project Description
ESmodels: An Epistemic Specification Solver
Anytime Computation of Cautious Consequences in Answer Set Programming
Building a Classification Model for Enrollment In Higher Educational Courses using Data Mining Techniques
Application of Methods for Syntax Analysis of Context-Free Languages to Query Evaluation of Logic Programs
Off-Policy Shaping Ensembles in Reinforcement Learning
Understanding model counting for $β$-acyclic CNF-formulas
On minimal sets of graded attribute implications
On the cost-complexity of multi-context systems
Integrating Vague Association Mining with Markov Model
n-Valued Refined Neutrosophic Logic and Its Applications to Physics
Counting Markov Blanket Structures
Learning Probabilistic Programs
Réseaux de radio cognitive : Allocation des ressources radio et accès dynamique au spectre
Possibilities of technologization of philosophical knowledge
Decision-Making with Complex Data Structures using Probabilistic Programming
Abduction and Dialogical Proof in Argumentation and Logic Programming
Strategy Synthesis for General Deductive Games Based on SAT Solving
A Plausibility Semantics for Abstract Argumentation Frameworks
Virus Detection in Multiplexed Nanowire Arrays using Hidden Semi-Markov models
$OntoMath^{PRO}$ Ontology: A Linked Data Hub for Mathematics
An evolutionary solver for linear integer programming
'Almost Sure' Chaotic Properties of Machine Learning Methods
MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning
Boundary properties of the inconsistency of pairwise comparisons in group decisions
Bayesian Multitask Learning with Latent Hierarchies
Robust Graphical Modeling with t-Distributions
Quantum Annealing for Variational Bayes Inference
Prediction with Advice of Unknown Number of Experts
Exponentiated Gradient Exploration for Active Learning
Fuzzy inequational logic
Controlled Natural Language Processing as Answer Set Programming: an Experiment
Matrix Completion under Interval Uncertainty
The New Approach on Fuzzy Decision Trees
Improving the Interpretability of Support Vector Machines-based Fuzzy Rules
Soft Neutrosophic Algebraic Structures and Their Generalization
Consensus and Consistency Level Optimization of Fuzzy Preference Relation: A Soft Computing Approach
Equilibrium States in Numerical Argumentation Networks
Mathematical Knowledge Representation: Semantic Models and Formalisms
On the Computational Efficiency of Training Neural Networks
Interactive Error Correction in Implicative Theories
Towards a Model Theory for Distributed Representations
Parameterizing the semantics of fuzzy attribute implications by systems of isotone Galois connections
An Unsupervised Ensemble-based Markov Random Field Approach to Microscope Cell Image Segmentation
Reasoning for ALCQ extended with a flexible meta-modelling hierarchy
A Comparison of learning algorithms on the Arcade Learning Environment
Conditional Generative Adversarial Nets
Hardware and Software manual for Evolution of Oil Droplets in a Chemo-Robotic Platform
Modeling Word Relatedness in Latent Dirichlet Allocation
Bounding the Probability of Causation in Mediation Analysis
Handling owl:sameAs via Rewriting
An Approach to Model Checking of Multi-agent Data Analysis
Integrating Fuzzy and Ant Colony System for Fuzzy Vehicle Routing Problem with Time Windows
Using Description Logics for RDF Constraint Checking and Closed-World Recognition
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations
A Note on Systematic Conflict Generation in CA-EN-type Causal Structures
Relations World: A Possibilistic Graphical Model
Automated Reasoning in Deontic Logic
A Unified View of Large-scale Zero-sum Equilibrium Computation
Falling Rule Lists
Discrete Bayesian Networks: The Exact Posterior Marginal Distributions
Rational Deployment of Multiple Heuristics in IDA*
Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
Unweighted Stochastic Local Search can be Effective for Random CSP Benchmarks
Improving the Deductive System DES with Persistence by Using SQL DBMS's
Belief Hierarchical Clustering
Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing
Value Iteration with Options and State Aggregation
Second International Nurse Rostering Competition (INRC-II) --- Problem Description and Rules ---
Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling
Slice Sampling for Probabilistic Programming
Consid{é}rant la d{é}pendance dans la th{é}orie des fonctions de croyance
Second-Order Belief Hidden Markov Models
Int{é}gration d'une mesure d'ind{é}pendance pour la fusion d'informations
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs
Inclusion within Continuous Belief Functions
A Flexible Coupling Approach to Multi-Agent Planning under Incomplete Information
A Modification of the Halpern-Pearl Definition of Causality
Towards the Ontology Web Search Engine
A Feature-based Classification Technique for Answering Multi-choice World History Questions
Fast Differentially Private Matrix Factorization
Structure Formation in Large Theories
LeoPARD --- A Generic Platform for the Implementation of Higher-Order Reasoners
Relations between MDDs and Tuples and Dynamic Modifications of MDDs based constraints
Norm Monitoring under Partial Action Observability
OntoSOC: Sociocultural Knowledge Ontology
Scalable Parallel Numerical Constraint Solver Using Global Load Balancing
On sets of graded attribute implications with witnessed non-redundancy
On the relation between accuracy and fairness in binary classification
New HSL Distance Based Colour Clustering Algorithm
A Logic of Knowing How
Feature Representation for Online Signature Verification
A Tool for Computing and Estimating the Volume of the Solution Space of SMT(LA)
Online Transfer Learning in Reinforcement Learning Domains
Emphatic Temporal-Difference Learning
Dependency-based Convolutional Neural Networks for Sentence Embedding
Archaeology in the Digital Age: From Paper to Databases
Towards Log-Linear Logics with Concrete Domains
First-order integer programming for MAP problems
Complexity and Compilation of GZ-Aggregates in Answer Set Programming
Solomonoff Induction Violates Nicod's Criterion
Optimizing the computation of overriding
Reinforcement Learning for the Unit Commitment Problem
RAPS: A Recommender Algorithm Based on Pattern Structures
Towards a Better Understanding of CAR, CDR, CADR and the Others
Adapting Stochastic Search For Real-time Dynamic Weighted Constraint Satisfaction
Computation of Stackelberg Equilibria of Finite Sequential Games
Implementing Efficient All Solutions SAT Solvers
Local Rademacher Complexity Bounds based on Covering Numbers
Towards a general framework for an observation and knowledge based model of occupant behaviour in office buildings
Gelisp: A Library to Represent Musical CSPs and Search Strategies
On oblivious branching programs with bounded repetition that cannot efficiently compute CNFs of bounded treewidth
Narrative Science Systems: A Review
Creating Scalable and Interactive Web Applications Using High Performance Latent Variable Models
An Efficient Implementation for WalkSAT
Empirical Study on Deep Learning Models for Question Answering
Redesigning pattern mining algorithms for supercomputers
Chaos of Protein Folding
Visualising interactive inferences with IDPD3
Submodular Hamming Metrics
Deep Multimodal Semantic Embeddings for Speech and Images
Complexity of the Description Logic ALCM
Communicating Semantics: Reference by Description
Planning in the Wild: Modeling Tools for PDDL
Bayesian Network Models for Adaptive Testing
Shaping Proto-Value Functions via Rewards
On the convergence of cycle detection for navigational reinforcement learning
Column-Oriented Datalog Materialization for Large Knowledge Graphs (Extended Technical Report)
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
A SAT model to mine flexible sequences in transactional datasets
COCO: The Bi-objective Black Box Optimization Benchmarking (bbob-biobj) Test Suite
Coordination of Players in Ride-Sharing Games by Signaling
Character-Level Question Answering with Attention
Extending DLR with Labelled Tuples, Projections, Functional Dependencies and Objectification (full version)
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
On the uniform one-dimensional fragment
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
Patterns on data described by vague limits, vague colimits and vague commutativity
Resource Allocation with Population Dynamics
HordeQBF: A Modular and Massively Parallel QBF Solver
Single-Image Depth Perception in the Wild
KOGNAC: Efficient Encoding of Large Knowledge Graphs
A global constraint for closed itemset mining
Text-based LSTM networks for Automatic Music Composition
Procedural urban environments for FPS games
A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion
Parameterized Compilation Lower Bounds for Restricted CNF-formulas
A Computational Model for Situated Task Learning with Interactive Instruction
Extracted Social Network Mining
AGM-Style Revision of Beliefs and Intentions from a Database Perspective (Preliminary Version)
Endgame Analysis of Dou Shou Qi
Mutual Transformation of Information and Knowledge
Selecting the Selection
Supervisory Control for Behavior Composition
Context Discovery for Model Learning in Partially Observable Environments
Learning a Driving Simulator
Interacting Conceptual Spaces
Stable Models for Infinitary Formulas with Extensional Atoms
Query Answering in Resource-Based Answer Set Semantics
Winograd Schemas and Machine Translation
Delta Epsilon Alpha Star: A PAC-Admissible Search Algorithm
Deeply Semantic Inductive Spatio-Temporal Learning
Mean Box Pooling: A Rich Image Representation and Output Embedding for the Visual Madlibs Task
Resolving Spatial-Time Conflicts In A Set Of Any-angle Or Angle-constrained Grid Paths
Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
Perceptual Reward Functions
A Geometric Framework for Convolutional Neural Networks
Natural Language Processing using Hadoop and KOSHIK
Free Lunch for Optimisation under the Universal Distribution
Evaluating Causal Models by Comparing Interventional Distributions
Towards Music Captioning: Generating Music Playlist Descriptions
Effectiveness of greedily collecting items in open world games
Causality and Responsibility for Formal Verification and Beyond
From Deterministic ODEs to Dynamic Structural Causal Models
Achievements in Answer Set Programming (Preliminary Report)
BreakID: Static Symmetry Breaking for ASP (System Description)
ALLSAT compressed with wildcards. Part 1: Converting CNF's to orthogonal DNF's
The Generalized Smallest Grammar Problem
Optimal Upper and Lower Bounds for Boolean Expressions by Dissociation
A Quantitative Version of the Gibbard-Satterthwaite Theorem for Three Alternatives
Overcoming Misleads In Logic Programs by Redefining Negation
Activity-Based Search for Black-Box Contraint-Programming Solvers
The matrices of argumentation frameworks
Bi-modal Gödel logic over [0,1]-valued Kripke frames
A Generalized Arc-Consistency Algorithm for a Class of Counting Constraints: Revised Edition that Incorporates One Correction
Quels formalismes temporels pour représenter des connaissances extraites de textes de recettes de cuisine ?
Modelling Constraint Solver Architecture Design as a Constraint Problem
A cognitive diversity framework for radar target classification
Entropy Search for Information-Efficient Global Optimization
Multi-granular Perspectives on Covering
Bootstrapping Intrinsically Motivated Learning with Human Demonstrations
Real-time face swapping as a tool for understanding infant self-recognition
Truncated Power Method for Sparse Eigenvalue Problems
Enhancing Support for Knowledge Works: A relatively unexplored vista of computing research
Multi-q Analysis of Image Patterns
Disjunctive Logic Programs versus Normal Logic Programs
IFP-Intuitionistic fuzzy soft set theory and its applications
Logical Fuzzy Preferences
Nested Aggregates in Answer Sets: An Application to a Priori Optimization
Roborobo! a Fast Robot Simulator for Swarm and Collective Robotics
Logical Probability Preferences
Logical Stochastic Optimization
Justificatory and Explanatory Argumentation for Committing Agents
Unveiling the link between logical fallacies and web persuasion
Efficient Computation of Mean Truncated Hitting Times on Very Large Graphs
Mining to Compact CNF Propositional Formulae
Temporal Description Logic for Ontology-Based Data Access (Extended Version)
Enacting Social Argumentative Machines in Semantic Wikipedia
Automating the Dispute Resolution in Task Dependency Network
Enhancements to ACL2 in Versions 5.0, 6.0, and 6.1
A Note on Topology Preservation in Classification, and the Construction of a Universal Neuron Grid
MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers
Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations
Bat Algorithm: Literature Review and Applications
Evolution Theory of Self-Evolving Autonomous Problem Solving Systems
An Integrated Framework for Diagnosis and Prognosis of Hybrid Systems
The Partner Units Configuration Problem: Completing the Picture
Microstrip Coupler Design Using Bat Algorithm
Combining finite and continuous solvers
Revisiting the Learned Clauses Database Reduction Strategies
Handwritten Character Recognition In Malayalam Scripts- A Review
Feature and Variable Selection in Classification
Machine Learner for Automated Reasoning 0.4 and 0.5
Parameter estimation based on interval-valued belief structures
Towards Ultra Rapid Restarts
A normative account of defeasible and probabilistic inference
Line Maps in Cluttered Environments
Reciprocity in Gift-Exchange-Games
A Superposition Calculus for Abductive Reasoning
A Geometric Method to Obtain the Generation Probability of a Sentence
The Best Templates Match Technique For Example Based Machine Translation
Multiscale probability transformation of basic probability assignment
Introduction to Neutrosophic Statistics
Rational Closure in SHIQ
A bio-inspired algorithm for fuzzy user equilibrium problem by aid of Physarum Polycephalum
Tableaux for Dynamic Logic of Propositional Assignments
ExpertBayes: Automatically refining manually built Bayesian networks
Graph Approximation and Clustering on a Budget
Kalman Temporal Differences
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
Towards a theory of granular sets
Notes on hierarchical ensemble methods for DAG-structured taxonomies
Exact Decoding on Latent Variable Conditional Models is NP-Hard
Knowledge Base of an Expert System Used for Dyslalic Children Therapy
Random Logic Programs: Linear Model
Communicating and resolving entity references
Feature Selection in Conditional Random Fields for Map Matching of GPS Trajectories
Action Recognition in the Frequency Domain
Simulating Non Stationary Operators in Search Algorithms
On Minimax Optimal Offline Policy Evaluation
On tensor rank of conditional probability tables in Bayesian networks
Neighborhood Selection and Rules Identification for Cellular Automata: A Rough Sets Approach
The Application of Differential Privacy for Rank Aggregation: Privacy and Accuracy
Gradient-based Taxis Algorithms for Network Robotics
Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search
A CSP implementation of the bigraph embedding problem
Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework
Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression
The category of networks of ontologies
Logic of temporal attribute implications
Feature Weight Tuning for Recursive Neural Networks
Turing Test for the Internet of Things
Stochastic Local Search for Pattern Set Mining
Efficient Decision-Making by Volume-Conserving Physical Object
Example Selection For Dictionary Learning
Towards a Consistent, Sound and Complete Conceptual Knowledge
KF metamodel formalization
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
A note about the generalisation of the C-tests
Minimizing Regret in Dynamic Decision Problems
Injury risk prediction for traffic accidents in Porto Alegre/RS, Brazil
Numerical Solution of Fuzzy Stochastic Differential Equation
Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
The Power of Randomization: Distributed Submodular Maximization on Massive Datasets
Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions
On Forgetting in Tractable Propositional Fragments
Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
Explaining robust additive utility models by sequences of preference swaps
Inductive Learning for Rule Generation from Ontology
Automated Reasoning for Robot Ethics
Pseudo Fuzzy Set
Transformation of basic probability assignments to probabilities based on a new entropy measure
Path Finding under Uncertainty through Probabilistic Inference
Online Fair Division: analysing a Food Bank problem
Probabilistic Zero-shot Classification with Semantic Rankings
Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges
An Introduction to Logics of Knowledge and Belief
Robustly Leveraging Prior Knowledge in Text Classification
Compositional Distributional Semantics with Long Short Term Memory
Transitive reasoning with imprecise probabilities
Autonomic Resource Management in Virtual Networks
Combining partially independent belief functions
A Rule-Based Short Query Intent Identification System
Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory
Recent advances on inconsistency indices for pairwise comparisons - a commentary
The Libra Toolkit for Probabilistic Models
Monte Carlo Localization in Hand-Drawn Maps
Dual Decomposition from the Perspective of Relax, Compensate and then Recover
RDF annotation of Second Life objects: Knowledge Representation meets Social Virtual reality
Knowledge reduction of dynamic covering decision information systems with varying attribute values
Tractable Query Answering and Optimization for Extensions of Weakly-Sticky Datalog+-
Fuzzy approaches to context variable in fuzzy geographically weighted clustering
Graphlet-based lazy associative graph classification
Formalizing Preference Utilitarianism in Physical World Models
x.ent: R Package for Entities and Relations Extraction based on Unsupervised Learning and Document Structure
Logical Conditional Preference Theories
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation
Maximum a Posteriori Estimation by Search in Probabilistic Programs
Theory of Semi-Instantiation in Abstract Argumentation
Private Disclosure of Information in Health Tele-monitoring
Prefix-Projection Global Constraint for Sequential Pattern Mining
Detecting Concept-level Emotion Cause in Microblogging
Interactive Knowledge Base Population
Formal Concept Analysis for Knowledge Discovery from Biological Data
Performing Bayesian Risk Aggregation using Discrete Approximation Algorithms with Graph Factorization
On SAT Models Enumeration in Itemset Mining
An Ensemble method for Content Selection for Data-to-text Systems
Variational Gaussian Copula Inference
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks
Sequential Extensions of Causal and Evidential Decision Theory
Dynamic Bayesian Ontology Languages
Argumentation Semantics for Prioritised Default Logic
Procedural Content Generation for GDL Descriptions of Simplified Boardgames
Factor Graphs for Quantum Probabilities
Identifying Avatar Aliases in Starcraft 2
Arabic Text Watermarking: A Review
Simulation of optical flow and fuzzy based obstacle avoidance system for mobile robots
Fuzzy Longest Common Subsequence Matching With FCM Using R
Drawing and Analyzing Causal DAGs with DAGitty
Duration and Interval Hidden Markov Model for Sequential Data Analysis
The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks
Model Guided Sampling Optimization for Low-dimensional Problems
Value function approximation via low-rank models
A Neural Attention Model for Abstractive Sentence Summarization
Better Document-level Sentiment Analysis from RST Discourse Parsing
Reinforcement Learning with Parameterized Actions
Natural scene statistics mediate the perception of image complexity
Loops with abelian inner mapping groups: An application of automated deduction
Sports highlights generation based on acoustic events detection: A rugby case study
Graph Kernels exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions
A Compositional Explanation of the Pet Fish Phenomenon
A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation
Quantum Look at two Common Logics: the Logic of Primitive Thinking and the Logic of Everyday Human Reasoning
Encoding Reality: Prediction-Assisted Cortical Learning Algorithm in Hierarchical Temporal Memory
Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base
Contamination-Free Measures and Algebraic Operations
The Rationale behind the Concept of Goal
A Model for Safety Case Confidence Assessment
Conditions for Normative Decision Making at the Fire Ground
Signal Representations on Graphs: Tools and Applications
Combining Fuzzy Cognitive Maps and Discrete Random Variables
SDDs are Exponentially More Succinct than OBDDs
Angrier Birds: Bayesian reinforcement learning
Fuzzy Object-Oriented Dynamic Networks. I
On Clustering Time Series Using Euclidean Distance and Pearson Correlation
Indicators of Good Student Performance in Moodle Activity Data
Proactive Message Passing on Memory Factor Networks
Top-N Recommender System via Matrix Completion
The Singularity Controversy, Part I: Lessons Learned and Open Questions: Conclusions from the Battle on the Legitimacy of the Debate
A Label Semantics Approach to Linguistic Hedges
The Utility of Hedged Assertions in the Emergence of Shared Categorical Labels
A First Attempt to Cloud-Based User Verification in Distributed System
Discussion on Mechanical Learning and Learning Machine
Greedy Deep Dictionary Learning
GECKA3D: A 3D Game Engine for Commonsense Knowledge Acquisition
Fuzzy Object-Oriented Dynamic Networks. II
Wayfinding and cognitive maps for pedestrian models
Region Based Approximation for High Dimensional Bayesian Network Models
Variations of the Similarity Function of TextRank for Automated Summarization
Machine olfaction using time scattering of sensor multiresolution graphs
Science Question Answering using Instructional Materials
Large-Scale Reasoning with OWL
Deep Exploration via Bootstrapped DQN
Recommendations as Treatments: Debiasing Learning and Evaluation
A General Modifier-based Framework for Inconsistency-Tolerant Query Answering
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Strong Backdoors for Default Logic
Causes for Query Answers from Databases, Datalog Abduction and View-Updates: The Presence of Integrity Constraints
Social planning for social HRI
SIFT: An Algorithm for Extracting Structural Information From Taxonomies
Thompson Sampling is Asymptotically Optimal in General Environments
Towards Neural Knowledge DNA
Learning to Blend Computer Game Levels
A Set Theoretic Approach for Knowledge Representation: the Representation Part
Penta and Hexa Valued Representation of Neutrosophic Information
Grounding Recursive Aggregates: Preliminary Report
Active Algorithms For Preference Learning Problems with Multiple Populations
Evolving Shepherding Behavior with Genetic Programming Algorithms
A System for Probabilistic Linking of Thesauri and Classification Systems
Learning Executable Semantic Parsers for Natural Language Understanding
An Expressive Probabilistic Temporal Logic
Properties of ABA+ for Non-Monotonic Reasoning
Spectral M-estimation with Applications to Hidden Markov Models
Algorithms for Batch Hierarchical Reinforcement Learning
Ordinal Conditional Functions for Nearly Counterfactual Revision
Reactive Policies with Planning for Action Languages
Graph Clustering Bandits for Recommendation
Online Learning of Commission Avoidant Portfolio Ensembles
Notes on a model for fuzzy computing
LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues
Combinatorial Aspects of the Distribution of Rough Objects
Adobe-MIT submission to the DSTC 4 Spoken Language Understanding pilot task
Function-Described Graphs for Structural Pattern Recognition
Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
Concept based Attention
Learning Representations for Counterfactual Inference
Real-Time Web Scale Event Summarization Using Sequential Decision Making
Natural Language Semantics and Computability
A New Method for Parallel Monte Carlo Tree Search
On Avoidance Learning with Partial Observability
On the Complexity of Connection Games
Combat Models for RTS Games
Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment
The Bees Algorithm for the Vehicle Routing Problem
Dynamic Bayesian Networks to simulate occupant behaviours in office buildings related to indoor air quality
As Cool as a Cucumber: Towards a Corpus of Contemporary Similes in Serbian
Extracting Higher-Order Goals from the Mizar Mathematical Library
Posterior Dispersion Indices
Compliant Conditions for Polynomial Time Approximation of Operator Counts
Internal Guidance for Satallax
Psychologically based Virtual-Suspect for Interrogative Interview Training
Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks
Information Theoretically Aided Reinforcement Learning for Embodied Agents
A structured argumentation framework for detaching conditional obligations
An interactive fuzzy goal programming algorithm to solve decentralized bi-level multiobjective fractional programming problem
Learning to Optimize
Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions
Structured Convolution Matrices for Energy-efficient Deep learning
Symbolic Music Data Version 1.0
DialPort: Connecting the Spoken Dialog Research Community to Real User Data
e-Commerce product classification: our participation at cDiscount 2015 challenge
Generative Adversarial Imitation Learning
A Probabilistic-Based Model for Binary CSP
Modal-set estimation with an application to clustering
DeepMath - Deep Sequence Models for Premise Selection
Deep Reinforcement Learning With Macro-Actions
The Mondrian Kernel
Proceedings First International Workshop on Hammers for Type Theories
Learning Abstract Classes using Deep Learning
Founded Semantics and Constraint Semantics of Logic Rules
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks
Automated Extraction of Number of Subjects in Randomised Controlled Trials
The VGLC: The Video Game Level Corpus
Epistemic Protocols for Distributed Gossiping
A Dynamic Epistemic Framework for Conformant Planning
"Show me the cup": Reference with Continuous Representations
Exploring high-level Perspectives on Self-Configuration Capabilities of Systems
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
Swift: Compiled Inference for Probabilistic Programming Languages
Learning Crosslingual Word Embeddings without Bilingual Corpora
Probabilistic Reasoning in the Description Logic ALCP with the Principle of Maximum Entropy (Full Version)
Why is Posterior Sampling Better than Optimism for Reinforcement Learning?
Meaningful Models: Utilizing Conceptual Structure to Improve Machine Learning Interpretability
Analysis of Double Covers of Factor Graphs
A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features
Real-Time Anomaly Detection for Streaming Analytics
How to Allocate Resources For Features Acquisition?
Mapping distributional to model-theoretic semantic spaces: a baseline
sk_p: a neural program corrector for MOOCs
Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy
Validation of Information Fusion
Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction
Neuromorphic Robot Dream
Personalized Emphasis Framing for Persuasive Message Generation
A MIP Backend for the IDP System
High Dimensional Human Guided Machine Learning
Multi Exit Configuration of Mesoscopic Pedestrian Simulation
Equilibrium Graphs
Feasibility of Post-Editing Speech Transcriptions with a Mismatched Crowd
Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
Reduced Space and Faster Convergence in Imperfect-Information Games via Regret-Based Pruning
Sequencing Chess
Exploration Potential
Prioritised Default Logic as Argumentation with Partial Order Default Priorities
Solving the Wastewater Treatment Plant Problem with SMT
Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
A globally-applicable disease ontology for biosurveillance; Anthology of Biosurveillance Diseases (ABD)
A Logic of Knowing Why
The Digital Synaptic Neural Substrate: Size and Quality Matters
Semiring Programming: A Framework for Search, Inference and Learning
NdFluents: A Multi-dimensional Contexts Ontology
Social Network Processes in the Isabelle and Coq Theorem Proving Communities
Learning to Translate for Multilingual Question Answering
AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline
Global Constraint Catalog, Volume II, Time-Series Constraints
Heuristic with elements of tabu search for Truck and Trailer Routing Problem
Semantic Parsing with Semi-Supervised Sequential Autoencoders
Deep unsupervised learning through spatial contrasting
A Tour of TensorFlow
Lifted Message Passing for the Generalized Belief Propagation
Places: An Image Database for Deep Scene Understanding
Deep Reinforcement Learning From Raw Pixels in Doom
Learning Macro-actions for State-Space Planning
On Deductive Systems of AC Semantics for Rough Sets
Multi-Objective Deep Reinforcement Learning
ABA+: Assumption-Based Argumentation with Preferences
PCG-Based Game Design Patterns
A Fuzzy Logic System to Analyze a Student's Lifestyle
Bank Card Usage Prediction Exploiting Geolocation Information
Improved Knowledge Base Completion by Path-Augmented TransR Model
Wind ramp event prediction with parallelized Gradient Boosted Regression Trees
Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm
Maximizing positive opinion influence using an evidential approach
Generalized Interval-valued OWA Operators with Interval Weights Derived from Interval-valued Overlap Functions
A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade
Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model
An Evolving Neuro-Fuzzy System with Online Learning/Self-learning
An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering
Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models
Reinforcement Learning in Conflicting Environments for Autonomous Vehicles
Characterization of an inconsistency ranking for pairwise comparison matrices
Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation
Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm
Hit-and-Run for Sampling and Planning in Non-Convex Spaces
Fuzzy Bayesian Learning
Robust Spectral Inference for Joint Stochastic Matrix Factorization
Strong Neutrosophic Graphs and Subgraph Topological Subspaces
The new hybrid COAW method for solving multi-objective problems
TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
Ways of Conditioning Generative Adversarial Networks
A Compare-Aggregate Model for Matching Text Sequences
Reinforcement Learning Approach for Parallelization in Filters Aggregation Based Feature Selection Algorithms
On interestingness measures of formal concepts
Song From PI: A Musically Plausible Network for Pop Music Generation
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
Recoverability of Joint Distribution from Missing Data
A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs
Neural Style Representations and the Large-Scale Classification of Artistic Style
Monte Carlo Connection Prover
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment
Generalized Dropout
Options Discovery with Budgeted Reinforcement Learning
Limbo: A Fast and Flexible Library for Bayesian Optimization
Deep Learning Approximation for Stochastic Control Problems
Deep Reinforcement Learning for Multi-Domain Dialogue Systems
Multiwinner Approval Rules as Apportionment Methods
"Model and Run" Constraint Networks with a MILP Engine
Analyzing Features for the Detection of Happy Endings in German Novels
Towards a new quantum cognition model
Generic and Efficient Solution Solves the Shortest Paths Problem in Square Runtime
Semantic Parsing of Mathematics by Context-based Learning from Aligned Corpora and Theorem Proving
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Low-dimensional Data Embedding via Robust Ranking
Unit Commitment using Nearest Neighbor as a Short-Term Proxy
Optimizing Quantiles in Preference-based Markov Decision Processes
Probabilistic Neural Programs
Comparison of the COG Defuzzification Technique and Its Variations to the GPA Index
Representing Independence Models with Elementary Triplets
Improving the Performance of Neural Networks in Regression Tasks Using Drawering
Coactive Critiquing: Elicitation of Preferences and Features
Knowledge Representation in Graphs using Convolutional Neural Networks
Decision Theory in an Algebraic Setting
Controlling Robot Morphology from Incomplete Measurements
Hierarchy through Composition with Linearly Solvable Markov Decision Processes
GOTM: a Goal-oriented Framework for Capturing Uncertainty of Medical Treatments
DeepCancer: Detecting Cancer through Gene Expressions via Deep Generative Learning
Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks
Encapsulating models and approximate inference programs in probabilistic modules
Crowdsourced Outcome Determination in Prediction Markets
TeKnowbase: Towards Construction of a Knowledge-base of Technical Concepts
A correlation coefficient of belief functions
Sample-efficient Deep Reinforcement Learning for Dialog Control
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning
Automated timetabling for small colleges and high schools using huge integer programs
Meta-Unsupervised-Learning: A supervised approach to unsupervised learning
A hybrid approach to supervised machine learning for algorithmic melody composition
PrASP Report
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs
From Preference-Based to Multiobjective Sequential Decision-Making
A pre-semantics for counterfactual conditionals and similar logics
A K-fold Method for Baseline Estimation in Policy Gradient Algorithms
OpenNMT: Open-Source Toolkit for Neural Machine Translation
Hedera: Scalable Indexing and Exploring Entities in Wikipedia Revision History
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks
Heterogeneous Information Network Embedding for Meta Path based Proximity
Binary Matrix Guessing Problem
ENIGMA: Efficient Learning-based Inference Guiding Machine
LAREX - A semi-automatic open-source Tool for Layout Analysis and Region Extraction on Early Printed Books
Logic Programming Petri Nets
Efficiently Summarising Event Sequences with Rich Interleaving Patterns
Organic Computing in the Spotlight
Comparative Study Of Data Mining Query Languages
Incremental Maintenance Of Association Rules Under Support Threshold Change
A Study of FOSS'2013 Survey Data Using Clustering Techniques
Redefinition of the concept of fuzzy set based on vague partition from the perspective of axiomatization
A Hybrid Evolutionary Algorithm Based on Solution Merging for the Longest Arc-Preserving Common Subsequence Problem
Two forms of minimality in ASPIC+
Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
Survey of modern Fault Diagnosis methods in networks
Convolutional Neural Network for Earthquake Detection and Location
A Historical Review of Forty Years of Research on CMAC
Graph Neural Networks and Boolean Satisfiability
Developing an ontology for the access to the contents of an archival fonds: the case of the Catasto Gregoriano
OntoMath Digital Ecosystem: Ontologies, Mathematical Knowledge Analytics and Management
Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision
Theorem Proving Based on Semantics of DNA Strand Graph
Quantifying Program Bias
'Viral' Turing Machines, Computation from Noise and Combinatorial Hierarchies
The Dialog State Tracking Challenge with Bayesian Approach
An Integer Programming Model for Binary Knapsack Problem with Value-Related Dependencies among Elements
Realization of Ontology Web Search Engine
A DIKW Paradigm to Cognitive Engineering
Ontologies in System Engineering: a Field Report
Boosted Generative Models
Towards A Rigorous Science of Interpretable Machine Learning
Bayesian Verification under Model Uncertainty
Stacked Thompson Bandits
Improving the Neural GPU Architecture for Algorithm Learning
Do Reichenbachian Common Cause Systems of Arbitrary Finite Size Exist?
The Statistical Recurrent Unit
Evaluating Singleplayer and Multiplayer in Human Computation Games
High-Resolution Multispectral Dataset for Semantic Segmentation
A Gentle Introduction to Epistemic Planning: The DEL Approach
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
Modeling the Ellsberg Paradox by Argument Strength
Abductive, Causal, and Counterfactual Conditionals Under Incomplete Probabilistic Knowledge
The Ontological Multidimensional Data Model
Axioms in Model-based Planners
Gait Pattern Recognition Using Accelerometers
Cost-Based Intuitionist Probabilities on Spaces of Graphs, Hypergraphs and Theorems
Reinforcement Learning for Transition-Based Mention Detection
Fuzzy Rankings: Properties and Applications
On Inconsistency Indices and Inconsistency Axioms in Pairwise Comparisons
InScript: Narrative texts annotated with script information
ParaGraphE: A Library for Parallel Knowledge Graph Embedding
A Visual Web Tool to Perform What-If Analysis of Optimization Approaches
Foundations for a Probabilistic Event Calculus
Improving Statistical Multimedia Information Retrieval Model by using Ontology
Ontology Based Pivoted normalization using Vector Based Approach for information Retrieval
Deep Exploration via Randomized Value Functions
Diversification-Based Learning in Computing and Optimization
Implications of the Fourth Industrial Age on Higher Education
Structured Parallel Programming for Monte Carlo Tree Search
Multi-Task Learning of Keyphrase Boundary Classification
Reprogramming Matter, Life, and Purpose
A simulated annealing approach to optimal storing in a multi-level warehouse
Finite-Time Stabilization of Longitudinal Control for Autonomous Vehicles via a Model-Free Approach
Best Practices for Applying Deep Learning to Novel Applications
Geometry of Policy Improvement
AppLP: A Dialogue on Applications of Logic Programming
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
Matching Media Contents with User Profiles by means of the Dempster-Shafer Theory
Minkowski Operations of Sets with Application to Robot Localization
Scavenger 0.1: A Theorem Prover Based on Conflict Resolution
Beliefs and Probability in Bacchus' l.p. Logic: A~3-Valued Logic Solution to Apparent Counter-intuition
CASP Solutions for Planning in Hybrid Domains
Dempster-Shafer Belief Function - A New Interpretation
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
Environment-Independent Task Specifications via GLTL
Generic LSH Families for the Angular Distance Based on Johnson-Lindenstrauss Projections and Feature Hashing LSH
Pseudorehearsal in actor-critic agents
Investigating Recurrence and Eligibility Traces in Deep Q-Networks
Beating Atari with Natural Language Guided Reinforcement Learning
A multi-method simulation of a high-frequency bus line using AnyLogic
Stochastic Constraint Programming as Reinforcement Learning
Learning from Ontology Streams with Semantic Concept Drift
Abstract Syntax Networks for Code Generation and Semantic Parsing
280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification
Fine-Grained Entity Typing with High-Multiplicity Assignments
A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations
Punny Captions: Witty Wordplay in Image Descriptions
Multimodal Word Distributions
No, This is not a Circle
Modeling Events as Machines
Kiwi - A Minimalist CP Solver
Quantum Mechanical Approach to Modelling Reliability of Sensor Reports
Imagining Probabilistic Belief Change as Imaging (Technical Report)
A Versatile, Sound Tool for Simplifying Definitions
Distributed Online Learning of Event Definitions
SLDR-DL: A Framework for SLD-Resolution with Deep Learning
Finding Bottlenecks: Predicting Student Attrition with Unsupervised Classifier
Block-Parallel IDA* for GPUs (Extended Manuscript)
CORe50: a New Dataset and Benchmark for Continuous Object Recognition
Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning
On the Complexity of Semantic Integration of OWL Ontologies
Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination
Learning Probabilistic Programs Using Backpropagation
Repeated Inverse Reinforcement Learning
Atari games and Intel processors
RankPL: A Qualitative Probabilistic Programming Language
Combining tabu search and graph reduction to solve the maximum balanced biclique problem
Ensemble Sampling
Note on Evolution and Forecasting of Requirements: Communications Example
pix2code: Generating Code from a Graphical User Interface Screenshot
Continual Learning in Generative Adversarial Nets
Beyond Parity: Fairness Objectives for Collaborative Filtering
Learning Causal Structures Using Regression Invariance
Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata
Probabilistic Program Abstractions
Abstract Argumentation / Persuasion / Dynamics
Deep Learning for Ontology Reasoning
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning
Generating Steganographic Text with LSTMs
Knowledge Base Completion: Baselines Strike Back
Unsupervised Learning of Disentangled Representations from Video
ICABiDAS: Intuition Centred Architecture for Big Data Analysis and Synthesis
A Joint Model for Question Answering and Question Generation
Marmara Turkish Coreference Corpus and Coreference Resolution Baseline
Design and Implementation of Modified Fuzzy based CPU Scheduling Algorithm
Towards balanced clustering - part 1 (preliminaries)
Off The Beaten Lane: AI Challenges In MOBAs Beyond Player Control
Towards Statistical Reasoning in Description Logics over Finite Domains (Full Version)
Towards Grounding Conceptual Spaces in Neural Representations
An Overview of Multi-Task Learning in Deep Neural Networks
Bib2vec: An Embedding-based Search System for Bibliographic Information
Collaborative vehicle routing: a survey
Bayesian Conditional Generative Adverserial Networks
The impact of Entropy and Solution Density on selected SAT heuristics
Entropy, neutro-entropy and anti-entropy for neutrosophic information
Data set operations to hide decision tree rules
Learning Hierarchical Information Flow with Recurrent Neural Modules
Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning
VAIN: Attentional Multi-agent Predictive Modeling
Programmable Agents
MAGIX: Model Agnostic Globally Interpretable Explanations
An approach to reachability analysis for feed-forward ReLU neural networks
Rational coordination with no communication or conventions
Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure?
Handling PDDL3.0 State Trajectory Constraints with Temporal Landmarks
Optimal choice: new machine learning problem and its solution
A Simulator for Hedonic Games
Well-supported phylogenies using largest subsets of core-genes by discrete particle swarm optimization
SUNNY-CP and the MiniZinc Challenge
Logic Programming for an Introductory Computer Science Course for High School Students
Learning Knowledge Graph Embeddings with Type Regularizer
New Fairness Metrics for Recommendation that Embrace Differences
Restricted Causal Inference Algorithm
Probabilistic Active Learning of Functions in Structural Causal Models
Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions
Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants
Ising Processing Units: Potential and Challenges for Discrete Optimization
Kernel Feature Selection via Conditional Covariance Minimization
A Deep Network with Visual Text Composition Behavior
Convergence Analysis of Optimization Algorithms
An Online Development Environment for Answer Set Programming
Evaluating Social Networks Using Task-Focused Network Inference
Towards an automated method based on Iterated Local Search optimization for tuning the parameters of Support Vector Machines
Similarity Search Over Graphs Using Localized Spectral Analysis
Conflict Analysis for Pythagorean Fuzzy Information Systems with Group Decision Making
Mechanics Automatically Recognized via Interactive Observation: Jumping
A Formal Framework to Characterize Interpretability of Procedures
Representation Learning for Grounded Spatial Reasoning
Fast Restricted Causal Inference
A Comprehensive Implementation of Conceptual Spaces
TensorLog: Deep Learning Meets Probabilistic DBs
Eigenlogic: Interpretable Quantum Observables with applications to Fuzzy Behavior of Vehicular Robots
Speeding-up ProbLog's Parameter Learning
Navigability with Imperfect Information
Video Highlight Prediction Using Audience Chat Reactions
Binary Voting with Delegable Proxy: An Analysis of Liquid Democracy
Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)
Analysis of Italian Word Embeddings
Providing Self-Aware Systems with Reflexivity
A Vision For Continuous Automated Game Design
Cost and Actual Causation
Evaluating Music Recommender Systems for Groups
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning
Deep Transfer in Reinforcement Learning by Language Grounding
Deep Recurrent Generative Decoder for Abstractive Text Summarization
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset
Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative
Cheryl's Birthday
Measuring Inconsistency in Argument Graphs
Hierarchically-Attentive RNN for Album Summarization and Storytelling
Tosca: Operationalizing Commitments Over Information Protocols
Automatic Selection of t-SNE Perplexity
Systematic Testing of Convolutional Neural Networks for Autonomous Driving
GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery
Deep Incremental Boosting
Energy saving for building heating via a simple and efficient model-free control design: First steps with computer simulations
A Measure for Dialog Complexity and its Application in Streamlining Service Operations
Learning body-affordances to simplify action spaces
Theoretical Foundation of Co-Training and Disagreement-Based Algorithms
Enriching Information Technology Course Materials by Using Youtube
TheoSea: Marching Theory to Light
Warp: a method for neural network interpretability applied to gene expression profiles
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
Visual Learning of Arithmetic Operations
A Framework for Constrained and Adaptive Behavior-Based Agents
NP-hardness of sortedness constraints
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
New Limits for Knowledge Compilation and Applications to Exact Model Counting
The Wreath Process: A totally generative model of geometric shape based on nested symmetries
On-the-Job Learning with Bayesian Decision Theory
Teaching Machines to Read and Comprehend
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives
On the Prior Sensitivity of Thompson Sampling
The Online Coupon-Collector Problem and Its Application to Lifelong Reinforcement Learning
Fast Online Clustering with Randomized Skeleton Sets
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
Attacker and Defender Counting Approach for Abstract Argumentation
Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates
Rare Speed-up in Automatic Theorem Proving Reveals Tradeoff Between Computational Time and Information Value
Reading Scene Text in Deep Convolutional Sequences
Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game
The Scope and Limits of Simulation in Cognitive Models
Using Hankel Matrices for Dynamics-based Facial Emotion Recognition and Pain Detection
Early Predictions of Movie Success: the Who, What, and When of Profitability
Smart Pacing for Effective Online Ad Campaign Optimization
Expectation Particle Belief Propagation
A Novel Method for Stock Forecasting based on Fuzzy Time Series Combined with the Longest Common/Repeated Sub-sequence
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve
Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest
Deep-Plant: Plant Identification with convolutional neural networks
Automated Benchmarking of Incremental SAT and QBF Solvers
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
Language Understanding for Text-based Games Using Deep Reinforcement Learning
Neuro-Fuzzy Algorithmic (NFA) Models and Tools for Estimation
Mixed Logical and Probabilistic Reasoning for Planning and Explanation Generation in Robotics
Extending SROIQ with Constraint Networks and Grounded Circumscription
Evolutionary Multimodal Optimization: A Short Survey
A Weakly Supervised Learning Approach based on Spectral Graph-Theoretic Grouping
Estimating Mutual Information by Local Gaussian Approximation
Qualitative Decision Methods for Multi-Attribute Decision Making
Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms
On the Linear Belief Compression of POMDPs: A re-examination of current methods
The QBF Gallery: Behind the Scenes
Ontology Bulding vs Data Harvesting and Cleaning for Smart-city Services
Mining for Causal Relationships: A Data-Driven Study of the Islamic State
Replication and Generalization of PRECISE
Fuzzy Logic Based Direct Torque Control Of Induction Motor With Space Vector Modulation
Security Games with Ambiguous Beliefs of Agents
Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspots
A Linearly-Convergent Stochastic L-BFGS Algorithm
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Simulating Brain Reaction to Methamphetamine Regarding Consumer Personality
Answering Fuzzy Conjunctive Queries over Finitely Valued Fuzzy Ontologies
OOASP: Connecting Object-oriented and Logic Programming
Multiple-Path Selection for new Highway Alignments using Discrete Algorithms
Generation of Multimedia Artifacts: An Extractive Summarization-based Approach
Talking about the Moving Image: A Declarative Model for Image Schema Based Embodied Perception Grounding and Language Generation
Sufficient and necessary conditions for Dynamic Programming in Valuation-Based Systems
Causal Decision Trees
Variable Elimination in the Fourier Domain
Molding CNNs for text: non-linear, non-consecutive convolutions
Reasoning in complex environments with the SelectScript declarative language
Distributed Deep Q-Learning
End-to-End Attention-based Large Vocabulary Speech Recognition
Fishing out Winners from Vote Streams
Warehouse Layout Method Based on Ant Colony and Backtracking Algorithm
Efficient Computation of Exact IRV Margins
The backtracking survey propagation algorithm for solving random K-SAT problems
Lifted Relational Neural Networks
The Max $K$-Armed Bandit: A PAC Lower Bound and tighter Algorithms
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Unsatisfiable Cores and Lower Bounding for Constraint Programming
Robot Language Learning, Generation, and Comprehension
Mining Combined Causes in Large Data Sets
A Comparison Between Decision Trees and Decision Tree Forest Models for Software Development Effort Estimation
Learning Structures of Bayesian Networks for Variable Groups
GR2RSS: Publishing Linked Open Commerce Data as RSS and Atom Feeds
What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment
Generating Weather Forecast Texts with Case Based Reasoning
Building a Truly Distributed Constraint Solver with JADE
Quantization based Fast Inner Product Search
Giraffe: Using Deep Reinforcement Learning to Play Chess
Research: Analysis of Transport Model that Approximates Decision Taker's Preferences
Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures
An Approach to the Analysis of the South Slavic Medieval Labels Using Image Texture
Bounded Situation Calculus Action Theories
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Learning Efficient Representations for Reinforcement Learning
Evolving TSP heuristics using Multi Expression Programming
Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs
Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies
Agent enabled Mining of Distributed Protein Data Banks
An Epsilon Hierarchical Fuzzy Twin Support Vector Regression
Multi-Attribute Proportional Representation
Sharing HOL4 and HOL Light proof knowledge
Premise Selection and External Provers for HOL4
Lazy Factored Inference for Functional Probabilistic Programming
Some Supplementaries to The Counting Semantics for Abstract Argumentation
Benchmarking for Bayesian Reinforcement Learning
Double Relief with progressive weighting function
Kernelized Deep Convolutional Neural Network for Describing Complex Images
Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models
Causal Model Analysis using Collider v-structure with Negative Percentage Mapping
Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture
Efficient Task Collaboration with Execution Uncertainty
(Blue) Taxi Destination and Trip Time Prediction from Partial Trajectories
Class Association Rules Mining based Rough Set Method
Learning from Synthetic Data Using a Stacked Multichannel Autoencoder
TransG : A Generative Mixture Model for Knowledge Graph Embedding
Proceedings Thirteenth International Workshop on the ACL2 Theorem Prover and Its Applications
Energy saving in smart homes based on consumer behaviour: A case study
Backdoors into Heterogeneous Classes of SAT and CSP
Exploiting Reduction Rules and Data Structures: Local Search for Minimum Vertex Cover in Massive Graphs
Telugu OCR Framework using Deep Learning
Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network
Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games
Boolean Hedonic Games
CRDT: Correlation Ratio Based Decision Tree Model for Healthcare Data Mining
Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection
Deep Multimodal Embedding: Manipulating Novel Objects with Point-clouds, Language and Trajectories
Discovery and Visualization of Nonstationary Causal Models
Optimal Release Time Decision from Fuzzy Mathematical Programming Perspective
Approximation and Heuristic Algorithms for Probabilistic Physical Search on General Graphs
Boolean Matrix Factorization and Noisy Completion via Message Passing
Learning dynamic Boltzmann machines with spike-timing dependent plasticity
Towards Unveiling the Ontology Key Features Altering Reasoner Performances
Two Phase $Q-$learning for Bidding-based Vehicle Sharing
Inferring Interpersonal Relations in Narrative Summaries
Taxonomy grounded aggregation of classifiers with different label sets
A New Approach for Scalable Analysis of Microbial Communities
Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing
Attribute2Image: Conditional Image Generation from Visual Attributes
Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures
Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization
Quantifying knowledge with a new calculus for belief functions - a generalization of probability theory
Locally Adaptive Translation for Knowledge Graph Embedding
What Makes it Difficult to Understand a Scientific Literature?
Reuse of Neural Modules for General Video Game Playing
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
Probabilistic Structural Controllability in Causal Bayesian Networks
Knowledge Sharing in Coalitions
A Novel Approach to Distributed Multi-Class SVM
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies
From rules to runs: A dynamic epistemic take on imperfect information games
Sensitivity analysis, multilinearity and beyond
Learning Discrete Bayesian Networks from Continuous Data
ShapeNet: An Information-Rich 3D Model Repository
Learning measures of semi-additive behaviour
Mobile Robots Adaptive Control Using Neural Networks
Using Linear Constraints for Logic Program Termination Analysis
Policy Gradient Methods for Off-policy Control
Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect
Origami: A 803 GOp/s/W Convolutional Network Accelerator
An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains
Data-driven Sequential Monte Carlo in Probabilistic Programming
From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction
BayesDB: A probabilistic programming system for querying the probable implications of data
Feature Representation for ICU Mortality
A thermodynamical approach towards multi-criteria decision making (MCDM)
A Survey of Available Corpora for Building Data-Driven Dialogue Systems
Quadripolar Relational Model: a framework for the description of borderline and narcissistic personality disorders
A Planning based Framework for Essay Generation
Can Pretrained Neural Networks Detect Anatomy?
Ontology-driven Information Extraction
Test-Driven Development of ontologies (extended version)
Towards Integrated Glance To Restructuring in Combinatorial Optimization
Remote Health Coaching System and Human Motion Data Analysis for Physical Therapy with Microsoft Kinect
Restricted Predicates for Hypothetical Datalog
On the Differential Privacy of Bayesian Inference
Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences
Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation
A Deep Generative Deconvolutional Image Model
Selecting the top-quality item through crowd scoring
Randomized Social Choice Functions Under Metric Preferences
Representation and Coding of Signal Geometry
The Max $K$-Armed Bandit: PAC Lower Bounds and Efficient Algorithms
Measuring pattern retention in anonymized data -- where one measure is not enough
RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles
Probabilistic Model-Based Approach for Heart Beat Detection
Multi-Level Cause-Effect Systems
Toward a Research Agenda in Adversarial Reasoning: Computational Approaches to Anticipating the Opponent's Intent and Actions
Device and System Level Design Considerations for Analog-Non-Volatile-Memory Based Neuromorphic Architectures
Using Data Analytics to Detect Anomalous States in Vehicles
Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning
GELATO and SAGE: An Integrated Framework for MS Annotation
Conditional probability generation methods for high reliability effects-based decision making
Taming the Noise in Reinforcement Learning via Soft Updates
Learning Natural Language Inference with LSTM
Closing the Gap Between Short and Long XORs for Model Counting
Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices
Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies
An (MI)LP-based Primal Heuristic for 3-Architecture Connected Facility Location in Urban Access Network Design
Selecting Near-Optimal Learners via Incremental Data Allocation
Computational Pathology: Challenges and Promises for Tissue Analysis
Wavelet Scattering on the Pitch Spiral
A Unified Approach for Learning the Parameters of Sum-Product Networks
Benders Decomposition for the Design of a Hub and Shuttle Public Transit System
Mutual Information and Diverse Decoding Improve Neural Machine Translation
Scalable Models for Computing Hierarchies in Information Networks
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
Open challenges in understanding development and evolution of speech forms: The roles of embodied self-organization, motivation and active exploration
Joint learning of ontology and semantic parser from text
Wikiometrics: A Wikipedia Based Ranking System
Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting
Identifying Stable Patterns over Time for Emotion Recognition from EEG
A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews
Git4Voc: Git-based Versioning for Collaborative Vocabulary Development
Basic Reasoning with Tensor Product Representations
The minimal hitting set generation problem: algorithms and computation
Submodular Optimization under Noise
Complexity of ITL model checking: some well-behaved fragments of the interval logic HS
Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs
A Method for Image Reduction Based on a Generalization of Ordered Weighted Averaging Functions
Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
It's about time: Online Macrotask Sequencing in Expert Crowdsourcing
$\mathbf{D^3}$: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
Studying Very Low Resolution Recognition Using Deep Networks
Word Existence Algorithm
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
Semantic Word Clusters Using Signed Normalized Graph Cuts
Sub-Optimal Multi-Phase Path Planning: A Method for Solving Rubik's Revenge
Online Event Recognition from Moving Vessel Trajectories
Coalition-based Planning of Military Operations: Adversarial Reasoning Algorithms in an Integrated Decision Aid
Bitwise Neural Networks
Towards Resolving Unidentifiability in Inverse Reinforcement Learning
Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection
Generalizing Prototype Theory: A Formal Quantum Framework
Fisher Motion Descriptor for Multiview Gait Recognition
Font Identification in Historical Documents Using Active Learning
Quantum machine learning with glow for episodic tasks and decision games
Learning and Tuning Meta-heuristics in Plan Space Planning
Efficient Hill-Climber for Multi-Objective Pseudo-Boolean Optimization
Numerical Atrribute Extraction from Clinical Texts
Towards a Cognitive Routing Engine for Software Defined Networks
Marvin: Semantic annotation using multiple knowledge sources
Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects
Finding the different patterns in buildings data using bag of words representation with clustering
A Factorized Recurrent Neural Network based architecture for medium to large vocabulary Language Modelling
Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
A Generalised Quantifier Theory of Natural Language in Categorical Compositional Distributional Semantics with Bialgebras
Formal Verification of Autonomous Vehicle Platooning
Harmonic Grammar in a DisCo Model of Meaning
Probabilistic Extension to the Concurrent Constraint Factor Oracle Model for Music Improvisation
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Find an Optimal Path in Static System and Dynamical System within Polynomial Runtime
Graying the black box: Understanding DQNs
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
Decoy Bandits Dueling on a Poset
Strategic disclosure of opinions on a social network
The IMP game: Learnability, approximability and adversarial learning beyond $Σ^0_1$
Value Iteration Networks
Time Resource Networks
Feature Based Task Recommendation in Crowdsourcing with Implicit Observations
Iterative Hierarchical Optimization for Misspecified Problems (IHOMP)
Adaptive Skills, Adaptive Partitions (ASAP)
Enabling Basic Normative HRI in a Cognitive Robotic Architecture
Detection of Cooperative Interactions in Logistic Regression Models
Identifying Diabetic Patients with High Risk of Readmission
A Minimalistic Approach to Sum-Product Network Learning for Real Applications
Look, Listen and Learn - A Multimodal LSTM for Speaker Identification
BPCMont: Business Process Change Management Ontology
Random Forest Based Approach for Concept Drift Handling
Surprising properties of dropout in deep networks
Extending Consequence-Based Reasoning to SRIQ
Towards reducing the multidimensionality of OLAP cubes using the Evolutionary Algorithms and Factor Analysis Methods
POMDP-lite for Robust Robot Planning under Uncertainty
Unsupervised Domain Adaptation Using Approximate Label Matching
A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes
Q($λ$) with Off-Policy Corrections
Contextual Media Retrieval Using Natural Language Queries
A Subsequence Interleaving Model for Sequential Pattern Mining
Symmetry Breaking Predicates for SAT-based DFA Identification
BioSpaun: A large-scale behaving brain model with complex neurons
11 x 11 Domineering is Solved: The first player wins
Auxiliary Deep Generative Models
Query Answering with Inconsistent Existential Rules under Stable Model Semantics
Ordonnancement d'entités pour la rencontre du web des documents et du web des données
Text Matching as Image Recognition
Interactive Storytelling over Document Collections
Recurrent Orthogonal Networks and Long-Memory Tasks
Enablers and Inhibitors in Causal Justifications of Logic Programs
Empath: Understanding Topic Signals in Large-Scale Text
Latent Skill Embedding for Personalized Lesson Sequence Recommendation
Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Parametric Prediction from Parametric Agents
A Quantum Computational Semantics for Epistemic Logical Operators. Part I: Epistemic Structures
Multilingual Twitter Sentiment Classification: The Role of Human Annotators
Time and Activity Sequence Prediction of Business Process Instances
A Survey on Domain-Specific Languages for Machine Learning in Big Data
Learning values across many orders of magnitude
Toward Game Level Generation from Gameplay Videos
Reinforcement Learning of POMDPs using Spectral Methods
Top-N Recommendation with Novel Rank Approximation
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks
Probably Approximately Correct Greedy Maximization
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
How effective can simple ordinal peer grading be?
Scalable Bayesian Rule Lists
Lie Access Neural Turing Machine
Investigating practical linear temporal difference learning
Easy Monotonic Policy Iteration
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Probabilistic Relational Model Benchmark Generation
Continuous Deep Q-Learning with Model-based Acceleration
Filter based Taxonomy Modification for Improving Hierarchical Classification
Automatic Differentiation Variational Inference
Hybrid Collaborative Filtering with Autoencoders
Automatic learning of gait signatures for people identification
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Learning Physical Intuition of Block Towers by Example
Causal inference for cloud computing
Sentiment Analysis in Scholarly Book Reviews
A Linked Data Scalability Challenge: Concept Reuse Leads to Semantic Decay
An Argument-based Creative Assistant for Harmonic Blending
Hierarchical Decision Making In Electricity Grid Management
Adaptive Visualisation System for Construction Building Information Models Using Saliency
Pairwise Choice Markov Chains
Implicit Discourse Relation Classification via Multi-Task Neural Networks
A Markovian-based Approach for Daily Living Activities Recognition
Hierarchical Linearly-Solvable Markov Decision Problems
High-dimensional Black-box Optimization via Divide and Approximate Conquer
On the physical realizability of quantum stochastic walks
Demonstrating the Feasibility of Automatic Game Balancing
Solving MaxSAT by Successive Calls to a SAT Solver
Image Captioning with Semantic Attention
On Learning High Dimensional Structured Single Index Models
A Signaling Game Approach to Databases Querying and Interaction
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
Item2Vec: Neural Item Embedding for Collaborative Filtering
Learning Network of Multivariate Hawkes Processes: A Time Series Approach
Controlling Search in Very large Commonsense Knowledge Bases: A Machine Learning Approach
Learning Domain-Invariant Subspace using Domain Features and Independence Maximization
Optimal Sensing via Multi-armed Bandit Relaxations in Mixed Observability Domains
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions
Hardware Acceleration for Boolean Satisfiability Solver by Applying Belief Propagation Algorithm
Mapping Temporal Variables into the NeuCube for Improved Pattern Recognition, Predictive Modelling and Understanding of Stream Data
Bank distress in the news: Describing events through deep learning
Sentence Pair Scoring: Towards Unified Framework for Text Comprehension
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
An Approximation Approach for Solving the Subpath Planning Problem
Multi-fidelity Gaussian Process Bandit Optimisation
Harnessing Deep Neural Networks with Logic Rules
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Characterization of neighborhood behaviours in a multi-neighborhood local search algorithm
Action-Affect Classification and Morphing using Multi-Task Representation Learning
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Cosolver2B: An Efficient Local Search Heuristic for the Travelling Thief Problem
Debugging Machine Learning Tasks
A Diagram Is Worth A Dozen Images
Load Disaggregation Based on Aided Linear Integer Programming
Pixel-Level Domain Transfer
Conditional Similarity Networks
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
Generating Visual Explanations
Shuffle and Learn: Unsupervised Learning using Temporal Order Verification
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
Towards Practical Bayesian Parameter and State Estimation
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification
Phoenix: A Self-Optimizing Chess Engine
Iterated Ontology Revision by Reinterpretation
Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding
A New Approach for Revising Logic Programs
Verifiability of Argumentation Semantics
Distributing Knowledge into Simple Bases
Characterizing Realizability in Abstract Argumentation
Neural Language Correction with Character-Based Attention
A Survey of League Championship Algorithm: Prospects and Challenges
Higher Order Recurrent Neural Networks
An Improved System for Sentence-level Novelty Detection in Textual Streams
Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking
Common-Description Learning: A Framework for Learning Algorithms and Generating Subproblems from Few Examples
Coalition Formability Semantics with Conflict-Eliminable Sets of Arguments
Fast Simulation of Probabilistic Boolean Networks (Technical Report)
Ontology-Mediated Queries: Combined Complexity and Succinctness of Rewritings via Circuit Complexity
Learning from the memory of Atari 2600
Brain Emotional Learning-Based Prediction Model (For Long-Term Chaotic Prediction Applications)
The KB paradigm and its application to interactive configuration
Energy Disaggregation for Real-Time Building Flexibility Detection
Robust Dialog State Tracking for Large Ontologies
Belief Merging by Source Reliability Assessment
Audio Event Detection using Weakly Labeled Data
Machine Learning Techniques with Ontology for Subjective Answer Evaluation
A Hierarchical Emotion Regulated Sensorimotor Model: Case Studies
Deep Neural Networks Under Stress
Characterizing Quantifier Fuzzification Mechanisms: a behavioral guide for practical applications
Optimizing human-interpretable dialog management policy using Genetic Algorithm
Anytime Inference in Valuation Algebras
OBDA Constraints for Effective Query Answering (Extended Version)
A Critical Examination of RESCAL for Completion of Knowledge Bases with Transitive Relations
High-Performance Computing for Scheduling Decision Support: A Parallel Depth-First Search Heuristic
Off-policy evaluation for slate recommendation
Digital Stylometry: Linking Profiles Across Social Networks
Learning Convolutional Neural Networks for Graphs
Fuzzy Sets Across the Natural Language Generation Pipeline
Dynamic Frame skip Deep Q Network
Heuristics for Planning, Plan Recognition and Parsing
AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Variational hybridization and transformation for large inaccurate noisy-or networks
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
Query-Efficient Imitation Learning for End-to-End Autonomous Driving
TensorLog: A Differentiable Deductive Database
Programming with a Differentiable Forth Interpreter
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Stochastic Patching Process
Generative Choreography using Deep Learning
DP-EM: Differentially Private Expectation Maximization
Spontaneous vs. Posed smiles - can we tell the difference?
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Genetic Architect: Discovering Genomic Structure with Learned Neural Architectures
Adaptive ADMM with Spectral Penalty Parameter Selection
Diagnosing editorial strategies of Chilean media on Twitter using an automatic news classifier
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests
Non-Gaussian Random Generators in Bacteria Foraging Algorithm for Multiobjective Optimization
Alternating Optimisation and Quadrature for Robust Control
Towards Bin Packing (preliminary problem survey, models with multiset estimates)
Learning Multiagent Communication with Backpropagation
Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks with Feedback Negative Sampling
Adaptive Neural Compilation
Ruling Out Static Latent Homophily in Citation Networks
The Symbolic Interior Point Method
Probabilistic Inference Modulo Theories
Kronecker Determinantal Point Processes
Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems
Model-Free Imitation Learning with Policy Optimization
Density estimation using Real NVP
Control of Memory, Active Perception, and Action in Minecraft
Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments
Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data
Parallel Markov Chain Monte Carlo via Spectral Clustering
Interdependent Scheduling Games
Determining the Characteristic Vocabulary for a Specialized Dictionary using Word2vec and a Directed Crawler
VIME: Variational Information Maximizing Exploration
Towards ontology driven learning of visual concept detectors
Technical Report: Directed Controller Synthesis of Discrete Event Systems
Uncertain programming model for multi-item solid transportation problem
Quantifying the probable approximation error of probabilistic inference programs
Hardness of the Pricing Problem for Chains in Barter Exchanges
A Survey of Qualitative Spatial and Temporal Calculi -- Algebraic and Computational Properties
Mining Software Components from Object-Oriented APIs
Post-Inference Prior Swapping
Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information
Selecting the Best Player Formation for Corner-Kick Situations Based on Bayes' Estimation
The belief noisy-or model applied to network reliability analysis
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning
Scene Grammars, Factor Graphs, and Belief Propagation
Effective Multi-Robot Spatial Task Allocation using Model Approximations
Generating Natural Language Inference Chains
Distance Metric Ensemble Learning and the Andrews-Curtis Conjecture
Coordination in Categorical Compositional Distributional Semantics
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
Unifying Count-Based Exploration and Intrinsic Motivation
Preliminaries of a Space Situational Awareness Ontology
Consistency and Trust in Peer Data Exchange Systems
Sorting out symptoms: design and evaluation of the 'babylon check' automated triage system
Multi-resource defensive strategies for patrolling games with alarm systems
Emotional Intensity analysis in Bipolar subjects
Sifting Common Information from Many Variables
SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks
Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation
Deep Successor Reinforcement Learning
Learning Language Games through Interaction
Exploring Implicit Human Responses to Robot Mistakes in a Learning from Demonstration Task
DISCO Nets: DISsimilarity COefficient Networks
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
Safe and Efficient Off-Policy Reinforcement Learning
Theoretical Robopsychology: Samu Has Learned Turing Machines
Arbitrage-Free Combinatorial Market Making via Integer Programming
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
Understanding User Instructions by Utilizing Open Knowledge for Service Robots
A Cognitive Architecture for the Implementation of Emotions in Computing Systems
Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)
MuFuRU: The Multi-Function Recurrent Unit
Cooperative Inverse Reinforcement Learning
Simple epistemic planning: generalised gossiping
Natural Language Generation enhances human decision-making with uncertain information
Tunable Online MUS/MSS Enumeration
WordNet2Vec: Corpora Agnostic Word Vectorization Method
Length bias in Encoder Decoder Models and a Case for Global Conditioning
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
The Mythos of Model Interpretability
Word Sense Disambiguation using a Bidirectional LSTM
A framework for detecting fraudulent activities in edo state tax collection system using investigative data mining
The Opacity of Backbones
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
Detecção de comunidades em redes complexas para identificar gargalos e desperdício de recursos em sistemas de ônibus
MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
Evidential Label Propagation Algorithm for Graphs
Robust Probabilistic Modeling with Bayesian Data Reweighting
Neural Associative Memory for Dual-Sequence Modeling
Visual-Inertial-Semantic Scene Representation for 3-D Object Detection
Estimating individual treatment effect: generalization bounds and algorithms
Using a Distributional Semantic Vector Space with a Knowledge Base for Reasoning in Uncertain Conditions
Bacteria Foraging Algorithm with Genetic Operators for the Solution of QAP and mQAP
Using Virtual Humans to Understand Real Ones
Micro-interventions in urban transport from pattern discovery on the flow of passengers and on the bus network
Spreadsheet Probabilistic Programming
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
Lifted Convex Quadratic Programming
Why is Compiling Lifted Inference into a Low-Level Language so Effective?
Impossibility in Belief Merging
Natural Language Generation as Planning under Uncertainty Using Reinforcement Learning
Strategic Attentive Writer for Learning Macro-Actions
ASAGA: Asynchronous Parallel SAGA
Learning Optimal Interventions
Robust Active Perception via Data-association aware Belief Space planning
Deep Reinforcement Learning Discovers Internal Models
Successor Features for Transfer in Reinforcement Learning
Unsupervised Risk Estimation Using Only Conditional Independence Structure
On the Expressive Power of Deep Neural Networks
Abducing Compliance of Incomplete Event Logs
Adding Context to Concept Trees
Bandit-Based Random Mutation Hill-Climbing
Product Classification in E-Commerce using Distributional Semantics
Polymetric Rhythmic Feel for a Cognitive Drum Computer
Complex Embeddings for Simple Link Prediction
Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings
The Schema Editor of OpenIoT for Semantic Sensor Networks
Neighborhood Mixture Model for Knowledge Base Completion
Étude de Problèmes d'Optimisation Combinatoire à Multiples Composantes Interdépendantes
Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information
Inferring Logical Forms From Denotations
Ancestral Causal Inference
Emulating Human Conversations using Convolutional Neural Network-based IR
An Approach to Stable Gradient Descent Adaptation of Higher-Order Neural Units
Analyzing the Behavior of Visual Question Answering Models
Robust Learning of Fixed-Structure Bayesian Networks
Learning to Poke by Poking: Experiential Learning of Intuitive Physics
LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
Sort Story: Sorting Jumbled Images and Captions into Stories
Human-Agent Decision-making: Combining Theory and Practice
Standard State Space Models of Unawareness (Extended Abstract)
Ceteris paribus logic in counterfactual reasoning
Preference at First Sight
Relating Knowledge and Coordinated Action: The Knowledge of Preconditions Principle
Parameterized Complexity Results for a Model of Theory of Mind Based on Dynamic Epistemic Logic
The optimality of coarse categories in decision-making and information storage
Translucent Players: Explaining Cooperative Behavior in Social Dilemmas
Neural Network Based Next-Song Recommendation
Precise deep neural network computation on imprecise low-power analog hardware
Proactive Decision Support using Automated Planning
Label Tree Embeddings for Acoustic Scene Classification
Assigning a Small Agreeable Set of Indivisible Items to Multiple Players
Quantum Simulation of a Quantum Stochastic Walk
Content-Based Top-N Recommendation using Heterogeneous Relations
Propagators and Solvers for the Algebra of Modular Systems
True Lies
Lifted Rule Injection for Relation Embeddings
A Reduction for Optimizing Lattice Submodular Functions with Diminishing Returns
A Learning Algorithm for Relational Logistic Regression: Preliminary Results
A Local Density-Based Approach for Local Outlier Detection
Adaptive Training of Random Mapping for Data Quantization
Technical Report: Towards a Universal Code Formatter through Machine Learning
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
Evaluation and selection of Medical Tourism sites: A rough AHP based MABAC approach
Credibilistic TOPSIS Model for Evaluation and Selection of Municipal Solid Waste Disposal Methods
Compression of Neural Machine Translation Models via Pruning
Clique-Width and Directed Width Measures for Answer-Set Programming
Ordering as privileged information
Contextual Symmetries in Probabilistic Graphical Models
A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness
Lifted Region-Based Belief Propagation
Towards A Virtual Assistant That Can Be Taught New Tasks In Any Domain By Its End-Users
Fractal Dimension Pattern Based Multiresolution Analysis for Rough Estimator of Person-Dependent Audio Emotion Recognition
Throwing fuel on the embers: Probability or Dichotomy, Cognitive or Linguistic?
Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset. Similarly for Neutrosophic Over-/Under-/Off- Logic, Probability, and Statistics
Domain Adaptation for Neural Networks by Parameter Augmentation
Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks
A Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Plan Caching
Can we reach Pareto optimal outcomes using bottom-up approaches?
Formal analysis of HTM Spatial Pooler performance under predefined operation conditions
Understanding the Abstract Dialectical Framework (Preliminary Report)
Modeling of Item-Difficulty for Ontology-based MCQs
Encoding Cryptographic Functions to SAT Using Transalg System
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
Generic Statistical Relational Entity Resolution in Knowledge Graphs
Bootstrap Model Aggregation for Distributed Statistical Learning
Affect Intensity Estimation Using Multiple Modalities
An extended MABAC for multi-attribute decision making using trapezoidal interval type-2 fuzzy numbers
Can mobile usage predict illiteracy in a developing country?
Towards Self-explanatory Ontology Visualization with Contextual Verbalization
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Rolling Horizon Coevolutionary Planning for Two-Player Video Games
Mapping Data to Ontologies with Exceptions Using Answer Set Programming
Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity
CaR-FOREST: Joint Classification-Regression Decision Forests for Overlapping Audio Event Detection
Translating Bayesian Networks into Entity Relationship Models, Extended Version
Explaining Deep Convolutional Neural Networks on Music Classification
Solving finite-domain linear constraints in presence of the $\texttt{alldifferent}$
Analysis of opinionated text for opinion mining
Augmenting Supervised Emotion Recognition with Rule-Based Decision Model
Extending Weakly-Sticky Datalog+/-: Query-Answering Tractability and Optimizations
Open Information Extraction
A Framework for Estimating Long Term Driver Behavior
Populations can be essential in tracking dynamic optima
Characterizing Driving Styles with Deep Learning
Minimum Vertex-type Sequence Indexingfor Clusters on Square Lattice
Large-scale Analysis of Chess Games with Chess Engines: A Preliminary Report
Random-Key Cuckoo Search for the Travelling Salesman Problem
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Intrinsically Motivated Multimodal Structure Learning
A Counterexample to the Forward Recursion in Fuzzy Critical Path Analysis Under Discrete Fuzzy Sets
Knowledge Representation on the Web revisited: Tools for Prototype Based Ontologies
Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay
Is spoken language all-or-nothing? Implications for future speech-based human-machine interaction
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Exploiting Vagueness for Multi-Agent Consensus
An Event Grouping Based Algorithm for University Course Timetabling Problem
Neural Contextual Conversation Learning with Labeled Question-Answering Pairs
Identifying Candidate Risk Factors for Prescription Drug Side Effects using Causal Contrast Set Mining
Indebted households profiling: a knowledge discovery from database approach
Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework
Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining
Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams
Constructing a Natural Language Inference Dataset using Generative Neural Networks
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
Modelling Office Energy Consumption: An Agent Based Approach
Latent Variable Discovery Using Dependency Patterns
Optimal resampling for the noisy OneMax problem
Inpainting of long audio segments with similarity graphs
Processing Natural Language About Ongoing Actions
Redundancy-free Verbalization of Individuals for Ontology Validation
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
Estimating Activity at Multiple Scales using Spatial Abstractions
Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder
OntoCat: Automatically categorizing knowledge in API Documentation
The Price of Anarchy in Auctions
Leveraging Unstructured Data to Detect Emerging Reliability Issues
Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems
Technical Report: Giving Hints for Logic Programming Examples without Revealing Solutions
Polling-systems-based Autonomous Vehicle Coordination in Traffic Intersections with No Traffic Signals
Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification
Mining Arguments from Cancer Documents Using Natural Language Processing and Ontologies
Harmonization of conflicting medical opinions using argumentation protocols and textual entailment - a case study on Parkinson disease
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
A DEMATEL-Based Completion Method for Incomplete Pairwise Comparison Matrix in AHP
N-opcode Analysis for Android Malware Classification and Categorization
Android Malware Detection Using Parallel Machine Learning Classifiers
VHT: Vertical Hoeffding Tree
Faceless Person Recognition; Privacy Implications in Social Media
A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams
Semi-supervised evidential label propagation algorithm for graph data
The DLVHEX System for Knowledge Representation: Recent Advances (System Description)
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool
PDDL+ Planning via Constraint Answer Set Programming
Human Pose Estimation in Space and Time using 3D CNN
A Novel Progressive Learning Technique for Multi-class Classification
From Community Detection to Community Deception
Ternary Neural Networks for Resource-Efficient AI Applications
Crowdsourcing with Unsure Option
Verifier Theory and Unverifiability
A case study of algorithm selection for the traveling thief problem
Lexical-Morphological Modeling for Legal Text Analysis
An Online Universal Classifier for Binary, Multi-class and Multi-label Classification
Spectral learning of dynamic systems from nonequilibrium data
Q-Learning with Basic Emotions
OpenTripPlanner, OpenStreetMap, General Transit Feed Specification: Tools for Disaster Relief and Recovery
Axiomatizing Category Theory in Free Logic
Automation of Pedestrian Tracking in a Crowded Situation
Unifying task specification in reinforcement learning
Deep Markov Random Field for Image Modeling
UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory
Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem
Latest Datasets and Technologies Presented in the Workshop on Grasping and Manipulation Datasets
An Integrated Classification Model for Financial Data Mining
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks
A centralized reinforcement learning method for multi-agent job scheduling in Grid
On Generation of Time-based Label Refinements
ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data
Joint Extraction of Events and Entities within a Document Context
Graph Aggregation
Instrumenting an SMT Solver to Solve Hybrid Network Reachability Problems
A Generic Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover
Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals
Column Networks for Collective Classification
Context Aware Nonnegative Matrix Factorization Clustering
NPCs as People, Too: The Extreme AI Personality Engine
A Formal Solution to the Grain of Truth Problem
Style Imitation and Chord Invention in Polyphonic Music with Exponential Families
Should Terminology Principles be re-examined?
Grammatical Templates: Improving Text Difficulty Evaluation for Language Learners
Continuous occurrence theory
NPCs Vote! Changing Voter Reactions Over Time Using the Extreme AI Personality Engine
Applications of Data Mining (DM) in Science and Engineering: State of the art and perspectives
Playing FPS Games with Deep Reinforcement Learning
Graph-Structured Representations for Visual Question Answering
Preorder-Based Triangle: A Modified Version of Bilattice-Based Triangle for Belief Revision in Nonmonotonic Reasoning
Extending Unification in $\mathcal{EL}$ to Disunification: The Case of Dismatching and Local Disunification
On the adoption of abductive reasoning for time series interpretation
TODIM and TOPSIS with Z-numbers
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images
Scope for Machine Learning in Digital Manufacturing
On the Phase Transition of Finding a Biclique in a larger Bipartite Graph
Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching
Enhanced LSTM for Natural Language Inference
An Ensemble Blocking Scheme for Entity Resolution of Large and Sparse Datasets
Semantic Similarity Strategies for Job Title Classification
Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches
Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention
Document Image Coding and Clustering for Script Discrimination
The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)
Language as a Latent Variable: Discrete Generative Models for Sentence Compression
Discovering Sound Concepts and Acoustic Relations In Text
Regulating Reward Training by Means of Certainty Prediction in a Neural Network-Implemented Pong Game
Optimizing positional scoring rules for rank aggregation
Fast Learning of Clusters and Topics via Sparse Posteriors
Pointer Sentinel Mixture Models
Towards Evidence-Based Ontology for Supporting Systematic Literature Review
Online Segment to Segment Neural Transduction
Top-N Recommendation on Graphs
Decision Making Based on Cohort Scores for Speaker Verification
Model-based Test Generation for Robotic Software: Automata versus Belief-Desire-Intention Agents
Weakly Supervised PLDA Training
A computer program for simulating time travel and a possible 'solution' for the grandfather paradox
UbuntuWorld 1.0 LTS - A Platform for Automated Problem Solving & Troubleshooting in the Ubuntu OS
Correct classification for big/smart/fast data machine learning
Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis
Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks
Evaluating Induced CCG Parsers on Grounded Semantic Parsing
Bacterial Foraging Optimized STATCOM for Stability Assessment in Power System
Outlier Detection from Network Data with Subnetwork Interpretation
Consistency Ensuring in Social Web Services Based on Commitments Structure
Towards deep learning with segregated dendrites
Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-value
A Probability Distribution Strategy with Efficient Clause Selection for Hard Max-SAT Formulas
Can Evolutionary Sampling Improve Bagged Ensembles?
One-Trial Correction of Legacy AI Systems and Stochastic Separation Theorems
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
Deep Visual Foresight for Planning Robot Motion
Embracing data abundance: BookTest Dataset for Reading Comprehension
A Constraint-Handling Technique for Genetic Algorithms using a Violation Factor
Tutorial on Answering Questions about Images with Deep Learning
Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing
Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Domain Adaptation with Soft-margin multiple feature-kernel learning beats Deep Learning for surveillance face recognition
The Predictive Context Tree: Predicting Contexts and Interactions
Visual Question Answering: Datasets, Algorithms, and Future Challenges
$\ell_1$ Regularized Gradient Temporal-Difference Learning
A Novel Representation of Neural Networks
Human Decision-Making under Limited Time
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Interpreting Neural Networks to Improve Politeness Comprehension
Ranking academic institutions on potential paper acceptance in upcoming conferences
Situational Awareness by Risk-Conscious Skills
Towards an Ontology-Driven Blockchain Design for Supply Chain Provenance
Extrapolation and learning equations
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation
A Chain-Detection Algorithm for Two-Dimensional Grids
Maximum entropy models for generation of expressive music
Deep Fruit Detection in Orchards
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders
A fuzzy expert system for earthquake prediction, case study: the Zagros range
An Information Theoretic Feature Selection Framework for Big Data under Apache Spark
Hadamard Product for Low-rank Bilinear Pooling
Distributional Inclusion Hypothesis for Tensor-based Composition
Localization for Wireless Sensor Networks: A Neural Network Approach
A Closed Form Solution to Multi-View Low-Rank Regression
Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering
Weekly maintenance scheduling using exact and genetic methods
Learning and Transfer of Modulated Locomotor Controllers
Decentralized Collaborative Learning of Personalized Models over Networks
VRPBench: A Vehicle Routing Benchmark Tool
Makespan Optimal Solving of Cooperative Path-Finding via Reductions to Propositional Satisfiability
Weighted Positive Binary Decision Diagrams for Exact Probabilistic Inference
Identifiability and Transportability in Dynamic Causal Networks
On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior
Census Signal Temporal Logic Inference for Multi-Agent Group Behavior Analysis
Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models
Deep Amortized Inference for Probabilistic Programs
Big Batch SGD: Automated Inference using Adaptive Batch Sizes
Embodiment of Learning in Electro-Optical Signal Processors
Dynamic Probabilistic Network Based Human Action Recognition
A Growing Long-term Episodic & Semantic Memory
Reasoning with Memory Augmented Neural Networks for Language Comprehension
KGEval: Estimating Accuracy of Automatically Constructed Knowledge Graphs
Learning Cost-Effective Treatment Regimes using Markov Decision Processes
Template Matching Advances and Applications in Image Analysis
Surprisal-Driven Zoneout
Frank-Wolfe Algorithms for Saddle Point Problems
Infinite-dimensional Log-Determinant divergences II: Alpha-Beta divergences
A self-tuning Firefly algorithm to tune the parameters of Ant Colony System (ACSFA)
Universal adversarial perturbations
New Liftable Classes for First-Order Probabilistic Inference
Distraction-Based Neural Networks for Document Summarization
Synthesis of Shared Control Protocols with Provable Safety and Performance Guarantees
Learning Scalable Deep Kernels with Recurrent Structure
Improving Sampling from Generative Autoencoders with Markov Chains
Probabilistic Model Checking for Complex Cognitive Tasks -- A case study in human-robot interaction
DPPred: An Effective Prediction Framework with Concise Discriminative Patterns
Edward: A library for probabilistic modeling, inference, and criticism
A Survey of Brain Inspired Technologies for Engineering
Chinese Poetry Generation with Planning based Neural Network
Mining Social Media for Open Innovation in Transportation Systems
Inference Compilation and Universal Probabilistic Programming
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Towards Blended Reactive Planning and Acting using Behavior Trees
Detecting Affordances by Visuomotor Simulation
Using Artificial Intelligence to Identify State Secrets
Bots as Virtual Confederates: Design and Ethics
An application of incomplete pairwise comparison matrices for ranking top tennis players
Limitations and Alternatives for the Evaluation of Large-scale Link Prediction
Inferring Coupling of Distributed Dynamical Systems via Transfer Entropy
Improving incremental recommenders with online bagging
Initialization and Coordinate Optimization for Multi-way Matching
Probabilistic Modeling of Progressive Filtering
A Hybrid Approach to Word Sense Disambiguation Combining Supervised and Unsupervised Learning
Learning Continuous Semantic Representations of Symbolic Expressions
Estimating Causal Direction and Confounding of Two Discrete Variables
QBF Solving by Counterexample-guided Expansion
Quasi-Recurrent Neural Networks
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
Dynamic Coattention Networks For Question Answering
Combining policy gradient and Q-learning
A Differentiable Physics Engine for Deep Learning in Robotics
Causes for Query Answers from Databases: Datalog Abduction, View-Updates, and Integrity Constraints
Learning to Act by Predicting the Future
Self-Wiring Question Answering Systems
An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Reinforcement-based Simultaneous Algorithm and its Hyperparameters Selection
Playing SNES in the Retro Learning Environment
Hierarchical compositional feature learning
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
Normalizing Flows on Riemannian Manifolds
Combining observational and experimental data to find heterogeneous treatment effects
The Data Complexity of Description Logic Ontologies
Cognitive Discriminative Mappings for Rapid Learning
The Neural Noisy Channel
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
Encoding monotonic multi-set preferences using CI-nets: preliminary report
A stochastically verifiable autonomous control architecture with reasoning
XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained Problems
Importance Sampling with Unequal Support
The Sum-Product Theorem: A Foundation for Learning Tractable Models
Neural Networks Models for Entity Discovery and Linking
UTCNN: a Deep Learning Model of Stance Classificationon on Social Media Text
Show me the material evidence: Initial experiments on evaluating hypotheses from user-generated multimedia data
Learning to Navigate in Complex Environments
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods
A Review on Algorithms for Constraint-based Causal Discovery
Leveraging Video Descriptions to Learn Video Question Answering
Learning to Gather Information via Imitation
Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction
When Saliency Meets Sentiment: Understanding How Image Content Invokes Emotion and Sentiment
Traversing Knowledge Graph in Vector Space without Symbolic Space Guidance
The NOESIS Network-Oriented Exploration, Simulation, and Induction System
An Evaluation of Information Sharing Parking Guidance Policies Using a Bayesian Approach
An integrated Graphical User Interface for Debugging Answer Set Programs
Variable Neighborhood Search Algorithms for the multi-depot dial-a-ride problem with heterogeneous vehicles and users
Driving CDCL Search
Towards Interconnected Virtual Reality: Opportunities, Challenges and Enablers
ProjE: Embedding Projection for Knowledge Graph Completion
Zero-Shot Visual Question Answering
Stream Packing for Asynchronous Multi-Context Systems using ASP
Learning to detect and localize many objects from few examples
Study on Feature Subspace of Archetypal Emotions for Speech Emotion Recognition
Fast Non-Parametric Tests of Relative Dependency and Similarity
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance
Towards a Mathematical Understanding of the Difficulty in Learning with Feedforward Neural Networks
Analysis of a Design Pattern for Teaching with Features and Labels
Navigational Rule Derivation: An algorithm to determine the effect of traffic signs on road networks
Team-maxmin equilibrium: efficiency bounds and algorithms
Variable Computation in Recurrent Neural Networks
Expert Gate: Lifelong Learning with a Network of Experts
Generative Deep Neural Networks for Dialogue: A Short Review
A Survey of Methods for Collective Communication Optimization and Tuning
Invertible Conditional GANs for image editing
Generating machine-executable plans from end-user's natural-language instructions
Non-Local Color Image Denoising with Convolutional Neural Networks
Learning From Graph Neighborhoods Using LSTMs
Memory Lens: How Much Memory Does an Agent Use?
Enforcing Relational Matching Dependencies with Datalog for Entity Resolution
Associative Adversarial Networks
Coherent Dialogue with Attention-based Language Models
An Efficient Training Algorithm for Kernel Survival Support Vector Machines
Interpreting Finite Automata for Sequential Data
CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression
An unexpected unity among methods for interpreting model predictions
Variational Intrinsic Control
Feature Importance Measure for Non-linear Learning Algorithms
Programs as Black-Box Explanations
iCaRL: Incremental Classifier and Representation Learning
Multi-Modal Mean-Fields via Cardinality-Based Clamping
A Spatio-Temporal Representation for the Orienteering Problem with Time-Varying Profits
Multiscale Inverse Reinforcement Learning using Diffusion Wavelets
On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems
GuessWhat?! Visual object discovery through multi-modal dialogue
An Analysis of Tournament Structure
New Trends in Neutrosophic Theory and Applications
Convolutional Experts Constrained Local Model for Facial Landmark Detection
Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement Learning
BliStrTune: Hierarchical Invention of Theorem Proving Strategies
Embedded Bandits for Large-Scale Black-Box Optimization
SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks
Long-Term Image Boundary Prediction
The BIN_COUNTS Constraint: Filtering and Applications
DeepSetNet: Predicting Sets with Deep Neural Networks
Improving Policy Gradient by Exploring Under-appreciated Rewards
Emergence of foveal image sampling from learning to attend in visual scenes
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
Maximizing Non-Monotone DR-Submodular Functions with Cardinality Constraints
Learning Filter Banks Using Deep Learning For Acoustic Signals
Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO
Dialogue Learning With Human-In-The-Loop
NewsQA: A Machine Comprehension Dataset
Exploration for Multi-task Reinforcement Learning with Deep Generative Models
Neural Combinatorial Optimization with Reinforcement Learning
Contextualizing Geometric Data Analysis and Related Data Analytics: A Virtual Microscope for Big Data Analytics
System-Generated Requests for Rewriting Proposals
Fusion of EEG and Musical Features in Continuous Music-emotion Recognition
SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data
The observer-assisted method for adjusting hyper-parameters in deep learning algorithms
Computer Assisted Composition with Recurrent Neural Networks
Robust Optimization for Tree-Structured Stochastic Network Design
CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional Variational Generation
Analysis of the Human-Computer Interaction on the Example of Image-based CAPTCHA by Association Rule Mining
On Coreferring Text-extracted Event Descriptions with the aid of Ontological Reasoning
An Evaluation of Models for Runtime Approximation in Link Discovery
A Compositional Object-Based Approach to Learning Physical Dynamics
Bootstrapping incremental dialogue systems: using linguistic knowledge to learn from minimal data
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed
Piecewise Latent Variables for Neural Variational Text Processing
Playing Doom with SLAM-Augmented Deep Reinforcement Learning
Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes
Inferring Cognitive Models from Data using Approximate Bayesian Computation
Automated assessment of non-native learner essays: Investigating the role of linguistic features
Structured Filtering
Asynchronous Stochastic Gradient MCMC with Elastic Coupling
Commonly Uncommon: Semantic Sparsity in Situation Recognition
A Matrix Splitting Perspective on Planning with Options
RecSys Challenge 2016: job recommendations based on preselection of offers and gradient boosting
DeepBach: a Steerable Model for Bach Chorales Generation
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework
Enhancing Use Case Points Estimation Method Using Soft Computing Techniques
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision
The Complexity of Bayesian Networks Specified by Propositional and Relational Languages
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)
Proportional Rankings
N-gram Opcode Analysis for Android Malware Detection
Deep learning in color: towards automated quark/gluon jet discrimination
Fleet Size and Mix Split-Delivery Vehicle Routing
Factored Contextual Policy Search with Bayesian Optimization
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
Cross-Lingual Predicate Mapping Between Linked Data Ontologies
Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer
Effect of Reward Function Choices in MDPs with Value-at-Risk
Mode Regularized Generative Adversarial Networks
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning
Inverses, Conditionals and Compositional Operators in Separative Valuation Algebra
Coupling Distributed and Symbolic Execution for Natural Language Queries
Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks
Advancing Bayesian Optimization: The Mixed-Global-Local (MGL) Kernel and Length-Scale Cool Down
Finding Better Active Learners for Faster Literature Reviews
Reinforcement Learning With Temporal Logic Rewards
Flu Detector: Estimating influenza-like illness rates from online user-generated content
Context-aware Sentiment Word Identification: sentiword2vec
A Unit Selection Methodology for Music Generation Using Deep Neural Networks
Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD)
Deep Active Learning for Dialogue Generation
Application of Advanced Record Linkage Techniques for Complex Population Reconstruction
Incorporating Human Domain Knowledge into Large Scale Cost Function Learning
An argumentative agent-based model of scientific inquiry
Sparse Factorization Layers for Neural Networks with Limited Supervision
Real-time interactive sequence generation and control with Recurrent Neural Network ensembles
Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints
Scalable Computation of Optimized Queries for Sequential Diagnosis
Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks
Learning through Dialogue Interactions by Asking Questions
Ontohub: A semantic repository for heterogeneous ontologies
Coupling Adaptive Batch Sizes with Learning Rates
Multi-Agent Path Finding with Delay Probabilities
Defensive Player Classification in the National Basketball Association
Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments
Supervised Quantum Learning without Measurements
An Alternative Softmax Operator for Reinforcement Learning
Reinforcement Learning Using Quantum Boltzmann Machines
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
Context and Interference Effects in the Combinations of Natural Concepts
An Empirical Study of Adequate Vision Span for Attention-Based Neural Machine Translation
Improving Tweet Representations using Temporal and User Context
A modified Physarum-inspired model for the user equilibrium traffic assignment problem
A Scalable Document-based Architecture for Text Analysis
Learning Features by Watching Objects Move
Computational Complexity of Testing Proportional Justified Representation
Parallelized Tensor Train Learning of Polynomial Classifiers
A Latent-class Model for Estimating Product-choice Probabilities from Clickstream Data
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games
Boolean kernels for collaborative filtering in top-N item recommendation
Understanding Error Correction and its Role as Part of the Communication Channel in Environments composed of Self-Integrating Systems
Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models
Counting Answer Sets via Dynamic Programming
Jointly Extracting Relations with Class Ties via Effective Deep Ranking
Highway and Residual Networks learn Unrolled Iterative Estimation
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
Convergence Rates for Greedy Kaczmarz Algorithms, and Faster Randomized Kaczmarz Rules Using the Orthogonality Graph
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
Solving Combinatorial Optimization problems with Quantum inspired Evolutionary Algorithm Tuned using a Novel Heuristic Method
Monte Carlo Sort for unreliable human comparisons
A Sparse Nonlinear Classifier Design Using AUC Optimization
Role of Simplicity in Creative Behaviour: The Case of the Poietic Generator
FastMask: Segment Multi-scale Object Candidates in One Shot
Deep neural heart rate variability analysis
Lifted Relational Algebra with Recursion and Connections to Modal Logic
Adaptive Lambda Least-Squares Temporal Difference Learning
A Joint Speaker-Listener-Reinforcer Model for Referring Expressions
Digital Advertising Traffic Operation: Machine Learning for Process Discovery
Non-Negative Matrix Factorization Test Cases
Learning Weighted Association Rules in Human Phenotype Ontology
Proceedings 29th and 30th Workshops on (Constraint) Logic Programming and 24th International Workshop on Functional and (Constraint) Logic Programming
STRIPS Planning in Infinite Domains
An affective computational model for machine consciousness
Truthful Facility Location with Additive Errors
Fuzzy finite element model updating using metaheuristic optimization algorithms
Stochastic Planning and Lifted Inference
A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control
Autoencoder Regularized Network For Driving Style Representation Learning
Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Understanding the complexity of #SAT using knowledge compilation
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities
Coupled Compound Poisson Factorization
Information Pursuit: A Bayesian Framework for Sequential Scene Parsing
Playtime Measurement with Survival Analysis
Reinforcement Learning via Recurrent Convolutional Neural Networks
Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition
A Convenient Category for Higher-Order Probability Theory
A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling
Towards Decoding as Continuous Optimization in Neural Machine Translation
Context-aware Captions from Context-agnostic Supervision
Real-time eSports Match Result Prediction
From First-Order Logic to Assertional Logic
A Savage-Like Axiomatization for Nonstandard Expected Utility
On the links between argumentation-based reasoning and nonmonotonic reasoning
Deep Probabilistic Programming
A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue
Agent-Agnostic Human-in-the-Loop Reinforcement Learning
Near Optimal Behavior via Approximate State Abstraction
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Achieving Privacy in the Adversarial Multi-Armed Bandit
Thompson Sampling For Stochastic Bandits with Graph Feedback
Towards prediction of rapid intensification in tropical cyclones with recurrent neural networks
From Community Detection to Community Profiling
Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling
Une mesure d'expertise pour le crowdsourcing
VOCSMAT: a connectionist-inspired treatment proposal for relational traumas
Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models
Ontology based system to guide internship assignment process
Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples
Interactive Learning from Policy-Dependent Human Feedback
Label Propagation on K-partite Graphs with Heterophily
What the Language You Tweet Says About Your Occupation
A Multichannel Convolutional Neural Network For Cross-language Dialog State Tracking
Space-Time Graph Modeling of Ride Requests Based on Real-World Data
Perceptually Optimized Image Rendering
Variability-Aware Design for Energy Efficient Computational Artificial Intelligence Platform
Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D
Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
Systems of natural-language-facilitated human-robot cooperation: A review
Pure Rough Mereology and Counting
Practical Reasoning with Norms for Autonomous Software Agents (Full Edition)
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy
Rhythm Transcription of Polyphonic Piano Music Based on Merged-Output HMM for Multiple Voices
Diversification Methods for Zero-One Optimization
Click Through Rate Prediction for Contextual Advertisment Using Linear Regression
Reinforcement Learning Algorithm Selection
Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning
Deep Reinforcement Learning for Robotic Manipulation-The state of the art
On the Semantics and Complexity of Probabilistic Logic Programs
Efficient Rank Aggregation via Lehmer Codes
Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies
Robust Order Scheduling in the Fashion Industry: A Multi-Objective Optimization Approach
Multilingual and Cross-lingual Timeline Extraction
The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving
Deep Learning with Low Precision by Half-wave Gaussian Quantization
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data
A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution
Exploring the bidimensional space: A dynamic logic point of view
View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network
Extracting Lifted Mutual Exclusion Invariants from Temporal Planning Domains
Representations of language in a model of visually grounded speech signal
Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections
Propagation via Kernelization: The Vertex Cover Constraint
Deep Generalized Canonical Correlation Analysis
Automatic Rule Extraction from Long Short Term Memory Networks
Causal Regularization
Optimal Detection of Faulty Traffic Sensors Used in Route Planning
Graph Based Relational Features for Collective Classification
Answer Set Solving with Bounded Treewidth Revisited
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
Group Scissor: Scaling Neuromorphic Computing Design to Large Neural Networks
Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing
Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy Diversification
Mechanism Design in Social Networks
A Morphology-aware Network for Morphological Disambiguation
Reservoir Computing Using Non-Uniform Binary Cellular Automata
Bilateral Multi-Perspective Matching for Natural Language Sentences
Offline bilingual word vectors, orthogonal transformations and the inverted softmax
Data-Intensive Supercomputing in the Cloud: Global Analytics for Satellite Imagery
Detection of Slang Words in e-Data using semi-Supervised Learning
On Detecting Adversarial Perturbations
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
Cyclic Dominance in the Spatial Coevolutionary Optional Prisoner's Dilemma Game
On the Discrepancy Between Kleinberg's Clustering Axioms and $k$-Means Clustering Algorithm Behavior
Local Search for Minimum Weight Dominating Set with Two-Level Configuration Checking and Frequency Based Scoring Function
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Automated Identification of Drug-Drug Interactions in Pediatric Congestive Heart Failure Patients
A Spacetime Approach to Generalized Cognitive Reasoning in Multi-scale Learning
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes
Revisiting Distributed Synchronous SGD
Hemingway: Modeling Distributed Optimization Algorithms
Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks
Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning
Survey of reasoning using Neural networks
Sample Efficient Policy Search for Optimal Stopping Domains
Synthesizing Imperative Programs from Examples Guided by Static Analysis
Delving Deeper into MOOC Student Dropout Prediction
Unsupervised Diverse Colorization via Generative Adversarial Networks
Task-driven Visual Saliency and Attention-based Visual Question Answering
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
Causal Inference by Stochastic Complexity
Solving DCOPs with Distributed Large Neighborhood Search
A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
Theoretical and Experimental Analysis of the Canadian Traveler Problem
Sequence-based Multimodal Apprenticeship Learning For Robot Perception and Decision Making
Embedding Knowledge Graphs Based on Transitivity and Antisymmetry of Rules
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
Contractibility for Open Global Constraints
Stochastic Variance Reduction Methods for Policy Evaluation
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Reinforcement Learning with Deep Energy-Based Policies
Synergistic Team Composition
Balancing Lexicographic Fairness and a Utilitarian Objective with Application to Kidney Exchange
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Combining the $k$-CNF and XOR Phase-Transitions
Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments
Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network
Optimal Categorical Attribute Transformation for Granularity Change in Relational Databases for Binary Decision Problems in Educational Data Mining
Robust Budget Allocation via Continuous Submodular Functions
Proportional Representation in Vote Streams
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Learning Conversational Systems that Interleave Task and Non-Task Content
Investigating the Characteristics of One-Sided Matching Mechanisms Under Various Preferences and Risk Attitudes
A Hypercat-enabled Semantic Internet of Things Data Hub: Technical Report
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Fast k-Nearest Neighbour Search via Prioritized DCI
Learning to Optimize Neural Nets
OptNet: Differentiable Optimization as a Layer in Neural Networks
Learning Social Affordance Grammar from Videos: Transferring Human Interactions to Human-Robot Interactions
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
Evolving Deep Neural Networks
Adaptive Matching for Expert Systems with Uncertain Task Types
Sampling Variations of Lead Sheets
SLIM: Semi-Lazy Inference Mechanism for Plan Recognition
Unsupervised Image-to-Image Translation Networks
DAWT: Densely Annotated Wikipedia Texts across multiple languages
Toward Controlled Generation of Text
A Laplacian Framework for Option Discovery in Reinforcement Learning
Belief Propagation in Conditional RBMs for Structured Prediction
End-to-End Task-Completion Neural Dialogue Systems
Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression
FeUdal Networks for Hierarchical Reinforcement Learning
Towards Monetary Incentives in Social Q&A Services
Generalised Discount Functions applied to a Monte-Carlo AImu Implementation
Controlling for Unobserved Confounds in Classification Using Correlational Constraints
Principles and Examples of Plausible Reasoning and Propositional Plausible Logic
Sound-Word2Vec: Learning Word Representations Grounded in Sounds
A new belief Markov chain model and its application in inventory prediction
Evidential supplier selection based on interval data fusion
Guarantees for Greedy Maximization of Non-submodular Functions with Applications
On the Limits of Learning Representations with Label-Based Supervision
Cooperative Epistemic Multi-Agent Planning for Implicit Coordination
Deep Robust Kalman Filter
A quantum dynamic belief decision making model
Multi-Robot Active Information Gathering with Periodic Communication
Cost-Optimal Learning of Causal Graphs
Towards Generalization and Simplicity in Continuous Control
Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields
Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data Clustering
A quantum dynamic belief model to explain the interference effects of categorization on decision making
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Learning the Probabilistic Structure of Cumulative Phenomena with Suppes-Bayes Causal Networks
Efficient Simulation of Financial Stress Testing Scenarios with Suppes-Bayes Causal Networks
Information Extraction in Illicit Domains
A Structured Self-attentive Sentence Embedding
Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization
Embedding Tarskian Semantics in Vector Spaces
Counterfactuals, indicative conditionals, and negation under uncertainty: Are there cross-cultural differences?
LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network
What can you do with a rock? Affordance extraction via word embeddings
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation
Learning Gradient Descent: Better Generalization and Longer Horizons
Applying the Wizard-of-Oz Technique to Multimodal Human-Robot Dialogue
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions
Real-Time Machine Learning: The Missing Pieces
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals
Prediction and Control with Temporal Segment Models
Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs
Task-based End-to-end Model Learning in Stochastic Optimization
Fuzzy Model Tree For Early Effort Estimation
Learning best K analogies from data distribution for case-based software effort estimation
Minimizing Maximum Regret in Commitment Constrained Sequential Decision Making
Understanding Black-box Predictions via Influence Functions
Weighted Voting Via No-Regret Learning
Making Neural QA as Simple as Possible but not Simpler
Exploring the Combination Rules of D Numbers From a Perspective of Conflict Redistribution
Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers
Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network
Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting
Minimax Regret Bounds for Reinforcement Learning
Machining of Spherical Component Fabricated by Selected Laser Melting: Strategies and Equipment
Particle Value Functions
Modeling Relational Data with Graph Convolutional Networks
Generalised Reichenbachian Common Cause Systems
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning
Multi-Timescale, Gradient Descent, Temporal Difference Learning with Linear Options
Object category understanding via eye fixations on freehand sketches
Evidence Updating for Stream-Processing in Big-Data: Robust Conditioning in Soft and Hard Fusion Environments
QMDP-Net: Deep Learning for Planning under Partial Observability
Distributed Constraint Problems for Utilitarian Agents with Privacy Concerns, Recast as POMDPs
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems
Interest-Driven Discovery of Local Process Models
Deep Learning for Explicitly Modeling Optimization Landscapes
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
\$1 Today or \$2 Tomorrow? The Answer is in Your Facebook Likes
Supervised Typing of Big Graphs using Semantic Embeddings
Information-theoretic Model Identification and Policy Search using Physics Engines with Application to Robotic Manipulation
Self corrective Perturbations for Semantic Segmentation and Classification
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning
Containment for Rule-Based Ontology-Mediated Queries
Distribution of Gaussian Process Arc Lengths
An overview of embedding models of entities and relationships for knowledge base completion
Semi-supervised Embedding in Attributed Networks with Outliers
Note Value Recognition for Piano Transcription Using Markov Random Fields
Supervisor Synthesis of POMDP based on Automata Learning
Smart Augmentation - Learning an Optimal Data Augmentation Strategy
Reasoning by Cases in Structured Argumentation
Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Team Formation for Scheduling Educational Material in Massive Online Classes
Open Vocabulary Scene Parsing
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
Socially Aware Motion Planning with Deep Reinforcement Learning
Transfer learning for music classification and regression tasks
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
Adversarial Transformation Networks: Learning to Generate Adversarial Examples
Mining Best Closed Itemsets for Projection-antimonotonic Constraints in Polynomial Time
Is This a Joke? Detecting Humor in Spanish Tweets
Inverse Reinforcement Learning from Incomplete Observation Data
Perception Driven Texture Generation
Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary
LabelBank: Revisiting Global Perspectives for Semantic Segmentation
On Convergence Property of Implicit Self-paced Objective
Spaceprint: a Mobility-based Fingerprinting Scheme for Public Spaces
Bandit-Based Model Selection for Deformable Object Manipulation
Enter the Matrix: A Virtual World Approach to Safely Interruptable Autonomous Systems
Efficient Parallel Translating Embedding For Knowledge Graphs
An Empirical Approach for Modeling Fuzzy Geographical Descriptors
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis
Evaluating Complex Task through Crowdsourcing: Multiple Views Approach
Reliable Decision Support using Counterfactual Models
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
Ontological Multidimensional Data Models and Contextual Data Qality
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection
Semi-Supervised Generation with Cluster-aware Generative Models
Multi-Advisor Reinforcement Learning
Deriving Probability Density Functions from Probabilistic Functional Programs
Adaptive Motion Gaming AI for Health Promotion
An Ontological Architecture for Orbital Debris Data
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
Multi-Label Learning with Global and Local Label Correlation
Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition
Landmark Guided Probabilistic Roadmap Queries
Conformative Filtering for Implicit Feedback Data
Encoder Based Lifelong Learning
From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards
Recurrent Environment Simulators
Thresholding Bandits with Augmented UCB
A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification
Basic Formal Properties of A Relational Model of The Mathematical Theory of Evidence
Mixed Graphical Models for Causal Analysis of Multi-modal Variables
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization
Stochastic Neural Networks for Hierarchical Reinforcement Learning
Semantically Consistent Regularization for Zero-Shot Recognition
WRPN: Training and Inference using Wide Reduced-Precision Networks
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
Finding Modes by Probabilistic Hypergraphs Shifting
Stigmergy-based modeling to discover urban activity patterns from positioning data
Beliefs in Markov Trees - From Local Computations to Local Valuation
Counterexample Guided Inductive Optimization
Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors
Deep Reinforcement Learning-based Image Captioning with Embedding Reward
Value Directed Exploration in Multi-Armed Bandits with Structured Priors
Virtual to Real Reinforcement Learning for Autonomous Driving
Fully Distributed and Asynchronized Stochastic Gradient Descent for Networked Systems
Solving ill-posed inverse problems using iterative deep neural networks
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data
Deep API Programmer: Learning to Program with APIs
An entity-driven recursive neural network model for chinese discourse coherence modeling
Incremental learning of high-level concepts by imitation
Optimizing Differentiable Relaxations of Coreference Evaluation Metrics
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
The Reactor: A Sample-Efficient Actor-Critic Architecture
RACE: Large-scale ReAding Comprehension Dataset From Examinations
Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm
A Novel Experimental Platform for In-Vessel Multi-Chemical Molecular Communications
Bayesian Hybrid Matrix Factorisation for Data Integration
Morpheo: Traceable Machine Learning on Hidden data
Anomaly detection and motif discovery in symbolic representations of time series
Understanding Negations in Information Processing: Learning from Replicating Human Behavior
Generalized Ideals and Co-Granular Rough Sets
25 Tweets to Know You: A New Model to Predict Personality with Social Media
Simultaneous Policy Learning and Latent State Inference for Imitating Driver Behavior
Using Contexts and Constraints for Improved Geotagging of Human Trafficking Webpages
Answering Complex Questions Using Open Information Extraction
A Large Self-Annotated Corpus for Sarcasm
OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images
Universal Adversarial Perturbations Against Semantic Image Segmentation
Importance Sampled Stochastic Optimization for Variational Inference
Network Dissection: Quantifying Interpretability of Deep Visual Representations
An Interpretable Knowledge Transfer Model for Knowledge Base Completion
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging
Learning to Acquire Information
Improved Neural Relation Detection for Knowledge Base Question Answering
On Singleton Arc Consistency for CSPs Defined by Monotone Patterns
A Semantic QA-Based Approach for Text Summarization Evaluation
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units
Governing Governance: A Formal Framework for Analysing Institutional Design and Enactment Governance
Modular Multi-Objective Deep Reinforcement Learning with Decision Values
A Review on Deep Learning Techniques Applied to Semantic Segmentation
Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playing
Naturalizing a Programming Language via Interactive Learning
Evaluating and Modelling Hanabi-Playing Agents
Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
Paying Attention to Descriptions Generated by Image Captioning Models
Multi-Task Video Captioning with Video and Entailment Generation
PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction
Learning of Human-like Algebraic Reasoning Using Deep Feedforward Neural Networks
Path Planning with Kinematic Constraints for Robot Groups
Semi-supervised Bayesian Deep Multi-modal Emotion Recognition
Molecular De Novo Design through Deep Reinforcement Learning
Taxonomy Induction using Hypernym Subsequences
Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
Training L1-Regularized Models with Orthant-Wise Passive Descent Algorithms
The loss surface of deep and wide neural networks
Using a new parsimonious AHP methodology combined with the Choquet integral: An application for evaluating social housing initiatives
A Generalization of Convolutional Neural Networks to Graph-Structured Data
No More Discrimination: Cross City Adaptation of Road Scene Segmenters
A quantitative assessment of the effect of different algorithmic schemes to the task of learning the structure of Bayesian Networks
Parseval Networks: Improving Robustness to Adversarial Examples
Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages
Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models
Learning to Ask: Neural Question Generation for Reading Comprehension
Defense semantics of argumentation: encoding reasons for accepting arguments
Deriving Quests from Open World Mechanics
Towards well-specified semi-supervised model-based classifiers via structural adaptation
MACA: A Modular Architecture for Conversational Agents
Argumentation-based Security for Social Good
The Problem of Coincidence in A Theory of Temporal Multiple Recurrence
An improved Ant Colony System for the Sequential Ordering Problem
Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning
A Rule-Based Computational Model of Cognitive Arithmetic
Lifelong Metric Learning
Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
Answer Set Programming for Non-Stationary Markov Decision Processes
Group invariance principles for causal generative models
Data Readiness Levels
Analogical Inference for Multi-Relational Embeddings
Interface and Data Biopolitics in the Age of Hyperconnectivity
People on Drugs: Credibility of User Statements in Health Communities
Experimental results : Reinforcement Learning of POMDPs using Spectral Methods
A New Medical Diagnosis Method Based on Z-Numbers
TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks
Credible Review Detection with Limited Information using Consistency Analysis
Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion
AirDraw: Leveraging Smart Watch Motion Sensors for Mobile Human Computer Interactions
Multimodal Affect Analysis for Product Feedback Assessment
Computing an Approximately Optimal Agreeable Set of Items
Scene Text Eraser
Geometric GAN
Machine Learning with World Knowledge: The Position and Survey
Safe and Nested Subgame Solving for Imperfect-Information Games
Word and Phrase Translation with word2vec
Solving a Path Planning Problem in a Partially Known Environment using a Swarm Algorithm
The Imprecisions of Precision Measures in Process Mining
Asynchronous Announcements
Sequential Dialogue Context Modeling for Spoken Language Understanding
Solving Multi-Objective MDP with Lexicographic Preference: An application to stochastic planning with multiple quantile objective
Flexible and Creative Chinese Poetry Generation Using Neural Memory
Context Attentive Bandits: Contextual Bandit with Restricted Context
Survey of Visual Question Answering: Datasets and Techniques
Solving Distributed Constraint Optimization Problems Using Logic Programming
Memetic search for identifying critical nodes in sparse graphs
Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
A First Empirical Study of Emphatic Temporal Difference Learning
Learning to see people like people
A Formal Characterization of the Local Search Topology of the Gap Heuristic
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Awareness improves problem-solving performance
Discrete Sequential Prediction of Continuous Actions for Deep RL
Simulated Penetration Testing and Mitigation Analysis
Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach
ResumeVis: A Visual Analytics System to Discover Semantic Information in Semi-structured Resume Data
Strategically knowing how
Exploiting the Pruning Power of Strong Local Consistencies Through Parallelization
Constrained Bayesian Networks: Theory, Optimization, and Applications
Probabilistically Safe Policy Transfer
Learning Hard Alignments with Variational Inference
Optimal Warping Paths are unique for almost every Pair of Time Series
Subjective Knowledge Acquisition and Enrichment Powered By Crowdsourcing
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
All-relevant feature selection using multidimensional filters with exhaustive search
Demystifying Relational Latent Representations
Small cities face greater impact from automation
AI, Native Supercomputing and The Revival of Moore's Law
Learning to Represent Haptic Feedback for Partially-Observable Tasks
Identification and Off-Policy Learning of Multiple Objectives Using Adaptive Clustering
Automatic Goal Generation for Reinforcement Learning Agents
Scalable Exact Parent Sets Identification in Bayesian Networks Learning with Apache Spark
Vehicle Routing with Drones
Online learnability of Statistical Relational Learning in anomaly detection
An evidential Markov decision making model
Continuous Implicit Authentication for Mobile Devices based on Adaptive Neuro-Fuzzy Inference System
The Conference Paper Assignment Problem: Using Order Weighted Averages to Assign Indivisible Goods
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
Induction of Interpretable Possibilistic Logic Theories from Relational Data
The Bag Semantics of Ontology-Based Data Access
VAE with a VampPrior
Model-Based Planning with Discrete and Continuous Actions
On Convergence and Stability of GANs
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Search Engine Guided Non-Parametric Neural Machine Translation
Fast Change Point Detection on Dynamic Social Networks
Mixed Membership Word Embeddings for Computational Social Science
Generalizing the Role of Determinization in Probabilistic Planning
Sketched Answer Set Programming
Statistical inference using SGD
A unified view of entropy-regularized Markov decision processes
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
A Unified Approach to Interpreting Model Predictions
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
Poincaré Embeddings for Learning Hierarchical Representations
Detection Algorithms for Communication Systems Using Deep Learning
Enhanced Experience Replay Generation for Efficient Reinforcement Learning
Explaining Transition Systems through Program Induction
Reinforcement Learning with a Corrupted Reward Channel
Symbolic LTLf Synthesis
Personalized and Private Peer-to-Peer Machine Learning
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Second-Order Word Embeddings from Nearest Neighbor Topological Features
Uplift Modeling with Multiple Treatments and General Response Types
Selective Classification for Deep Neural Networks
Predictive Analytics for Enhancing Travel Time Estimation in Navigation Apps of Apple, Google, and Microsoft
An effective algorithm for hyperparameter optimization of neural networks
Data-driven Random Fourier Features using Stein Effect
Safe Model-based Reinforcement Learning with Stability Guarantees
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
Counterfactual Multi-Agent Policy Gradients
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning
Principled Hybrids of Generative and Discriminative Domain Adaptation
Online Edge Grafting for Efficient MRF Structure Learning
Cross-Domain Perceptual Reward Functions
Learning Structured Text Representations
Finding Robust Solutions to Stable Marriage
Neural Attribute Machines for Program Generation
Filtering Variational Objectives
Together We Know How to Achieve: An Epistemic Logic of Know-How
Distributed Robust Subspace Recovery
Discovering Reliable Approximate Functional Dependencies
Multimodal Machine Learning: A Survey and Taxonomy
Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation
ASR error management for improving spoken language understanding
Logical and Inequality Implications for Reducing the Size and Complexity of Quadratic Unconstrained Binary Optimization Problems
Classification regions of deep neural networks
Analysis of universal adversarial perturbations
Bayesian GAN
Risk-Sensitive Cooperative Games for Human-Machine Systems
Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical Analysis
Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
Kernel Implicit Variational Inference
Machine Learned Learning Machines
Contextual Explanation Networks
Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation
Preliminary results on Ontology-based Open Data Publishing
Deep Learning is Robust to Massive Label Noise
Semi-Supervised Learning for Detecting Human Trafficking
Morphological Error Detection in 3D Segmentations
Towards Learned Clauses Database Reduction Strategies Based on Dominance Relationship
Propositional Knowledge Representation in Restricted Boltzmann Machines
Adversarial Generation of Natural Language
Non-Markovian Control with Gated End-to-End Memory Policy Networks
The Atari Grand Challenge Dataset
End-to-End Differentiable Proving
Controllable Invariance through Adversarial Feature Learning
A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit
Descriptions of Objectives and Processes of Mechanical Learning
Free energy-based reinforcement learning using a quantum processor
Diversified Top-k Partial MaxSAT Solving
Teaching Machines to Describe Images via Natural Language Feedback
One button machine for automating feature engineering in relational databases
Grounding Symbols in Multi-Modal Instructions
Enhancing workflow-nets with data for trace completion
Discovering Discrete Latent Topics with Neural Variational Inference
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Knowledge Representation in Bicategories of Relations
Modeling Latent Attention Within Neural Networks
Joint Matrix-Tensor Factorization for Knowledge Base Inference
Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics
3D Pathfinding and Collision Avoidance Using Uneven Search-space Quantization and Visual Cone Search
A method for the online construction of the set of states of a Markov Decision Process using Answer Set Programming
Batched Large-scale Bayesian Optimization in High-dimensional Spaces
Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning
A WL-SPPIM Semantic Model for Document Classification
Parameter Space Noise for Exploration
Epistemic Logic with Functional Dependency Operator
Guided Interaction Exploration in Artifact-centric Process Models
Improving Max-Sum through Decimation to Solve Loopy Distributed Constraint Optimization Problems
Recurrent computations for visual pattern completion
Stochastic Global Optimization Algorithms: A Systematic Formal Approach
InfoVAE: Information Maximizing Variational Autoencoders
Can Computers overcome Humans? Consciousness interaction and its implications
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Generalized Value Iteration Networks: Life Beyond Lattices
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes
Dynamic Discovery of Type Classes and Relations in Semantic Web Data
Dynamic Integration of Background Knowledge in Neural NLU Systems
Setting Players' Behaviors in World of Warcraft through Semi-Supervised Learning
The FastMap Algorithm for Shortest Path Computations
TIP: Typifying the Interpretability of Procedures
Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment
Symmetry Learning for Function Approximation in Reinforcement Learning
A Focal Any-Angle Path-finding Algorithm Based on A* on Visibility Graphs
Rethinking Skip-thought: A Neighborhood based Approach
Image Matching via Loopy RNN
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning
Data-Efficient Policy Evaluation Through Behavior Policy Search
YellowFin and the Art of Momentum Tuning
Neural Domain Adaptation for Biomedical Question Answering
Deep reinforcement learning from human preferences
Semantic Entity Retrieval Toolkit
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
Fuzzy Recommendations in Marketing Campaigns
Recommendations for Marketing Campaigns in Telecommunication Business based on the footprint analysis
A Supervised Approach to Extractive Summarisation of Scientific Papers
On Natural Language Generation of Formal Argumentation
Zero-Shot Relation Extraction via Reading Comprehension
Optimization by a quantum reinforcement algorithm
Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection
Simultaneous merging multiple grid maps using the robust motion averaging
Neural Models for Key Phrase Detection and Question Generation
Conjunctions of Among Constraints
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
From Propositional Logic to Plausible Reasoning: A Uniqueness Theorem
Value-Decomposition Networks For Cooperative Multi-Agent Learning
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
Evaluating the quality of tourist agendas customized to different travel styles
Capacity Releasing Diffusion for Speed and Locality
Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers
Learning to Schedule Deadline- and Operator-Sensitive Tasks
Solving Integer Linear Programs with a Small Number of Global Variables and Constraints
Multi-Label Annotation Aggregation in Crowdsourcing
User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario
meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting
Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning
Dualing GANs
The Complexity of Campaigning
Session Analysis using Plan Recognition
A Thorough Formalization of Conceptual Spaces
Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic
Optimal modularity and memory capacity of neural networks
Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes
Word-Entity Duet Representations for Document Ranking
Toward Real-Time Decentralized Reinforcement Learning using Finite Support Basis Functions
NPGLM: A Non-Parametric Method for Temporal Link Prediction
Structure Learning in Motor Control:A Deep Reinforcement Learning Model
Web-STAR: Towards a Visual Web-Based IDE for a Story Comprehension System
CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Gated-Attention Architectures for Task-Oriented Language Grounding
A Framework for Accurate Drought Forecasting System Using Semantics-Based Data Integration Middleware
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation
A-NICE-MC: Adversarial Training for MCMC
Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis
Finding optimal finite biological sequences over finite alphabets: the OptiFin toolbox
Specifying Non-Markovian Rewards in MDPs Using LDL on Finite Traces (Preliminary Version)
Random Forests for Industrial Device Functioning Diagnostics Using Wireless Sensor Networks
There and Back Again: A General Approach to Learning Sparse Models
The Boolean Solution Problem from the Perspective of Predicate Logic - Extended Version
Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability
Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog
Neural Question Answering at BioASQ 5B
Developing Bug-Free Machine Learning Systems With Formal Mathematics
Relating Complexity-theoretic Parameters with SAT Solver Performance
TimeNet: Pre-trained deep recurrent neural network for time series classification
Gradient Episodic Memory for Continual Learning
Training a Fully Convolutional Neural Network to Route Integrated Circuits
Strategyproof Mechanisms for Additively Separable Hedonic Games and Fractional Hedonic Games
A Pig, an Angel and a Cactus Walk Into a Blender: A Descriptive Approach to Visual Blending
Generative Bridging Network in Neural Sequence Prediction
Hierarchical Attentive Recurrent Tracking
Path planning for Robotic Mobile Fulfillment Systems
DynASP2.5: Dynamic Programming on Tree Decompositions in Action
Default Logic and Bounded Treewidth
Neural SLAM: Learning to Explore with External Memory
Path Integral Networks: End-to-End Differentiable Optimal Control
Indoor UAV scheduling with Restful Task Assignment Algorithm
Speaker Identification in each of the Neutral and Shouted Talking Environments based on Gender-Dependent Approach Using SPHMMs
Providing Effective Real-time Feedback in Simulation-based Surgical Training
A ROS multi-ontology references services: OWL reasoners and application prototyping issues
Statistical Analysis of Dice CAPTCHA Usability
A reliability-based approach for influence maximization using the evidence theory
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes
Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model
A study of existing Ontologies in the IoT-domain
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management
Teacher-Student Curriculum Learning
Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study
Submodular Function Maximization for Group Elevator Scheduling
A Reverse Hex Solver
Modeling preference time in middle distance triathlons
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels
Visualizing the Consequences of Evidence in Bayesian Networks
Conditional generation of multi-modal data using constrained embedding space mapping
Window-of-interest based Multi-objective Evolutionary Search for Satisficing Concepts
Dissipative quantum bifurcation machine: Quantum heating of coupled nonlinear oscillators
Interpretable & Explorable Approximations of Black Box Models
Unsupervised Submodular Rank Aggregation on Score-based Permutations
Sentiment Identification in Code-Mixed Social Media Text
The impossibility of "fairness": a generalized impossibility result for decisions
Graph Based Recommendations: From Data Representation to Feature Extraction and Application
SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guarantee
Learning to Design Games: Strategic Environments in Deep Reinforcement Learning
Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object Rotation
Model enumeration in propositional circumscription via unsatisfiable core analysis
Hindsight Experience Replay
Optimal Vehicle Dispatching Schemes via Dynamic Pricing
CNN features are also great at unsupervised classification
Cross-linguistic differences and similarities in image descriptions
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control
Long-Term Memory Networks for Question Answering
Networked Fairness in Cake Cutting
Methods for finding leader--follower equilibria with multiple followers
A parallel corpus of Python functions and documentation strings for automated code documentation and code generation
Measuring Relations Between Concepts In Conceptual Spaces
Evaluating race and sex diversity in the world's largest companies using deep neural networks
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning
Neural Machine Translation between Herbal Prescriptions and Diseases
Understanding State Preferences With Text As Data: Introducing the UN General Debate Corpus
Towards Crafting Text Adversarial Samples
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration
Lexicographic choice functions
A Survey on Resilient Machine Learning
Accelerated Variance Reduced Stochastic ADMM
Automated Game Design Learning
CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis
Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings
Value Prediction Network
Deep Learning for Sensor-based Activity Recognition: A Survey
Using RDF Summary Graph For Keyword-based Semantic Searches
Source-Target Inference Models for Spatial Instruction Understanding
Independence, Conditionality and Structure of Dempster-Shafer Belief Functions
Identification and Interpretation of Belief Structure in Dempster-Shafer Theory
Automatic Mapping of NES Games with Mappy
Large Scale Variable Fidelity Surrogate Modeling
A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors
Dependency Injection for Programming by Optimization
Constraints, Lazy Constraints, or Propagators in ASP Solving: An Empirical Analysis
Large-scale Video Classification guided by Batch Normalized LSTM Translator
Stable Distribution Alignment Using the Dual of the Adversarial Distance
Clingo goes Linear Constraints over Reals and Integers
Neural Networks for Information Retrieval
Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media
Hot-Rodding the Browser Engine: Automatic Configuration of JavaScript Compilers
On (Anti)Conditional Independence in Dempster-Shafer Theory
Predicting Abandonment in Online Coding Tutorials
Bayesian Optimization for Probabilistic Programs
Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic
Reliability Assessment of Distribution System Using Fuzzy Logic for Modelling of Transformer and Line Uncertainties
GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures
Tunnel Effects in Cognition: A new Mechanism for Scientific Discovery and Education
Improving Adherence to Heart Failure Management Guidelines via Abductive Reasoning
Online Multi-Armed Bandit
Coalition formation for Multi-agent Pursuit based on Neural Network and AGRMF Model
graph2vec: Learning Distributed Representations of Graphs
The Power of Constraint Grammars Revisited
PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking
Houdini: Fooling Deep Structured Prediction Models
Order-Free RNN with Visual Attention for Multi-Label Classification
Grounding Spatio-Semantic Referring Expressions for Human-Robot Interaction
On-line Building Energy Optimization using Deep Reinforcement Learning
Deformable Part-based Fully Convolutional Network for Object Detection
Entropy-based Pruning for Learning Bayesian Networks using BIC
Imagination-Augmented Agents for Deep Reinforcement Learning
Crowdsourcing Multiple Choice Science Questions
Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons
The Role of Conversation Context for Sarcasm Detection in Online Interactions
Computing LPMLN Using ASP and MLN Solvers
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach
Video Question Answering via Attribute-Augmented Attention Network Learning
Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems
An All-in-One Network for Dehazing and Beyond
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
An Infinite Hidden Markov Model With Similarity-Biased Transitions
On the Computation of Paracoherent Answer Sets
A Distributional Perspective on Reinforcement Learning
A Framework for Easing the Development of Applications Embedding Answer Set Programming
Society-in-the-Loop: Programming the Algorithmic Social Contract
Preference Reasoning in Matching Procedures: Application to the Admission Post-Baccalaureat Platform
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
Likelihood Estimation for Generative Adversarial Networks
Learning Rare Word Representations using Semantic Bridging
Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation
Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms
Extracting Core Claims from Scientific Articles
Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning
Mutual Alignment Transfer Learning
Structural Regularities in Text-based Entity Vector Spaces
Un modèle pour la représentation des connaissances temporelles dans les documents historiques
Price and Profit Awareness in Recommender Systems
A Survey on Multi-Task Learning
Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints
Closed-Loop Policies for Operational Tests of Safety-Critical Systems
The Advantage of Evidential Attributes in Social Networks
A Decidable Very Expressive Description Logic for Databases (Extended Version)
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Guiding Reinforcement Learning Exploration Using Natural Language
Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Common Knowledge in a Logic of Gossips
Preservation of Semantic Properties during the Aggregation of Abstract Argumentation Frameworks
An Epistemic Foundation for Authentication Logics (Extended Abstract)
Group Recommendations: Axioms, Impossibilities, and Random Walks
Argument-based Belief in Topological Structures
Reconciling Bayesian Epistemology and Narration-based Approaches to Judiciary Fact-finding
A New Modal Framework for Epistemic Logic
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
Deep Residual Learning for Weakly-Supervised Relation Extraction
Learning to Teach Reinforcement Learning Agents
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
Data-Driven Stochastic Robust Optimization: A General Computational Framework and Algorithm for Optimization under Uncertainty in the Big Data Era
Recurrent Ladder Networks
The Topology of Statistical Verifiability
Photographic Image Synthesis with Cascaded Refinement Networks
Method and apparatus for automatic text input insertion in digital devices with a restricted number of keys
Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results
Developing Knowledge-enhanced Chronic Disease Risk Prediction Models from Regional EHR Repositories
Fashioning with Networks: Neural Style Transfer to Design Clothes
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
Learned in Translation: Contextualized Word Vectors
A Labelling Framework for Probabilistic Argumentation
Quantum Projective Simulation with Hamiltonian Evolution: A study in reinforcement learning
Neural Rating Regression with Abstractive Tips Generation for Recommendation
Fast Preprocessing for Robust Face Sketch Synthesis
CREST: Convolutional Residual Learning for Visual Tracking
Using Program Induction to Interpret Transition System Dynamics
Hierarchical Subtask Discovery With Non-Negative Matrix Factorization
"I can assure you [$\ldots$] that it's going to be all right" -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships
On the Importance of Consistency in Training Deep Neural Networks
Fairness-aware machine learning: a perspective
Graph-based Features for Automatic Online Abuse Detection
Independently Controllable Factors
Effective sketching methods for value function approximation
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
Agent based Tools for Modeling and Simulation of Self-Organization in Peer-to-Peer, Ad-Hoc and other Complex Networks
Game theory models for communication between agents: a review
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
Boosting Variational Inference: an Optimization Perspective
e-QRAQ: A Multi-turn Reasoning Dataset and Simulator with Explanations
Declarative Statistics
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning
Training of Deep Neural Networks based on Distance Measures using RMSProp
Why Adaptively Collected Data Have Negative Bias and How to Correct for It
STARDATA: A StarCraft AI Research Dataset
A Characterization of Monotone Influence Measures for Data Classification
Asking Too Much? The Rhetorical Role of Questions in Political Discourse
Generative Statistical Models with Self-Emergent Grammar of Chord Sequences
Real-Time Visual Localisation in a Tagged Environment
Reinforced Video Captioning with Entailment Rewards
Shortcut-Stacked Sentence Encoders for Multi-Domain Inference
Multibiometric Secure System Based on Deep Learning
Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems
Beyond the technical challenges for deploying Machine Learning solutions in a software company
Learning how to Active Learn: A Deep Reinforcement Learning Approach
Stochastic Optimization with Bandit Sampling
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
Decoupled Learning of Environment Characteristics for Safe Exploration
The Tensor Memory Hypothesis
Role of Secondary Attributes to Boost the Prediction Accuracy of Students Employability Via Data Mining
Addendum to: Summary Information for Reasoning About Hierarchical Plans
Preference fusion and Condorcet's Paradox under uncertainty
Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs
Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddings
Counterexample Guided Inductive Optimization Applied to Mobile Robots Path Planning (Extended Version)
Deep Object-Centric Representations for Generalizable Robot Learning
Motion Planning under Partial Observability using Game-Based Abstraction
Benchmark Environments for Multitask Learning in Continuous Domains
Graph Classification via Deep Learning with Virtual Nodes
Learning from Noisy Label Distributions
Automatic Summarization of Online Debates
Weighted parallel SGD for distributed unbalanced-workload training system
New Ideas for Brain Modelling 4
Maximum A Posteriori Inference in Sum-Product Networks
mAnI: Movie Amalgamation using Neural Imitation
Visualizing and Exploring Dynamic High-Dimensional Datasets with LION-tSNE
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description
The Size of a Hyperball in a Conceptual Space
Cultural Structures of Knowledge from Wikipedia Networks of First Links
Exploring Directional Path-Consistency for Solving Constraint Networks
LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions
An Improved Residual LSTM Architecture for Acoustic Modeling
Human Uncertainty and Ranking Error -- The Secret of Successful Evaluation in Predictive Data Mining
A Stronger Foundation for Computer Science and P=NP
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction
A Brief Survey of Deep Reinforcement Learning
A novel agent-based simulation framework for sensing in complex adaptive environments
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method
Software-Defined Robotics -- Idea & Approach
Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks
A Batch Noise Contrastive Estimation Approach for Training Large Vocabulary Language Models
More cat than cute? Interpretable Prediction of Adjective-Noun Pairs
Neural Block Sampling
Vector Space Model as Cognitive Space for Text Classification
Comparative Benchmarking of Causal Discovery Techniques
Probabilistic Relation Induction in Vector Space Embeddings
The CARESSES EU-Japan project: making assistive robots culturally competent
Network Model Selection for Task-Focused Attributed Network Inference
On a Formal Model of Safe and Scalable Self-driving Cars
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Human Action Recognition System using Good Features and Multilayer Perceptron Network
Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs
Classification of Radiology Reports Using Neural Attention Models
Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
On Relaxing Determinism in Arithmetic Circuits
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems
Non-linear Convolution Filters for CNN-based Learning
Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition
Single Reference Image based Scene Relighting via Material Guided Filtering
A Survey of Human Activity Recognition Using WiFi CSI
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
A Study on Neural Network Language Modeling
Learning Generalized Reactive Policies using Deep Neural Networks
Achieving Proportional Representation via Voting
Reinforcement Mechanism Design for e-commerce
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids
$k$-Nearest Neighbor Augmented Neural Networks for Text Classification
3D Object Reconstruction from a Single Depth View with Adversarial Learning
Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks
RIOT: a Novel Stochastic Method for Rapidly Configuring Cloud-Based Workflows
On Type-Aware Entity Retrieval
The Convergence of Machine Learning and Communications
Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection
Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks
Safe Reinforcement Learning via Shielding
Unifying DAGs and UGs
How 5G (and concomitant technologies) will revolutionize healthcare
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning
Modelling Protagonist Goals and Desires in First-Person Narrative
Quality and Diversity Optimization: A Unifying Modular Framework
Pros and cons gamification and gaming in classroom
End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design
Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events
Inference of Fine-Grained Event Causality from Blogs and Films
Learning what to read: Focused machine reading
Order-Planning Neural Text Generation From Structured Data
Inferring Networked Device Categories from Low-Level Activity Indicators
An Automated Compatibility Prediction Engine using DISC Theory Based Classification and Neural Networks
XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual Classification
Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue
Difficulty-level Modeling of Ontology-based Factual Questions
Interactive Attention Networks for Aspect-Level Sentiment Classification
Automation of Android Applications Testing Using Machine Learning Activities Classification
A Computer Composes A Fabled Problem: Four Knights vs. Queen
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
Speeding-up the decision making of a learning agent using an ion trap quantum processor
A Generic Approach for Escaping Saddle points
Learning the PE Header, Malware Detection with Minimal Domain Knowledge
Fine-tuning deep CNN models on specific MS COCO categories
A second order primal-dual method for nonsmooth convex composite optimization
Machine Learning and Social Robotics for Detecting Early Signs of Dementia
Bayesian Optimisation for Safe Navigation under Localisation Uncertainty
Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling
Learning from lions: inferring the utility of agents from their trajectories
Inferring Generative Model Structure with Static Analysis
Object-Oriented Knowledge Extraction using Universal Exploiters
Identifying Mirror Symmetry Density with Delay in Spiking Neural Networks
Semantic Preserving Embeddings for Generalized Graphs
Uncertainty measurement with belief entropy on interference effect in Quantum-Like Bayesian Networks
Variable Annealing Length and Parallelism in Simulated Annealing
Computational Machines in a Coexistence with Concrete Universals and Data Streams
Expert Opinion Extraction from a Biomedical Database
Cellular Automaton Based Simulation of Large Pedestrian Facilities - A Case Study on the Staten Island Ferry Terminals
A Planning Approach to Monitoring Behavior of Computer Programs
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations
Art of singular vectors and universal adversarial perturbations
A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling
RRA: Recurrent Residual Attention for Sequence Learning
End-to-End United Video Dehazing and Detection
Affective Neural Response Generation
Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds
Refining Source Representations with Relation Networks for Neural Machine Translation
Information Design in Crowdfunding under Thresholding Policies
Parallelizing Linear Recurrent Neural Nets Over Sequence Length
On labeling Android malware signatures using minhashing and further classification with Structural Equation Models
A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables
Action Schema Networks: Generalised Policies with Deep Learning
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning
Neural Network Based Nonlinear Weighted Finite Automata
Visualizations for an Explainable Planning Agent
Workflow Complexity for Collaborative Interactions: Where are the Metrics? -- A Challenge
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning
A Framework for Generalizing Graph-based Representation Learning Methods
Warmstarting of Model-based Algorithm Configuration
KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
The Conditional Analogy GAN: Swapping Fashion Articles on People Images
Denoising Autoencoders for Overgeneralization in Neural Networks
Fast semi-supervised discriminant analysis for binary classification of large data-sets
One-Shot Visual Imitation Learning via Meta-Learning
Shared Learning : Enhancing Reinforcement in $Q$-Ensembles
ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks
Query-based Attention CNN for Text Similarity Map
Feature-Fused SSD: Fast Detection for Small Objects
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades
Supervising Unsupervised Learning
Embedding Deep Networks into Visual Explanations
The Uncertainty Bellman Equation and Exploration
Scene-centric Joint Parsing of Cross-view Videos
Process-oriented Iterative Multiple Alignment for Medical Process Mining
Reinforcement Learning Based Conversational Search Assistant
A Categorical Approach for Recognizing Emotional Effects of Music
AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms
Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks
Direction-Aware Semi-Dense SLAM
Relational Marginal Problems: Theory and Estimation
ZhuSuan: A Library for Bayesian Deep Learning
Kernel Cross-Correlator
The shortest way to visit all metro lines in a city
Deep Graph Attention Model
Feedforward and Recurrent Neural Networks Backward Propagation and Hessian in Matrix Form
When is a Convolutional Filter Easy To Learn?
DropoutDAgger: A Bayesian Approach to Safe Imitation Learning
On the Complexity of Robust Stable Marriage
Online algorithms for POMDPs with continuous state, action, and observation spaces
A Comparative Quantitative Analysis of Contemporary Big Data Clustering Algorithms for Market Segmentation in Hospitality Industry
Incorrigibility in the CIRL Framework
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning
Interactive Music Generation with Positional Constraints using Anticipation-RNNs
Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization
Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting
Deep Reinforcement Learning that Matters
Verifying Properties of Binarized Deep Neural Networks
Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning
A Voting-Based System for Ethical Decision Making
Temporal Pattern Mining from Evolving Networks
Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks
On Compiling DNNFs without Determinism
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward
Feature Engineering for Predictive Modeling using Reinforcement Learning
Convolutional neural networks that teach microscopes how to image
Exact Learning of Lightweight Description Logic Ontologies
Neural Optimizer Search with Reinforcement Learning
Complexity of Scheduling Charging in the Smart Grid
Robust Optimization of Unconstrained Binary Quadratic Problems
Defining a Lingua Franca to Open the Black Box of a Naïve Bayes Recommender
EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure
Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network
Inverse Reinforcement Learning with Conditional Choice Probabilities
Predicting Runtime Distributions using Deep Neural Networks
Code Attention: Translating Code to Comments by Exploiting Domain Features
OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World
Humanoid Robots as Agents of Human Consciousness Expansion
On overfitting and asymptotic bias in batch reinforcement learning with partial observability
Quantum Memristors in Quantum Photonics
Generalized Quantum Reinforcement Learning with Quantum Technologies
Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups
Semi-Supervised Hierarchical Semantic Object Parsing
Object-Oriented Knowledge Representation and Data Storage Using Inhomogeneous Classes
Towards Classification of Web ontologies using the Horizontal and Vertical Segmentation
Prioritized Norms in Formal Argumentation
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
Self-supervised learning: When is fusion of the primary and secondary sensor cue useful?
Intrusions in Marked Renewal Processes
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
HDLTex: Hierarchical Deep Learning for Text Classification
Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps
"Let me convince you to buy my product ... ": A Case Study of an Automated Persuasive System for Fashion Products
Deep Learning Based Cryptographic Primitive Classification
Non-iterative Label Propagation on Optimal Leading Forest
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
Enhanced Quantum Synchronization via Quantum Machine Learning
Ensemble Classifier for Eye State Classification using EEG Signals
Towards automation of data quality system for CERN CMS experiment
Fooling Vision and Language Models Despite Localization and Attention Mechanism
User and Developer Interaction with Editable and Readable Ontologies
Embodied Evolution in Collective Robotics: A Review
Lexical Disambiguation in Natural Language Questions (NLQs)
A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning
DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting
Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment
A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks
Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition
Heuristic Online Goal Recognition in Continuous Domains
Distance-based Confidence Score for Neural Network Classifiers
Are we Done with Object Recognition? The iCub robot's Perspective
Improving Efficiency in Convolutional Neural Network with Multilinear Filters
Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Overcoming Exploration in Reinforcement Learning with Demonstrations
A Neural Comprehensive Ranker (NCR) for Open-Domain Question Answering
Provably Minimally-Distorted Adversarial Examples
Training an adaptive dialogue policy for interactive learning of visually grounded word meanings
Human motion primitive discovery and recognition
Vision-based deep execution monitoring
Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization
Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning
Learning event representation: As sparse as possible, but not sparser
Deep Abstract Q-Networks
Improving speech recognition by revising gated recurrent units
Sensor Synthesis for POMDPs with Reachability Objectives
Supervised Q-walk for Learning Vector Representation of Nodes in Networks
Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming
Context Embedding Networks
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
Learning Graphical Models from a Distributed Stream
Stacked Structure Learning for Lifted Relational Neural Networks
Learnable Explicit Density for Continuous Latent Space and Variational Inference
Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency for Sequence Modeling
Generating Nontrivial Melodies for Music as a Service
Rainbow: Combining Improvements in Deep Reinforcement Learning
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
Texture Fuzzy Segmentation using Skew Divergence Adaptive Affinity Functions
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits
Recurrent Network-based Deterministic Policy Gradient for Solving Bipedal Walking Challenge on Rugged Terrains
On formalizing fairness in prediction with machine learning
Function space analysis of deep learning representation layers
Coresets for Dependency Networks
Geo-referencing Place from Everyday Natural Language Descriptions
Causality and Temporal Dependencies in the Design of Fault Management Systems
Prior Knowledge based mutation prioritization towards causal variant finding in rare disease
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
Safe Semi-Supervised Learning of Sum-Product Networks
Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis
Emergent Complexity via Multi-Agent Competition
Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations
End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies
Deep Semantic Abstractions of Everyday Human Activities: On Commonsense Representations of Human Interactions
A novel prestack sparse azimuthal AVO inversion
Counterfactual Conditionals in Quantified Modal Logic
Synkhronos: a Multi-GPU Theano Extension for Data Parallelism
Machine Learning Bell Nonlocality in Quantum Many-body Systems
Measurement Context Extraction from Text: Discovering Opportunities and Gaps in Earth Science
DisSent: Sentence Representation Learning from Explicit Discourse Relations
Sign-Constrained Regularized Loss Minimization
Marginal sequential Monte Carlo for doubly intractable models
Clusters of Driving Behavior from Observational Smartphone Data
Identifying On-time Reward Delivery Projects with Estimating Delivery Duration on Kickstarter
HyperENTM: Evolving Scalable Neural Turing Machines through HyperNEAT
Bayesian Hypernetworks
Recent Advances in Zero-shot Recognition
Functional Decision Theory: A New Theory of Instrumental Rationality
Network Model Selection Using Task-Focused Minimum Description Length
Learners that Use Little Information
Multi-Value Rule Sets
Learning Infinite RBMs with Frank-Wolfe
The Complete Extensions do not form a Complete Semilattice
Manifold Regularization for Kernelized LSTD
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control
Generalization in Deep Learning
Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation
Mining Frequent Patterns in Process Models
A Survey on Optical Character Recognition System
Characterizing Driving Context from Driver Behavior
Gradient-free Policy Architecture Search and Adaptation
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
Distributed algorithm for empty vehicles management in personal rapid transit (PRT) network
The Hard Problems Are Almost Everywhere For Random CNF-XOR Formulas
Convergence diagnostics for stochastic gradient descent with constant step size
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Constructing Datasets for Multi-hop Reading Comprehension Across Documents
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
Asymmetric Actor Critic for Image-Based Robot Learning
The Effects of Memory Replay in Reinforcement Learning
A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization
Graph Embedding with Rich Information through Heterogeneous Network
Characterization of Gradient Dominance and Regularity Conditions for Neural Networks
Emergent Translation in Multi-Agent Communication
Adapting general-purpose speech recognition engine output for domain-specific natural language question answering
Consequentialist conditional cooperation in social dilemmas with imperfect information
Decision Trees for Helpdesk Advisor Graphs
Swift Linked Data Miner: Mining OWL 2 EL class expressions directly from online RDF datasets
A Two-Phase Safe Vehicle Routing and Scheduling Problem: Formulations and Solution Algorithms
On Using Linear Diophantine Equations to Tune the extent of Look Ahead while Hiding Decision Tree Rules
Spoken Language Biomarkers for Detecting Cognitive Impairment
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework
Solving the "false positives" problem in fraud prediction
ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection
A Learning-to-Infer Method for Real-Time Power Grid Topology Identification
Deep Neural Network Approximation using Tensor Sketching
The Complexity of Graph-Based Reductions for Reachability in Markov Decision Processes
Safety-Aware Apprenticeship Learning
Hierarchical State Abstractions for Decision-Making Problems with Computational Constraints
Investigating the feature collection for semantic segmentation via single skip connection
Listening to the World Improves Speech Command Recognition
Deep Health Care Text Classification
Serving deep learning models in a serverless platform
Max-Margin Invariant Features from Transformed Unlabeled Data
Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention
Feature learning in feature-sample networks using multi-objective optimization
FashionBrain Project: A Vision for Understanding Europe's Fashion Data Universe
Klout Topics for Modeling Interests and Expertise of Users Across Social Networks
Understanding Grounded Language Learning Agents
Audiovisual Analytics Vocabulary and Ontology (AAVO): initial core and example expansion
Distributional Reinforcement Learning with Quantile Regression
Group Fairness in Multiwinner Voting
An efficient SAT formulation for learning multiple criteria non-compensatory sorting rules from examples
Towards a new paradigm for assistive technology at home: research challenges, design issues and performance assessment
Detection and Analysis of Human Emotions through Voice and Speech Pattern Processing
Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information
Dual Skipping Networks
Long-Distance Loop Closure Using General Object Landmarks
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Vehicle Routing Problem with Vector Profits (VRPVP) with Max-Min Criterion
Regularization for Deep Learning: A Taxonomy
Training Probabilistic Spiking Neural Networks with First-to-spike Decoding
Tensorizing Generative Adversarial Nets
Understanding Hidden Memories of Recurrent Neural Networks
Rough extreme learning machine: a new classification method based on uncertainty measure
Graph Attention Networks
The loss surface and expressivity of deep convolutional neural networks
Unsupervised Neural Machine Translation
Eigenoption Discovery through the Deep Successor Representation
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
Generating Natural Adversarial Examples
Parametrizing filters of a CNN with a GAN
Regret Minimization for Partially Observable Deep Reinforcement Learning
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
Whodunnit? Crime Drama as a Case for Natural Language Understanding
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm
Unsupervised Machine Translation Using Monolingual Corpora Only
DCN+: Mixed Objective and Deep Residual Coattention for Question Answering
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering
Automata Guided Hierarchical Reinforcement Learning for Zero-shot Skill Composition
Pomegranate: fast and flexible probabilistic modeling in python
Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Servant of Many Masters: Shifting priorities in Pareto-optimal sequential decision-making
Minimal Exploration in Structured Stochastic Bandits
Building Data-driven Models with Microstructural Images: Generalization and Interpretability
Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Weight-Based Variable Ordering in the Context of High-Level Consistencies
SPARK: Static Program Analysis Reasoning and Retrieving Knowledge
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Mandolin: A Knowledge Discovery Framework for the Web of Data
Decentralised firewall for malware detection
The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learning
Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging Recording
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
Semantic Web Today: From Oil Rigs to Panama Papers
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Strategies for Conceptual Change in Convolutional Neural Networks
Multilingual Speech Recognition With A Single End-To-End Model
RoboCupSimData: A RoboCup soccer research dataset
Neural Language Modeling by Jointly Learning Syntax and Lexicon
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm
Bounding and Counting Linear Regions of Deep Neural Networks
Weighted Transformer Network for Machine Translation
Adaptive Bayesian Sampling with Monte Carlo EM
Alpha-expansion is Exact on Stable Instances
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Learning Overcomplete HMMs
Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks
Recurrent Autoregressive Networks for Online Multi-Object Tracking
Block-Sparse Recurrent Neural Networks
Faster Fuzzing: Reinitialization with Deep Neural Models
Inverse Reward Design
Learning Sparse Visual Representations with Leaky Capped Norm Regularizers
Clustering with feature selection using alternating minimization, Application to computational biology
Exploration in NetHack with Secret Discovery
Information Directed Sampling for Stochastic Bandits with Graph Feedback
Large-scale Cloze Test Dataset Designed by Teachers
CogSciK: Clustering for Cognitive Science Motivated Decision Making
Discovering Representative Examples for Program Synthesis
Heuristic Optimization for Automated Distribution System Planning in Network Integration Studies
Repairing Ontologies via Axiom Weakening
Open-World Knowledge Graph Completion
Scalable Log Determinants for Gaussian Process Kernel Learning
Learning Multi-Modal Word Representation Grounded in Visual Context
Fast Meta-Learning for Adaptive Hierarchical Classifier Design
Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication
A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem
DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers
Stochastic Deep Learning in Memristive Networks
Self-Supervised Intrinsic Image Decomposition
Saliency Prediction for Mobile User Interfaces
Lattice embeddings between types of fuzzy sets. Closed-valued fuzzy sets
Learning with Options that Terminate Off-Policy
Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records
CARLA: An Open Urban Driving Simulator
Applications of Deep Learning and Reinforcement Learning to Biological Data
Optimised Maintenance of Datalog Materialisations
Stream Reasoning in Temporal Datalog
Deep Within-Class Covariance Analysis for Acoustic Scene Classification
Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems
MojiTalk: Generating Emotional Responses at Scale
Commonsense LocatedNear Relation Extraction
Evaluation of trackers for Pan-Tilt-Zoom Scenarios
High-Order Attention Models for Visual Question Answering
Learning Abduction under Partial Observability
Simple And Efficient Architecture Search for Convolutional Neural Networks
Solving the Resource Constrained Project Scheduling Problem Using the Parallel Tabu Search Designed for the CUDA Platform
Phonemic and Graphemic Multilingual CTC Based Speech Recognition
Multilingual Adaptation of RNN Based ASR Systems
A unified decision making framework for supply and demand management in microgrid networks
Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks
Web Robot Detection in Academic Publishing
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
Deep Rewiring: Training very sparse deep networks
Saliency-based Sequential Image Attention with Multiset Prediction
Goal-Driven Query Answering for Existential Rules with Equality
Weakly-supervised Semantic Parsing with Abstract Examples
Loss Functions for Multiset Prediction
Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs
Hibikino-Musashi@Home 2017 Team Description Paper
A Generally Applicable, Highly Scalable Measurement Computation and Optimization Approach to Sequential Model-Based Diagnosis
Exploiting Layerwise Convexity of Rectifier Networks with Sign Constrained Weights
Markov Decision Processes with Continuous Side Information
Fast Predictive Simple Geodesic Regression
Quantile Markov Decision Process
K3, L3, LP, RM3, A3, FDE: How to Make Many-Valued Logics Work for You
Bootstrapped synthetic likelihood
A General Neural Network Hardware Architecture on FPGA
Using Noisy Extractions to Discover Causal Knowledge
Budget-Constrained Multi-Armed Bandits with Multiple Plays
Hindsight policy gradients
Enabling Reasoning with LegalRuleML
A Robust Genetic Algorithm for Learning Temporal Specifications from Data
One Model for the Learning of Language
Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities
3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network
Using KL-divergence to focus Deep Visual Explanation
Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games
Learning to Play Othello with Deep Neural Networks
Dependent landmark drift: robust point set registration based on the Gaussian mixture model with a statistical shape model
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments
Is prioritized sweeping the better episodic control?
Scalable Recollections for Continual Lifelong Learning
A Generalized Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles of Complex Types
Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
Run, skeleton, run: skeletal model in a physics-based simulation
Computational Results for Extensive-Form Adversarial Team Games
Facets, Tiers and Gems: Ontology Patterns for Hypernormalisation
FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
Implementing the Deep Q-Network
Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders
Cross Temporal Recurrent Networks for Ranking Question Answer Pairs
Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition
Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data
Situationally Aware Options
Constructive Preference Elicitation over Hybrid Combinatorial Spaces
Quantifying Performance of Bipedal Standing with Multi-channel EMG
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
Relating Input Concepts to Convolutional Neural Network Decisions
Recurrent Relational Networks for Complex Relational Reasoning
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces
Robust Stackelberg Equilibria in Extensive-Form Games and Extension to Limited Lookahead
Asymmetric Action Abstractions for Multi-Unit Control in Adversarial Real-Time Games
The Stochastic Firefighter Problem
Decomposition Strategies for Constructive Preference Elicitation
A correlational analysis of multiagent sensorimotor interactions: clustering autonomous and controllable entities
RGB-D-based Human Motion Recognition with Deep Learning: A Survey
Safer Classification by Synthesis
Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning
Ethical Challenges in Data-Driven Dialogue Systems
Cascade Attribute Learning Network
D numbers theory based game-theoretic framework in adversarial decision making under fuzzy environment
Generalizing Hamiltonian Monte Carlo with Neural Networks
Generative Adversarial Network for Abstractive Text Summarization
Pedagogical learning
MAVOT: Memory-Augmented Video Object Tracking
A general unified framework for interval pairwise comparison matrices
Deep Reinforcement Learning for Sepsis Treatment
Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation
Production Ready Chatbots: Generate if not Retrieve
Classifier Selection with Permutation Tests
Table-to-text Generation by Structure-aware Seq2seq Learning
Distilling a Neural Network Into a Soft Decision Tree
Tensor Completion Algorithms in Big Data Analytics
Homomorphic Parameter Compression for Distributed Deep Learning Training
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
Quantitative CBA: Small and Comprehensible Association Rule Classification Models
Hierarchical Policy Search via Return-Weighted Density Estimation
Crossmodal Attentive Skill Learner
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
Backprop as Functor: A compositional perspective on supervised learning
A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
FearNet: Brain-Inspired Model for Incremental Learning
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
A reinforcement learning algorithm for building collaboration in multi-agent systems
TensorFlow Distributions
PSIque: Next Sequence Prediction of Satellite Images using a Convolutional Sequence-to-Sequence Network
Efficient exploration with Double Uncertain Value Networks
Saliency Weighted Convolutional Features for Instance Search
Deep Reinforcement Learning for De-Novo Drug Design
Extreme Dimension Reduction for Handling Covariate Shift
Now Playing: Continuous low-power music recognition
Embedding Words as Distributions with a Bayesian Skip-gram Model
Improving Latent User Models in Online Social Media
Video Captioning via Hierarchical Reinforcement Learning
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care
Learning to Learn from Weak Supervision by Full Supervision
ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism
Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation
Calculating Semantic Similarity between Academic Articles using Topic Event and Ontology
Deep Neural Networks for Multiple Speaker Detection and Localization
Comparing Deep Reinforcement Learning and Evolutionary Methods in Continuous Control
Improving Smiling Detection with Race and Gender Diversity
A double competitive strategy based learning automata algorithm
Novel Exploration Techniques (NETs) for Malaria Policy Interventions
Interactive Reinforcement Learning for Object Grounding via Self-Talking
From knowledge-based to data-driven modeling of fuzzy rule-based systems: A critical reflection
Will humans even write code in 2040 and what would that mean for extreme heterogeneity in computing?
Evaluation of Alzheimer's Disease by Analysis of MR Images using Multilayer Perceptrons and Kohonen SOM Classifiers as an Alternative to the ADC Maps
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Hierarchical Actor-Critic
Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals
Characterizing and Computing Causes for Query Answers in Databases from Database Repairs and Repair Programs
A Deeper Look at Experience Replay
Learning User Intent from Action Sequences on Interactive Systems
Examining Cooperation in Visual Dialog Models
Multimodal Storytelling via Generative Adversarial Imitation Learning
Determinism in the Certification of UNSAT Proofs
Neural Cross-Lingual Entity Linking
An analysis of incorporating an external language model into a sequence-to-sequence model
Learning General Latent-Variable Graphical Models with Predictive Belief Propagation and Hilbert Space Embeddings
Distance-based Self-Attention Network for Natural Language Inference
Adversarial Examples that Fool Detectors
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
A Deep Network Model for Paraphrase Detection in Short Text Messages
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient
Columnar Database Techniques for Creating AI Features
Stochastic Dual Coordinate Descent with Bandit Sampling
FlagIt: A System for Minimally Supervised Human Trafficking Indicator Mining
A Class of Logistic Functions for Approximating State-Inclusive Koopman Operators
S-Shaped vs. V-Shaped Transfer Functions for Antlion Optimization Algorithm in Feature Selection Problems
Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
Bayesian Q-learning with Assumed Density Filtering
Robust Deep Reinforcement Learning with Adversarial Attacks
DeepConfig: Automating Data Center Network Topologies Management with Machine Learning
MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications
Learning Robust Dialog Policies in Noisy Environments
The Eigenoption-Critic Framework
In a Nutshell: Sequential Parameter Optimization
Benchmarking Single Image Dehazing and Beyond
Toward `verifying' a Water Treatment System
Mining Non-Redundant Sets of Generalizing Patterns from Sequence Databases
Interpretable Policies for Reinforcement Learning by Genetic Programming
Consideration on Example 2 of "An Algorithm of General Fuzzy InferenceWith The Reductive Property"
Review of Design of Speech Recognition and Text Analytics based Digital Banking Customer Interface and Future Directions of Technology Adoption
Reasoning in Systems with Elements that Randomly Switch Characteristics
Intrinsic Point of Interest Discovery from Trajectory Data
Proximodistal Exploration in Motor Learning as an Emergent Property of Optimization
Constraint and Mathematical Programming Models for Integrated Port Container Terminal Operations
CoDraw: Visual Dialog for Collaborative Drawing
Pre-training Attention Mechanisms
Impossibility of deducing preferences and rationality from human policy
Morphology dictates a robot's ability to ground crowd-proposed language
Ray: A Distributed Framework for Emerging AI Applications
SchNet - a deep learning architecture for molecules and materials
Visual Explanations from Hadamard Product in Multimodal Deep Networks
'Indifference' methods for managing agent rewards
Nonparametric Inference for Auto-Encoding Variational Bayes
Parallel Complexity of Forward and Backward Propagation
Learning Representations from Road Network for End-to-End Urban Growth Simulation
Heinrich Behmann's Contributions to Second-Order Quantifier Elimination from the View of Computational Logic
Large-Scale Vandalism Detection with Linear Classifiers - The Conkerberry Vandalism Detector at WSDM Cup 2017
Safe Policy Improvement with Baseline Bootstrapping
Mining Smart Card Data for Travelers' Mini Activities
Column Generation for Interaction Coverage in Combinatorial Software Testing
On Data-Dependent Random Features for Improved Generalization in Supervised Learning
Machine Learning for Vehicular Networks
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning
Block-diagonal Hessian-free Optimization for Training Neural Networks
Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography images
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning
An Ensemble Model with Ranking for Social Dialogue
Context-aware Path Ranking for Knowledge Base Completion
Bit-Vector Model Counting using Statistical Estimation
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations
Fair Forests: Regularized Tree Induction to Minimize Model Bias
CSGNet: Neural Shape Parser for Constructive Solid Geometry
Inverse Classification for Comparison-based Interpretability in Machine Learning
Rank Pruning for Dominance Queries in CP-Nets
Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration
Interpretable Counting for Visual Question Answering
Towards Collaborative Conceptual Exploration
Building Robust Deep Neural Networks for Road Sign Detection
An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems
Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture
Toward Continual Learning for Conversational Agents
The Merits of Sharing a Ride
Kernel Robust Bias-Aware Prediction under Covariate Shift
Deep Learning Interior Tomography for Region-of-Interest Reconstruction
Characterizing optimal hierarchical policy inference on graphs via non-equilibrium thermodynamics
Learning Structural Weight Uncertainty for Sequential Decision-Making
A Compare-Propagate Architecture with Alignment Factorization for Natural Language Inference
Game-theoretic Network Centrality: A Review
Neurally Plausible Model of Robot Reaching Inspired by Infant Motor Babbling
Scalable Hash-Based Estimation of Divergence Measures
Automated rating of recorded classroom presentations using speech analysis in kazakh
Multi-Objective Vehicle Routing Problem Applied to Large Scale Post Office Deliveries
Um Sistema Multiagente no Combate ao Braqueamento de Capitais
ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling
Social Media Analysis based on Semanticity of Streaming and Batch Data
Approximate Ranking from Pairwise Comparisons
Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
A Quantitative Analysis of Multi-Winner Rules
Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning
Secrecy by Witness-Functions under Equational Theories
Entropy production rate as a criterion for inconsistency in decision theory
Automated Conjecturing VII: The Graph Brain Project & Big Mathematics
A Comprehensive Survey of Ontology Summarization: Measures and Methods
On the inherent competition between valid and spurious inductive inferences in Boolean data
Approximate FPGA-based LSTMs under Computation Time Constraints
Indian Regional Movie Dataset for Recommender Systems
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors
How to find a GSMem malicious activity via an AI approach
DeepTraffic: Driving Fast through Dense Traffic with Deep Reinforcement Learning
Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes
An Ontology for Satellite Databases
Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives
Deep In-GPU Experience Replay
A Formalization of Kant's Second Formulation of the Categorical Imperative
Adaptive Graph Convolutional Neural Networks
Eliciting Worker Preference for Task Completion
Reasoning about Unforeseen Possibilities During Policy Learning
Neural Program Synthesis with Priority Queue Training
Using probabilistic programs as proposals
Topic-based Evaluation for Conversational Bots
EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs
Formalized Conceptual Spaces with a Geometric Representation of Correlations
Model-Based Action Exploration for Learning Dynamic Motion Skills
Interactive Learning of Acyclic Conditional Preference Networks
Planning with Trust for Human-Robot Collaboration
Combining Symbolic and Function Evaluation Expressions In Neural Programs
A Computational Model of Commonsense Moral Decision Making
Fairness in Supervised Learning: An Information Theoretic Approach
Which Training Methods for GANs do actually Converge?
Better Runtime Guarantees Via Stochastic Domination
Non-Parametric Transformation Networks
tau-FPL: Tolerance-Constrained Learning in Linear Time
Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up
Building a Conversational Agent Overnight with Dialogue Self-Play
Topic Modeling on Health Journals with Regularized Variational Inference
Empirical Explorations in Training Networks with Discrete Activations
Social Network based Short-Term Stock Trading System
Learning Features For Relational Data
Considerations regarding security issues impact on systems availability
Unseen Class Discovery in Open-world Classification
A Generalized Dempster--Shafer Evidence Theory
Toward Scalable Verification for Safety-Critical Deep Networks
Layered TPOT: Speeding up Tree-based Pipeline Optimization
Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations
Optimal Weighting for Exam Composition
Integrating planning for task-completion dialogue policy learning
Demonstration of Topological Data Analysis on a Quantum Processor
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
A high-performance analog Max-SAT solver and its application to Ramsey numbers
Visualization of Hyperspectral Images Using Moving Least Squares
Efficient Learning of Optimal Markov Network Topology with k-Tree Modeling
Cross-Domain Transfer in Reinforcement Learning using Target Apprentice
Extreme Learning Machine with Local Connections
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral-Regularization Algorithms
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Comparison Training for Computer Chinese Chess
Analyzing Language Learned by an Active Question Answering Agent
Mitigating Unwanted Biases with Adversarial Learning
DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning
Clustering with Deep Learning: Taxonomy and New Methods
A Classification Refinement Strategy for Semantic Segmentation
Optimal Transport on Discrete Domains
PointCNN
Evaluation of Interactive Machine Learning Systems
Intrinsic dimension of concept lattices
PRNN: Recurrent Neural Network with Persistent Memory
MAttNet: Modular Attention Network for Referring Expression Comprehension
Discovering Markov Blanket from Multiple interventional Datasets
Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning
Sine Cosine Crow Search Algorithm: A powerful hybrid meta heuristic for global optimization
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification
Knowledge Graph Embedding with Multiple Relation Projections
Safe Exploration in Continuous Action Spaces
FlashRL: A Reinforcement Learning Platform for Flash Games
A Sheaf Model of Contradictions and Disagreements. Preliminary Report and Discussion
Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data
Representing the Insincere: Strategically Robust Proportional Representation
On the Inter-relationships among Drift rate, Forgetting rate, Bias/variance profile and Error
Improving Active Learning in Systematic Reviews
An Improved Tabu Search Heuristic for Static Dial-A-Ride Problem
Evaluating approaches for supervised semantic labeling
Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks
Personalized Survival Prediction with Contextual Explanation Networks
Algorithms for the Greater Good! On Mental Modeling and Acceptable Symbiosis in Human-AI Collaboration
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
Features, Projections, and Representation Change for Generalized Planning
A Rational Distributed Process-level Account of Independence Judgment
A Cross Entropy based Optimization Algorithm with Global Convergence Guarantees
Deep Learning Works in Practice. But Does it Work in Theory?
Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations
Deep Reinforcement Learning for Programming Language Correction
Deep Predictive Models in Interactive Music
Lifted Filtering via Exchangeable Decomposition
Learning Families of Formal Languages from Positive and Negative Information
Cluster-based Approach to Improve Affect Recognition from Passively Sensed Data
Recursive Feature Generation for Knowledge-based Learning
Deep Learning with Data Dependent Implicit Activation Function
Dual Recurrent Attention Units for Visual Question Answering
3D Object Dense Reconstruction from a Single Depth View
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Adaptive Memory Networks
Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming
Interpretable Deep Convolutional Neural Networks via Meta-learning
Visual Interpretability for Deep Learning: a Survey
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation
Intriguing Properties of Randomly Weighted Networks: Generalizing While Learning Next to Nothing
Memory Fusion Network for Multi-view Sequential Learning
Incorporating Literals into Knowledge Graph Embeddings
Pose Flow: Efficient Online Pose Tracking
Plan Explanations as Model Reconciliation -- An Empirical Study
Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle
Task-Aware Compressed Sensing with Generative Adversarial Networks
Interactive Grounded Language Acquisition and Generalization in a 2D World
The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface
Guided Policy Exploration for Markov Decision Processes using an Uncertainty-Based Value-of-Information Criterion
Abstractly Interpreting Argumentation Frameworks for Sharpening Extensions
Learning from Richer Human Guidance: Augmenting Comparison-Based Learning with Feature Queries
Utility Decomposition with Deep Corrections for Scalable Planning under Uncertainty
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation
A Survey Of Methods For Explaining Black Box Models
Improving Variational Encoder-Decoders in Dialogue Generation
FastNet
IONet: Learning to Cure the Curse of Drift in Inertial Odometry
Scalable Meta-Learning for Bayesian Optimization
Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
Efficient collective swimming by harnessing vortices through deep reinforcement learning
Efficient Large-Scale Multi-Modal Classification
Learning Role-based Graph Embeddings
Cognitive Business Process Management for Adaptive Cyber-Physical Processes
Web-Based Implementation of Travelling Salesperson Problem Using Genetic Algorithm
Balancing Two-Player Stochastic Games with Soft Q-Learning
Learning Robust Options
Slice Sampling Particle Belief Propagation
ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback
Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks
Generalization of an Upper Bound on the Number of Nodes Needed to Achieve Linear Separability
Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification
Local Contrast Learning
To the problem of "The Instrumental complex for ontological engineering purpose" software system design
Beyond Markov Logic: Efficient Mining of Prediction Rules in Large Graphs
Graph Planning with Expected Finite Horizon
Beyond the One Step Greedy Approach in Reinforcement Learning
Learning a SAT Solver from Single-Bit Supervision
Formal Ontology Learning from English IS-A Sentences
The Need for Speed of AI Applications: Performance Comparison of Native vs. Browser-based Algorithm Implementations
Learning Multiple Levels of Representations with Kernel Machines
Influence-Directed Explanations for Deep Convolutional Networks
Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks
Detecting and Correcting for Label Shift with Black Box Predictors
A note on reinforcement learning with Wasserstein distance regularisation, with applications to multipolicy learning
ProofWatch: Watchlist Guidance for Large Theories in E
A New Algorithmic Decision for Categorical Syllogisms via Caroll's Diagrams
State Representation Learning for Control: An Overview
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
Deep Reinforcement Learning for Solving the Vehicle Routing Problem
Global Model Interpretation via Recursive Partitioning
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering
signSGD: compressed optimisation for non-convex problems
Learning Robust and Adaptive Real-World Continuous Control Using Simulation and Transfer Learning
On the Relative Succinctness of Sentential Decision Diagrams
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Barista - a Graphical Tool for Designing and Training Deep Neural Networks
Attention based Sentence Extraction from Scientific Articles using Pseudo-Labeled data
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control
Evolved Policy Gradients
Challenging Images For Minds and Machines
Learning via social awareness: improving sketch representations with facial feedback
Disjoint Multi-task Learning between Heterogeneous Human-centric Tasks
PlayeRank: Multi-dimensional and role-aware rating of soccer player performance
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Deep Learning and Data Assimilation for Real-Time Production Prediction in Natural Gas Wells
Who Killed Albert Einstein? From Open Data to Murder Mystery Games
Learning Deep Disentangled Embeddings with the F-Statistic Loss
Reinforcement Learning from Imperfect Demonstrations
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero
Deep Learning Based Speech Beamforming
High Dimensional Bayesian Optimization Using Dropout
Mean Field Multi-Agent Reinforcement Learning
Admissible Time Series Motif Discovery with Missing Data
Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays
Truth Validation with Evidence
Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning
An Anytime Algorithm for Task and Motion MDPs
A Unified View of Causal and Non-causal Feature Selection
Deep Generative Model for Joint Alignment and Word Representation
Measuring Human-perceived Similarity in Heterogeneous Collections
Monte Carlo Q-learning for General Game Playing
Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps
Dropout Model Evaluation in MOOCs
Towards a Continuous Knowledge Learning Engine for Chatbots
Online Continuous Submodular Maximization
Implicit Robot-Human Communication in Adversarial and Collaborative Environments
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty
Optimizing Interactive Systems with Data-Driven Objectives
Graphical Models for Non-Negative Data Using Generalized Score Matching
Convergence of Online Mirror Descent Algorithms
Scalable Alignment Kernels via Space-Efficient Feature Maps
Estimating scale-invariant future in continuous time
Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning
Memorize or generalize? Searching for a compositional RNN in a haystack
Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning
Robust Estimation via Robust Gradient Estimation
Learning High-level Representations from Demonstrations
Deep Echo State Networks for Diagnosis of Parkinson's Disease
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss
Divide, Denoise, and Defend against Adversarial Attacks
Deep Learning for Joint Source-Channel Coding of Text
Fourier Policy Gradients
Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations
Using Automatic Generation of Relaxation Constraints to Improve the Preimage Attack on 39-step MD4
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements
Robust Maximization of Non-Submodular Objectives
Combining Textual Content and Structure to Improve Dialog Similarity
Meta-Reinforcement Learning of Structured Exploration Strategies
Learning to Play with Intrinsically-Motivated Self-Aware Agents
Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation
Epistemic Graphs for Representing and Reasoning with Positive and Negative Influences of Arguments
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Manipulating and Measuring Model Interpretability
Convergent Actor-Critic Algorithms Under Off-Policy Training and Function Approximation
Pooling homogeneous ensembles to build heterogeneous ensembles
L2-Nonexpansive Neural Networks
Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise
Generating High-Quality Query Suggestion Candidates for Task-Based Search
Towards an Understanding of Entity-Oriented Search Intents
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
Reliable Intersection Control in Non-cooperative Environments
A Polynomial Time Subsumption Algorithm for Nominal Safe $\mathcal{ELO}_\bot$ under Rational Closure
Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds
High Order Recurrent Neural Networks for Acoustic Modelling
Learning to Make Predictions on Graphs with Autoencoders
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
Coloring black boxes: visualization of neural network decisions
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments
Optimal Stochastic Delivery Planning in Full-Truckload and Less-Than-Truckload Delivery
Unsupervised Grammar Induction with Depth-bounded PCFG
Visualizing the Flow of Discourse with a Concept Ontology
Learning Optimal Policies from Observational Data
Deep learning in radiology: an overview of the concepts and a survey of the state of the art
GraphRNN: A Deep Generative Model for Graphs
Cakewalk Sampling
Domain Specific Design Patterns: Designing For Conversational User Interfaces
One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data
Self-organizing maps and generalization: an algorithmic description of Numerosity and Variability Effects
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
Reinforcement and Imitation Learning for Diverse Visuomotor Skills
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents
Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System
Generalized Binary Search For Split-Neighborly Problems
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
Bioinformatics and Medicine in the Era of Deep Learning
Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing
Ab initio Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism
The Emergence of Spectral Universality in Deep Networks
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Investigating Human Priors for Playing Video Games
DeepSOFA: A Real-Time Continuous Acuity Score Framework using Deep Learning
Escort: Efficient Sparse Convolutional Neural Networks on GPUs
General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
Quantum cognition goes beyond-quantum: modeling the collective participant in psychological measurements
DiGrad: Multi-Task Reinforcement Learning with Shared Actions
Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction
Model-Ensemble Trust-Region Policy Optimization
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning
Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Modeling reverse thinking for machine learning
Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent Reinforcement Learning Approach
Facial Expression Recognition Based on Complexity Perception Classification Algorithm
Learning Flexible and Reusable Locomotion Primitives for a Microrobot
Representation Learning in Partially Observable Environments using Sensorimotor Prediction
Q-CP: Learning Action Values for Cooperative Planning
The Power Mean Laplacian for Multilayer Graph Clustering
Composable Planning with Attributes
Semi-Supervised Online Structure Learning for Composite Event Recognition
Hierarchical Imitation and Reinforcement Learning
Semi-parametric Topological Memory for Navigation
Gesture-based Piloting of an Aerial Robot using Monocular Vision
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
Estimating Total Search Space Size for Specific Piece Sets in Chess
Essentially No Barriers in Neural Network Energy Landscape
Impact of Biases in Big Data
Multi-Instance Dynamic Ordinal Random Fields for Weakly-supervised Facial Behavior Analysis
Understanding the Loss Surface of Neural Networks for Binary Classification
Optimization with Gradient-Boosted Trees and Risk Control
Multi-Agent Imitation Learning for Driving Simulation
An Ensemble Framework of Voice-Based Emotion Recognition System for Films and TV Programs
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
A Swift Heuristic Method for Work Order Scheduling under the Skilled-Workforce Constraint
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
On Cognitive Preferences and the Interpretability of Rule-based Models
Improving Multi-Step Traffic Flow Prediction
Localization under Topological Uncertainty for Lane Identification of Autonomous Vehicles
Exploring Novel Game Spaces with Fluidic Games
A real-time rule-based system for bridge management based on CART decision tree and SMO algorithms
DAGs with NO TEARS: Smooth Optimization for Structure Learning
N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
One-Class Adversarial Nets for Fraud Detection
Towards Automatic & Personalised Mobile Health Interventions: An Interactive Machine Learning Perspective
ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks
Explain Yourself: A Natural Language Interface for Scrutable Autonomous Robots
Optimal Stochastic Package Delivery Planning with Deadline: A Cardinality Minimization in Routing
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning
Annotation Artifacts in Natural Language Inference Data
Smoothed Action Value Functions for Learning Gaussian Policies
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification
Object cosegmentation using deep Siamese network
Multi-Channel Pyramid Person Matching Network for Person Re-Identification
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
Extracting Action Sequences from Texts Based on Deep Reinforcement Learning
Generating Contradictory, Neutral, and Entailing Sentences
OntoWind: An Improved and Extended Wind Energy Ontology
Accelerated Methods for Deep Reinforcement Learning
Sever: A Robust Meta-Algorithm for Stochastic Optimization
Satisficing in Time-Sensitive Bandit Learning
An efficient framework for learning sentence representations
Simultaneous Task Allocation and Planning Under Uncertainty
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes
Compositional Attention Networks for Machine Reasoning
Concise Fuzzy Representation of Big Graphs: a Dimensionality Reduction Approach
Feudal Reinforcement Learning for Dialogue Management in Large Domains
Deep Neural Network Compression with Single and Multiple Level Quantization
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings
A New Model for Evaluating Range-Based Anomaly Detection Algorithms
Evolutionary Architecture Search For Deep Multitask Networks
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting
A Deep Learning Based Behavioral Approach to Indoor Autonomous Navigation
Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts
Measuring Conflict in a Multi-Source Environment as a Normal Measure
On Cryptographic Attacks Using Backdoors for SAT
Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies
An Agent-Based Simulation of Residential Location Choice of Tenants in Tehran, Iran
Impacts of transport development on residence choice of renter households: An agent-based evaluation
Learning to Explore with Meta-Policy Gradient
Feature extraction without learning in an analog Spatial Pooler memristive-CMOS circuit design of Hierarchical Temporal Memory
Learning to Play General Video-Games via an Object Embedding Network
Knowledge-based Recurrent Attentive Neural Network for Traffic Sign Detection
Complex activity patterns generated by short-term synaptic plasticity
Imitation Learning with Concurrent Actions in 3D Games
Averaging Weights Leads to Wider Optima and Better Generalization
Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
Challenges in Discriminating Profanity from Hate Speech
Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
Unraveling Go gaming nature by Ising Hamiltonian and common fate graphs: tactics and statistics
Beyond Patient Monitoring: Conversational Agents Role in Telemedicine & Healthcare Support For Home-Living Elderly Individuals
A Meaning-based Statistical English Math Word Problem Solver
Vulnerability of Deep Learning
Some HCI Priorities for GDPR-Compliant Machine Learning
Snap Machine Learning
Learning to Cluster for Proposal-Free Instance Segmentation
Argumentation theory for mathematical argument
Learning recurrent dynamics in spiking networks
The Web as a Knowledge-base for Answering Complex Questions
Computing and Testing Pareto Optimal Committees
Batched quantum state exponentiation and quantum Hebbian learning
Simple random search provides a competitive approach to reinforcement learning
Automated Curriculum Learning by Rewarding Temporally Rare Events
Neural Text Generation: Past, Present and Beyond
English-Catalan Neural Machine Translation in the Biomedical Domain through the cascade approach
Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition
Ontology-Based Reasoning about the Trustworthiness of Cyber-Physical Systems
Enslaving the Algorithm: From a "Right to an Explanation" to a "Right to Better Decisions"?
Inference in Probabilistic Graphical Models by Graph Neural Networks
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation
Speech Emotion Recognition Considering Local Dynamic Features
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
Multi-view Metric Learning in Vector-valued Kernel Spaces
Expeditious Generation of Knowledge Graph Embeddings
On-demand Relational Concept Analysis
Scalable Generalized Dynamic Topic Models
Scan transcription of two-dimensional shapes as an alternative neuromorphic concept
Stacked Cross Attention for Image-Text Matching
Robust Blind Deconvolution via Mirror Descent
Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization
Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning
A framework for Culture-aware Robots based on Fuzzy Logic
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
The Rapidly Changing Landscape of Conversational Agents
Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft
Towards Universal Representation for Unseen Action Recognition
Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings
Neuronal Circuit Policies
Foundations of Prescriptive Process Monitoring
From Random Differential Equations to Structural Causal Models: the stochastic case
2CoBel : An Efficient Belief Function Extension for Two-dimensional Continuous Spaces
A mosaic of Chu spaces and Channel Theory with applications to Object Identification and Mereological Complexity
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs
Datasheets for Datasets
Multi-range Reasoning for Machine Comprehension
A Resourceful Reframing of Behavior Trees
code2vec: Learning Distributed Representations of Code
Connectionist Recommendation in the Wild
Accelerating Empowerment Computation with UCT Tree Search
MLE-induced Likelihood for Markov Random Fields
Image Semantic Transformation: Faster, Lighter and Stronger
Reinforcement Learning for Fair Dynamic Pricing
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation
Comprehending Real Numbers: Development of Bengali Real Number Speech Corpus
Forward-Backward Reinforcement Learning
Neuroevolution for RTS Micro
What deep learning can tell us about higher cognitive functions like mindreading?
Bundled fragments of first-order modal logic: (un)decidability
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy
Best arm identification in multi-armed bandits with delayed feedback
A Review of Literature on Parallel Constraint Solving
A real-time warning system for rear-end collision based on random forest classifier
Modified SMOTE Using Mutual Information and Different Sorts of Entropies
Actor-Critic based Training Framework for Abstractive Summarization
3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation
Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering
Welfare Without Taxation - Autonomous production revenues for Universal Basic Income
Regularizing RNNs for Caption Generation by Reconstructing The Past with The Present
3D Pose Estimation and 3D Model Retrieval for Objects in the Wild
Learning to Anonymize Faces for Privacy Preserving Action Detection
Visual Robot Task Planning
Efficient Encodings of Conditional Cardinality Constraints
Modeling Individual Differences in Game Behavior using HMM
Attentional Multilabel Learning over Graphs: A Message Passing Approach
Aggregated Momentum: Stability Through Passive Damping
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
Regional Priority Based Anomaly Detection using Autoencoders
Towards Explanation of DNN-based Prediction with Guided Feature Inversion
Predictions of short-term driving intention using recurrent neural network on sequential data
Investigating Capsule Networks with Dynamic Routing for Text Classification
Specification-Driven Multi-Perspective Predictive Business Process Monitoring (Extended Version)
Universal Planning Networks
Unsupervised Learning of Sequence Representations by Autoencoders
CIKM AnalytiCup 2017 Lazada Product Title Quality Challenge An Ensemble of Deep and Shallow Learning to predict the Quality of Product Titles
Transferring Common-Sense Knowledge for Object Detection
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples
NegPSpan: efficient extraction of negative sequential patterns with embedding constraints
Clinical Concept Embeddings Learned from Massive Sources of Medical Data
Abstractive Tabular Dataset Summarization via Knowledge Base Semantic Embeddings
Stochastic Adversarial Video Prediction
Hypertree Decompositions Revisited for PGMs
Variational Rejection Sampling
The Kanerva Machine: A Generative Distributed Memory
End-to-End Saliency Mapping via Probability Distribution Prediction
A Human Mixed Strategy Approach to Deep Reinforcement Learning
A Survey of Miss-Ratio Curve Construction Techniques
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
Noise-resistant Deep Learning for Object Classification in 3D Point Clouds Using a Point Pair Descriptor
Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices
End-to-End Learning of Communications Systems Without a Channel Model
Comparing Dependencies in Probability Theory and General Rough Sets: Part-A
Compositional Obverter Communication Learning From Raw Visual Input
Predictive Process Monitoring Methods: Which One Suits Me Best?
Programmatically Interpretable Reinforcement Learning
Efficient Reciprocal Collision Avoidance between Heterogeneous Agents Using CTMAT
Hindsight is Only 50/50: Unsuitability of MDP based Approximate POMDP Solvers for Multi-resolution Information Gathering
A Proposal of Interactive Growing Hierarchical SOM
Fast Conditional Independence Test for Vector Variables with Large Sample Sizes
Active Mini-Batch Sampling using Repulsive Point Processes
A Generation Method of Immunological Memory in Clonal Selection Algorithm by using Restricted Boltzmann Machines
Policy Gradient With Value Function Approximation For Collective Multiagent Planning
A theory of consciousness: computation, algorithm, and neurobiological realization
A review of possible effects of cognitive biases on interpretation of rule-based machine learning models
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection
Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks
Segmentation of Multiple Sclerosis lesion in brain MR images using Fuzzy C-Means
QA4IE: A Question Answering based Framework for Information Extraction
A Hierarchical Latent Structure for Variational Conversation Modeling
Exploring Disentangled Feature Representation Beyond Face Identification
Towards Training Probabilistic Topic Models on Neuromorphic Multi-chip Systems
Introduction to Iltis: An Interactive, Web-Based System for Teaching Logic
Personalization of Health Interventions using Cluster-Based Reinforcement Learning
Understanding disentangling in $β$-VAE
Universal Successor Representations for Transfer Reinforcement Learning
CoT: Cooperative Training for Generative Modeling
A Variable Neighborhood Search for Flying Sidekick Traveling Salesman Problem
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
Reasoning about Safety of Learning-Enabled Components in Autonomous Cyber-physical Systems
Emergent Communication through Negotiation
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
Predicting Twitter User Socioeconomic Attributes with Network and Language Information
Market Making via Reinforcement Learning
Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Incomplete Contracting and AI Alignment
STAIR Actions: A Video Dataset of Everyday Home Actions
Global SNR Estimation of Speech Signals using Entropy and Uncertainty Estimates from Dropout Networks
Regularized Greedy Column Subset Selection
CubeNet: Equivariance to 3D Rotation and Translation
Outline Objects using Deep Reinforcement Learning
Discovery and usage of joint attention in images
CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web
Online Fall Detection using Recurrent Neural Networks
Monitoring and Executing Workflows in Linked Data Environments
Intelligent Probabilistic Inference
An Introduction to Collective Intelligence
Channel-Independent and Sensor-Independent Stimulus Representations
The Cyborg Astrobiologist: First Field Experience
Field geology with a wearable computer: 1st results of the Cyborg Astrobiologist System
Nurse Rostering with Genetic Algorithms
Survey on Various Gesture Recognition Techniques for Interfacing Machines Based on Ambient Intelligence
SNF Project Locomotion: Final report 2009-2010
SNF Project Locomotion: Progress report 2008-2009
Discovering Stock Price Prediction Rules of Bombay Stock Exchange Using Rough Fuzzy Multi Layer Perception Networks
Galactic-scale macro-engineering: Looking for signs of other intelligent species, as an exercise in hope for our own
Semantic Enrichment of Mobile Phone Data Records Using Background Knowledge
Autonomics: an autonomous and intelligent economic platform and next generation money tool
Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm
Geometry of Interest (GOI): Spatio-Temporal Destination Extraction and Partitioning in GPS Trajectory Data
Visual Dialog
Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video
Sim-To-Real Optimization Of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play
Automatic Recognition of Space-Time Constellations by Learning on the Grassmann Manifold (Extended Version)
The Biological Concept of Neoteny in Evolutionary Colour Image Segmentation - Simple Experiments in Simple Non-Memetic Genetic Algorithms
Understanding the Social Cascading of Geekspeak and the Upshots for Social Cognitive Systems
The "Wow! signal" of the terrestrial genetic code
A DDoS-Aware IDS Model Based on Danger Theory and Mobile Agents
Distinguishing cause from effect using observational data: methods and benchmarks
SENNS: Sparse Extraction Neural NetworkS for Feature Extraction
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0
The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic
An Improved Search Algorithm for Optimal Multiple-Sequence Alignment
Formal Model of Uncertainty for Possibilistic Rules
An Anytime Algorithm for Optimal Coalition Structure Generation
Probabilistic and Non-Monotonic Inference
A General Non-Probabilistic Theory of Inductive Reasoning
Graphs in machine learning: an introduction
Swarms, Phase Transitions, and Collective Intelligence
Neural network design for J function approximation in dynamic programming
Weak subsumption Constraints for Type Diagnosis: An Incremental Algorithm
The Effect of Resource Limits and Task Complexity on Collaborative Planning in Dialogue
A Framework for Natural Language Interfaces to Temporal Databases
FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text
Time, Tense and Aspect in Natural Language Database Interfaces
Discovery of Linguistic Relations Using Lexical Attraction
From spin glasses to hard satisfiable formulas
First-Order Conditional Logic Revisited
Similarity-Based Models of Word Cooccurrence Probabilities
Hypertree Decompositions and Tractable Queries
An Empirical Approach to Temporal Reference Resolution (journal version)
Minimum Description Length Induction, Bayesianism, and Kolmogorov Complexity
The "Fodor"-FODOR fallacy bites back
An Algebraic Programming Style for Numerical Software and its Optimization
General Principles of Learning-Based Multi-Agent Systems
Automatic Generation of Constraint Propagation Algorithms for Small Finite Domains
Knowledge in Multi-Agent Systems: Initial Configurations and Broadcast
Computing large and small stable models
SLT-Resolution for the Well-Founded Semantics
On the tractable counting of theory models and its application to belief revision and truth maintenance
Linear Tabulated Resolution Based on Prolog Control Strategy
A Consistency-Based Model for Belief Change: Preliminary Report
Detecting Unsolvable Queries for Definite Logic Programs
Constraint Programming viewed as Rule-based Programming
DATALOG with constraints - an answer-set programming system
Constraint Exploration and Envelope of Simulation Trajectories
Using Learning-based Filters to Detect Rule-based Filtering Obsolescence
Two Steps Feature Selection and Neural Network Classification for the TREC-8 Routing
Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model
Creativity and Delusions: A Neurocomputational Approach
Belief Revision: A Critique
On Properties of Update Sequences Based on Causal Rejection
Arc consistency for soft constraints
Computing Preferred Answer Sets by Meta-Interpretation in Answer Set Programming
Collusion in Unrepeated, First-Price Auctions with an Uncertain Number of Participants
A Modal Logic Framework for Multi-agent Belief Fusion
Linear Programming helps solving large multi-unit combinatorial auctions
Nonmonotonic Logics and Semantics
A Framework for Compiling Preferences in Logic Programs
Complexity of Manipulating Elections with Few Candidates
Modeling Complex Domains of Actions and Change
A Polynomial Translation of Logic Programs with Nested Expressions into Disjunctive Logic Programs: Preliminary Report
The partition semantics of questions, syntactically
Extremal Optimization: an Evolutionary Local-Search Algorithm
The DLV System for Knowledge Representation and Reasoning
Vanquishing the XCB Question: The Methodology Discovery of the Last Shortest Single Axiom for the Equivalential Calculus
Propositional satisfiability in declarative programming
Theoretical Analyses of Cross-Validation Error and Voting in Instance-Based Learning
Many Hard Examples in Exact Phase Transitions with Application to Generating Hard Satisfiable Instances
Unfolding Partiality and Disjunctions in Stable Model Semantics
A Neural Network Assembly Memory Model with Maximum-Likelihood Recall and Recognition Properties
Complex Systems
Cluster-based Specification Techniques in Dempster-Shafer Theory
On rho in a Decision-Theoretic Apparatus of Dempster-Shafer Theory
Updating beliefs with incomplete observations
On the Existence and Convergence Computable Universal Priors
Minimum Model Semantics for Logic Programs with Negation-as-Failure
Complexity of Determining Nonemptiness of the Core
Universal Voting Protocol Tweaks to Make Manipulation Hard
Bridging the gap between modal temporal logics and constraint-based QSR as an ALC(D) spatio-temporalisation with weakly cyclic TBoxes
A ternary Relation Algebra of directed lines
Neural realisation of the SP theory: cell assemblies revisited
Coherent Keyphrase Extraction via Web Mining
A Neural Network Assembly Memory Model Based on an Optimal Binary Signal Detection Theory
Clustering by compression
Computational complexity and simulation of rare events of Ising spin glasses
Distribution of Mutual Information from Complete and Incomplete Data
A Simple Proportional Conflict Redistribution Rule
Outlier Detection by Logic Programming
Normal forms for Answer Sets Programming
Comparing Multi-Target Trackers on Different Force Unit Levels
Image Colour Segmentation by Genetic Algorithms
On Image Filtering, Noise and Morphological Size Intensity Diagrams
On the existence of stable models of non-stratified logic programs
From truth to computability II
Towards Automated Integration of Guess and Check Programs in Answer Set Programming: A Meta-Interpreter and Applications
Generating Hard Satisfiable Formulas by Hiding Solutions Deceptively
The Bayesian Decision Tree Technique with a Sweeping Strategy
Summarization from Medical Documents: A Survey
Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data
Comparison of the Bayesian and Randomised Decision Tree Ensembles within an Uncertainty Envelope Technique
Knowledge Representation Issues in Semantic Graphs for Relationship Detection
Temporal and Spatial Data Mining with Second-Order Hidden Models
A Unified Subspace Outlier Ensemble Framework for Outlier Detection in High Dimensional Spaces
Preferential and Preferential-discriminative Consequence relations
Competitive on-line learning with a convex loss function
Deriving a Stationary Dynamic Bayesian Network from a Logic Program with Recursive Loops
Conjunctive Query Containment and Answering under Description Logics Constraints
Measuring Semantic Similarity by Latent Relational Analysis
MAP estimation via agreement on (hyper)trees: Message-passing and linear programming
Does a Plane Imitate a Bird? Does Computer Vision Have to Follow Biological Paradigms?
Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with Experimental Data
Branch-and-Prune Search Strategies for Numerical Constraint Solving
Minimum Cost Homomorphisms to Proper Interval Graphs and Bigraphs
Open Answer Set Programming with Guarded Programs
New results on rewrite-based satisfiability procedures
Retraction and Generalized Extension of Computing with Words
A Knowledge-Based Approach for Selecting Information Sources
Modal Logics of Topological Relations
Supervisory Control of Fuzzy Discrete Event Systems: A Formal Approach
Understanding Design Fundamentals: How Synthesis and Analysis Drive Creativity, Resulting in Emergence
Expressing Implicit Semantic Relations without Supervision
Searching for Globally Optimal Functional Forms for Inter-Atomic Potentials Using Parallel Tempering and Genetic Programming
Automated verification of weak equivalence within the SMODELS system
Higher-Order Termination: from Kruskal to Computability
Sensor Scheduling for Optimal Observability Using Estimation Entropy
Semantic results for ontic and epistemic change
Decentralized Failure Diagnosis of Stochastic Discrete Event Systems
A Logical Approach to Efficient Max-SAT solving
Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform Mass Spectrometry Data
A Neutrosophic Description Logic
On the Benefits of Inoculation, an Example in Train Scheduling
Interactive Configuration by Regular String Constraints
Truncating the loop series expansion for Belief Propagation
Generic Global Constraints based on MDDs
Axiomatic Theory of Algorithms: Computability and Decidability in Algorithmic Classes
Generating Functions For Kernels of Digraphs (Enumeration & Asymptotics for Nim Games)
Attribute Exploration of Discrete Temporal Transitions
Quantum Computer as a Probabilistic Inference Engine
Entangled Quantum Networks
Markovian Entanglement Networks
A study of structural properties on profiles HMMs
Clustering Co-occurrence of Maximal Frequent Patterns in Streams
Clustering with Lattices in the Analysis of Graph Patterns
Virtual Sensor Based Fault Detection and Classification on a Plasma Etch Reactor
A preliminary analysis on metaheuristics methods applied to the Haplotype Inference Problem
Using RDF to Model the Structure and Process of Systems
Efficient Tabling Mechanisms for Transaction Logic Programs
On the deduction of galaxy abundances with evolutionary neural networks
Fitness landscape of the cellular automata majority problem: View from the Olympus
Lagrangian Relaxation for MAP Estimation in Graphical Models
Fuzzy Modeling of Electrical Impedance Tomography Image of the Lungs
Discriminated Belief Propagation
Performance Bounds for Lambda Policy Iteration and Application to the Game of Tetris
Towards a Sound Theory of Adaptation for the Simple Genetic Algorithm
Dimensionality Reduction and Reconstruction using Mirroring Neural Networks and Object Recognition based on Reduced Dimension Characteristic Vector
Automatic Pattern Classification by Unsupervised Learning Using Dimensionality Reduction of Data with Mirroring Neural Networks
A Common View on Strong, Uniform, and Other Notions of Equivalence in Answer-Set Programming
Sequential operators in computability logic
TRUST-TECH based Methods for Optimization and Learning
Le terme et le concept : fondements d'une ontoterminologie
Design and Implementation of Aggregate Functions in the DLV System
Automated Termination Proofs for Logic Programs by Term Rewriting
A $O(\log m)$, deterministic, polynomial-time computable approximation of Lewis Carroll's scoring rule
On Kernelization of Supervised Mahalanobis Distance Learners
An Analysis of Key Factors for the Success of the Communal Management of Knowledge
Logic Mining Using Neural Networks
Towards applied theories based on computability logic
Constructing Folksonomies from User-specified Relations on Flickr
Feature Selection for Bayesian Evaluation of Trauma Death Risk
The end of Sleeping Beauty's nightmare
Unveiling the mystery of visual information processing in human brain
Message-passing for Maximum Weight Independent Set
Electricity Demand and Energy Consumption Management System
Approximating acyclicity parameters of sparse hypergraphs
Mining Meaning from Wikipedia
Achieving compositionality of the stable model semantics for Smodels programs
An Evidential Path Logic for Multi-Relational Networks
Experimental Evidence for Quantum Structure in Cognition
Classical Logical versus Quantum Conceptual Thought: Examples in Economics, Decision theory and Concept Theory
Embedding Non-Ground Logic Programs into Autoepistemic Logic for Knowledge Base Combination
Modeling Social Annotation: a Bayesian Approach
Learning Class-Level Bayes Nets for Relational Data
Physics of risk and uncertainty in quantum decision making
A New Clustering Algorithm Based Upon Flocking On Complex Network
Approximate inference on planar graphs using Loop Calculus and Belief Propagation
Improvements of real coded genetic algorithms based on differential operators preventing premature convergence
Syntactic Confluence Criteria for Positive/Negative-Conditional Term Rewriting Systems
A Self-Contained and Easily Accessible Discussion of the Method of Descente Infinie and Fermat's Only Explicitly Known Proof by Descente Infinie
Learning DTW Global Constraint for Time Series Classification
Modeling the Experience of Emotion
On Requirements for Programming Exercises from an E-learning Perspective
Complexity of Terminating Preference Elicitation
Switcher-random-walks: a cognitive-inspired mechanism for network exploration
Fully Automated Approaches to Analyze Large-Scale Astronomy Survey Data
CP-logic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming
KiWi: A Scalable Subspace Clustering Algorithm for Gene Expression Analysis
Fast Algorithms for Mining Interesting Frequent Itemsets without Minimum Support
Toggling operators in computability logic
Automated Epilepsy Diagnosis Using Interictal Scalp EEG
Characterizations of Stable Model Semantics for Logic Programs with Arbitrary Constraint Atoms
Do not Choose Representation just Change: An Experimental Study in States based EA
A Minimum Description Length Approach to Multitask Feature Selection
Mining Compressed Repetitive Gapped Sequential Patterns Efficiently
Exact Indexing for Massive Time Series Databases under Time Warping Distance
Recommender Systems for the Conference Paper Assignment Problem
On Chase Termination Beyond Stratification
Constructive Decision Theory
Survival of the flexible: explaining the recent dominance of nature-inspired optimization within a rapidly evolving world
Strategic Positioning in Tactical Scenario Planning
Generalized Collective Inference with Symmetric Clique Potentials
Robustness and Adaptiveness Analysis of Future Fleets
Apply Local Clustering Method to Improve the Running Speed of Ant Colony Optimization
The Cost of Stability in Coalitional Games
On the Internal Topological Structure of Plane Regions
Resource Matchmaking Algorithm using Dynamic Rough Set in Grid Environment
Interactive Data Integration through Smart Copy & Paste
Greedy Gossip with Eavesdropping
A Convergent Online Single Time Scale Actor Critic Algorithm
Dealing with incomplete agents' preferences and an uncertain agenda in group decision making via sequential majority voting
Reduced-Rank Hidden Markov Models
Scaling Analysis of Affinity Propagation
A Component Based Heuristic Search Method with Evolutionary Eliminations
An Evolutionary Squeaky Wheel Optimisation Approach to Personnel Scheduling
Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis
Sum of Us: Strategyproof Selection from the Selectors
Industrial-Strength Formally Certified SAT Solving
Maximin affinity learning of image segmentation
Differentially Private Empirical Risk Minimization
On Finding Predictors for Arbitrary Families of Processes
Believe It or Not: Adding Belief Annotations to Databases
Why so? or Why no? Functional Causality for Explaining Query Answers
Web-Based Expert System for Civil Service Regulations: RCSES
Comparing Simulation Output Accuracy of Discrete Event and Agent Based Models: A Quantitive Approach
A Decidable Class of Nested Iterated Schemata (extended version)
Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm
SIFT-based Ear Recognition by Fusion of Detected Keypoints from Color Similarity Slice Regions
Detecting Motifs in System Call Sequences
Establishment of Relationships between Material Design and Product Design Domains by Hybrid FEM-ANN Technique
Implementation of an Innovative Bio Inspired GA and PSO Algorithm for Controller design considering Steam GT Dynamics
Message-Passing Algorithms: Reparameterizations and Splittings
Redundancy, Deduction Schemes, and Minimum-Size Bases for Association Rules
A Contextual-Bandit Approach to Personalized News Article Recommendation
Indexer Based Dynamic Web Services Discovery
Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron
The role of semantics in mining frequent patterns from knowledge bases in description logics with rules
Agreement Maintenance Based on Schema and Ontology Change in P2P Environment
Modelling and simulating retail management practices: a first approach
Optimisation of a Crossdocking Distribution Centre Simulation Model
A Formal Approach to Modeling the Memory of a Living Organism
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization
Integrating Real-Time Analysis With The Dendritic Cell Algorithm Through Segmentation
On Tsallis Entropy Bias and Generalized Maximum Entropy Models
Belief Propagation for Min-cost Network Flow: Convergence and Correctness
Terrorism Event Classification Using Fuzzy Inference Systems
Genetic Algorithms for Multiple-Choice Problems
Decision Support Systems (DSS) in Construction Tendering Processes
Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach
Intelligent System for Speaker Identification using Lip features with PCA and ICA
Discrete geometric analysis of message passing algorithm on graphs
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data
Scalable Probabilistic Databases with Factor Graphs and MCMC
Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection
Métodos para la Selección y el Ajuste de Características en el Problema de la Detección de Spam
Uncovering the Riffled Independence Structure of Rankings
Begin, After, and Later: a Maximal Decidable Interval Temporal Logic
Modelling Reactive and Proactive Behaviour in Simulation
PAC learnability of a concept class under non-atomic measures: a problem by Vidyasagar
Soft Approximations and uni-int Decision Making
Approximate Counting for Complex-Weighted Boolean Constraint Satisfaction Problems
On The Complexity and Completeness of Static Constraints for Breaking Row and Column Symmetry
An axiomatic formalization of bounded rationality based on a utility-information equivalence
An svm multiclassifier approach to land cover mapping
Logic-Based Decision Support for Strategic Environmental Assessment
New Results for the MAP Problem in Bayesian Networks
A Program-Level Approach to Revising Logic Programs under the Answer Set Semantics
Stable marriage problems with quantitative preferences
Comparison Of Modified Dual Ternary Indexing And Multi-Key Hashing Algorithms For Music Information Retrieval
A Homogeneous Reaction Rule Language for Complex Event Processing
Approximate Judgement Aggregation
NESVM: a Fast Gradient Method for Support Vector Machines
Experimental Evaluation of Branching Schemes for the CSP
Gaussian Process Bandits for Tree Search: Theory and Application to Planning in Discounted MDPs
The Complexity of Causality and Responsibility for Query Answers and non-Answers
Multiplex Structures: Patterns of Complexity in Real-World Networks
On the Doubt about Margin Explanation of Boosting
Modeling and Analyzing Adaptive User-Centric Systems in Real-Time Maude
An Algebraic Study of Bilattice-based Logics
Mining Knowledge in Astrophysical Massive Data Sets
Grounded Symbols in the Brain Computational Foundations for Perceptual Symbol System
Analysing the behaviour of robot teams through relational sequential pattern mining
Predictive State Temporal Difference Learning
Supervised Random Walks: Predicting and Recommending Links in Social Networks
Biologically Inspired Design Principles for Scalable, Robust, Adaptive, Decentralized Search and Automated Response (RADAR)
Quantum randomness and free will
Distributed Graph Coloring: An Approach Based on the Calling Behavior of Japanese Tree Frogs
Learning restricted Bayesian network structures
URSA: A System for Uniform Reduction to SAT
Experimental Comparison of Representation Methods and Distance Measures for Time Series Data
On the size of data structures used in symbolic model checking
Data Conflict Resolution Using Trust Mappings
SAPFOCS: a metaheuristic based approach to part family formation problems in group technology
Dyna-H: a heuristic planning reinforcement learning algorithm applied to role-playing-game strategy decision systems
Evolutionary Mechanics: new engineering principles for the emergence of flexibility in a dynamic and uncertain world
A Context-theoretic Framework for Compositionality in Distributional Semantics
Finding undetected protein associations in cell signaling by belief propagation
A Human-Centric Approach to Group-Based Context-Awareness
Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing
Speeding up SAT solver by exploring CNF symmetries : Revisited
Emergence through Selection: The Evolution of a Scientific Challenge
Graph Coalition Structure Generation
Ologs: a categorical framework for knowledge representation
Decision Theory with Prospect Interference and Entanglement
Foundations for Understanding and Building Conscious Systems using Stable Parallel Looped Dynamics
Bisimulations for fuzzy automata
A Wiki for Business Rules in Open Vocabulary, Executable English
A Discrete Evolutionary Model for Chess Players' Ratings
Language, Emotions, and Cultures: Emotional Sapir-Whorf Hypothesis
Reduced Ordered Binary Decision Diagram with Implied Literals: A New knowledge Compilation Approach
Decentralized Constraint Satisfaction
Doubly Robust Policy Evaluation and Learning
Formal and Computational Properties of the Confidence Boost of Association Rules
When is social computation better than the sum of its parts?
Counting Homomorphisms and Partition Functions
A Simplified and Improved Free-Variable Framework for Hilbert's epsilon as an Operator of Indefinite Committed Choice
Backdoors to Tractable Answer-Set Programming
Learning invariant features through local space contraction
Combining Ontology Development Methodologies and Semantic Web Platforms for E-government Domain Ontology Development
The Impact of Mutation Rate on the Computation Time of Evolutionary Dynamic Optimization
Bayesian and L1 Approaches to Sparse Unsupervised Learning
On Kinds of Indiscernibility in Logic and Metaphysics
Belief-propagation algorithm and the Ising model on networks with arbitrary distributions of motifs
Acquiring Correct Knowledge for Natural Language Generation
Theory and Algorithms for Partial Order Based Reduction in Planning
The Rate of Convergence of AdaBoost
Concurrent Auctions Across The Supply Chain
Existence of Multiagent Equilibria with Limited Agents
The Influence of Global Constraints on Similarity Measures for Time-Series Databases
Local Optima Networks of NK Landscapes with Neutrality
Higher Order Programming to Mine Knowledge for a Modern Medical Expert System
The PITA System: Tabling and Answer Subsumption for Reasoning under Uncertainty
An iterative feature selection method for GRNs inference by exploring topological properties
Controlling wheelchairs by body motions: A learning framework for the adaptive remapping of space
Exploiting Agent and Type Independence in Collaborative Graphical Bayesian Games
Specifying and Staging Mixed-Initiative Dialogs with Program Generation and Transformation
Reputation-based Incentive Protocols in Crowdsourcing Applications
The Ditmarsch Tale of Wonders - The Dynamics of Lying
Selectivity in Probabilistic Causality: Drawing Arrows from Inputs to Stochastic Outputs
Comparing System Dynamics and Agent-Based Simulation for Tumour Growth and its Interactions with Effector Cells
Making Use of Advances in Answer-Set Programming for Abstract Argumentation Systems
Geographic Trough Filling for Internet Datacenters
Cosmological parameter estimation using Particle Swarm Optimization (PSO)
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
Risk-Sensitive Reinforcement Learning Applied to Control under Constraints
Learning where to Attend with Deep Architectures for Image Tracking
Kara: A System for Visualising and Visual Editing of Interpretations for Answer-Set Programs
Latent Semantic Learning with Structured Sparse Representation for Human Action Recognition
Higher-Order Markov Tag-Topic Models for Tagged Documents and Images
Cooperative Information Sharing to Improve Distributed Learning in Multi-Agent Systems
Individual and Domain Adaptation in Sentence Planning for Dialogue
Embedding Description Logic Programs into Default Logic
Semantic-Driven e-Government: Application of Uschold and King Ontology Building Methodology for Semantic Ontology Models Development
Contextually Guided Semantic Labeling and Search for 3D Point Clouds
Constraint Satisfaction Tractability from Semi-lattice Operations on Infinite Sets
Developing Embodied Multisensory Dialogue Agents
Interactive Character Posing by Sparse Coding
Pbm: A new dataset for blog mining
A Pareto-metaheuristic for a bi-objective winner determination problem in a combinatorial reverse auction
A probabilistic methodology for multilabel classification
Empowerment for Continuous Agent-Environment Systems
Optimization in SMT with LA(Q) Cost Functions
Decentralized Multi-agent Plan Repair in Dynamic Environments
An efficient high-quality hierarchical clustering algorithm for automatic inference of software architecture from the source code of a software system
Refinement Modal Logic
Dynamic Mechanism Design for Markets with Strategic Resources
Strong Backdoors to Nested Satisfiability
Hybrid Batch Bayesian Optimization
Algorithms for Learning Kernels Based on Centered Alignment
Towards Electronic Shopping of Composite Product
Learning High-Dimensional Mixtures of Graphical Models
Multi source feedback based performance appraisal system using Fuzzy logic decision support system
Role-Dynamics: Fast Mining of Large Dynamic Networks
Algorithms and Complexity Results for Exact Bayesian Structure Learning
Sparse-posterior Gaussian Processes for general likelihoods
Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks
Learning loopy graphical models with latent variables: Efficient methods and guarantees
The Initial Conditions of the Universe from Constrained Simulations
Transforming Graph Representations for Statistical Relational Learning
Directed Information Graphs
Kepler Eclipsing Binary Stars. III. Classification of Kepler Eclipsing Binary Light Curves with Locally Linear Embedding
Leveraging Usage Data for Linked Data Movie Entity Summarization
Large-Scale Automatic Labeling of Video Events with Verbs Based on Event-Participant Interaction
Robot Navigation using Reinforcement Learning and Slow Feature Analysis
Mesh Learning for Classifying Cognitive Processes
Bayesian Discovery of Linear Acyclic Causal Models
L2 Regularization for Learning Kernels
Tree Projections and Structural Decomposition Methods: The Power of Local Consistency and Larger Islands of Tractability
kLog: A Language for Logical and Relational Learning with Kernels
Foreword: A Computable Universe, Understanding Computation and Exploring Nature As Computation
Approximate Equalities on Rough Intuitionistic Fuzzy Sets and an Analysis of Approximate Equalities
Fuzzy Knowledge Representation Based on Possibilistic and Necessary Bayesian Networks
Possibilistic Pertinence Feedback and Semantic Networks for Goal's Extraction
Concepts and Their Dynamics: A Quantum-Theoretic Modeling of Human Thought
A weighted combination similarity measure for mobility patterns in wireless networks
Fuzzy Knowledge Representation, Learning and Optimization with Bayesian Analysis in Fuzzy Semantic Networks
The third open Answer Set Programming competition
Constrained Approximate Maximum Entropy Learning of Markov Random Fields
Modelling local and global phenomena with sparse Gaussian processes
Simple Regret Optimization in Online Planning for Markov Decision Processes
Feature Based Fuzzy Rule Base Design for Image Extraction
On the Complexity of Existential Positive Queries
Ant Robotics: Covering Continuous Domains by Multi-A(ge)nt Systems
Learning the Experts for Online Sequence Prediction
Modeling Latent Variable Uncertainty for Loss-based Learning
Shift-Invariance Sparse Coding for Audio Classification
Causal Bounds and Instruments
Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach
Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model
The evolution of representation in simple cognitive networks
Discriminative Learning via Semidefinite Probabilistic Models
Matrix Tile Analysis
Stochastic Optimal Control in Continuous Space-Time Multi-Agent Systems
A Self-Supervised Terrain Roughness Estimator for Off-Road Autonomous Driving
Design, Evaluation and Analysis of Combinatorial Optimization Heuristic Algorithms
Conceptual Modelling and The Quality of Ontologies: Endurantism Vs. Perdurantism
Set-valued dynamic treatment regimes for competing outcomes
Isabelle/jEdit --- a Prover IDE within the PIDE framework
Recovering Articulated Object Models from 3D Range Data
Active Model Selection
Joint discovery of haplotype blocks and complex trait associations from SNP sequences
Probabilistic index maps for modeling natural signals
Ontology for Cellular Communication
Meta-Learning of Exploration/Exploitation Strategies: The Multi-Armed Bandit Case
Semantic Information Retrieval Using Ontology In University Domain
Toward an Integrated Framework for Automated Development and Optimization of Online Advertising Campaigns
On Finding Optimal Polytrees
Comparison of different T-norm operators in classification problems
More than Word Frequencies: Authorship Attribution via Natural Frequency Zoned Word Distribution Analysis
Content-based Text Categorization using Wikitology
Monte Carlo Search Algorithm Discovery for One Player Games
Optimized Look-Ahead Tree Policies: A Bridge Between Look-Ahead Tree Policies and Direct Policy Search
Conquering the rating bound problem in neighborhood-based collaborative filtering: a function recovery approach
Parametric Constructive Kripke-Semantics for Standard Multi-Agent Belief and Knowledge (Knowledge As Unbiased Belief)
On firm specific characteristics of pharmaceutical generics and incentives to permanence under fuzzy conditions
Cultural Algorithm Toolkit for Multi-objective Rule Mining
RIO: Minimizing User Interaction in Ontology Debugging
Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments
Lattice structures of fixed points of the lower approximations of two types of covering-based rough sets
A Cookbook for Temporal Conceptual Data Modelling with Description Logics
Semi-automatic annotation process for procedural texts: An application on cooking recipes
Test-cost-sensitive attribute reduction of data with normal distribution measurement errors
Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming
Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining
Conflict-driven ASP Solving with External Sources
Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs
Reply to Comments on Neuroelectrodynamics: Where are the Real Conceptual Pitfalls?
Mining Permission Request Patterns from Android and Facebook Applications (extended author version)
Multi-view constrained clustering with an incomplete mapping between views
Inferring the Underlying Structure of Information Cascades
Quick Summary
Uncertain Congestion Games with Assorted Human Agent Populations
Markov Determinantal Point Processes
A Model-Based Approach to Rounding in Spectral Clustering
Latent Composite Likelihood Learning for the Structured Canonical Correlation Model
Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants
Creating a level playing field for all symbols in a discretization
Typed Answer Set Programming and Inverse Lambda Algorithms
Learning classifier systems with memory condition to solve non-Markov problems
Parameterized Complexity and Kernel Bounds for Hard Planning Problems
Surprisingly Rational: Probability theory plus noise explains biases in judgment
Algorithm Runtime Prediction: Methods & Evaluation
Learning using Local Membership Queries
Secured Wireless Communication using Fuzzy Logic based High Speed Public-Key Cryptography (FLHSPKC)
Composite Strategy for Multicriteria Ranking/Sorting (methodological issues, examples)
Distributed Non-Stochastic Experts
Construction of Energy Functions for Lattice Heteropolymer Models: A Case Study in Constraint Satisfaction Programming and Adiabatic Quantum Optimization
Objective Improvement in Information-Geometric Optimization
Visualization and clustering by 3D cellular automata: Application to unstructured data
TwitterPaul: Extracting and Aggregating Twitter Predictions
Intrusion Detection on Smartphones
TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks
Learning-Assisted Automated Reasoning with Flyspeck
Evolutionarily Stable Sets in Quantum Penny Flip Games
Training Support Vector Machines Using Frank-Wolfe Optimization Methods
Autonomous Navigation by Robust Scan Matching Technique
Foundations of scientific research (Foundations of Research Activities)
Deciding Monotone Duality and Identifying Frequent Itemsets in Quadratic Logspace
Bag-of-Words Representation for Biomedical Time Series Classification
Tree Projections and Structural Decomposition Methods: Minimality and Game-Theoretic Characterization
1 Billion Pages = 1 Million Dollars? Mining the Web to Play "Who Wants to be a Millionaire?"
Preference-based Graphic Models for Collaborative Filtering
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
A Generalized Mean Field Algorithm for Variational Inference in Exponential Families
Learning Module Networks
Modeling in OWL 2 without Restrictions
Taming the Infinite Chase: Query Answering under Expressive Integrity Constraints
Increasing Air Traffic: What is the Problem?
Online Learning for Ground Trajectory Prediction
Interactive Ant Colony Optimisation (iACO) for Early Lifecycle Software Design
Distributed optimization of deeply nested systems
Discovering Basic Emotion Sets via Semantic Clustering on a Twitter Corpus
Autonomously Learning to Visually Detect Where Manipulation Will Succeed
Maximizing a Nonnegative, Monotone, Submodular Function Constrained to Matchings
Applying Strategic Multiagent Planning to Real-World Travel Sharing Problems
Learning with Scope, with Application to Information Extraction and Classification
An Information-Theoretic External Cluster-Validity Measure
Probabilistic entailment in the setting of coherence: The role of quasi conjunction and inclusion relation
Utilizing ASP for Generating and Visualizing Argumentation Frameworks
Extending FO(ID) with Knowledge Producing Definitions: Preliminary Results
Verification of Agent-Based Artifact Systems
Crowd Labeling: a survey
The IBMAP approach for Markov networks structure learning
Bayesian Classification and Feature Selection from Finite Data Sets
A Two-round Variant of EM for Gaussian Mixtures
The Anchors Hierachy: Using the triangle inequality to survive high dimensional data
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach
When you talk about "Information processing" what actually do you have in mind?
From 9-IM Topological Operators to Qualitative Spatial Relations using 3D Selective Nef Complexes and Logic Rules for bodies
Developing Parallel Dependency Graph In Improving Game Balancing
On Supervised Selection of Bayesian Networks
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions: A Survey of Approximation and Consistency Results
Multi-Robot Informative Path Planning for Active Sensing of Environmental Phenomena: A Tale of Two Algorithms
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering
A solution concept for games with altruism and cooperation
Towards a theory of good SAT representations
Exploiting Social Tags for Cross-Domain Collaborative Filtering
Geodesic-based Salient Object Detection
Variational Algorithms for Marginal MAP
Using Modified Partitioning Around Medoids Clustering Technique in Mobile Network Planning
Arriving on time: estimating travel time distributions on large-scale road networks
Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness: Extended Version
K-Nearest Neighbour algorithm coupled with logistic regression in medical case-based reasoning systems. Application to prediction of access to the renal transplant waiting list in Brittany
Fairness in Academic Course Timetabling
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model
Viterbi training in PRISM
Heart Disease Prediction System using Associative Classification and Genetic Algorithm
A Massively Parallel Associative Memory Based on Sparse Neural Networks
On the speed of constraint propagation and the time complexity of arc consistency testing
Formalizing the Confluence of Orthogonal Rewriting Systems
A Community Based Algorithm for Large Scale Web Service Composition
What does mathoverflow tell us about the production of mathematics?
High Level Pattern Classification via Tourist Walks in Networks
Semantic-based Anomalous Pattern Discovery in Moving Object Trajectories
The Dynamically Extended Mind -- A Minimal Modeling Case Study
Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing
Modelling and Analysing Cargo Screening Processes: A Project Outline
Investigating Mathematical Models of Immuno-Interactions with Early-Stage Cancer under an Agent-Based Modelling Perspective
Using a bag of Words for Automatic Medical Image Annotation with a Latent Semantic
Loop Calculus and Bootstrap-Belief Propagation for Perfect Matchings on Arbitrary Graphs
Is protein folding problem really a NP-complete one ? First investigations
New Results on Equilibria in Strategic Candidacy
Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy
The Rise and Fall of Semantic Rule Updates Based on SE-Models
A Multi-Engine Approach to Answer Set Programming
A Decomposition of the Max-min Fair Curriculum-based Course Timetabling Problem
Computation of Diet Composition for Patients Suffering from Kidney and Urinary Tract Diseases with the Fuzzy Genetic System
Activity Modeling in Smart Home using High Utility Pattern Mining over Data Streams
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
Evidence and plausibility in neighborhood structures
Routing in Wireless Mesh Networks: Two Soft Computing Based Approaches
Soft Computing Framework for Routing in Wireless Mesh Networks: An Integrated Cost Function Approach
On Nicod's Condition, Rules of Induction and the Raven Paradox
Probability Distinguishes Different Types of Conditional Statements
The Fundamental Learning Problem that Genetic Algorithms with Uniform Crossover Solve Efficiently and Repeatedly As Evolution Proceeds
Learning Markov networks with context-specific independences
Parameterized Complexity Results for Plan Reuse
Model checking coalitional games in shortage resource scenarios
Numerical response of the magnetic permeability as a funcion of the frecuency of NiZn ferrites using Genetic Algorithm
Time-Series Classification Through Histograms of Symbolic Polynomials
Levels of Integration between Low-Level Reasoning and Task Planning
Herding the Crowd: Automated Planning for Crowdsourced Planning
Inducing Honest Reporting Without Observing Outcomes: An Application to the Peer-Review Process
Cultural Evolution Entails (Creativity Entails (Concept Combination Entails Quantum Structure))
Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management
The multi-vehicle covering tour problem: building routes for urban patrolling
Partition-Merge: Distributed Inference and Modularity Optimization
Learning Lambek grammars from proof frames
Toward robust phase-locking in Melibe swim central pattern generator models
SAT-based Preprocessing for MaxSAT (extended version)
A practical approach to ontology-enabled control systems for astronomical instrumentation
Dissociation and Propagation for Approximate Lifted Inference with Standard Relational Database Management Systems
On the Tractability of Minimal Model Computation for Some CNF Theories
Unsupervised learning human's activities by overexpressed recognized non-speech sounds
Structural Weights in Ontology Matching
Clustering Markov Decision Processes For Continual Transfer
Analyzing Evolutionary Optimization in Noisy Environments
Characterizing and Extending Answer Set Semantics using Possibility Theory
Test Set Selection using Active Information Acquisition for Predictive Models
High Throughput Virtual Screening with Data Level Parallelism in Multi-core Processors
Balancing bike sharing systems (BBSS): instance generation from the CitiBike NYC data
Mining Malware Specifications through Static Reachability Analysis
Fair assignment of indivisible objects under ordinal preferences
Predictive User Modeling with Actionable Attributes
Formal Ontology Learning on Factual IS-A Corpus in English using Description Logics
A New Approach to Constraint Weight Learning for Variable Ordering in CSPs
Proceedings 2nd Workshop on GRAPH Inspection and Traversal Engineering
Quantitative methods for Phylogenetic Inference in Historical Linguistics: An experimental case study of South Central Dravidian
Data Smashing
Constraint Solvers for User Interface Layout
Learning optimization models in the presence of unknown relations
Speeding up SOR Solvers for Constraint-based GUIs with a Warm-Start Strategy
Gödel, Tarski, Turing and the conundrum of free will
Does Restraining End Effect Matter in EMD-Based Modeling Framework for Time Series Prediction? Some Experimental Evidences
Mechanisms for Making Crowds Truthful
Learning Document-Level Semantic Properties from Free-Text Annotations
Trust-Based Mechanisms for Robust and Efficient Task Allocation in the Presence of Execution Uncertainty
Complex Question Answering: Unsupervised Learning Approaches and Experiments
Highly comparative feature-based time-series classification
Constructing Reference Sets from Unstructured, Ungrammatical Text
Cooperative Games with Overlapping Coalitions
False-Name Manipulations in Weighted Voting Games
Stackelberg vs. Nash in Security Games: An Extended Investigation of Interchangeability, Equivalence, and Uniqueness
Multi-Robot Adversarial Patrolling: Facing a Full-Knowledge Opponent
Centrality-as-Relevance: Support Sets and Similarity as Geometric Proximity
Cause Identification from Aviation Safety Incident Reports via Weakly Supervised Semantic Lexicon Construction
Modelling Observation Correlations for Active Exploration and Robust Object Detection
A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data
Generalized Biwords for Bitext Compression and Translation Spotting
Text Relatedness Based on a Word Thesaurus
GGP with Advanced Reasoning and Board Knowledge Discovery
Using Neural Network to Propose Solutions to Threats in Attack Patterns
Towards Unsupervised Learning of Temporal Relations between Events
Coalition Structure Generation over Graphs
Context-based Word Acquisition for Situated Dialogue in a Virtual World
Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages
Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis
Representing, reasoning and answering questions about biological pathways - various applications
A Novel Method for Comparative Analysis of DNA Sequences by Ramanujan-Fourier Transform
Counterfactual Estimation and Optimization of Click Metrics for Search Engines
Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets
Cancer Prognosis Prediction Using Balanced Stratified Sampling
Adaptive MCMC-Based Inference in Probabilistic Logic Programs
Multi-agent Inverse Reinforcement Learning for Zero-sum Games
Venture: a higher-order probabilistic programming platform with programmable inference
A New Paradigm for Minimax Search
SSS* = Alpha-Beta + TT
Nearly Optimal Minimax Tree Search?
Outer-Product Hidden Markov Model and Polyphonic MIDI Score Following
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic
Inferring Social Status and Rich Club Effects in Enterprise Communication Networks
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace
A Control Dichotomy for Pure Scoring Rules
Approximate Equilibrium and Incentivizing Social Coordination
A Formal Analysis of Required Cooperation in Multi-agent Planning
Nonmonotonic Reasoning as a Temporal Activity
LTLf and LDLf Monitoring: A Technical Report
Finding Inner Outliers in High Dimensional Space
Semantics and Compilation of Answer Set Programming with Generalized Atoms
A Mathematical Theory of Learning
FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test
Lifted Variable Elimination for Probabilistic Logic Programming
An Agent-based Modeling Framework for Sociotechnical Simulation of Water Distribution Contamination Events
Bound Founded Answer Set Programming
Credal Model Averaging for classification: representing prior ignorance and expert opinions
The Design of the Fifth Answer Set Programming Competition
Contextual Abductive Reasoning with Side-Effects
Transaction Logic with (Complex) Events
Properties of Stable Model Semantics Extensions
That's sick dude!: Automatic identification of word sense change across different timescales
An Ordinal Bargaining Solution with Fixed-Point Property
Efficient Model Learning for Human-Robot Collaborative Tasks
HEPGAME and the Simplification of Expressions
Semantic Composition and Decomposition: From Recognition to Generation
Inverse Graphics with Probabilistic CAD Models
The Complexity of Reasoning with FODD and GFODD
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
A New Rational Algorithm for View Updating in Relational Databases
Are There Good Mistakes? A Theoretical Analysis of CEGIS
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Backwards State-space Reduction for Planning in Dynamic Knowledge Bases
Characterization of graphs for protein structure modeling and recognition of solubility
People are Strange when you're a Stranger: Impact and Influence of Bots on Social Networks
Calculating Ultra-Strong and Extended Solutions for Nine Men's Morris, Morabaraba, and Lasker
Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis
Computational Analysis of Perfect-Information Position Auctions
A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks
A QoS based Routing Approach using Genetic Algorithms for Bandwidth Maximization in Network
Non-Convex Rank Minimization via an Empirical Bayesian Approach
Video Face Editing Using Temporal-Spatial-Smooth Warping
Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
A Study of Proxies for Shapley Allocations of Transport Costs
Knowledge Engineering for Planning-Based Hypothesis Generation
Integrating active sensing into reactive synthesis with temporal logic constraints under partial observations
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition
Gamma Processes, Stick-Breaking, and Variational Inference
Ontology-based Representation and Reasoning on Process Models: A Logic Programming Approach
Computational Beauty: Aesthetic Judgment at the Intersection of Art and Science
eTutor: Online Learning for Personalized Education
A Parallel and Efficient Algorithm for Learning to Match
Estimating the intrinsic dimension in fMRI space via dataset fractal analysis - Counting the `cpu cores' of the human brain
Towards a Visual Turing Challenge
Learning of Agent Capability Models with Applications in Multi-agent Planning
The Spaces of Data, Information, and Knowledge
Answering Conjunctive Queries over $\mathcal{EL}$ Knowledge Bases with Transitive and Reflexive Roles
On Sparse Discretization for Graphical Games
Building the Observer into the System: Toward a Realistic Description of Human Interaction with the World
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization
Learning Fuzzy Controllers in Mobile Robotics with Embedded Preprocessing
Aggregating partial rankings with applications to peer grading in massive online open courses
Toward a Universal Cortical Algorithm: Examining Hierarchical Temporal Memory in Light of Frontal Cortical Function
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives
Solving the Periodic Timetabling Problem using a Genetic Algorithm
Behaviour Trees for Evolutionary Robotics
Probability Theory without Bayes' Rule
Investigation of a chaotic spiking neuron model
Long-term causal effects via behavioral game theory
Social Participation Ontology: community documentation, enhancements and use examples
High performance photonic reservoir computer based on a coherently driven passive cavity
What do we learn about development from baby robots?
Managing large-scale scientific hypotheses as uncertain and probabilistic data
A New Efficient Method for Calculating Similarity Between Web Services
User Clustering in Online Advertising via Topic Models
Compositional Distributional Semantics with Compact Closed Categories and Frobenius Algebras
Language Models for Image Captioning: The Quirks and What Works
The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning
Do PageRank-based author rankings outperform simple citation counts?
Exploring Strategy-Proofness, Uniqueness, and Pareto Optimality for the Stable Matching Problem with Couples
A Theory of Formal Synthesis via Inductive Learning
Margins, Kernels and Non-linear Smoothed Perceptrons
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
DopeLearning: A Computational Approach to Rap Lyrics Generation
A New Fundamental Evidence of Non-Classical Structure in the Combination of Natural Concepts
Modular Action Language ALM
Parallel Streaming Signature EM-tree: A Clustering Algorithm for Web Scale Applications
Variational Inference with Normalizing Flows
A survey of SMS based Information Systems
A U.S. Research Roadmap for Human Computation
Multidefender Security Games
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
Using Monte Carlo method for searching partitionings of hard variants of Boolean satisfiability problem
A model building framework for Answer Set Programming with external computations
Markov Logic Networks for Natural Language Question Answering
Evidential relational clustering using medoids
Scaling Monte Carlo Tree Search on Intel Xeon Phi
Lift-Based Bidding in Ad Selection
Tree-based Visualization and Optimization for Image Collection
Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach
Fairness Constraints: Mechanisms for Fair Classification
Dual-normal Logic Programs - the Forgotten Class
Incorporating Belief Function in SVM for Phoneme Recognition
Communication: Words and Conceptual Systems
A Gauss-Newton Method for Markov Decision Processes
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
Boosting in the presence of outliers: adaptive classification with non-convex loss functions
Within-Brain Classification for Brain Tumor Segmentation
Disjunctive Answer Set Solvers via Templates
Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
Layer-Specific Adaptive Learning Rates for Deep Networks
Hybridization of Interval CP and Evolutionary Algorithms for Optimizing Difficult Problems
Normalization of Relative and Incomplete Temporal Expressions in Clinical Narratives
High Performance Latent Variable Models
Time-resolved emission from bright hot pixels of an active region observed in the EUV band with SDO/AIA and multi-stranded loop modeling
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning
Learning Causal Graphs with Small Interventions
Learning Adversary Behavior in Security Games: A PAC Model Perspective
Toward an Efficient Multi-class Classification in an Open Universe
Adaptive information-theoretic bounded rational decision-making with parametric priors
Combining Privileged Information to Improve Context-Aware Recommender Systems
Detecting events and key actors in multi-person videos
Instantaneous Modelling and Reverse Engineering of DataConsistent Prime Models in Seconds!
Characterizing Concept Drift
Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling
Convolutional Models for Joint Object Categorization and Pose Estimation
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
Active exploration of sensor networks from a robotics perspective
Censoring Representations with an Adversary
Behavior Query Discovery in System-Generated Temporal Graphs
Joint Word Representation Learning using a Corpus and a Semantic Lexicon
Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning
Non-Sentential Utterances in Dialogue: Experiments in Classification and Interpretation
Multi-Agent Continuous Transportation with Online Balanced Partitioning
Interpretable Two-level Boolean Rule Learning for Classification
Learning with Memory Embeddings
Semantic Folding Theory And its Application in Semantic Fingerprinting
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
Bicycle-Sharing System Analysis and Trip Prediction
Asking the metaquestions in constraint tractability
An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming
Feature extraction using Latent Dirichlet Allocation and Neural Networks: A case study on movie synopses
Deep Cross Residual Learning for Multitask Visual Recognition
The Curious Robot: Learning Visual Representations via Physical Interactions
A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories
Learning to Track at 100 FPS with Deep Regression Networks
How deep is knowledge tracing?
Differential Evolution for Efficient AUV Path Planning in Time Variant Uncertain Underwater Environment
Efficient Classification of Multi-Labelled Text Streams by Clashing
Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment
A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents
A Discrete Firefly Algorithm to Solve a Rich Vehicle Routing Problem Modelling a Newspaper Distribution System with Recycling Policy
Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN
The STRANDS Project: Long-Term Autonomy in Everyday Environments
Elicitation for Preferences Single Peaked on Trees
A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids
Toward Efficient Task Assignment and Motion Planning for Large Scale Underwater Mission
End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks
Preference Elicitation For Single Crossing Domain
Annotation Order Matters: Recurrent Image Annotator for Arbitrary Length Image Tagging
Inductive Coherence
Learning Sparse Additive Models with Interactions in High Dimensions
Extending the Harper Identity to Iterated Belief Change
Contribution to the Formal Specification and Verification of a Multi-Agent Robotic System
Proving the Incompatibility of Efficiency and Strategyproofness via SMT Solving
Parallel Strategies Selection
DisCSPs with Privacy Recast as Planning Problems for Utility-based Agents
Conversational Markers of Constructive Discussions
Distributed Flexible Nonlinear Tensor Factorization
Defining Concepts of Emotion: From Philosophy to Science
The Z-loss: a shift and scale invariant classification loss belonging to the Spherical Family
Formulating Semantics of Probabilistic Argumentation by Characterizing Subgraphs: Theory and Empirical Results
A Web-based Tool for Identifying Strategic Intervention Points in Complex Systems
Combining Answer Set Programming and Domain Heuristics for Solving Hard Industrial Problems (Application Paper)
Paraconsistency and Word Puzzles
Self-Organising Maps in Computer Security
The Power of Non-Ground Rules in Answer Set Programming
Iterative Learning of Answer Set Programs from Context Dependent Examples
Towards the Self-constructive Brain: emergence of adaptive behavior
Blankets Joint Posterior score for learning Markov network structures
Revisiting Causality Inference in Memory-less Transition Networks
Facial Expression Recognition Using a Hybrid CNN-SIFT Aggregator
Stochastic Rank-1 Bandits
Inferring unknown biological function by integration of GO annotations and gene expression data
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies
lpopt: A Rule Optimization Tool for Answer Set Programming
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Planning With Discrete Harmonic Potential Fields
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games
Fathom: Reference Workloads for Modern Deep Learning Methods
State Duration and Interval Modeling in Hidden Semi-Markov Model for Sequential Data Analysis
Multi-View Fuzzy Clustering with Minimax Optimization for Effective Clustering of Data from Multiple Sources
Title Generation for User Generated Videos
Activity Networks with Delays An application to toxicity analysis
A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features
Modelling Cyber-Security Experts' Decision Making Processes using Aggregation Operators
Language Detection For Short Text Messages In Social Media
What makes ImageNet good for transfer learning?
Binary Particle Swarm Optimization versus Hybrid Genetic Algorithm for Inferring Well Supported Phylogenetic Trees
Knowledge Representation Analysis of Graph Mining
Splitting and Updating Hybrid Knowledge Bases (Extended Version)
A Multi-Purpose Scenario-based Simulator for Smart House Environments
Integrating Testing and Interactive Theorem Proving
Kernel Belief Propagation
Strange Beta: An Assistance System for Indoor Rock Climbing Route Setting Using Chaotic Variations and Machine Learning
Well-Definedness and Efficient Inference for Probabilistic Logic Programming under the Distribution Semantics
Characterizing and Improving Generalized Belief Propagation Algorithms on the 2D Edwards-Anderson Model
Bayesian Locality Sensitive Hashing for Fast Similarity Search
Improving parameter learning of Bayesian nets from incomplete data
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models
A quantitative Gibbard-Satterthwaite theorem without neutrality
Data Mining Session-Based Patient Reported Outcomes (PROs) in a Mental Health Setting: Toward Data-Driven Clinical Decision Support and Personalized Treatment
An Information Theoretic Analysis of Decision in Computer Chess
The Diversity Paradox: How Nature Resolves an Evolutionary Dilemma
A Study on Using Uncertain Time Series Matching Algorithms in MapReduce Applications
A Study of CAPTCHAs for Securing Web Services
Document Clustering based on Topic Maps
A comparison of two suffix tree-based document clustering algorithms
On the definition of a confounder
On the complexity of strong Nash equilibrium: Hard-to-solve instances and smoothed complexity
Pattern-Based Constraint Satisfaction and Logic Puzzles
On Appropriate Selection of Fuzzy Aggregation Operators in Medical Decision Support System
From Constraints to Resolution Rules, Part II: chains, braids, confluence and T&E
A Fuzzy Logic Based Certain Trust Model for E-Commerce
A Junction Tree Framework for Undirected Graphical Model Selection
Improvement/Extension of Modular Systems as Combinatorial Reengineering (Survey)
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections
Towards an Extension of the 2-tuple Linguistic Model to Deal With Unbalanced Linguistic Term sets
The Stochastic Gradient Descent for the Primal L1-SVM Optimization Revisited
Inference and learning in probabilistic logic programs using weighted Boolean formulas
Measuring Cultural Relativity of Emotional Valence and Arousal using Semantic Clustering and Twitter
A Time and Space Efficient Junction Tree Architecture
Exploring The Contribution of Unlabeled Data in Financial Sentiment Analysis
Measure Transformer Semantics for Bayesian Machine Learning
Cognitive residues of similarity
Extended Distributed Learning Automata:A New Method for Solving Stochastic Graph Optimization Problems
Exploiting Binary Floating-Point Representations for Constraint Propagation: The Complete Unabridged Version
How Did Humans Become So Creative? A Computational Approach
Efficient Computation of the Shapley Value for Game-Theoretic Network Centrality
A Feature Subset Selection Algorithm Automatic Recommendation Method
Sharing Rewards in Cooperative Connectivity Games
Decentralized Anti-coordination Through Multi-agent Learning
Using content features to enhance performance of user-based collaborative filtering performance of user-based collaborative filtering
Planning for Decentralized Control of Multiple Robots Under Uncertainty
Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves
Using the Crowd to Generate Content for Scenario-Based Serious-Games
Assessing the Reach and Impact of Game-Based Learning Approaches to Cultural Competency and Behavioural Change
Discriminative Functional Connectivity Measures for Brain Decoding
Incremental Learning of Event Definitions with Inductive Logic Programming
A predictive analytics approach to reducing avoidable hospital readmission
Algorithms for multi-armed bandit problems
Solving MaxSAT and #SAT on structured CNF formulas
A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search
On the satisfiability problem for SPARQL patterns
Conjunction and Negation of Natural Concepts: A Quantum-theoretic Modeling
Tree-like Queries in OWL 2 QL: Succinctness and Complexity Results
Eigenspace Method for Spatiotemporal Hotspot Detection
Neural tuning size is a key factor underlying holistic face processing
Typed Hilbert Epsilon Operators and the Semantics of Determiner Phrases (Invited Lecture)
Low-Autocorrelation Binary Sequences: On Improved Merit Factors and Runtime Predictions to Achieve Them
Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition
Cognitive Surveillance: Why does it never appear among the AVSS Conferences topics?
Combining predictions from linear models when training and test inputs differ
An interacting replica approach applied to the traveling salesman problem
Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package
Data classification using the Dempster-Shafer method
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
Building Program Vector Representations for Deep Learning
Hardness of parameter estimation in graphical models
Performance analysis of a 240 thread tournament level MCTS Go program on the Intel Xeon Phi
A Tabu Search Algorithm for the Multi-period Inspector Scheduling Problem
Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): Comparing multi-electrode recordings from simulated and biological mammalian cortical tissue
Efficient Feature Group Sequencing for Anytime Linear Prediction
IP Tracing and Active Network Response
Optimal high-level descriptions of dynamical systems
Entanglement-Based Machine Learning on a Quantum Computer
Interference Effects in Quantum Belief Networks
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Problem Theory
Highly comparative fetal heart rate analysis
Symmetric Weighted First-Order Model Counting
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
When Computer Vision Gazes at Cognition
Plan or not: Remote Human-robot Teaming with Incomplete Task Information
The Computational Complexity of Structure-Based Causality
Teaching Deep Convolutional Neural Networks to Play Go
Appropriate Causal Models and the Stability of Causation
On the Inductive Bias of Dropout
A Multi-criteria neutrosophic group decision making metod based TOPSIS for supplier selection
Belief as Willingness to Bet
Revisiting Non-Progressive Influence Models: Scalable Influence Maximization
Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method
Using temporal abduction for biosignal interpretation: A case study on QRS detection
Belief Revision, Minimal Change and Relaxation: A General Framework based on Satisfaction Systems, and Applications to Description Logics
Tractability and Decompositions of Global Cost Functions
Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification
Collaborative Filtering Bandits
Speeding up Permutation Testing in Neuroimaging
An Efficient Metric of Automatic Weight Generation for Properties in Instance Matching Technique
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation
Computational Curiosity (A Book Draft)
Influence-Optimistic Local Values for Multiagent Planning --- Extended Version
Low-Cost Learning via Active Data Procurement
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Describing Videos by Exploiting Temporal Structure
When Are Tree Structures Necessary for Deep Learning of Representations?
23-bit Metaknowledge Template Towards Big Data Knowledge Discovery and Management
A Meta-Analysis of the Anomaly Detection Problem
Estimating the Probability of Meeting a Deadline in Hierarchical Plans
Poker Cash Game: a Thermodynamic Description
Mapping-equivalence and oid-equivalence of single-function object-creating conjunctive queries
Sequential Relevance Maximization with Binary Feedback
Modeling State-Conditional Observation Distribution using Weighted Stereo Samples for Factorial Speech Processing Models
Doubly Robust Policy Evaluation and Optimization
Quantum Structure of Negation and Conjunction in Human Thought
Dynamic Move Tables and Long Branches with Backtracking in Computer Chess
Reduced Basis Decomposition: a Certified and Fast Lossy Data Compression Algorithm
Boosting Convolutional Features for Robust Object Proposals
Construction of FuzzyFind Dictionary using Golay Coding Transformation for Searching Applications
Is Poker a Skill Game? New Insights from Statistical Physics
Two Timescale Stochastic Approximation with Controlled Markov noise and Off-policy temporal difference learning
ELM-Based Distributed Cooperative Learning Over Networks
Large Margin Nearest Neighbor Embedding for Knowledge Representation
Detecting Falls with X-Factor Hidden Markov Models
On mining complex sequential data by means of FCA and pattern structures
Automated Analysis and Prediction of Job Interview Performance
Negatively Correlated Search
Hybrid Genetic Algorithm and Lasso Test Approach for Inferring Well Supported Phylogenetic Trees based on Subsets of Chloroplastic Core Genes
Groupwise registration of aerial images
How do you revise your belief set with %$;@*?
Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes
Generalized Support and Formal Development of Constraint Propagators
Concept Extraction to Identify Adverse Drug Reactions in Medical Forums: A Comparison of Algorithms
A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution
Combining Existential Rules and Transitivity: Next Steps
Explanation of Stagnation at Points that are not Local Optima in Particle Swarm Optimization by Potential Analysis
Manipulation is Harder with Incomplete Votes
Discovering Valuable Items from Massive Data
Quizz: Targeted crowdsourcing with a billion (potential) users
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
Probabilistic Numerics and Uncertainty in Computations
Bootstrapping Skills
Leading Tree in DPCLUS and Its Impact on Building Hierarchies
Place classification with a graph regularized deep neural network model
Leveraging Word Embeddings for Spoken Document Summarization
Spatial Symmetry Driven Pruning Strategies for Efficient Declarative Spatial Reasoning
SAT-based Analysis of Large Real-world Feature Models is Easy
Hybrid Algorithm for Multi-Objective Optimization by Greedy Hypervolume Maximization
The Extreme Value Machine
Skopus: Mining top-k sequential patterns under leverage
Exact and approximate inference in graphical models: variable elimination and beyond
Characterization of Logic Program Revision as an Extension of Propositional Revision
Adaptive Automation: Leveraging Machine Learning to Support Uninterrupted Automated Testing of Software Applications
Predicting respiratory motion for real-time tumour tracking in radiotherapy
Perceptron like Algorithms for Online Learning to Rank
Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services
Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL
Type-Constrained Representation Learning in Knowledge Graphs
No Regret Bound for Extreme Bandits
SETI via Leakage from Light Sails in Exoplanetary Systems
Beyond-Quantum Modeling of Question Order Effects and Response Replicability in Psychological Measurements
Schema Independent Relational Learning
A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics
Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithm
Computing Stable Coalitions: Approximation Algorithms for Reward Sharing
Real-time Top-K Predictive Query Processing over Event Streams
A unified heuristic and an annotated bibliography for a large class of earliness-tardiness scheduling problems
A Behavior Analysis-Based Game Bot Detection Approach Considering Various Play Styles
Recurrent Reinforcement Learning: A Hybrid Approach
Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems
Bio-Inspired Human Action Recognition using Hybrid Max-Product Neuro-Fuzzy Classifier and Quantum-Behaved PSO
On Reasoning with RDF Statements about Statements using Singleton Property Triples
Mapping Heritability of Large-Scale Brain Networks with a Billion Connections {\em via} Persistent Homology
Extraction of evidence tables from abstracts of randomized clinical trials using a maximum entropy classifier and global constraints
A Simulated Annealing Approach to Bayesian Inference
Visual Generalized Coordinates
Evaluation of Protein-protein Interaction Predictors with Noisy Partially Labeled Data Sets
Reasoning about Entailment with Neural Attention
Minimum Weight Perfect Matching via Blossom Belief Propagation
Designing Behaviour in Bio-inspired Robots Using Associative Topologies of Spiking-Neural-Networks
Symbol Emergence in Robotics: A Survey
Knowledge-based system for collaborative process specification
Supporting interoperability of collaborative networks through engineering of a service-based Mediation Information System (MISE 2.0)
Symbolic Neutrosophic Theory
The GTR-model: a universal framework for quantum-like measurements
Neural Enquirer: Learning to Query Tables with Natural Language
Assessing forensic evidence by computing belief functions
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
On the Min-cost Traveling Salesman Problem with Drone
A Restricted Visual Turing Test for Deep Scene and Event Understanding
Fast Algorithms for Game-Theoretic Centrality Measures
Optical SETI Observations of the Anomalous Star KIC 8462852
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation
Query Answering over Contextualized RDF/OWL Knowledge with Forall-Existential Bridge Rules: Decidable Finite Extension Classes (Post Print)
Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease
Symphony from Synapses: Neocortex as a Universal Dynamical Systems Modeller using Hierarchical Temporal Memory
Solving stable matching problems using answer set programming
Deep Active Object Recognition by Joint Label and Action Prediction
Blind, Greedy, and Random: Ordinal Approximation Algorithms for Matching and Clustering
Probabilistic Programming with Gaussian Process Memoization
Learning the Preferences of Ignorant, Inconsistent Agents
On Voting and Facility Location
A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction
Addressing Complex and Subjective Product-Related Queries with Customer Reviews
News Across Languages - Cross-Lingual Document Similarity and Event Tracking
Heuristic algorithms for finding distribution reducts in probabilistic rough set model
The ERA of FOLE: Foundation
Deep Reinforcement Learning in Large Discrete Action Spaces
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model
On the Foundations of the Brussels Operational-Realistic Approach to Cognition
Modeling Variations of First-Order Horn Abduction in Answer Set Programming
Simple, Robust and Optimal Ranking from Pairwise Comparisons
Programming in logic without logic programming
How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites
Learning Preferences for Manipulation Tasks from Online Coactive Feedback
DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks
Large Collection of Diverse Gene Set Search Queries Recapitulate Known Protein-Protein Interactions and Gene-Gene Functional Associations
Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal Embedding
Funnel Libraries for Real-Time Robust Feedback Motion Planning
Engineering Safety in Machine Learning
Graded Entailment for Compositional Distributional Semantics
The DARPA Twitter Bot Challenge
Persuasive Teachable Agent for Intergenerational Learning
Multi-Object Reasoning with Constrained Goal Models
Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty - An Extended Version
Adaptive Subgradient Methods for Online AUC Maximization
Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences
Recognition of Visually Perceived Compositional Human Actions by Multiple Spatio-Temporal Scales Recurrent Neural Networks
Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes
Classification Accuracy as a Proxy for Two Sample Testing
Scalable Text Mining with Sparse Generative Models
Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
Network of Bandits insure Privacy of end-users
Modeling Human Ad Hoc Coordination
A Truthful Mechanism with Biparameter Learning for Online Crowdsourcing
Identifying Structures in Social Conversations in NSCLC Patients through the Semi-Automatic extraction of Topical Taxonomies
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Communication-Efficient Learning of Deep Networks from Decentralized Data
Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space
Determining the best attributes for surveillance video keywords generation
A Motion Planning Strategy for the Active Vision-Based Mapping of Ground-Level Structures
Augur: Mining Human Behaviors from Fiction to Power Interactive Systems
Moving Target Defense for Web Applications using Bayesian Stackelberg Games
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Stochastic Shortest Path with Energy Constraints in POMDPs
Enhancing Genetic Algorithms using Multi Mutations
Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
A Neutrosophic Recommender System for Medical Diagnosis Based on Algebraic Neutrosophic Measures
Analyzing Games with Ambiguous Player Types using the ${\rm MINthenMAX}$ Decision Model
Unscented Bayesian Optimization for Safe Robot Grasping
Learning Shared Representations in Multi-task Reinforcement Learning
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
UTA-poly and UTA-splines: additive value functions with polynomial marginals
Building a Fine-Grained Entity Typing System Overnight for a New X (X = Language, Domain, Genre)
Inferring Fine-grained Details on User Activities and Home Location from Social Media: Detecting Drinking-While-Tweeting Patterns in Communities
Bayesian Opponent Exploitation in Imperfect-Information Games
Sequential Voting Promotes Collective Discovery in Social Recommendation Systems
Turing learning: a metric-free approach to inferring behavior and its application to swarms
One-Shot Generalization in Deep Generative Models
Neural Aggregation Network for Video Face Recognition
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Generating Natural Questions About an Image
Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks
Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity
Using Enthymemes to Fill the Gap between Logical Argumentation and Revision of Abstract Argumentation Frameworks
Greedy Strategies and Larger Islands of Tractability for Conjunctive Queries and Constraint Satisfaction Problems
Look-ahead before you leap: end-to-end active recognition by forecasting the effect of motion
Adaptive Candidate Generation for Scalable Edge-discovery Tasks on Data Graphs
A heuristic algorithm for a single vehicle static bike sharing rebalancing problem
A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding
Movement Coordination in Human-Robot Teams: A Dynamical Systems Approach
ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines
Beyond knowing that: a new generation of epistemic logics
Markov Chain methods for the bipartite Boolean quadratic programming problem
Low-Complexity Stochastic Generalized Belief Propagation
The GPU-based Parallel Ant Colony System
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
Ask Your Neurons: A Deep Learning Approach to Visual Question Answering
A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models
Causal Discovery for Manufacturing Domains
Online Optimization Methods for the Quantification Problem
Monotone Retargeting for Unsupervised Rank Aggregation with Object Features
Generalized Linear Models for Aggregated Data
Gearbox Fault Detection through PSO Exact Wavelet Analysis and SVM Classifier
Enhanced Twitter Sentiment Classification Using Contextual Information
Heart Rate Variability and Respiration Signal as Diagnostic Tools for Late Onset Sepsis in Neonatal Intensive Care Units
Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering
Detecting Novel Processes with CANDIES -- An Holistic Novelty Detection Technique based on Probabilistic Models
The Information-Collecting Vehicle Routing Problem: Stochastic Optimization for Emergency Storm Response
Bayesian Variable Selection for Globally Sparse Probabilistic PCA
Interactive Debugging of Knowledge Bases
Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers
Quantifying the accuracy of approximate diffusions and Markov chains
Anomaly Detection in XML-Structured SOAP Messages Using Tree-Based Association Rule Mining
Optimal Number of Choices in Rating Contexts
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
Backprop KF: Learning Discriminative Deterministic State Estimators
Unsupervised Learning for Physical Interaction through Video Prediction
A note on privacy preserving iteratively reweighted least squares
Yum-me: A Personalized Nutrient-based Meal Recommender System
Data Programming: Creating Large Training Sets, Quickly
Dimension Projection among Languages based on Pseudo-relevant Documents for Query Translation
Toward a general, scaleable framework for Bayesian teaching with applications to topic models
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
Low-Cost Scene Modeling using a Density Function Improves Segmentation Performance
TensorFlow: A system for large-scale machine learning
Hybrid Perturbation methods based on Statistical Time Series models
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Adversarial Feature Learning
Applications of Probabilistic Programming (Master's thesis, 2015)
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
Towards a Job Title Classification System
ECMdd: Evidential c-medoids clustering with multiple prototypes
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
A Formal Calculus for International Relations Computation and Evaluation
Face valuing: Training user interfaces with facial expressions and reinforcement learning
Policy Networks with Two-Stage Training for Dialogue Systems
Fuzzy-Klassen Model for Development Disparities Analysis based on Gross Regional Domestic Product Sector of a Region
Community Structure in Industrial SAT Instances
Store Location Selection via Mining Search Query Logs of Baidu Maps
A framework for redescription set construction
Entropy/IP: Uncovering Structure in IPv6 Addresses
Invariant recognition drives neural representations of action sequences
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
Assessing Human Error Against a Benchmark of Perfection
Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning
An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels
A Comparative Analysis of classification data mining techniques : Deriving key factors useful for predicting students performance
The LAMBADA dataset: Word prediction requiring a broad discourse context
Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation
Knowledge-Defined Networking
Graphical Models for Optimal Power Flow
Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning
Efficient Attack Graph Analysis through Approximate Inference
E-commerce in Your Inbox: Product Recommendations at Scale
Resolving Distributed Knowledge
An Axiomatic Approach to Routing
Enriching Linked Datasets with New Object Properties
A Game-Theoretic Approach to Word Sense Disambiguation
Non-Monotonic Spatial Reasoning with Answer Set Programming Modulo Theories
STransE: a novel embedding model of entities and relationships in knowledge bases
Information integration from distributed threshold-based interactions
Algebraic foundations for qualitative calculi and networks
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
The SERENDIP III 70 cm Search for Extraterrestrial Intelligence
A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning
Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
Application of Statistical Relational Learning to Hybrid Recommendation Systems
Click Carving: Segmenting Objects in Video with Point Clicks
Optimal control for a robotic exploration, pick-up and delivery problem
One-Shot Session Recommendation Systems with Combinatorial Items
Mixed Strategy for Constrained Stochastic Optimal Control
Cost-Optimal Algorithms for Planning with Procedural Control Knowledge
Fundamental Parameters of Main-Sequence Stars in an Instant with Machine Learning
Document Clustering Games in Static and Dynamic Scenarios
Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge
Extended Graded Modalities in Strategy Logic
DeepQA: Improving the estimation of single protein model quality with deep belief networks
Juxtaposition of System Dynamics and Agent-based Simulation for a Case Study in Immunosenescence
Simulating user learning in authoritative technology adoption: An agent based model for council-led smart meter deployment planning in the UK
Optimising Rule-Based Classification in Temporal Data
Exploring Differences in Interpretation of Words Essential in Medical Expert-Patient Communication
Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Novel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization
Automated Prediction of Temporal Relations
Classification of Alzheimer's Disease Structural MRI Data by Deep Learning Convolutional Neural Networks
An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia
Multiple scan data association by convex variational inference
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
Robust Contextual Outlier Detection: Where Context Meets Sparsity
A novel online multi-label classifier for high-speed streaming data applications
Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks
Fitted Learning: Models with Awareness of their Limits
DESPOT: Online POMDP Planning with Regularization
Modelling Creativity: Identifying Key Components through a Corpus-Based Approach
"Flow Size Difference" Can Make a Difference: Detecting Malicious TCP Network Flows Based on Benford's Law
Even Good Bots Fight: The Case of Wikipedia
Concordance and the Smallest Covering Set of Preference Orderings
The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research
Towards Deep Symbolic Reinforcement Learning
Context-aware Sequential Recommendation
Temporal Logic Programs with Variables
An Efficient Method of Partitioning High Volumes of Multidimensional Data for Parallel Clustering Algorithms
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation
Multi-document abstractive summarization using ILP based multi-sentence compression
Pose-Selective Max Pooling for Measuring Similarity
Towards the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference
Predictive modelling of football injuries
Testing Quantum Models of Conjunction Fallacy on the World Wide Web
An Ontology of Preference-Based Multiobjective Metaheuristics
A Fast Factorization-based Approach to Robust PCA
Hierarchical Memory Networks for Answer Selection on Unknown Words
Learning from the Hindsight Plan -- Episodic MPC Improvement
ICE: Information Credibility Evaluation on Social Media via Representation Learning
Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs
Technical Report: Graph-Structured Sparse Optimization for Connected Subgraph Detection
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery
Network Structure Inference, A Survey: Motivations, Methods, and Applications
A new algorithm for identity verification based on the analysis of a handwritten dynamic signature
Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots
DeepDGA: Adversarially-Tuned Domain Generation and Detection
Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System
Adaptive Convolutional ELM For Concept Drift Handling in Online Stream Data
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
A Music-generating System Inspired by the Science of Complex Adaptive Systems
Revisiting Multiple Instance Neural Networks
Personalizing a Dialogue System with Transfer Reinforcement Learning
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model
Truthful Mechanisms for Matching and Clustering in an Ordinal World
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies
Safety Verification of Deep Neural Networks
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web
A Review of 40 Years of Cognitive Architecture Research: Core Cognitive Abilities and Practical Applications
Fast Low-rank Shared Dictionary Learning for Image Classification
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes
Optimal Belief Approximation
Integrating Topic Models and Latent Factors for Recommendation
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
From Node Embedding To Community Embedding
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
Extensions and Limitations of the Neural GPU
Predicting Domain Generation Algorithms with Long Short-Term Memory Networks
Maximizing Investment Value of Small-Scale PV in a Smart Grid Environment
Neural Architecture Search with Reinforcement Learning
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
Neuro-Symbolic Program Synthesis
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
Entropic Causal Inference
Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge
Recognizing and Eliciting Weakly Single Crossing Profiles on Trees
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
Zero-resource Machine Translation by Multimodal Encoder-decoder Network with Multimedia Pivot
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
Causal Inference in Observational Data
The Effects of Relative Importance of User Constraints in Cloud of Things Resource Discovery: A Case Study
Explicablility as Minimizing Distance from Expected Behavior
Fictitious play for cooperative action selection in robot teams
Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs
Learning to reinforcement learn
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression
Faster variational inducing input Gaussian process classification
Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions
A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective
A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games
Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising
Dynamic Key-Value Memory Networks for Knowledge Tracing
The Off-Switch Game
Learning Python Code Suggestion with a Sparse Pointer Network
Decision Support Systems in Fisheries and Aquaculture: A systematic review
Blocking and Other Enhancements for Bottom-Up Model Generation Methods
Split-door criterion for causal identification: Automatic search for natural experiments
Learning Concept Hierarchies through Probabilistic Topic Modeling
Fractional Order AGC for Distributed Energy Resources Using Robust Optimization
Measuring and modeling the perception of natural and unconstrained gaze in humans and machines
Capacity and Trainability in Recurrent Neural Networks
Choquet integral in decision analysis - lessons from the axiomatization
Proportional Justified Representation
Joint Causal Inference from Multiple Contexts
Spatial Decompositions for Large Scale SVMs
Self-critical Sequence Training for Image Captioning
Active Search for Sparse Signals with Region Sensing
Cognitive Deep Machine Can Train Itself
Summary - TerpreT: A Probabilistic Programming Language for Program Induction
A New Type-II Fuzzy Logic Based Controller for Non-linear Dynamical Systems with Application to a 3-PSP Parallel Robot
A Multi-Pass Approach to Large-Scale Connectomics
Fixpoint Approximation of Strategic Abilities under Imperfect Information
Learning in the Machine: Random Backpropagation and the Deep Learning Channel
Safety Verification and Control for Collision Avoidance at Road Intersections
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Knowledge Elicitation via Sequential Probabilistic Inference for High-Dimensional Prediction
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
A Model of Multi-Agent Consensus for Vague and Uncertain Beliefs
Online Reinforcement Learning for Real-Time Exploration in Continuous State and Action Markov Decision Processes
Knowledge Completion for Generics using Guided Tensor Factorization
Web-based Argumentation
Retrieving sinusoids from nonuniformly sampled data using recursive formulation
Attentive Explanations: Justifying Decisions and Pointing to the Evidence
Dynamical Kinds and their Discovery
Adversarial Message Passing For Graphical Models
A User Simulator for Task-Completion Dialogues
Optimal Target Assignment and Path Finding for Teams of Agents
Web-based Semantic Similarity for Emotion Recognition in Web Objects
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids
Action-Driven Object Detection with Top-Down Visual Attentions
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit
Liquid Democracy: An Analysis in Binary Aggregation and Diffusion
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
Sequence-to-point learning with neural networks for nonintrusive load monitoring
When the map is better than the territory
Counterfactual Prediction with Deep Instrumental Variables Networks
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control
Toward sensitive document release with privacy guarantees
Learning local trajectories for high precision robotic tasks : application to KUKA LBR iiwa Cartesian positioning
Pareto Efficient Multi Objective Optimization for Local Tuning of Analogy Based Estimation
Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks
IoFClime: The fuzzy logic and the Internet of Things to control indoor temperature regarding the outdoor ambient conditions
Linear Disentangled Representation Learning for Facial Actions
Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition
Learning to Invert: Signal Recovery via Deep Convolutional Networks
Unknowable Manipulators: Social Network Curator Algorithms
Control Capacity of Partially Observable Dynamic Systems in Continuous Time
Parsimonious Inference on Convolutional Neural Networks: Learning and applying on-line kernel activation rules
Semantic Evolutionary Concept Distances for Effective Information Retrieval in Query Expansion
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
Constraint programming for planning test campaigns of communications satellites
Incorporating Prior Information in Compressive Online Robust Principal Component Analysis
Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code
Dynamic time warping distance for message propagation classification in Twitter
The Causal Frame Problem: An Algorithmic Perspective
Entropic Causality and Greedy Minimum Entropy Coupling
Feature base fusion for splicing forgery detection based on neuro fuzzy
Decision structure of risky choice
Credal Networks under Epistemic Irrelevance
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
Spatial Projection of Multiple Climate Variables using Hierarchical Multitask Learning
Comparing Dataset Characteristics that Favor the Apriori, Eclat or FP-Growth Frequent Itemset Mining Algorithms
Procedural Content Generation via Machine Learning (PCGML)
Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving
Autonomous Braking System via Deep Reinforcement Learning
Energy Saving Additive Neural Network
Phase Transitions of the Typical Algorithmic Complexity of the Random Satisfiability Problem Studied with Linear Programming
A VLA Search for Radio Signals from M31 and M33
A Minimax Algorithm Better Than Alpha-beta?: No and Yes
Similarity Preserving Representation Learning for Time Series Analysis
Constraint Answer Set Solver EZCSP and Why Integration Schemas Matter
Efficient Multi-task Feature and Relationship Learning
Frustratingly Short Attention Spans in Neural Language Modeling
Theoretical and Practical Advances on Smoothing for Extensive-Form Games
Direct Estimation of Information Divergence Using Nearest Neighbor Ratios
Towards a Unified Taxonomy of Biclustering Methods
Threshold Constraints with Guarantees for Parity Objectives in Markov Decision Processes
A Circuit-Based Approach to Efficient Enumeration
Polynomial Time Efficient Construction Heuristics for Vertex Separation Minimization Problem
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
Learning to Multi-Task by Active Sampling
Towards a Common Implementation of Reinforcement Learning for Multiple Robotic Tasks
Knowledge Graph Completion via Complex Tensor Factorization
Regularizing Face Verification Nets For Pain Intensity Regression
Proactive Resource Management in LTE-U Systems: A Deep Learning Perspective
Diverse Weighted Bipartite b-Matching
Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
Learning What Data to Learn
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning
Analysing Congestion Problems in Multi-agent Reinforcement Learning
Truth and Regret in Online Scheduling
Conversion Rate Optimization through Evolutionary Computation
Large-Scale Evolution of Image Classifiers
Count-Based Exploration with Neural Density Models
Multi-step Reinforcement Learning: A Unifying Algorithm
Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training
Activation Maximization Generative Adversarial Nets
Learning a Unified Control Policy for Safe Falling
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection
Interpretable Structure-Evolving LSTM
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
Numerical Integration and Dynamic Discretization in Heuristic Search Planning over Hybrid Domains
Symbol Grounding via Chaining of Morphisms
Bayesian Optimization with Gradients
High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data
A computational investigation of sources of variability in sentence comprehension difficulty in aphasia
Towards Moral Autonomous Systems
Learned Optimizers that Scale and Generalize
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
Database Learning: Toward a Database that Becomes Smarter Every Time
Searching for Exoplanets Around X-Ray Binaries with Accreting White Dwarfs, Neutron Stars, and Black Holes
Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks
Evolving Game Skill-Depth using General Video Game AI Agents
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation
Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality
Learning Correspondence Structures for Person Re-identification
Recurrent Topic-Transition GAN for Visual Paragraph Generation
ZM-Net: Real-time Zero-shot Image Manipulation Network
RobustFill: Neural Program Learning under Noisy I/O
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions
Unsupervised Basis Function Adaptation for Reinforcement Learning
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
An Analysis of Visual Question Answering Algorithms
Dialectical Rough Sets, Parthood and Figures of Opposition
FairJudge: Trustworthy User Prediction in Rating Platforms
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
On the Reliable Detection of Concept Drift from Streaming Unlabeled Data
Contextual Data Collection for Smart Cities
Human-Aware Sensor Network Ontology: Semantic Support for Empirical Data Collection
Tackling Dynamic Vehicle Routing Problem with Time Windows by means of Ant Colony System
The quality of priority ratios estimation in relation to a selected prioritization procedure and consistency measure for a Pairwise Comparison Matrix
An Automated Text Categorization Framework based on Hyperparameter Optimization
Pay Attention to Those Sets! Learning Quantification from Images
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Quality Aware Network for Set to Set Recognition
On Generalized Bellman Equations and Temporal-Difference Learning
A Security Monitoring Framework For Virtualization Based HEP Infrastructures
Effective Warm Start for the Online Actor-Critic Reinforcement Learning based mHealth Intervention
Larger is Better: The Effect of Learning Rates Enjoyed by Stochastic Optimization with Progressive Variance Reduction
A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations
Intrusion Prevention and Detection in Grid Computing - The ALICE Case
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities
A hybrid spatial data mining approach based on fuzzy topological relations and MOSES evolutionary algorithm
Misspecified Linear Bandits
A General Theory for Training Learning Machine
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models
Sharing deep generative representation for perceived image reconstruction from human brain activity
Event Stream-Based Process Discovery using Abstract Representations
Consensus measure of rankings
Network-based coverage of mutational profiles reveals cancer genes
Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary
A Partitioning Algorithm for Detecting Eventuality Coincidence in Temporal Double recurrence
Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation
Quantifying Mental Health from Social Media with Neural User Embeddings
Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner
The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement
Fast k-means based on KNN Graph
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations
Exploring Latent Semantic Factors to Find Useful Product Reviews
Item Recommendation with Evolving User Preferences and Experience
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections
Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks
Clingcon: The Next Generation
Monaural Audio Speaker Separation with Source Contrastive Estimation
Relaxation heuristics for the set multicover problem with generalized upper bound constraints
Comparison-Based Choices
Online Article Ranking as a Constrained, Dynamic, Multi-Objective Optimization Problem
Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees
REMIX: Automated Exploration for Interactive Outlier Detection
Distributed Vector Representation Of Shopping Items, The Customer And Shopping Cart To Build A Three Fold Recommendation System
Evolving Ensemble Fuzzy Classifier
I Probe, Therefore I Am: Designing a Virtual Journalist with Human Emotions
Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
Learning Convolutional Text Representations for Visual Question Answering
Parameter Adaptation and Criticality in Particle Swarm Optimization
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning
Learning to Mix n-Step Returns: Generalizing lambda-Returns for Deep Reinforcement Learning
Shallow Updates for Deep Reinforcement Learning
Near-Feasible Stable Matchings with Budget Constraints
Thinking Fast and Slow with Deep Learning and Tree Search
MMD GAN: Towards Deeper Understanding of Moment Matching Network
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval
Compiling quantum circuits to realistic hardware architectures using temporal planners
Logic Tensor Networks for Semantic Image Interpretation
Operation Frames and Clubs in Kidney Exchange
Generating Time-Based Label Refinements to Discover More Precise Process Models
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
Multi-shot ASP solving with clingo
Bayesian Unification of Gradient and Bandit-based Learning for Accelerated Global Optimisation
Multi-Labelled Value Networks for Computer Go
Strength Factors: An Uncertainty System for a Quantified Modal Logic
Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
The Sample Complexity of Online One-Class Collaborative Filtering
Cross-modal Common Representation Learning by Hybrid Transfer Network
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Hyperparameter Optimization: A Spectral Approach
Visuospatial Skill Learning for Robots
Event Representations for Automated Story Generation with Deep Neural Nets
Emergence of Invariance and Disentangling in Deep Representations
Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach
Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning
Where is my forearm? Clustering of body parts from simultaneous tactile and linguistic input using sequential mapping
Item Silk Road: Recommending Items from Information Domains to Social Users
Deep Optimization for Spectrum Repacking
Learning Large-Scale Topological Maps Using Sum-Product Networks
Optimal Auctions through Deep Learning
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self
SEVEN: Deep Semi-supervised Verification Networks
Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks
Meta learning Framework for Automated Driving
Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation
Autonomous Reactive Mission Scheduling and Task-Path Planning Architecture for Autonomous Underwater Vehicle
Identifying Spatial Relations in Images using Convolutional Neural Networks
The Opacity of Backbones and Backdoors Under a Weak Assumption
Learning a visuomotor controller for real world robotic grasping using simulated depth images
Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks
Device Placement Optimization with Reinforcement Learning
Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic
Accelerating Innovation Through Analogy Mining
Consistent feature attribution for tree ensembles
Scalable Co-Optimization of Morphology and Control in Embodied Machines
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification
Ensemble Framework for Real-time Decision Making
Inter-Session Modeling for Session-Based Recommendation
Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation
Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models
Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms
Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios
Stochastic Bandit Models for Delayed Conversions
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
Tableaux for Policy Synthesis for MDPs with PCTL* Constraints
Hashing Over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning
Automated Problem Identification: Regression vs Classification via Evolutionary Deep Networks
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Information-gain computation
An HTM based cortical algorithm for detection of seismic waves
Application of Fuzzy Assessing for Reliability Decision Making
Emergence of Locomotion Behaviours in Rich Environments
PELESent: Cross-domain polarity classification using distant supervision
Best-Effort Inductive Logic Programming via Fine-grained Cost-based Hypothesis Generation
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Learning Heuristic Search via Imitation
An Optimal Bayesian Network Based Solution Scheme for the Constrained Stochastic On-line Equi-Partitioning Problem
A Simple Neural Attentive Meta-Learner
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
Exoplanet Transits as the Foundation of an Interstellar Communications Network
Kafnets: kernel-based non-parametric activation functions for neural networks
Disentangling Motion, Foreground and Background Features in Videos
Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification
Lenient Multi-Agent Deep Reinforcement Learning
On consistency of optimal pricing algorithms in repeated posted-price auctions with strategic buyer
When You Must Forget: beyond strong persistence when forgetting in answer set programming
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
Detection, Recognition and Tracking of Moving Objects from Real-time Video via Visual Vocabulary Model and Species Inspired PSO
Object Tracking based on Quantum Particle Swarm Optimization
Reverse Curriculum Generation for Reinforcement Learning
Logic Programming approaches for routing fault-free and maximally-parallel Wavelength Routed Optical Networks on Chip (Application paper)
Hybrid Conditional Planning using Answer Set Programming
Learning model-based planning from scratch
Representing Hybrid Automata by Action Language Modulo Theories
Discretization-free Knowledge Gradient Methods for Bayesian Optimization
RAIL: Risk-Averse Imitation Learning
Outcome-Oriented Predictive Process Monitoring: Review and Benchmark
Domain Recursion for Lifted Inference with Existential Quantifiers
Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks
Evidence combination for a large number of sources
Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities
A Logic for Global and Local Announcements
Relaxing Exclusive Control in Boolean Games
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Balancing Explicability and Explanation in Human-Aware Planning
A Novel Neural Network Model Specified for Representing Logical Relations
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning
Object-Oriented Sokoban Solver: A Serious Game Project for OOAD and AI Education
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
An Effective Training Method For Deep Convolutional Neural Network
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model
Measuring Catastrophic Forgetting in Neural Networks
Regulating Highly Automated Robot Ecologies: Insights from Three User Studies
PowerAI DDL
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
An Approach with Toric Varieties for Singular Learning Machines
Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval
A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence
Robust Computer Algebra, Theorem Proving, and Oracle AI
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
An automatic water detection approach based on Dempster-Shafer theory for multi spectral images
A Simple and Realistic Pedestrian Model for Crowd Simulation and Application
SESA: Supervised Explicit Semantic Analysis
Universal limits to parallel processing capability of network architectures
Resilient Linear Classification: An Approach to Deal with Attacks on Training Data
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
Belief Tree Search for Active Object Recognition
Optimization of Ensemble Supervised Learning Algorithms for Increased Sensitivity, Specificity, and AUC of Population-Based Colorectal Cancer Screenings
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Learning to Plan Chemical Syntheses
Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier - A Review
DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain
Geometric Enclosing Networks
Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs
Evaluating Visual Conversational Agents via Cooperative Human-AI Games
Learning Musical Relations using Gated Autoencoders
A Data and Model-Parallel, Distributed and Scalable Framework for Training of Deep Networks in Apache Spark
Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization
Efficient Online Inference for Infinite Evolutionary Cluster models with Applications to Latent Social Event Discovery
Fake News in Social Networks
What caused what? An irreducible account of actual causation
Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence
Finding Streams in Knowledge Graphs to Support Fact Checking
On the Compressive Power of Deep Rectifier Networks for High Resolution Representation of Class Boundaries
Area Protection in Adversarial Path-Finding Scenarios with Multiple Mobile Agents on Graphs: a theoretical and experimental study of target-allocation strategies for defense coordination
Learning 6-DOF Grasping Interaction with Deep Geometry-aware 3D Representations
Divergence, Entropy, Information: An Opinionated Introduction to Information Theory
Hamiltonian Maker-Breaker games on small graphs
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer
Robust Task Clustering for Deep Many-Task Learning
Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction
Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
A Deep Learning Approach for Population Estimation from Satellite Imagery
Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative Machine Learning approaches
Incorporating Feedback into Tree-based Anomaly Detection
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
Behavior Trees in Robotics and AI: An Introduction
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Smile for the Camera: Privacy and Policy Implications of Emotion AI
From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
Using Summarization to Discover Argument Facets in Online Ideological Dialog
Maintaining Ad-Hoc Communication Network in Area Protection Scenarios with Adversarial Agents
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning
Interacting Attention-gated Recurrent Networks for Recommendation
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction
Unsupervised Generative Modeling Using Matrix Product States
Measuring the Similarity of Sentential Arguments in Dialog
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge
Representation Learning for Visual-Relational Knowledge Graphs
A Deep Reinforcement Learning Chatbot
Prosocial learning agents solve generalized Stag Hunts better than selfish ones
Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
MBMF: Model-Based Priors for Model-Free Reinforcement Learning
Fairness Testing: Testing Software for Discrimination
On better training the infinite restricted Boltzmann machines
Autonomous Quadrotor Landing using Deep Reinforcement Learning
Combining Strategic Learning and Tactical Search in Real-Time Strategy Games
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
Multimodal Content Analysis for Effective Advertisements on YouTube
Variational Reasoning for Question Answering with Knowledge Graph
Computing the Shapley Value in Allocation Problems: Approximations and Bounds, with an Application to the Italian VQR Research Assessment Program
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest
Robustness Analysis of Visual QA Models by Basic Questions
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
Motif-based Rule Discovery for Predicting Real-valued Time Series
Unsupervised state representation learning with robotic priors: a robustness benchmark
A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
The Geometric Block Model
SKOS Concepts and Natural Language Concepts: an Analysis of Latent Relationships in KOSs
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification
Push and Pull Search for Solving Constrained Multi-objective Optimization Problems
AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video
Towards Cognitive-and-Immersive Systems: Experiments in a Shared (or common) Blockworld Framework
Leveraging Distributional Semantics for Multi-Label Learning
Model-Powered Conditional Independence Test
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics
Learning Complex Swarm Behaviors by Exploiting Local Communication Protocols with Deep Reinforcement Learning
Non-Depth-First Search against Independent Distributions on an AND-OR Tree
Influence of Personal Preferences on Link Dynamics in Social Networks
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Extracting Ontological Knowledge from Textual Descriptions
Mining a Sub-Matrix of Maximal Sum
Bayesian Filtering for ODEs with Bounded Derivatives
The Consciousness Prior
Long Text Generation via Adversarial Training with Leaked Information
Ultra-Dense HetNets Meet Big Data: Green Frameworks, Techniques, and Approaches
Object-oriented Neural Programming (OONP) for Document Understanding
Exact MAP inference in general higher-order graphical models using linear programming
Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture
Automatic Error Analysis of Human Motor Performance for Interactive Coaching in Virtual Reality
Dose Prediction with U-net: A Feasibility Study for Predicting Dose Distributions from Contours using Deep Learning on Prostate IMRT Patients
FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification
Research on several key technologies in practical speech emotion recognition
Angriffserkennung für industrielle Netzwerke innerhalb des Projektes IUNO
Introducing machine learning for power system operation support
Traffic Optimization For a Mixture of Self-interested and Compliant Agents
Edina: Building an Open Domain Socialbot with Self-dialogues
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation
SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control
Privacy-Preserving Deep Inference for Rich User Data on The Cloud
HANDY: A Hybrid Association Rules Mining Approach for Network Layer Discovery of Services for Mobile Ad hoc Network
Exploration in Feature Space for Reinforcement Learning
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
Projection Based Weight Normalization for Deep Neural Networks
Distributed Kernel K-Means for Large Scale Clustering
Towards Agent-Based Model Specification in Smart Grid: A Cognitive Agent-based Computing Approach
On Preemption and Overdetermination in Formal Theories of Causality
Adapting a Formal Model Theory to Applications in Augmented Personalized Medicine
Mixed Precision Training
Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
Neural Program Meta-Induction
Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Community Aware Random Walk for Network Embedding
Mental Sampling in Multimodal Representations
Self-Supervised Visual Planning with Temporal Skip Connections
A systematic study of the class imbalance problem in convolutional neural networks
A retrieval-based dialogue system utilizing utterance and context embeddings
Safe Medicine Recommendation via Medical Knowledge Graph Embedding
Reply With: Proactive Recommendation of Email Attachments
Spontaneous Symmetry Breaking in Neural Networks
Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning
Auditing Black-Box Models Using Transparent Model Distillation With Side Information
Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue
A Bayesian Perspective on Generalization and Stochastic Gradient Descent
Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation
Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF
Protein Folding Optimization using Differential Evolution Extended with Local Search and Component Reinitialization
Classification Driven Dynamic Image Enhancement
A Novel Stochastic Stratified Average Gradient Method: Convergence Rate and Its Complexity
Exploiting generalization in the subspaces for faster model-based learning
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
A Memristor-Based Optimization Framework for AI Applications
Model Identification via Physics Engines for Improved Policy Search
Multi-Objective Approaches to Markov Decision Processes with Uncertain Transition Parameters
Inductive Representation Learning in Large Attributed Graphs
SRE: Semantic Rules Engine For the Industrial Internet-Of-Things Gateways
On modeling vagueness and uncertainty in data-to-text systems through fuzzy sets
Inverse Reinforcement Learning Under Noisy Observations
Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy
Transfer Learning to Learn with Multitask Neural Model Search
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics
Semantic Code Repair using Neuro-Symbolic Transformation Networks
Super-polynomial and exponential improvements for quantum-enhanced reinforcement learning
Improve SAT-solving with Machine Learning
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
Abnormal Spatial-Temporal Pattern Analysis for Niagara Frontier Border Wait Times
Fraternal Dropout
Separation of Water and Fat Magnetic Resonance Imaging Signals Using Deep Learning with Convolutional Neural Networks
Erratum: Link prediction in drug-target interactions network using similarity indices
Piecewise Linear Neural Network verification: A comparative study
School bus routing by maximizing trip compatibility
SCDA: School Compatibility Decomposition Algorithm for Solving the Multi-School Bus Routing and Scheduling Problem
Beautiful and damned. Combined effect of content quality and social ties on user engagement
Adaptive coordination of working-memory and reinforcement learning in non-human primates performing a trial-and-error problem solving task
Learning to Represent Programs with Graphs
Framework for evaluation of sound event detection in web videos
Provable defenses against adversarial examples via the convex outer adversarial polytope
Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise
Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities
Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples
Transaction Fraud Detection Using GRU-centered Sandwich-structured Model
HPX Smart Executors
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Visually-Aware Fashion Recommendation and Design with Generative Image Models
Continuous DR-submodular Maximization: Structure and Algorithms
Learning K-way D-dimensional Discrete Code For Compact Embedding Representations
Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks
"Dave...I can assure you...that it's going to be all right..." -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
Differential Performance Debugging with Discriminant Regression Trees
Unified Spectral Clustering with Optimal Graph
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FILE: data/arxiv/artificial intelligence_134_15000_200_abs.txt
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The singularity refers to an idea that once a machine having an artificial intelligence surpassing the human intelligence capacity is created, it will trigger explosive technological and intelligence growth. I propose to test the hypothesis that machine intelligence capacity can grow autonomously starting with an intelligence comparable to that of bacteria - microbial intelligence. The goal will be to demonstrate that rapid growth in intelligence capacity can be realized at all in artificial computing systems. I propose the following three properties that may allow an artificial intelligence to exhibit a steady growth in its intelligence capacity: (i) learning with the ability to modify itself when exposed to more data, (ii) acquiring new functionalities (skills), and (iii) expanding or replicating itself. The algorithms must demonstrate a rapid growth in skills of dataprocessing and analysis and gain qualitatively different functionalities, at least until the current computing technology supports their scalable development. The existing algorithms that already encompass some of these or similar properties, as well as missing abilities that must yet be implemented, will be reviewed in this work. Future computational tests could support or oppose the hypothesis that artificial intelligence can potentially grow to the level of superintelligence which overcomes the limitations in hardware by producing necessary processing resources or by changing the physical realization of computation from using chip circuits to using quantum computing principles.
Nowadays, considering the speed of the processes and the amount of data used in cyber defense, it cannot be expected to have an effective defense by using only human power without the help of automation systems. However, for the effective defense against dynamically evolving attacks on networks, it is difficult to develop software with conventional fixed algorithms. This can be achieved by using artificial intelligence methods that provide flexibility and learning capability. The likelihood of developing cyber defense capabilities through increased intelligence of defense systems is quite high. Given the problems associated with cyber defense in real life, it is clear that many cyber defense problems can be successfully solved only when artificial intelligence methods are used. In this article, the current artificial intelligence practices and techniques are reviewed and the use and importance of artificial intelligence in cyber defense systems is mentioned. The aim of this article is to be able to explain the use of these methods in the field of cyber defense with current examples by considering and analyzing the artificial intelligence technologies and methodologies that are currently being developed and integrating them with the role and adaptation of the technology and methodology in the defense of cyberspace.
The large distances involved in interstellar travel require a high degree of spacecraft autonomy, realized by artificial intelligence. The breadth of tasks artificial intelligence could perform on such spacecraft involves maintenance, data collection, designing and constructing an infrastructure using in-situ resources. Despite its importance, existing publications on artificial intelligence and interstellar travel are limited to cursory descriptions where little detail is given about the nature of the artificial intelligence. This article explores the role of artificial intelligence for interstellar travel by compiling use cases, exploring capabilities, and proposing typologies, system and mission architectures. Estimations for the required intelligence level for specific types of interstellar probes are given, along with potential system and mission architectures, covering those proposed in the literature but also presenting novel ones. Finally, a generic design for interstellar probes with an AI payload is proposed. Given current levels of increase in computational power, a spacecraft with a similar computational power as the human brain would have a mass from dozens to hundreds of tons in a 2050-2060 timeframe. Given that the advent of the first interstellar missions and artificial general intelligence are estimated to be by the mid-21st century, a more in-depth exploration of the relationship between the two should be attempted, focusing on neglected areas such as protecting the artificial intelligence payload from radiation in interstellar space and the role of artificial intelligence in self-replication.
The first decade of this century has seen the nascency of the first mathematical theory of general artificial intelligence. This theory of Universal Artificial Intelligence (UAI) has made significant contributions to many theoretical, philosophical, and practical AI questions. In a series of papers culminating in book (Hutter, 2005), an exciting sound and complete mathematical model for a super intelligent agent (AIXI) has been developed and rigorously analyzed. While nowadays most AI researchers avoid discussing intelligence, the award-winning PhD thesis (Legg, 2008) provided the philosophical embedding and investigated the UAI-based universal measure of rational intelligence, which is formal, objective and non-anthropocentric. Recently, effective approximations of AIXI have been derived and experimentally investigated in JAIR paper (Veness et al. 2011). This practical breakthrough has resulted in some impressive applications, finally muting earlier critique that UAI is only a theory. For the first time, without providing any domain knowledge, the same agent is able to self-adapt to a diverse range of interactive environments. For instance, AIXI is able to learn from scratch to play TicTacToe, Pacman, Kuhn Poker, and other games by trial and error, without even providing the rules of the games. These achievements give new hope that the grand goal of Artificial General Intelligence is not elusive. This article provides an informal overview of UAI in context. It attempts to gently introduce a very theoretical, formal, and mathematical subject, and discusses philosophical and technical ingredients, traits of intelligence, some social questions, and the past and future of UAI.
The rise of deep learning has brought artificial intelligence (AI) to the forefront. The ultimate goal of AI is to realize machines with human mind and consciousness, but existing achievements mainly simulate intelligent behavior on computer platforms. These achievements all belong to weak AI rather than strong AI. How to achieve strong AI is not known yet in the field of intelligence science. Currently, this field is calling for a new paradigm, especially Theory of Cognitive Relativity (TCR). The TCR aims to summarize a simple and elegant set of first principles about the nature of intelligence, at least including the Principle of World's Relativity and the Principle of Symbol's Relativity. The Principle of World's Relativity states that the subjective world an intelligent agent can observe is strongly constrained by the way it perceives the objective world. The Principle of Symbol's Relativity states that an intelligent agent can use any physical symbol system to express what it observes in its subjective world. The two principles are derived from scientific facts and life experience. Thought experiments show that they are important to understand high-level intelligence and necessary to establish a scientific theory of mind and consciousness. Rather than brain-like intelligence, the TCR indeed advocates a promising change in direction to realize true AI, i.e. artificial general intelligence or artificial consciousness, particularly different from humans' and animals'. Furthermore, a TCR creed has been presented and extended to reveal the secrets of consciousness and to guide realization of conscious machines. In the sense that true AI could be diversely implemented in a brain-different way, the TCR would probably drive an intelligence revolution in combination with some additional first principles.
With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions Bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of interest. However, research on edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence. Specifically, we first review the background and motivation for artificial intelligence running at the network edge. We then provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training/inference at the network edge. Finally, we discuss future research opportunities on edge intelligence. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions and inspire further research ideas on edge intelligence.
AI technology has a long history which is actively and constantly changing and growing. It focuses on intelligent agents, which contain devices that perceive the environment and based on which takes actions in order to maximize goal success chances. In this paper, we will explain the modern AI basics and various representative applications of AI. In the context of the modern digitalized world, AI is the property of machines, computer programs, and systems to perform the intellectual and creative functions of a person, independently find ways to solve problems, be able to draw conclusions and make decisions. Most artificial intelligence systems have the ability to learn, which allows people to improve their performance over time. The recent research on AI tools, including machine learning, deep learning and predictive analysis intended toward increasing the planning, learning, reasoning, thinking and action taking ability. Based on which, the proposed research intends towards exploring on how the human intelligence differs from the artificial intelligence. Moreover, we critically analyze what AI of today is capable of doing, why it still cannot reach human intelligence and what are the open challenges existing in front of AI to reach and outperform human level of intelligence. Furthermore, it will explore the future predictions for artificial intelligence and based on which potential solution will be recommended to solve it within next decades.
This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artifical life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate the evolution of the environment into our approach in order to refine the ontogenesis of the artificial system, and to compare it with the enaction paradigm. The growing complexity of the ontogenetic mechanisms to be activated can therefore be compensated by an interactive guidance system emanating from the environment. This proposition does not however resolve that of the relevance of the meaning created by the machine (sense-making). Such reflections lead us to integrate human interaction into this environment in order to construct relevant meaning in terms of participative artificial intelligence. This raises a number of questions with regards to setting up an enactive interaction. The article concludes by exploring a number of issues, thereby enabling us to associate current approaches with the principles of morphogenesis, guidance, the phenomenology of interactions and the use of minimal enactive interfaces in setting up experiments which will deal with the problem of artificial intelligence in a variety of enaction-based ways.
The elusive quest for intelligence in artificial intelligence prompts us to consider that instituting human-level intelligence in systems may be (still) in the realm of utopia. In about a quarter century, we have witnessed the winter of AI (1990) being transformed and transported to the zenith of tabloid fodder about AI (2015). The discussion at hand is about the elements that constitute the canonical idea of intelligence. The delivery of intelligence as a pay-per-use-service, popping out of an app or from a shrink-wrapped software defined point solution, is in contrast to the bio-inspired view of intelligence as an outcome, perhaps formed from a tapestry of events, cross-pollinated by instances, each with its own microcosm of experiences and learning, which may not be discrete all-or-none functions but continuous, over space and time. The enterprise world may not require, aspire or desire such an engaged solution to improve its services for enabling digital transformation through the deployment of digital twins, for example. One might ask whether the "work-flow on steroids" version of decision support may suffice for intelligence? Are we harking back to the era of rule based expert systems? The image conjured by the publicity machines offers deep solutions with human-level AI and preposterous claims about capturing the "brain in a box" by 2020. Even emulating insects may be difficult in terms of real progress. Perhaps we can try to focus on worms (Caenorhabditis elegans) which may be better suited for what business needs to quench its thirst for so-called intelligence in AI.
This paper summarises how the "SP theory of intelligence" and its realisation in the "SP computer model" simplifies and integrates concepts across artificial intelligence and related areas, and thus provides a promising foundation for the development of a general, human-level thinking machine, in accordance with the main goal of research in artificial general intelligence. The key to this simplification and integration is the powerful concept of "multiple alignment", borrowed and adapted from bioinformatics. This concept has the potential to be the "double helix" of intelligence, with as much significance for human-level intelligence as has DNA for biological sciences. Strengths of the SP system include: versatility in the representation of diverse kinds of knowledge; versatility in aspects of intelligence (including: strengths in unsupervised learning; the processing of natural language; pattern recognition at multiple levels of abstraction that is robust in the face of errors in data; several kinds of reasoning (including: one-step `deductive' reasoning; chains of reasoning; abductive reasoning; reasoning with probabilistic networks and trees; reasoning with 'rules'; nonmonotonic reasoning and reasoning with default values; Bayesian reasoning with 'explaining away'; and more); planning; problem solving; and more); seamless integration of diverse kinds of knowledge and diverse aspects of intelligence in any combination; and potential for application in several areas (including: helping to solve nine problems with big data; helping to develop human-level intelligence in autonomous robots; serving as a database with intelligence and with versatility in the representation and integration of several forms of knowledge; serving as a vehicle for medical knowledge and as an aid to medical diagnosis; and several more).
Artificial General Intelligence is a field of research aiming to distill the principles of intelligence that operate independently of a specific problem domain or a predefined context and utilize these principles in order to synthesize systems capable of performing any intellectual task a human being is capable of and eventually go beyond that. While "narrow" artificial intelligence which focuses on solving specific problems such as speech recognition, text comprehension, visual pattern recognition, robotic motion, etc. has shown quite a few impressive breakthroughs lately, understanding general intelligence remains elusive. In the paper we offer a novel theoretical approach to understanding general intelligence. We start with a brief introduction of the current conceptual approach. Our critique exposes a number of serious limitations that are traced back to the ontological roots of the concept of intelligence. We then propose a paradigm shift from intelligence perceived as a competence of individual agents defined in relation to an a priori given problem domain or a goal, to intelligence perceived as a formative process of self-organization by which intelligent agents are individuated. We call this process open-ended intelligence. Open-ended intelligence is developed as an abstraction of the process of cognitive development so its application can be extended to general agents and systems. We introduce and discuss three facets of the idea: the philosophical concept of individuation, sense-making and the individuation of general cognitive agents. We further show how open-ended intelligence can be framed in terms of a distributed, self-organizing network of interacting elements and how such process is scalable. The framework highlights an important relation between coordination and intelligence and a new understanding of values. We conclude with a number of questions for future research.
One of the main research areas in Artificial Intelligence is the coding of agents (programs) which are able to learn by themselves in any situation. This means that agents must be useful for purposes other than those they were created for, as, for example, playing chess. In this way we try to get closer to the pristine goal of Artificial Intelligence. One of the problems to decide whether an agent is really intelligent or not is the measurement of its intelligence, since there is currently no way to measure it in a reliable way. The purpose of this project is to create an interpreter that allows for the execution of several environments, including those which are generated randomly, so that an agent (a person or a program) can interact with them. Once the interaction between the agent and the environment is over, the interpreter will measure the intelligence of the agent according to the actions, states and rewards the agent has undergone inside the environment during the test. As a result we will be able to measure agents' intelligence in any possible environment, and to make comparisons between several agents, in order to determine which of them is the most intelligent. In order to perform the tests, the interpreter must be able to randomly generate environments that are really useful to measure agents' intelligence, since not any randomly generated environment will serve that purpose.
Today, available methods that assess AI systems are focused on using empirical techniques to measure the performance of algorithms in some specific tasks (e.g., playing chess, solving mazes or land a helicopter). However, these methods are not appropriate if we want to evaluate the general intelligence of AI and, even less, if we compare it with human intelligence. The ANYNT project has designed a new method of evaluation that tries to assess AI systems using well known computational notions and problems which are as general as possible. This new method serves to assess general intelligence (which allows us to learn how to solve any new kind of problem we face) and not only to evaluate performance on a set of specific tasks. This method not only focuses on measuring the intelligence of algorithms, but also to assess any intelligent system (human beings, animals, AI, aliens?,...), and letting us to place their results on the same scale and, therefore, to be able to compare them. This new approach will allow us (in the future) to evaluate and compare any kind of intelligent system known or even to build/find, be it artificial or biological. This master thesis aims at ensuring that this new method provides consistent results when evaluating AI algorithms, this is done through the design and implementation of prototypes of universal intelligence tests and their application to different intelligent systems (AI algorithms and humans beings). From the study we analyze whether the results obtained by two different intelligent systems are properly located on the same scale and we propose changes and refinements to these prototypes in order to, in the future, being able to achieve a truly universal intelligence test.
The overarching problem in artificial intelligence (AI) is that we do not understand the intelligence process well enough to enable the development of adequate computational models. Much work has been done in AI over the years at lower levels, but a big part of what has been missing involves the high level, abstract, general nature of intelligence. We address this gap by developing a model for general intelligence. To accomplish this, we focus on three basic aspects of intelligence. First, we must realize the general order and nature of intelligence at a high level. Second, we must come to know what these realizations mean with respect to the overall intelligence process. Third, we must describe these realizations as clearly as possible. We propose a hierarchical model to help capture and exploit the order within intelligence. The underlying order involves patterns of signals that become organized, stored and activated in space and time. These patterns can be described using a simple, general hierarchy, with physical signals at the lowest level, information in the middle, and abstract signal representations at the top. This high level perspective provides a big picture that literally helps us see the intelligence process, thereby enabling fundamental realizations, a better understanding and clear descriptions of the intelligence process. The resulting model can be used to support all kinds of information processing across multiple levels of abstraction. As computer technology improves, and as cooperation increases between humans and computers, people will become more efficient and more productive in performing their information processing tasks.
To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.
Artificial Intelligence frameworks should allow for ever more autonomous and general systems in contrast to very narrow and restricted (human pre-defined) domain systems, in analogy to how the brain works. Self-constructive Artificial Intelligence ($SCAI$) is one such possible framework. We herein propose that $SCAI$ is based on three principles of organization: self-growing, self-experimental and self-repairing. Self-growing: the ability to autonomously and incrementally construct structures and functionality as needed to solve encountered (sub)problems. Self-experimental: the ability to internally simulate, anticipate and take decisions based on these expectations. Self-repairing: the ability to autonomously re-construct a previously successful functionality or pattern of interaction lost from a possible sub-component failure (damage). To implement these principles of organization, a constructive architecture capable of evolving adaptive autonomous agents is required. We present Schema-based learning as one such architecture capable of incrementally constructing a myriad of internal models of three kinds: predictive schemas, dual (inverse models) schemas and goal schemas as they are necessary to autonomously develop increasing functionality. We claim that artificial systems, whether in the digital or in the physical world, can benefit very much form this constructive architecture and should be organized around these principles of organization. To illustrate the generality of the proposed framework, we include several test cases in structural adaptive navigation in artificial intelligence systems in Paper II of this series, and resilient robot motor control in Paper III of this series. Paper IV of this series will also include $SCAI$ for problem structural discovery in predictive Business Intelligence.
It is very important to adhere strictly to ethical and social influences when delivering most of our life to artificial intelligence systems. With industry 4.0, the internet of things, data analysis and automation have begun to be of great importance in our lives. With the Yapanese version of Industry 5.0, it has come to our attention that machine-human interaction and human intelligence are working in harmony with the cognitive computer. In this context, robots working on artificial intelligence algorithms co-ordinated with the development of technology have begun to enter our lives. But the consequences of the recent complaints of the Robots have been that important issues have arisen about how to be followed in terms of intellectual property and ethics. Although there are no laws regulating robots in our country at present, laws on robot ethics and rights abroad have entered into force. This means that it is important that we organize the necessary arrangements in the way that robots and artificial intelligence are so important in the new world order. In this study, it was aimed to examine the existing rules of machine and robot ethics and to set an example for the arrangements to be made in our country, and various discussions were given in this context.
Motivated by Shannon's model and recent rehabilitation of self-supervised artificial intelligence having a "World Model", this paper propose an unified intelligence-communication (UIC) model for describing a single agent and any multi-agent system. Firstly, the environment is modelled as the generic communication channel between agents. Secondly, the UIC model adopts a learning-agent model for unifying several well-adopted agent architecture, e.g. rule-based agent model in complex adaptive systems, layered model for describing human-level intelligence, world-model based agent model. The model may also provide an unified approach to investigate a multi-agent system (MAS) having multiple action-perception modalities, e.g. explicitly information transfer and implicit information transfer. This treatise would be divided into three parts, and this first part provides an overview of the UIC model without introducing cumbersome mathematical analysis and optimizations. In the second part of this treatise, case studies with quantitative analysis driven by the UIC model would be provided, exemplifying the adoption of the UIC model in multi-agent system. Specifically, two representative cases would be studied, namely the analysis of a natural multi-agent system, as well as the co-design of communication, perception and action in an artificial multi-agent system. In the third part of this treatise, the paper provides further insights and future research directions motivated by the UIC model, such as unification of single intelligence and collective intelligence, a possible explanation of intelligence emergence and a dual model for agent-environment intelligence hypothesis. Notes: This paper is a Previewed Version, the extended full-version would be released after being accepted.
The concept of "task" is at the core of artificial intelligence (AI): Tasks are used for training and evaluating AI systems, which are built in order to perform and automatize tasks we deem useful. In other fields of engineering theoretical foundations allow thorough evaluation of designs by methodical manipulation of well understood parameters with a known role and importance; this allows an aeronautics engineer, for instance, to systematically assess the effects of wind speed on an airplane's performance and stability. No framework exists in AI that allows this kind of methodical manipulation: Performance results on the few tasks in current use (cf. board games, question-answering) cannot be easily compared, however similar or different. The issue is even more acute with respect to artificial *general* intelligence systems, which must handle unanticipated tasks whose specifics cannot be known beforehand. A *task theory* would enable addressing tasks at the *class* level, bypassing their specifics, providing the appropriate formalization and classification of tasks, environments, and their parameters, resulting in more rigorous ways of measuring, comparing, and evaluating intelligent behavior. Even modest improvements in this direction would surpass the current ad-hoc nature of machine learning and AI evaluation. Here we discuss the main elements of the argument for a task theory and present an outline of what it might look like for physical tasks.
Developing a reliable parametric cost model at the conceptual stage of the project is crucial for projects managers and decision-makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost prediction. However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples. Artificial intelligence (AI) and machine learning (ML) algorithms include numerous models and algorithms for supervised regression applications. Therefore, a comparison analysis for AI models is required to guide practitioners to the appropriate model. The study focuses on investigating twenty artificial intelligence (AI) techniques which are conducted for cost modeling such as fuzzy logic (FL) model, artificial neural networks (ANNs), multiple regression analysis (MRA), case-based reasoning (CBR), hybrid models, and ensemble methods such as scalable boosting trees (XGBoost). Field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the applied ML models. Out of 20 AI techniques, the results showed that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on Mean Absolute Percentage Error (MAPE) and adjusted R2. Nonlinear adaptability, handling missing values and outliers, model interpretation and uncertainty have been discussed for the twenty developed AI models. Keywords: Artificial intelligence, Machine learning, ensemble methods, XGBoost, evolutionary fuzzy rules generation, Conceptual cost, and parametric cost model.
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence paradigm focusing on the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. There are several paradigms of CI [like artificial neural networks, evolutionary computations, swarm intelligence, artificial immune systems, fuzzy systems and many others], each of these has its origins in biological systems [biological neural systems, natural Darwinian evolution, social behavior, immune system, interactions of organisms with their environment]. Most of those paradigms evolved into separate machine learning (ML) techniques, where probabilistic methods are used complementary with CI techniques in order to effectively combine elements of learning, adaptation, evolution and Fuzzy logic to create heuristic algorithms that are, in some sense, intelligent. The current trend is to develop consensus techniques, since no single machine learning algorithms is superior to others in all possible situations. In order to overcome this problem several meta-approaches were proposed in ML focusing on the integration of results from different methods into single prediction. We discuss here the Landau theory for the nonlinear equation that can describe the adaptive integration of information acquired from an ensemble of independent learning agents. The influence of each individual agent on other learners is described similarly to the social impact theory. The final decision outcome for the consensus system is calculated using majority rule in the stationary limit, yet the minority solutions can survive inside the majority population as the complex intermittent clusters of opposite opinion.
This paper describes a novel method for building affectively intelligent human-interactive agents. The method is based on a key sociological insight that has been developed and extensively verified over the last twenty years, but has yet to make an impact in artificial intelligence. The insight is that resource bounded humans will, by default, act to maintain affective consistency. Humans have culturally shared fundamental affective sentiments about identities, behaviours, and objects, and they act so that the transient affective sentiments created during interactions confirm the fundamental sentiments. Humans seek and create situations that confirm or are consistent with, and avoid and supress situations that disconfirm or are inconsistent with, their culturally shared affective sentiments. This "affect control principle" has been shown to be a powerful predictor of human behaviour. In this paper, we present a probabilistic and decision-theoretic generalisation of this principle, and we demonstrate how it can be leveraged to build affectively intelligent artificial agents. The new model, called BayesAct, can maintain multiple hypotheses about sentiments simultaneously as a probability distribution, and can make use of an explicit utility function to make value-directed action choices. This allows the model to generate affectively intelligent interactions with people by learning about their identity, predicting their behaviours using the affect control principle, and taking actions that are simultaneously goal-directed and affect-sensitive. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for artificially intelligent systems that interact with humans in two different settings: an exam practice assistant (tutor) and an assistive device for persons with a cognitive disability.
Theatrical improvisation (impro or improv) is a demanding form of live, collaborative performance. Improv is a humorous and playful artform built on an open-ended narrative structure which simultaneously celebrates effort and failure. It is thus an ideal test bed for the development and deployment of interactive artificial intelligence (AI)-based conversational agents, or artificial improvisors. This case study introduces an improv show experiment featuring human actors and artificial improvisors. We have previously developed a deep-learning-based artificial improvisor, trained on movie subtitles, that can generate plausible, context-based, lines of dialogue suitable for theatre (Mathewson and Mirowski 2017). In this work, we have employed it to control what a subset of human actors say during an improv performance. We also give human-generated lines to a different subset of performers. All lines are provided to actors with headphones and all performers are wearing headphones. This paper describes a Turing test, or imitation game, taking place in a theatre, with both the audience members and the performers left to guess who is a human and who is a machine. In order to test scientific hypotheses about the perception of humans versus machines we collect anonymous feedback from volunteer performers and audience members. Our results suggest that rehearsal increases proficiency and possibility to control events in the performance. That said, consistency with real world experience is limited by the interface and the mechanisms used to perform the show. We also show that human-generated lines are shorter, more positive, and have less difficult words with more grammar and spelling mistakes than the artificial improvisor generated lines.
Web intelligence can be considered as a subset of Artificial Intelligence. It uses existing data in web to produce new data, knowledge and wisdom to support decision making and new predictions for web users. Artificial Intelligence is ever changing and evolving field of computer science and it is extensively used in wide array of web based business applications. Although it is used substantially in web based systems in developed countries, it is not examined whether it is being substantially used in Sri Lanka. Every Sri Lankan citizen depends on Public Service more or less throughout his/ her life time and at least more than 3 times: at birth, marriage and death. So providing most of these services to its citizen, Sri Lankan Government uses more or less of its country web portal. This paper presents a model to evaluate web intelligence capability based on weight to key functionalities with respect to web intelligence. The government websites were checked by the proposed criteria to show the potential of using web intelligent technology to provide website based services. The result indicates that the use of web intelligence techniques openly and publicly to provide web based services through government web portal to its citizens is not satisfactory. It also indicates that lack of using the technologies pertaining to web intelligence in the public service web hinders the most of the advantages that citizen and government can gain from such technological involvement.
The study of arguments as abstract entities and their interaction as introduced by Dung (Artificial Intelligence 177, 1995) has become one of the most active research branches within Artificial Intelligence and Reasoning. A main issue for abstract argumentation systems is the selection of acceptable sets of arguments. Value-based argumentation, as introduced by Bench-Capon (J. Logic Comput. 13, 2003), extends Dung's framework. It takes into account the relative strength of arguments with respect to some ranking representing an audience: an argument is subjectively accepted if it is accepted with respect to some audience, it is objectively accepted if it is accepted with respect to all audiences. Deciding whether an argument is subjectively or objectively accepted, respectively, are computationally intractable problems. In fact, the problems remain intractable under structural restrictions that render the main computational problems for non-value-based argumentation systems tractable. In this paper we identify nontrivial classes of value-based argumentation systems for which the acceptance problems are polynomial-time tractable. The classes are defined by means of structural restrictions in terms of the underlying graphical structure of the value-based system. Furthermore we show that the acceptance problems are intractable for two classes of value-based systems that where conjectured to be tractable by Dunne (Artificial Intelligence 171, 2007).
People who design, use, and are affected by autonomous artificially intelligent agents want to be able to \emph{trust} such agents -- that is, to know that these agents will perform correctly, to understand the reasoning behind their actions, and to know how to use them appropriately. Many techniques have been devised to assess and influence human trust in artificially intelligent agents. However, these approaches are typically ad hoc, and have not been formally related to each other or to formal trust models. This paper presents a survey of \emph{algorithmic assurances}, i.e. programmed components of agent operation that are expressly designed to calibrate user trust in artificially intelligent agents. Algorithmic assurances are first formally defined and classified from the perspective of formally modeled human-artificially intelligent agent trust relationships. Building on these definitions, a synthesis of research across communities such as machine learning, human-computer interaction, robotics, e-commerce, and others reveals that assurance algorithms naturally fall along a spectrum in terms of their impact on an agent's core functionality, with seven notable classes ranging from integral assurances (which impact an agent's core functionality) to supplemental assurances (which have no direct effect on agent performance). Common approaches within each of these classes are identified and discussed; benefits and drawbacks of different approaches are also investigated.
Evaluation has always been a key challenge in the development of artificial intelligence (AI) based software, due to the technical complexity of the software artifact and, often, its embedding in complex sociotechnical processes. Recent advances in machine learning (ML) enabled by deep neural networks has exacerbated the challenge of evaluating such software due to the opaque nature of these ML-based artifacts. A key related issue is the (in)ability of such systems to generate useful explanations of their outputs, and we argue that the explanation and evaluation problems are closely linked. The paper models the elements of a ML-based AI system in the context of public sector decision (PSD) applications involving both artificial and human intelligence, and maps these elements against issues in both evaluation and explanation, showing how the two are related. We consider a number of common PSD application patterns in the light of our model, and identify a set of key issues connected to explanation and evaluation in each case. Finally, we propose multiple strategies to promote wider adoption of AI/ML technologies in PSD, where each is distinguished by a focus on different elements of our model, allowing PSD policy makers to adopt an approach that best fits their context and concerns.
We discuss the objectives of any endeavor in creating artificial intelligence, AI, and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. This suggests that our attempts at AI could have been misguided; what we actually need to strive for can be termed artificial curiosity, AC, and intelligence happens as a consequence of those efforts. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. We discuss what these essential doctrines might be and why their establishment is required to form connections, possibly growing, between a knowledge store that has been built up and new pieces of information that curiosity will bring back. As more findings are acquired and more bonds are fermented, we need a way to, periodically, reduce the amount of data; in the sense, it is important to capture the critical characteristics of what has been accumulated or produce a summary of what has been gathered. We start with the intuition for this line of reasoning and formalize it with a series of models (and iterative improvements) that will be necessary to make the incubation of intelligence a reality. Our discussion provides conceptual modifications to the Turing Test and to Searle's Chinese room argument. We discuss the future implications for society as AI becomes an integral part of life.
We present an alternative methodology for the analysis of algorithms, based on the concept of expected discounted reward. This methodology naturally handles algorithms that do not always terminate, so it can (theoretically) be used with partial algorithms for undecidable problems, such as those found in artificial general intelligence (AGI) and automated theorem proving. We mention an approach to self-improving AGI enabled by this methodology. Aug 2017 addendum: This article was originally written with multiple audiences in mind. It is really best put in the following terms. Goertzel, Hutter, Legg, and others have developed a definition of an intelligence score for a general abstract agent: expected lifetime reward in a random environment. AIXI is generally the optimal agent according to this score, but there may be reasons to analyze other agents and compare score values. If we want to use this definition of intelligence in practice, perhaps we can start by analyzing some simple agents. Common algorithms can be thought of as simple agents (environment is input, reward is based on running time) so we take the goal of applying the agent intelligence score to algorithms. That is, we want to find, what are the IQ scores of algorithms? We can do some very simple analysis, but the real answer is that even for simple algorithms, the intelligence score is too difficult to work with in practice.
The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years. A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the immune have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. A comparison of the Artificial Immune Systems to other well-known algorithms, areas for future work, tips & tricks and a list of resources will round this tutorial off. It should be noted that as Artificial Immune Systems is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from time to time and from those examples given here.
The rapid advancement of machine learning techniques has re-energized research into general artificial intelligence. While the idea of domain-agnostic meta-learning is appealing, this emerging field must come to terms with its relationship to human cognition and the statistics and structure of the tasks humans perform. The position of this article is that only by aligning our agents' abilities and environments with those of humans do we stand a chance at developing general artificial intelligence (GAI). A broad reading of the famous 'No Free Lunch' theorem is that there is no universally optimal inductive bias or, equivalently, bias-free learning is impossible. This follows from the fact that there are an infinite number of ways to extrapolate data, any of which might be the one used by the data generating environment; an inductive bias prefers some of these extrapolations to others, which lowers performance in environments using these adversarial extrapolations. We may posit that the optimal GAI is the one that maximally exploits the statistics of its environment to create its inductive bias; accepting the fact that this agent is guaranteed to be extremely sub-optimal for some alternative environments. This trade-off appears benign when thinking about the environment as being the physical universe, as performance on any fictive universe is obviously irrelevant. But, we should expect a sharper inductive bias if we further constrain our environment. Indeed, we implicitly do so by defining GAI in terms of accomplishing that humans consider useful. One common version of this is need the for 'common-sense reasoning', which implicitly appeals to the statistics of physical universe as perceived by humans.
Artificial Intelligence (AI) - the phenomenon of machines being able to solve problems that require human intelligence - has in the past decade seen an enormous rise of interest due to significant advances in effectiveness and use. The health sector, one of the most important sectors for societies and economies worldwide, is particularly interesting for AI applications, given the ongoing digitalisation of all types of health information. The potential for AI assistance in the health domain is immense, because AI can support medical decision making at reduced costs, everywhere. However, due to the complexity of AI algorithms, it is difficult to distinguish good from bad AI-based solutions and to understand their strengths and weaknesses, which is crucial for clarifying responsibilities and for building trust. For this reason, the International Telecommunication Union (ITU) has established a new Focus Group on "Artificial Intelligence for Health" (FG-AI4H) in partnership with the World Health Organization (WHO). Health and care services are usually the responsibility of a government - even when provided through private insurance systems - and thus under the responsibility of WHO/ITU member states. FG-AI4H will identify opportunities for international standardization, which will foster the application of AI to health issues on a global scale. In particular, it will establish a standardized assessment framework with open benchmarks for the evaluation of AI-based methods for health, such as AI-based diagnosis, triage or treatment decisions.
The fields of artificial intelligence and neuroscience have a long history of fertile bi-directional interactions. On the one hand, important inspiration for the development of artificial intelligence systems has come from the study of natural systems of intelligence, the mammalian neocortex in particular. On the other, important inspiration for models and theories of the brain have emerged from artificial intelligence research. A central question at the intersection of these two areas is concerned with the processes by which neocortex learns, and the extent to which they are analogous to the back-propagation training algorithm of deep networks. Matching the data efficiency, transfer and generalization properties of neocortical learning remains an area of active research in the field of deep learning. Recent advances in our understanding of neuronal, synaptic and dendritic physiology of the neocortex suggest new approaches for unsupervised representation learning, perhaps through a new class of objective functions, which could act alongside or in lieu of back-propagation. Such local learning rules have implicit rather than explicit objectives with respect to the training data, facilitating domain adaptation and generalization. Incorporating them into deep networks for representation learning could better leverage unlabelled datasets to offer significant improvements in data efficiency of downstream supervised readout learning, and reduce susceptibility to adversarial perturbations, at the cost of a more restricted domain of applicability.
This article is about how the "SP theory of intelligence" and its realisation in the "SP machine" (both outlined in the article) may help to solve computer-related problems in the design of autonomous robots, meaning robots that do not depend on external intelligence or power supplies, are mobile, and are designed to exhibit as much human-like intelligence as possible. The article is about: how to increase the computational and energy efficiency of computers and reduce their bulk; how to achieve human-like versatility in intelligence; and likewise for human-like adaptability in intelligence. The SP system has potential for substantial gains in computational and energy efficiency and reductions in the bulkiness of computers: by reducing the size of data to be processed; by exploiting statistical information that the system gathers; and via an updated version of Donald Hebb's concept of a "cell assembly". Towards human-like versatility in intelligence, the SP system has strengths in unsupervised learning, natural language processing, pattern recognition, information retrieval, several kinds of reasoning, planning, problem solving, and more, with seamless integration amongst structures and functions. The SP system's strengths in unsupervised learning and other aspects of intelligence may help to achieve human-like adaptability in intelligence via: the learning of natural language; learning to see; building 3D models of objects and of a robot's surroundings; learning regularities in the workings of a robot and in the robot's environment; exploration and play; learning major skills; and secondary forms of learning. Also discussed are: how the SP system may process parallel streams of information; generalisation of knowledge, correction of over-generalisations, and learning from dirty data; how to cut the cost of learning; and reinforcements, motivations, goals, and demonstration.
What would a human hundreds or thousands times more intelligent than the brightest human ever born be like? We must admit we can hardly guess. A human being of such intelligence will be so radically different from us that it can hardly, if at all, be recognized as human. If we had to go back along the evolutionary tree to identify a creature 1000 times less intelligent than the average contemporary human, we will have to go really far back. Would it be a kind of a lizard? An insect perhaps? Considering this, how can we possibly aspire to have a grasp of something a thousand times more intelligent than us? When it comes to intelligence, even the very attempt to quantify it is highly misleading. Now if we attend to a seemingly adjacent question, what would a machine with such capacity for intelligence be like? Just coming up with an approximate metaphor requires a huge stretch of the imagination, meaning that almost anything goes... What would a society of such super intelligent agents, be they human, machines or an amalgam of both, be like? Well, here we are transported into the realm of pure speculation. Technological Singularity is referred to as the event of artificial intelligence surpassing the intelligence of humans and shortly after augmenting itself far beyond that. It is no wonder that the mathematical concept of singularity has become the symbol of an event so disruptive and so far reaching that it is impossible to conceptually or even metaphorically grasp, much less to predict.
Human-Computer Interaction with the traditional User Interface is done using a specified in advance script dialog menu, mainly based on human intellect and unproductive use of navigation. This approach does not lead to making qualitative decision in control systems, where the situations and processes cannot be structured in advance. Any dynamic changes in the controlled business process (as example, in organizational unit of the information fuzzy control system) make it necessary to modify the script dialogue in User Interface. This circumstance leads to a redesign of the components of the User Interface and of the entire control system. In the Intelligent User Interface, where the dialog situations are unknown in advance, fuzzy structured and artificial intelligence is crucial, the redesign described above is impossible. To solve this and other problems, we propose the data, information and knowledge based technology of Smart/ Intelligent User Interface (IUI) design, which interacts with users and systems in natural and other languages, utilizing the principles of Situational Control and Fuzzy Logic theories, Artificial Intelligence, Linguistics, Knowledge Base technologies and others. The proposed technology of IUI design is defined by multi-agents of Situational Control and of data, information and knowledge, modelling of Fuzzy Logic Inference, Generalization, Representation and Explanation of knowledge, Planning and Decision-making, Dialog Control, Reasoning and Systems Thinking, Fuzzy Control of organizational unit in real-time, fuzzy conditions, heterogeneous domains, and multi-lingual communication under uncertainty and in Fuzzy Environment.
Society has become more dependent on automated intelligent systems, at the same time, these systems have become more and more complicated. Society's expectation regarding the capabilities and intelligence of such systems has also grown. We have become a more complicated society with more complicated problems. As the expectation of intelligent systems rises, we discover many more applications for artificial intelligence. Additionally, as the difficulty level and computational requirements of such problems rise, there is a need to distribute the problem solving. Although the field of multiagent systems (MAS) and distributed artificial intelligence (DAI) is relatively young, the importance and applicability of this technology for solving today's problems continue to grow. In multiagent systems, the main goal is to provide fruitful cooperation among agents in order to enrich the support given to all user activities. This paper deals with the development of a multiagent system aimed at solving the reservation problems encountered in rural tourism. Due to their benefits over the last few years, online travel agencies have become a very useful instrument in planning vacations. A MAS concept (which is based on the Internet exploitation) can improve this activity and provide clients with a new, rapid and efficient way of making accommodation arrangements.
This philosophical paper explores the relation between modern scientific simulations and the future of the universe. We argue that a simulation of an entire universe will result from future scientific activity. This requires us to tackle the challenge of simulating open-ended evolution at all levels in a single simulation. The simulation should encompass not only biological evolution, but also physical evolution (a level below) and cultural evolution (a level above). The simulation would allow us to probe what would happen if we would "replay the tape of the universe" with the same or different laws and initial conditions. We also distinguish between real-world and artificial-world modelling. Assuming that intelligent life could indeed simulate an entire universe, this leads to two tentative hypotheses. Some authors have argued that we may already be in a simulation run by an intelligent entity. Or, if such a simulation could be made real, this would lead to the production of a new universe. This last direction is argued with a careful speculative philosophical approach, emphasizing the imperative to find a solution to the heat death problem in cosmology. The reader is invited to consult Annex 1 for an overview of the logical structure of this paper. -- Keywords: far future, future of science, ALife, simulation, realization, cosmology, heat death, fine-tuning, physical eschatology, cosmological natural selection, cosmological artificial selection, artificial cosmogenesis, selfish biocosm hypothesis, meduso-anthropic principle, developmental singularity hypothesis, role of intelligent life.
Social intelligence in natural and artificial systems is usually measured by the evaluation of associated traits or tasks that are deemed to represent some facets of social behaviour. The amalgamation of these traits is then used to configure the intuitive notion of social intelligence. Instead, in this paper we start from a parametrised definition of social intelligence as the expected performance in a set of environments with several agents, and we assess and derive tests from it. This definition makes several dependencies explicit: (1) the definition depends on the choice (and weight) of environments and agents, (2) the definition may include both competitive and cooperative behaviours depending on how agents and rewards are arranged into teams, (3) the definition mostly depends on the abilities of other agents, and (4) the actual difference between social intelligence and general intelligence (or other abilities) depends on these choices. As a result, we address the problem of converting this definition into a more precise one where some fundamental properties ensuring social behaviour (such as action and reward dependency and anticipation on competitive/cooperative behaviours) are met as well as some other more instrumental properties (such as secernment, boundedness, symmetry, validity, reliability, efficiency), which are convenient to convert the definition into a practical test. From the definition and the formalised properties, we take a look at several representative multi-agent environments, tests and games to see whether they meet these properties.
Understanding and using natural processes for intelligent functionalities, referred to as natural intelligence, has recently attracted interest from a variety of fields, including post-silicon computing for artificial intelligence and decision making in the behavioural sciences. In a past study, we successfully used the wave-particle duality of single photons to solve the two-armed bandit problem, which constitutes the foundation of reinforcement learning and decision making. In this study, we propose and confirm a hierarchical architecture for single-photon-based reinforcement learning and decision making that verifies the scalability of the principle. Specifically, the four-armed bandit problem is solved given zero prior knowledge in a two-layer hierarchical architecture, where polarization is autonomously adapted in order to effect adequate decision making using single-photon measurements. In the hierarchical structure, the notion of layer-dependent decisions emerges. The optimal solutions in the coarse layer and in the fine layer, however, conflict with each other in some contradictive problems. We show that while what we call a tournament strategy resolves such contradictions, the probabilistic nature of single photons allows for the direct location of the optimal solution even for contradictive problems, hence manifesting the exploration ability of single photons. This study provides insights into photon intelligence in hierarchical architectures for future artificial intelligence as well as the potential of natural processes for intelligent functionalities.
General game playing artificial intelligence has recently seen important advances due to the various techniques known as 'deep learning'. However the advances conceal equally important limitations in their reliance on: massive data sets; fortuitously constructed problems; and absence of any human-level complexity, including other human opponents. On the other hand, deep learning systems which do beat human champions, such as in Go, do not generalise well. The power of deep learning simultaneously exposes its weakness. Given that deep learning is mostly clever reconfigurations of well-established methods, moving beyond the state of art calls for forward-thinking visionary solutions, not just more of the same. I present the argument that general game playing artificial intelligence will require a generalised player model. This is because games are inherently human artefacts which therefore, as a class of problems, contain cases which require a human-style problem solving approach. I relate this argument to the performance of state of art general game playing agents. I then describe a concept for a formal category theoretic basis to a generalised player model. This formal model approach integrates my existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.
Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.
This article presents an extensive literature review of technology based intervention methodologies for individuals facing Autism Spectrum Disorder (ASD). Reviewed methodologies include: contemporary Computer Aided Systems (CAS), Computer Vision Assisted Technologies (CVAT) and Virtual Reality (VR) or Artificial Intelligence (AI)-Assisted interventions. The research over the past decade has provided enough demonstrations that individuals with ASD have a strong interest in technology based interventions, which are useful in both, clinical settings as well as at home and classrooms. Despite showing great promise, research in developing an advanced technology based intervention that is clinically quantitative for ASD is minimal. Moreover, the clinicians are generally not convinced about the potential of the technology based interventions due to non-empirical nature of published results. A major reason behind this lack of acceptability is that a vast majority of studies on distinct intervention methodologies do not follow any specific standard or research design. We conclude from our findings that there remains a gap between the research community of computer science, psychology and neuroscience to develop an AI assisted intervention technology for individuals suffering from ASD. Following the development of a standardized AI based intervention technology, a database needs to be developed, to devise effective AI algorithms.
The General Data Protection Regulation (GDPR) is a European Union regulation that will replace the existing Data Protection Directive on 25 May 2018. The most significant change is a huge increase in the maximum fine that can be levied for breaches of the regulation. Yet fewer than half of UK companies are fully aware of GDPR - and a number of those who were preparing for it stopped doing so when the Brexit vote was announced. A last-minute rush to become compliant is therefore expected, and numerous companies are starting to offer advice, checklists and consultancy on how to comply with GDPR. In such an environment, artificial intelligence technologies ought to be able to assist by providing best advice; asking all and only the relevant questions; monitoring activities; and carrying out assessments. The paper considers four areas of GDPR compliance where rule based technologies and/or machine learning techniques may be relevant: * Following compliance checklists and codes of conduct; * Supporting risk assessments; * Complying with the new regulations regarding technologies that perform automatic profiling; * Complying with the new regulations concerning recognising and reporting breaches of security. It concludes that AI technology can support each of these four areas. The requirements that GDPR (or organisations that need to comply with GDPR) state for explanation and justification of reasoning imply that rule-based approaches are likely to be more helpful than machine learning approaches. However, there may be good business reasons to take a different approach in some circumstances.
Big data, data science, deep learning, artificial intelligence are the key words of intense hype related with a job market in full evolution, that impose to adapt the contents of our university professional trainings. Which artificial intelligence is mostly concerned by the job offers? Which methodologies and technologies should be favored in the training programs? Which objectives, tools and educational resources do we needed to put in place to meet these pressing needs? We answer these questions in describing the contents and operational resources in the Data Science orientation of the specialty Applied Mathematics at INSA Toulouse. We focus on basic mathematics training (Optimization, Probability, Statistics), associated with the practical implementation of the most performing statistical learning algorithms, with the most appropriate technologies and on real examples. Considering the huge volatility of the technologies, it is imperative to train students in seft-training, this will be their technological watch tool when they will be in professional activity. This explains the structuring of the educational site github.com/wikistat into a set of tutorials. Finally, to motivate the thorough practice of these tutorials, a serious game is organized each year in the form of a prediction contest between students of Master degrees in Applied Mathematics for IA.
Artificial intelligence (AI) holds great promise to empower us with knowledge and augment our effectiveness. We can -- and must -- ensure that we keep humans safe and in control, particularly with regard to government and public sector applications that affect broad populations. How can AI development teams harness the power of AI systems and design them to be valuable to humans? Diverse teams are needed to build trustworthy artificial intelligent systems, and those teams need to coalesce around a shared set of ethics. There are many discussions in the AI field about ethics and trust, but there are few frameworks available for people to use as guidance when creating these systems. The Human-Machine Teaming (HMT) Framework for Designing Ethical AI Experiences described in this paper, when used with a set of technical ethics, will guide AI development teams to create AI systems that are accountable, de-risked, respectful, secure, honest, and usable. To support the team's efforts, activities to understand people's needs and concerns will be introduced along with the themes to support the team's efforts. For example, usability testing can help determine if the audience understands how the AI system works and complies with the HMT Framework. The HMT Framework is based on reviews of existing ethical codes and best practices in human-computer interaction and software development. Human-machine teams are strongest when human users can trust AI systems to behave as expected, safely, securely, and understandably. Using the HMT Framework to design trustworthy AI systems will provide support to teams in identifying potential issues ahead of time and making great experiences for humans.
The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. This study aimed to explore psychiatrists' opinions about the potential impact of innovations in artificial intelligence and machine learning on psychiatric practice. In Spring 2019, we conducted a web-based survey of 791 psychiatrists from 22 countries worldwide. The survey measured opinions about the likelihood future technology would fully replace physicians in performing ten key psychiatric tasks. This study involved qualitative descriptive analysis of written response to three open-ended questions in the survey. Comments were classified into four major categories in relation to the impact of future technology on patient-psychiatric interactions, the quality of patient medical care, the profession of psychiatry, and health systems. Overwhelmingly, psychiatrists were skeptical that technology could fully replace human empathy. Many predicted that 'man and machine' would increasingly collaborate in undertaking clinical decisions, with mixed opinions about the benefits and harms of such an arrangement. Participants were optimistic that technology might improve efficiencies and access to care, and reduce costs. Ethical and regulatory considerations received limited attention. This study presents timely information of psychiatrists' view about the scope of artificial intelligence and machine learning on psychiatric practice. Psychiatrists expressed divergent views about the value and impact of future technology with worrying omissions about practice guidelines, and ethical and regulatory issues.
This paper provides an overview of the SP theory of intelligence and its central idea that artificial intelligence, mainstream computing, and much of human perception and cognition, may be understood as information compression. The background and origins of the SP theory are described, and the main elements of the theory, including the key concept of multiple alignment, borrowed from bioinformatics but with important differences. Associated with the SP theory is the idea that redundancy in information may be understood as repetition of patterns, that compression of information may be achieved via the matching and unification (merging) of patterns, and that computing and information compression are both fundamentally probabilistic. It appears that the SP system is Turing-equivalent in the sense that anything that may be computed with a Turing machine may, in principle, also be computed with an SP machine. One of the main strengths of the SP theory and the multiple alignment concept is in modelling concepts and phenomena in artificial intelligence. Within that area, the SP theory provides a simple but versatile means of representing different kinds of knowledge, it can model both the parsing and production of natural language, with potential for the understanding and translation of natural languages, it has strengths in pattern recognition, with potential in computer vision, it can model several kinds of reasoning, and it has capabilities in planning, problem solving, and unsupervised learning. The paper includes two examples showing how alternative parsings of an ambiguous sentence may be modelled as multiple alignments, and another example showing how the concept of multiple alignment may be applied in medical diagnosis.
Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the transfer of histopathological slides into digital representations. The combination of different data sources (images, patient records, and *omics data) together with current advances in artificial intelligence/machine learning enable to make novel information accessible and quantifiable to a human expert, which is not yet available and not exploited in current medical settings. The grand goal is to reach a level of usable intelligence to understand the data in the context of an application task, thereby making machine decisions transparent, interpretable and explainable. The foundation of such an "augmented pathologist" needs an integrated approach: While machine learning algorithms require many thousands of training examples, a human expert is often confronted with only a few data points. Interestingly, humans can learn from such few examples and are able to instantly interpret complex patterns. Consequently, the grand goal is to combine the possibilities of artificial intelligence with human intelligence and to find a well-suited balance between them to enable what neither of them could do on their own. This can raise the quality of education, diagnosis, prognosis and prediction of cancer and other diseases. In this paper we describe some (incomplete) research issues which we believe should be addressed in an integrated and concerted effort for paving the way towards the augmented pathologist.
Triggered by modern technologies, our possibilities may now expand beyond the unthinkable. Cars externally may look similar to decades ago, but a dramatic revolution happened inside the cabin as a result of their computation, communications, and storage capabilities. With the advent of Electric Autonomous Vehicles (EAVs), Artificial Intelligence and ecological technologies found the best synergy. Several transportation problems may be solved (accidents, emissions, and congestion among others), and the foundation of Machine-to-Machine (M2M) economy could be established, in addition to value-added services such as infotainment (information and entertainment). In the world where intelligent technologies are pervading everyday life, software and algorithms play a major role. Software has been lately introduced in virtually every technological product available on the market, from phones to television sets to cars and even housing. Artificial Intelligence is one of the consequences of this pervasive presence of algorithms. The role of software is becoming dominant and technology is, at times pervasive, of our existence. Concerns, such as privacy and security, demand high attention and have been already explored to some level of detail. However, intelligent agents and actors are often considered as perfect entities that will overcome human error-prone nature. This may not always be the case and we advocate that the notion of reputation is also applicable to intelligent artificial agents, in particular to EAVs.
This paper presents a tentative outline for the construction of an artificial, generally intelligent system (AGI). It is argued that building a general data compression algorithm solving all problems up to a complexity threshold should be the main thrust of research. A measure for partial progress in AGI is suggested. Although the details are far from being clear, some general properties for a general compression algorithm are fleshed out. Its inductive bias should be flexible and adapt to the input data while constantly searching for a simple, orthogonal and complete set of hypotheses explaining the data. It should recursively reduce the size of its representations thereby compressing the data increasingly at every iteration. Abstract Based on that fundamental ability, a grounded reasoning system is proposed. It is argued how grounding and flexible feature bases made of hypotheses allow for resourceful thinking. While the simulation of representation contents on the mental stage accounts for much of the power of propositional logic, compression leads to simple sets of hypotheses that allow the detection and verification of universally quantified statements. Abstract Together, it is highlighted how general compression and grounded reasoning could account for the birth and growth of first concepts about the world and the commonsense reasoning about them.
Artificial Intelligence (AI) technologies could be broadly categorised into Analytics and Autonomy. Analytics focuses on algorithms offering perception, comprehension, and projection of knowledge gleaned from sensorial data. Autonomy revolves around decision making, and influencing and shaping the environment through action production. A smart autonomous system (SAS) combines analytics and autonomy to understand, learn, decide and act autonomously. To be useful, SAS must be trusted and that requires testing. Lifelong learning of a SAS compounds the testing process. In the remote chance that it is possible to fully test and certify the system pre-release, which is theoretically an undecidable problem, it is near impossible to predict the future behaviours that these systems, alone or collectively, will exhibit. While it may be feasible to severely restrict such systems\textquoteright \ learning abilities to limit the potential unpredictability of their behaviours, an undesirable consequence may be severely limiting their utility. In this paper, we propose the architecture for a watchdog AI (WAI) agent dedicated to lifelong functional testing of SAS. We further propose system specifications including a level of abstraction whereby humans shepherd a swarm of WAI agents to oversee an ecosystem made of humans and SAS. The discussion extends to the challenges, pros, and cons of the proposed concept.
One of the common artificial intelligence applications in electronic games consists of making an artificial agent learn how to execute some determined task successfully in a game environment. One way to perform this task is through machine learning algorithms capable of learning the sequence of actions required to win in a given game environment. There are several supervised learning techniques able to learn the correct answer for a problem through examples. However, when learning how to play electronic games, the correct answer might only be known by the end of the game, after all the actions were already taken. Thus, not being possible to measure the accuracy of each individual action to be taken at each time step. A way for dealing with this problem is through Neuroevolution, a method which trains Artificial Neural Networks using evolutionary algorithms. In this article, we introduce a framework for testing optimization algorithms with artificial agent controllers in electronic games, called EvoMan, which is inspired in the action-platformer game Mega Man II. The environment can be configured to run in different experiment modes, as single evolution, coevolution and others. To demonstrate some challenges regarding the proposed platform, as initial experiments we applied Neuroevolution using Genetic Algorithms and the NEAT algorithm, in the context of competitively coevolving two distinct agents in this game.
Today, more and more, it is necessary that most applications and documents developed in previous or current technologies to be accessible online on cloud-based infrastructures. That is why the migration of legacy systems including their hosts of documents to new technologies and online infrastructures, using modern Artificial Intelligence techniques, is absolutely necessary. With the advancement of Artificial Intelligence and Deep Learning with its multitude of applications, a new area of research is emerging - that of automated systems development and maintenance. The underlying work objective that led to this paper aims to research and develop truly intelligent systems able to analyze user interfaces from various sources and generate real and usable inferences ranging from architecture analysis to actual code generation. One key element of such systems is that of artificial scene detection and analysis based on deep learning computer vision systems. Computer vision models and particularly deep directed acyclic graphs based on convolutional modules are generally constructed and trained based on natural images datasets. Due to this fact, the models will develop during the training process natural image feature detectors apart from the base graph modules that will learn basic primitive features. In the current paper, we will present the base principles of a deep neural pipeline for computer vision applied to artificial scenes (scenes generated by user interfaces or similar). Finally, we will present the conclusions based on experimental development and benchmarking against state-of-the-art transfer-learning implemented deep vision models.
What is the nature of curiosity? Is there any scientific way to understand the origin of this mysterious force that drives the behavior of even the stupidest naturally intelligent systems and is completely absent in their smartest artificial analogs? Can we build AI systems that could be curious about something, systems that would have an intrinsic motivation to learn? Is such a motivation quantifiable? Is it implementable? I will discuss this problem from the standpoint of physics. The relationship between physics and intelligence is a consequence of the fact that correctly predicted information is nothing but an energy resource, and the process of thinking can be viewed as a process of accumulating and spending this resource through the acts of perception and, respectively, decision making. The natural motivation of any autonomous system to keep this accumulation/spending balance as high as possible allows one to treat the problem of describing the dynamics of thinking processes as a resource optimization problem. Here I will propose and discuss a simple theoretical model of such an autonomous system which I call the Autonomous Turing Machine (ATM). The potential attractiveness of ATM lies in the fact that it is the model of a self-propelled AI for which the only available energy resource is the information itself. For ATM, the problem of optimal thinking, learning, and decision-making becomes conceptually simple and mathematically well tractable. This circumstance makes the ATM an ideal playground for studying the dynamics of intelligent behavior and allows one to quantify many seemingly unquantifiable features of genuine intelligence.
Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as `Partially Detected Intelligent Transportation Systems'. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.
In the coming years, the future of military combat will include, on one hand, artificial intelligence-optimized complex command, control, communications, computers, intelligence, surveillance and reconnaissance (C4ISR) and networks and, on the other hand, autonomous intelligent Things fighting autonomous intelligent Things at a fast pace. Under this perspective, enemy forces will seek to disable or disturb our autonomous Things and our complex infrastructures and systems. Autonomy, scale and complexity in our defense systems will trigger new cyber-attack strategies, and autonomous intelligent malware (AIM) will be part of the picture. Should these cyber-attacks succeed while human operators remain unaware or unable to react fast enough due to the speed, scale or complexity of the mission, systems or attacks, missions would fail, our networks and C4ISR would be heavily disrupted, and command and control would be disabled. New cyber-defense doctrines and technologies are therefore required. Autonomous cyber defense (ACyD) is a new field of research and technology driven by the defense sector in anticipation of such threats to future military infrastructures, systems and operations. It will be implemented via swarms of autonomous intelligent cyber-defense agents (AICAs) that will fight AIM within our networks and systems. This paper presents this cyber-defense technology of the future, the current state of the art in this field and its main challenges. First, we review the rationale of the ACyD concept and its associated AICA technology. Then, we present the current research results from NATO's IST-152 Research Task Group on the AICA Reference Architecture. We then develop the 12 main technological challenges that must be resolved in the coming years, besides ethical and political issues.
The influence of Artificial Intelligence (AI) and Artificial Life (ALife) technologies upon society, and their potential to fundamentally shape the future evolution of humankind, are topics very much at the forefront of current scientific, governmental and public debate. While these might seem like very modern concerns, they have a long history that is often disregarded in contemporary discourse. Insofar as current debates do acknowledge the history of these ideas, they rarely look back further than the origin of the modern digital computer age in the 1940s-50s. In this paper we explore the earlier history of these concepts. We focus in particular on the idea of self-reproducing and evolving machines, and potential implications for our own species. We show that discussion of these topics arose in the 1860s, within a decade of the publication of Darwin's The Origin of Species, and attracted increasing interest from scientists, novelists and the general public in the early 1900s. After introducing the relevant work from this period, we categorise the various visions presented by these authors of the future implications of evolving machines for humanity. We suggest that current debates on the co-evolution of society and technology can be enriched by a proper appreciation of the long history of the ideas involved.
Autonomous lifelong development and learning is a fundamental capability of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives. Deep learning (DL) approaches made great advances in artificial intelligence, but are still far away from human learning. As argued convincingly by Lake et al., differences include human capabilities to learn causal models of the world from very little data, leveraging compositional representations and priors like intuitive physics and psychology. However, there are other fundamental differences between current DL systems and human learning, as well as technical ingredients to fill this gap, that are either superficially, or not adequately, discussed by Lake et al. These fundamental mechanisms relate to autonomous development and learning. They are bound to play a central role in artificial intelligence in the future. Current DL systems require engineers to manually specify a task-specific objective function for every new task, and learn through off-line processing of large training databases. On the contrary, humans learn autonomously open-ended repertoires of skills, deciding for themselves which goals to pursue or value, and which skills to explore, driven by intrinsic motivation/curiosity and social learning through natural interaction with peers. Such learning processes are incremental, online, and progressive. Human child development involves a progressive increase of complexity in a curriculum of learning where skills are explored, acquired, and built on each other, through particular ordering and timing. Finally, human learning happens in the physical world, and through bodily and physical experimentation, under severe constraints on energy, time, and computational resources. In the two last decades, the field of Developmental and Cognitive Robotics (Cangelosi and Schlesinger, 2015, Asada et al., 2009), in strong interaction with developmental psychology and neuroscience, has achieved significant advances in computational
We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspective of Fermilab, America's premier national laboratory for High Energy Physics (HEP). We believe the NAIRDSP should be extended in light of the rapid pace of development and innovation in the field of Artificial Intelligence (AI) since 2016, and present our recommendations below. AI has profoundly impacted many areas of human life, promising to dramatically reshape society --- e.g., economy, education, science --- in the coming years. We are still early in this process. It is critical to invest now in this technology to ensure it is safe and deployed ethically. Science and society both have a strong need for accuracy, efficiency, transparency, and accountability in algorithms, making investments in scientific AI particularly valuable. Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership. Moreover, investments in AI will be important for maintaining US leadership in the physical sciences.
Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent these fields can learn and benefit from each other. QML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently, we have witnessed breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world. Conversely, ML already permeates cutting-edge technologies, and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been demonstrated for interactive learning, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments, and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement, researchers have also broached the fundamental issue of quantum generalizations of ML/AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is described by quantum mechanics. In this review, we describe the main ideas, recent developments, and progress in a broad spectrum of research investigating machine learning and artificial intelligence in the quantum domain.
Since its inception, artificial intelligence has relied upon a theoretical foundation centered around perfect rationality as the desired property of intelligent systems. We argue, as others have done, that this foundation is inadequate because it imposes fundamentally unsatisfiable requirements. As a result, there has arisen a wide gap between theory and practice in AI, hindering progress in the field. We propose instead a property called bounded optimality. Roughly speaking, an agent is bounded-optimal if its program is a solution to the constrained optimization problem presented by its architecture and the task environment. We show how to construct agents with this property for a simple class of machine architectures in a broad class of real-time environments. We illustrate these results using a simple model of an automated mail sorting facility. We also define a weaker property, asymptotic bounded optimality (ABO), that generalizes the notion of optimality in classical complexity theory. We then construct universal ABO programs, i.e., programs that are ABO no matter what real-time constraints are applied. Universal ABO programs can be used as building blocks for more complex systems. We conclude with a discussion of the prospects for bounded optimality as a theoretical basis for AI, and relate it to similar trends in philosophy, economics, and game theory.
Cognitive radio networks (CRNs) are networks of nodes equipped with cognitive radios that can optimize performance by adapting to network conditions. While cognitive radio networks (CRN) are envisioned as intelligent networks, relatively little research has focused on the network level functionality of CRNs. Although various routing protocols, incorporating varying degrees of adaptiveness, have been proposed for CRNs, it is imperative for the long term success of CRNs that the design of cognitive routing protocols be pursued by the research community. Cognitive routing protocols are envisioned as routing protocols that fully and seamless incorporate AI-based techniques into their design. In this paper, we provide a self-contained tutorial on various AI and machine-learning techniques that have been, or can be, used for developing cognitive routing protocols. We also survey the application of various classes of AI techniques to CRNs in general, and to the problem of routing in particular. We discuss various decision making techniques and learning techniques from AI and document their current and potential applications to the problem of routing in CRNs. We also highlight the various inference, reasoning, modeling, and learning sub tasks that a cognitive routing protocol must solve. Finally, open research issues and future directions of work are identified.
We construct a complexity-based morphospace to study systems-level properties of conscious & intelligent systems. The axes of this space label 3 complexity types: autonomous, cognitive & social. Given recent proposals to synthesize consciousness, a generic complexity-based conceptualization provides a useful framework for identifying defining features of conscious & synthetic systems. Based on current clinical scales of consciousness that measure cognitive awareness and wakefulness, we take a perspective on how contemporary artificially intelligent machines & synthetically engineered life forms measure on these scales. It turns out that awareness & wakefulness can be associated to computational & autonomous complexity respectively. Subsequently, building on insights from cognitive robotics, we examine the function that consciousness serves, & argue the role of consciousness as an evolutionary game-theoretic strategy. This makes the case for a third type of complexity for describing consciousness: social complexity. Having identified these complexity types, allows for a representation of both, biological & synthetic systems in a common morphospace. A consequence of this classification is a taxonomy of possible conscious machines. We identify four types of consciousness, based on embodiment: (i) biological consciousness, (ii) synthetic consciousness, (iii) group consciousness (resulting from group interactions), & (iv) simulated consciousness (embodied by virtual agents within a simulated reality). This taxonomy helps in the investigation of comparative signatures of consciousness across domains, in order to highlight design principles necessary to engineer conscious machines. This is particularly relevant in the light of recent developments at the crossroads of cognitive neuroscience, biomedical engineering, artificial intelligence & biomimetics.
We propose Cognitive Databases, an approach for transparently enabling Artificial Intelligence (AI) capabilities in relational databases. A novel aspect of our design is to first view the structured data source as meaningful unstructured text, and then use the text to build an unsupervised neural network model using a Natural Language Processing (NLP) technique called word embedding. This model captures the hidden inter-/intra-column relationships between database tokens of different types. For each database token, the model includes a vector that encodes contextual semantic relationships. We seamlessly integrate the word embedding model into existing SQL query infrastructure and use it to enable a new class of SQL-based analytics queries called cognitive intelligence (CI) queries. CI queries use the model vectors to enable complex queries such as semantic matching, inductive reasoning queries such as analogies, predictive queries using entities not present in a database, and, more generally, using knowledge from external sources. We demonstrate unique capabilities of Cognitive Databases using an Apache Spark based prototype to execute inductive reasoning CI queries over a multi-modal database containing text and images. We believe our first-of-a-kind system exemplifies using AI functionality to endow relational databases with capabilities that were previously very hard to realize in practice.
The goal of creating Artificial General Intelligence (AGI) -- or in other words of creating Turing machines (modern computers) that can behave in a way that mimics human intelligence -- has occupied AI researchers ever since the idea of AI was first proposed. One common theme in these discussions is the thesis that the ability of a machine to conduct convincing dialogues with human beings can serve as at least a sufficient criterion of AGI. We argue that this very ability should be accepted also as a necessary condition of AGI, and we provide a description of the nature of human dialogue in particular and of human language in general against this background. We then argue that it is for mathematical reasons impossible to program a machine in such a way that it could master human dialogue behaviour in its full generality. This is (1) because there are no traditional explicitly designed mathematical models that could be used as a starting point for creating such programs; and (2) because even the sorts of automated models generated by using machine learning, which have been used successfully in areas such as machine translation, cannot be extended to cope with human dialogue. If this is so, then we can conclude that a Turing machine also cannot possess AGI, because it fails to fulfil a necessary condition thereof. At the same time, however, we acknowledge the potential of Turing machines to master dialogue behaviour in highly restricted contexts, where what is called ``narrow'' AI can still be of considerable utility.
Recent developments in machine-learning algorithms have led to impressive performance increases in many traditional application scenarios of artificial intelligence research. In the area of deep reinforcement learning, deep learning functional architectures are combined with incremental learning schemes for sequential tasks that include interaction-based, but often delayed feedback. Despite their impressive successes, modern machine-learning approaches, including deep reinforcement learning, still perform weakly when compared to flexibly adaptive biological systems in certain naturally occurring scenarios. Such scenarios include transfers to environments different than the ones in which the training took place or environments that dynamically change, both of which are often mastered by biological systems through a capability that we here term "fluid adaptivity" to contrast it from the much slower adaptivity ("crystallized adaptivity") of the prior learning from which the behavior emerged. In this article, we derive and discuss research strategies, based on analyzes of fluid adaptivity in biological systems and its neuronal modeling, that might aid in equipping future artificially intelligent systems with capabilities of fluid adaptivity more similar to those seen in some biologically intelligent systems. A key component of this research strategy is the dynamization of the problem space itself and the implementation of this dynamization by suitably designed flexibly interacting modules.
The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years. A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the immune have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. A comparison of the Artificial Immune Systems to other well-known algorithms, areas for future work, tips & tricks and a list of resources will round this tutorial off. It should be noted that as Artificial Immune Systems is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from time to time and from those examples given here.
Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the Eureka system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, Eureka uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. Eureka has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that Eureka outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications.
By introducing elements of information mining to tax analysis, by means of data mining software and advanced computational concepts of artificial intelligence, the problem of tax evader's crime against public property has been addressed. Through an empirical approach from a hypothetical case of use, induction algorithms, neural networks and bayesian networks are applied to determine the feasibility of its heuristic application by the tax public administrator. Different strategies are explored to facilitate the work of local and regional federal tax inspectors, considering their limited computational capabilities, but equally effective for those social scientist committed to handcrafting tax research. ----- Apresentando a introdu\c{c}\~ao de elementos de explora\c{c}\~ao de informa\c{c}\~oes para an\'alise fiscal, por meio de software de minera\c{c}\~ao de dados e conceitos avan\c{c}ados computacionais de intelig\^encia artificial, foi abordado o problema do crime de sonegador fiscal contra o patrim\^onio p\'ublico. Atrav\'es de uma abordagem emp\'irica a partir de um caso hipot\'etico de uso, os algoritmos de indu\c{c}\~ao, redes neurais e redes bayesianas s\~ao aplicados para determinar a viabilidade de sua aplica\c{c}\~ao heur\'istica pelo administrador p\'ublico tribut\'ario. Diferentes estrat\'egias s\~ao exploradas para facilitar o trabalho dos inspectores tribut\'arios federais locais e regionais, tendo em conta as suas capacidades computacionais limitados, mas igualmente eficaz para aqueles cientista social comprometido com a investiga\c{c}\~ao fiscal.
Hutchinson, Lo and Poggio raised the question that if learning works can learn the Black-Scholes formula, and they proposed the network mapping the ratio of underlying price to strike $S_t/K$ and the time to maturity $\tau$ directly into the ratio of option price to strike $C_t/K$. In this paper we propose a novel descision function and study the network mapping $S_t/K$ and $\tau$ into the ratio of time value to strike $V_t/K$. Time values' appearance in artificial intelligence fits into traders' natural intelligence. Empirical experiments will be carried out to demonstrate that it significantly improves Hutchinson-Lo-Poggio's original model by faster learning and better generalization performance. In order to take a conceptual viewpoint and to prove that $V_t/K$ but not $C_t/K$ can be approximated by superpositions of logistic functions on its domain of definition, we work on the theory of universal approximation on unbounded domains. We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in $L^{p}(\RR^2 \times [0, 1]^{n})$ unless it is constant zero, and that an artificial neural network with a single hidden layer and logistic activation is a universal approximator of $L^{2}(\RR \times [0, 1]^{n})$. Our work partially generalizes Cybenko's fundamental universal approximation theorem on the unit hypercube $[0, 1]^{n}$.
In the article a turn-based game played on four computers connected via network is investigated. There are three computers with natural intelligence and one with artificial intelligence. Game table is seen by each player's own view point in all players' monitors. Domino pieces are three dimensional. For distributed systems TCP/IP protocol is used. In order to get 3D image, Microsoft XNA technology is applied. Domino 101 game is nondeterministic game that is result of the game depends on the initial random distribution of the pieces. Number of the distributions is equal to the multiplication of following combinations: . Moreover, in this game that is played by four people, players are divided into 2 pairs. Accordingly, we cannot predict how the player uses the dominoes that is according to the dominoes of his/her partner or according to his/her own dominoes. The fact that the natural intelligence can be a player in any level affects the outcome. These reasons make it difficult to develop an AI. In the article four levels of AI are developed. The AI in the first level is equivalent to the intelligence of a child who knows the rules of the game and recognizes the numbers. The AI in this level plays if it has any domino, suitable to play or says pass. In most of the games which can be played on the internet, the AI does the same. But the AI in the last level is a master player, and it can develop itself according to its competitors' levels.
Mycotoxin contamination in certain agricultural systems have been a serious concern for human and animal health. Mycotoxins are toxic substances produced mostly as secondary metabolites by fungi that grow on seeds and feed in the field, or in storage. The food-borne Mycotoxins likely to be of greatest significance for human health in tropical developing countries are Aflatoxins and Fumonisins. Chili pepper is also prone to Aflatoxin contamination during harvesting, production and storage periods.Various methods used for detection of Mycotoxins give accurate results, but they are slow, expensive and destructive. Destructive method is testing a material that degrades the sample under investigation. Whereas, non-destructive testing will, after testing, allow the part to be used for its intended purpose. Ultrasonic methods, Multispectral image processing methods, Terahertz methods, X-ray and Thermography have been very popular in nondestructive testing and characterization of materials and health monitoring. Image processing methods are used to improve the visual quality of the pictures and to extract useful information from them. In this proposed work, the chili pepper samples will be collected, and the X-ray, multispectral images of the samples will be processed using image processing methods. The term "Computational Intelligence" referred as simulation of human intelligence on computers. It is also called as "Artificial Intelligence" (AI) approach. The techniques used in AI approach are Neural network, Fuzzy logic and evolutionary computation. Finally, the computational intelligence method will be used in addition to image processing to provide best, high performance and accurate results for detecting the Mycotoxin level in the samples collected.
This article describes existing and expected benefits of the "SP theory of intelligence", and some potential applications. The theory aims to simplify and integrate ideas across artificial intelligence, mainstream computing, and human perception and cognition, with information compression as a unifying theme. It combines conceptual simplicity with descriptive and explanatory power across several areas of computing and cognition. In the "SP machine" -- an expression of the SP theory which is currently realized in the form of a computer model -- there is potential for an overall simplification of computing systems, including software. The SP theory promises deeper insights and better solutions in several areas of application including, most notably, unsupervised learning, natural language processing, autonomous robots, computer vision, intelligent databases, software engineering, information compression, medical diagnosis and big data. There is also potential in areas such as the semantic web, bioinformatics, structuring of documents, the detection of computer viruses, data fusion, new kinds of computer, and the development of scientific theories. The theory promises seamless integration of structures and functions within and between different areas of application. The potential value, worldwide, of these benefits and applications is at least $190 billion each year. Further development would be facilitated by the creation of a high-parallel, open-source version of the SP machine, available to researchers everywhere.
Nowadays, represented by Deep Learning techniques, the field of machine learning is experiencing unprecedented prosperity and its influence is demonstrated in academia, industry and civil society. "Intelligent" has become a label which could not be neglected for most applications; celebrities and scientists also warned that the development of full artificial intelligence may spell the end of the human race. It seems that the answer to building a computer system that could automatically improve with experience is right on the next corner. While for AI and machine learning researchers, it is a consensus that we are not anywhere near the core technique which could bring the Terminator, Number 5 or R2D2 into real life, and there is not even a formal definition about what is intelligence, or one of its basic properties: Learning. Therefore, even though researchers know these concerns are not necessary currently, there is no generalized explanation about why these concerns are not necessary, and what properties people should take into account that would make these concerns to be necessary. In this paper, starts from analysing the relation between information and its representation, a necessary condition for a model to be a learning model is proposed. This condition and related future works could be used to verify whether a system is able to learn or not, and enrich our understanding of learning: one important property of Intelligence.
Reinforcement learning is a general and powerful framework with which to study and implement artificial intelligence. Recent advances in deep learning have enabled RL algorithms to achieve impressive performance in restricted domains such as playing Atari video games (Mnih et al., 2015) and, recently, the board game Go (Silver et al., 2016). However, we are still far from constructing a generally intelligent agent. Many of the obstacles and open questions are conceptual: What does it mean to be intelligent? How does one explore and learn optimally in general, unknown environments? What, in fact, does it mean to be optimal in the general sense? The universal Bayesian agent AIXI (Hutter, 2005) is a model of a maximally intelligent agent, and plays a central role in the sub-field of general reinforcement learning (GRL). Recently, AIXI has been shown to be flawed in important ways; it doesn't explore enough to be asymptotically optimal (Orseau, 2010), and it can perform poorly with certain priors (Leike and Hutter, 2015). Several variants of AIXI have been proposed to attempt to address these shortfalls: among them are entropy-seeking agents (Orseau, 2011), knowledge-seeking agents (Orseau et al., 2013), Bayes with bursts of exploration (Lattimore, 2013), MDL agents (Leike, 2016a), Thompson sampling (Leike et al., 2016), and optimism (Sunehag and Hutter, 2015). We present AIXIjs, a JavaScript implementation of these GRL agents. This implementation is accompanied by a framework for running experiments against various environments, similar to OpenAI Gym (Brockman et al., 2016), and a suite of interactive demos that explore different properties of the agents, similar to REINFORCEjs (Karpathy, 2015). We use AIXIjs to present numerous experiments illustrating fundamental properties of, and differences between, these agents.
Deep learning has revolutionized speech recognition, image recognition, and natural language processing since 2010, each involving a single modality in the input signal. However, many applications in artificial intelligence involve more than one modality. It is therefore of broad interest to study the more difficult and complex problem of modeling and learning across multiple modalities. In this paper, a technical review of the models and learning methods for multimodal intelligence is provided. The main focus is the combination of vision and natural language, which has become an important area in both computer vision and natural language processing research communities. This review provides a comprehensive analysis of recent work on multimodal deep learning from three new angles - learning multimodal representations, the fusion of multimodal signals at various levels, and multimodal applications. On multimodal representation learning, we review the key concept of embedding, which unifies the multimodal signals into the same vector space and thus enables cross-modality signal processing. We also review the properties of the many types of embedding constructed and learned for general downstream tasks. On multimodal fusion, this review focuses on special architectures for the integration of the representation of unimodal signals for a particular task. On applications, selected areas of a broad interest in current literature are covered, including caption generation, text-to-image generation, and visual question answering. We believe this review can facilitate future studies in the emerging field of multimodal intelligence for the community.
The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.
Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to the vast capabilities of convolutional neural networks, that can extract useful features from noisy and complex data. Games are excellent tools to test and push the boundaries of novel RL algorithms because they give valuable insight into how well an algorithm can perform in isolated environments without the real-life consequences. Real-time strategy games (RTS) is a genre that has tremendous complexity and challenges the player in short and long-term planning. There is much research that focuses on applied RL in RTS games, and novel advances are therefore anticipated in the not too distant future. However, there are to date few environments for testing RTS AIs. Environments in the literature are often either overly simplistic, such as microRTS, or complex and without the possibility for accelerated learning on consumer hardware like StarCraft II. This paper introduces the Deep RTS game environment for testing cutting-edge artificial intelligence algorithms for RTS games. Deep RTS is a high-performance RTS game made specifically for artificial intelligence research. It supports accelerated learning, meaning that it can learn at a magnitude of 50 000 times faster compared to existing RTS games. Deep RTS has a flexible configuration, enabling research in several different RTS scenarios, including partially observable state-spaces and map complexity. We show that Deep RTS lives up to our promises by comparing its performance with microRTS, ELF, and StarCraft II on high-end consumer hardware. Using Deep RTS, we show that a Deep Q-Network agent beats random-play agents over 70% of the time. Deep RTS is publicly available at https://github.com/cair/DeepRTS.
A system with artificial intelligence usually relies on symbol manipulation, at least partly and implicitly. However, the interpretation of the symbols - what they represent and what they are about - is ultimately left to humans, as designers and users of the system. How symbols can acquire meaning for the system itself, independent of external interpretation, is an unsolved problem. Some grounding of symbols can be obtained by embodiment, that is, by causally connecting symbols (or sub-symbolic variables) to the physical environment, such as in a robot with sensors and effectors. However, a causal connection as such does not produce representation and aboutness of the kind that symbols have for humans. Here I present a theory that explains how humans and other living organisms have acquired the capability to have symbols and sub-symbolic variables that represent, refer to, and are about something else. The theory shows how reference can be to physical objects, but also to abstract objects, and even how it can be misguided (errors in reference) or be about non-existing objects. I subsequently abstract the primary components of the theory from their biological context, and discuss how and under what conditions the theory could be implemented in artificial agents. A major component of the theory is the strong nonlinearity associated with (potentially unlimited) self-reproduction. The latter is likely not acceptable in artificial systems. It remains unclear if goals other than those inherently serving self-reproduction can have aboutness and if such goals could be stabilized.
The field of machine ethics is concerned with the question of how to embed ethical behaviors, or a means to determine ethical behaviors, into artificial intelligence (AI) systems. The goal is to produce artificial moral agents (AMAs) that are either implicitly ethical (designed to avoid unethical consequences) or explicitly ethical (designed to behave ethically). Van Wynsberghe and Robbins' (2018) paper Critiquing the Reasons for Making Artificial Moral Agents critically addresses the reasons offered by machine ethicists for pursuing AMA research; this paper, co-authored by machine ethicists and commentators, aims to contribute to the machine ethics conversation by responding to that critique. The reasons for developing AMAs discussed in van Wynsberghe and Robbins (2018) are: it is inevitable that they will be developed; the prevention of harm; the necessity for public trust; the prevention of immoral use; such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. In this paper, each co-author addresses those reasons in turn. In so doing, this paper demonstrates that the reasons critiqued are not shared by all co-authors; each machine ethicist has their own reasons for researching AMAs. But while we express a diverse range of views on each of the six reasons in van Wynsberghe and Robbins' critique, we nevertheless share the opinion that the scientific study of AMAs has considerable value.
A recurring topic in interstellar exploration and the search for extraterrestrial intelligence (SETI) is the role of artificial intelligence. More precisely, these are programs or devices that are capable of performing cognitive tasks that have been previously associated with humans such as image recognition, reasoning, decision-making etc. Such systems are likely to play an important role in future deep space missions, notably interstellar exploration, where the spacecraft needs to act autonomously. This article explores the drivers for an interstellar mission with a computation-heavy payload and provides an outline of a spacecraft and mission architecture that supports such a payload. Based on existing technologies and extrapolations of current trends, it is shown that AI spacecraft development and operation will be constrained and driven by three aspects: power requirements for the payload, power generation capabilities, and heat rejection capabilities. A likely mission architecture for such a probe is to get into an orbit close to the star in order to generate maximum power for computational activities, and then to prepare for further exploration activities. Given current levels of increase in computational power, such a payload with a similar computational power as the human brain would have a mass of hundreds to dozens of tons in a 2050 - 2060 timeframe.
New Artificial Human Optimization (AHO) Field Algorithms can be created from scratch or by adding the concept of Artificial Humans into other existing Optimization Algorithms. Particle Swarm Optimization (PSO) has been very popular for solving complex optimization problems due to its simplicity. In this work, new Artificial Human Optimization Field Algorithms are created by modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO research articles based on Human Behavior, Human Cognition and Human Thinking etc. But there are no Hybrid PSO articles which based on concepts like Human Disease, Human Kindness and Human Relaxation. This paper proposes new AHO Field algorithms based on these research gaps. Some existing Hybrid PSO algorithms are given a new name in this work so that it will be easy for future AHO researchers to find these novel Artificial Human Optimization Field Algorithms. A total of 6 Artificial Human Optimization Field algorithms titled "Human Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)", "Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", "Human Thinking Particle Swarm Optimization (HTPSO)" and "Human Disease Particle Swarm Optimization (HDPSO)" are tested by applying these novel algorithms on Ackley, Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results obtained are compared with PSO algorithm.
Many computer models such as cellular automata and artificial neural networks have been developed and successfully applied. However, in some cases, these models might be restrictive on the possible solutions or their solutions might be difficult to interpret. To overcome this problem, we outline a new approach, the so-called allagmatic method, that automatically programs and executes models with as little limitations as possible while maintaining human interpretability. Earlier we described a metamodel and its building blocks according to the philosophical concepts of structure (spatial dimension) and operation (temporal dimension). They are entity, milieu, and update function that together abstractly describe cellular automata, artificial neural networks, and possibly any kind of computer model. By automatically combining these building blocks in an evolutionary computation, interpretability might be increased by the relationship to the metamodel, and models might be translated into more interpretable models via the metamodel. We propose generic and object-oriented programming to implement the entities and their milieus as dynamic and generic arrays and the update function as a method. We show two experiments where a simple cellular automaton and an artificial neural network are automatically programmed, compiled, and executed. A target state is successfully evolved and learned in the cellular automaton and artificial neural network, respectively. We conclude that the allagmatic method can create and execute cellular automaton and artificial neural network models in an automated manner with the guidance of philosophy.
We seek causes through science, religion, and in everyday life. We get excited when a big rock causes a big splash, and we get scared when it tumbles without a cause. But our causal cognition is usually biased. The 'why' is influenced by the 'who'. It is influenced by the 'self', and by 'others'. We share rituals, we watch action movies, and we influence each other to believe in the same causes. Human mind is packed with subjectivity because shared cognitive biases bring us together. But they also make us vulnerable. An artificial mind is deemed to be more objective than the human mind. After many years of science-fiction fantasies about even-minded androids, they are now sold as personal or expert assistants, as brand advocates, as policy or candidate supporters, as network influencers. Artificial agents have been stunningly successful in disseminating artificial causal beliefs among humans. As malicious artificial agents continue to manipulate human cognitive biases, and deceive human communities into ostensive but expansive causal illusions, the hope for defending us has been vested into developing benevolent artificial agents, tasked with preventing and mitigating cognitive distortions inflicted upon us by their malicious cousins. Can the distortions of human causal cognition be corrected on a more solid foundation of artificial causal cognition? In the present paper, we study a simple model of causal cognition, viewed as a quest for causal models. We show that, under very mild and hard to avoid assumptions, there are always self-confirming causal models, which perpetrate self-deception, and seem to preclude a royal road to objectivity.
The ability to predict the intentions of people based solely on their visual actions is a skill only performed by humans and animals. The intelligence of current computer algorithms has not reached this level of complexity, but there are several research efforts that are working towards it. With the number of classification algorithms available, it is hard to determine which algorithm works best for a particular situation. In classification of visual human intent data, Hidden Markov Models (HMM), and their variants, are leading candidates. The inability of HMMs to provide a probability in the observation to observation linkages is a big downfall in this classification technique. If a person is visually identifying an action of another person, they monitor patterns in the observations. By estimating the next observation, people have the ability to summarize the actions, and thus determine, with pretty good accuracy, the intention of the person performing the action. These visual cues and linkages are important in creating intelligent algorithms for determining human actions based on visual observations. The Evidence Feed Forward Hidden Markov Model is a newly developed algorithm which provides observation to observation linkages. The following research addresses the theory behind Evidence Feed Forward HMMs, provides mathematical proofs of their learning of these parameters to optimize the likelihood of observations with a Evidence Feed Forwards HMM, which is important in all computational intelligence algorithm, and gives comparative examples with standard HMMs in classification of both visual action data and measurement data; thus providing a strong base for Evidence Feed Forward HMMs in classification of many types of problems.
Social Robot Lumen is an Artificial Intelligence development project that aims to create an Artificial Intelligence (AI) which allows a humanoid robot to communicate with human being naturally. In this study, Lumen will be developed to be a tour guide in Electrical Engineering Days 2015 exhibition. In developing an AI, there are a lot of modules that need to be developed separately. To make the development easier, we need a computational platform which becomes basis for all developers to give easiness in developing the modules in parallel way. That computational platform that developed by the writer is called Lumen Server. Lumen Server has two main function, which are to be a bridge between all Lumen intelligence modules with NAO robot, and to be the communication bridge between those Lumen intelligence modules. For the second function, Lumen Server implements the AMQP protocol using RabbitMQ. Besides that, writer also developed a control system for robot movement called Lumen Motion. Lumen motion is implemented by modelling the movement of NAO robot and also by creating a control system using fuzzy logic controller. Writer also developed a program that connects all Lumen intelligence modules so that Lumen can act like a tour guide. The implementation of this program uses FSM and event-driven program. From implementation result, all the features which were designed are successfully implemented. By the developing of this computational platform, it can ease the development of Lumen in the future. For next development, it must be focused on creating integration system so that Lumen can be more responsive to the environment. ----- Sosial Robot Lumen adalah proyek pengembangan kecerdasan buatan yang bertujuan untuk menciptakan kecerdasan buatan atau artificial intelligence (AI) yang memungkinkan robot untuk dapat berkomunikasi dengan manusia secara alami.
In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, real-time simulation number, and matching degree of predicted moves. Additionally, the FML for player performance evaluation and linguistic descriptions for game results commentary are presented. We experimentally verify and validate the performance of the FDAA and variants of the FHMCS by testing five games in 2016 and 60 games of Google Master Go, a new version of the AlphaGo program, in January 2017. The experimental results demonstrate that the proposed FDAA can work effectively for Go applications.
The combination of Artificial Intelligence (AI) and Internet-of-Things (IoT), which is denoted as AI-powered Internet-of-Things (AIoT), is capable of processing huge amount of data generated from a large number of devices and handling complex problems in social infrastructures. As AI and IoT technologies are becoming mature, in this paper, we propose to apply AIoT technologies for traffic light control, which is an essential component for intelligent transportation system, to improve the efficiency of smart city's road system. Specifically, various sensors such as surveillance cameras provide real-time information for intelligent traffic light control system to observe the states of both motorized traffic and non-motorized traffic. In this paper, we propose an intelligent traffic light control solution by using distributed multi-agent Q learning, considering the traffic information at the neighboring intersections as well as local motorized and non-motorized traffic, to improve the overall performance of the entire control system. By using the proposed multi-agent Q learning algorithm, our solution is targeting to optimize both the motorized and non-motorized traffic. In addition, we considered many constraints/rules for traffic light control in the real world, and integrate these constraints in the learning algorithm, which can facilitate the proposed solution to be deployed in real operational scenarios. We conducted numerical simulations for a real-world map with real-world traffic data. The simulation results show that our proposed solution outperforms existing solutions in terms of vehicle and pedestrian queue lengths, waiting time at intersections, and many other key performance metrics.
This report - a major revision of its previous release - describes a reference architecture for intelligent software agents performing active, largely autonomous cyber-defense actions on military networks of computing and communicating devices. The report is produced by the North Atlantic Treaty Organization (NATO) Research Task Group (RTG) IST-152 "Intelligent Autonomous Agents for Cyber Defense and Resilience". In a conflict with a technically sophisticated adversary, NATO military tactical networks will operate in a heavily contested battlefield. Enemy software cyber agents - malware - will infiltrate friendly networks and attack friendly command, control, communications, computers, intelligence, surveillance, and reconnaissance and computerized weapon systems. To fight them, NATO needs artificial cyber hunters - intelligent, autonomous, mobile agents specialized in active cyber defense. With this in mind, in 2016, NATO initiated RTG IST-152. Its objective has been to help accelerate the development and transition to practice of such software agents by producing a reference architecture and technical roadmap. This report presents the concept and architecture of an Autonomous Intelligent Cyber-defense Agent (AICA). We describe the rationale of the AICA concept, explain the methodology and purpose that drive the definition of the AICA Reference Architecture, and review some of the main features and challenges of AICAs.
Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation.
In the last five years, edge computing has attracted tremendous attention from industry and academia due to its promise to reduce latency, save bandwidth, improve availability, and protect data privacy to keep data secure. At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services. These two trends, combined together, have created a new horizon: Edge Intelligence (EI). The development of EI requires much attention from both the computer systems research community and the AI community to meet these demands. However, existing computing techniques used in the cloud are not applicable to edge computing directly due to the diversity of computing sources and the distribution of data sources. We envision that there missing a framework that can be rapidly deployed on edge and enable edge AI capabilities. To address this challenge, in this paper we first present the definition and a systematic review of EI. Then, we introduce an Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability. We analyze four fundamental EI techniques which are used to build OpenEI and identify several open problems based on potential research directions. Finally, four typical application scenarios enabled by OpenEI are presented.
Given a knowledge base KB containing first-order and statistical facts, we consider a principled method, called the random-worlds method, for computing a degree of belief that some formula Phi holds given KB. If we are reasoning about a world or system consisting of N individuals, then we can consider all possible worlds, or first-order models, with domain {1,...,N} that satisfy KB, and compute the fraction of them in which Phi is true. We define the degree of belief to be the asymptotic value of this fraction as N grows large. We show that when the vocabulary underlying Phi and KB uses constants and unary predicates only, we can naturally associate an entropy with each world. As N grows larger, there are many more worlds with higher entropy. Therefore, we can use a maximum-entropy computation to compute the degree of belief. This result is in a similar spirit to previous work in physics and artificial intelligence, but is far more general. Of equal interest to the result itself are the limitations on its scope. Most importantly, the restriction to unary predicates seems necessary. Although the random-worlds method makes sense in general, the connection to maximum entropy seems to disappear in the non-unary case. These observations suggest unexpected limitations to the applicability of maximum-entropy methods.
Most modern formalisms used in Databases and Artificial Intelligence for describing an application domain are based on the notions of class (or concept) and relationship among classes. One interesting feature of such formalisms is the possibility of defining a class, i.e., providing a set of properties that precisely characterize the instances of the class. Many recent articles point out that there are several ways of assigning a meaning to a class definition containing some sort of recursion. In this paper, we argue that, instead of choosing a single style of semantics, we achieve better results by adopting a formalism that allows for different semantics to coexist. We demonstrate the feasibility of our argument, by presenting a knowledge representation formalism, the description logic muALCQ, with the above characteristics. In addition to the constructs for conjunction, disjunction, negation, quantifiers, and qualified number restrictions, muALCQ includes special fixpoint constructs to express (suitably interpreted) recursive definitions. These constructs enable the usual frame-based descriptions to be combined with definitions of recursive data structures such as directed acyclic graphs, lists, streams, etc. We establish several properties of muALCQ, including the decidability and the computational complexity of reasoning, by formulating a correspondence with a particular modal logic of programs called the modal mu-calculus.
Existing plan synthesis approaches in artificial intelligence fall into two categories -- domain independent and domain dependent. The domain independent approaches are applicable across a variety of domains, but may not be very efficient in any one given domain. The domain dependent approaches need to be (re)designed for each domain separately, but can be very efficient in the domain for which they are designed. One enticing alternative to these approaches is to automatically synthesize domain independent planners given the knowledge about the domain and the theory of planning. In this paper, we investigate the feasibility of using existing automated software synthesis tools to support such synthesis. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used to derive a domain-customized planner through a semi-automatic combination of a declarative theory of planning, and the declarative control knowledge specific to a given domain, to semi-automatically combine them to derive domain-customized planners. We discuss what it means to write a declarative theory of planning and control knowledge for KIDS, and illustrate our approach by generating a class of domain-specific planners using state space refinements. Our experiments show that the synthesized planners can outperform classical refinement planners (implemented as instantiations of UCP, Kambhampati & Srivastava, 1995), using the same control knowledge. We will contrast the costs and benefits of the synthesis approach with conventional methods for customizing domain independent planners.
In this paper we propose a random CSP model, called Model GB, which is a natural generalization of standard Model B. It is proved that Model GB in which each constraint is easy to satisfy exhibits non-trivial behaviour (not trivially satisfiable or unsatisfiable) as the number of variables approaches infinity. A detailed analysis to obtain an asymptotic estimate (good to 1+o(1)) of the average number of nodes in a search tree used by the backtracking algorithm on Model GB is also presented. It is shown that the average number of nodes required for finding all solutions or proving that no solution exists grows exponentially with the number of variables. So this model might be an interesting distribution for studying the nature of hard instances and evaluating the performance of CSP algorithms. In addition, we further investigate the behaviour of the average number of nodes as r (the ratio of constraints to variables) varies. The results indicate that as r increases, random CSP instances get easier and easier to solve, and the base for the average number of nodes that is exponential in r tends to 1 as r approaches infinity. Therefore, although the average number of nodes used by the backtracking algorithm on random CSP is exponential, many CSP instances will be very easy to solve when r is sufficiently large.
The constraint satisfaction problem (CSP) is a general problem central to computer science and artificial intelligence. Although the CSP is NP-hard in general, considerable effort has been spent on identifying tractable subclasses. The main two approaches consider structural properties (restrictions on the hypergraph of constraint scopes) and relational properties (restrictions on the language of constraint relations). Recently, some authors have considered hybrid properties that restrict the constraint hypergraph and the relations simultaneously. Our key contribution is the novel concept of a CSP pattern and classes of problems defined by forbidden patterns (which can be viewed as forbidding generic subproblems). We describe the theoretical framework which can be used to reason about classes of problems defined by forbidden patterns. We show that this framework generalises relational properties and allows us to capture known hybrid tractable classes. Although we are not close to obtaining a dichotomy concerning the tractability of general forbidden patterns, we are able to make some progress in a special case: classes of problems that arise when we can only forbid binary negative patterns (generic subproblems in which only inconsistent tuples are specified). In this case we are able to characterise very large classes of tractable and NP-hard forbidden patterns. This leaves the complexity of just one case unresolved and we conjecture that this last case is tractable.
Human intuition has been simulated by several research projects using artificial intelligence techniques. Most of these algorithms or models lack the ability to handle complications or diversions. Moreover, they also do not explain the factors influencing intuition and the accuracy of the results from this process. In this paper, we present a simple series based model for implementation of human-like intuition using the principles of connectivity and unknown entities. By using Poker hand datasets and Car evaluation datasets, we compare the performance of some well-known models with our intuition model. The aim of the experiment was to predict the maximum accurate answers using intuition based models. We found that the presence of unknown entities, diversion from the current problem scenario, and identifying weakness without the normal logic based execution, greatly affects the reliability of the answers. Generally, the intuition based models cannot be a substitute for the logic based mechanisms in handling such problems. The intuition can only act as a support for an ongoing logic based model that processes all the steps in a sequential manner. However, when time and computational cost are very strict constraints, this intuition based model becomes extremely important and useful, because it can give a reasonably good performance. Factors affecting intuition are analyzed and interpreted through our model.
As computational agents are developed for increasingly complicated e-commerce applications, the complexity of the decisions they face demands advances in artificial intelligence techniques. For example, an agent representing a seller in an auction should try to maximize the seller's profit by reasoning about a variety of possibly uncertain pieces of information, such as the maximum prices various buyers might be willing to pay, the possible prices being offered by competing sellers, the rules by which the auction operates, the dynamic arrival and matching of offers to buy and sell, and so on. A naive application of multiagent reasoning techniques would require the seller's agent to explicitly model all of the other agents through an extended time horizon, rendering the problem intractable for many realistically-sized problems. We have instead devised a new strategy that an agent can use to determine its bid price based on a more tractable Markov chain model of the auction process. We have experimentally identified the conditions under which our new strategy works well, as well as how well it works in comparison to the optimal performance the agent could have achieved had it known the future. Our results show that our new strategy in general performs well, outperforming other tractable heuristic strategies in a majority of experiments, and is particularly effective in a 'seller?s market', where many buy offers are available.
Belief merging is an important but difficult problem in Artificial Intelligence, especially when sources of information are pervaded with uncertainty. Many merging operators have been proposed to deal with this problem in possibilistic logic, a weighted logic which is powerful for handling inconsistency and deal- ing with uncertainty. They often result in a possibilistic knowledge base which is a set of weighted formulas. Although possibilistic logic is inconsistency tolerant, it suers from the well-known "drowning effect". Therefore, we may still want to obtain a consistent possi- bilistic knowledge base as the result of merg- ing. In such a case, we argue that it is not always necessary to keep weighted informa- tion after merging. In this paper, we define a merging operator that maps a set of pos- sibilistic knowledge bases and a formula rep- resenting the integrity constraints to a clas- sical knowledge base by using lexicographic ordering. We show that it satisfies nine pos- tulates that generalize basic postulates for propositional merging given in [11]. These postulates capture the principle of minimal change in some sense. We then provide an algorithm for generating the resulting knowl- edge base of our merging operator. Finally, we discuss the compatibility of our merging operator with propositional merging and es- tablish the advantage of our merging opera- tor over existing semantic merging operators in the propositional case.
The problems associated with scaling involve active and challenging research topics in the area of artificial intelligence. The purpose is to solve real world problems by means of AI technologies, in cases where the complexity of representation of the real world problem is potentially combinatorial. In this paper, we present a novel approach to cope with the scaling issues in Bayesian belief networks for ship classification. The proposed approach divides the conceptual model of a complex ship classification problem into a set of small modules that work together to solve the classification problem while preserving the functionality of the original model. The possible ways of explaining sensor returns (e.g., the evidence) for some features, such as portholes along the length of a ship, are sometimes combinatorial. Thus, using an exhaustive approach, which entails the enumeration of all possible explanations, is impractical for larger problems. We present a network structure (referred to as Sequential Decomposition, SD) in which each observation is associated with a set of legitimate outcomes which are consistent with the explanation of each observed piece of evidence. The results show that the SD approach allows one to represent feature-observation relations in a manageable way and achieve the same explanatory power as an exhaustive approach.
Shafer's theory of belief and the Bayesian theory of probability are two alternative and mutually inconsistent approaches toward modelling uncertainty in artificial intelligence. To help reduce the conflict between these two approaches, this paper reexamines expected utility theory-from which Bayesian probability theory is derived. Expected utility theory requires the decision maker to assign a utility to each decision conditioned on every possible event that might occur. But frequently the decision maker cannot foresee all the events that might occur, i.e., one of the possible events is the occurrence of an unforeseen event. So once we acknowledge the existence of unforeseen events, we need to develop some way of assigning utilities to decisions conditioned on unforeseen events. The commonsensical solution to this problem is to assign similar utilities to events which are similar. Implementing this commonsensical solution is equivalent to replacing Bayesian subjective probabilities over the space of foreseen and unforeseen events by random set theory probabilities over the space of foreseen events. This leads to an expected utility principle in which normalized variants of Shafer's commonalities play the role of subjective probabilities. Hence allowing for unforeseen events in decision analysis causes Bayesian probability theory to become much more similar to Shaferian theory.
In recent years, researchers in decision analysis and artificial intelligence (Al) have used Bayesian belief networks to build models of expert opinion. Using standard methods drawn from the theory of computational complexity, workers in the field have shown that the problem of probabilistic inference in belief networks is difficult and almost certainly intractable. K N ET, a software environment for constructing knowledge-based systems within the axiomatic framework of decision theory, contains a randomized approximation scheme for probabilistic inference. The algorithm can, in many circumstances, perform efficient approximate inference in large and richly interconnected models of medical diagnosis. Unlike previously described stochastic algorithms for probabilistic inference, the randomized approximation scheme computes a priori bounds on running time by analyzing the structure and contents of the belief network. In this article, we describe a randomized algorithm for probabilistic inference and analyze its performance mathematically. Then, we devote the major portion of the paper to a discussion of the algorithm's empirical behavior. The results indicate that the generation of good trials (that is, trials whose distribution closely matches the true distribution), rather than the computation of numerous mediocre trials, dominates the performance of stochastic simulation. Key words: probabilistic inference, belief networks, stochastic simulation, computational complexity theory, randomized algorithms.
Many Artificial Intelligence systems depend on the agent's updating its beliefs about the world on the basis of experience. Experiments constitute one type of experience, so scientific methodology offers a natural environment for examining the issues attendant to using this class of evidence. This paper presents a framework which structures the process of using scientific data from research reports for the purpose of making decisions, using decision analysis as the basis for the structure and using medical research as the general scientific domain. The structure extends the basic influence diagram for updating belief in an object domain parameter of interest by expanding the parameter into four parts: those of the patient, the population, the study sample, and the effective study sample. The structure uses biases to perform the transformation of one parameter into another, so that, for instance, selection biases, in concert with the population parameter, yield the study sample parameter. The influence diagram structure provides decision theoretic justification for practices of good clinical research such as randomized assignment and blindfolding of care providers. The model covers most research designs used in medicine: case-control studies, cohort studies, and controlled clinical trials, and provides an architecture to separate clearly between statistical knowledge and domain knowledge. The proposed general model can be the basis for clinical epidemiological advisory systems, when coupled with heuristic pruning of irrelevant biases; of statistical workstations, when the computational machinery for calculation of posterior distributions is added; and of meta-analytic reviews, when multiple studies may impact on a single population parameter.
The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systems
We introduce GOTCHAs (Generating panOptic Turing Tests to Tell Computers and Humans Apart) as a way of preventing automated offline dictionary attacks against user selected passwords. A GOTCHA is a randomized puzzle generation protocol, which involves interaction between a computer and a human. Informally, a GOTCHA should satisfy two key properties: (1) The puzzles are easy for the human to solve. (2) The puzzles are hard for a computer to solve even if it has the random bits used by the computer to generate the final puzzle --- unlike a CAPTCHA. Our main theorem demonstrates that GOTCHAs can be used to mitigate the threat of offline dictionary attacks against passwords by ensuring that a password cracker must receive constant feedback from a human being while mounting an attack. Finally, we provide a candidate construction of GOTCHAs based on Inkblot images. Our construction relies on the usability assumption that users can recognize the phrases that they originally used to describe each Inkblot image --- a much weaker usability assumption than previous password systems based on Inkblots which required users to recall their phrase exactly. We conduct a user study to evaluate the usability of our GOTCHA construction. We also generate a GOTCHA challenge where we encourage artificial intelligence and security researchers to try to crack several passwords protected with our scheme.
Prime implicates and prime implicants have proven relevant to a number of areas of artificial intelligence, most notably abductive reasoning and knowledge compilation. The purpose of this paper is to examine how these notions might be appropriately extended from propositional logic to the modal logic K. We begin the paper by considering a number of potential definitions of clauses and terms for K. The different definitions are evaluated with respect to a set of syntactic, semantic, and complexity-theoretic properties characteristic of the propositional definition. We then compare the definitions with respect to the properties of the notions of prime implicates and prime implicants that they induce. While there is no definition that perfectly generalizes the propositional notions, we show that there does exist one definition which satisfies many of the desirable properties of the propositional case. In the second half of the paper, we consider the computational properties of the selected definition. To this end, we provide sound and complete algorithms for generating and recognizing prime implicates, and we show the prime implicate recognition task to be PSPACE-complete. We also prove upper and lower bounds on the size and number of prime implicates. While the paper focuses on the logic K, all of our results hold equally well for multi-modal K and for concept expressions in the description logic ALC.
Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must react quickly to user commands and to other agents actions. On the downside, real-time search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by re-visiting the same problem states repeatedly. The situation changed recently with a new algorithm, D LRTA*, which attempted to eliminate learning by automatically selecting subgoals. D LRTA* is well poised for video games, except it has a complex and memory-demanding pre-computation phase during which it builds a database of subgoals. In this paper, we propose a simpler and more memory-efficient way of pre-computing subgoals thereby eliminating the main obstacle to applying state-of-the-art real-time search methods in video games. The new algorithm solves a number of randomly chosen problems off-line, compresses the solutions into a series of subgoals and stores them in a database. When presented with a novel problem on-line, it queries the database for the most similar previously solved case and uses its subgoals to solve the problem. In the domain of pathfinding on four large video game maps, the new algorithm delivers solutions eight times better while using 57 times less memory and requiring 14% less pre-computation time.
Domain-independent planning is one of the foundational areas in the field of Artificial Intelligence. A description of a planning task consists of an initial world state, a goal, and a set of actions for modifying the world state. The objective is to find a sequence of actions, that is, a plan, that transforms the initial world state into a goal state. In optimal planning, we are interested in finding not just a plan, but one of the cheapest plans. A prominent approach to optimal planning these days is heuristic state-space search, guided by admissible heuristic functions. Numerous admissible heuristics have been developed, each with its own strengths and weaknesses, and it is well known that there is no single "best heuristic for optimal planning in general. Thus, which heuristic to choose for a given planning task is a difficult question. This difficulty can be avoided by combining several heuristics, but that requires computing numerous heuristic estimates at each state, and the tradeoff between the time spent doing so and the time saved by the combined advantages of the different heuristics might be high. We present a novel method that reduces the cost of combining admissible heuristics for optimal planning, while maintaining its benefits. Using an idealized search space model, we formulate a decision rule for choosing the best heuristic to compute at each state. We then present an active online learning approach for learning a classifier with that decision rule as the target concept, and employ the learned classifier to decide which heuristic to compute at each state. We evaluate this technique empirically, and show that it substantially outperforms the standard method for combining several heuristics via their pointwise maximum.
This book-length article combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence (AI). The behavior of future AI systems can be described by mathematical equations, which are adapted to analyze possible unintended AI behaviors and ways that AI designs can avoid them. This article makes the case for utility-maximizing agents and for avoiding infinite sets in agent definitions. It shows how to avoid agent self-delusion using model-based utility functions and how to avoid agents that corrupt their reward generators (sometimes called "perverse instantiation") using utility functions that evaluate outcomes at one point in time from the perspective of humans at a different point in time. It argues that agents can avoid unintended instrumental actions (sometimes called "basic AI drives" or "instrumental goals") by accurately learning human values. This article defines a self-modeling agent framework and shows how it can avoid problems of resource limits, being predicted by other agents, and inconsistency between the agent's utility function and its definition (one version of this problem is sometimes called "motivated value selection"). This article also discusses how future AI will differ from current AI, the politics of AI, and the ultimate use of AI to help understand the nature of the universe and our place in it.
In this position paper, I argue that standardized tests for elementary science such as SAT or Regents tests are not very good benchmarks for measuring the progress of artificial intelligence systems in understanding basic science. The primary problem is that these tests are designed to test aspects of knowledge and ability that are challenging for people; the aspects that are challenging for AI systems are very different. In particular, standardized tests do not test knowledge that is obvious for people; none of this knowledge can be assumed in AI systems. Individual standardized tests also have specific features that are not necessarily appropriate for an AI benchmark. I analyze the Physics subject SAT in some detail and the New York State Regents Science test more briefly. I also argue that the apparent advantages offered by using standardized tests are mostly either minor or illusory. The one major real advantage is that the significance is easily explained to the public; but I argue that even this is a somewhat mixed blessing. I conclude by arguing that, first, more appropriate collections of exam style problems could be assembled, and second, that there are better kinds of benchmarks than exam-style problems. In an appendix I present a collection of sample exam-style problems that test kinds of knowledge missing from the standardized tests.
Undergraduate students of artificial intelligence often struggle with representing knowledge as logical sentences. This is a skill that seems to require extensive practice to obtain, suggesting a teaching strategy that involves the assignment of numerous exercises involving the formulation of some bit of knowledge, communicated using a natural language such as English, as a sentence in some logic. The number of such exercises needed to master this skill is far too large to allow typical artificial intelligence course teaching teams to provide prompt feedback on student efforts. Thus, an automated assessment system for such exercises is needed to ensure that students receive an adequate amount of practice, with the rapid delivery of feedback allowing students to identify errors in their understanding and correct them. This paper describes an automated grading system for knowledge representation exercises using first-order logic. A resolution theorem prover, \textit{Prover9}, is used to check if a student-submitted formula is logically equivalent to a solution provided by the instructor. This system has been used by students enrolled in undergraduate artificial intelligence classes for several years. Use of this teaching tool resulted in a statistically significant improvement on first-order logic knowledge representation questions appearing on the course final examination. This article explains how this system works, provides an analysis of changes in student learning outcomes, and explores potential enhancements of this system, including the possibility of providing rich formative feedback by replacing the resolution theorem prover with a tableaux-based method.
This article addresses an open problem in the area of cognitive systems and architectures: namely the problem of handling (in terms of processing and reasoning capabilities) complex knowledge structures that can be at least plausibly comparable, both in terms of size and of typology of the encoded information, to the knowledge that humans process daily for executing everyday activities. Handling a huge amount of knowledge, and selectively retrieve it ac- cording to the needs emerging in different situational scenarios, is an important aspect of human intelligence. For this task, in fact, humans adopt a wide range of heuristics (Gigerenzer and Todd) due to their bounded rationality (Simon, 1957). In this perspective, one of the re- quirements that should be considered for the design, the realization and the evaluation of intelligent cognitively inspired systems should be represented by their ability of heuristically identify and retrieve, from the general knowledge stored in their artificial Long Term Memory (LTM), that one which is synthetically and contextually relevant. This require- ment, however, is often neglected. Currently, artificial cognitive systems and architectures are not able, de facto, to deal with complex knowledge structures that can be even slightly comparable to the knowledge heuris- tically managed by humans. In this paper I will argue that this is not only a technological problem but also an epistemological one and I will briefly sketch a proposal for a possible solution.
Learning models of artificial intelligence can nowadays perform very well on a large variety of tasks. However, in practice different task environments are best handled by different learning models, rather than a single, universal, approach. Most non-trivial models thus require the adjustment of several to many learning parameters, which is often done on a case-by-case basis by an external party. Meta-learning refers to the ability of an agent to autonomously and dynamically adjust its own learning parameters, or meta-parameters. In this work we show how projective simulation, a recently developed model of artificial intelligence, can naturally be extended to account for meta-learning in reinforcement learning settings. The projective simulation approach is based on a random walk process over a network of clips. The suggested meta-learning scheme builds upon the same design and employs clip networks to monitor the agent's performance and to adjust its meta-parameters "on the fly". We distinguish between "reflexive adaptation" and "adaptation through learning", and show the utility of both approaches. In addition, a trade-off between flexibility and learning-time is addressed. The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.
Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time. The relations bond ideas, arguments, thoughts, and feelings, re-shape them during a speech, and finally build knowledge out of all information provided in the conversation. Speakers share a common interest to discuss. It is expected that each speaker's reply includes duplicated forms of words from previous speakers. However, linguistic adaptation is observed and evolves in a more complex path than just transferring slightly modified versions of common concepts. A conversation aiming a benefit at the end shows an emergent cooperation inducing the adaptation. Not only cooperation, but also competition drives the adaptation or an opposite scenario and one can capture the dynamic process by tracking how the concepts are linguistically linked. To uncover salient complex dynamic events in verbal communications, we attempt to discover self-organized linguistic relations hidden in a conversation with explicitly stated winners and losers. We examine open access data of the United States Supreme Court. Our understanding is crucial in big data research to guide how transition states in opinion mining and decision-making should be modeled and how this required knowledge to guide the model should be pinpointed, by filtering large amount of data.
Unifying seemingly disparate algorithmic ideas to produce better performing algorithms has been a longstanding goal in reinforcement learning. As a primary example, TD($\lambda$) elegantly unifies one-step TD prediction with Monte Carlo methods through the use of eligibility traces and the trace-decay parameter $\lambda$. Currently, there are a multitude of algorithms that can be used to perform TD control, including Sarsa, $Q$-learning, and Expected Sarsa. These methods are often studied in the one-step case, but they can be extended across multiple time steps to achieve better performance. Each of these algorithms is seemingly distinct, and no one dominates the others for all problems. In this paper, we study a new multi-step action-value algorithm called $Q(\sigma)$ which unifies and generalizes these existing algorithms, while subsuming them as special cases. A new parameter, $\sigma$, is introduced to allow the degree of sampling performed by the algorithm at each step during its backup to be continuously varied, with Sarsa existing at one extreme (full sampling), and Expected Sarsa existing at the other (pure expectation). $Q(\sigma)$ is generally applicable to both on- and off-policy learning, but in this work we focus on experiments in the on-policy case. Our results show that an intermediate value of $\sigma$, which results in a mixture of the existing algorithms, performs better than either extreme. The mixture can also be varied dynamically which can result in even greater performance.
The area of computation called artificial intelligence (AI) is falsified by describing a previous 1972 falsification of AI by British applied mathematician James Lighthill. It is explained how Lighthill's arguments continue to apply to current AI. It is argued that AI should use the Popperian scientific method in which it is the duty of every scientist to attempt to falsify theories and if theories are falsified to replace or modify them. The paper describes the Popperian method in detail and discusses Paul Nurse's application of the method to cell biology that also involves questions of mechanism and behavior. Arguments used by Lighthill in his original 1972 report that falsified AI are discussed. The Lighthill arguments are then shown to apply to current AI. The argument uses recent scholarship to explain Lighthill's assumptions and to show how the arguments based on those assumptions continue to falsify modern AI. An important focus of the argument involves Hilbert's philosophical programme that defined knowledge and truth as provable formal sentences. Current AI takes the Hilbert programme as dogma beyond criticism while Lighthill as a mid 20th century applied mathematician had abandoned it. The paper uses recent scholarship to explain John von Neumann's criticism of AI that I claim was assumed by Lighthill. The paper discusses computer chess programs to show Lighthill's combinatorial explosion still applies to AI but not humans. An argument showing that Turing Machines (TM) are not the correct description of computation is given. The paper concludes by advocating studying computation as Peter Naur's Dataology.
Molecular variants of vitamin B12, siderophores and glycans occur. To take up variant forms, bacteria may express an array of receptors. The gut microbe Bacteroides thetaiotaomicron has three different receptors to take up variants of vitamin B12 and 88 receptors to take up various glycans. The design of receptor arrays reflects key processes that shape cellular evolution. Competition may focus each species on a subset of the available nutrient diversity. Some gut bacteria can take up only a narrow range of carbohydrates, whereas species such as B.~thetaiotaomicron can digest many different complex glycans. Comparison of different nutrients, habitats, and genomes provide opportunity to test hypotheses about the breadth of receptor arrays. Another important process concerns fluctuations in nutrient availability. Such fluctuations enhance the value of cellular sensors, which gain information about environmental availability and adjust receptor deployment. Bacteria often adjust receptor expression in response to fluctuations of particular carbohydrate food sources. Some species may adjust expression of uptake receptors for specific siderophores. How do cells use sensor information to control the response to fluctuations? That question about regulatory wiring relates to problems that arise in control theory and artificial intelligence. Control theory clarifies how to analyze environmental fluctuations in relation to the design of sensors and response systems. Recent advances in deep learning studies of artificial intelligence focus on the architecture of regulatory wiring and the ways in which complex control networks represent and classify environmental states. I emphasize the similar design problems that arise in cellular evolution, control theory, and artificial intelligence. I connect those broad concepts to testable hypotheses for bacterial uptake of B12, siderophores and glycans.
Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has recently proven useful. In recent years, there have been significant advances in the accuracy of parsing algorithms. In this article, we perform an empirical, task-oriented evaluation to determine how parsing accuracy influences the performance of a state-of-the-art rule-based sentiment analysis system that determines the polarity of sentences from their parse trees. In particular, we evaluate the system using four well-known dependency parsers, including both current models with state-of-the-art accuracy and more innacurate models which, however, require less computational resources. The experiments show that all of the parsers produce similarly good results in the sentiment analysis task, without their accuracy having any relevant influence on the results. Since parsing is currently a task with a relatively high computational cost that varies strongly between algorithms, this suggests that sentiment analysis researchers and users should prioritize speed over accuracy when choosing a parser; and parsing researchers should investigate models that improve speed further, even at some cost to accuracy.
There have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relatively advanced computer games. There is undoubtedly an anticipation that Deep Reinforcement Learning will play a major role when the first AI masters the complicated game plays needed to beat a professional Real-Time Strategy game player. For this to be possible, there needs to be a game environment that targets and fosters AI research, and specifically Deep Reinforcement Learning. Some game environments already exist, however, these are either overly simplistic such as Atari 2600 or complex such as Starcraft II from Blizzard Entertainment. We propose a game environment in between Atari 2600 and Starcraft II, particularly targeting Deep Reinforcement Learning algorithm research. The environment is a variant of Tower Line Wars from Warcraft III, Blizzard Entertainment. Further, as a proof of concept that the environment can harbor Deep Reinforcement algorithms, we propose and apply a Deep Q-Reinforcement architecture. The architecture simplifies the state space so that it is applicable to Q-learning, and in turn improves performance compared to current state-of-the-art methods. Our experiments show that the proposed architecture can learn to play the environment well, and score 33% better than standard Deep Q-learning which in turn proves the usefulness of the game environment.
The hard problem in artificial intelligence asks how the shuffling of syntactical symbols in a program can lead to systems which experience semantics and qualia. We address this question in three stages. First, we introduce a new class of human semantic symbols which appears when unexpected and drastic environmental change causes humans to become surprised, confused, uncertain, and in extreme cases, unresponsive, passive and dysfunctional. For this class of symbols, pre-learned programs become inoperative so these syntactical programs cannot be the source of experienced qualia. Second, we model the dysfunctional human response to a radically changed environment as being the natural response of any learning machine facing novel inputs from well outside its previous training set. In this situation, learning machines are unable to extract information from their input and will typically enter a dynamical state characterized by null outputs and a lack of response. This state immediately predicts and explains the characteristics of the semantic experiences of humans in similar circumstances. In the third stage, we consider learning machines trained to implement multiple functions in simple sequential programs using environmental data to specify subroutine names, control flow instructions, memory calls, and so on. Drastic change in any of these environmental inputs can again lead to inoperative programs. By examining changes specific to people or locations we can model human cognitive symbols featuring these dependencies, such as attachment and grief. Our approach links known dynamical machines states with human qualia and thus offers new insight into the hard problem of artificial intelligence.
All artificial Intelligence (AI) systems make errors. These errors are unexpected, and differ often from the typical human mistakes ("non-human" errors). The AI errors should be corrected without damage of existing skills and, hopefully, avoiding direct human expertise. This paper presents an initial summary report of project taking new and systematic approach to improving the intellectual effectiveness of the individual AI by communities of AIs. We combine some ideas of learning in heterogeneous multiagent systems with new and original mathematical approaches for non-iterative corrections of errors of legacy AI systems. The mathematical foundations of AI non-destructive correction are presented and a series of new stochastic separation theorems is proven. These theorems provide a new instrument for the development, analysis, and assessment of machine learning methods and algorithms in high dimension. They demonstrate that in high dimensions and even for exponentially large samples, linear classifiers in their classical Fisher's form are powerful enough to separate errors from correct responses with high probability and to provide efficient solution to the non-destructive corrector problem. In particular, we prove some hypotheses formulated in our paper `Stochastic Separation Theorems' (Neural Networks, 94, 255--259, 2017), and answer one general problem published by Donoho and Tanner in 2009.
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy. We performed a prospective validation study of the accuracy and safety of an AI powered triage and diagnostic system. Identical cases were evaluated by both an AI system and human doctors. Differential diagnoses and triage outcomes were evaluated by an independent judge, who was blinded from knowing the source (AI system or human doctor) of the outcomes. Independently of these cases, vignettes from publicly available resources were also assessed to provide a benchmark to previous studies and the diagnostic component of the MRCGP exam. Overall we found that the Babylon AI powered Triage and Diagnostic System was able to identify the condition modelled by a clinical vignette with accuracy comparable to human doctors (in terms of precision and recall). In addition, we found that the triage advice recommended by the AI System was, on average, safer than that of human doctors, when compared to the ranges of acceptable triage provided by independent expert judges, with only a minimal reduction in appropriateness.
In recent years there has been a sharp rise in networking applications, in which significant events need to be classified but only a few training instances are available. These are known as cases of one-shot learning. Examples include analyzing network traffic under zero-day attacks, and computer vision tasks by sensor networks deployed in the field. To handle this challenging task, organizations often use human analysts to classify events under high uncertainty. Existing algorithms use a threshold-based mechanism to decide whether to classify an object automatically or send it to an analyst for deeper inspection. However, this approach leads to a significant waste of resources since it does not take the practical temporal constraints of system resources into account. Our contribution is threefold. First, we develop a novel Deep Reinforcement One-shot Learning (DeROL) framework to address this challenge. The basic idea of the DeROL algorithm is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data. Then, in real-time, DeROL maps the current state of the one-shot learning process to operational actions based on the trained deep-Q network, to maximize the objective function. Second, we develop the first open-source software for practical artificially intelligent one-shot classification systems with limited resources for the benefit of researchers in related fields. Third, we present an extensive experimental study using the OMNIGLOT dataset for computer vision tasks and the UNSW-NB15 dataset for intrusion detection tasks that demonstrates the versatility and efficiency of the DeROL framework.
5th generation networks are envisioned to provide seamless and ubiquitous connection to 1000-fold more devices and is believed to provide ultra-low latency and higher data rates up to tens of Gbps. Different technologies enabling these requirements are being developed including mmWave communications, Massive MIMO and beamforming, Device to Device (D2D) communications and Heterogeneous Networks. D2D communication is a promising technology to enable applications requiring high bandwidth such as online streaming and online gaming etc. It can also provide ultra- low latencies required for applications like vehicle to vehicle communication for autonomous driving. D2D communication can provide higher data rates with high energy efficiency and spectral efficiency compared to conventional communication. The performance benefits of D2D communication can be best achieved when D2D users reuses the spectrum being utilized by the conventional cellular users. This spectrum sharing in a multi-tier heterogeneous network will introduce complex interference among D2D users and cellular users which needs to be resolved. Motivated by limited number of surveys for interference mitigation and resource allocation in D2D enabled heterogeneous networks, we have surveyed different conventional and artificial intelligence based interference mitigation and resource allocation schemes developed in recent years. Our contribution lies in the analysis of conventional interference mitigation techniques and their shortcomings. Finally, the strengths of AI based techniques are determined and open research challenges deduced from the recent research are presented.
The Artificial Intelligence (AI) revolution foretold of during the 1960s is well underway in the second decade of the 21st century. Its period of phenomenal growth likely lies ahead. Still, we believe, there are crucial lessons that biology can offer that will enable a prosperous future for AI. For machines in general, and for AI's especially, operating over extended periods or in extreme environments will require energy usage orders of magnitudes more efficient than exists today. In many operational environments, energy sources will be constrained. Any plans for AI devices operating in a challenging environment must begin with the question of how they are powered, where fuel is located, how energy is stored and made available to the machine, and how long the machine can operate on specific energy units. Hence, the materials and technologies that provide the needed energy represent a critical challenge towards future use-scenarios of AI and should be integrated into their design. Here we make four recommendations for stakeholders and especially decision makers to facilitate a successful trajectory for this technology. First, that scientific societies and governments coordinate Biomimetic Research for Energy-efficient, AI Designs (BREAD); a multinational initiative and a funding strategy for investments in the future integrated design of energetics into AI. Second, that biomimetic energetic solutions be central to design consideration for future AI. Third, that a pre-competitive space be organized between stakeholder partners and fourth, that a trainee pipeline be established to ensure the human capital required for success in this area.
Conventional methods for visual assessment of civil infrastructures have certain limitations, such as subjectivity of the collected data, long inspection time, and high cost of labor. Although some new technologies i.e. robotic techniques that are currently in practice can collect objective, quantified data, the inspectors own expertise is still critical in many instances since these technologies are not designed to work interactively with human inspector. This study aims to create a smart, human centered method that offers significant contributions to infrastructure inspection, maintenance, management practice, and safety for the bridge owners. By developing a smart Mixed Reality framework, which can be integrated into a wearable holographic headset device, a bridge inspector, for example, can automatically analyze a certain defect such as a crack that he or she sees on an element, display its dimension information in real-time along with the condition state. Such systems can potentially decrease the time and cost of infrastructure inspections by accelerating essential tasks of the inspector such as defect measurement, condition assessment and data processing to management systems. The human centered artificial intelligence will help the inspector collect more quantified and objective data while incorporating inspectors professional judgement. This study explains in detail the described system and related methodologies of implementing attention guided semi supervised deep learning into mixed reality technology, which interacts with the human inspector during assessment. Thereby, the inspector and the AI will collaborate or communicate for improved visual inspection.
People frequently face challenging decision-making problems in which outcomes are uncertain or unknown. Artificial intelligence (AI) algorithms exist that can outperform humans at learning such tasks. Thus, there is an opportunity for AI agents to assist people in learning these tasks more effectively. In this work, we use a multi-armed bandit as a controlled setting in which to explore this direction. We pair humans with a selection of agents and observe how well each human-agent team performs. We find that team performance can beat both human and agent performance in isolation. Interestingly, we also find that an agent's performance in isolation does not necessarily correlate with the human-agent team's performance. A drop in agent performance can lead to a disproportionately large drop in team performance, or in some settings can even improve team performance. Pairing a human with an agent that performs slightly better than them can make them perform much better, while pairing them with an agent that performs the same can make them them perform much worse. Further, our results suggest that people have different exploration strategies and might perform better with agents that match their strategy. Overall, optimizing human-agent team performance requires going beyond optimizing agent performance, to understanding how the agent's suggestions will influence human decision-making.
Humor is an essential human trait. Efforts to understand humor have called out links between humor and the foundations of cognition, as well as the importance of humor in social engagement. As such, it is a promising and important subject of study, with relevance for artificial intelligence and human-computer interaction. Previous computational work on humor has mostly operated at a coarse level of granularity, e.g., predicting whether an entire sentence, paragraph, document, etc., is humorous. As a step toward deep understanding of humor, we seek fine-grained models of attributes that make a given text humorous. Starting from the observation that satirical news headlines tend to resemble serious news headlines, we build and analyze a corpus of satirical headlines paired with nearly identical but serious headlines. The corpus is constructed via Unfun.me, an online game that incentivizes players to make minimal edits to satirical headlines with the goal of making other players believe the results are serious headlines. The edit operations used to successfully remove humor pinpoint the words and concepts that play a key role in making the original, satirical headline funny. Our analysis reveals that the humor tends to reside toward the end of headlines, and primarily in noun phrases, and that most satirical headlines follow a certain logical pattern, which we term false analogy. Overall, this paper deepens our understanding of the syntactic and semantic structure of satirical news headlines and provides insights for building humor-producing systems.
PURPOSE OF REVIEW: Despite the impressive results of recent artificial intelligence (AI) applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric ophthalmology patients and how AI techniques can address these challenges, surveys recent applications of AI to pediatric ophthalmology, and discusses future directions in the field. RECENT FINDINGS: The most significant advances involve the automated detection of retinopathy of prematurity (ROP), yielding results that rival experts. Machine learning (ML) has also been successfully applied to the classification of pediatric cataracts, prediction of post-operative complications following cataract surgery, detection of strabismus and refractive error, prediction of future high myopia, and diagnosis of reading disability via eye tracking. In addition, ML techniques have been used for the study of visual development, vessel segmentation in pediatric fundus images, and ophthalmic image synthesis. SUMMARY: AI applications could significantly benefit clinical care for pediatric ophthalmology patients by optimizing disease detection and grading, broadening access to care, furthering scientific discovery, and improving clinical efficiency. These methods need to match or surpass physician performance in clinical trials before deployment with patients. Due to widespread use of closed-access data sets and software implementations, it is difficult to directly compare the performance of these approaches, and reproducibility is poor. Open-access data sets and software implementations could alleviate these issues, and encourage further AI applications to pediatric ophthalmology. KEYWORDS: pediatric ophthalmology, machine learning, artificial intelligence, deep learning
This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we consider how these automation mechanisms can be leveraged to offer AI-aaS. Use cases for AI-aaS are easily found in addressing smart applications in sectors such as transportation, manufacturing, energy, water, air quality, and emissions. We propose an architectural scheme based on SDIs where each AI-aaS application is comprised of a monitoring, analysis, policy, execution plus knowledge (MAPE-K) loop (MKL). Each application is composed as one or more specific service chains embedded in SDI, some of which will include a Machine Learning (ML) pipeline. Our model includes a new training plane and an AI-aaS plane to deal with the model-development and operational phases of AI applications. We also consider the role of an ML/MKL sandbox in ensuring coherency and consistency in the operation of multiple parallel MKL loops. We present experimental measurement results for three AI-aaS applications deployed on the SAVI testbed: 1. Compressing monitored data in SDI using autoencoders; 2. Traffic monitoring to allocate CPUs resources to VNFs; and 3. Highway segment classification in smart transportation.
We introduce DaiMoN, a decentralized artificial intelligence model network, which incentivizes peer collaboration in improving the accuracy of machine learning models for a given classification problem. It is an autonomous network where peers may submit models with improved accuracy and other peers may verify the accuracy improvement. The system maintains an append-only decentralized ledger to keep the log of critical information, including who has trained the model and improved its accuracy, when it has been improved, by how much it has improved, and where to find the newly updated model. DaiMoN rewards these contributing peers with cryptographic tokens. A main feature of DaiMoN is that it allows peers to verify the accuracy improvement of submitted models without knowing the test labels. This is an essential component in order to mitigate intentional model overfitting by model-improving peers. To enable this model accuracy evaluation with hidden test labels, DaiMoN uses a novel learnable Distance Embedding for Labels (DEL) function proposed in this paper. Specific to each test dataset, DEL scrambles the test label vector by embedding it in a low-dimension space while approximately preserving the distance between the dataset's test label vector and a label vector inferred by the classifier. It therefore allows proof-of-improvement (PoI) by peers without providing them access to true test labels. We provide analysis and empirical evidence that under DEL, peers can accurately assess model accuracy. We also argue that it is hard to invert the embedding function and thus, DEL is resilient against attacks aiming to recover test labels in order to cheat. Our prototype implementation of DaiMoN is available at https://github.com/steerapi/daimon.
Data-driven computational approaches have evolved to enable extraction of information from medical images with a reliability, accuracy and speed which is already transforming their interpretation and exploitation in clinical practice. While similar benefits are longed for in the field of interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theatres are extremely complex and typically rely on poorly integrated intra-operative devices, sensors, and support infrastructures. Taking stock of some of the most exciting developments in machine learning and artificial intelligence for computer assisted interventions, we highlight the crucial need to take context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer assisted intervention, or CAI4CAI, arises as an emerging opportunity feeding into the broader field of surgical data science. Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human-AI actor team; how to design interventional systems and associated cognitive shared control schemes for online uncertainty-aware collaborative decision making ultimately producing more precise and reliable interventions.
The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties. This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks. These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV among actual homeless youth in Los Angeles. This represents one of the first-ever deployments of computer science based influence maximization algorithms in this domain. Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons that can be gleaned for future deployment of such algorithms. The positive results from these deployments illustrate the enormous potential of AI in addressing societally relevant problems.
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Seeding the Singularity for A.I
Use of Artificial Intelligence Techniques / Applications in Cyber Defense
Artificial Intelligence for Interstellar Travel
One Decade of Universal Artificial Intelligence
Theory of Cognitive Relativity: A Promising Paradigm for True AI
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
Artificial Intelligence and its Role in Near Future
Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop
Intelligence in Artificial Intelligence
The SP Theory of Intelligence as a Foundation for the Development of a General, Human-Level Thinking Machine
Open Ended Intelligence: The individuation of Intelligent Agents
An architecture for the evaluation of intelligent systems
Analysis of first prototype universal intelligence tests: evaluating and comparing AI algorithms and humans
A Model for General Intelligence
On the Measure of Intelligence
Towards Self-constructive Artificial Intelligence: Algorithmic basis (Part I)
Robotics Rights and Ethics Rules
An Unified Intelligence-Communication Model for Multi-Agent System Part-I: Overview
Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like
Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction
Landau Theory of Adaptive Integration in Computational Intelligence
Affect Control Processes: Intelligent Affective Interaction using a Partially Observable Markov Decision Process
Improbotics: Exploring the Imitation Game using Machine Intelligence in Improvised Theatre
A Model for Web-Intelligence Index to Evaluate the Web Intelligence Capacity of Government Web Sites of Sri Lanka
Algorithms and Complexity Results for Persuasive Argumentation
"Dave...I can assure you...that it's going to be all right..." -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships
Hows and Whys of Artificial Intelligence for Public Sector Decisions: Explanation and Evaluation
Artificial Intelligence: A Child's Play
Analysis of Algorithms and Partial Algorithms
Artificial Immune Systems Tutorial
Minimally Naturalistic Artificial Intelligence
Focus Group on Artificial Intelligence for Health
Neocortical plasticity: an unsupervised cake but no free lunch
Autonomous robots and the SP theory of intelligence
A World of Views: A World of Interacting Post-human Intelligences
Intelligent User Interface in Fuzzy Environment
Applying Artificial Intelligence and Internet Techniques in Rural Tourism Domain
The Future of Scientific Simulations: from Artificial Life to Artificial Cosmogenesis
Definition and properties to assess multi-agent environments as social intelligence tests
Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence
How to advance general game playing artificial intelligence by player modelling
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
Can Autism be Catered with Artificial Intelligence-Assisted Intervention Technology? A Literature Review
Using Artificial Intelligence to Support Compliance with the General Data Protection Regulation
Wikistat 2.0: Educational Resources for Artificial Intelligence
Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development
Artificial Intelligence and the Future of Psychiatry: Qualitative Findings from a Global Physician Survey
Computing as compression: the SP theory of intelligence
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology
Reputation in M2M Economy
Artificial general intelligence through recursive data compression and grounded reasoning: a position paper
Lifelong Testing of Smart Autonomous Systems by Shepherding a Swarm of Watchdog Artificial Intelligence Agents
An electronic-game framework for evaluating coevolutionary algorithms
CloudifierNet -- Deep Vision Models for Artificial Image Processing
Can Turing machine be curious about its Turing test results? Three informal lectures on physics of intelligence
Intelligent Traffic Signal Control: Using Reinforcement Learning with Partial Detection
When Autonomous Intelligent Goodware will Fight Autonomous Intelligent Malware: A Possible Future of Cyber Defense
Past Visions of Artificial Futures: One Hundred and Fifty Years under the Spectre of Evolving Machines
Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human--like learning
Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan"
Machine learning \& artificial intelligence in the quantum domain
Provably Bounded-Optimal Agents
Artificial Intelligence Based Cognitive Routing for Cognitive Radio Networks
The Morphospace of Consciousness
Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities
There is no Artificial General Intelligence
From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility
Artificial Immune Systems
Adaptive Parallel Iterative Deepening Search
Elementos de ingeniería de explotación de la información aplicados a la investigación tributaria fiscal
The option pricing model based on time values: an application of the universal approximation theory on unbounded domains
A Distributed AI Aided 3D Domino Game
A study on non-destructive method for detecting Toxin in pepper using Neural networks
The SP theory of intelligence: benefits and applications
What is Learning? A primary discussion about information and Representation
AIXIjs: A Software Demo for General Reinforcement Learning
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications
Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
How the symbol grounding of living organisms can be realized in artificial agents
Responses to a Critique of Artificial Moral Agents
Artificial Intelligence Probes for Interstellar Exploration and Colonization
Novel Artificial Human Optimization Field Algorithms - The Beginning
Automatic Programming of Cellular Automata and Artificial Neural Networks Guided by Philosophy
Causality and deceit: Do androids watch action movies?
Evidence Feed Forward Hidden Markov Model: A New Type of Hidden Markov Model
Design and implementation of computational platform for social-humanoid robot Lumen as an exhibition guide in Electrical Engineering Days 2015
FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go
Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning
Autonomous Intelligent Cyber-defense Agent (AICA) Reference Architecture. Release 2.0
The Responsibility Quantification (ResQu) Model of Human Interaction with Automation
OpenEI: An Open Framework for Edge Intelligence
Random Worlds and Maximum Entropy
A Uniform Framework for Concept Definitions in Description Logics
Synthesizing Customized Planners from Specifications
An Average Analysis of Backtracking on Random Constraint Satisfaction Problems
The tractability of CSP classes defined by forbidden patterns
Implementing Human-like Intuition Mechanism in Artificial Intelligence
Use of Markov Chains to Design an Agent Bidding Strategy for Continuous Double Auctions
Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation
A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification
A Bayesian Variant of Shafer's Commonalities For Modelling Unforeseen Events
An Empirical Evaluation of a Randomized Algorithm for Probabilistic Inference
A Decision-Theoretic Model for Using Scientific Data
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
GOTCHA Password Hackers!
Prime Implicates and Prime Implicants: From Propositional to Modal Logic
Case-Based Subgoaling in Real-Time Heuristic Search for Video Game Pathfinding
Online Speedup Learning for Optimal Planning
Ethical Artificial Intelligence
The Limitations of Standardized Science Tests as Benchmarks for Artificial Intelligence Research: Position Paper
Using Automated Theorem Provers to Teach Knowledge Representation in First-Order Logic
Some Epistemological Problems with the Knowledge Level in Cognitive Architectures
Meta-learning within Projective Simulation
Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups
Multi-step Reinforcement Learning: A Unifying Algorithm
A Popperian Falsification of Artificial Intelligence - Lighthill Defended
Receptor uptake arrays for vitamin B12, siderophores and glycans shape bacterial communities
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis
Towards a Deep Reinforcement Learning Approach for Tower Line Wars
Null Dynamical State Models of Human Cognitive Dysfunction
Augmented Artificial Intelligence: a Conceptual Framework
A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems
A Survey of Conventional and Artificial Intelligence / Learning based Resource Allocation and Interference Mitigation Schemes in D2D Enabled Networks
Making BREAD: Biomimetic strategies for Artificial Intelligence Now and in the Future
Artificial Intelligence Assisted Infrastructure Assessment Using Mixed Reality Systems
Human-AI Learning Performance in Multi-Armed Bandits
Reverse-Engineering Satire, or "Paper on Computational Humor Accepted Despite Making Serious Advances"
Artificial Intelligence for Pediatric Ophthalmology
Artificial Intelligence as a Services (AI-aaS) on Software-Defined Infrastructure
DaiMoN: A Decentralized Artificial Intelligence Model Network
CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions
Artificial Intelligence for Low-Resource Communities: Influence Maximization in an Uncertain World
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Multiband NFC for High-Throughput Wireless Computer Vision Sensor Network
Real-time Tracking Based on Neuromrophic Vision
Reconfiguring the Imaging Pipeline for Computer Vision
I'm sorry to say, but your understanding of image processing fundamentals is absolutely wrong
A Survey on Deep Learning Methods for Robot Vision
Negative Results in Computer Vision: A Perspective
CloudCV: Large Scale Distributed Computer Vision as a Cloud Service
Computers Should Be Uniters Not Dividers: A Vision of Computer-Enhanced Happy Future
The Evolution of First Person Vision Methods: A Survey
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision
The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models
Crowdsourcing in Computer Vision
Recent Advances in Transient Imaging: A Computer Graphics and Vision Perspective
Motion Segmentation by SCC on the Hopkins 155 Database
Vector Quantization for Machine Vision
Minimal Problems for the Calibrated Trifocal Variety
Learning Inference Models for Computer Vision
Sparse models for Computer Vision
Universal representations:The missing link between faces, text, planktons, and cat breeds
A Survey on Recent Advances of Computer Vision Algorithms for Egocentric Video
CS591 Report: Application of siamesa network in 2D transformation
Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions
On-Board Vision Processing For Small UAVs: Time to Rethink Strategy
Computer Vision Systems in Road Vehicles: A Review
$EVA^2$ : Exploiting Temporal Redundancy in Live Computer Vision
Addressing the non-functional requirements of computer vision systems: A case study
SideEye: A Generative Neural Network Based Simulator of Human Peripheral Vision
Toward Designing Intelligent PDEs for Computer Vision: An Optimal Control Approach
Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition
Robotics Vision-based Heuristic Reasoning for Underwater Target Tracking and Navigation
A Possible Model of Noise Enhanced Visual Perception in Human Vision
Accelerated Convolutions for Efficient Multi-Scale Time to Contact Computation in Julia
Robust Fitting in Computer Vision: Easy or Hard?
A real time vehicles detection algorithm for vision based sensors
Annotation Methodologies for Vision and Language Dataset Creation
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Fast k Nearest Neighbor Search using GPU
When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
Exploiting skeletal structure in computer vision annotation with Benders decomposition
Simulations for Validation of Vision Systems
Accelerating Convolutional Neural Networks for Continuous Mobile Vision via Cache Reuse
Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities
Complex Networks: New Concepts and Tools for Real-Time Imaging and Vision
Analyzing the Performance of Multilayer Neural Networks for Object Recognition
Learning a Loopy Model For Semantic Segmentation Exactly
siftservice.com - Turning a Computer Vision algorithm into a World Wide Web Service
gvnn: Neural Network Library for Geometric Computer Vision
Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data
Design and Evaluation of Vision-based Head and Face Tracking Interfaces for Assistive Input
ChainerCV: a Library for Deep Learning in Computer Vision
Openmv: A Python powered, extensible machine vision camera
Adversarial Examples that Fool both Human and Computer Vision
The Affine Transforms for Image Enhancement in the Context of Logarithmic Models
DimensionApp : android app to estimate object dimensions
Deep Learning applied to NLP
Interstitial Content Detection
Secrets in Computing Optical Flow by Convolutional Networks
Edge Computing and Dynamic Vision Sensing for Low Delay Access to Visual Medical Information
Consistent sets of lines with no colorful incidence
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
Is Image Super-resolution Helpful for Other Vision Tasks?
Performance Evaluation of Vision-Based Algorithms for MAVs
Active Control of Camera Parameters for Object Detection Algorithms
What Next? A Dozen Information-Technology Research Goals
Selective Image Super-Resolution
Computer Vision Accelerators for Mobile Systems based on OpenCL GPGPU Co-Processing
Human perception in computer vision
Inspiring Computer Vision System Solutions
Embedded Platforms for Computer Vision-based Advanced Driver Assistance Systems: a Survey
Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision
Utility-Based Control for Computer Vision
A survey on Human Computer Interaction Mechanism Using Finger Tracking
Image Analysis in Astronomy for very large vision machine
On the closed-form solution of the rotation matrix arising in computer vision problems
A Hilbert Scheme in Computer Vision
Domain Adaptations for Computer Vision Applications
The Chow Form of the Essential Variety in Computer Vision
Reflection Invariance: an important consideration of image orientation
Parameterizing Region Covariance: An Efficient Way To Apply Sparse Codes On Second Order Statistics
ResearchDoom and CocoDoom: Learning Computer Vision with Games
Replicator Equation: Applications Revisited
Two Hilbert schemes in computer vision
An Extremely Efficient Chess-board Detection for Non-trivial Photos
Understanding the visual speech signal
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition
Fine-grained Activity Recognition in Baseball Videos
An efficient circle detection scheme in digital images using ant system algorithm
GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training
Review on Computer Vision Techniques in Emergency Situation
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
3D Visual Tracking with Particle and Kalman Filters
Automatic liver segmentation method in CT images
A Stochastic Grammar for Natural Shapes
A Minimal Six-Point Auto-Calibration Algorithm
On Minimal Accuracy Algorithm Selection in Computer Vision and Intelligent Systems
Comparisons of Reasoning Mechanisms for Computer Vision
Talk to the Hand: Generating a 3D Print from Photographs
Algebraic Relations and Triangulation of Unlabeled Image Points
Solutions of Quadratic First-Order ODEs applied to Computer Vision Problems
Thin Structure Estimation with Curvature Regularization
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks
The Incremental Multiresolution Matrix Factorization Algorithm
BioTracker: An Open-Source Computer Vision Framework for Visual Animal Tracking
Cloud Computing framework for Computer Vision Research:An Introduction
Intelligent Indoor Mobile Robot Navigation Using Stereo Vision
Robot Vision Architecture for Autonomous Clothes Manipulation
An Image Processing based Object Counting Approach for Machine Vision Application
Virtual Worlds as Proxy for Multi-Object Tracking Analysis
Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow
Robust positioning of drones for land use monitoring in strong terrain relief using vision-based navigation
Use of Computer Vision to Detect Tangles in Tangled Objects
Distributed and Parallel Net Imaging
Illustrating Color Evolution and Color Blindness by the Decoding Model of Color Vision
Vision-based Human Gender Recognition: A Survey
Efficient Point-to-Subspace Query in $\ell^1$: Theory and Applications in Computer Vision
Clustering Learning for Robotic Vision
Pooling-Invariant Image Feature Learning
Robust Subspace Recovery via Bi-Sparsity Pursuit
Challenges in video based object detection in maritime scenario using computer vision
IAT - Image Annotation Tool: Manual
An Approach for Noise Removal on Depth Images
Color Homography Color Correction
Tackling Corruption With Agents & ICT: A Vision
An Analysis of Action Recognition Datasets for Language and Vision Tasks
Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation
Commonsense Scene Semantics for Cognitive Robotics: Towards Grounding Embodied Visuo-Locomotive Interactions
SpeedMachines: Anytime Structured Prediction
Mahotas: Open source software for scriptable computer vision
Crowd Behavior Analysis: A Review where Physics meets Biology
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach
Deep Learning with Energy-efficient Binary Gradient Cameras
An Empirical Study of Adequate Vision Span for Attention-Based Neural Machine Translation
TasselNet: Counting maize tassels in the wild via local counts regression network
Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram
A Computer Vision System to Localize and Classify Wastes on the Streets
QRkit: Sparse, Composable QR Decompositions for Efficient and Stable Solutions to Problems in Computer Vision
Model Validation for Vision Systems via Graphics Simulation
Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems
Leader Follower Formation Control of Ground Vehicles Using Camshift Based Guidance
Simplified vision based automatic navigation for wheat harvesting in low income economies
Assessing The Performance Bounds Of Local Feature Detectors: Taking Inspiration From Electronics Design Practices
A Survey of Current Datasets for Vision and Language Research
Calorie Counter: RGB-Depth Visual Estimation of Energy Expenditure at Home
Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars
System-theoretic approach to image interest point detection
Procams-Based Cybernetics
Computing the Stereo Matching Cost with a Convolutional Neural Network
NeRD: a Neural Response Divergence Approach to Visual Salience Detection
Compression Rate Method for Empirical Science and Application to Computer Vision
Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision
A Standalone Markerless 3D Tracker for Handheld Augmented Reality
Real Time Object Tracking Based on Inter-frame Coding: A Review
Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics
Template Matching based Object Detection Using HOG Feature Pyramid
An Empirical Evaluation of Deep Learning on Highway Driving
Median and Mode Ellipse Parameterization for Robust Contour Fitting
Defensive Distillation is Not Robust to Adversarial Examples
Compensating for Large In-Plane Rotations in Natural Images
EmotioNet Challenge: Recognition of facial expressions of emotion in the wild
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art
Computer vision-based food calorie estimation: dataset, method, and experiment
Exploration of object recognition from 3D point cloud
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications
A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
Some considerations on how the human brain must be arranged in order to make its replication in a thinking machine possible
Resource-Aware Programming for Robotic Vision
Arch2030: A Vision of Computer Architecture Research over the Next 15 Years
Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes
Differential Invariants under Gamma Correction
A Conjecture about a "vision" model for blind men
Free actions and Grassmanian variety
Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision
Texture Defect Detection in Gradient Space
Fast and numerically stable circle fit
Examining Representational Similarity in ConvNets and the Primate Visual Cortex
Recovery of structure of looped jointed objects from multiframes
Projective reconstruction in algebraic vision
Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems
What Will I Do Next? The Intention from Motion Experiment
Complex Networks, Simple Vision
Entropy And Vision
Isometric Embeddings in Imaging and Vision: Facts and Fiction
Picture Collage with Genetic Algorithm and Stereo vision
A Unified Quantitative Model of Vision and Audition
Araguaia Medical Vision Lab at ISIC 2017 Skin Lesion Classification Challenge
Semantic Robot Vision Challenge: Current State and Future Directions
Model-based Influence Diagrams for Machine Vision
3D Vision Guided Robotic Charging Station for Electric and Plug-in Hybrid Vehicles
Vision Based Game Development Using Human Computer Interaction
Object Detection Through Exploration With A Foveated Visual Field
The Digital Humanities Unveiled: Perceptions Held by Art Historians and Computer Scientists about Computer Vision Technology
Hardware based Scale- and Rotation-Invariant Feature Extraction: A Retrospective Analysis and Future Directions
ROI Segmentation for Feature Extraction from Human Facial Images
Causal graph-based video segmentation
A Survey on Eye-Gaze Tracking Techniques
A Novel Method for Vectorization
Hierarchical structure-and-motion recovery from uncalibrated images
Resnet in Resnet: Generalizing Residual Architectures
Training Sparse Neural Networks
A Novel Approach for Image Segmentation based on Histograms computed from Hue-data
Widening siamese architectures for stereo matching
Kernel Spectral Curvature Clustering (KSCC)
Toward Parts-Based Scene Understanding with Pixel-Support Parts-Sparse Pictorial Structures
Designing an FPGA Synthesizable Computer Vision Algorithm to Detect the Greening of Potatoes
A Cognitive Model for Humanoid Robot Navigation and Mapping using Alderbaran NAO
Sparse Modeling for Image and Vision Processing
On Vectorization of Deep Convolutional Neural Networks for Vision Tasks
Predicting Motivations of Actions by Leveraging Text
Edge Detection for Pattern Recognition: A Survey
Deep Motion Features for Visual Tracking
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Coarse-to-Fine Lifted MAP Inference in Computer Vision
CNN Fixations: An unraveling approach to visualize the discriminative image regions
Automated Identification of Trampoline Skills Using Computer Vision Extracted Pose Estimation
Semantic 3D Reconstruction with Finite Element Bases
Training and Testing Object Detectors with Virtual Images
A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
Retinal Vessel Segmentation Using A New Topological Method
Competitive Analysis of Minimum-Cut Maximum Flow Algorithms in Vision Problems
Pixels to Voxels: Modeling Visual Representation in the Human Brain
Play and Learn: Using Video Games to Train Computer Vision Models
A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image
DeepCorrect: Correcting DNN models against Image Distortions
First-Order Modeling and Stability Analysis of Illusory Contours
Bregman Divergences for Infinite Dimensional Covariance Matrices
Memory-Efficient Design Strategy for a Parallel Embedded Integral Image Computation Engine
AirDraw: Leveraging Smart Watch Motion Sensors for Mobile Human Computer Interactions
Field Geology with a Wearable Computer: First Results of the Cyborg Astrobiologist System
Rethinking the Inception Architecture for Computer Vision
ABHIVYAKTI: A Vision Based Intelligent System for Elder and Sick Persons
Implementation of a Vision System for a Landmine Detecting Robot Using Artificial Neural Network
On Considering Uncertainty and Alternatives in Low-Level Vision
Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images
Design and Analysis of a Single-Camera Omnistereo Sensor for Quadrotor Micro Aerial Vehicles (MAVs)
Generic decoding of seen and imagined objects using hierarchical visual features
Detecting People in Cubist Art
What value do explicit high level concepts have in vision to language problems?
To Know Where We Are: Vision-Based Positioning in Outdoor Environments
Möbius Invariants of Shapes and Images
Set-Point Regulation of Linear Continuous-Time Systems using Neuromorphic Vision Sensors
Learning Grimaces by Watching TV
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
Utilizing Semantic Visual Landmarks for Precise Vehicle Navigation
Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation
Incremental Color Quantization for Color-Vision-Deficient Observers Using Mobile Gaming Data
A Neural Markovian Concurrent Image Labeling Algorithm
On the Cell-based Complexity of Recognition of Bounded Configurations by Finite Dynamic Cellular Automata
Fast Planar Correlation Clustering for Image Segmentation
Gender Recognition in Walk Gait through 3D Motion by Quadratic Bezier Curve and Statistical Techniques
Fingertip Detection: A Fast Method with Natural Hand
Content Based Image Retrieval System Using NOHIS-tree
An Integrated System for 3D Gaze Recovery and Semantic Analysis of Human Attention
Rigid-Motion Scattering for Texture Classification
A Review of Image Mosaicing Techniques
Analysis of Gait Pattern to Recognize the Human Activities
Clustering Approach Towards Image Segmentation: An Analytical Study
Learning Multi-Scale Representations for Material Classification
A Robust Point Sets Matching Method
Attributes as Semantic Units between Natural Language and Visual Recognition
Hands Deep in Deep Learning for Hand Pose Estimation
On Computing the Translations Norm in the Epipolar Graph
Learning to Compare Image Patches via Convolutional Neural Networks
Rigid Multiview Varieties
The use of deep learning in image segmentation, classification and detection
A probabilistic tour of visual attention and gaze shift computational models
Inferring low-dimensional microstructure representations using convolutional neural networks
Towards Applying the OPRA Theory to Shape Similarity
Fast Convolutional Sparse Coding in the Dual Domain
Can you find a face in a HEVC bitstream?
Going Further with Point Pair Features
Keypoint-based object tracking and localization using networks of low-power embedded smart cameras
Synthesizing Novel Pairs of Image and Text
Attention on Attention: Architectures for Visual Question Answering (VQA)
Modeling Visual Information Processing in Brain: A Computer Vision Point of View and Approach
The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM
A New Computational Framework For 2D Shape-Enclosing Contours
Quantized Convolutional Neural Networks for Mobile Devices
A clever elimination strategy for efficient minimal solvers
Hand Gesture Real Time Paint Tool - Box
A computer verified, monadic, functional implementation of the integral
A Meta-Theory of Boundary Detection Benchmarks
ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition
Learning to Transfer Privileged Information
When Computer Vision Gazes at Cognition
Investigating Natural Image Pleasantness Recognition using Deep Features and Eye Tracking for Loosely Controlled Human-computer Interaction
Multiscale Computing in the Exascale Era
Humans and deep networks largely agree on which kinds of variation make object recognition harder
Geometric Analysis of the Conformal Camera for Intermediate-Level Vision and Perisaccadic Perception
Modeling Instantaneous Changes In Natural Scenes
Iterative Grassmannian Optimization for Robust Image Alignment
A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges
ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks using Angle Sensitive Pixels
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper
Convolutional Networks in Visual Environments
Cyborg Systems as Platforms for Computer-Vision Algorithm-Development for Astrobiology
Image decomposition with anisotropic diffusion applied to leaf-texture analysis
Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
A Computer Vision System for Attention Mapping in SLAM based 3D Models
Efficient Regularization of Squared Curvature
On Learning Where To Look
What you need to know about the state-of-the-art computational models of object-vision: A tour through the models
Measurement of Road Traffic Parameters Based on Multi-Vehicle Tracking
Enhancing Feature Tracking With Gyro Regularization
Visualization Regularizers for Neural Network based Image Recognition
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
Invariant Representative Cocycles of Cohomology Generators using Irregular Graph Pyramids
A Fast Semidefinite Approach to Solving Binary Quadratic Problems
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval
Estimating the Potential Speedup of Computer Vision Applications on Embedded Multiprocessors
Superpixelizing Binary MRF for Image Labeling Problems
Origami: A 803 GOp/s/W Convolutional Network Accelerator
Total Variation Applications in Computer Vision
Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?
Tutorial on Answering Questions about Images with Deep Learning
Visual Question Answering: Datasets, Algorithms, and Future Challenges
Photographic home styles in Congress: a computer vision approach
Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media
Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild
On human motion prediction using recurrent neural networks
Facial Affect Estimation in the Wild Using Deep Residual and Convolutional Networks
Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation
Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
Towards a Dedicated Computer Vision Tool set for Crowd Simulation Models
A Study on Topological Descriptors for the Analysis of 3D Surface Texture
AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
Non-local Neural Networks
Latency and Throughput Characterization of Convolutional Neural Networks for Mobile Computer Vision
Deep Learning For Computer Vision Tasks: A review
A Machine Learning Approach to Recovery of Scene Geometry from Images
A Multi-Camera Image Processing and Visualization System for Train Safety Assessment
A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors
Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research
Intelligent Health Recommendation System for Computer Users
Biologically Inspired Hierarchical Model for Feature Extraction and Localization
Perception games, the image understanding and interpretational geometry
On Ullman's theorem in computer vision
A dyadic solution of relative pose problems
Automatic system for counting cells with elliptical shape
Assessing the Value of 3D Reconstruction in Building Construction
Convolutional Neural Networks Applied to House Numbers Digit Classification
Fusing image representations for classification using support vector machines
Investigating the performance of Correspondence Algorithms in Vision based Driver-assistance in Indoor Environment
Stopping Criterion for the Mean Shift Iterative Algorithm
Reduced egomotion estimation drift using omnidirectional views
A Vision on the Status and Evolution of HEP Physics Software Tools
A Novel Georeferenced Dataset for Stereo Visual Odometry
A State Of the Art Report on Research in Multiple RGB-D sensor Setups
The role of RGB-D benchmark datasets: an overview
An Experimental Comparison of Trust Region and Level Sets
DeepPose: Human Pose Estimation via Deep Neural Networks
An Adaptive Dictionary Learning Approach for Modeling Dynamical Textures
Proceedings of The 38th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM), 2014
Homotopy equivalence of finite digital images
Pattern Encoding on the Poincare Sphere
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
Autonomous Farm Vehicles: Prototype of Power Reaper
Riemannian Metric Learning for Symmetric Positive Definite Matrices
Advances in Human Action Recognition: A Survey
A Framework for Fast Face and Eye Detection
Fine-grained Recognition Datasets for Biodiversity Analysis
Human Head Pose Estimation by Facial Features Location
An Extension to Hough Transform Based on Gradient Orientation
The Fast Bilateral Solver
Bidirectional Warping of Active Appearance Model
Loss Functions for Neural Networks for Image Processing
Structure from Motion on a Sphere
Amodal Instance Segmentation
GPU-based Image Analysis on Mobile Devices
Descriptor learning for omnidirectional image matching
Object Tracking in Videos: Approaches and Issues
Reading Ancient Coin Legends: Object Recognition vs. OCR
Image Acquisition in an Underwater Vision System with NIR and VIS Illumination
Real-time Pedestrian Surveillance with Top View Cumulative Grids
Imaging with Rays: Microscopy, Medical Imaging, and Computer Vision
Action Recognition in the Frequency Domain
Speeding-up Graphical Model Optimization via a Coarse-to-fine Cascade of Pruning Classifiers
Convex Color Image Segmentation with Optimal Transport Distances
Learning to Linearize Under Uncertainty
InAR:Inverse Augmented Reality
Low Rank Representation on Riemannian Manifold of Square Root Densities
SPECFACE - A Dataset of Human Faces Wearing Spectacles
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
Evaluation of Object Detection Proposals Under Condition Variations
On the Relation between two Rotation Metrics
2D SEM images turn into 3D object models
Region Graph Based Method for Multi-Object Detection and Tracking using Depth Cameras
Road Detection through Supervised Classification
Interactive Image Segmentation From A Feedback Control Perspective
Boundary conditions for Shape from Shading
Depth Estimation Through a Generative Model of Light Field Synthesis
Distortion Varieties
Cloud Dictionary: Sparse Coding and Modeling for Point Clouds
Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision
Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection
From visual words to a visual grammar: using language modelling for image classification
A Fast HOG Descriptor Using Lookup Table and Integral Image
Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot
Convolutional Neural Pyramid for Image Processing
High-Quality Correspondence and Segmentation Estimation for Dual-Lens Smart-Phone Portraits
Unsupervised Learning by Predicting Noise
CORe50: a New Dataset and Benchmark for Continuous Object Recognition
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
Generalized Convolutional Neural Networks for Point Cloud Data
Identifying 3 moss species by deep learning, using the "chopped picture" method
3D Pose Regression using Convolutional Neural Networks
Two-stream Flow-guided Convolutional Attention Networks for Action Recognition
Exploring Geometric Property Thresholds For Filtering Non-Text Regions In A Connected Component Based Text Detection Application
A LBP Based Correspondence Identification Scheme for Multi-view Sensing Network
Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
Can the early human visual system compete with Deep Neural Networks?
Conditional Autoencoders with Adversarial Information Factorization
Leaf Identification Using a Deep Convolutional Neural Network
Guided Labeling using Convolutional Neural Networks
Enhanced Characterness for Text Detection in the Wild
Visual Based Navigation of Mobile Robots
Image Registration Techniques: A Survey
Learning audio and image representations with bio-inspired trainable feature extractors
Generating Instance Segmentation Annotation by Geometry-guided GAN
Bridging Cognitive Programs and Machine Learning
Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches
Satellite imagery analysis for operational damage assessment in Emergency situations
An Introduction to Image Synthesis with Generative Adversarial Nets
Transferring Rich Deep Features for Facial Beauty Prediction
A Pyramid CNN for Dense-Leaves Segmentation
Joint interpretation of on-board vision and static GPS cartography for determination of correct speed limit
Enhancement Techniques for Local Content Preservation and Contrast Improvement in Images
Efficient Selection of Disambiguating Actions for Stereo Vision
Understanding How Image Quality Affects Deep Neural Networks
Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition
Unsupervised Video Analysis Based on a Spatiotemporal Saliency Detector
A New Approach of Gray Images Binarization with Threshold Methods
UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory
Recurrent 3D Attentional Networks for End-to-End Active Object Recognition in Cluttered Scenes
A Dataset for Developing and Benchmarking Active Vision
Enhancing human color vision by breaking binocular redundancy
Person Re-Identification with Vision and Language
Enhancing Underwater Imagery using Generative Adversarial Networks
Fusion of stereo and still monocular depth estimates in a self-supervised learning context
Multi-GPU Training of ConvNets
Computational Models for Multiview Dense Depth Maps of Dynamic Scene
Pixel Normalization from Numeric Data as Input to Neural Networks
Self-organizing neural networks in classification and image recognition
chi2TeX Semi-automatic translation from chiwriter to LaTeX
Segmentation for radar images based on active contour
Extension of Path Probability Method to Approximate Inference over Time
Logical methods of object recognition on satellite images using spatial constraints
Extending Bron Kerbosch for Solving the Maximum Weight Clique Problem
Kunchenko's Polynomials for Template Matching
Kernel diff-hash
A Topological Code for Plane Images
Watersheds on edge or node weighted graphs "par l'exemple"
Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification
The complex-valued encoding for dicision-making based on aliasing data
Continuous Optimization for Fields of Experts Denoising Works
Electrocardiography Separation of Mother and Baby
Next Generation Multicuts for Semi-Planar Graphs
Calculate distance to object in the area where car, using video analysis
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results
Towards Miss Universe Automatic Prediction: The Evening Gown Competition
On the Existence of a Projective Reconstruction
Convolutional Neural Networks learn compact local image descriptors
Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Image Enhancement Using a Generalization of Homographic Function
Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How Many Objects can iCub Learn?
An On-line Variational Bayesian Model for Multi-Person Tracking from Cluttered Scenes
Polyhedron Volume-Ratio-based Classification for Image Recognition
Photographic dataset: random peppercorns
Biconvex Relaxation for Semidefinite Programming in Computer Vision
Review of Action Recognition and Detection Methods
The HASYv2 dataset
Challenge of Multi-Camera Tracking
ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection
Automatic skin lesion segmentation with fully convolutional-deconvolutional networks
A Dynamic Programming Solution to Bounded Dejittering Problems
Graphcut Texture Synthesis for Single-Image Superresolution
Online Handwritten Mathematical Expressions Recognition System Using Fuzzy Neural Network
Human-Level Intelligence or Animal-Like Abilities?
Network Analysis for Explanation
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Building an Integrated Mobile Robotic System for Real-Time Applications in Construction
Exploiting deep residual networks for human action recognition from skeletal data
Human Emotional Facial Expression Recognition
Fast and Accurate Surface Normal Integration on Non-Rectangular Domains
In Quest of Image Semantics: Are We Looking for It Under the Right Lamppost?
View Based Methods can achieve Bayes-Optimal 3D Recognition
Many-to-Many Graph Matching: a Continuous Relaxation Approach
Asymmetric Totally-corrective Boosting for Real-time Object Detection
Featureless 2D-3D Pose Estimation by Minimising an Illumination-Invariant Loss
A Unified Multiscale Framework for Discrete Energy Minimization
Analysis of Multi-Scale Fractal Dimension to Classify Human Motion
Discrete Energy Minimization, beyond Submodularity: Applications and Approximations
GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures
Unsupervised Feature Learning for low-level Local Image Descriptors
Multiview Hessian Discriminative Sparse Coding for Image Annotation
Random Binary Mappings for Kernel Learning and Efficient SVM
Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution
Submodularization for Quadratic Pseudo-Boolean Optimization
Circle detection on images using Learning Automata
Fast and Robust Archetypal Analysis for Representation Learning
Calibration of Multiple Fish-Eye Cameras Using a Wand
Mobile Camera Array Calibration for Light Field Acquisition
Visual Word Selection without Re-Coding and Re-Pooling
Caffe: Convolutional Architecture for Fast Feature Embedding
Show and Tell: A Neural Image Caption Generator
Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors
Detection of Non-Stationary Photometric Perturbations on Projection Screens
The Cross-Depiction Problem: Computer Vision Algorithms for Recognising Objects in Artwork and in Photographs
Discovering Attribute Shades of Meaning with the Crowd
Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices
Leveraging Context to Support Automated Food Recognition in Restaurants
Deep Mean Maps
Natural Language Object Retrieval
Data-dependent Initializations of Convolutional Neural Networks
Context-aware CNNs for person head detection
Finding Optimal Combination of Kernels using Genetic Programming
Parametric Object Motion from Blur
Learning by tracking: Siamese CNN for robust target association
Deep Learning the City : Quantifying Urban Perception At A Global Scale
Hard Negative Mining for Metric Learning Based Zero-Shot Classification
Linking Image and Text with 2-Way Nets
Sparse Image Representation with Epitomes
Bayesian ensemble learning for image denoising
Large-margin Learning of Compact Binary Image Encodings
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
Unsupervised Network Pretraining via Encoding Human Design
Efficient SDP Inference for Fully-connected CRFs Based on Low-rank Decomposition
Adaptive Locally Affine-Invariant Shape Matching
Towards Distortion-Predictable Embedding of Neural Networks
Dictionary Learning and Sparse Coding for Third-order Super-symmetric Tensors
Color-Phase Analysis for Sinusoidal Structured Light in Rapid Range Imaging
Color-Stripe Structured Light Robust to Surface Color and Discontinuity
Loss Functions for Top-k Error: Analysis and Insights
Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression
Remote Health Coaching System and Human Motion Data Analysis for Physical Therapy with Microsoft Kinect
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
Space-Time Representation of People Based on 3D Skeletal Data: A Review
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization
Survey on the attention based RNN model and its applications in computer vision
Robust Multi-body Feature Tracker: A Segmentation-free Approach
Learning Attributes Equals Multi-Source Domain Generalization
Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images
Hierarchical Piecewise-Constant Super-regions
Superpixel Hierarchy
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks
Small-Variance Nonparametric Clustering on the Hypersphere
Complexity of Discrete Energy Minimization Problems
Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
Semantic Decomposition and Recognition of Long and Complex Manipulation Action Sequences
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression
DeepSetNet: Predicting Sets with Deep Neural Networks
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth
EgoReID: Cross-view Self-Identification and Human Re-identification in Egocentric and Surveillance Videos
Guaranteed Parameter Estimation for Discrete Energy Minimization
Domain Adaptation for Visual Applications: A Comprehensive Survey
The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment
Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning
Deep Unsupervised Similarity Learning using Partially Ordered Sets
DeepPermNet: Visual Permutation Learning
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
A Review on Deep Learning Techniques Applied to Semantic Segmentation
Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps
Unmasking the abnormal events in video
Self-supervised learning of visual features through embedding images into text topic spaces
A watershed-based algorithm to segment and classify cells in fluorescence microscopy images
Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
Learning Robust Representations for Computer Vision
Dual-Glance Model for Deciphering Social Relationships
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
Attentive Semantic Video Generation using Captions
The Conditional Analogy GAN: Swapping Fashion Articles on People Images
Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines
A Hierarchical Probabilistic Model for Facial Feature Detection
Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion
Drought Stress Classification using 3D Plant Models
Visual speech recognition: aligning terminologies for better understanding
High efficiency compression for object detection
Structured learning and detailed interpretation of minimal object images
Wasserstein Divergence for GANs
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
The challenge of simultaneous object detection and pose estimation: a comparative study
A Benchmark and Evaluation of Non-Rigid Structure from Motion
Learning Semantic Segmentation with Diverse Supervision
Deep Visual Domain Adaptation: A Survey
Challenging Images For Minds and Machines
Multispectral Image Intrinsic Decomposition via Low Rank Constraint
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
Who Let The Dogs Out? Modeling Dog Behavior From Visual Data
3D Pose Estimation and 3D Model Retrieval for Objects in the Wild
Vision as Adaptive Epistemology
High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision
Google's Cloud Vision API Is Not Robust To Noise
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
Efficient variational inference in large-scale Bayesian compressed sensing
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
Escort: Efficient Sparse Convolutional Neural Networks on GPUs
Does a Plane Imitate a Bird? Does Computer Vision Have to Follow Biological Paradigms?
A Low Cost Vision Based Hybrid Fiducial Mark Tracking Technique for Mobile Industrial Robots
Inverse Graphics with Probabilistic CAD Models
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Hierarchical Multi-scale Attention Networks for Action Recognition
Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery
Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons
A Multi-Stage Multi-Task Neural Network for Aerial Scene Interpretation and Geolocalization
A Novice Guide towards Human Motion Analysis and Understanding
Introducing the Computable Universe
Soft Computing Techniques for Change Detection in remotely sensed images : A Review
A Novel Architecture for Computing Approximate Radon Transform
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
Playing for Data: Ground Truth from Computer Games
A $p$-adic RanSaC algorithm for stereo vision using Hensel lifting
A Multi-Agents Architecture to Learn Vision Operators and their Parameters
Simplifying Energy Optimization using Partial Enumeration
Unsupervised feature learning by augmenting single images
Fast Localization of Facial Landmark Points
Scalable Matting: A Sub-linear Approach
Optimal measurement of visual motion across spatial and temporal scales
Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach
A Pattern Recognition System for Detecting Use of Mobile Phones While Driving
Consensus Message Passing for Layered Graphical Models
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
Low Cost Semi-Autonomous Agricultural Robots In Pakistan-Vision Based Navigation Scalable methodology for wheat harvesting
Learning Visual Features from Large Weakly Supervised Data
Automatically selecting inference algorithms for discrete energy minimisation
Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes
Searching for Objects using Structure in Indoor Scenes
Object Detection from Video Tubelets with Convolutional Neural Networks
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations
A Study of Vision based Human Motion Recognition and Analysis
Learning to relate images: Mapping units, complex cells and simultaneous eigenspaces
Automated Switching System for Skin Pixel Segmentation in Varied Lighting
Distributed Low-rank Subspace Segmentation
Building Proteins in a Day: Efficient 3D Molecular Reconstruction
A Neural Algorithm of Artistic Style
An Uncertain Future: Forecasting from Static Images using Variational Autoencoders
A quantitative analysis of tilt in the Café Wall illusion: a bioplausible model for foveal and peripheral vision
Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation
Template Matching Advances and Applications in Image Analysis
Visual motion processing and human tracking behavior
The More You Know: Using Knowledge Graphs for Image Classification
Chord Angle Deviation using Tangent (CADT), an Efficient and Robust Contour-based Corner Detector
Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing
DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition
Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System
Gaussian Processes with Context-Supported Priors for Active Object Localization
Superpixel-based Semantic Segmentation Trained by Statistical Process Control
Place recognition: An Overview of Vision Perspective
Detecting and Grouping Identical Objects for Region Proposal and Classification
Learning Uncertain Convolutional Features for Accurate Saliency Detection
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing
Mass Displacement Networks
Deep Scene Text Detection with Connected Component Proposals
In search of inliers: 3d correspondence by local and global voting
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms
Identifying Mirror Symmetry Density with Delay in Spiking Neural Networks
Learning Compact Geometric Features
Scene-centric Joint Parsing of Cross-view Videos
Playing for Benchmarks
Multi-Task Learning by Deep Collaboration and Application in Facial Landmark Detection
Object Referring in Visual Scene with Spoken Language
The Perception-Distortion Tradeoff
Vision Based Railway Track Monitoring using Deep Learning
xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
Excitation Backprop for RNNs
Deep Depth Inference using Binocular and Monocular Cues
Discriminant Projection Representation-based Classification for Vision Recognition
On the Duality Between Retinex and Image Dehazing
OneDataShare: A Vision for Cloud-hosted Data Transfer Scheduling and Optimization as a Service
Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld
Toward Natural Gesture/Speech Control of a Large Display
A Tool for Integer Homology Computation: Lambda-At Model
Camera Calibration: a USU Implementation
Top-Down Unsupervised Image Segmentation (it sounds like oxymoron, but actually it is not)
Computational Vision in Nature and Technology
Co-occurrence Matrix and Fractal Dimension for Image Segmentation
Pedestrian Detection with Unsupervised Multi-Stage Feature Learning
A Prototyping Environment for Integrated Artificial Attention Systems
Semantic Annotation: The Mainstay of Semantic Web
Text Based Approach For Indexing And Retrieval Of Image And Video: A Review
Revised Version of a JCIT Paper-Comparison of Feature Point Extraction Algorithms for Vision Based Autonomous Aerial Refueling
On the Convergence of the Mean Shift Algorithm in the One-Dimensional Space
Flying Objects Detection from a Single Moving Camera
Fingertip in the Eye: A cascaded CNN pipeline for the real-time fingertip detection in egocentric videos
Graph-based denoising for time-varying point clouds
Client-Driven Content Extraction Associated with Table
Evidential Reasoning in Parallel Hierarchical Vision Programs
Sparkle Vision: Seeing the World through Random Specular Microfacets
Human Shape Variation - An Efficient Implementation using Skeleton
A Novel Approach For Finger Vein Verification Based on Self-Taught Learning
Preprint ARPPS Augmented Reality Pipeline Prospect System
Person Recognition in Personal Photo Collections
Depth Superresolution using Motion Adaptive Regularization
Feature-based Recursive Observer Design for Homography Estimation
Autonomous Ingress of a UAV through a window using Monocular Vision
25 years of CNNs: Can we compare to human abstraction capabilities?
Vanishing point detection with convolutional neural networks
Fully-Trainable Deep Matching
Revisiting Winner Take All (WTA) Hashing for Sparse Datasets
Scene Flow Estimation: A Survey
Inverse Compositional Spatial Transformer Networks
Mirrored Light Field Video Camera Adapter
A/D Converter Architectures for Energy-Efficient Vision Processor
Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination
See, Hear, and Read: Deep Aligned Representations
Nonlinear Embedding Transform for Unsupervised Domain Adaptation
Original Loop-closure Detection Algorithm for Monocular vSLAM
Multiple-Kernel Local-Patch Descriptor
Capacity limitations of visual search in deep convolutional neural network
Monocular Navigation in Large Scale Dynamic Environments
Visual Reasoning with Natural Language
Simulating Structure-from-Motion
Image matting with normalized weight and semi-supervised learning
Superpixel clustering with deep features for unsupervised road segmentation
A Parallel Algorithm for Dilated Contour Extraction from Bilevel Images
Least squares fitting of circles and lines
Field geology with a wearable computer: 1st results of the Cyborg Astrobiologist System
Parametrical Neural Networks and Some Other Similar Architectures
Spatio-Temporal Electromagnetic Field Shapes and their Logical Processing
Automatic Detection of Pulmonary Embolism using Computational Intelligence
Bayesian Nonlinear Principal Component Analysis Using Random Fields
Necessary Conditions for Discontinuities of Multidimensional Size Functions
Mapping Images with the Coherence Length Diagrams
Active Testing for Face Detection and Localization
Bilateral filters: what they can and cannot do
Geometric Models with Co-occurrence Groups
Visual Concept Detection and Real Time Object Detection
Modelling Distributed Shape Priors by Gibbs Random Fields of Second Order
Eye Pupil Location Using Webcam
The ideal of the trifocal variety
Simulation of Fractional Brownian Surfaces via Spectral Synthesis on Manifolds
Genetic Stereo Matching Algorithm with Fuzzy Fitness
Similarity- based approach for outlier detection
Massively Deep Artificial Neural Networks for Handwritten Digit Recognition
Time-domain multiscale shape identification in electro-sensing
CITlab ARGUS for Arabic Handwriting
Effective persistent homology of digital images
Understanding Deep Convolutional Networks
Handwritten Recognition Using SVM, KNN and Neural Network
Synthesising Dynamic Textures using Convolutional Neural Networks
A filter based approach for inbetweening
Combinational neural network using Gabor filters for the classification of handwritten digits
Algebraic Image Processing
Determination of Digital Straight Segments Using the Slope
On the impact of quantum computing technology on future developments in high-performance scientific computing
Using Self-Contradiction to Learn Confidence Measures in Stereo Vision
Egocentric Height Estimation
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network
Human-like Clustering with Deep Convolutional Neural Networks
An End-to-End Compression Framework Based on Convolutional Neural Networks
Adaptive Deep Learning through Visual Domain Localization
Event-based Moving Object Detection and Tracking
Recovering an Algebraic Curve Using its Projections From Different Points. Applications to Static and Dynamic Computational Vision
Formulation Of A N-Degree Polynomial For Depth Estimation using a Single Image
Extracting Parts of 2D Shapes Using Local and Global Interactions Simultaneously
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation
An efficient Exact-PGA algorithm for constant curvature manifolds
Saliency Driven Object recognition in egocentric videos with deep CNN
Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation
Fast Multi-frame Stereo Scene Flow with Motion Segmentation
Folded Recurrent Neural Networks for Future Video Prediction
The Cyborg Astrobiologist: First Field Experience
Compressive adaptive computational ghost imaging
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization
Potts model, parametric maxflow and k-submodular functions
Performance Evaluation of Raster Based Shape Vectors in Object Recognition
Correlation Filters with Limited Boundaries
Real Time Speckle Image De-Noising
Fast and Accurate Bilateral Filtering using Gauss-Polynomial Decomposition
DeepFool: a simple and accurate method to fool deep neural networks
MAX-CSP, Graph Cuts and Statistical Physics
On learning optimized reaction diffusion processes for effective image restoration
Fast and Robust Hand Tracking Using Detection-Guided Optimization
Energy-Efficient ConvNets Through Approximate Computing
LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning
Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals
Spatially Adaptive Computation Time for Residual Networks
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning
Efficiently Computing Piecewise Flat Embeddings for Data Clustering and Image Segmentation
Computer methods for 3D motion tracking in real-time
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
Gazing into the Abyss: Real-time Gaze Estimation
Some Applications of Algebraic Curves to Computational Vision
Making a Science of Model Search
Dimensionality Reduction and Reconstruction using Mirroring Neural Networks and Object Recognition based on Reduced Dimension Characteristic Vector
Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions
Seeking multi-thresholds for image segmentation with Learning Automata
Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM
Going Deeper in Facial Expression Recognition using Deep Neural Networks
Diving deeper into mentee networks
Multiview Differential Geometry of Curves
An improved computer vision method for detecting white blood cells
Understanding Image Virality
Computational Imaging for VLBI Image Reconstruction
Digitizing Municipal Street Inspections Using Computer Vision
Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task
Revisiting Deep Intrinsic Image Decompositions
Deep Learning in the Automotive Industry: Applications and Tools
Order embeddings and character-level convolutions for multimodal alignment
Comparing deep neural networks against humans: object recognition when the signal gets weaker
Discriminative Optimization: Theory and Applications to Computer Vision Problems
Learning Discriminative Alpha-Beta-divergence for Positive Definite Matrices (Extended Version)
Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence
An Adaptive Fuzzy-Based System to Simulate, Quantify and Compensate Color Blindness
Translation of "Zur Ermittlung eines Objektes aus zwei Perspektiven mit innerer Orientierung" by Erwin Kruppa (1913)
AFT*: Integrating Active Learning and Transfer Learning to Reduce Annotation Efforts
Hand Gesture Controlled Drones: An Open Source Library
Face Detection with Effective Feature Extraction
Vision-Based Navigation I: A navigation filter for fusing DTM/correspondence updates
WILI - Web Interface for people with Lowvision Issues
Invariance of visual operations at the level of receptive fields
Binocular disparity as an explanation for the moon illusion
Egocentric vision IT technologies for Alzheimer disease assessment and studies
Analysis of Amoeba Active Contours
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Electrotactile vision substitution for 3D trajectory following
Neural perceptual model to global-local vision for recognition of the logical structure of administrative documents
Are all training examples equally valuable?
An Improved Tracking using IMU and Vision Fusion for Mobile Augmented Reality Applications
Structured Hough Voting for Vision-based Highway Border Detection
Vision and Learning for Deliberative Monocular Cluttered Flight
Capturing the Dynamics of Pedestrian Traffic Using a Machine Vision System
Relaxed Multiple-Instance SVM with Application to Object Discovery
In the sight of my wearable camera: Classifying my visual experience
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
Preprint Extending Touch-less Interaction on Vision Based Wearable Device
Object Level Deep Feature Pooling for Compact Image Representation
Compression Artifacts Reduction by a Deep Convolutional Network
Vision System and Depth Processing for DRC-HUBO+
Optimizing Gaze Direction in a Visual Navigation Task
Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance
All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles
A Light-powered, Always-On, Smart Camera with Compressed Domain Gesture Detection
Vision-based Traffic Flow Prediction using Dynamic Texture Model and Gaussian Process
Introspective Perception: Learning to Predict Failures in Vision Systems
From Monocular SLAM to Autonomous Drone Exploration
MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
Dual Attention Networks for Multimodal Reasoning and Matching
Wearable Vision Detection of Environmental Fall Risks using Convolutional Neural Networks
Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard Species using Artificial Vision Algorithms
Sparse Factorization Layers for Neural Networks with Limited Supervision
Egocentric Video Description based on Temporally-Linked Sequences
Connecting Look and Feel: Associating the visual and tactile properties of physical materials
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Shading Annotations in the Wild
Topometric Localization with Deep Learning
Rotational Rectification Network: Enabling Pedestrian Detection for Mobile Vision
Team Applied Robotics: A closer look at our robotic picking system
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Recognizing Objects In-the-wild: Where Do We Stand?
Towards Decentralised Resilient Community Cloud Infrastructures
Adapting Engineering Education to Industrie 4.0 Vision
Separating Self-Expression and Visual Content in Hashtag Supervision
Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking
Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering
Deep Collaborative Weight-based Classification
The organizing vision of integrated health information systems
Dynamic Vision Sensors for Human Activity Recognition
Robust event-stream pattern tracking based on correlative filter
A Comprehensive Analysis of Deep Regression
Compare and Contrast: Learning Prominent Visual Differences
Computational Unification: a Vision for Connecting Researchers
The Cyborg Astrobiologist: Porting from a wearable computer to the Astrobiology Phone-cam
Image Processing in Optical Guidance for Autonomous Landing of Lunar Probe
Bio-inspired speed detection and discrimination
Personalised product design using virtual interactive techniques
Generalized Boundaries from Multiple Image Interpretations
Mouse Simulation Using Two Coloured Tapes
Linearized Alternating Direction Method with Adaptive Penalty and Warm Starts for Fast Solving Transform Invariant Low-Rank Textures
Color Assessment and Transfer for Web Pages
A comparative study on face recognition techniques and neural network
Image Registration for Stability Testing of MEMS
Computer simulation based parameter selection for resistance exercise
A Novel Equation based Classifier for Detecting Human in Images
Is Bottom-Up Attention Useful for Scene Recognition?
Fast Approximate $K$-Means via Cluster Closures
Fast Training of Convolutional Networks through FFTs
Multi Modal Face Recognition Using Block Based Curvelet Features
Affine Subspace Representation for Feature Description
Optimal Radiometric Calibration for Camera-Display Communication
A Dataset for Movie Description
Classification of Occluded Objects using Fast Recurrent Processing
Galaxy morphology - an unsupervised machine learning approach
A Simple Yet Effective Improvement to the Bilateral Filter for Image Denoising
Accelerating Very Deep Convolutional Networks for Classification and Detection
Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion
Facial Expression Recognition Using Sparse Gaussian Conditional Random Field
A dense subgraph based algorithm for compact salient image region detection
Convolutional Feature Masking for Joint Object and Stuff Segmentation
Object Detectors Emerge in Deep Scene CNNs
Simple Image Description Generator via a Linear Phrase-Based Approach
An exploration of parameter redundancy in deep networks with circulant projections
Convolutional Channel Features
Talking about the Moving Image: A Declarative Model for Image Schema Based Embodied Perception Grounding and Language Generation
Toward a Taxonomy and Computational Models of Abnormalities in Images
A framework for robust object multi-detection with a vote aggregation and a cascade filtering
Human Attention Estimation for Natural Images: An Automatic Gaze Refinement Approach
Deep Learning For Smile Recognition
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks
Do We Need Binary Features for 3D Reconstruction?
Resource Constrained Structured Prediction
Revisiting Active Perception
Fast and High-Quality Bilateral Filtering Using Gauss-Chebyshev Approximation
Movie Description
Pooling Faces: Template based Face Recognition with Pooled Face Images
Deep Structured-Output Regression Learning for Computational Color Constancy
Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research
Fixed-point Factorized Networks
Geometry of 3D Environments and Sum of Squares Polynomials
Easy-setup eye movement recording system for human-computer interaction
Second-order Convolutional Neural Networks
A Bag-of-Words Equivalent Recurrent Neural Network for Action Recognition
A Holistic Approach for Optimizing DSP Block Utilization of a CNN implementation on FPGA
Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises
WRPN: Training and Inference using Wide Reduced-Precision Networks
Feature Enhancement in Visually Impaired Images
A Fast Method For Computing Principal Curvatures From Range Images
Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration
WRPN: Wide Reduced-Precision Networks
Newton-type Methods for Inference in Higher-Order Markov Random Fields
Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images
Saliency Preservation in Low-Resolution Grayscale Images
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Normalization of Neural Networks using Analytic Variance Propagation
Hyperdrive: A Systolically Scalable Binary-Weight CNN Inference Engine for mW IoT End-Nodes
Distribution-Aware Binarization of Neural Networks for Sketch Recognition
Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification
Image-based Vehicle Classification System
Fine-graind Image Classification via Combining Vision and Language
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition
Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer
Towards Quality Advancement of Underwater Machine Vision with Generative Adversarial Networks
Fruit Quantity and Quality Estimation using a Robotic Vision System
A meshfree particle method for a vision-based macroscopic pedestrian model
Parallel Stroked Multi Line: a model-based method for compressing large fingerprint databases
Decoding visemes: improving machine lipreading (PhD thesis)
An Upper Limit of AC Huffman Code Length in JPEG Compression
Mobile Augmented Reality Applications
A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition
Kernelized Locality-Sensitive Hashing for Semi-Supervised Agglomerative Clustering
Eye-GUIDE (Eye-Gaze User Interface Design) Messaging for Physically-Impaired People
Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks
Model-Driven Applications of Fractional Derivatives and Integrals
Mobile Crowd Sensing and Computing: When Participatory Sensing Meets Participatory Social Media
Deeply Semantic Inductive Spatio-Temporal Learning
Web Similarity
PatchBatch: a Batch Augmented Loss for Optical Flow
Randomized Iterative Reconstruction for Sparse View X-ray Computed Tomography
Making data center computations fast, but not so furious
Snapshot Difference Imaging using Time-of-Flight Sensors
pix2code: Generating Code from a Graphical User Interface Screenshot
Dragon: A Computation Graph Virtual Machine Based Deep Learning Framework
Computational complexity lower bounds of certain discrete Radon transform approximations
White Noise from the White Goods? Conceptual and Empirical Perspectives on Ambient Domestic Computing
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
Aqua Computing: Coupling Computing and Communications
Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation
Market-Oriented Cloud Computing and the Cloudbus Toolkit
Efficient Minimization of Higher Order Submodular Functions using Monotonic Boolean Functions
Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts
Computer vision tools for the non-invasive assessment of autism-related behavioral markers
A brief experience on journey through hardware developments for image processing and its applications on Cryptography
Feature Selection with Annealing for Computer Vision and Big Data Learning
Efficient Visual Coding: From Retina To V2
Learning to see like children: proof of concept
On the Performance of ConvNet Features for Place Recognition
Robust Optimization for Deep Regression
Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation
Learning to Track at 100 FPS with Deep Regression Networks
Learning Spatially Regularized Correlation Filters for Visual Tracking
Egocentric Meets Top-view
A System View of the Recognition and Interpretation of Observed Human Shape, Pose and Action
High-Contrast Color-Stripe Pattern for Rapid Structured-Light Range Imaging
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition
Tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach
Generating Natural Questions About an Image
UAV-based Autonomous Image Acquisition with Multi-View Stereo Quality Assurance by Confidence Prediction
Image Classification of Grapevine Buds using Scale-Invariant Features Transform, Bag of Features and Support Vector Machines
Perception-aware Path Planning
Seeing into Darkness: Scotopic Visual Recognition
Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey
Crowd Counting by Adapting Convolutional Neural Networks with Side Information
EgoTransfer: Transferring Motion Across Egocentric and Exocentric Domains using Deep Neural Networks
GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild
Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale
Context-based Object Viewpoint Estimation: A 2D Relational Approach
When Unsupervised Domain Adaptation Meets Tensor Representations
The iNaturalist Species Classification and Detection Dataset
3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds
PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces
Learning Invariant Riemannian Geometric Representations Using Deep Nets
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks
Fine-Grained Car Detection for Visual Census Estimation
Rapid and Robust Automated Macroscopic Wood Identification System using Smartphone with Macro-lens
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
Person Recognition in Social Media Photos
Material Classification using Neural Networks
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Dream Formulations and Deep Neural Networks: Humanistic Themes in the Iconology of the Machine-Learned Image
3D non-rigid registration using color: Color Coherent Point Drift
HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification
Deep Learning Object Detection Methods for Ecological Camera Trap Data
Non-Linear Temporal Subspace Representations for Activity Recognition
DIY Human Action Data Set Generation
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution
A Linear Shift Invariant Multiscale Transform
General Theory of Image Normalization
A Differential Invariant for Zooming
Boosting the Differences: A fast Bayesian classifier neural network
Distorted English Alphabet Identification : An application of Difference Boosting Algorithm
Correlation over Decomposed Signals: A Non-Linear Approach to Fast and Effective Sequences Comparison
Fingerprint based bio-starter and bio-access
IS (Iris Security)
Factor Temporal Prognosis of Tick-Borne Encephalitis Foci Functioning on the South of Russian Far East
Conditional Expressions for Blind Deconvolution: Multi-point form
Simple method to eliminate blur based on Lane and Bates algorithm
Riemannian level-set methods for tensor-valued data
Learning Similarity for Character Recognition and 3D Object Recognition
A Class of LULU Operators on Multi-Dimensional Arrays
Increasing Linear Dynamic Range of Commercial Digital Photocamera Used in Imaging Systems with Optical Coding
Conceptualization of seeded region growing by pixels aggregation. Part 1: the framework
Higher Order Moments Generation by Mellin Transform for Compound Models of Clutter
Generalized Prediction Intervals for Arbitrary Distributed High-Dimensional Data
Audio Classification from Time-Frequency Texture
Obtaining Depth Maps From Color Images By Region Based Stereo Matching Algorithms
Dipole and Quadrupole Moments in Image Processing
Dipole Vectors in Images Processing
Sparse image representation by discrete cosine/spline based dictionaries
How Do Interactive Virtual Operas Shift Relationships between Music, Text and Image?
Properties of the Discrete Pulse Transform for Multi-Dimensional Arrays
L2-optimal image interpolation and its applications to medical imaging
Polyharmonic Daubechies type wavelets in Image Processing and Astronomy, II
Multiplierless Modules for Forward and Backward Integer Wavelet Transform
Template-based matching using weight maps
A radial version of the Central Limit Theorem
Multimodal diff-hash
An image processing of a Raphael's portrait of Leonardo
The watershed concept and its use in segmentation : a brief history
Image Restoration with Signal-dependent Camera Noise
Learning in Riemannian Orbifolds
Efficient Topology-Controlled Sampling of Implicit Shapes
Visual Vocabulary Learning and Its Application to 3D and Mobile Visual Search
Identifications of concealed weapon in a Human Body
Visual Transfer Learning: Informal Introduction and Literature Overview
Detection of elliptical shapes via cross-entropy clustering
GMM-Based Hidden Markov Random Field for Color Image and 3D Volume Segmentation
Coded aperture compressive temporal imaging
Software Requirements Specification - Softbody Simulation System
Submodularity of a Set Label Disagreement Function
Wavelet methods for shape perception in electro-sensing
Q-learning optimization in a multi-agents system for image segmentation
Stitched Panoramas from Toy Airborne Video Cameras
Heat kernel coupling for multiple graph analysis
ARIANNA: pAth Recognition for Indoor Assisted NavigatioN with Augmented perception
Image processing using miniKanren
On Quadratization of Pseudo-Boolean Functions
Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications
A graph-based mathematical morphology reader
RPCA-KFE: Key Frame Extraction for Consumer Video based Robust Principal Component Analysis
Comparative analysis of common edge detection techniques in context of object extraction
A review over the applicability of image entropy in analyses of remote sensing datasets
Enhanced EZW Technique for Compression of Image by Setting Detail Retaining Pass Number
Recognition of Handwritten Persian/Arabic Numerals Based on Robust Feature Set and K-NN Classifier
Gabor-like Image Filtering using a Neural Microcircuit
Challenge IEEE-ISBI/TCB : Application of Covariance matrices and wavelet marginals
Optical Character Recognition, Using K-Nearest Neighbors
V-variable image compression
Blob indentation identification via curvature measurement
Proceedings of The 39th Annual Workshop of the Austrian Association for Pattern Recognition (OAGM), 2015
Sparse 3D convolutional neural networks
Benchmarking KAZE and MCM for Multiclass Classification
VeinPLUS: A Transillumination and Reflection-based Hand Vein Database
Shedding Light on the Asymmetric Learning Capability of AdaBoost
Handwriting Recognition
Piecewise Linear Activation Functions For More Efficient Deep Networks
A Simple Hierarchical Pooling Data Structure for Loop Closure
Node Specificity in Convolutional Deep Nets Depends on Receptive Field Position and Size
Some medical applications of example-based super-resolution
Automatic detection of moving objects in video surveillance
Critical Points for Two-view Triangulation
Stroke-Based Cursive Character Recognition
Facial transformations of ancient portraits: the face of Caesar
Evidential Reasoning in Image Understanding
Developing and Analyzing Boundary Detection Operators Using Probabilistic Models
An implementation of the relational k-means algorithm
Head Gesture Recognition using Optical Flow based Classification with Reinforcement of GMM based Background Subtraction
No more meta-parameter tuning in unsupervised sparse feature learning
Transfer Learning for Video Recognition with Scarce Training Data for Deep Convolutional Neural Network
Gray level image enhancement using the Bernstein polynomials
Multi-valued Color Representation Based on Frank t-norm Properties
Shannon, Tsallis and Kaniadakis entropies in bi-level image thresholding
Quantum image classification using principal component analysis
Time-causal and time-recursive spatio-temporal receptive fields
Pose Estimation Based on 3D Models
Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
Feature Learning for Interaction Activity Recognition in RGBD Videos
Geometry and dimensionality reduction of feature spaces in primary visual cortex
gSLICr: SLIC superpixels at over 250Hz
Overcomplete Dictionary Learning with Jacobi Atom Updates
Motion trails from time-lapse video
A proposal project for a blind image quality assessment by learning distortions from the full reference image quality assessments
Multiclass Classification of Cervical Cancer Tissues by Hidden Markov Model
Automatic Detection and Decoding of Photogrammetric Coded Targets
Content Aware Neural Style Transfer
Efficient Robust Mean Value Calculation of 1D Features
Closed Form for Some Gaussian Convolutions
Fast calculation of correlations in recognition systems
Exploiting Facial Landmarks for Emotion Recognition in the Wild
Color Homography
An Alternative Matting Laplacian
Face Detection with the Faster R-CNN
Inference on subspheres model for directional data
Depth Estimation from Single Image using Sparse Representations
Ashwin: Plug-and-Play System for Machine-Human Image Annotation
Stamp processing with examplar features
Image Based Camera Localization: an Overview
Comparing Face Detection and Recognition Techniques
Multi-Camera Occlusion and Sudden-Appearance-Change Detection Using Hidden Markovian Chains
Effective sparse representation of X-Ray medical images
Path-following based Point Matching using Similarity Transformation
Group Visual Sentiment Analysis
Photographic dataset: playing cards
Spatially Aware Melanoma Segmentation Using Hybrid Deep Learning Techniques
Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks
Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble
Segmenting Dermoscopic Images
Segmentation of skin lesions based on fuzzy classification of pixels and histogram thresholding
A Hybrid Deep Learning Approach for Texture Analysis
Surface Normals in the Wild
Collaborative Low-Rank Subspace Clustering
Adaptive Cost Function for Pointcloud Registration
Improved underwater image enhancement algorithms based on partial differential equations (PDEs)
Blood capillaries and vessels segmentation in optical coherence tomography angiogram using fuzzy C-means and Curvelet transform
Deep Learning Methods for Efficient Large Scale Video Labeling
Rotation Invariance Neural Network
A Bayesian algorithm for detecting identity matches and fraud in image databases
Improved Human Emotion Recognition Using Symmetry of Facial Key Points with Dihedral Group
UPSET and ANGRI : Breaking High Performance Image Classifiers
Image Segmentation Algorithms Overview
A step towards procedural terrain generation with GANs
Evaluation of Hashing Methods Performance on Binary Feature Descriptors
A comment on the paper Prediction of Kidney Function from Biopsy Images using Convolutional Neural Networks
Multigraded Cayley-Chow forms
Elliptification of Rectangular Imagery
Fruit recognition from images using deep learning
Mathematics of Deep Learning
Invariants of multidimensional time series based on their iterated-integral signature
Detecting and counting tiny faces
A predictor-corrector method for the training of deep neural networks
Aggregated Sparse Attention for Steering Angle Prediction
A Survey of Deep Learning Techniques for Mobile Robot Applications
Not quite unreasonable effectiveness of machine learning algorithms
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses
QCMC: Quasi-conformal Parameterizations for Multiply-connected domains
Fast 2-D Complex Gabor Filter with Kernel Decomposition
Pre-Symmetry Sets of 3D shapes
The Expressive Power of Binary Submodular Functions
Deformable Model with a Complexity Independent from Image Resolution
On landmark selection and sampling in high-dimensional data analysis
Hodge Theory on Metric Spaces
Learning an Interactive Segmentation System
Computing the output distribution and selection probabilities of a stack filter from the DNF of its positive Boolean function
Clinical gait data analysis based on Spatio-Temporal features
Multilinear Biased Discriminant Analysis: A Novel Method for Facial Action Unit Representation
Perception of Motion and Architectural Form: Computational Relationships between Optical Flow and Perspective
Fully Automatic Expression-Invariant Face Correspondence
Automatic Tuning of Interactive Perception Applications
SignsWorld; Deeping Into the Silence World and Hearing Its Signs (State of the Art)
Vision Paper: Towards an Understanding of the Limits of Map-Reduce Computation
Object Recognition with Multi-Scale Pyramidal Pooling Networks
Supervised Texture Classification Using a Novel Compression-Based Similarity Measure
Color Constancy based on Image Similarity via Bilayer Sparse Coding
Stable Segmentation of Digital Image
Automatic Detection of Texture Defects Using Texture-Periodicity and Gabor Wavelets
Image-based Face Detection and Recognition: "State of the Art"
Joint optimization of fitting & matching in multi-view reconstruction
Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition
Higher-order Segmentation via Multicuts
Active Sensing as Bayes-Optimal Sequential Decision Making
Recognition of Indian Sign Language in Live Video
Infrared face recognition: a literature review
6th International Symposium on Attention in Cognitive Systems 2013
Efficient Energy Minimization for Enforcing Statistics
Combining Spatio-Temporal Appearance Descriptors and Optical Flow for Human Action Recognition in Video Data
A Novel Illumination-Invariant Loss for Monocular 3D Pose Estimation
A fast and robust algorithm to count topologically persistent holes in noisy clouds
Face Detection from still and Video Images using Unsupervised Cellular Automata with K means clustering algorithm
Low-Rank Modeling and Its Applications in Image Analysis
Hallucinating optimal high-dimensional subspaces
Face Recognition Methods & Applications
Learning detectors quickly using structured covariance matrices
Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods
A Reverse Hierarchy Model for Predicting Eye Fixations
Face Detection with a 3D Model
Part-based R-CNNs for Fine-grained Category Detection
Tree-based iterated local search for Markov random fields with applications in image analysis
Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow
Scalable Greedy Algorithms for Transfer Learning
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input
CIDEr: Consensus-based Image Description Evaluation
Scale-Invariant Convolutional Neural Networks
Features in Concert: Discriminative Feature Selection meets Unsupervised Clustering
Volumetric Bias in Segmentation and Reconstruction: Secrets and Solutions
See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG
Learning to See by Moving
Dense Semantic Correspondence where Every Pixel is a Classifier
Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM
GazeDPM: Early Integration of Gaze Information in Deformable Part Models
Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
LogDet Rank Minimization with Application to Subspace Clustering
End-to-end Convolutional Network for Saliency Prediction
Learning a Discriminative Model for the Perception of Realism in Composite Images
A Markov Random Field and Active Contour Image Segmentation Model for Animal Spots Patterns
Efficient non-greedy optimization of decision trees
FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications
Applying deep learning to classify pornographic images and videos
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies
Fast Object Localization Using a CNN Feature Map Based Multi-Scale Search
Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle
Efficient Splitting-based Method for Global Image Smoothing
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification
Congruences and Concurrent Lines in Multi-View Geometry
Who Leads the Clothing Fashion: Style, Color, or Texture? A Computational Study
Vehicles Recognition Using Fuzzy Descriptors of Image Segments
MAS for video objects segmentation and tracking based on active contours and SURF descriptor
Generalized Max Pooling
Fingers' Angle Calculation using Level-Set Method
VideoSET: Video Summary Evaluation through Text
Visual Speech Recognition
F-formation Detection: Individuating Free-standing Conversational Groups in Images
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation
Towards Open World Recognition
Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks
Texture analysis using volume-radius fractal dimension
A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images
Reconciling saliency and object center-bias hypotheses in explaining free-viewing fixations
Clustering Assisted Fundamental Matrix Estimation
Exploring Integral Image Word Length Reduction Techniques for SURF Detector
Anticipating Visual Representations from Unlabeled Video
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
Imaging Time-Series to Improve Classification and Imputation
Compressing Convolutional Neural Networks
Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines
Using User Generated Online Photos to Estimate and Monitor Air Pollution in Major Cities
Iterative Thresholded Bi-Histogram Equalization for Medical Image Enhancement
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
Vision-Based Road Detection using Contextual Blocks
Learning Social Relation Traits from Face Images
ShapeNet: An Information-Rich 3D Model Repository
Enhanced image feature coverage: Key-point selection using genetic algorithms
Randomized Low-Rank Dynamic Mode Decomposition for Motion Detection
Can Pretrained Neural Networks Detect Anatomy?
Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
Efficient Globally Optimal 2D-to-3D Deformable Shape Matching
Lipreading with Long Short-Term Memory
Learnt quasi-transitive similarity for retrieval from large collections of faces
Image Captioning with Semantic Attention
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
From line segments to more organized Gestalts
Lightweight Unsupervised Domain Adaptation by Convolutional Filter Reconstruction
Sub-pixel accuracy edge fitting by means of B-spline
InterActive: Inter-Layer Activeness Propagation
Facial Expression Recognition from World Wild Web
CNN based texture synthesize with Semantic segment
Bacterial foraging optimization based brain magnetic resonance image segmentation
Weighted Residuals for Very Deep Networks
Attention Correctness in Neural Image Captioning
Convolution by Evolution: Differentiable Pattern Producing Networks
Robust 3D Hand Pose Estimation in Single Depth Images: from Single-View CNN to Multi-View CNNs
Theta-RBM: Unfactored Gated Restricted Boltzmann Machine for Rotation-Invariant Representations
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data
Can DMD obtain a Scene Background in Color?
Grid Loss: Detecting Occluded Faces
UnrealCV: Connecting Computer Vision to Unreal Engine
Visual Saliency Detection Based on Multiscale Deep CNN Features
End-to-End Eye Movement Detection Using Convolutional Neural Networks
Image Aesthetic Assessment: An Experimental Survey
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Optimization of Convolutional Neural Network using Microcanonical Annealing Algorithm
Tangled Splines
Learning Robust Video Synchronization without Annotations
Universal adversarial perturbations
Real-Time Image Distortion Correction: Analysis and Evaluation of FPGA-Compatible Algorithms
RenderGAN: Generating Realistic Labeled Data
Semi-Dense 3D Semantic Mapping from Monocular SLAM
Deep Variational Inference Without Pixel-Wise Reconstruction
Factorized Bilinear Models for Image Recognition
AutoScaler: Scale-Attention Networks for Visual Correspondence
Multi-Scale Saliency Detection using Dictionary Learning
Fast Video Classification via Adaptive Cascading of Deep Models
Quad-networks: unsupervised learning to rank for interest point detection
Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study
Harmonic Networks: Deep Translation and Rotation Equivariance
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching
Smart Content Recognition from Images Using a Mixture of Convolutional Neural Networks
Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection
Computing Egomotion with Local Loop Closures for Egocentric Videos
Wide-Residual-Inception Networks for Real-time Object Detection
End-to-End Interpretation of the French Street Name Signs Dataset
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop Objects
Recent Advances in Features Extraction and Description Algorithms: A Comprehensive Survey
In Defense of the Triplet Loss for Person Re-Identification
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
Visually grounded learning of keyword prediction from untranscribed speech
A Paradigm Shift: Detecting Human Rights Violations Through Web Images
Graph Partitioning with Acyclicity Constraints
Generalized Rank Pooling for Activity Recognition
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition
Simultaneous Stereo Video Deblurring and Scene Flow Estimation
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
On the Two-View Geometry of Unsynchronized Cameras
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks
Locality Preserving Projections for Grassmann manifold
Survey of Visual Question Answering: Datasets and Techniques
Learning Image Relations with Contrast Association Networks
Unrolled Optimization with Deep Priors
A Random-Fern based Feature Approach for Image Matching
Network Sketching: Exploiting Binary Structure in Deep CNNs
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks
Changing Views on Curves and Surfaces
The Surfacing of Multiview 3D Drawings via Lofting and Occlusion Reasoning
Coresets for Triangulation
EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation
Head Detection with Depth Images in the Wild
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks
Fashioning with Networks: Neural Style Transfer to Design Clothes
Associative Domain Adaptation
Training Deep Networks to be Spatially Sensitive
A discriminative view of MRF pre-processing algorithms
Random Binary Trees for Approximate Nearest Neighbour Search in Binary Space
ChromaTag: A Colored Marker and Fast Detection Algorithm
Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation
Towards Semantic Fast-Forward and Stabilized Egocentric Videos
Human Action Recognition System using Good Features and Multilayer Perceptron Network
Robust Stereo Feature Descriptor for Visual Odometry
Synthesising Wider Field Images from Narrow-Field Retinal Video Acquired Using a Low-Cost Direct Ophthalmoscope (Arclight) Attached to a Smartphone
Performance Guaranteed Network Acceleration via High-Order Residual Quantization
Medical Image Analysis using Convolutional Neural Networks: A Review
Reversible Architectures for Arbitrarily Deep Residual Neural Networks
A Computational Model of Afterimages based on Simultaneous and Successive Contrasts
Social Style Characterization from Egocentric Photo-streams
3D Reconstruction with Low Resolution, Small Baseline and High Radial Distortion Stereo Images
Face Retrieval using Frequency Decoded Local Descriptor
Modeling Image Virality with Pairwise Spatial Transformer Networks
Human Detection for Night Surveillance using Adaptive Background Subtracted Image
Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations
Toward predictive machine learning for active vision
Feed Forward and Backward Run in Deep Convolution Neural Network
Learning Multi-Modal Word Representation Grounded in Visual Context
Image Registration of Very Large Images via Genetic Programming
A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles
Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
Can We Teach Computers to Understand Art? Domain Adaptation for Enhancing Deep Networks Capacity to De-Abstract Art
Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition
SuperPoint: Self-Supervised Interest Point Detection and Description
Aggregated Channels Network for Real-Time Pedestrian Detection
Learning to Prune Filters in Convolutional Neural Networks
A neural model of the locust visual system for detection of object approaches with real-world scenes
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks
From Hashing to CNNs: Training BinaryWeight Networks via Hashing
Sampling Superquadric Point Clouds with Normals
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Neural Photometric Stereo Reconstruction for General Reflectance Surfaces
Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective
Automatic Pixelwise Object Labeling for Aerial Imagery Using Stacked U-Nets
TOMAAT: volumetric medical image analysis as a cloud service
Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses
Lifting Layers: Analysis and Applications
Context Encoding for Semantic Segmentation
FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces
Review of Deep Learning
Markerless Inside-Out Tracking for Interventional Applications
End-to-End Saliency Mapping via Probability Distribution Prediction
A Multi-Layer Approach to Superpixel-based Higher-order Conditional Random Field for Semantic Image Segmentation
Evaluation of the visual odometry methods for semi-dense real-time
Community Cloud Computing
Implementation of Hand Detection based Techniques for Human Computer Interaction
Massivizing Computer Systems: a Vision to Understand, Design, and Engineer Computer Ecosystems through and beyond Modern Distributed Systems
Locally Served Network Computers
Visualising the structure of architectural open spaces based on shape analysis
An explicit formula for the number of tunnels in digital objects
Neural Networks with Complex and Quaternion Inputs
Neural Network Clustering Based on Distances Between Objects
Rough Sets Computations to Impute Missing Data
Towards understanding and modelling office daily life
Approximation of a Fractional Order System by an Integer Order Model Using Particle Swarm Optimization Technique
Faster Retrieval with a Two-Pass Dynamic-Time-Warping Lower Bound
An Iterative Fingerprint Enhancement Algorithm Based on Accurate Determination of Orientation Flow
Improvements of the 3D images captured with Time-of-Flight cameras
Breast Cancer Detection Using Multilevel Thresholding
Real-Time Implementation of Order-Statistics Based Directional Filters
Cost-Effective Implementation of Order-Statistics Based Vector Filters Using Minimax Approximations
Classification with Scattering Operators
Handwritten Character Recognition of South Indian Scripts: A Review
Multi Layer Analysis
Robust seed selection algorithm for k-means type algorithms
Multimodal similarity-preserving hashing
Nonparametric Edge Detection in Speckled Imagery
A Plea for Neutral Comparison Studies in Computational Sciences
Creation of Digital Test Form for Prepress Department
Coupled quasi-harmonic bases
Applications of Clifford's Geometric Algebra
Angles between subspaces
Analysing Word Importance for Image Annotation
Removal and Contraction Operations in $n$D Generalized Maps for Efficient Homology Computation
Rough Clustering Based Unsupervised Image Change Detection
Gabor Filter and Rough Clustering Based Edge Detection
Definition of Visual Speech Element and Research on a Method of Extracting Feature Vector for Korean Lip-Reading
Inverse Renormalization Group Transformation in Bayesian Image Segmentations
The color of smiling: computational synaesthesia of facial expressions
Research on the fast Fourier transform of image based on GPU
Computational models of attention
Faster method for Deep Belief Network based Object classification using DWT
Fast $(1+ε)$-approximation of the Löwner extremal matrices of high-dimensional symmetric matrices
Artificial Neural Networks for Detection of Malaria in RBCs
An Optimization Method For Slice Interpolation Of Medical Images
Unsupervised Deep Haar Scattering on Graphs
Exact Decoding on Latent Variable Conditional Models is NP-Hard
Multiple Object Recognition with Visual Attention
Complex-Valued Hough Transforms for Circles
Quantum Energy Regression using Scattering Transforms
DESAT: an SSW tool for SDO/AIA image de-saturation
A Unified Deep Neural Network for Speaker and Language Recognition
Image Retrieval Based on Binary Signature ang S-kGraph
A massively parallel multi-level approach to a domain decomposition method for the optical flow estimation with varying illumination
Neuron detection in stack images: a persistent homology interpretation
Learning to Compose Neural Networks for Question Answering
Automatic Moth Detection from Trap Images for Pest Management
Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting
Lazy Evaluation of Convolutional Filters
Quantitative Analysis of Saliency Models
Interpreting extracted rules from ensemble of trees: Application to computer-aided diagnosis of breast MRI
A Scalable and Robust Framework for Intelligent Real-time Video Surveillance
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering
Imaging around corners with single-pixel detector by computational ghost imaging
An Efficient Algebraic Solution to the Perspective-Three-Point Problem
Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification
Enhanced Local Binary Patterns for Automatic Face Recognition
Low-Precision Batch-Normalized Activations
Lensless computational imaging through deep learning
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks
Punny Captions: Witty Wordplay in Image Descriptions
Time Stretch Inspired Computational Imaging
Prune the Convolutional Neural Networks with Sparse Shrink
BlitzNet: A Real-Time Deep Network for Scene Understanding
Adaptive strategies for solving parameterized systems using homotopy continuation
Heat Kernel Smoothing in Irregular Image Domains
Genetic Algorithm-Based Solver for Very Large Multiple Jigsaw Puzzles of Unknown Dimensions and Piece Orientation
On Nearest Neighbors in Non Local Means Denoising
Parallel transport in shape analysis: a scalable numerical scheme
BLADE: Filter Learning for General Purpose Computational Photography
The Enhanced Hybrid MobileNet
Violable Contracts and Governance for Blockchain Applications
SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm
WRPN & Apprentice: Methods for Training and Inference using Low-Precision Numerics
FutureMapping: The Computational Structure of Spatial AI Systems
Mobility Enhancement for Elderly
The Cyborg Astrobiologist: Testing a Novelty-Detection Algorithm on Two Mobile Exploration Systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah
Yin and Yang: Balancing and Answering Binary Visual Questions
The Stixel world: A medium-level representation of traffic scenes
3D Interpreter Networks for Viewer-Centered Wireframe Modeling
Efficient Point-to-Subspace Query in $\ell^1$ with Application to Robust Object Instance Recognition
Structured learning of sum-of-submodular higher order energy functions
Service-oriented Communities: Visions and Contributions towards Social Organizations
Measuring Atmospheric Scattering from Digital Images of Urban Scenery using Temporal Polarization-Based Vision
Sequential Score Adaptation with Extreme Value Theory for Robust Railway Track Inspection
Circle detection using isosceles triangles sampling
Understanding learned CNN features through Filter Decoding with Substitution
Deep multi-scale video prediction beyond mean square error
Sparse Coral Classification Using Deep Convolutional Neural Networks
The Curious Robot: Learning Visual Representations via Physical Interactions
Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes
Single Image 3D Interpreter Network
We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based Methods
Steps Towards a Theory of Visual Information: Active Perception, Signal-to-Symbol Conversion and the Interplay Between Sensing and Control
A Bimodal Co-Sparse Analysis Model for Image Processing
Multi-Projector Color Structured-Light Vision
Weighted Schatten $p$-Norm Minimization for Image Denoising and Background Subtraction
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
Detecting Violent and Abnormal Crowd activity using Temporal Analysis of Grey Level Co-occurrence Matrix (GLCM) Based Texture Measures
Derivatives and inverse of a linear-nonlinear multi-layer spatial vision model
ModelHub: Towards Unified Data and Lifecycle Management for Deep Learning
PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition
Pay Attention to Those Sets! Learning Quantification from Images
Machine Vision System for 3D Plant Phenotyping
WebVision Challenge: Visual Learning and Understanding With Web Data
Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Towards social pattern characterization in egocentric photo-streams
Automatic Ground Truths: Projected Image Annotations for Omnidirectional Vision
Constrained Deep Transfer Feature Learning and its Applications
Grad-CAM++: Generalized Gradient-based Visual Explanations for Deep Convolutional Networks
Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization
In-Bed Pose Estimation: Deep Learning with Shallow Dataset
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision
Visual Psychophysics for Making Face Recognition Algorithms More Explainable
Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges
Why Size Matters: Feature Coding as Nystrom Sampling
ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines
HFirst: A Temporal Approach to Object Recognition
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mobile Cloud Computing: A Review on Smartphone Augmentation Approaches
Computation of the Hausdorff distance between sets of line segments in parallel
Physical Computing With No Clock to Implement the Gaussian Pyramid of SIFT Algorithm
The Theory of Computational Quasi-conformal Geometry on Point Clouds
Accelerated Distance Computation with Encoding Tree for High Dimensional Data
TRIM: Triangulating Images for Efficient Registration
On Using Micro-Clouds to Deliver the Fog
Optimal Piecewise Linear Function Approximation for GPU-based Applications
Flexible Camera Calibration Using a New Analytical Radial Undistortion Formula with Application to Mobile Robot Localization
Entity Based Peer-to-Peer in a Data Grid Environment
A Java Based Architecture of P2P-Grid Middleware
Space and camera path reconstruction for omni-directional vision
lambda-Connectedness Determination for Image Segmentation
A Nonparametric Approach to 3D Shape Analysis from Digital Camera Images - I. in Memory of W.P. Dayawansa
Multi-Label MRF Optimization via Least Squares s-t Cuts
Metric and Kernel Learning using a Linear Transformation
ICT in Universities of the Western Himalayan Region in India: Status, Performance- An Assessment
Synthesis of supervised classification algorithm using intelligent and statistical tools
Matching 2-D Ellipses to 3-D Circles with Application to Vehicle Pose Estimation
Binarizing Business Card Images for Mobile Devices
Recognition of handwritten Roman Numerals using Tesseract open source OCR engine
Randomized hybrid linear modeling by local best-fit flats
LACBoost and FisherBoost: Optimally Building Cascade Classifiers
Incremental Training of a Detector Using Online Sparse Eigen-decomposition
Repairing People Trajectories Based on Point Clustering
Online Adaptive Decision Fusion Framework Based on Entropic Projections onto Convex Sets with Application to Wildfire Detection in Video
Natural images from the birthplace of the human eye
Modeling Dynamic Swarms
SHREC 2011: robust feature detection and description benchmark
Automatic Detection of Ringworm using Local Binary Pattern (LBP)
A Medial Axis Based Thinning Strategy for Character Images
Variational Gaussian Process Dynamical Systems
Generalised Object Detection and Semantic Analysis: Casino Example using Matlab
Spatiotemporal Gabor filters: a new method for dynamic texture recognition
Multi-column Deep Neural Networks for Image Classification
Using Barriers to Reduce the Sensitivity to Edge Miscalculations of Casting-Based Object Projection Feature Estimation
Texture Classification Approach Based on Combination of Edge & Co-occurrence and Local Binary Pattern
Non-sparse Linear Representations for Visual Tracking with Online Reservoir Metric Learning
Robust Head Pose Estimation Using Contourlet Transform
Spectral Graph Cut from a Filtering Point of View
Generalized sequential tree-reweighted message passing
Poisson noise reduction with non-local PCA
The Stability of Convergence of Curve Evolutions in Vector Fields
Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis
A Survey of Recent View-based 3D Model Retrieval Methods
Discriminative Sparse Coding on Multi-Manifold for Data Representation and Classification
Full Object Boundary Detection by Applying Scale Invariant Features in a Region Merging Segmentation Algorithm
An Automatic Algorithm for Object Recognition and Detection Based on ASIFT Keypoints
Tracking Revisited using RGBD Camera: Baseline and Benchmark
Training Effective Node Classifiers for Cascade Classification
Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences
ChESS - Quick and Robust Detection of Chess-board Features
Robust Face Recognition via Block Sparse Bayesian Learning
Sparse Camera Network for Visual Surveillance -- A Comprehensive Survey
Image Interpolation Using Kriging Technique for Spatial Data
Good Recognition is Non-Metric
Object Detection in Real Images
Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey
A Method for Visuo-Spatial Classification of Freehand Shapes Freely Sketched
Computer vision applications for coronagraphic optical alignment and image processing
Automatic Parameter Adaptation for Multi-object Tracking
Sparse Norm Filtering
Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks
Characterizing Ambiguity in Light Source Invariant Shape from Shading
Saliency-Guided Perceptual Grouping Using Motion Cues in Region-Based Artificial Visual Attention
A General Two-Step Approach to Learning-Based Hashing
Surface Registration Using Genetic Algorithm in Reduced Search Space
Flexible Visual Quality Inspection in Discrete Manufacturing
Using the Random Sprays Retinex Algorithm for Global Illumination Estimation
Multiclass Road Sign Detection using Multiplicative Kernel
Global Localization Based on 3D Planar Surface Segments
Classifying Traffic Scenes Using The GIST Image Descriptor
An Overview and Evaluation of Various Face and Eyes Detection Algorithms for Driver Fatigue Monitoring Systems
Contextual Hypergraph Modelling for Salient Object Detection
Object Recognition System Design in Computer Vision: a Universal Approach
Second-order Shape Optimization for Geometric Inverse Problems in Vision
Recognizing Image Style
Comparative Study Of Image Edge Detection Algorithms
A compact formula for the derivative of a 3-D rotation in exponential coordinates
An Algorithmic Theory of Dependent Regularizers, Part 1: Submodular Structure
Deep Convolutional Ranking for Multilabel Image Annotation
Estimation of Human Body Shape and Posture Under Clothing
Using Web Co-occurrence Statistics for Improving Image Categorization
Near-separable Non-negative Matrix Factorization with $\ell_1$- and Bregman Loss Functions
What is usual in unusual videos? Trajectory snippet histograms for discovering unusualness
A bi-level view of inpainting - based image compression
Object Tracking via Non-Euclidean Geometry: A Grassmann Approach
Summarisation of Short-Term and Long-Term Videos using Texture and Colour
Cross-Scale Cost Aggregation for Stereo Matching
On learning to localize objects with minimal supervision
Quality-based Multimodal Classification Using Tree-Structured Sparsity
Blind Recognition of Touched Keys: Attack and Countermeasures
Capturing and Recognizing Objects Appearance Employing Eigenspace
Traffic Monitoring Using M2M Communication
Automatic Tracker Selection w.r.t Object Detection Performance
Cost-Effective HITs for Relative Similarity Comparisons
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
Indoor Activity Detection and Recognition for Sport Games Analysis
Relative Facial Action Unit Detection
Better Feature Tracking Through Subspace Constraints
An Intelligent Pixel Replication Technique by Binary Decomposition for Digital Image Zooming
Speeding up Convolutional Neural Networks with Low Rank Expansions
ESSP: An Efficient Approach to Minimizing Dense and Nonsubmodular Energy Functions
Adapted Approach for Fruit Disease Identification using Images
On the Optimal Solution of Weighted Nuclear Norm Minimization
Automated Fabric Defect Inspection: A Survey of Classifiers
Geometric Polynomial Constraints in Higher-Order Graph Matching
Layered Logic Classifiers: Exploring the `And' and `Or' Relations
Circle detection by Harmony Search Optimization
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
Optimizing Ranking Measures for Compact Binary Code Learning
Simultaneous Detection and Segmentation
Orientation covariant aggregation of local descriptors with embeddings
Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
Pushbroom Stereo for High-Speed Navigation in Cluttered Environments
On Pairwise Costs for Network Flow Multi-Object Tracking
Scene Image is Non-Mutually Exclusive - A Fuzzy Qualitative Scene Understanding
Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding
MKL-RT: Multiple Kernel Learning for Ratio-trace Problems via Convex Optimization
Learning visual biases from human imagination
Supervised mid-level features for word image representation
On The Effect of Hyperedge Weights On Hypergraph Learning
A Solution for Multi-Alignment by Transformation Synchronisation
A Weighted Common Subgraph Matching Algorithm
Edge Detection based on Kernel Density Estimation
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets
Part Detector Discovery in Deep Convolutional Neural Networks
6 Seconds of Sound and Vision: Creativity in Micro-Videos
Deep Deconvolutional Networks for Scene Parsing
ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections
Iteratively Reweighted Graph Cut for Multi-label MRFs with Non-convex Priors
Mid-level Deep Pattern Mining
Visual Representations: Defining Properties and Deep Approximations
Analysing domain shift factors between videos and images for object detection
An Effective Image Feature Classiffication using an improved SOM
HOG based Fast Human Detection
Combining Language and Vision with a Multimodal Skip-gram Model
Deep Image: Scaling up Image Recognition
Correntropy Induced L2 Graph for Robust Subspace Clustering
Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences
Robust Face Recognition by Constrained Part-based Alignment
A Light Transport Model for Mitigating Multipath Interference in TOF Sensors
Point Context: An Effective Shape Descriptor for RST-invariant Trajectory Recognition
Joint Object and Part Segmentation using Deep Learned Potentials
On a fast bilateral filtering formulation using functional rearrangements
Activity recognition from videos with parallel hypergraph matching on GPUs
Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation
Learning Style Similarity for Searching Infographics
A Deeper Look at Dataset Bias
A Two-Layer Local Constrained Sparse Coding Method for Fine-Grained Visual Categorization
Monocular Object Instance Segmentation and Depth Ordering with CNNs
MRF Optimization by Graph Approximation
CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research
Reproducible Evaluation of Pan-Tilt-Zoom Tracking
Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
Design and Implementation of Real-time Algorithms for Eye Tracking and PERCLOS Measurement for on board Estimation of Alertness of Drivers
The Minimum Spanning Tree of Maximum Entropy
Image Segmentation Using Hierarchical Merge Tree
Fast Detection of Curved Edges at Low SNR
Training a Convolutional Neural Network for Appearance-Invariant Place Recognition
Improved Deep Convolutional Neural Network For Online Handwritten Chinese Character Recognition using Domain-Specific Knowledge
Learning to count with deep object features
Parsimonious Labeling
Visual Data Deblocking using Structural Layer Priors
Feature Representation in Convolutional Neural Networks
Neural Network Classifiers for Natural Food Products
Lifting GIS Maps into Strong Geometric Context for Scene Understanding
Data-free parameter pruning for Deep Neural Networks
Relating Cascaded Random Forests to Deep Convolutional Neural Networks for Semantic Segmentation
Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian
Action recognition in still images by latent superpixel classification
Bregman Iteration for Correspondence Problems: A Study of Optical Flow
Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition
Egocentric Field-of-View Localization Using First-Person Point-of-View Devices
Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition
DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations
Wide-Area Image Geolocalization with Aerial Reference Imagery
Fine-Grained Product Class Recognition for Assisted Shopping
Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations
Towards Reversible De-Identification in Video Sequences Using 3D Avatars and Steganography
Personalized Age Progression with Aging Dictionary
Image Parsing with a Wide Range of Classes and Scene-Level Context
Geometric Context from Videos
Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context
Linear Shape Deformation Models with Local Support Using Graph-based Structured Matrix Factorisation
Regional Active Contours based on Variational level sets and Machine Learning for Image Segmentation
Color Space Transformation Network
Background Modeling Using Adaptive Pixelwise Kernel Variances in a Hybrid Feature Space
Recovering hard-to-find object instances by sampling context-based object proposals
Review of Person Re-identification Techniques
An Efficient Multilinear Optimization Framework for Hypergraph Matching
Weakly Supervised Deep Detection Networks
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer
Online Supervised Hashing for Ever-Growing Datasets
From Images to Sentences through Scene Description Graphs using Commonsense Reasoning and Knowledge
Visual7W: Grounded Question Answering in Images
An Adaptive Data Representation for Robust Point-Set Registration and Merging
Learning to Assign Orientations to Feature Points
Standard methods for inexpensive pollen loads authentication by means of computer vision and machine learning
Robust Face Alignment Using a Mixture of Invariant Experts
Sensory Polymorphism and Behavior: When Machine Vision Meets Monkey Eyes
Moral Lineage Tracing
Learning Structured Inference Neural Networks with Label Relations
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
WIDER FACE: A Face Detection Benchmark
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
Ground-truth dataset and baseline evaluations for image base-detail separation algorithms
TransCut: Transparent Object Segmentation from a Light-Field Image
Constrained Structured Regression with Convolutional Neural Networks
DenseCap: Fully Convolutional Localization Networks for Dense Captioning
Recurrent Instance Segmentation
Iterative Instance Segmentation
Real-Time Depth Refinement for Specular Objects
PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents
RGBD Datasets: Past, Present and Future
Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images
A Convolutional Neural Network Neutrino Event Classifier
LOMo: Latent Ordinal Model for Facial Analysis in Videos
A CNN Based Scene Chinese Text Recognition Algorithm With Synthetic Data Engine
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution
Training Region-based Object Detectors with Online Hard Example Mining
Joint Unsupervised Learning of Deep Representations and Image Clusters
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
Visual Storytelling
Deep Feature Based Contextual Model for Object Detection
Improving the Robustness of Deep Neural Networks via Stability Training
ACD: Action Concept Discovery from Image-Sentence Corpora
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling
Deep Saliency with Encoded Low level Distance Map and High Level Features
WarpNet: Weakly Supervised Matching for Single-view Reconstruction
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
Automatic 3D Reconstruction of Manifold Meshes via Delaunay Triangulation and Mesh Sweeping
Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters
Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps
Mesh Interest Point Detection Based on Geometric Measures and Sparse Refinement
Faster R-CNN Features for Instance Search
Sparse vs. Non-sparse: Which One Is Better for Practical Visual Tracking?
Leveraging Union of Subspace Structure to Improve Constrained Clustering
Comparative study and enhancement of Camera Tampering Detection algorithms
End-to-End Localization and Ranking for Relative Attributes
Fashion Landmark Detection in the Wild
Learning Dynamic Hierarchical Models for Anytime Scene Labeling
SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation
About Pyramid Structure in Convolutional Neural Networks
Seeing with Humans: Gaze-Assisted Neural Image Captioning
Semantic Understanding of Scenes through the ADE20K Dataset
A Recurrent Encoder-Decoder Network for Sequential Face Alignment
Detecting Vanishing Points using Global Image Context in a Non-Manhattan World
A 4D Light-Field Dataset and CNN Architectures for Material Recognition
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition
Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks
Multi-Person Pose Estimation with Local Joint-to-Person Associations
Measuring Machine Intelligence Through Visual Question Answering
Spatio-Colour Asplünd 's Metric and Logarithmic Image Processing for Colour Images (LIPC)
A Multiple Component Matching Framework for Person Re-Identification
Discriminately Decreasing Discriminability with Learned Image Filters
Runtime Guarantees for Regression Problems
Insights from Classifying Visual Concepts with Multiple Kernel Learning
Image Retrieval using Histogram Factorization and Contextual Similarity Learning
A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization
A Health Monitoring System for Elder and Sick Persons
A Bag of Visual Words Approach for Symbols-Based Coarse-Grained Ancient Coin Classification
Compact Relaxations for MAP Inference in Pairwise MRFs with Piecewise Linear Priors
Seeing What You're Told: Sentence-Guided Activity Recognition In Video
Brain MRI Segmentation with Fast and Globally Convex Multiphase Active Contours
Graph Cuts with Interacting Edge Costs - Examples, Approximations, and Algorithms
Automatic Estimation of Live Coffee Leaf Infection based on Image Processing Techniques
Circle detection using electro-magnetism optimization
Continuous Action Recognition Based on Sequence Alignment
Shape-from-intrinsic operator
Log-Euclidean Bag of Words for Human Action Recognition
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Interactively Test Driving an Object Detector: Estimating Performance on Unlabeled Data
Very Deep Convolutional Networks for Large-Scale Image Recognition
Comparing Feature Detectors: A bias in the repeatability criteria, and how to correct it
Visual Words for Automatic Lip-Reading
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning
Metric Learning Driven Multi-Task Structured Output Optimization for Robust Keypoint Tracking
Actions and Attributes from Wholes and Parts
Web image annotation by diffusion maps manifold learning algorithm
Candidate Constrained CRFs for Loss-Aware Structured Prediction
Optimizing Over Radial Kernels on Compact Manifolds
Iranian cashes recognition using mobile
Visual Scene Representations: Contrast, Scaling and Occlusion
Gabor wavelets combined with volumetric fractal dimension applied to texture analysis
Detect2Rank : Combining Object Detectors Using Learning to Rank
Driver distraction detection and recognition using RGB-D sensor
Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation
Clothing Co-Parsing by Joint Image Segmentation and Labeling
Crowded Scene Analysis: A Survey
Modeling Brain Circuitry over a Wide Range of Scales
A Comprehensive Survey on Pose-Invariant Face Recognition
Rectified Factor Networks
Total variation on a tree
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
Bethe Learning of Conditional Random Fields via MAP Decoding
What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
Dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture
3D Object Class Detection in the Wild
Sign Language Fingerspelling Classification from Depth and Color Images using a Deep Belief Network
Efficient piecewise training of deep structured models for semantic segmentation
Separable time-causal and time-recursive spatio-temporal receptive fields
Ego-Object Discovery
Kernelized Low Rank Representation on Grassmann Manifolds
Low Rank Representation on Grassmann Manifolds: An Extrinsic Perspective
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
Learning Multiple Visual Tasks while Discovering their Structure
Color Constancy Using CNNs
Visual Recognition Using Directional Distribution Distance
Preprint Touch-less Interactive Augmented Reality Game on Vision Based Wearable Device
FlowNet: Learning Optical Flow with Convolutional Networks
Identifying Reliable Annotations for Large Scale Image Segmentation
A Flexible Tensor Block Coordinate Ascent Scheme for Hypergraph Matching
Hyperspectral Image Classification and Clutter Detection via Multiple Structural Embeddings and Dimension Reductions
Unsupervised domain adaption dictionary learning for visual recognition
A Novel Approach Towards Clustering Based Image Segmentation
Boosting Optical Character Recognition: A Super-Resolution Approach
ICDAR 2015 Text Reading in the Wild Competition
Slow and steady feature analysis: higher order temporal coherence in video
Time Series Classification using the Hidden-Unit Logistic Model
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty
Kernelized Multiview Projection
Image Representations and New Domains in Neural Image Captioning
Seeing Behind the Camera: Identifying the Authorship of a Photograph
An end-to-end generative framework for video segmentation and recognition
Convexity Shape Constraints for Image Segmentation
Object Proposals for Text Extraction in the Wild
Deep Multi-task Learning for Railway Track Inspection
Facial Descriptors for Human Interaction Recognition In Still Images
Attribute-Graph: A Graph based approach to Image Ranking
Algebraic Clustering of Affine Subspaces
Automatic Concept Discovery from Parallel Text and Visual Corpora
Learning FRAME Models Using CNN Filters
Attribute2Image: Conditional Image Generation from Visual Attributes
Sublabel-Accurate Relaxation of Nonconvex Energies
Scalable domain adaptation of convolutional neural networks
Pseudo-Bayesian Robust PCA: Algorithms and Analyses
3D Reconstruction of Crime Scenes and Design Considerations for an Interactive Investigation Tool
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation
A Latent-Variable Lattice Model
G-CNN: an Iterative Grid Based Object Detector
Denoising and Completion of 3D Data via Multidimensional Dictionary Learning
Multimodal Classification of Events in Social Media
Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features
Gamifying Video Object Segmentation
Kernelized LRR on Grassmann Manifolds for Subspace Clustering
Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs
Facial Expression Recognition in the Wild using Rich Deep Features
A Comparative Study of Object Trackers for Infrared Flying Bird Tracking
Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices
A Grassmannian Graph Approach to Affine Invariant Feature Matching
A Large Dataset of Object Scans
DAP3D-Net: Where, What and How Actions Occur in Videos?
Global Deconvolutional Networks for Semantic Segmentation
Contextual Media Retrieval Using Natural Language Queries
Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition
GOGMA: Globally-Optimal Gaussian Mixture Alignment
Weakly Supervised Localization using Deep Feature Maps
MOT16: A Benchmark for Multi-Object Tracking
Shallow and Deep Convolutional Networks for Saliency Prediction
Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences
Temporally coherent 4D reconstruction of complex dynamic scenes
Deep Interactive Object Selection
Pushing the Limits of Deep CNNs for Pedestrian Detection
Learning Domain-Invariant Subspace using Domain Features and Independence Maximization
Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution
Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Learning Image Matching by Simply Watching Video
Towards Viewpoint Invariant 3D Human Pose Estimation
Robust cDNA microarray image segmentation and analysis technique based on Hough circle transform
A Diagram Is Worth A Dozen Images
Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
Recognizing Car Fluents from Video
Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
Video Interpolation using Optical Flow and Laplacian Smoothness
Generating Visual Explanations
Multi-Cue Zero-Shot Learning with Strong Supervision
Latent Embeddings for Zero-shot Classification
Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry
Comparison of Optimization Methods in Optical Flow Estimation
Patch-based Texture Synthesis for Image Inpainting
Robust Optical Flow Estimation of Double-Layer Images under Transparency or Reflection
Learning Discriminative Features with Class Encoder
SemiContour: A Semi-supervised Learning Approach for Contour Detection
LIME: A Method for Low-light IMage Enhancement
Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition
Spontaneous vs. Posed smiles - can we tell the difference?
An Analysis of Deep Neural Network Models for Practical Applications
Real-Time Human Motion Capture with Multiple Depth Cameras
SNN: Stacked Neural Networks
Learning the image processing pipeline
Generalizing the Convolution Operator to extend CNNs to Irregular Domains
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation
Machine Learning Techniques and Applications For Ground-based Image Analysis
Multiple Human Tracking in RGB-D Data: A Survey
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
The ND-IRIS-0405 Iris Image Dataset
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets
DecomposeMe: Simplifying ConvNets for End-to-End Learning
Eye Tracking for Everyone
Pragmatic factors in image description: the case of negations
An active efficient coding model of the optokinetic nystagmus
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
Unsupervised Learning of 3D Structure from Images
Deep Learning of Appearance Models for Online Object Tracking
Learning to Hash with Binary Deep Neural Network
Geometry-Informed Material Recognition
Binary Hashing with Semidefinite Relaxation and Augmented Lagrangian
On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization
A Local-Global Approach to Semantic Segmentation in Aerial Images
Interactive Illumination Invariance
Feature Descriptors for Tracking by Detection: a Benchmark
Temporal Model Adaptation for Person Re-Identification
Semantic Clustering for Robust Fine-Grained Scene Recognition
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
Stereo Video Deblurring
Deep Retinal Image Understanding
Reconstructing Articulated Rigged Models from RGB-D Videos
Dense Motion Estimation for Smoke
Learning Action Concept Trees and Semantic Alignment Networks from Image-Description Data
Robust Structure from Motion in the Presence of Outliers and Missing Data
An empirical study on the effects of different types of noise in image classification tasks
Generative Visual Manipulation on the Natural Image Manifold
Towards Deep Compositional Networks
3D Face Reconstruction by Learning from Synthetic Data
GeThR-Net: A Generalized Temporally Hybrid Recurrent Neural Network for Multimodal Information Fusion
Learning camera viewpoint using CNN to improve 3D body pose estimation
A scalable convolutional neural network for task-specified scenarios via knowledge distillation
On Support Relations and Semantic Scene Graphs
The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)
Realtime Hierarchical Clustering based on Boundary and Surface Statistics
Visual Fashion-Product Search at SK Planet
Optimistic and Pessimistic Neural Networks for Scene and Object Recognition
Video Summarization using Deep Semantic Features
Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking
Real-Time RGB-D based Template Matching Pedestrian Detection
A novel and effective scoring scheme for structure classification and pairwise similarity measurement
Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets
Multiple Instance Learning Convolutional Neural Networks for Object Recognition
Spatio-Temporal Attention Models for Grounded Video Captioning
Enhanced Object Detection via Fusion With Prior Beliefs from Image Classification
Maxmin convolutional neural networks for image classification
A New Distance Measure for Non-Identical Data with Application to Image Classification
A Detailed Rubric for Motion Segmentation
Initialization and Coordinate Optimization for Multi-way Matching
UMDFaces: An Annotated Face Dataset for Training Deep Networks
Boosting Image Captioning with Attributes
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
Gender Politics in the 2016 U.S. Presidential Election: A Computer Vision Approach
Real Time Video Analysis using Smart Phone Camera for Stroboscopic Image
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
Cross Domain Knowledge Transfer for Person Re-identification
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
DeepVO: A Deep Learning approach for Monocular Visual Odometry
Recurrent Memory Addressing for describing videos
Multi-Scale Anisotropic Fourth-Order Diffusion Improves Ridge and Valley Localization
Dense Captioning with Joint Inference and Visual Context
Recurrent Attention Models for Depth-Based Person Identification
3D Image Reconstruction from X-Ray Measurements with Overlap
Alternating Direction Graph Matching
Multi-View 3D Object Detection Network for Autonomous Driving
Multi-Modal Mean-Fields via Cardinality-Based Clamping
Robotic Grasp Detection using Deep Convolutional Neural Networks
Discriminative Correlation Filter with Channel and Spatial Reliability
GuessWhat?! Visual object discovery through multi-modal dialogue
Fast deterministic tourist walk for texture analysis
It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation
Object Detection Free Instance Segmentation With Labeling Transformations
Deep, Dense, and Low-Rank Gaussian Conditional Random Fields
Image Based Appraisal of Real Estate Properties
POSEidon: Face-from-Depth for Driver Pose Estimation
Object-Centric Representation Learning from Unlabeled Videos
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
Food Image Recognition by Using Convolutional Neural Networks (CNNs)
On-Demand Learning for Deep Image Restoration
Richer Convolutional Features for Edge Detection
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation
Understanding and Mapping Natural Beauty
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
Deep Convolutional Poses for Human Interaction Recognition in Monocular Videos
How do people explore virtual environments?
Deep Function Machines: Generalized Neural Networks for Topological Layer Expression
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
A Message Passing Algorithm for the Minimum Cost Multicut Problem
A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching
Video Propagation Networks
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
Deeply Aggregated Alternating Minimization for Image Restoration
Wide-Slice Residual Networks for Food Recognition
Globally Optimal Object Tracking with Fully Convolutional Networks
Quantum Clustering and Gaussian Mixtures
Rotation equivariant vector field networks
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation
Learning from Synthetic Humans
See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content
CNN-based Segmentation of Medical Imaging Data
Two-view 3D Reconstruction for Food Volume Estimation
Bandwidth limited object recognition in high resolution imagery
Complex Event Recognition from Images with Few Training Examples
Normative theory of visual receptive fields
Large Scale Novel Object Discovery in 3D
Side Information in Robust Principal Component Analysis: Algorithms and Applications
An Analysis of 1-to-First Matching in Iris Recognition
An Experimental Study of Deep Convolutional Features For Iris Recognition
Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation
Guided Optical Flow Learning
Predicting Privileged Information for Height Estimation
Joint Discovery of Object States and Manipulation Actions
On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
Graph Based Over-Segmentation Methods for 3D Point Clouds
Deep Multi-camera People Detection
EMNIST: an extension of MNIST to handwritten letters
Learning to Detect Human-Object Interactions
Unsupervised Diverse Colorization via Generative Adversarial Networks
Analyzing Learned Convnet Features with Dirichlet Process Gaussian Mixture Models
II-FCN for skin lesion analysis towards melanoma detection
Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm
Graph-based Isometry Invariant Representation Learning
Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning
Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction
Deep Head Pose Estimation from Depth Data for In-car Automotive Applications
SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
Viraliency: Pooling Local Virality
Evaluating Deep Convolutional Neural Networks for Material Classification
Detection of Human Rights Violations in Images: Can Convolutional Neural Networks help?
Zero-Shot Learning - The Good, the Bad and the Ugly
End-to-end Binary Representation Learning via Direct Binary Embedding
Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths
Joint Epipolar Tracking (JET): Simultaneous optimization of epipolar geometry and feature correspondences
Texture segmentation with Fully Convolutional Networks
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Convolutional neural network architecture for geometric matching
On the Limitation of Convolutional Neural Networks in Recognizing Negative Images
Self corrective Perturbations for Semantic Segmentation and Classification
Planar Object Tracking in the Wild: A Benchmark
Deep Residual Learning for Instrument Segmentation in Robotic Surgery
Learned Multi-Patch Similarity
MIHash: Online Hashing with Mutual Information
Introduction To The Monogenic Signal
Multiple Instance Detection Network with Online Instance Classifier Refinement
Compositional Human Pose Regression
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images
3D Object Reconstruction from Hand-Object Interactions
Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network
Deep Depth From Focus
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition
Improving Vision-based Self-positioning in Intelligent Transportation Systems via Integrated Lane and Vehicle Detection
Classification of Diabetic Retinopathy Images Using Multi-Class Multiple-Instance Learning Based on Color Correlogram Features
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
Seismic facies recognition based on prestack data using deep convolutional autoencoder
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking
DOPE: Distributed Optimization for Pairwise Energies
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
Deep Contextual Recurrent Residual Networks for Scene Labeling
Provable Self-Representation Based Outlier Detection in a Union of Subspaces
Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive Features For Semantic Matching
Harvesting Multiple Views for Marker-less 3D Human Pose Annotations
A Fuzzy Brute Force Matching Method for Binary Image Features
Accurate Optical Flow via Direct Cost Volume Processing
Joint Layout Estimation and Global Multi-View Registration for Indoor Reconstruction
Inception Recurrent Convolutional Neural Network for Object Recognition
BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography
Action Understanding with Multiple Classes of Actors
Object Discovery via Cohesion Measurement
Generalized orderless pooling performs implicit salient matching
Rotation Averaging and Strong Duality
Attributes2Classname: A discriminative model for attribute-based unsupervised zero-shot learning
Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks
Unsupervised learning of object landmarks by factorized spatial embeddings
What Can Help Pedestrian Detection?
Generative Cooperative Net for Image Generation and Data Augmentation
Residual Squeeze VGG16
CHAM: action recognition using convolutional hierarchical attention model
Predicting the Driver's Focus of Attention: the DR(eye)VE Project
Learning 3D Object Categories by Looking Around Them
Neural Style Transfer: A Review
Object-Level Context Modeling For Scene Classification with Context-CNN
Revisiting IM2GPS in the Deep Learning Era
Cooperative Learning with Visual Attributes
Localized LRR on Grassmann Manifolds: An Extrinsic View
Sparse Coding on Stereo Video for Object Detection
Classification and Retrieval of Digital Pathology Scans: A New Dataset
Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks
Look, Listen and Learn
Hashing as Tie-Aware Learning to Rank
Deep Learning Improves Template Matching by Normalized Cross Correlation
Real-Time Background Subtraction Using Adaptive Sampling and Cascade of Gaussians
Dilated Residual Networks
Towards Metamerism via Foveated Style Transfer
ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification
Line Profile Based Segmentation Algorithm for Touching Corn Kernels
Learning by Association - A versatile semi-supervised training method for neural networks
Deep Frame Interpolation
Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units
Deep Alignment Network: A convolutional neural network for robust face alignment
Learning to Learn from Noisy Web Videos
Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection
Point Linking Network for Object Detection
Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment
Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes
Teaching Compositionality to CNNs
Recent Progress of Face Image Synthesis
Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions
Synthesis of Near-regular Natural Textures
Deep Network Flow for Multi-Object Tracking
Illuminating Pedestrians via Simultaneous Detection & Segmentation
What's Mine is Yours: Pretrained CNNs for Limited Training Sonar ATR
A selectional auto-encoder approach for document image binarization
Weighted Singular Value Thresholding and its Application to Background Estimation
Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges
Automatic Understanding of Image and Video Advertisements
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Leveraging the Path Signature for Skeleton-based Human Action Recognition
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL
Rethinking Reprojection: Closing the Loop for Pose-aware ShapeReconstruction from a Single Image
Show and Recall: Learning What Makes Videos Memorable
A robotic vision system to measure tree traits
Image Projective Invariants
Object-Extent Pooling for Weakly Supervised Single-Shot Localization
Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning
Compact Model Representation for 3D Reconstruction
Joint Background Reconstruction and Foreground Segmentation via A Two-stage Convolutional Neural Network
Graph-Theoretic Spatiotemporal Context Modeling for Video Saliency Detection
ssEMnet: Serial-section Electron Microscopy Image Registration using a Spatial Transformer Network with Learned Features
Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks
Modelling the Scene Dependent Imaging in Cameras with a Deep Neural Network
Product recognition in store shelves as a sub-graph isomorphism problem
Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images
Serious Games Application for Memory Training Using Egocentric Images
Human Pose Forecasting via Deep Markov Models
Photographic Image Synthesis with Cascaded Refinement Networks
Analysis and Optimization of Convolutional Neural Network Architectures
PROBE-GK: Predictive Robust Estimation using Generalized Kernels
Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-based Supervision
Best Viewpoint Tracking for Camera Mounted on Robotic Arm with Dynamic Obstacles
Depth Super-Resolution Meets Uncalibrated Photometric Stereo
Learning Feature Pyramids for Human Pose Estimation
Improved Speech Reconstruction from Silent Video
Deep Metric Learning with Angular Loss
Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularization
Face Parsing via Recurrent Propagation
A Framework for Visually Realistic Multi-robot Simulation in Natural Environment
A Unified Model for Near and Remote Sensing
Learning to Synthesize a 4D RGBD Light Field from a Single Image
Image Quality Assessment Guided Deep Neural Networks Training
Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning
Learning Blind Motion Deblurring
Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames
Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback
DesnowNet: Context-Aware Deep Network for Snow Removal
GANs for Biological Image Synthesis
A deep architecture for unified aesthetic prediction
MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation
An Efficient Single Chord-based Accumulation Technique (SCA) to Detect More Reliable Corners
Recognizing Involuntary Actions from 3D Skeleton Data Using Body States
Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction
A Robust Indoor Scene Recognition Method based on Sparse Representation
Leaf Counting with Deep Convolutional and Deconvolutional Networks
3D Binary Signatures
Distributed Bundle Adjustment
Adaptive SVM+: Learning with Privileged Information for Domain Adaptation
Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation
Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network
Single Shot Text Detector with Regional Attention
Human Detection and Tracking for Video Surveillance A Cognitive Science Approach
Photometric stereo for strong specular highlights
Dense Face Alignment
The Devil is in the Tails: Fine-grained Classification in the Wild
Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach
Why Do Deep Neural Networks Still Not Recognize These Images?: A Qualitative Analysis on Failure Cases of ImageNet Classification
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations
Deep Generative Filter for Motion Deblurring
Joint Learning of Set Cardinality and State Distribution
A Tutorial on Deep Learning for Music Information Retrieval
Unsupervised object discovery for instance recognition
Exploring Food Detection using CNNs
Feature-Fused SSD: Fast Detection for Small Objects
Variational Methods for Normal Integration
Matterport3D: Learning from RGB-D Data in Indoor Environments
When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition
Visual Question Generation as Dual Task of Visual Question Answering
Robust Facial Landmark Detection under Significant Head Poses and Occlusion
Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection
Multi-view pose estimation with mixtures-of-parts and adaptive viewpoint selection
Attribute Recognition by Joint Recurrent Learning of Context and Correlation
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Unified Deep Supervised Domain Adaptation and Generalization
Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light
PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design
Group Affect Prediction Using Multimodal Distributions
Contrastive Learning for Image Captioning
Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with Small Deep-Neural-Network Architectures
Face Sketch Matching via Coupled Deep Transform Learning
Handwritten digit string recognition by combination of residual network and RNN-CTC
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network
Describing Natural Images Containing Novel Objects with Knowledge Guided Assitance
Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words
Backtracking Regression Forests for Accurate Camera Relocalization
Complete 3D Scene Parsing from Single RGBD Image
Deep Spatial Regression Model for Image Crowd Counting
Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System
Deterministic Approximate Methods for Maximum Consensus Robust Fitting
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
Multi-level Residual Networks from Dynamical Systems View
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
A Connection between Feed-Forward Neural Networks and Probabilistic Graphical Models
Image Patch Matching Using Convolutional Descriptors with Euclidean Distance
Clothing Retrieval with Visual Attention Model
Robust Saliency Detection via Fusing Foreground and Background Priors
A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement
Set-to-Set Hashing with Applications in Visual Recognition
Object-Centric Photometric Bundle Adjustment with Deep Shape Prior
An EEG-based Image Annotation System
MSR-net:Low-light Image Enhancement Using Deep Convolutional Network
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
Deep Residual Text Detection Network for Scene Text
AON: Towards Arbitrarily-Oriented Text Recognition
Evaluation of trackers for Pan-Tilt-Zoom Scenarios
A Novel SDASS Descriptor for Fully Encoding the Information of 3D Local Surface
Sliced Wasserstein Distance for Learning Gaussian Mixture Models
People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting
Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines
Zero-Annotation Object Detection with Web Knowledge Transfer
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks
Thoracic Disease Identification and Localization with Limited Supervision
Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards
Repulsion Loss: Detecting Pedestrians in a Crowd
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss
RGB-D-based Human Motion Recognition with Deep Learning: A Survey
Deep Expander Networks: Efficient Deep Networks from Graph Theory
Self-Supervised Vision-Based Detection of the Active Speaker as a Prerequisite for Socially-Aware Language Acquisition
Visual Feature Attribution using Wasserstein GANs
Interactive Robot Learning of Gestures, Language and Affordances
Appearance-and-Relation Networks for Video Classification
Coplanar Repeats by Energy Minimization
Interpretable Facial Relational Network Using Relational Importance
A Generative Model of 3D Object Layouts in Apartments
HoME: a Household Multimodal Environment
Towards Alzheimer's Disease Classification through Transfer Learning
Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care
3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues
Spatially-Adaptive Filter Units for Deep Neural Networks
Embodied Question Answering
Hybrid VAE: Improving Deep Generative Models using Partial Observations
Semantic Photometric Bundle Adjustment on Natural Sequences
Inertial-aided Rolling Shutter Relative Pose Estimation
Improving Smiling Detection with Race and Gender Diversity
A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation
Learning Deep Representations for Word Spotting Under Weak Supervision
DR-Net: Transmission Steered Single Image Dehazing Network with Weakly Supervised Refinement
Feature Generating Networks for Zero-Shot Learning
AI Oriented Large-Scale Video Management for Smart City: Technologies, Standards and Beyond
4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications
Separating Reflection and Transmission Images in the Wild
Beyond the Pixel-Wise Loss for Topology-Aware Delineation
Hybrid eye center localization using cascaded regression and hand-crafted model fitting
Deep Image Smoothing based on Texture and Structure Guidance
Learning 2D Gabor Filters by Infinite Kernel Learning Regression
A Frequency Domain Neural Network for Fast Image Super-resolution
Class Rectification Hard Mining for Imbalanced Deep Learning
3D Facial Expression Reconstruction using Cascaded Regression
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
CoDraw: Visual Dialog for Collaborative Drawing
Semantic Visual Localization
An ILP Solver for Multi-label MRFs with Connectivity Constraints
Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks
Light Field Segmentation From Super-pixel Graph Representation
Automatic Estimation of Ice Bottom Surfaces from Radar Imagery
Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation
Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features
Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network
Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions
Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos
SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition
Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction
MSDNN: Multi-Scale Deep Neural Network for Salient Object Detection
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention
Unsupervised Representation Learning with Laplacian Pyramid Auto-encoders
Brenier approach for optimal transportation between a quasi-discrete measure and a discrete measure
TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark
RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment
DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning
C2MSNet: A Novel approach for single image haze removal
From Neuronal Models to Neuronal Dynamics and Image Processing
Neural Algebra of Classifiers
Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection
Comparative Study of ECO and CFNet Trackers in Noisy Environment
Hierarchical Spatial Transformer Network
Object-based reasoning in VQA
Image Captioning at Will: A Versatile Scheme for Effectively Injecting Sentiments into Image Descriptions
A Survey of Recent Advances in Texture Representation
Synchronization Detection and Recovery of Steganographic Messages with Adversarial Learning
In Defense of Classical Image Processing: Fast Depth Completion on the CPU
Single Image Reflection Removal Using Deep Encoder-Decoder Network
HoloFace: Augmenting Human-to-Human Interactions on HoloLens
When can $l_p$-norm objective functions be minimized via graph cuts?
The edge cloud: A holistic view of communication, computation and caching
Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model
Background subtraction using the factored 3-way restricted Boltzmann machines
A comprehensive review of 3D point cloud descriptors
Unsupervised Typography Transfer
Pros and Cons of GAN Evaluation Measures
Disjoint Multi-task Learning between Heterogeneous Human-centric Tasks
Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning
Do deep nets really need weight decay and dropout?
Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks
Continuous Relaxation of MAP Inference: A Nonconvex Perspective
xView: Objects in Context in Overhead Imagery
End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching
Deep Unsupervised Learning of Visual Similarities
Pulling Out All the Tops with Computer Vision and Deep Learning
Facial Expression Recognition Based on Complexity Perception Classification Algorithm
DeepDefense: Training Deep Neural Networks with Improved Robustness
Monocular Depth Estimation using Multi-Scale Continuous CRFs as Sequential Deep Networks
Focal Loss Dense Detector for Vehicle Surveillance
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition
Exponential Discriminative Metric Embedding in Deep Learning
Motion deblurring of faces
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Indoor Scene Understanding in 2.5/3D: A Survey
Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks
Beyond Gröbner Bases: Basis Selection for Minimal Solvers
Particle Identification In Camera Image Sensors Using Computer Vision
Video Based Reconstruction of 3D People Models
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data
Unpaired Image Captioning by Language Pivoting
Self-Supervised Monocular Image Depth Learning and Confidence Estimation
Diverse M-Best Solutions by Dynamic Programming
Deja Vu: Motion Prediction in Static Images
Progressive Structure from Motion
Discrete Potts Model for Generating Superpixels on Noisy Images
Buried object detection from B-scan ground penetrating radar data using Faster-RCNN
Maximum Consensus Parameter Estimation by Reweighted $\ell_1$ Methods
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering
Noise generation for compression algorithms
Scene Graph Parsing as Dependency Parsing
Generalized Hadamard-Product Fusion Operators for Visual Question Answering
DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation
Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition
Learning Structure and Strength of CNN Filters for Small Sample Size Training
Learning to Anonymize Faces for Privacy Preserving Action Detection
Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition
Unsupervised Correlation Analysis
Entrenamiento de una red neuronal para el reconocimiento de imagenes de lengua de senas capturadas con sensores de profundidad
MegaDepth: Learning Single-View Depth Prediction from Internet Photos
Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images
Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection
Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning
Identifying Cross-Depicted Historical Motifs
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems
POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors
Fast Single Image Rain Removal via a Deep Decomposition-Composition Network
Image Segmentation using Sparse Subset Selection
k-NN Graph Construction: a Generic Online Approach
Large Field and High Resolution: Detecting Needle in Haystack
Geometrical analysis of polynomial lens distortion models
French Word Recognition through a Quick Survey on Recurrent Neural Networks Using Long-Short Term Memory RNN-LSTM
Deformation Aware Image Compression
Outline Objects using Deep Reinforcement Learning
FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification
Comparatives, Quantifiers, Proportions: A Multi-Task Model for the Learning of Quantities from Vision
Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing
Video Face Matching using Subset Selection and Clustering of Probabilistic Multi-Region Histograms
The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience
Fast Neuromimetic Object Recognition using FPGA Outperforms GPU Implementations
Efficient Dictionary Learning with Sparseness-Enforcing Projections
Design, Implementation and Simulation of a Cloud Computing System for Enhancing Real-time Video Services by using VANET and Onboard Navigation Systems
Design of a Mobile Face Recognition System for Visually Impaired Persons
Local Color Contrastive Descriptor for Image Classification
Compact Convolutional Neural Network Cascade for Face Detection
Fast Randomized Singular Value Thresholding for Low-rank Optimization
FPNN: Field Probing Neural Networks for 3D Data
Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
Automated Linear-Time Detection and Quality Assessment of Superpixels in Uncalibrated True- or False-Color RGB Images
CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi
Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images
Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices
FPGA based Parallelized Architecture of Efficient Graph based Image Segmentation Algorithm
Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System
muNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification
Sistema de Navegação Autônomo Baseado em Visão Computacional
Super-resolution of spatiotemporal event-stream image captured by the asynchronous temporal contrast vision sensor
Optimization over Geodesics for Exact Principal Geodesic Analysis
Privacy, Trust and Identity in Pervasive Computing: A Review of Technical Challenges and Future Research Directions
Preserving privacy for secure and outsourcing for Linear Programming in cloud computing
Open Quantum Systems and Quantum Algorithms
Complexity of Representation and Inference in Compositional Models with Part Sharing
Cubical Cohomology Ring of 3D Photographs
A Simplified Phase Model for Oscillator Based Computing
Recurrent computations for visual pattern completion
Secure SURF with Fully Homomorphic Encryption
Recurrent Segmentation for Variable Computational Budgets
Probabilistic Adaptive Computation Time
Approximate FPGA-based LSTMs under Computation Time Constraints
Fast and accurate computation of orthogonal moments for texture analysis
Efficient and Deep Person Re-Identification using Multi-Level Similarity
Mapping the Physical Properties of Cosmic Hot Gas with Hyper-spectral Imaging
Theory of ferroelectrics: A vision for the next decade and beyond
Geometric Morphology of Granular Materials
On a cepstrum-based speech detector robust to white noise
Non-negative sparse coding
2D Electrophoresis Gel Image and Diagnosis of a Disease
On the complexity of curve fitting algorithms
A rigorous definition of axial lines: ridges on isovist fields
Extraction of topological features from communication network topological patterns using self-organizing feature maps
Q-valued neural network as a system of fast identification and pattern recognition
Estimating mutual information and multi--information in large networks
Near Perfect Decoding of LDPC Codes
Bayesian Restoration of Digital Images Employing Markov Chain Monte Carlo a Review
Stability in multidimensional Size Theory
A Note on Approximate Nearest Neighbor Methods
On reconstructing n-point configurations from the distribution of distances or areas
The Parameter-Less Self-Organizing Map algorithm
Medical Image Segmentation and Localization using Deformable Templates
The Fuzzy Vault for fingerprints is Vulnerable to Brute Force Attack
Colour image segmentation by the vector-valued Allen-Cahn phase-field model: a multigrid solution
Area distances of Convex Plane Curves and Improper Affine Spheres
KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification
Discrete schemes for Gaussian curvature and their convergence
Fast Wavelet-Based Visual Classification
Intrusion Detection Using Cost-Sensitive Classification
Visual Grouping by Neural Oscillators
A Vision-based Computed Torque Control for Parallel Kinematic Machines
Real-time Texture Error Detection
Combinatorial Ricci Curvature and Laplacians for Image Processing
Two-Dimensional ARMA Modeling for Breast Cancer Detection and Classification
Search-based Structured Prediction
Accelerating Competitive Learning Graph Quantization
On the equivalence between hierarchical segmentations and ultrametric watersheds
CLD-shaped Brushstrokes in Non-Photorealistic Rendering
A Comprehensive Review of Image Enhancement Techniques
Image Segmentation by Using Threshold Techniques
Image processing of a spectrogram produced by Spectrometer Airglow Temperature Imager
Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human Face Recognition - A Comparative Study
Fast Color Space Transformations Using Minimax Approximations
A Fast Switching Filter for Impulsive Noise Removal from Color Images
A family of statistical symmetric divergences based on Jensen's inequality
Surface Curvature Effects on Reflectance from Translucent Materials
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
Convex Analysis and Optimization with Submodular Functions: a Tutorial
Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics
Adaptive Cluster Expansion (ACE): A Multilayer Network for Estimating Probability Density Functions
The Development of Dominance Stripes and Orientation Maps in a Self-Organising Visual Cortex Network (VICON)
Diffusion-geometric maximally stable component detection in deformable shapes
A Self-Organising Neural Network for Processing Data from Multiple Sensors
Automatic segmentation of HeLa cell images
Off-Line Handwritten Signature Identification Using Rotated Complex Wavelet Filters
Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction
Spatial Features for Multi-Font/Multi-Size Kannada Numerals and Vowels Recognition
Efficient and Accurate Gaussian Image Filtering Using Running Sums
An Automatic Clustering Technique for Optimal Clusters
A Novel Approach to Texture classification using statistical feature
Determining a rotation of a tetrahedron from a projection
Investigation to implicate data on clouds
Invariant Scattering Convolution Networks
A comparative evaluation of two algorithms of detection of masses on mammograms
Using Hausdorff Distance for New Medical Image Annotation
Reconstruction error in a motion capture system
A Complete Workflow for Development of Bangla OCR
Skin-color based videos categorization
A Privacy-Aware Bayesian Approach for Combining Classifier and Cluster Ensembles
Texture Analysis And Characterization Using Probability Fractal Descriptors
Neural Networks for Handwritten English Alphabet Recognition
Pilgrims Face Recognition Dataset -- HUFRD
Rapid Feature Extraction for Optical Character Recognition
A Missing and Found Recognition System for Hajj and Umrah
A Novel Approach of Harris Corner Detection of Noisy Images using Adaptive Wavelet Thresholding Technique
An Implementation of Computer Graphics as Prepress Image Enhancement Process
Multibiometric: Feature Level Fusion Using FKP Multi-Instance biometric
A Survey of Multibiometric Systems
Classification of Hepatic Lesions using the Matching Metric
A Comparative study of Arabic handwritten characters invariant feature
Artificial Neural Network Based Optical Character Recognition
An Effective Fingerprint Classification and Search Method
Viewpoint Invariant Object Detector
A Scale-Space Theory for Text
Optical Flow on Evolving Surfaces with an Application to the Analysis of 4D Microscopy Data
Application of Hopfield Network to Saccades
Learning Graphical Model Parameters with Approximate Marginal Inference
A Geometric Descriptor for Cell-Division Detection
Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks
Four Side Distance: A New Fourier Shape Signature
K Means Segmentation of Alzheimers Disease in PET scan datasets: An implementation
Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique
A Robust Rapid Approach to Image Segmentation with Optimal Thresholding and Watershed Transform
Template matching with noisy patches: A contrast-invariant GLR test
Image Compression By Embedding Five Modulus Method Into JPEG
Bubbles are rational
BiEntropy - The Approximate Entropy of a Finite Binary String
Rotation invariants of two dimensional curves based on iterated integrals
Quaternion Fourier Transform on Quaternion Fields and Generalizations
Introduction to Clifford's Geometric Algebra
A Face-like Structure Detection on Planet and Satellite Surfaces using Image Processing
Image Fusion Technologies In Commercial Remote Sensing Packages
Fuzzy Fibers: Uncertainty in dMRI Tractography
Handwritten Digits Recognition using Deep Convolutional Neural Network: An Experimental Study using EBlearn
Integration of 3D Object Recognition and Planning for Robotic Manipulation: A Preliminary Report
The Immune System: the ultimate fractionated cyber-physical system
Optical Flow on Evolving Surfaces with Space and Time Regularisation
Wavelet and Fast Fourier Transform based analysis of Solar Image
Generic Deep Networks with Wavelet Scattering
Deep learning for class-generic object detection
Bangla Text Recognition from Video Sequence: A New Focus
A sparse Kaczmarz solver and a linearized Bregman method for online compressed sensing
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
Embed System for Robotic Arm with 3 Degree of Freedom Controller using Computational Vision on Real-Time
Unsupervised Text Extraction from G-Maps
Sinogram constrained TV-minimization for metal artifact reduction in CT
Selecting a Small Set of Optimal Gestures from an Extensive Lexicon
Study on performance improvement of oil paint image filter algorithm using parallel pattern library
Implementation And Performance Evaluation Of Background Subtraction Algorithms
Efficient Tracking of a Moving Object using Inter-Frame Coding
An FPGA-based Parallel Architecture for Face Detection using Mixed Color Models
Hyperspectral Imaging and Analysis for Sparse Reconstruction and Recognition
Boosted Markov Networks for Activity Recognition
Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering
Real-time emotion recognition for gaming using deep convolutional network features
Finding Action Tubes
An Analytical Study of different Document Image Binarization Methods
A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction
On a spatial-temporal decomposition of the optical flow
Using Ensemble Models in the Histological Examination of Tissue Abnormalities
On the Problem of Detecting When Two Implicit Plane Algebraic Curves Are Similar
Boosting-like Deep Learning For Pedestrian Detection
General Deformations of Point Configurations Viewed By a Pinhole Model Camera
Planar Ultrametric Rounding for Image Segmentation
Multiscale Adaptive Representation of Signals: I. The Basic Framework
SnowWatch: Snow Monitoring through Acquisition and Analysis of User-Generated Content
Background Image Generation Using Boolean Operations
Elasticity-based Matching by Minimizing the Symmetric Difference of Shapes
Confusing Deep Convolution Networks by Relabelling
Robust Large-Scale Localization in 3D Point Clouds Revisited
Improvised Salient Object Detection and Manipulation
3D Time-lapse Reconstruction from Internet Photos
Deep Multimodal Semantic Embeddings for Speech and Images
Learning Deep Structure-Preserving Image-Text Embeddings
Screen Content Image Segmentation Using Sparse-Smooth Decomposition
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
A robust autoassociative memory with coupled networks of Kuramoto-type oscillators
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
Persistence Lenses: Segmentation, Simplification, Vectorization, Scale Space and Fractal Analysis of Images
Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks
Labeling Topics with Images using Neural Networks
Supervised Classification of RADARSAT-2 Polarimetric Data for Different Land Features
Early Methods for Detecting Adversarial Images
Shape and Centroid Independent Clustring Algorithm for Crowd Management Applications
Mean Box Pooling: A Rich Image Representation and Output Embedding for the Visual Madlibs Task
Does V-NIR based Image Enhancement Come with Better Features?
Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using CNN Features
Transfer Learning for Endoscopic Image Classification
Facial Surface Analysis using Iso-Geodesic Curves in Three Dimensional Face Recognition System
Planar Pixelations and Image Recognition
Efficient Learning of Sparse Invariant Representations
Classification with Invariant Scattering Representations
Real-time face swapping as a tool for understanding infant self-recognition
Multi-q Analysis of Image Patterns
Efficient Parallel Estimation for Markov Random Fields
Euclidean Upgrade from a Minimal Number of Segments
A review on handwritten character and numeral recognition for Roman, Arabic, Chinese and Indian scripts
Classification Tree Diagrams in Health Informatics Applications
Group-sparse Matrix Recovery
A DCT Approximation for Image Compression
Graph Approximation and Clustering on a Budget
Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network
Committees of deep feedforward networks trained with few data
Learning to Deblur
Image processing
One-Dimensional Vector based Pattern Matching
Compute Less to Get More: Using ORC to Improve Sparse Filtering
A Concept Learning Approach to Multisensory Object Perception
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
Gradient Boundary Histograms for Action Recognition
An algorithm for improving Non-Local Means operators via low-rank approximation
Object Recognition Using Deep Neural Networks: A Survey
Unsupervised Neural Architecture for Saliency Detection: Extended Version
CITlab ARGUS for historical handwritten documents
Contour Detection Using Contrast Formulas in the Framework of Logarithmic Models
CITlab ARGUS for historical data tables
Permutohedral Lattice CNNs
Extraction of Salient Sentences from Labelled Documents
A Novel Feature Selection and Extraction Technique for Classification
Spectral classification using convolutional neural networks
A Discrete Tchebichef Transform Approximation for Image and Video Coding
Quantum Pairwise Symmetry: Applications in 2D Shape Analysis
Ring artifacts correction in compressed sensing tomographic reconstruction
Efficient batchwise dropout training using submatrices
Why Use Sobolev Metrics on the Space of Curves
Over-Sampling in a Deep Neural Network
DRAW: A Recurrent Neural Network For Image Generation
Towards radio astronomical imaging using an arbitrary basis
Separable and non-separable data representation for pattern discrimination
Pixel-wise Deep Learning for Contour Detection
Predicting People's 3D Poses from Short Sequences
Path-SGD: Path-Normalized Optimization in Deep Neural Networks
Optical Flow on Evolving Sphere-Like Surfaces
Localized Multiple Kernel Learning---A Convex Approach
Partial Functional Correspondence
Spectral Collaborative Representation based Classification for Hand Gestures recognition on Electromyography Signals
Tracking Direction of Human Movement - An Efficient Implementation using Skeleton
Places205-VGGNet Models for Scene Recognition
Turing's Imitation Game has been Improved
Accelerated graph-based spectral polynomial filters
Natural scene statistics mediate the perception of image complexity
MMSE Estimation for Poisson Noise Removal in Images
Creation of a Deep Convolutional Auto-Encoder in Caffe
Simple Baseline for Visual Question Answering
RNN Fisher Vectors for Action Recognition and Image Annotation
Multilinear Subspace Clustering
Supervised Texture Segmentation: A Comparative Study
A Theory of Local Matching: SIFT and Beyond
Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences
Generate Image Descriptions based on Deep RNN and Memory Cells for Images Features
Cell segmentation with random ferns and graph-cuts
Position paper: Towards an observer-oriented theory of shape comparison
The red one!: On learning to refer to things based on their discriminative properties
Fast Bilateral Filtering of Vector-Valued Images
Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases
Hierarchical Clustering in Face Similarity Score Space
Stereotyping and Bias in the Flickr30K Dataset
Towards Multi-Agent Communication-Based Language Learning
CITlab ARGUS for historical handwritten documents
Multi-View Treelet Transform
Structured Convolution Matrices for Energy-efficient Deep learning
3D zigzag for multislicing, multiband and video processing
Find your Way by Observing the Sun and Other Semantic Cues
Bayesian Inference of Bijective Non-Rigid Shape Correspondence
Hierarchical Manifold Clustering on Diffusion Maps for Connectomics (MIT 18.S096 final project)
A convolutional approach to reflection symmetry
Learning Robust Representations of Text
Improving analytical tomographic reconstructions through consistency conditions
Spatio-Temporal Sentiment Hotspot Detection Using Geotagged Photos
A Tour of TensorFlow
ECAT: Event Capture Annotation Tool
Generating captions without looking beyond objects
GPU-accelerated real-time stixel computation
Theory and computer simulation of the moiré patterns in single-layer cylindrical particles
Short-term prediction of localized cloud motion using ground-based sky imagers
Deep Neural Networks for HDR imaging
GPU-based Pedestrian Detection for Autonomous Driving
A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation
Fuzzy Statistical Matrices for Cell Classification
Generalized Dropout
Learning Invariant Representations Of Planar Curves
A neuro-mathematical model for geometrical optical illusions
Large-Scale Shape Retrieval with Sparse 3D Convolutional Neural Networks
Point Pair Feature based Object Detection for Random Bin Picking
Template Matching with Deformable Diversity Similarity
Automated Inference on Sociopsychological Impressions of Attractive Female Faces
Photo-Quality Evaluation based on Computational Aesthetics: Review of Feature Extraction Techniques
Re-evaluating Automatic Metrics for Image Captioning
A Framework for Wasserstein-1-Type Metrics
Geometric features for voxel-based surface recognition
Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper)
A method of limiting performance loss of CNNs in noisy environments
Evolution-Preserving Dense Trajectory Descriptors
Vehicle Speed Detecting App
Changing Model Behavior at Test-Time Using Reinforcement Learning
Discrete Wavelet Transform Based Algorithm for Recognition of QRS Complexes
Optical Flow-based 3D Human Motion Estimation from Monocular Video
Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction
Object classification in images of Neoclassical furniture using Deep Learning
On the Interplay between Strong Regularity and Graph Densification
Semantic Instance Segmentation via Deep Metric Learning
Learned Watershed: End-to-End Learning of Seeded Segmentation
Creativity: Generating Diverse Questions using Variational Autoencoders
Least square ellipsoid fitting using iterative orthogonal transformations
Introspective Classification with Convolutional Nets
Introspective Generative Modeling: Decide Discriminatively
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
Data Augmentation for Low-Resource Neural Machine Translation
Speech-Based Visual Question Answering
Quantum Mechanical Approach to Modelling Reliability of Sensor Reports
Imagination improves Multimodal Translation
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
Mirror version of similar triangles method for constrained optimization problems
Megapixel Size Image Creation using Generative Adversarial Networks
Deep learning evaluation using deep linguistic processing
The in-town monitoring system for ambulance dispatch centre
Online Convolutional Dictionary Learning for Multimodal Imaging
Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network
The $\mathcal{E}$-Average Common Submatrix: Approximate Searching in a Restricted Neighborhood
Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions
Impulsive noise removal from color images with morphological filtering
Tensor-based approach to accelerate deformable part models
Domain Adaptation for Resume Classification Using Convolutional Neural Networks
On recent advances in 2D Constrained Delaunay triangulation algorithms
Learning Visually Grounded Sentence Representations
Video Highlight Prediction Using Audience Chat Reactions
Improved Face Detection and Alignment using Cascade Deep Convolutional Network
Estimating speech from lip dynamics
Hierarchically-Attentive RNN for Album Summarization and Storytelling
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition
GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery
Learning Rotation for Kernel Correlation Filter
STNet: Selective Tuning of Convolutional Networks for Object Localization
Exact Blur Measure Outperforms Conventional Learned Features for Depth Finding
Machine learning methods for histopathological image analysis
A Bottom Up Procedure for Text Line Segmentation of Latin Script
A Finite Element Computational Framework for Active Contours on Graphs
Real time ridge orientation estimation for fingerprint images
ComFlux: External Composition and Adaptation of Pervasive Applications
Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description
Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field
Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute
Fingerprint Orientation Refinement through Iterative Smoothing
Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes
A deep learning-based method for relative location prediction in CT scan images
Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay
Sentiment Classification using Images and Label Embeddings
High performance ultra-low-precision convolutions on mobile devices
Distributed Mapper
Attention networks for image-to-text
Learning a Complete Image Indexing Pipeline
Lightweight Neural Networks
An Artificial Neural Network Architecture Based on Context Transformations in Cortical Minicolumns
AVEID: Automatic Video System for Measuring Engagement In Dementia
Texture Object Segmentation Based on Affine Invariant Texture Detection
ObamaNet: Photo-realistic lip-sync from text
Combining Stereo Disparity and Optical Flow for Basic Scene Flow
Fine-tuned Language Models for Text Classification
The WiLI benchmark dataset for written language identification
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
Plummer Autoencoders
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers
Learning to Count Objects in Natural Images for Visual Question Answering
Affine Differential Invariants for Invariant Feature Point Detection
Rigid Point Registration with Expectation Conditional Maximization
Contour Parametrization via Anisotropic Mean Curvature Flows
A Framework for Video-Driven Crowd Synthesis
Enhanced navigation systems in GPS denied environments for visually impaired people: A Survey
The Three Pillars of Machine-Based Programming
Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual Network
Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment
A Modified Image Comparison Algorithm Using Histogram Features
VoroTop: Voronoi Cell Topology Visualization and Analysis Toolkit
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks
Vision-Guided Robot Hearing
Robot In a Room: Toward Perfect Object Recognition in Closed Environments
Rapid Online Analysis of Local Feature Detectors and Their Complementarity
Background subtraction - separating the modeling and the inference
Free-Space Detection with Self-Supervised and Online Trained Fully Convolutional Networks
Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging
Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications
A Restricted Visual Turing Test for Deep Scene and Event Understanding
2D Visual Place Recognition for Domestic Service Robots at Night
Vision-based Engagement Detection in Virtual Reality
Learning Deep CNN Denoiser Prior for Image Restoration
Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)
Visual pathways from the perspective of cost functions and multi-task deep neural networks
Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion
Learning Affinity via Spatial Propagation Networks
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
DDD17: End-To-End DAVIS Driving Dataset
Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond
Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data
Grounding Referring Expressions in Images by Variational Context
A vision based system for underwater docking
Superpixel based Class-Semantic Texton Occurrences for Natural Roadside Vegetation Segmentation
TSSD: Temporal Single-Shot Object Detection Based on Attention-Aware LSTM
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
A Survey on Mobile Edge Computing: The Communication Perspective
Behavior Subtraction
Multiple View Reconstruction of Calibrated Images using Singular Value Decomposition
Gray Image extraction using Fuzzy Logic
Fast 3D Salient Region Detection in Medical Images using GPUs
Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization
Ubic: Bridging the gap between digital cryptography and the physical world
A robust and adaptable method for face detection based on Color Probabilistic Estimation Technique
Discovering Discriminative Cell Attributes for HEp-2 Specimen Image Classification
Fast Robust PCA on Graphs
Deep convolutional neural networks for pedestrian detection
Convolutional neural networks with low-rank regularization
A Focused Dynamic Attention Model for Visual Question Answering
A New Trusted and E-Commerce Architecture for Cloud Computing
Dictionary learning for fast classification based on soft-thresholding
Extracting man-made objects from remote sensing images via fast level set evolutions
Multi-Atlas Segmentation of Biomedical Images: A Survey
Visual Saliency Based on Multiscale Deep Features
Spherical Conformal Parameterization of Genus-0 Point Clouds for Meshing
Local Multi-Grouped Binary Descriptor with Ring-based Pooling Configuration and Optimization
Dynamic Parallel and Distributed Graph Cuts
Recent Advances in Convolutional Neural Networks
Deep convolutional networks for automated detection of posterior-element fractures on spine CT
A Powerful Generative Model Using Random Weights for the Deep Image Representation
FALCON: Feature Driven Selective Classification for Energy-Efficient Image Recognition
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
Oriented bounding boxes using multiresolution contours for fast interference detection of arbitrary geometry objects
The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives
Geometric deep learning: going beyond Euclidean data
Geometric deep learning on graphs and manifolds using mixture model CNNs
Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes
Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Smart Mining for Deep Metric Learning
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism
One-Shot Concept Learning by Simulating Evolutionary Instinct Development
Reading Scene Text with Attention Convolutional Sequence Modeling
Compact Environment-Invariant Codes for Robust Visual Place Recognition
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
Knowledge Projection for Deep Neural Networks
Efficient Implementation of a Recognition System Using the Cortex Ventral Stream Model
Weakly Supervised One-Shot Detection with Attention Siamese Networks
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath Similarity
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
Using Convolutional Neural Networks for Determining Reticulocyte Percentage in Cats
Fast Subspace Clustering Based on the Kronecker Product
Group Normalization
Context-aware Deep Feature Compression for High-speed Visual Tracking
Quantum Analogue Computing
Large-scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts
A Novel Framework for Robustness Analysis of Visual QA Models
Oracle Complexity and Nontransitivity in Pattern Recognition
Novel Runtime Systems Support for Adaptive Compositional Modeling on the Grid
The Computational Complexity of Orientation Search Problems in Cryo-Electron Microscopy
Analogue Quantum Computers for Data Analysis
The Fast Haar Wavelet Transform for Signal & Image Processing
A Note on the Membrane Computer
Distribution of the search of evolutionary product unit neural networks for classification
A programme to determine the exact interior of any connected digital picture
Entropy Computation of Document Images in Run-Length Compressed Domain
The Shortlist Method for Fast Computation of the Earth Mover's Distance and Finding Optimal Solutions to Transportation Problems
Trainable and Dynamic Computing: Error Backpropagation through Physical Media
Improving the performance of the linear systems solvers using CUDA
Distributed Machine Learning via Sufficient Factor Broadcasting
Approaching the Computational Color Constancy as a Classification Problem through Deep Learning
Distributed Machine Learning via Sufficient Factor Broadcasting
Simplified firefly algorithm for 2D image key-points search
Quantifying Creativity in Art Networks
Depth Reconstruction and Computer-Aided Polyp Detection in Optical Colonoscopy Video Frames
Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras
Preserving the value of large scale data analytics over time through selective re-computation
A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields
Free Space Estimation using Occupancy Grids and Dynamic Object Detection
Some observations on computer lip-reading: moving from the dream to the reality
SkipNet: Learning Dynamic Routing in Convolutional Networks
Convolutional Networks with Adaptive Computation Graphs
Depth-Adaptive Computational Policies for Efficient Visual Tracking
SBNet: Sparse Blocks Network for Fast Inference
MEDEA: Automated Measure and on-line Analysis in Astronomy and Astrophysics for Very Large Vision Machine
Paving the Way for Image Understanding: A New Kind of Image Decomposition is Desired
Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot
Camera motion estimation through planar deformation determination
Multi-Dimensional Recurrent Neural Networks
Evaluation for Uncertain Image Classification and Segmentation
The bispectrum as a source of phase-sensitive invariants for Fourier descriptors: a group-theoretic approach
A possible low-level explanation of "temporal dynamics of brightness induction and White's illusion"
Feature Level Clustering of Large Biometric Database
Visual Infrared Video Fusion for Night Vision using Background Estimation
Feature Selection via Sparse Approximation for Face Recognition
Vision-Based Navigation III: Pose and Motion from Omnidirectional Optical Flow and a Digital Terrain Map
Vision-Based Navigation II: Error Analysis for a Navigation Algorithm based on Optical-Flow and a Digital Terrain Map
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
Estimating 3D Human Shapes from Measurements
How important are Deformable Parts in the Deformable Parts Model?
Joint Reconstruction of Multi-view Compressed Images
A Distributed Algorithm for Gathering Many Fat Mobile Robots in the Plane
3D Scene Grammar for Parsing RGB-D Pointclouds
A Multi-Orientation Analysis Approach to Retinal Vessel Tracking
Object Recognition with Imperfect Perception and Redundant Description
Handwritten and Printed Text Separation in Real Document
Expressing Relational and Temporal Knowledge in Visual Probabilistic Networks
iCub World: Friendly Robots Help Building Good Vision Data-Sets
Speedy Object Detection based on Shape
Top-down and Bottom-up Feature Combination for Multi-sensor Attentive Robots
Scalable $k$-NN graph construction
CSIFT Based Locality-constrained Linear Coding for Image Classification
Glasgow's Stereo Image Database of Garments
Large Scale Visual Recommendations From Street Fashion Images
Convex Relaxations of SE(2) and SE(3) for Visual Pose Estimation
Efficient Background Modeling Based on Sparse Representation and Outlier Iterative Removal
Regression-Based Image Alignment for General Object Categories
Globally Optimal Joint Image Segmentation and Shape Matching Based on Wasserstein Modes
Unified mobile public health care system (UMPHCS) for underdeveloped countries
GASP : Geometric Association with Surface Patches
Anisotropic Agglomerative Adaptive Mean-Shift
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval
A Latent Clothing Attribute Approach for Human Pose Estimation
Exploring Human Vision Driven Features for Pedestrian Detection
Visual Summary of Egocentric Photostreams by Representative Keyframes
Parametric Regression on the Grassmannian
Salient Structure Detection by Context-Guided Visual Search
Driver Gaze Region Estimation Without Using Eye Movement
Subspace Alignment Based Domain Adaptation for RCNN Detector
Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
Occlusion-Aware Object Localization, Segmentation and Pose Estimation
Action-Conditional Video Prediction using Deep Networks in Atari Games
Single Image Dehazing through Improved Atmospheric Light Estimation
Event-based Camera Pose Tracking using a Generative Event Model
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
Depth Extraction from Videos Using Geometric Context and Occlusion Boundaries
Accurate Vision-based Vehicle Localization using Satellite Imagery
Bioinspired Visual Motion Estimation
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
Labeled pupils in the wild: A dataset for studying pupil detection in unconstrained environments
Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation
Horizon Lines in the Wild
Counting Everyday Objects in Everyday Scenes
Reversible Image Merging for Low-level Machine Vision
Deep Residual Networks with Exponential Linear Unit
Invariant feature extraction from event based stimuli
Photometric Bundle Adjustment for Vision-Based SLAM
Learning Joint Representations of Videos and Sentences with Web Image Search
Camera Pose Estimation from Lines using Plücker Coordinates
Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition
Multiple objects tracking in surveillance video using color and Hu moments
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
A Biomimetic Model of the Outer Plexiform Layer by Incorporating Memristive Devices
Shadow Estimation Method for "The Episolar Constraint: Monocular Shape from Shadow Correspondence"
A Novel Approach for Canvas Accessibility Problem in HTML5
Fast Edge Detection Using Structured Forests
Road Detection via On--line Label Transfer
Video (language) modeling: a baseline for generative models of natural videos
A Stable Multi-Scale Kernel for Topological Machine Learning
An Expressive Deep Model for Human Action Parsing from A Single Image
The NUbots Team Description Paper 2015
Predicting opponent team activity in a RoboCup environment
Building Statistical Shape Spaces for 3D Human Modeling
Predicting Complete 3D Models of Indoor Scenes
Holistically-Nested Edge Detection
TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking
Circle-based Eye Center Localization (CECL)
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
Kernelized Deep Convolutional Neural Network for Describing Complex Images
Deep Multimodal Embedding: Manipulating Novel Objects with Point-clouds, Language and Trajectories
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
The Next Best Underwater View
Cross-dimensional Weighting for Aggregated Deep Convolutional Features
Unsupervised Temporal Segmentation of Repetitive Human Actions Based on Kinematic Modeling and Frequency Analysis
Domain Adaptation and Transfer Learning in StochasticNets
Analysis of Vessel Connectivities in Retinal Images by Cortically Inspired Spectral Clustering
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
On Some Properties of Calibrated Trifocal Tensors
Facial age estimation using BSIF and LBP
Joint Object-Material Category Segmentation from Audio-Visual Cues
Towards Declarative Safety Rules for Perception Specification Architectures
Bit-Planes: Dense Subpixel Alignment of Binary Descriptors
Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects
Density-based Denoising of Point Cloud
Temporally Robust Global Motion Compensation by Keypoint-based Congealing
Multi-modal Tracking for Object based SLAM
Cross-modal Supervision for Learning Active Speaker Detection in Video
Rich Image Captioning in the Wild
WEPSAM: Weakly Pre-Learnt Saliency Model
Hierarchical Modeling of Multidimensional Data in Regularly Decomposed Spaces: Applications in Image Analysis
Learning Action Maps of Large Environments via First-Person Vision
Software Assumptions Failure Tolerance: Role, Strategies, and Visions
Chained Predictions Using Convolutional Neural Networks
HARRISON: A Benchmark on HAshtag Recommendation for Real-world Images in Social Networks
Local Perturb-and-MAP for Structured Prediction
Action Classification via Concepts and Attributes
End-to-End Instance Segmentation with Recurrent Attention
Modeling Photographic Composition via Triangles
Deeper Depth Prediction with Fully Convolutional Residual Networks
Multimodal Residual Learning for Visual QA
Shallow Networks for High-Accuracy Road Object-Detection
Predictive Coding for Dynamic Vision : Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model
Learning without Forgetting
Camera Elevation Estimation from a Single Mountain Landscape Photograph
Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation
FusionNet: 3D Object Classification Using Multiple Data Representations
Human Pose Estimation in Space and Time using 3D CNN
Deep Markov Random Field for Image Modeling
Bottom-up Instance Segmentation using Deep Higher-Order CRFs
Track Facial Points in Unconstrained Videos
Image denoising via group sparsity residual constraint
Development of a Fuzzy Expert System based Liveliness Detection Scheme for Biometric Authentication
Non-flat Road Detection Based on A Local Descriptor
A compact representation for minimizers of $k$-submodular functions
Real-time Halfway Domain Reconstruction of Motion and Geometry
mdBrief - A Fast Online Adaptable, Distorted Binary Descriptor for Real-Time Applications Using Calibrated Wide-Angle Or Fisheye Cameras
SoundNet: Learning Sound Representations from Unlabeled Video
CRF-CNN: Modeling Structured Information in Human Pose Estimation
Can fully convolutional networks perform well for general image restoration problems?
Light Field Stitching for Extended Synthetic Aperture
Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range
Answering Image Riddles using Vision and Reasoning through Probabilistic Soft Logic
SANet: Structure-Aware Network for Visual Tracking
Robust end-to-end deep audiovisual speech recognition
Object Detection using Image Processing
What Can Be Predicted from Six Seconds of Driver Glances?
Did Evolution get it right? An evaluation of Near-Infrared imaging in semantic scene segmentation using deep learning
Sequential Person Recognition in Photo Albums with a Recurrent Network
Embedded Line Scan Image Sensors: The Low Cost Alternative for High Speed Imaging
The Mehler-Fock Transform and some Applications in Texture Analysis and Color Processing
UnrealStereo: A Synthetic Dataset for Analyzing Stereo Vision
Efficient Optical flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone
Beyond Holistic Object Recognition: Enriching Image Understanding with Part States
Automatic Interpretation of Unordered Point Cloud Data for UAV Navigation in Construction
Design, Control and Visual Navigation of the DelftaCopter
Overlapping Cover Local Regression Machines
What are the visual features underlying human versus machine vision?
Understanding trained CNNs by indexing neuron selectivity
Relative Camera Pose Estimation Using Convolutional Neural Networks
Attentional Network for Visual Object Detection
Towards Autonomous UAV Landing Based on Infrared Beacons and Particle Filtering
Handwritten Arabic Numeral Recognition using Deep Learning Neural Networks
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI
Weighted Motion Averaging for the Registration of Multi-View Range Scans
An Optimization Framework with Flexible Inexact Inner Iterations for Nonconvex and Nonsmooth Programming
Context Aware Query Image Representation for Particular Object Retrieval
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation
Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks
Deformable Convolutional Networks
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders
Learning to Predict: A Fast Re-constructive Method to Generate Multimodal Embeddings
Satellite Image-based Localization via Learned Embeddings
Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising
Weakly Supervised Dense Video Captioning
Encoder Based Lifelong Learning
Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database
Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes
Reconstruction of~3-D Rigid Smooth Curves Moving Free when Two Traceable Points Only are Available
Virtual to Real Reinforcement Learning for Autonomous Driving
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
On Face Segmentation, Face Swapping, and Face Perception
Camera Pose Filtering with Local Regression Geodesics on the Riemannian Manifold of Dual Quaternions
Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
Face Recognition Machine Vision System Using Eigenfaces
You said that?
Distribution of degrees of freedom over structure and motion of rigid bodies
Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups
Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network
Magnetic-Visual Sensor Fusion based Medical SLAM for Endoscopic Capsule Robot
Fashion Forward: Forecasting Visual Style in Fashion
Exploring the structure of a real-time, arbitrary neural artistic stylization network
VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach
CASENet: Deep Category-Aware Semantic Edge Detection
Reflection Invariant and Symmetry Detection
CortexNet: a Generic Network Family for Robust Visual Temporal Representations
Image Matching via Loopy RNN
Vision-based Real Estate Price Estimation
The Devil is in the Decoder
Emotional Filters: Automatic Image Transformation for Inducing Affect
Cascaded Scene Flow Prediction using Semantic Segmentation
Scene Graph Generation from Objects, Phrases and Region Captions
Structure-measure: A New Way to Evaluate Foreground Maps
Associations among Image Assessments as Cost Functions in Linear Decomposition: MSE, SSIM, and Correlation Coefficient
Adversarial Robustness: Softmax versus Openmax
Curvature sensing by vision and touch
Weakly Supervised Image Annotation and Segmentation with Objects and Attributes
Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB
SeDAR - Semantic Detection and Ranging: Humans can localise without LiDAR, can robots?
One-Shot Learning for Semantic Segmentation
Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction
Conversational Exploratory Search via Interactive Storytelling
Direct Pose Estimation with a Monocular Camera
Learning quadrangulated patches for 3D shape parameterization and completion
Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens
Emerging Topics in Assistive Reading Technology: From Presentation to Content Accessibility
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
UAV and Service Robot Coordination for Indoor Object Search Tasks
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
Are we Done with Object Recognition? The iCub robot's Perspective
Vision-based deep execution monitoring
Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks
Classification of Time-Series Images Using Deep Convolutional Neural Networks
Visual gesture variability between talkers in continuous visual speech
Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
Multiframe Scene Flow with Piecewise Rigid Motion
End-to-end Driving via Conditional Imitation Learning
Scalable Dense Monocular Surface Reconstruction
Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery
Dropout Sampling for Robust Object Detection in Open-Set Conditions
Real-time Convolutional Neural Networks for Emotion and Gender Classification
SEGCloud: Semantic Segmentation of 3D Point Clouds
Class Correlation affects Single Object Localization using Pre-trained ConvNets
Squeeze-SegNet: A new fast Deep Convolutional Neural Network for Semantic Segmentation
The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching
W-Net: A Deep Model for Fully Unsupervised Image Segmentation
Learning Depth from Monocular Videos using Direct Methods
Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks
Visual to Sound: Generating Natural Sound for Videos in the Wild
Vision Recognition using Discriminant Sparse Optimization Learning
Learning to Navigate by Growing Deep Networks
Flexible Stereo: Constrained, Non-rigid, Wide-baseline Stereo Vision for Fixed-wing Aerial Platforms
Query-Efficient Black-box Adversarial Examples (superceded)
Low-Shot Learning with Imprinted Weights
Human-Centric Data Cleaning [Vision]
ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks
A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing
Low-Shot Learning from Imaginary Data
Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver Prediction
Learning random-walk label propagation for weakly-supervised semantic segmentation
Enhanced Image Classification With Data Augmentation Using Position Coordinates
Deep Image Super Resolution via Natural Image Priors
Vehicle Pose and Shape Estimation through Multiple Monocular Vision
Joint 3D Reconstruction of a Static Scene and Moving Objects
Salient Object Detection by Lossless Feature Reflection
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning
Generating goal-directed visuomotor plans based on learning using a predictive coding type deep visuomotor recurrent neural network model
Review of Visual Saliency Detection with Comprehensive Information
Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction
Object Captioning and Retrieval with Natural Language
MAGSAC: marginalizing sample consensus
Modeling Camera Effects to Improve Deep Vision for Real and Synthetic Data
Stacked Cross Attention for Image-Text Matching
Robust Blind Deconvolution via Mirror Descent
Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
Random Polyhedral Scenes: An Image Generator for Active Vision System Experiments
Robust Video Content Alignment and Compensation for Rain Removal in a CNN Framework
Learning Kinematic Descriptions using SPARE: Simulated and Physical ARticulated Extendable dataset
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
Guide Me: Interacting with Deep Networks
Performance Evaluation of 3D Correspondence Grouping Algorithms
Monocular Vision based Collaborative Localization for Micro Aerial Vehicle Swarms
The Sound of Pixels
PCN: Part and Context Information for Pedestrian Detection with CNNs
Anisotropic k-Nearest Neighbor Search Using Covariance Quadtree
SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions
Automatic Pattern Classification by Unsupervised Learning Using Dimensionality Reduction of Data with Mirroring Neural Networks
Learning Graph Matching
Point-Set Registration: Coherent Point Drift
Boosting k-NN for categorization of natural scenes
Towards automated high-throughput screening of C. elegans on agar
Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns
The Object Projection Feature Estimation Problem in Unsupervised Markerless 3D Motion Tracking
3D Model Assisted Image Segmentation
Filling-Based Techniques Applied to Object Projection Feature Estimation
Probabilistic index maps for modeling natural signals
Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces
An Image Based Technique for Enhancement of Underwater Images
A Survey of Appearance Models in Visual Object Tracking
Filtering for More Accurate Dense Tissue Segmentation in Digitized Mammograms
A Study of Actor and Action Semantic Retention in Video Supervoxel Segmentation
One-Shot Adaptation of Supervised Deep Convolutional Models
Learning Human Pose Estimation Features with Convolutional Networks
Infrared face recognition: a comprehensive review of methodologies and databases
Extraction of Line Word Character Segments Directly from Run Length Compressed Printed Text Documents
Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling
Computational Beauty: Aesthetic Judgment at the Intersection of Art and Science
Transferring Rich Feature Hierarchies for Robust Visual Tracking
A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model
VQA: Visual Question Answering
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures
Weakly-Supervised Alignment of Video With Text
Texture Modelling with Nested High-order Markov-Gibbs Random Fields
Representational Distance Learning for Deep Neural Networks
Semantic Object Parsing with Local-Global Long Short-Term Memory
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Using Apache Lucene to Search Vector of Locally Aggregated Descriptors
Efficient Optimization for Rank-based Loss Functions
A machine learning method for the large-scale evaluation of urban visual environment
Efficient Continuous Relaxations for Dense CRF
Absolute Pose Estimation from Line Correspondences using Direct Linear Transformation
Fast Trajectory Simplification Algorithm for Natural User Interfaces in Robot Programming by Demonstration
Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture
Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence
3D-Assisted Image Feature Synthesis for Novel Views of an Object
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
A Framework for Symmetric Part Detection in Cluttered Scenes
Fast image-based obstacle detection from unmanned surface vehicles
FaceNet: A Unified Embedding for Face Recognition and Clustering
Fast keypoint detection in video sequences
Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
FPA-CS: Focal Plane Array-based Compressive Imaging in Short-wave Infrared
Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation and Evaluation
A Novel Feature Extraction Method for Scene Recognition Based on Centered Convolutional Restricted Boltzmann Machines
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
Semantic Image Segmentation via Deep Parsing Network
Selecting Relevant Web Trained Concepts for Automated Event Retrieval
LIBSVX: A Supervoxel Library and Benchmark for Early Video Processing
A Semi-Automated Method for Object Segmentation in Infant's Egocentric Videos to Study Object Perception
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
On Complex Valued Convolutional Neural Networks
Continuous 3D Label Stereo Matching using Local Expansion Moves
Aerial image geolocalization from recognition and matching of roads and intersections
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
How Deep is the Feature Analysis underlying Rapid Visual Categorization?
Deep learning trends for focal brain pathology segmentation in MRI
On the usability of deep networks for object-based image analysis
Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?
EM-Based Mixture Models Applied to Video Event Detection
Message-passing algorithms for synchronization problems over compact groups
Adaptive mixed norm optical flow estimation
Fast On-Line Kernel Density Estimation for Active Object Localization
Kernel Cross-View Collaborative Representation based Classification for Person Re-Identification
Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection
Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision
Procedural Generation of Videos to Train Deep Action Recognition Networks
Differential Angular Imaging for Material Recognition
Superpixel Segmentation Using Gaussian Mixture Model
Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification
Speckle Reduction with Trained Nonlinear Diffusion Filtering
Parallel Structure from Motion from Local Increment to Global Averaging
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation
Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling
Coordinating Filters for Faster Deep Neural Networks
Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
Object Detection Using Deep CNNs Trained on Synthetic Images
Detekcja upadku i wybranych akcji na sekwencjach obrazów cyfrowych
Recurrent Residual Learning for Action Recognition
Spectral Filter Tracking
Multi-Branch Fully Convolutional Network for Face Detection
Deep Neural Network Capacity
Linear Differential Constraints for Photo-polarimetric Height Estimation
Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable
Self-Guiding Multimodal LSTM - when we do not have a perfect training dataset for image captioning
Convolutional Long Short-Term Memory Networks for Recognizing First Person Interactions
An Evolutionary Computing Enriched RS Attack Resilient Medical Image Steganography Model for Telemedicine Applications
Generic 3D Representation via Pose Estimation and Matching
A Survey on Hardware Implementations of Visual Object Trackers
Crowd counting via scale-adaptive convolutional neural network
Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN
Detection-aided liver lesion segmentation using deep learning
Single-epoch supernova classification with deep convolutional neural networks
Robust Kronecker Component Analysis
Dynamic Graph CNN for Learning on Point Clouds
Game of Sketches: Deep Recurrent Models of Pictionary-style Word Guessing
Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches
Full-Frame Scene Coordinate Regression for Image-Based Localization
A Weighted Sparse Sampling and Smoothing Frame Transition Approach for Semantic Fast-Forward First-Person Videos
Computational Optimal Transport
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Deep learning and its application to medical image segmentation
HDM-Net: Monocular Non-Rigid 3D Reconstruction with Learned Deformation Model
Learning Free-Form Deformations for 3D Object Reconstruction
Learning Beyond Human Expertise with Generative Models for Dental Restorations
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning
Accelerated Optimization in the PDE Framework: Formulations for the Manifold of Diffeomorphisms
VESICLE: Volumetric Evaluation of Synaptic Interfaces using Computer vision at Large Scale
Tracing the boundaries of materials in transparent vessels using computer vision
WhittleSearch: Interactive Image Search with Relative Attribute Feedback
From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example
Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes
The Cafe Wall Illusion: Local and Global Perception from multiple scale to multiscale
Improved Inception-Residual Convolutional Neural Network for Object Recognition
Estimacao Temporal da Deformacao entre Objectos utilizando uma Metodologia Fisica
3D-Ultrasound probe calibration for computer-guided diagnosis and therapy
Multiclass Approaches for Support Vector Machine Based Land Cover Classification
An Exponential Lower Bound on the Complexity of Regularization Paths
A Combinatorial Algorithm to Compute Regularization Paths
Critical Analysis of Middleware Architectures for Large Scale Distributed Systems
Genus Computing for 3D digital objects: algorithm and implementation
Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering
Evolution of Things
Comparison of Persistent Homologies for Vector Functions: from continuous to discrete and back
A Simple and Correct Even-Odd Algorithm for the Point-in-Polygon Problem for Complex Polygons
Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques
Optimal Computational Trade-Off of Inexact Proximal Methods
Different Operating Systems Compatible for Image Prepress Process in Color Management: Analysis and Performance Testing
Computing Consensus Curves
On Automation and Medical Image Interpretation, With Applications for Laryngeal Imaging
Speech: A Challenge to Digital Signal Processing Technology for Human-to-Computer Interaction
Fast Linearized Alternating Direction Minimization Algorithm with Adaptive Parameter Selection for Multiplicative Noise Removal
Computer Aided ECG Analysis - State of the Art and Upcoming Challenges
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching
Numerical Computation of Weil-Peterson Geodesics in the Universal Teichmüller Space
Beyond visual P300 based brain-computer interfacing paradigms
Word Spotting in Cursive Handwritten Documents using Modified Character Shape Codes
Computing support for advanced medical data analysis and imaging
Stereo on a budget
FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation
Computing With Contextual Numbers
Fast Disk Conformal Parameterization of Simply-connected Open Surfaces
A Video Database of Human Faces under Near Infra-Red Illumination for Human Computer Interaction Aplications
Efficient Hand Articulations Tracking using Adaptive Hand Model and Depth map
Iris Codes Classification Using Discriminant and Witness Directions
Towards a Generic Application Partitioning and Retraction Framework for Pervasive Environments
MatConvNet - Convolutional Neural Networks for MATLAB
Accelerated graph-based nonlinear denoising filters
Evolution of active categorical image classification via saccadic eye movement
A Systematic Approach to Blocking Convolutional Neural Networks
Feature Extraction and Soft Computing Methods for Aerospace Structure Defect Classification
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
An affective computational model for machine consciousness
Development of JavaScript-based deep learning platform and application to distributed training
Large-scale image analysis using docker sandboxing
Dynamic Computational Time for Visual Attention
Streaming Algorithm for Euler Characteristic Curves of Multidimensional Images
Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs
meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
High-Performance Out-of-core Block Randomized Singular Value Decomposition on GPU
Video Salient Object Detection Using Spatiotemporal Deep Features
Adaptive PCA for Time-Varying Data
Improving Efficiency in Convolutional Neural Network with Multilinear Filters
Which phoneme-to-viseme maps best improve visual-only computer lip-reading?
Optimal Control of Wireless Computing Networks
Learning Random Fourier Features by Hybrid Constrained Optimization
clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local Convolutions
A Quantization-Friendly Separable Convolution for MobileNets
Cloudbus Toolkit for Market-Oriented Cloud Computing
A U.S. Research Roadmap for Human Computation
21st Century Computer Architecture
Binary-decomposed DCNN for accelerating computation and compressing model without retraining
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference
Assisted Video Sequences Indexing : Motion Analysis Based on Interest Points
Digital Color Imaging
Structure from Motion: Theoretical Foundations of a Novel Approach Using Custom Built Invariants
A Virtual Library of Technical Publications
A Theory of Cross-Validation Error
The Management of Context-Sensitive Features: A Review of Strategies
Statistical efficiency of curve fitting algorithms
Differential Methods in Catadioptric Sensor Design with Applications to Panoramic Imaging
A New Analytical Radial Distortion Model for Camera Calibration
Rational Radial Distortion Models with Analytical Undistortion Formulae
Geometrical Complexity of Classification Problems
Computerized Face Detection and Recognition
Blind Detection and Compensation of Camera Lens Geometric Distortions
Search Process and Probabilistic Bifix Approach
The Poincare conjecture for digital spaces. Properties of digital n-dimensional disks and spheres
Connection between continuous and digital n-manifolds and the Poincare conjecture
A kernel method for canonical correlation analysis
Conditional Expressions for Blind Deconvolution: Derivative form
A novel set of rotationally and translationally invariant features for images based on the non-commutative bispectrum
Contains and Inside relationships within combinatorial Pyramids
Which Point Configurations are Determined by the Distribution of their Pairwise Distances?
Matrices of Forests and the Analysis of Digraphs
Hilbert series of subspace arrangements
Numerically Invariant Signature Curves
Brainy light sensors with no diffraction limitations
Multiresolution Approximation of Polygonal Curves in Linear Complexity
An Independent Evaluation of Subspace Face Recognition Algorithms
MI image registration using prior knowledge
A structure from motion inequality
Variational local structure estimation for image super-resolution
High-Order Nonparametric Belief-Propagation for Fast Image Inpainting
Lossless Representation of Graphs using Distributions
An Affinity Propagation Based method for Vector Quantization Codebook Design
Affine Geometry of Space Curves
Graph kernels between point clouds
A Fast Hierarchical Multilevel Image Segmentation Method using Unbiased Estimators
Some Aspects of Testing Process for Transport Streams in Digital Video Broadcasting
Efficient implementation of GALS systems over commercial synchronous FPGAs: a new approach
Spatio-activity based object detection
A multilateral filtering method applied to airplane runway image
The Euler-Poincare theory of Metamorphosis
Fusion de classifieurs pour la classification d'images sonar
Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification
Conceptualization of seeded region growing by pixels aggregation. Part 2: how to localize a final partition invariant about the seeded region initialisation order
Conceptualization of seeded region growing by pixels aggregation. Part 3: a wide range of algorithms
An image processing analysis of skin textures
Design and Implementation a 8 bits Pipeline Analog to Digital Converter in the Technology 0.6 μm CMOS Process
A Fuzzy Commitment Scheme
Modeling and Control with Local Linearizing Nadaraya Watson Regression
Detecting the Most Unusual Part of a Digital Image
Stroke Fragmentation based on Geometry Features and HMM
Over-enhancement Reduction in Local Histogram Equalization using its Degrees of Freedom
The Digital Restoration of Da Vinci's Sketches
Digital Restoration of Ancient Papyri
Color Dipole Moments for Edge Detection
Better Global Polynomial Approximation for Image Rectification
Information Distance in Multiples
Quality assessment of the MPEG-4 scalable video CODEC
Coding cells of digital spaces: a framework to write generic digital topology algorithms
Adaptive Regularization of Ill-Posed Problems: Application to Non-rigid Image Registration
Multiple pattern classification by sparse subspace decomposition
Introducing New AdaBoost Features for Real-Time Vehicle Detection
Non-photorealistic image processing: an Impressionist rendering
Laser Actuated Presentation System
Kannada Character Recognition System A Review
Fusion of Multiple Matchers using SVM for Offline Signature Identification
Geometric approach to sampling and communication
Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection
Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory
Recognition of Handwritten Roman Script Using Tesseract Open source OCR Engine
Trends and Techniques in Visual Gaze Analysis
Signature Recognition using Multi Scale Fourier Descriptor And Wavelet Transform
Classification via Incoherent Subspaces
On the Subspace of Image Gradient Orientations
Face Synthesis (FASY) System for Generation of a Face Image from Human Description
Optimization of Weighted Curvature for Image Segmentation
A Two Stage Classification Approach for Handwritten Devanagari Characters
A novel approach for handwritten Devnagari character recognition
FPGA Based Assembling of Facial Components for Human Face Construction
Uncertainty of visual measurement and efficient allocation of sensory resources
A Fast Decision Technique for Hierarchical Hough Transform for Line Detection
Orthogonal multifilters image processing of astronomical images from scanned photographic plates
A Miniature-Based Image Retrieval System
On Euclidean Norm Approximations
Variational Iteration Method for Image Restoration
Distance Measures for Reduced Ordering Based Vector Filters
A Fuzzy Clustering Model for Fuzzy Data with Outliers
Detecting Image Forgeries using Geometric Cues
Affine-invariant diffusion geometry for the analysis of deformable 3D shapes
Diffusion framework for geometric and photometric data fusion in non-rigid shape analysis
Chernoff information of exponential families
All Roads Lead To Rome
Image Retrieval Method Using Top-surf Descriptor
Approximation of Besov vectors by Paley-Wiener vectors in Hilbert spaces
Gaussian Affine Feature Detector
Fuzzy Rules and Evidence Theory for Satellite Image Analysis
From a Modified Ambrosio-Tortorelli to a Randomized Part Hierarchy Tree
Hue Histograms to Spatiotemporal Local Features for Action Recognition
Improving digital signal interpolation: L2-optimal kernels with kernel-invariant interpolation speed
Intent Inference and Syntactic Tracking with GMTI Measurements
Pose Estimation from a Single Depth Image for Arbitrary Kinematic Skeletons
Visual Secret Sharing Scheme using Grayscale Images
Online Vehicle Detection For Estimating Traffic Status
A Variation of the Box-Counting Algorithm Applied to Colour Images
BSVM: A Banded Suport Vector Machine
A Fuzzy View on k-Means Based Signal Quantization with Application in Iris Segmentation
Label-Specific Training Set Construction from Web Resource for Image Annotation
Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs
A Invertible Dimension Reduction of Curves on a Manifold
Hamiltonian Streamline Guided Feature Extraction with Applications to Face Detection
Edge detection based on morphological amoebas
Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves
Conjugate Variables as a Resource in Signal and Image Processing
An Efficient Codebook Initialization Approach for LBG Algorithm
A Non-Iterative Solution to the Four-Point Three-Views Pose Problem in Case of Collinear Cameras
On the digital homology groups of digital images
Detachable Object Detection: Segmentation and Depth Ordering From Short-Baseline Video
Probabilistic prototype models for attributed graphs
Squiggle - A Glyph Recognizer for Gesture Input
Towards an interoperable information infrastructure providing decision support for genomic medicine
Graph Regularized Nonnegative Matrix Factorization for Hyperspectral Data Unmixing
Sparsity and Robustness in Face Recognition
Covariant fractional extension of the modified Laplace-operator used in 3D-shape recovery
Good Pairs of Adjacency Relations in Arbitrary Dimensions
A Single Euler Number Feature for Multi-font Multi-size Kannada Numeral Recognition
A self-portrait of young Leonardo
Compressed sensing of astronomical images: orthogonal wavelets domains
Ward's Hierarchical Clustering Method: Clustering Criterion and Agglomerative Algorithm
Les crashs sont rationnels
Adaptive Noise Reduction Scheme for Salt and Pepper
Polynomial Regression on Riemannian Manifolds
An efficient FPGA implementation of MRI image filtering and tumor characterization using Xilinx system generator
Multiscale Fractal Descriptors Applied to Nanoscale Images
Image Labeling and Segmentation using Hierarchical Conditional Random Field Model
Cognitive Memory Network
Comparing Background Subtraction Algorithms and Method of Car Counting
Improving feature selection algorithms using normalised feature histograms
Using Covariance Matrices as Feature Descriptors for Vehicle Detection from a Fixed Camera
A feature extraction technique based on character geometry for character recognition
Stochastic-Based Pattern Recognition Analysis
Image Fusion and Re-Modified SPIHT for Fused Image
Integrated three-dimensional reconstruction using reflectance fields
SVD-EBP Algorithm for Iris Pattern Recognition
Simultaneous Object Detection, Tracking, and Event Recognition
Compensating Interpolation Distortion by Using New Optimized Modular Method
Optimal Weights Mixed Filter for Removing Mixture of Gaussian and Impulse Noises
Gray Level Co-Occurrence Matrices: Generalisation and Some New Features
Potentials and Limits of Super-Resolution Algorithms and Signal Reconstruction from Sparse Data
An efficient hierarchical graph based image segmentation
On multi-view feature learning
Is margin preserved after random projection?
Portraits of Julius Caesar: a proposal for 3D analysis
Statistical Translation, Heat Kernels and Expected Distances
Large Scale Variational Bayesian Inference for Structured Scale Mixture Models
Online Exploration of Polygons with Holes
Anatomical Structure Segmentation in Liver MRI Images
HMRF-EM-image: Implementation of the Hidden Markov Random Field Model and its Expectation-Maximization Algorithm
Content Based Multimedia Information Retrieval to Support Digital Libraries
Multisegmentation through wavelets: Comparing the efficacy of Daubechies vs Coiflets
A Survey Of Activity Recognition And Understanding The Behavior In Video Survelliance
A QCQP Approach to Triangulation
A phase-sensitive method for filtering on the sphere
Information-theoretic Dictionary Learning for Image Classification
Trace transform based method for color image domain identification
Video Data Visualization System: Semantic Classification And Personalization
A Comparative Study between Moravec and Harris Corner Detection of Noisy Images Using Adaptive Wavelet Thresholding Technique
Multimodal diffusion geometry by joint diagonalization of Laplacians
Writing Reusable Digital Geometry Algorithms in a Generic Image Processing Framework
An Efficient Color Face Verification Based on 2-Directional 2-Dimensional Feature Extraction
Model based neuro-fuzzy ASR on Texas processor
Environmental Sounds Spectrogram Classification using Log-Gabor Filters and Multiclass Support Vector Machines
Refinability of splines from lattice Voronoi cells
Subset Selection for Gaussian Markov Random Fields
Reproduction of Images by Gamut Mapping and Creation of New Test Charts in Prepress Process
Enhanced Techniques for PDF Image Segmentation and Text Extraction
Semisupervised Classifier Evaluation and Recalibration
A notion of continuity in discrete spaces and applications
Level Set Estimation from Compressive Measurements using Box Constrained Total Variation Regularization
Three dimensional tracking of gold nanoparticles using digital holographic microscopy
A polygon-based interpolation operator for super-resolution imaging
Novel Architecture for 3D model in virtual communities from detected face
MLPACK: A Scalable C++ Machine Learning Library
Resolution Enhancement of Range Images via Color-Image Segmentation
The fortresses of Ejin: an example of outlining a site from satellite images
Performance Evaluation of Random Set Based Pedestrian Tracking Algorithms
Localisation of Numerical Date Field in an Indian Handwritten Document
Efficient Superimposition Recovering Algorithm
Improving Perceptual Color Difference using Basic Color Terms
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
Unmixing of Hyperspectral Data Using Robust Statistics-based NMF
Robust Face Recognition using Local Illumination Normalization and Discriminant Feature Point Selection
A Learning Framework for Morphological Operators using Counter-Harmonic Mean
Multi-target tracking algorithms in 3D
Approximating rational Bezier curves by constrained Bezier curves of arbitrary degree
Perceptually Motivated Shape Context Which Uses Shape Interiors
Blinking Molecule Tracking
Adaptive Foreground and Shadow Detection inImage Sequences
Matrix Approximation under Local Low-Rank Assumption
Recurrent Online Clustering as a Spatio-Temporal Feature Extractor in DeSTIN
Indoor Semantic Segmentation using depth information
Gradient Driven Learning for Pooling in Visual Pipeline Feature Extraction Models
On the Product Rule for Classification Problems
Sparse MRI for motion correction
A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks
A new compressive video sensing framework for mobile broadcast
Shape Characterization via Boundary Distortion
Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique
Verifying a platform for digital imaging: a multi-tool strategy
A new approach for an unitary risk theory
Image compression using anti-forensics method
A survey on sensing methods and feature extraction algorithms for SLAM problem
Asynchronous Cellular Operations on Gray Images Extracting Topographic Shape Features and Their Relations
An Entropy-based Learning Algorithm of Bayesian Conditional Trees
Generalizing k-means for an arbitrary distance matrix
A Comparative Analysis on the Applicability of Entropy in remote sensing
Analysis Of Interest Points Of Curvelet Coefficients Contributions Of Microscopic Images And Improvement Of Edges
Blockwise SURE Shrinkage for Non-Local Means
A novel automatic thresholding segmentation method with local adaptive thresholds
K-Algorithm A Modified Technique for Noise Removal in Handwritten Documents
OPS-QFTs: A new type of quaternion Fourier transforms based on the orthogonal planes split with one or two general pure quaternions
Algebraic foundations of split hypercomplex nonlinear adaptive filtering
Clifford Fourier-Mellin transform with two real square roots of -1 in Cl(p,q), p+q=2
Image segmentation by optimal and hierarchical piecewise constant approximations
An Overview of the Research on Texture Based Plant Leaf Classification
Persian Heritage Image Binarization Competition (PHIBC 2012)
A Unified Framework of Elementary Geometric Transformation Representation
A two-layer Conditional Random Field for the classification of partially occluded objects
Making Laplacians commute
Who and Where: People and Location Co-Clustering
A Robust Alternating Direction Method for Constrained Hybrid Variational Deblurring Model
The Linearized Bregman Method via Split Feasibility Problems: Analysis and Generalizations
A Novel Approach in detecting pose orientation of a 3D face required for face
A method for nose-tip based 3D face registration using maximum intensity algorithm
Estimation of intrinsic volumes from digital grey-scale images
A Non-Local Means Filter for Removing the Poisson Noise
A new look at reweighted message passing
Exploration and Exploitation in Visuomotor Prediction of Autonomous Agents
Dense Scattering Layer Removal
Misfire Detection in IC Engine using Kstar Algorithm
Can Facial Uniqueness be Inferred from Impostor Scores?
Gender Classification Using Gradient Direction Pattern
Efficient Information Theoretic Clustering on Discrete Lattices
Neighborhood filters and the decreasing rearrangement
Skin Texture Recognition Using Neural Networks
Detection of Partially Visible Objects
A novel framework for image forgery localization
Image forgery detection based on the fusion of machine learning and block-matching methods
The Power of Asymmetry in Binary Hashing
Analysis and Understanding of Various Models for Efficient Representation and Accurate Recognition of Human Faces
Co-Sparse Textural Similarity for Image Segmentation
Evaluation of Plane Detection with RANSAC According to Density of 3D Point Clouds
Some Improvements on Deep Convolutional Neural Network Based Image Classification
Learning Transformations for Classification Forests
An Efficient Edge Detection Technique by Two Dimensional Rectangular Cellular Automata
Key point selection and clustering of swimmer coordination through Sparse Fisher-EM
Content Based Image Indexing and Retrieval
Gesture recognition based mouse events
A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation
Visual Tracking using Particle Swarm Optimization
Edge detection of binary images using the method of masks
Hierarchical pixel clustering for image segmentation
Delegating Custom Object Detection Tasks to a Universal Classification System
Use HMM and KNN for classifying corneal data
Temporal Image Fusion
Splines and Wavelets on Geophysically Relevant Manifolds
Sublinear Models for Graphs
Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means
Image reconstruction from limited range projections using orthogonal moments
Using n-grams models for visual semantic place recognition
Consensus in the Wasserstein Metric Space of Probability Measures
Weyl group orbit functions in image processing
Color to Gray and Back transformation for distributing color digital images
Decreasing Weighted Sorted $\ell_1$ Regularization
Generic Object Detection With Dense Neural Patterns and Regionlets
Learning Fine-grained Image Similarity with Deep Ranking
A General Homogeneous Matrix Formulation to 3D Rotation Geometric Transformations
Code Minimization for Fringe Projection Based 3D Stereo Sensors by Calibration Improvement
Entropy Based Cartoon Texture Separation
Improving Image Clustering using Sparse Text and the Wisdom of the Crowds
Cellular Automata based adaptive resampling technique for the processing of remotely sensed imagery
Classification of Basmati Rice Grain Variety using Image Processing and Principal Component Analysis
Imaging with Kantorovich-Rubinstein discrepancy
An iterative approach to Hough transform without re-voting
An landcover fuzzy logic classification by maximumlikelihood
Performance evaluation of wavelet scattering network in image texture classification in various color spaces
Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network
Turkish Presidential Elections TRT Publicity Speech Facial Expression Analysis
Introduction to Clustering Algorithms and Applications
Fuzzy and entropy facial recognition
Binary matrices of optimal autocorrelations as alignment marks
Comment on "Ensemble Projection for Semi-supervised Image Classification"
Tree-Structure Bayesian Compressive Sensing for Video
Shape and Color Object Tracking for Real-Time Robotic Navigation
Online Tracking of Skin Colour Regions Against a Complex Background
The HAWKwood Database
Improve CAPTCHA's Security Using Gaussian Blur Filter
Remote sensing image classification exploiting multiple kernel learning
Vehicle Detection and Tracking Techniques: A Concise Review
A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes
Improved depth imaging by constrained full-waveform inversion
An Unsupervised Ensemble-based Markov Random Field Approach to Microscope Cell Image Segmentation
New similarity index based on entropy and group theory
Multilinear Principal Component Analysis Network for Tensor Object Classification
Conditional Generative Adversarial Nets
Abnormal Object Recognition: A Comprehensive Study
Collecting Image Description Datasets using Crowdsourcing
On Coarse Graining of Information and Its Application to Pattern Recognition
Ten Years of Pedestrian Detection, What Have We Learned?
A Unified Semantic Embedding: Relating Taxonomies and Attributes
Viewpoints and Keypoints
Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction
Real time Detection of Lane Markers in Urban Streets
On color image quality assessment using natural image statistics
Open-source code for manifold-based 3D rotation recovery of X-ray scattering patterns
HSI based colour image equalization using iterative nth root and nth power
Learning to Recognize Pedestrian Attribute
Implementation of Auto Monitoring and Short-Message-Service System via GSM Modem
Constructing Binary Descriptors with a Stochastic Hill Climbing Search
Filtered Channel Features for Pedestrian Detection
Feature Sampling Strategies for Action Recognition
Embedding of binary image in the Gray planes
Hyper-parameter optimization of Deep Convolutional Networks for object recognition
Vector Quantization by Minimizing Kullback-Leibler Divergence
Pose Induction for Novel Object Categories
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes
Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph
Object Class Detection and Classification using Multi Scale Gradient and Corner Point based Shape Descriptors
Fast Guided Filter
Comparing persistence diagrams through complex vectors
Noise in Structured-Light Stereo Depth Cameras: Modeling and its Applications
Training Deeper Convolutional Networks with Deep Supervision
Distributed Lustre activity tracking
Modified Hausdorff Fractal Dimension (MHFD)
Vanishing Point Attracts Eye Movements in Scene Free-viewing
Improved Microaneurysm Detection using Deep Neural Networks
Exploring Nearest Neighbor Approaches for Image Captioning
Unsupervised Segmentation of Overlapping Cervical Cell Cytoplasm
New HSL Distance Based Colour Clustering Algorithm
Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition
A comparative study between proposed Hyper Kurtosis based Modified Duo-Histogram Equalization (HKMDHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) for Contrast Enhancement Purpose of Low Contrast Human Brain CT scan images
Discrete Independent Component Analysis (DICA) with Belief Propagation
Using Dimension Reduction to Improve the Classification of High-dimensional Data
Symbolic Segmentation Using Algorithm Selection
Geometry of Graph Edit Distance Spaces
Feature Representation for Online Signature Verification
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
Convolutional Color Constancy
Reinventing Pocket Microscopy
Closed Curves and Elementary Visual Object Identification
Learning Robust Deep Face Representation
Hand Gesture Recognition Library
Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization
Effective Object Tracking in Unstructured Crowd Scenes
Nonlinear Spectral Analysis via One-homogeneous Functionals - Overview and Future Prospects
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer
Simultaneous Deep Transfer Across Domains and Tasks
Free-hand Sketch Synthesis with Deformable Stroke Models
Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis
Interactive multiclass segmentation using superpixel classification
Fast sequential forensic camera identification
Dynamical spectral unmixing of multitemporal hyperspectral images
What's the point? Frame-wise Pointing Gesture Recognition with Latent-Dynamic Conditional Random Fields
A Deep Siamese Network for Scene Detection in Broadcast Videos
Pixel-wise Segmentation of Street with Neural Networks
Properties of the Sample Mean in Graph Spaces and the Majorize-Minimize-Mean Algorithm
Robust Registration of Calcium Images by Learned Contrast Synthesis
Cell identification in whole-brain multiview images of neural activation
Generating Images from Captions with Attention
Sliced Wasserstein Kernels for Probability Distributions
A GMM-Based Stair Quality Model for Human Perceived JPEG Images
A Directional Diffusion Algorithm for Inpainting
Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest
Deeply-Recursive Convolutional Network for Image Super-Resolution
Uncovering Temporal Context for Video Question and Answering
Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression
Cross-scale predictive dictionaries
Quantitative Analysis of Particles Segregation
What Players do with the Ball: A Physically Constrained Interaction Modeling
Unsupervised Deep Embedding for Clustering Analysis
Acceleration of the PDHGM on strongly convex subspaces
Top-k Multiclass SVM
Multi-view 3D Models from Single Images with a Convolutional Network
Multi-Volume High Resolution RGB-D Mapping with Dynamic Volume Placement
Where To Look: Focus Regions for Visual Question Answering
Exploring Person Context and Local Scene Context for Object Detection
MidRank: Learning to rank based on subsequences
On-line Recognition of Handwritten Mathematical Symbols
Modeling Visual Compatibility through Hierarchical Mid-level Elements
A Classification Leveraged Object Detector
Keyboard Based Control of Four Dimensional Rotations
Recurrent Attentional Networks for Saliency Detection
An incremental linear-time learning algorithm for the Optimum-Path Forest classifier
Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
Single-Image Depth Perception in the Wild
Low-Rank Matrix Recovery using Gabidulin Codes in Characteristic Zero
Visual saliency detection: a Kalman filter based approach
From Dynamic to Static Semantics, Quantitatively
Can Boosting with SVM as Week Learners Help?
Right whale recognition using convolutional neural networks
Evaluation of the Effect of Improper Segmentation on Word Spotting
Improving Human Action Recognition by Non-action Classification
Scalable Gaussian Processes for Supervised Hashing
Semantic Reasoning for Context-aware Internet of Things Applications
Towards Conceptual Compression
Learning Robust Features using Deep Learning for Automatic Seizure Detection
Union is strength in lossy image compression
Kalman's shrinkage for wavelet-based despeckling of SAR images
Neural shrinkage for wavelet-based SAR despeckling
One-Class Slab Support Vector Machine
Permutation NMF
Spoofing 2D Face Detection: Machines See People Who Aren't There
Bootstrapping Face Detection with Hard Negative Examples
A Factorization Approach to Inertial Affine Structure from Motion
Canonical Correlation Inference for Mapping Abstract Scenes to Text
Scout-It: Interior tomography using modified scout acquisition
Stacked Approximated Regression Machine: A Simple Deep Learning Approach
Dynamic Hand Gesture Recognition for Wearable Devices with Low Complexity Recurrent Neural Networks
Generating Synthetic Data for Text Recognition
Temporally Consistent Motion Segmentation from RGB-D Video
In the Saddle: Chasing Fast and Repeatable Features
Fine Hand Segmentation using Convolutional Neural Networks
MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms
Human Action Recognition without Human
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
A statistical model of tristimulus measurements within and between OLED displays
Methods of Hierarchical Clustering
Considerations and Results in Multimedia and DVB Application Development on Philips Nexperia Platform
Convergence of Variational Regularization Methods for Imaging on Riemannian Manifolds
An Algorithmic Solution to the Five-Point Pose Problem Based on the Cayley Representation of Rotations
Alignment of Microtubule Imagery
Non-Gaussian Scale Space Filtering with 2 by 2 Matrix of Linear Filters
Controlled Total Variation regularization for inverse problems
Face Recognition Based on SVM and 2DPCA
Age group and gender recognition from human facial images
A software for aging faces applied to ancient marble busts
Multiscale Fractal Descriptors Applied to Texture Classification
Image Compression predicated on Recurrent Iterated Function Systems
Digit Recognition in Handwritten Weather Records
A Convex Approach for Image Hallucination
Bingham Procrustean Alignment for Object Detection in Clutter
Fast image segmentation and restoration using parametric curve evolution with junctions and topology changes
Invertibility and Robustness of Phaseless Reconstruction
Edge-detection applied to moving sand dunes on Mars
Stability of Phase Retrievable Frames
An Estimation Method of Measuring Image Quality for Compressed Images of Human Face
Foreground segmentation based on multi-resolution and matting
Handwritten Character Recognition In Malayalam Scripts- A Review
FTVd is beyond Fast Total Variation regularized Deconvolution
Deconstruction of compound objects from image sets
Ambiguous Proximity Distribution
The constitution of visual perceptual units in the functional architecture of V1
Image retrieval with hierarchical matching pursuit
Improvement Tracking Dynamic Programming using Replication Function for Continuous Sign Language Recognition
Denosing Using Wavelets and Projections onto the L1-Ball
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
Why are images smooth?
Robust Outlier Detection Technique in Data Mining: A Univariate Approach
R-CNNs for Pose Estimation and Action Detection
Saccadic Eye Movements and the Generalized Pareto Distribution
Weakly-supervised Discovery of Visual Pattern Configurations
How good are detection proposals, really?
Multi-tensor Completion for Estimating Missing Values in Video Data
Identifying Synapses Using Deep and Wide Multiscale Recursive Networks
A feasible roadmap for developing volumetric probability atlas of localized prostate cancer
Convolutional Networks for Image Processing by Coupled Oscillator Arrays
DISA at ImageCLEF 2014 Revised: Search-based Image Annotation with DeCAF Features
Tensity Research Based on the Information of Eye Movement
A Survey on Heterogeneous Face Recognition: Sketch, Infra-red, 3D and Low-resolution
Deep Regression for Face Alignment
A Global Approach for Solving Edge-Matching Puzzles
Ctrax extensions for tracking in difficult lighting conditions
Image Classification with A Deep Network Model based on Compressive Sensing
A Deep Graph Embedding Network Model for Face Recognition
Location Recognition Over Large Time Lags
Feedforward semantic segmentation with zoom-out features
Covariance estimation using conjugate gradient for 3D classification in Cryo-EM
Colorisation et texturation temps réel d'environnements urbains par système mobile avec scanner laser et caméra fish-eye
The myth of the Digital Earth between fragmentation and wholeness
Edge Preserving Multi-Modal Registration Based On Gradient Intensity Self-Similarity
Oriented Edge Forests for Boundary Detection
A survey of modern optical character recognition techniques
The application of the Bayes Ying Yang harmony based GMMs in on-line signature verification
An Algebraical Model for Gray Level Images
Image Dynamic Range Enhancement in the Context of Logarithmic Models
Gray Level Image Enhancement Using Polygonal Functions
Visual Instance Retrieval with Deep Convolutional Networks
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
Deep metric learning using Triplet network
A New Way to Factorize Linear Cameras
Fractal descriptors based on the probability dimension: a texture analysis and classification approach
Functional correspondence by matrix completion
Domain-Size Pooling in Local Descriptors: DSP-SIFT
A multistep segmentation algorithm for vessel extraction in medical imaging
Deep Roto-Translation Scattering for Object Classification
Hierarchical Maximum-Margin Clustering
Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks
Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions
Real Time Implementation of Spatial Filtering On FPGA
Clustering by Descending to the Nearest Neighbor in the Delaunay Graph Space
Cross-Modality Hashing with Partial Correspondence
Some enumerations of binary digital images
Probabilistic Zero-shot Classification with Semantic Rankings
On debiasing restoration algorithms: applications to total-variation and nonlocal-means
Frequency Domain TOF: Encoding Object Depth in Modulation Frequency
Fully Connected Deep Structured Networks
Designing A Composite Dictionary Adaptively From Joint Examples
Learning to Detect Vehicles by Clustering Appearance Patterns
Characterizing driving behavior using automatic visual analysis
Novel Super-Resolution Method Based on High Order Nonlocal-Means
Pattern Recognition of Bearing Faults using Smoother Statistical Features
Initialization Strategies of Spatio-Temporal Convolutional Neural Networks
RANSAC based three points algorithm for ellipse fitting of spherical object's projection
Real-time multi-view deconvolution
Direct l_(2,p)-Norm Learning for Feature Selection
Robust Anomaly Detection Using Semidefinite Programming
Knowledge driven Offline to Online Script Conversion
On-line Handwritten Devanagari Character Recognition using Fuzzy Directional Features
A Multicomponent Approach to Nonrigid Registration of Diffusion Tensor Images
Image Subset Selection Using Gabor Filters and Neural Networks
Connectivity Preserving Multivalued Functions in Digital Topology
Extraction of Protein Sequence Motif Information using PSO K-Means
What Do Deep CNNs Learn About Objects?
Unsupervised Feature Learning from Temporal Data
Joint Learning of Distributed Representations for Images and Texts
Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors - Extended Version
Preprint Imagining In-Air Interaction for Hemiplegia Sufferer
Segmentation of Subspaces in Sequential Data
Caffe con Troll: Shallow Ideas to Speed Up Deep Learning
SIFT Vs SURF: Quantifying the Variation in Transformations
Linear Spatial Pyramid Matching Using Non-convex and non-negative Sparse Coding for Image Classification
Image Segmentation and Restoration Using Parametric Contours With Free Endpoints
Mid-level Elements for Object Detection
Improved repeatability measures for evaluating performance of feature detectors
Fast R-CNN
A Review of Feature and Data Fusion with Medical Images
Classify Images with Conceptor Network
Well-posedness of a nonlinear integro-differential problem and its rearranged formulation
Wavelets and continuous wavelet transform for autostereoscopic multiview images
Fast ConvNets Using Group-wise Brain Damage
Fast Geometric Fit Algorithm for Sphere Using Exact Solution
Technical Report: Image Captioning with Semantically Similar Images
Deep Structured Models For Group Activity Recognition
Deep Secure Encoding: An Application to Face Recognition
Automatic Layer Separation using Light Field Imaging
Autonomous 3D Reconstruction Using a MAV
Automatic vehicle tracking and recognition from aerial image sequences
Spectral Motion Synchronization in SE(3)
Land Use Classification in Remote Sensing Images by Convolutional Neural Networks
Partial matching face recognition method for rehabilitation nursing robots beds
On Hyperspectral Classification in the Compressed Domain
Single and Multiple Illuminant Estimation Using Convolutional Neural Networks
On the convergence of the sparse possibilistic c-means algorithm
Unconstrained Face Verification using Deep CNN Features
Simulation of optical flow and fuzzy based obstacle avoidance system for mobile robots
A Deep Pyramid Deformable Part Model for Face Detection
Introducing Geometry in Active Learning for Image Segmentation
An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography
Image Type Water Meter Character Recognition Based on Embedded DSP
Diffusion tensor imaging with deterministic error bounds
Semantic Video Segmentation : Exploring Inference Efficiency
Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets
NoSPaM Manual - A Tool for Node-Specific Triad Pattern Mining
Image Set Querying Based Localization
Homotopy relations for digital images
Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks
Retinex filtering of foggy images: generation of a bulk set with selection and ranking
Light Field Reconstruction Using Shearlet Transform
Double Sparse Multi-Frame Image Super Resolution
Active Learning for Delineation of Curvilinear Structures
ASIST: Automatic Semantically Invariant Scene Transformation
Image reconstruction from dense binary pixels
On The Continuous Steering of the Scale of Tight Wavelet Frames
Is Hamming distance the only way for matching binary image feature descriptors?
Video captioning with recurrent networks based on frame- and video-level features and visual content classification
Robust Dictionary based Data Representation
Deep Tracking: Visual Tracking Using Deep Convolutional Networks
Effects of GIMP Retinex Filtering Evaluated by the Image Entropy
Car Segmentation and Pose Estimation using 3D Object Models
Cost-based Feature Transfer for Vehicle Occupant Classification
How can one sample images with sampling rates close to the theoretical minimum?
Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study
Angrier Birds: Bayesian reinforcement learning
Quality Adaptive Low-Rank Based JPEG Decoding with Applications
Stochastic Dykstra Algorithms for Metric Learning on Positive Semi-Definite Cone
Visual Script and Language Identification
Creativity in Machine Learning
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
Proactive Message Passing on Memory Factor Networks
Eye detection in digital images: challenges and solutions
When is Clustering Perturbation Robust?
Unsupervised Deep Hashing for Large-scale Visual Search
Image and Information
Improved Eigenfeature Regularization for Face Identification
Gabor Wavelets in Image Processing
Triplet Similarity Embedding for Face Verification
Convolutional Radio Modulation Recognition Networks
Deep Feature-based Face Detection on Mobile Devices
Multi-resolution Compressive Sensing Reconstruction
Context-guided diffusion for label propagation on graphs
A Survey of Semantic Segmentation
Automatic Building Extraction in Aerial Scenes Using Convolutional Networks
Car Type Recognition with Deep Neural Networks
Robust Detection of Intensity Variant Clones in Forged and JPEG Compressed Images
SHAPE: Linear-Time Camera Pose Estimation With Quadratic Error-Decay
On the Accuracy of Point Localisation in a Circular Camera-Array
A straightforward method to assess motion blur for different types of displays
Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition
A novel and automatic pectoral muscle identification algorithm for mediolateral oblique (MLO) view mammograms using ImageJ
Proximal groupoid patterns In digital images
From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators
Towards Building an RGBD-M Scanner
Learning zeroth class dictionary for human action recognition
U-CATCH: Using Color ATtribute of image patCHes in binary descriptors
Deep video gesture recognition using illumination invariants
Learning Representations for Automatic Colorization
Multi-velocity neural networks for gesture recognition in videos
Stacked Hourglass Networks for Human Pose Estimation
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Position and Vector Detection of Blind Spot motion with the Horn-Schunck Optical Flow
Object Recognition Based on Amounts of Unlabeled Data
LIFT: Learned Invariant Feature Transform
Confidence driven TGV fusion
Detecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference
Compression Artifacts Removal Using Convolutional Neural Networks
Discovering Useful Parts for Pose Estimation in Sparsely Annotated Datasets
Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns
Mining Discriminative Triplets of Patches for Fine-Grained Classification
Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition
On Image segmentation using Fractional Gradients-Learning Model Parameters using Approximate Marginal Inference
Weakly Supervised Learning of Affordances
Image-level Classification in Hyperspectral Images using Feature Descriptors, with Application to Face Recognition
Improved Image Boundaries for Better Video Segmentation
Multimodal Sparse Coding for Event Detection
Image segmentation with superpixel-based covariance descriptors in low-rank representation
Improving Weakly-Supervised Object Localization By Micro-Annotation
Contour-based 3d tongue motion visualization using ultrasound image sequences
Development of a 3D tongue motion visualization platform based on ultrasound image sequences
A Formal Evaluation of PSNR as Quality Measurement Parameter for Image Segmentation Algorithms
Trajectory probability hypothesis density filter
Latent Bi-constraint SVM for Video-based Object Recognition
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities
Incorporating long-range consistency in CNN-based texture generation
TRex: A Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications
A practical local tomography reconstruction algorithm based on known subregion
Learning Abstract Classes using Deep Learning
Automatic 3D Reconstruction for Symmetric Shapes
Preserving Color in Neural Artistic Style Transfer
Model-based Deep Hand Pose Estimation
Attribute Recognition from Adaptive Parts
Application of Convolutional Neural Network for Image Classification on Pascal VOC Challenge 2012 dataset
Adaptive Gray World-Based Color Normalization of Thin Blood Film Images
New version of Gram-Schmidt Process with inverse for Signal and Image Processing
Information-theoretical label embeddings for large-scale image classification
A Topological Lowpass Filter for Quasiperiodic Signals
Learning the Roots of Visual Domain Shift
Detection of surface defects on ceramic tiles based on morphological techniques
Combined Classifiers for Invariant Face Recognition
A Statistical Test for Joint Distributions Equivalence
Instance Normalization: The Missing Ingredient for Fast Stylization
Online Trajectory Segmentation and Summary With Applications to Visualization and Retrieval
Local Feature Detectors, Descriptors, and Image Representations: A Survey
SEMBED: Semantic Embedding of Egocentric Action Videos
Defeating Image Obfuscation with Deep Learning
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes
Making a Case for Learning Motion Representations with Phase
Semantic Video Trailers
Rectifier Neural Network with a Dual-Pathway Architecture for Image Denoising
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups
A Perspective on Deep Imaging
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition
Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
Robust Estimation of Multiple Inlier Structures
Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability
Super-resolving multiresolution images with band-independant geometry of multispectral pixels
Automatic Construction of a Recurrent Neural Network based Classifier for Vehicle Passage Detection
Simultaneous Low-rank Component and Graph Estimation for High-dimensional Graph Signals: Application to Brain Imaging
Automated Breast Lesion Segmentation in Ultrasound Images
Semi Automatic Color Segmentation of Document Pages
Pano2CAD: Room Layout From A Single Panorama Image
Redefining Binarization and the Visual Archetype
Training a Feedback Loop for Hand Pose Estimation
Low-dose CT denoising with convolutional neural network
Fast Image Classification by Boosting Fuzzy Classifiers
Convex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost
Places: An Image Database for Deep Scene Understanding
Deep disentangled representations for volumetric reconstruction
On the Existence of a Sample Mean in Dynamic Time Warping Spaces
M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification
Mixed context networks for semantic segmentation
Kernel Alignment for Unsupervised Transfer Learning
Utilization of Deep Reinforcement Learning for saccadic-based object visual search
Detecting Rainfall Onset Using Sky Images
Record Counting in Historical Handwritten Documents with Convolutional Neural Networks
Tool and Phase recognition using contextual CNN features
Recent advances in content based video copy detection
The TUM LapChole dataset for the M2CAI 2016 workflow challenge
Best-Buddies Tracking
An All-In-One Convolutional Neural Network for Face Analysis
Rough Set Based Color Channel Selection
Deep Convolutional Neural Network for 6-DOF Image Localization
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data
X-ray Scattering Image Classification Using Deep Learning
Evaluating Urbanization from Satellite and Aerial Images by means of a statistical approach to the texture analysis
Optimized clothes segmentation to boost gender classification in unconstrained scenarios
Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)
A DNN Framework For Text Image Rectification From Planar Transformations
Herding Generalizes Diverse M -Best Solutions
Automatic discovery of discriminative parts as a quadratic assignment problem
CIFAR-10: KNN-based Ensemble of Classifiers
Neural Style Representations and the Large-Scale Classification of Artistic Style
Optical Flow Requires Multiple Strategies (but only one network)
Generalized BackPropagation, Étude De Cas: Orthogonality
A Bayesian approach to type-specific conic fitting
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
TextBoxes: A Fast Text Detector with a Single Deep Neural Network
The subset-matched Jaccard index for evaluation of Segmentation for Plant Images
Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems
Cascaded Neural Networks with Selective Classifiers and its evaluation using Lung X-ray CT Images
3D Fully Convolutional Network for Vehicle Detection in Point Cloud
Deep Watershed Transform for Instance Segmentation
Semantic Segmentation using Adversarial Networks
Multimodal Latent Variable Analysis
3D Ultrasound image segmentation: A Survey
Machine Learning for Dental Image Analysis
Efficient Pose and Cell Segmentation using Column Generation
Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks
Zero-Shot Learning posed as a Missing Data Problem
A method for the segmentation of images based on thresholding and applied to vesicular textures
Deep Pyramidal Residual Networks with Separated Stochastic Depth
Stereo image de-fencing using smartphones
ImageNet pre-trained models with batch normalization
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation
A series of maximum entropy upper bounds of the differential entropy
Autoencoder-based holographic image restoration
Observation of dynamics inside an unlabeled live cell using bright-field photon microscopy: Evaluation of organelles' trajectories
Compressive Image Recovery Using Recurrent Generative Model
Design of Image Matched Non-Separable Wavelet using Convolutional Neural Network
Fast, Dense Feature SDM on an iPhone
A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems
Feature Encoding in Band-limited Distributed Surveillance Systems
Local Sparse Approximation for Image Restoration with Adaptive Block Size Selection
A Statistical Approach to Continuous Self-Calibrating Eye Gaze Tracking for Head-Mounted Virtual Reality Systems
Stochastic Multidimensional Scaling
Meta-Unsupervised-Learning: A supervised approach to unsupervised learning
Super-Resolution Reconstruction of Electrical Impedance Tomography Images
Vid2speech: Speech Reconstruction from Silent Video
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps
Greedy Search for Descriptive Spatial Face Features
On Classification of Distorted Images with Deep Convolutional Neural Networks
Green-Blue Stripe Pattern for Range Sensing from a Single Image
Visual Multiple-Object Tracking for Unknown Clutter Rate
Visualizing Residual Networks
Systematic study of color spaces and components for the segmentation of sky/cloud images
Analysis of the noise in back-projection light field acquisition and its optimization
Using Convolutional Neural Networks to Count Palm Trees in Satellite Images
LAREX - A semi-automatic open-source Tool for Layout Analysis and Region Extraction on Early Printed Books
Super-resolution Using Constrained Deep Texture Synthesis
Face Detection using Deep Learning: An Improved Faster RCNN Approach
ImageNet MPEG-7 Visual Descriptors - Technical Report
Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network
Fast and easy blind deblurring using an inverse filter and PROBE
Robust features for facial action recognition
An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning
An Implementation of Faster RCNN with Study for Region Sampling
An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks
Effective face landmark localization via single deep network
Multi-Resolution Dual-Tree Wavelet Scattering Network for Signal Classification
Visual Discovery at Pinterest
CityPersons: A Diverse Dataset for Pedestrian Detection
Derivative Based Focal Plane Array Nonuniformity Correction
Learning Compact Appearance Representation for Video-based Person Re-Identification
Mimicking Ensemble Learning with Deep Branched Networks
How ConvNets model Non-linear Transformations
Unifying local and non-local signal processing with graph CNNs
Optimal rates of estimation for multi-reference alignment
An Extensive Technique to Detect and Analyze Melanoma: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2017
Label Refinement Network for Coarse-to-Fine Semantic Segmentation
Introduction to Nonnegative Matrix Factorization
Towards CNN Map Compression for camera relocalisation
Estimating the resolution of real images
Skin Lesion Classification using Class Activation Map
Generative Compression
High-Resolution Multispectral Dataset for Semantic Segmentation
Prior-based Hierarchical Segmentation Highlighting Structures of Interest
Gait Pattern Recognition Using Accelerometers
Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval
Combining Residual Networks with LSTMs for Lipreading
Users prefer Guetzli JPEG over same-sized libjpeg
Fully Convolutional Networks to Detect Clinical Dermoscopic Features
Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge
Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow
Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation
Recurrent Models for Situation Recognition
Knowledge distillation using unlabeled mismatched images
IOD-CNN: Integrating Object Detection Networks for Event Recognition
Episode-Based Active Learning with Bayesian Neural Networks
Important New Developments in Arabographic Optical Character Recognition (OCR)
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series
Sentiment Recognition in Egocentric Photostreams
SAR image despeckling through convolutional neural networks
Clustering in Hilbert simplex geometry
Spatiotemporal Networks for Video Emotion Recognition
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Enhance Feature Discrimination for Unsupervised Hashing
Detail-revealing Deep Video Super-resolution
Fast Learning and Prediction for Object Detection using Whitened CNN Features
Solving the L1 regularized least square problem via a box-constrained smooth minimization
UC Merced Submission to the ActivityNet Challenge 2016
Saliency-guided Adaptive Seeding for Supervoxel Segmentation
Camera Calibration by Global Constraints on the Motion of Silhouettes
A Comment on "Analysis of Video Image Sequences Using Point and Line Correspondences"
On Measuring Bias in Online Information
Derivation of the Asymptotic Eigenvalue Distribution for Causal 2D-AR Models under Upscaling
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
Exploring epoch-dependent stochastic residual networks
Panorama to panorama matching for location recognition
A Dual Sparse Decomposition Method for Image Denoising
Speeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units
Full-Page Text Recognition: Learning Where to Start and When to Stop
Partially Occluded Leaf Recognition via Subgraph Matching and Energy Optimization
Outline Colorization through Tandem Adversarial Networks
Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference
Offline Handwritten Recognition of Malayalam District Name - A Holistic Approach
Statistical learning of rational wavelet transform for natural images
Generative Convolutional Networks for Latent Fingerprint Reconstruction
Recurrent Soft Attention Model for Common Object Recognition
Diving Performance Assessment by means of Video Processing
Skin lesion detection based on an ensemble of deep convolutional neural network
Collaborative Descriptors: Convolutional Maps for Preprocessing
Obstacle Avoidance Using Stereo Camera
Probabilistic Image Colorization
Convolutional Sparse Representations with Gradient Penalties
Using Satellite Imagery for Good: Detecting Communities in Desert and Mapping Vaccination Activities
TraX: The visual Tracking eXchange Protocol and Library
Automated Robotic Monitoring and Inspection of Steel Structures and Bridges
A Closed-Form Model for Image-Based Distant Lighting
A Correspondence Relaxation Approach for 3D Shape Reconstruction
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
Automated Body Structure Extraction from Arbitrary 3D Mesh
Intel RealSense Stereoscopic Depth Cameras
Research on Bi-mode Biometrics Based on Deep Learning
Static Gesture Recognition using Leap Motion
What's In A Patch, I: Tensors, Differential Geometry and Statistical Shading Analysis
What's In A Patch, II: Visualizing generic surfaces
Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry
MUTAN: Multimodal Tucker Fusion for Visual Question Answering
What are the Receptive, Effective Receptive, and Projective Fields of Neurons in Convolutional Neural Networks?
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
CrossNets : A New Approach to Complex Learning
Shake-Shake regularization
View-Invariant Recognition of Action Style Self-Dissimilarity
Convolutional Networks with MuxOut Layers as Multi-rate Systems for Image Upscaling
On the mathematics of beauty: beautiful images
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems
Unsupervised Learning of Disentangled Representations from Video
Bridge Simulation and Metric Estimation on Landmark Manifolds
Face R-CNN
A Kind of Affine Weighted Moment Invariants
Mobile vs. point guards
Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN
Shape-Color Differential Moment Invariants under Affine Transformations
An Overview of Multi-Task Learning in Deep Neural Networks
Using Deep Networks for Drone Detection
User-driven mobile robot storyboarding: Learning image interest and saliency from pairwise image comparisons
Pedestrian Prediction by Planning using Deep Neural Networks
A Novel VHR Image Change Detection Algorithm Based on Image Fusion and Fuzzy C-Means Clustering
Coupled Support Vector Machines for Supervised Domain Adaptation
Irregular Convolutional Neural Networks
Robust Lane Tracking with Multi-mode Observation Model and Particle Filtering
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
Online Convolutional Dictionary Learning
Chatbots as Conversational Recommender Systems in Urban Contexts
Persistence Diagrams with Linear Machine Learning Models
Joint Pose and Principal Curvature Refinement Using Quadrics
High-Quality Face Image SR Using Conditional Generative Adversarial Networks
Generative Adversarial Models for People Attribute Recognition in Surveillance
Effective Approaches to Batch Parallelization for Dynamic Neural Network Architectures
Identity Alignment by Noisy Pixel Removal
Adaptive Binarization for Weakly Supervised Affordance Segmentation
On Study of the Reliable Fully Convolutional Networks with Tree Arranged Outputs (TAO-FCN) for Handwritten String Recognition
Optical Mapping Near-eye Three-dimensional Display with Correct Focus Cues
Cultivating DNN Diversity for Large Scale Video Labelling
Make Your Bone Great Again : A study on Osteoporosis Classification
Exploiting Convolutional Representations for Multiscale Human Settlement Detection
Pictures of Combinatorial Cubes
Convolutional Sparse Coding: Boundary Handling Revisited
Automatic breast cancer grading in lymph nodes using a deep neural network
A Jointly Learned Deep Architecture for Facial Attribute Analysis and Face Detection in the Wild
Understanding Aesthetics in Photography using Deep Convolutional Neural Networks
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks
Superposition de calques monochromes d'opacités variables
Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"
Deep Generative Adversarial Neural Networks for Realistic Prostate Lesion MRI Synthesis
Learning to Hallucinate Face Images via Component Generation and Enhancement
Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative
SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
Isointense infant brain MRI segmentation with a dilated convolutional neural network
Anveshak - A Groundtruth Generation Tool for Foreground Regions of Document Images
Analysis of Convolutional Neural Networks for Document Image Classification
Document Image Binarization with Fully Convolutional Neural Networks
Systematic Testing of Convolutional Neural Networks for Autonomous Driving
Unsupervised Incremental Learning of Deep Descriptors From Video Streams
Deep Incremental Boosting
Augmentor: An Image Augmentation Library for Machine Learning
Colorimetric Calibration of a Digital Camera
An Improved Neural Segmentation Method Based on U-NET
Brain Abnormality Detection by Deep Convolutional Neural Network
Learning a Multi-View Stereo Machine
Deformable Modeling for Human Body Acquired from Depth Sensors
e-Counterfeit: a mobile-server platform for document counterfeit detection
Reflection Separation and Deblurring of Plenoptic Images
Shape Registration with Directional Data
An Optimized Union-Find Algorithm for Connected Components Labeling Using GPUs
DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
Deep Learning for Medical Image Analysis
Dual-fisheye lens stitching for 360-degree imaging
A simple expression for the map of Asplund's distances with the multiplicative Logarithmic Image Processing (LIP) law
Pix2face: Direct 3D Face Model Estimation
Adversarial nets with perceptual losses for text-to-image synthesis
Assessing verticalization effects on urban safety perception
Lensless-camera based machine learning for image classification
Learning Loss for Knowledge Distillation with Conditional Adversarial Networks
Gaussian Filter in CRF Based Semantic Segmentation
Sushi Dish - Object detection and classification from real images
Dataset Augmentation with Synthetic Images Improves Semantic Segmentation
A Reproducible Study on Remote Heart Rate Measurement
Feasibility of Corneal Imaging for Handheld Augmented Reality
Towards Around-Device Interaction using Corneal Imaging
A Comparison on Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging
Learning Dilation Factors for Semantic Segmentation of Street Scenes
A Survey of Efficient Regression of General-Activity Human Poses from Depth Images
A Geometric Approach to Harmonic Color Palette Design
Detecting Hands in Egocentric Videos: Towards Action Recognition
Robust Sparse Coding via Self-Paced Learning
An Iterative Regression Approach for Face Pose Estimation from RGB Images
Generic Sketch-Based Retrieval Learned without Drawing a Single Sketch
Asian Stamps Identification and Classification System
Correlating Satellite Cloud Cover with Sky Cameras
SalNet360: Saliency Maps for omni-directional images with CNN
Semi-Automated Nasal PAP Mask Sizing using Facial Photographs
A First Derivative Potts Model for Segmentation and Denoising Using ILP
Smart Mirror: Intelligent Makeup Recommendation and Synthesis
Measurement of amplitude of the moiré patterns in digital autostereoscopic 3D display
Realizing Half-Diminished Reality from Video Stream of Manipulating Objects
LADAR-Based Mover Detection from Moving Vehicles
Fast Vehicle Detection in Aerial Imagery
Image similarity using Deep CNN and Curriculum Learning
Generative Adversarial Networks with Inverse Transformation Unit
Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction
A New Multifocus Image Fusion Method Using Contourlet Transform
Scale Adaptive Clustering of Multiple Structures
Exact Camera Location Recovery by Least Unsquared Deviations
A Variational Approach to Shape-from-shading Under Natural Illumination
Decoding visemes: improving machine lipreading
Robust non-local means filter for ultrasound image denoising
Variational Grid Setting Network
Gaussian Three-Dimensional kernel SVM for Edge Detection Applications
IQ of Neural Networks
Energy-Based Spherical Sparse Coding
Video Denoising and Enhancement via Dynamic Video Layering
A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation
Human Pose Regression by Combining Indirect Part Detection and Contextual Information
Does Normalization Methods Play a Role for Hyperspectral Image Classification?
A Sequential Thinning Algorithm For Multi-Dimensional Binary Patterns
Automatic Streaming Segmentation of Stereo Video Using Bilateral Space
Towards In-Transit Analytics for Industry 4.0
Hardware design for binarization and thinning of fingerprint images
Face Transfer with Generative Adversarial Network
The Robust Reading Competition Annotation and Evaluation Platform
Using Deep Convolutional Networks for Gesture Recognition in American Sign Language
Unsupervised Object Discovery and Segmentation of RGBD-images
Light-weight place recognition and loop detection using road markings
Rethinking Convolutional Semantic Segmentation Learning
Neural Stain-Style Transfer Learning using GAN for Histopathological Images
Multi-modal Aggregation for Video Classification
Stochastic variance reduced multiplicative update for nonnegative matrix factorization
Learning Graph Convolution Filters from Data Manifold
Random Subspace Two-dimensional LDA for Face Recognition
Image Captioning and Classification of Dangerous Situations
Identifying Rings in IFU Surveys
Frangi-Net: A Neural Network Approach to Vessel Segmentation
Hand Gesture Recognition with Leap Motion
Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video
A Public Image Database for Benchmark of Plant Seedling Classification Algorithms
Hidden Markov Random Field Iterative Closest Point
A Forward-Backward Approach for Visualizing Information Flow in Deep Networks
Segmenting Brain Tumors with Symmetry
Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks
Multi-Image Semantic Matching by Mining Consistent Features
A Face Fairness Framework for 3D Meshes
Integral Human Pose Regression
Cost-Effective Active Learning for Melanoma Segmentation
Joint Cuts and Matching of Partitions in One Graph
SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again
Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition
Revisiting hand-crafted feature for action recognition: a set of improved dense trajectories
Highlighting objects of interest in an image by integrating saliency and depth
Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems
Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion
High Dynamic Range Imaging Technology
Towards High Performance Video Object Detection
Spatial PixelCNN: Generating Images from Patches
Automatic Recognition of Coal and Gangue based on Convolution Neural Network
Context Augmentation for Convolutional Neural Networks
Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene
Population-based Respiratory 4D Motion Atlas Construction and its Application for VR Simulations of Liver Punctures
Online and Batch Supervised Background Estimation via L1 Regression
Burst Denoising with Kernel Prediction Networks
Shape from Shading through Shape Evolution
Noise Level Estimation for Overcomplete Dictionary Learning Based on Tight Asymptotic Bounds
Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2
CycleGAN Face-off
Training Ensembles to Detect Adversarial Examples
Review. Machine learning techniques for traffic sign detection
Object Classification using Ensemble of Local and Deep Features
Learning Low-shot facial representations via 2D warping
Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking
AI2-THOR: An Interactive 3D Environment for Visual AI
Bipartite Graph Matching for Keyframe Summary Evaluation
Encoding CNN Activations for Writer Recognition
A Bidirectional Adaptive Bandwidth Mean Shift Strategy for Clustering
Texture Synthesis with Recurrent Variational Auto-Encoder
Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project
Adversarial Patch
Extrapolating Expected Accuracies for Large Multi-Class Problems
Sky detection and log illumination refinement for PDE-based hazy image contrast enhancement
A PDE-based log-agnostic illumination correction algorithm
Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space
LoopSmart: Smart Visual SLAM Through Surface Loop Closure
Detection and segmentation of the Left Ventricle in Cardiac MRI using Deep Learning
LaVAN: Localized and Visible Adversarial Noise
End-to-end detection-segmentation network with ROI convolution
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
Comparative Study on Generative Adversarial Networks
Semi-supervised Fisher vector network
Generalizing, Decoding, and Optimizing Support Vector Machine Classification
Learning Deep Features for One-Class Classification
Image Captioning using Deep Neural Architectures
Light-weight pixel context encoders for image inpainting
Food recognition and recipe analysis: integrating visual content, context and external knowledge
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Personalized Human Activity Recognition Using Convolutional Neural Networks
Abnormal Heartbeat Detection Using Recurrent Neural Networks
Using Deep Autoencoders for Facial Expression Recognition
Towards an Understanding of Neural Networks in Natural-Image Spaces
Histogram of Oriented Depth Gradients for Action Recognition
E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text
Learning Video-Story Composition via Recurrent Neural Network
Weighted Nonlocal Total Variation in Image Processing
Perceptual Compressive Sensing
Convolutional neural network-based regression for depth prediction in digital holography
Learning Attribute Representation for Human Activity Recognition
Multi-task Learning for Continuous Control
ClassSim: Similarity between Classes Defined by Misclassification Ratios of Trained Classifiers
Rollable Latent Space for SAR Target Recognition of Un-seen Views
Structural Recurrent Neural Network (SRNN) for Group Activity Analysis
Face Detection Using Improved Faster RCNN
A Multiresolution Deep Learning Framework for Automated Annotation of Reflectance Confocal Microscopy Images
ShakeDrop regularization
Texture Segmentation Based Video Compression Using Convolutional Neural Networks
Convolutional Hashing for Automated Scene Matching
Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural Priors for Semantic Image Segmentation
Combinets: Learning New Classifiers via Recombination
On the Universal Approximability of Quantized ReLU Neural Networks
Fooling OCR Systems with Adversarial Text Images
Spectral Normalization for Generative Adversarial Networks
Fast, Trainable, Multiscale Denoising
A new foundational crisis in mathematics, is it really happening?
Robustness of Rotation-Equivariant Networks to Adversarial Perturbations
A survey on trajectory clustering analysis
Stochastic Video Generation with a Learned Prior
Improved Techniques For Weakly-Supervised Object Localization
ChatPainter: Improving Text to Image Generation using Dialogue
Adversarial vulnerability for any classifier
Edge-Based Recognition of Novel Objects for Robotic Grasping
Semantic segmentation of trajectories with agent models
Generating High Quality Visible Images from SAR Images Using CNNs
Using Deep Learning for Segmentation and Counting within Microscopy Data
Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework
Knowledge Transfer with Jacobian Matching
Contained Neural Style Transfer for Decorated Logo Generation
Protecting JPEG Images Against Adversarial Attacks
Categorical Mixture Models on VGGNet activations
Revisiting Decomposable Submodular Function Minimization with Incidence Relations
Noise2Noise: Learning Image Restoration without Clean Data
idtracker.ai: Tracking all individuals in large collectives of unmarked animals
Discriminability objective for training descriptive captions
Target Driven Instance Detection
Principal Component Analysis with Tensor Train Subspace
Using accumulation to optimize deep residual neural nets
Temporal Human Action Segmentation via Dynamic Clustering
Varying k-Lipschitz Constraint for Generative Adversarial Networks
Activity Detection with Latent Sub-event Hierarchy Learning
Learning Region Features for Object Detection
Face Recognition Techniques: A Survey
Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks
Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks
Non-rigid 3D Shape Registration using an Adaptive Template
Guided Image Inpainting: Replacing an Image Region by Pulling Content from Another Image
Aligning Across Large Gaps in Time
What Do We Understand About Convolutional Networks?
Deep Convolutional Compressed Sensing for LiDAR Depth Completion
Iterative Low-Rank Approximation for CNN Compression
Design of a PCIe Interface Card Control Software Based on WDF
Unsupervised Domain Adaptation: A Multi-task Learning-based Method
Semantic See-Through Rendering on Light Fields
Unsupervised Learning and Segmentation of Complex Activities from Video
Weakening the Detecting Capability of CNN-based Steganalysis
Two-Stream Neural Networks for Tampered Face Detection
Snap Angle Prediction for 360$^{\circ}$ Panorama
Learnable Image Encryption
A Neuronal Planar Modeling for Handwriting Signature based on Automatic Segmentation
Multimodal Biometric Authentication Using Choquet Integral and Genetic Algorithm
Closed-form detector for solid sub-pixel targets in multivariate t-distributed background clutter
Telepresence System based on Simulated Holographic Display
Supervised Convolutional Sparse Coding
YOLOv3: An Incremental Improvement
AMNet: Memorability Estimation with Attention
The Monge-Kantorovich Optimal Transport Distance for Image Comparison
Two Stream 3D Semantic Scene Completion
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network
MGGAN: Solving Mode Collapse using Manifold Guided Training
Extraction of Airways using Graph Neural Networks
Seed-Point Based Geometric Partitioning of Nuclei Clumps
An efficient CNN for spectral reconstruction from RGB images
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems
Analytical Cost Metrics : Days of Future Past
Analysis and approximation of some Shape-from-Shading models for non-Lambertian surfaces
Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Image De-raining Using a Conditional Generative Adversarial Network
Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US
Global Minimum for a Finsler Elastica Minimal Path Approach
Virtual Kathakali : Gesture Driven Metamorphosis
Neural Architectures for Robot Intelligence
Cleaning the USNO-B Catalog through automatic detection of optical artifacts
The source coding game with a cheating switcher
Augmenting Light Field to model Wave Optics effects
SVM-based Multiview Face Recognition by Generalization of Discriminant Analysis
Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm
A stochastic model of human visual attention with a dynamic Bayesian network
Fuzzy Logic of Speed and Steering Control System for Three Dimensional Line Following of an Autonomous Vehicle
A Peer-to-Peer Middleware Framework for Resilient Persistent Programming
Combinatorial Continuous Maximal Flows
Hybrid Linear Modeling via Local Best-fit Flats
Introduction to the Bag of Features Paradigm for Image Classification and Retrieval
A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
Block-Sparse Recovery via Convex Optimization
A bio-inspired image coder with temporal scalability
Robust Mobile Object Tracking Based on Multiple Feature Similarity and Trajectory Filtering
Distributed Multi-view Matching in Networks with Limited Communications
A Theory for Optical flow-based Transport on Image Manifolds
Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
Autonomous Cleaning of Corrupted Scanned Documents - A Generative Modeling Approach
Generalized Principal Component Analysis (GPCA)
Fixed-Rank Representation for Unsupervised Visual Learning
Improvement of ISOM by using filter
Kinects and Human Kinetics: A New Approach for Studying Crowd Behavior
A Hash based Approach for Secure Keyless Steganography in Lossless RGB Images
Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications
A Multi-View Embedding Space for Modeling Internet Images, Tags, and their Semantics
Efficient Multiple Object Tracking Using Mutually Repulsive Active Membranes
A Model of OpenEHR Based Electronic Medical Record In Indonesia
Kernel Sparse Models for Automated Tumor Segmentation
Improved Foreground Detection via Block-based Classifier Cascade with Probabilistic Decision Integration
Separable Dictionary Learning
Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images
Nonmyopic View Planning for Active Object Detection
Efficient pedestrian detection by directly optimize the partial area under the ROC curve
Determining Leishmania Infection Levels by Automatic Analysis of Microscopy Images
Human Face Recognition using Gabor based Kernel Entropy Component Analysis
Associative embeddings for large-scale knowledge transfer with self-assessment
Automatic Detection of Calibration Grids in Time-of-Flight Images
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
Matching Image Sets via Adaptive Multi Convex Hull
Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification
A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
Fast Supervised Hashing with Decision Trees for High-Dimensional Data
Cascades of Regression Tree Fields for Image Restoration
Robust and Efficient Subspace Segmentation via Least Squares Regression
Circle detection using Discrete Differential Evolution Optimization
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Video Face Editing Using Temporal-Spatial-Smooth Warping
Robust Statistical Approach for Extraction of Moving Human Silhouettes from Videos
Hierarchical Adaptive Structural SVM for Domain Adaptation
A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle
Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling
Learning to Rank Binary Codes
Towards Scene Understanding with Detailed 3D Object Representations
Deep Learning Face Attributes in the Wild
Correlation Adaptive Subspace Segmentation by Trace Lasso
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
Multi-view Convolutional Neural Networks for 3D Shape Recognition
A Vision Based System for Monitoring the Loss of Attention in Automotive Drivers
Predicting Important Objects for Egocentric Video Summarization
High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps
Riemannian Gaussian Distributions on the Space of Symmetric Positive Definite Matrices
Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
Deep Networks for Image Super-Resolution with Sparse Prior
Better Exploiting OS-CNNs for Better Event Recognition in Images
Coherent Motion Segmentation in Moving Camera Videos using Optical Flow Orientations
Basic Level Categorization Facilitates Visual Object Recognition
Learning Articulated Motion Models from Visual and Lingual Signals
Compositional Memory for Visual Question Answering
What Objective Does Self-paced Learning Indeed Optimize?
Learning to Generate Images with Perceptual Similarity Metrics
Deep Manifold Traversal: Changing Labels with Convolutional Features
Superpixel Convolutional Networks using Bilateral Inceptions
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources
NetVLAD: CNN architecture for weakly supervised place recognition
Voronoi Region-Based Adaptive Unsupervised Color Image Segmentation
Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks
Integrating Local Material Recognition with Large-Scale Perceptual Attribute Discovery
Image segmentation of cross-country scenes captured in IR spectrum
Using Deep Learning for Image-Based Plant Disease Detection
Learning Visual Storylines with Skipping Recurrent Neural Networks
The THUMOS Challenge on Action Recognition for Videos "in the Wild"
An information theoretic formulation of the Dictionary Learning and Sparse Coding Problems on Statistical Manifolds
$\ell_p$-Box ADMM: A Versatile Framework for Integer Programming
Understanding Human-Centric Images: From Geometry to Fashion
SANTIAGO: Spine Association for Neuron Topology Improvement and Graph Optimization
Highly Efficient Compact Pose SLAM with SLAM++
Refining Geometry from Depth Sensors using IR Shading Images
A geometric analysis of subspace clustering with outliers
Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition
Integration of spatio-temporal contrast sensitivity with a multi-slice channelized Hotelling observer
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
Multispectral Palmprint Encoding and Recognition
Seeing the Big Picture: Deep Embedding with Contextual Evidences
3D ShapeNets: A Deep Representation for Volumetric Shapes
ImageNet Large Scale Visual Recognition Challenge
Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations
Evaluation of Output Embeddings for Fine-Grained Image Classification
3D Hand Pose Detection in Egocentric RGB-D Images
Brachiaria species identification using imaging techniques based on fractal descriptors
Learning Contour-Fragment-based Shape Model with And-Or Tree Representation
Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object Recognition and Detection
Simulation of Color Blindness and a Proposal for Using Google Glass as Color-correcting Tool
NEFI: Network Extraction From Images
Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data
DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking
Convex Optimization for Parallel Energy Minimization
Lifting Object Detection Datasets into 3D
Convolutional Neural Network-Based Image Representation for Visual Loop Closure Detection
The adaptable buffer algorithm for high quantile estimation in non-stationary data streams
Capturing Hands in Action using Discriminative Salient Points and Physics Simulation
DeepStereo: Learning to Predict New Views from the World's Imagery
Kernel Cuts: MRF meets Kernel & Spectral Clustering
Unsupervised Learning from Narrated Instruction Videos
Recurrent Network Models for Human Dynamics
Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
Learning Sampling Distributions for Efficient Object Detection
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces
Deep Attributes from Context-Aware Regional Neural Codes
DeepSat - A Learning framework for Satellite Imagery
LEWIS: Latent Embeddings for Word Images and their Semantics
Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
Truncated Max-of-Convex Models
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression
Crater Detection via Convolutional Neural Networks
Low-rank Matrix Factorization under General Mixture Noise Distributions
Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues
Factors in Finetuning Deep Model for object detection
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements
Self-Transfer Learning for Fully Weakly Supervised Object Localization
Automatic Face Reenactment
Face Attribute Prediction Using Off-the-Shelf CNN Features
PlaNet - Photo Geolocation with Convolutional Neural Networks
Augur: Mining Human Behaviors from Fiction to Power Interactive Systems
Exploring the coevolution of predator and prey morphology and behavior
SSSC-AM: A Unified Framework for Video Co-Segmentation by Structured Sparse Subspace Clustering with Appearance and Motion Features
Revisiting Batch Normalization For Practical Domain Adaptation
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding
Learning to Navigate the Energy Landscape
Fractal Dimension Invariant Filtering and Its CNN-based Implementation
BreakingNews: Article Annotation by Image and Text Processing
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
Perceptually Consistent Color-to-Gray Image Conversion
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
Neural Dataset Generality
Automatic Image Annotation via Label Transfer in the Semantic Space
Viziometrics: Analyzing Visual Information in the Scientific Literature
Dual Local-Global Contextual Pathways for Recognition in Aerial Imagery
Siamese Instance Search for Tracking
EventNet Version 1.1 Technical Report
A Sparse Representation of Complete Local Binary Pattern Histogram for Human Face Recognition
Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines
Apparent Age Estimation Using Ensemble of Deep Learning Models
A constrained clustering based approach for matching a collection of feature sets
Max-Margin Feature Selection
FVQA: Fact-based Visual Question Answering
Revisiting Visual Question Answering Baselines
Scene Text Detection via Holistic, Multi-Channel Prediction
Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network
Tubelets: Unsupervised action proposals from spatiotemporal super-voxels
Piecewise convexity of artificial neural networks
Improved Deep Learning of Object Category using Pose Information
Visual Question Answering: A Survey of Methods and Datasets
Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking
Ego2Top: Matching Viewers in Egocentric and Top-view Videos
Deep Learning Human Mind for Automated Visual Classification
Automatic Visual Theme Discovery from Joint Image and Text Corpora
Poisson Noise Reduction with Higher-order Natural Image Prior Model
Automated Visual Fin Identification of Individual Great White Sharks
From Facial Expression Recognition to Interpersonal Relation Prediction
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
Pose-Selective Max Pooling for Measuring Similarity
End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering
Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes
A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation
Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies
Laplacian regularized low rank subspace clustering
Local Similarity-Aware Deep Feature Embedding
MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors
Learning Detailed Face Reconstruction from a Single Image
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation
Multimodal Memory Modelling for Video Captioning
DSAC - Differentiable RANSAC for Camera Localization
The Freiburg Groceries Dataset
Finding Mirror Symmetry via Registration
Improving training of deep neural networks via Singular Value Bounding
Self-Supervised Video Representation Learning With Odd-One-Out Networks
Towards Robust Deep Neural Networks with BANG
Fully Convolutional Crowd Counting On Highly Congested Scenes
3D Bounding Box Estimation Using Deep Learning and Geometry
Unsupervised Human Action Detection by Action Matching
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
A Video-Based Method for Objectively Rating Ataxia
Defining the Pose of any 3D Rigid Object and an Associated Distance
Scale Coding Bag of Deep Features for Human Attribute and Action Recognition
Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks
DARN: a Deep Adversial Residual Network for Intrinsic Image Decomposition
Learning Non-Lambertian Object Intrinsics across ShapeNet Categories
Symbolic Representation and Classification of Logos
Feedback Networks
AENet: Learning Deep Audio Features for Video Analysis
Urban Scene Segmentation with Laser-Constrained CRFs
An OpenCL(TM) Deep Learning Accelerator on Arria 10
Action Recognition: From Static Datasets to Moving Robots
Deep Learning Features at Scale for Visual Place Recognition
A Survey of Structure from Motion
Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks
Automating Image Analysis by Annotating Landmarks with Deep Neural Networks
A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network
YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
Reconstruction-Based Disentanglement for Pose-invariant Face Recognition
Sparse Representation based Multi-sensor Image Fusion: A Review
One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network
Enhanced Facial Recognition Framework based on Skin Tone and False Alarm Rejection
Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification
Scene Recognition by Combining Local and Global Image Descriptors
Deep representation learning for human motion prediction and classification
Synthesizing Training Data for Object Detection in Indoor Scenes
Visual Translation Embedding Network for Visual Relation Detection
Age Progression/Regression by Conditional Adversarial Autoencoder
MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos
Shape DNA: Basic Generating Functions for Geometric Moment Invariants
NoScope: Optimizing Neural Network Queries over Video at Scale
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics
Development of An Android Application for Object Detection Based on Color, Shape, or Local Features
Learning Rank Reduced Interpolation with Principal Component Analysis
Using Human Brain Activity to Guide Machine Learning
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking
Encouraging LSTMs to Anticipate Actions Very Early
ZM-Net: Real-time Zero-shot Image Manipulation Network
Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
Transductive Zero-Shot Learning with Adaptive Structural Embedding
Active Convolution: Learning the Shape of Convolution for Image Classification
Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network
Bundle Optimization for Multi-aspect Embedding
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
Unsupervised learning from video to detect foreground objects in single images
Configurable, Photorealistic Image Rendering and Ground Truth Synthesis by Sampling Stochastic Grammars Representing Indoor Scenes
Generating Descriptions with Grounded and Co-Referenced People
Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor
Three-Dimensional Segmentation of Vesicular Networks of Fungal Hyphae in Macroscopic Microscopy Image Stacks
Deep Generative Adversarial Compression Artifact Removal
Improving Pairwise Ranking for Multi-label Image Classification
Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories
Visual Recognition of Paper Analytical Device Images for Detection of Falsified Pharmaceuticals
Unsupervised object segmentation in video by efficient selection of highly probable positive features
A Labeling-Free Approach to Supervising Deep Neural Networks for Retinal Blood Vessel Segmentation
Automatic Viseme Vocabulary Construction to Enhance Continuous Lip-reading
Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree
Submodular Trajectory Optimization for Aerial 3D Scanning
A Dual-Source Approach for 3D Human Pose Estimation from a Single Image
CAD Priors for Accurate and Flexible Instance Reconstruction
READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
Learning Robust Object Recognition Using Composed Scenes from Generative Models
Towards seamless multi-view scene analysis from satellite to street-level
Hierarchical Cellular Automata for Visual Saliency
Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting
Learning a Robust Society of Tracking Parts
Continuous Video to Simple Signals for Swimming Stroke Detection with Convolutional Neural Networks
Discriminatively Learned Hierarchical Rank Pooling Networks
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision
r-BTN: Cross-domain Face Composite and Synthesis from Limited Facial Patches
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery
Measurement-Adaptive Sparse Image Sampling and Recovery
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Recovering 6D Object Pose: Multi-modal Analyses on Challenges
Modeling Multi-Object Configurations via Medial/Skeletal Linking Structures
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition
Interactive 3D Modeling with a Generative Adversarial Network
Fine-Grained Categorization via CNN-Based Automatic Extraction and Integration of Object-Level and Part-Level Features
Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation
Deep Hashing Network for Unsupervised Domain Adaptation
A New Urban Objects Detection Framework Using Weakly Annotated Sets
Laplacian-Steered Neural Style Transfer
Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction
Object Tracking based on Quantum Particle Swarm Optimization
Dominant Sets for "Constrained" Image Segmentation
Faster Than Real-time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses
Supervising Neural Attention Models for Video Captioning by Human Gaze Data
Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming
Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Loss
Concise Radiometric Calibration Using The Power of Ranking
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
Generalizing the Convolution Operator in Convolutional Neural Networks
Automated Assessment of Facial Wrinkling: a case study on the effect of smoking
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Extreme clicking for efficient object annotation
Transitive Invariance for Self-supervised Visual Representation Learning
Exploring Temporal Preservation Networks for Precise Temporal Action Localization
Joint Multi-Person Pose Estimation and Semantic Part Segmentation
SSH: Single Stage Headless Face Detector
Conditional Adversarial Network for Semantic Segmentation of Brain Tumor
Practical Block-wise Neural Network Architecture Generation
Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning
On Image Classification: Correlation v.s. Causality
WordSup: Exploiting Word Annotations for Character based Text Detection
Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
Fast Image Processing with Fully-Convolutional Networks
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
Scene Text Recognition with Sliding Convolutional Character Models
Towards Automated Cadastral Boundary Delineation from UAV Data
Focusing Attention: Towards Accurate Text Recognition in Natural Images
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition
Image Matching: An Application-oriented Benchmark
To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation
Normal Integration: A Survey
Morphable Face Models - An Open Framework
Recognition of feature curves on 3D shapes using an algebraic approach to Hough transforms
Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images
Depth estimation using structured light flow -- analysis of projected pattern flow on an object's surface --
Rethinking Feature Discrimination and Polymerization for Large-scale Recognition
DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels
Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation
3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
DART: Distribution Aware Retinal Transform for Event-based Cameras
Beautiful and damned. Combined effect of content quality and social ties on user engagement
An Iterative Co-Saliency Framework for RGBD Images
Interpreting Convolutional Neural Networks Through Compression
Remote Sensing Image Fusion Based on Two-stream Fusion Network
Global versus Localized Generative Adversarial Nets
Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries
Action-Attending Graphic Neural Network
Parameter Reference Loss for Unsupervised Domain Adaptation
Few-shot Learning by Exploiting Visual Concepts within CNNs
On the Relations of Correlation Filter Based Trackers and Struck
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism
SPP-Net: Deep Absolute Pose Regression with Synthetic Views
Mapping the world population one building at a time
Note on Attacking Object Detectors with Adversarial Stickers
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss
Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels
Future Frame Prediction for Anomaly Detection -- A New Baseline
Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval
Implementation of Deep Convolutional Neural Network in Multi-class Categorical Image Classification
What have we learned from deep representations for action recognition?
Convex Relaxations for Pose Graph Optimization with Outliers
Adversarial Spheres
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks
Image Provenance Analysis at Scale
EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking
Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network
When Vehicles See Pedestrians with Phones:A Multi-Cue Framework for Recognizing Phone-based Activities of Pedestrians
Supersaliency: Predicting Smooth Pursuit-Based Attention with Slicing CNNs Improves Fixation Prediction for Naturalistic Videos
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions
Learning the Synthesizability of Dynamic Texture Samples
Face recognition for monitoring operator shift in railways
Every Smile is Unique: Landmark-Guided Diverse Smile Generation
Generating Triples with Adversarial Networks for Scene Graph Construction
Triplet-based Deep Similarity Learning for Person Re-Identification
DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)
Generating retinal flow maps from structural optical coherence tomography with artificial intelligence
Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning
Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval
Joint Pixel and Feature-level Domain Adaptation in the Wild
Cross-View Image Synthesis using Conditional GANs
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss
Queuing Theory Guided Intelligent Traffic Scheduling through Video Analysis using Dirichlet Process Mixture Model
Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences
Unsupervised Representation Learning by Predicting Image Rotations
End-to-End Video Captioning with Multitask Reinforcement Learning
PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection
Speeding-up Object Detection Training for Robotics with FALKON
Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection
Seeing Voices and Hearing Faces: Cross-modal biometric matching
CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM
Face Alignment in Full Pose Range: A 3D Total Solution
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
Geometric Consistency for Self-Supervised End-to-End Visual Odometry
Capsules for Object Segmentation
Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification
Multimodal Unsupervised Image-to-Image Translation
The Generalized Universal Law of Generalization
L.T.Kuzin: Research Program
A hybrid MLP-PNN architecture for fast image superresolution
Accurate and robust image superresolution by neural processing of local image representations
Matching Edges in Images ; Application to Face Recognition
Next Generation Language Resources using GRID
Tactical games & behavioral self-organization
SEAL: Common Core Libraries and Services for LHC Applications
Inverse problems in imaging systems and the general Bayesian inversion frawework
Extreme Learning Machine for land cover classification
3D/2D Registration of Mapping Catheter Images for Arrhythmia Interventional Assistance
A New Method to Extract Dorsal Hand Vein Pattern using Quadratic Inference Function
Analytical shape determination of fiber-like objects with Virtual Image Correlation
Automatic diagnosis of retinal diseases from color retinal images
Medical Image Compression using Wavelet Decomposition for Prediction Method
A Study on Potential of Integrating Multimodal Interaction into Musical Conducting Education
Normalized Information Distance is Not Semicomputable
Revisiting Complex Moments For 2D Shape Representation and Image Normalization
A Review of Research on Devnagari Character Recognition
Statistical Multiresolution Dantzig Estimation in Imaging: Fundamental Concepts and Algorithmic Framework
Exploring New Directions in Iris Recognition
Off-Line Arabic Handwriting Character Recognition Using Word Segmentation
Wavelet Based Normal and Abnormal Heart Sound Identification using Spectrogram Analysis
An anisotropy preserving metric for DTI processing
On the Relation Between the Common Labelling and the Median Graph
Parallel D2-Clustering: Large-Scale Clustering of Discrete Distributions
A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts
Computing Motion with 3D Memristive Grid
Non-constant bounded holomorphic functions of hyperbolic numbers - Candidates for hyperbolic activation functions
Time Efficient Approach To Offline Hand Written Character Recognition Using Associative Memory Net
GPU Accelerated Particle Visualization with Splotch
Correcting Multi-focus Images via Simple Standard Deviation for Image Fusion
Multiple Kernel Learning for Brain-Computer Interfacing
Approximating persistent homology for a cloud of $n$ points in a subquadratic time
Stopping Rules for Bag-of-Words Image Search and Its Application in Appearance-Based Localization
Software Architecture and Subclassing Technique for Semiconductor Manufacturing Machines
Evaluation of Image Segmentation and Filtering With ANN in the Papaya Leaf
Block Motion Based Dynamic Texture Analysis: A Review
Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness
A Multi-threshold Segmentation Approach Based on Artificial Bee Colony Optimization
Fast Separable Non-Local Means
Neighborhood Rank Order Coding for Robust Texture Analysis and Feature Extraction
Symmetric angular momentum coupling, the quantum volume operator and the 7-spin network: a computational perspective
A Two-phase Decision Support Framework for the Automatic Screening of Digital Fundus Images
Online SLAM with Any-time Self-calibration and Automatic Change Detection
Deep Learning for Medical Image Segmentation
A Real Time Facial Expression Classification System Using Local Binary Patterns
Describing Multimedia Content using Attention-based Encoder--Decoder Networks
Compression of Fully-Connected Layer in Neural Network by Kronecker Product
GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection
Multilingual Image Description with Neural Sequence Models
Color graph based wavelet transform with perceptual information
Computational models: Bottom-up and top-down aspects
Efficient Training of Very Deep Neural Networks for Supervised Hashing
TEMPO: Feature-Endowed Teichmüller Extremal Mappings of Point Clouds
Reservoir computing for spatiotemporal signal classification without trained output weights
Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure
Fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback
Robot Networks with Homonyms: The Case of Patterns Formation
Algorithmic Optimisations for Iterative Deconvolution Methods
Medial Meshes for Volume Approximation
Binary Stereo Matching
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
Non-Verbal Communication Analysis in Victim-Offender Mediations
Adjusted least squares fitting of algebraic hypersurfaces
Nonparametric Nearest Neighbor Descent Clustering based on Delaunay Triangulation
A Probabilistic Theory of Deep Learning
Fast Methods for Eikonal Equations: an Experimental Survey
A Survey of Multithreading Image Analysis
Exploring the influence of scale on artist attribution
Detection and Analysis of Emotion From Speech Signals
Camera Calibration from Dynamic Silhouettes Using Motion Barcodes
Rapid Exact Signal Scanning with Deep Convolutional Neural Networks
FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation
Learning the Semantics of Manipulation Action
A simple method for estimating the fractal dimension from digital images: The compression dimension
Generating Images with Perceptual Similarity Metrics based on Deep Networks
Local High-order Regularization on Data Manifolds
Depth-Based Object Tracking Using a Robust Gaussian Filter
Effective Computer Model For Recognizing Nationality From Frontal Image
Persistent Homology of Attractors For Action Recognition
Deep Self-Convolutional Activations Descriptor for Dense Cross-Modal Correspondence
Accelerating Deep Learning with Shrinkage and Recall
Network as a Service: The New Vista of Opportunities
Human Computer Interaction Using Marker Based Hand Gesture Recognition
NIST: An Image Classification Network to Image Semantic Retrieval
EmoFit: Affect Monitoring System for Sedentary Jobs
Label distribution based facial attractiveness computation by deep residual learning
Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity
Machine learning methods for accurate delineation of tumors in PET images
Optical Flow Estimation using a Spatial Pyramid Network
Low-rank Bilinear Pooling for Fine-Grained Classification
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition
End-to-End Training of Hybrid CNN-CRF Models for Stereo
Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices
Security-related Research in Ubiquitous Computing -- Results of a Systematic Literature Review
Detection of Face using Viola Jones and Recognition using Back Propagation Neural Network
An Epipolar Line from a Single Pixel
Unsupervised Construction of Human Body Models Using Principles of Organic Computing
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization
A Real-time Hand Gesture Recognition and Human-Computer Interaction System
Evaluation and Prediction of Polygon Approximations of Planar Contours for Shape Analysis
Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes
A New Adaptive Video Super-Resolution Algorithm With Improved Robustness to Innovations
Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance
Exploring the Imposition of Synaptic Precision Restrictions For Evolutionary Synthesis of Deep Neural Networks
Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules
Discrete Gyrator Transforms: Computational Algorithms and Applications
Learned Primal-dual Reconstruction
Salt-n-pepper noise filtering using Cellular Automata
Streamlined Deployment for Quantized Neural Networks
White Matter Fiber Segmentation Using Functional Varifolds
Texture Fuzzy Segmentation using Skew Divergence Adaptive Affinity Functions
Learning Wasserstein Embeddings
Detection and Analysis of Human Emotions through Voice and Speech Pattern Processing
Convolutional Drift Networks for Video Classification
HyperNetworks with statistical filtering for defending adversarial examples
Randomized Nonnegative Matrix Factorization
Fast Predictive Simple Geodesic Regression
Gradually Updated Neural Networks for Large-Scale Image Recognition
Compression for Smooth Shape Analysis
A Survey of FPGA Based Neural Network Accelerator
The Internet of Battle Things
Can Computers Create Art?
Worst-case Optimal Submodular Extensions for Marginal Estimation
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
KRISM --- Krylov Subspace-based Optical Computing of Hyperspectral Images
Efficient Large-Scale Multi-Modal Classification
Efficient Neural Architecture Search via Parameter Sharing
Lightweight Classification of IoT Malware based on Image Recognition
FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy
Ensemble computation approach to the Hough transform
Real-Time End-to-End Action Detection with Two-Stream Networks
Facial Expression Analysis under Partial Occlusion: A Survey
Solving Inverse Computational Imaging Problems using Deep Pixel-level Prior
A Deep Learning Approach for Multimodal Deception Detection
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
Sparse Adversarial Perturbations for Videos
Sharing and Preserving Computational Analyses for Posterity with encapsulator
Efficient Hardware Realization of Convolutional Neural Networks using Intra-Kernel Regular Pruning
Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector
The similarity metric
Evolutionary Computational Method of Facial Expression Analysis for Content-based Video Retrieval using 2-Dimensional Cellular Automata
Simplification and integration in computing and cognition: the SP theory and the multiple alignment concept
The SP theory of intelligence: benefits and applications
Efficient Legendre moment computation for grey level images
Real-Time Human-Computer Interaction Based on Face and Hand Gesture Recognition
An NBDMMM Algorithm Based Framework for Allocation of Resources in Cloud
AiiDA: Automated Interactive Infrastructure and Database for Computational Science
Joint System and Algorithm Design for Computationally Efficient Fan Beam Coded Aperture X-ray Coherent Scatter Imaging
DyVEDeep: Dynamic Variable Effort Deep Neural Networks
Computer-aided position planning of miniplates to treat facial bone defects
An Integrated Soft Computing Approach to a Multi-biometric Security Model
The Neural Representation Benchmark and its Evaluation on Brain and Machine
Sparsey: Event Recognition via Deep Hierarchical Spare Distributed Codes
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video
Using Self-Organising Mappings to Learn the Structure of Data Manifolds
ScheduleNanny: Using GPS to Learn the User's Significant Locations, Travel Times and Schedule
A simple effective method for curvatures estimation on triangular meshes
Spatiotemporal sensistivity and visual attention for efficient rendering of dynamic environments
Fast Lexically Constrained Viterbi Algorithm (FLCVA): Simultaneous Optimization of Speed and Memory
A Fast and Accurate Nonlinear Spectral Method for Image Recognition and Registration
Grid Added Value to Address Malaria
Evolutionary Optimisation Methods for Template Based Image Registration
Construction of Bayesian Deformable Models via Stochastic Approximation Algorithm: A Convergence Study
Empirical Evaluation of Four Tensor Decomposition Algorithms
Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model
Network QoS Management in Cyber-Physical Systems
A Novel Clustering Algorithm Based on Quantum Games
On Design and Implementation of the Distributed Modular Audio Recognition Framework: Requirements and Specification Design Document
Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face Recognition
RIOT: I/O-Efficient Numerical Computing without SQL
An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image
A Method for Extraction and Recognition of Isolated License Plate Characters
Color Image Clustering using Block Truncation Algorithm
An Optimal Method For Wake Detection In SAR Images Using Radon Transformation Combined With Wavelet Filters
CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series
Sequential Clustering based Facial Feature Extraction Method for Automatic Creation of Facial Models from Orthogonal Views
Reversible Image Authentication with Tamper Localization Based on Integer Wavelet Transform
Fingerprint Verification based on Gabor Filter Enhancement
Robust Multi biometric Recognition Using Face and Ear Images
Performance analysis of Non Linear Filtering Algorithms for underwater images
Genetic Programming Framework for Fingerprint Matching
Gesture Recognition with a Focus on Important Actions by Using a Path Searching Method in Weighted Graph
Stability of multidimensional persistent homology with respect to domain perturbations
A New Image Steganography Based On First Component Alteration Technique
An Improved Image Mining Technique For Brain Tumour Classification Using Efficient Classifier
A Comparative Study of Removal Noise from Remote Sensing Image
Text Region Extraction from Business Card Images for Mobile Devices
Facial Gesture Recognition Using Correlation And Mahalanobis Distance
Sliding window approach based Text Binarisation from Complex Textual images
Tuning CLD Maps
Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning
New Visual Cryptography Algorithm For Colored Image
Deblured Gaussian Blurred Images
Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier
Bayesian estimation of regularization and PSF parameters for Wiener-Hunt deconvolution
Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition
An Effective Fingerprint Verification Technique
Future management needs of a "software-driven" science community
Multiple Classifier Combination for Off-line Handwritten Devnagari Character Recognition
Survey of Nearest Neighbor Techniques
Maximum Likelihood Mosaics
Segmentation of Camera Captured Business Card Images for Mobile Devices
A correspondence-less approach to matching of deformable shapes
An Effect of Spatial Filtering in Visualization of Coronary Arteries Imaging
Visualization techniques for data mining of Latur district satellite imagery
DWT Based Fingerprint Recognition using Non Minutiae Features
Fingerprint: DWT, SVD Based Enhancement and Significant Contrast for Ridges and Valleys Using Fuzzy Measures
Fingerprint recognition using standardized fingerprint model
Fast multi-scale edge-detection in medical ultrasound signals
Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation
Devnagari document segmentation using histogram approach
Improvements on "Fast space-variant elliptical filtering using box splines"
Speculative Parallel Evaluation Of Classification Trees On GPGPU Compute Engines
A Facial Expression Classification System Integrating Canny, Principal Component Analysis and Artificial Neural Network
3D Model Retrieval Based on Semantic and Shape Indexes
Probabilistic Motion Estimation Based on Temporal Coherence
Information Distance: New Developments
A Multimodal Biometric System Using Linear Discriminant Analysis For Improved Performance
A Novel Approach to Fast Image Filtering Algorithm of Infrared Images based on Intro Sort Algorithm
A General Solver Based on Sparse Resultants
Comparing Methods for segmentation of Microcalcification Clusters in Digitized Mammograms
Very Short Literature Survey From Supervised Learning To Surrogate Modeling
New approach using Bayesian Network to improve content based image classification systems
Parametric annealing: a stochastic search method for human pose tracking
Volumetric Mapping of Genus Zero Objects via Mass Preservation
Fingerprint Gender Classification using Wavelet Transform and Singular Value Decomposition
Optimizing Face Recognition Using PCA
Investigation of Color Constancy for Ubiquitous Wireless LAN/Camera Positioning: An Initial Outcome
Bayesian Random Fields: The Bethe-Laplace Approximation
Speckle Reduction using Stochastic Distances
Cups Products in Z2-Cohomology of 3D Polyhedral Complexes
Assessment of SAR Image Filtering using Adaptive Stack Filters
Fixed Interfaces, Adaptive Interfaces... What is next? Total movability - a new paradigm for the user interface
A Novel Approach of Color Image Hiding using RGB Color planes and DWT
Combinatorial Gradient Fields for 2D Images with Empirically Convergent Separatrices
Segmentation of Breast Regions in Mammogram Based on Density: A Review
A Complete System for Candidate Polyps Detection in Virtual Colonoscopy
Inference of Fine-grained Attributes of Bengali Corpus for Stylometry Detection
3D Face Recognition using Significant Point based SULD Descriptor
A New Algorithm Based Entropic Threshold for Edge Detection in Images
The role of colour preattentive processing in human-computer interaction task efficiency: a preliminary study
A recursive divide-and-conquer approach for sparse principal component analysis
Self Authentication of image through Daubechies Transform technique (SADT)
Fast and Robust Linear Motion Deblurring
A Novel Directional Weighted Minimum Deviation (DWMD) Based Filter for Removal of Random Valued Impulse Noise
Toward New Vision in Teaching Calculus
A Self-Organizing Neural Scheme for Door Detection in Different Environments
Barnes-Hut-SNE
An ANN-based Method for Detecting Vocal Fold Pathology
Automatic symmetry based cluster approach for anomalous brain identification in PET scan image : An Analysis
GBM Volumetry using the 3D Slicer Medical Image Computing Platform
Font Acknowledgment and Character Extraction of Digital and Scanned Images
Determining Points on Handwritten Mathematical Symbols
Exploiting Data Parallelism in the yConvex Hypergraph Algorithm for Image Representation using GPGPUs
Conversion of Braille to Text in English, Hindi and Tamil Languages
Content Based Image Retrieval System using Feature Classification with Modified KNN Algorithm
Selection Mammogram Texture Descriptors Based on Statistics Properties Backpropagation Structure
Haptic Science and Technology
Development of Comprehensive Devnagari Numeral and Character Database for Offline Handwritten Character Recognition
Contextually learnt detection of unusual motion-based behaviour in crowded public spaces
Contour polygonal approximation using shortest path in networks
Neural Network Application on Foliage Plant Identification
Multi-Sensor Image Fusion Based on Moment Calculation
Geometric Feature Based Face-Sketch Recognition
Region and Location Based Indexing and Retrieval of MR-T2 Brain Tumor Images
End-to-end Phoneme Sequence Recognition using Convolutional Neural Networks
Sparse similarity-preserving hashing
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Image Processing based Systems and Techniques for the Recognition of Ancient and Modern Coins
Automated Coin Recognition System using ANN
Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator
Leaf Classification Using Shape, Color, and Texture Features
License Plate Recognition (LPR): A Review with Experiments for Malaysia Case Study
smart application for AMS using Face Recognition
On Performance of Logical-Clustering Of Flow-Sensors
Multi-view Face Analysis Based on Gabor Features
Ant Colony based Feature Selection Heuristics for Retinal Vessel Segmentation
A-infinity Persistence
Indoor 3D Video Monitoring Using Multiple Kinect Depth-Cameras
An inertial forward-backward algorithm for monotone inclusions
Stabilizing dual-energy X-ray computed tomography reconstructions using patch-based regularization
Extraction of Projection Profile, Run-Histogram and Entropy Features Straight from Run-Length Compressed Text-Documents
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
Icon Based Information Retrieval and Disease Identification in Agriculture
Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video
A Study of Local Binary Pattern Method for Facial Expression Detection
An evolutionary computational based approach towards automatic image registration
Recognition of Isolated Words using Zernike and MFCC features for Audio Visual Speech Recognition
Object Proposal Generation using Two-Stage Cascade SVMs
A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans
Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient
Self Organization Map based Texture Feature Extraction for Efficient Medical Image Categorization
Bi-l0-l2-Norm Regularization for Blind Motion Deblurring
A Convex Approach to Consensus on SO(n)
A graph Laplacian regularization for hyperspectral data unmixing
Salient Object Detection: A Discriminative Regional Feature Integration Approach
Affective Facial Expression Processing via Simulation: A Probabilistic Model
Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction
Fast forward feature selection for the nonlinear classification of hyperspectral images
A new ADMM algorithm for the Euclidean median and its application to robust patch regression
Microscopic Advances with Large-Scale Learning: Stochastic Optimization for Cryo-EM
Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises
Wise Computing: Towards Endowing System Development with True Wisdom
A Cheap System for Vehicle Speed Detection
A Proximal Bregman Projection Approach to Continuous Max-Flow Problems Using Entropic Distances
Deep Learning for Object Saliency Detection and Image Segmentation
Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications
How Far Can You Get By Combining Change Detection Algorithms?
A Review Paper: Noise Models in Digital Image Processing
Graph edit distance : a new binary linear programming formulation
Randomized Robust Subspace Recovery for High Dimensional Data Matrices
SAR Imaging of Moving Target based on Knowledge-aided Two-dimensional Autofocus
Teaching Logic to Information Systems Students: Challenges and Opportunities
A National Effort for Motivating Indian Students and Teachers towards Algorithmic Research
Manitest: Are classifiers really invariant?
Thinning Algorithm Using Hypergraph Based Morphological Operators
Large-scale subspace clustering using sketching and validation
A Latent Source Model for Patch-Based Image Segmentation
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Stacked Attention Networks for Image Question Answering
Neural Module Networks
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization
An Introduction to Convolutional Neural Networks
Sparseness helps: Sparsity Augmented Collaborative Representation for Classification
R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
Local Binary Pattern for Word Spotting in Handwritten Historical Document
Incremental Reconstruction of Urban Environments by Edge-Points Delaunay Triangulation
A Classifier-guided Approach for Top-down Salient Object Detection
Kernelized Covariance for Action Recognition
Image Colorization Using a Deep Convolutional Neural Network
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition
Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image Recognition
FPGA system for real-time computational extended depth of field imaging using phase aperture coding
Optimal Filtered Backprojection for Fast and Accurate Tomography Reconstruction
Adapting Deep Network Features to Capture Psychological Representations
Temporal Registration in In-Utero Volumetric MRI Time Series
Dynamic Network Surgery for Efficient DNNs
The Symmetry of a Simple Optimization Problem in Lasso Screening
Learning Temporal Transformations From Time-Lapse Videos
Cast and Self Shadow Segmentation in Video Sequences using Interval based Eigen Value Representation
On the Cohomology of 3D Digital Images
Fast Approximate L_infty Minimization: Speeding Up Robust Regression
Semi-Optimal Edge Detector based on Simple Standard Deviation with Adjusted Thresholding
Arabic Text Recognition in Video Sequences
Toward Cloud-based Vehicular Networks with Efficient Resource Management
A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model
Performance of Hull-Detection Algorithms For Proton Computed Tomography Reconstruction
Direct Processing of Run Length Compressed Document Image for Segmentation and Characterization of a Specified Block
Realtime Multilevel Crowd Tracking using Reciprocal Velocity Obstacles
Survey on Sparse Coded Features for Content Based Face Image Retrieval
Visual Saliency Model using SIFT and Comparison of Learning Approaches
Combined Approach for Image Segmentation
Local Decorrelation For Improved Detection
Optimization Methods for Convolutional Sparse Coding
Recurrent Models of Visual Attention
Convex Hulls under Uncertainty
A Computational Model of the Short-Cut Rule for 2D Shape Decomposition
Real-time Crowd Tracking using Parameter Optimized Mixture of Motion Models
Going Deeper with Convolutions
Approximation errors of online sparsification criteria
Histogram of Oriented Principal Components for Cross-View Action Recognition
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit
Fast Steerable Principal Component Analysis
Analytical Comparison of Noise Reduction Filters for Image Restoration Using SNR Estimation
Image Data Compression for Covariance and Histogram Descriptors
Py3DFreeHandUS: a library for voxel-array reconstruction using Ultrasonography and attitude sensors
Self-informed neural network structure learning
Transformation Properties of Learned Visual Representations
Fast and Robust Feature Matching for RGB-D Based Localization
Gradient Difference based approach for Text Localization in Compressed domain
Semi-supervised Segmentation Fusion of Multi-spectral and Aerial Images
Learning Descriptors for Object Recognition and 3D Pose Estimation
Matrix Product State for Feature Extraction of Higher-Order Tensors
A Survey On Video Forgery Detection
Tomographic Image Reconstruction using Training images
Brain Tumor Segmentation: A Comparative Analysis
Properties of simple sets in digital spaces. Contractions of simple sets preserving the homotopy type of a digital space
Comparisons of wavelet functions in QRS signal to noise ratio enhancement and detection accuracy
Multimodal Convolutional Neural Networks for Matching Image and Sentence
Computational Cost Reduction in Learned Transform Classifications
Speeding Up Neural Networks for Large Scale Classification using WTA Hashing
Learning to Answer Questions From Image Using Convolutional Neural Network
Robust Face Recognition with Structural Binary Gradient Patterns
Understanding deep features with computer-generated imagery
Implementation of Training Convolutional Neural Networks
Facial Expressions recognition Based on Principal Component Analysis (PCA)
Learning both Weights and Connections for Efficient Neural Networks
Extract an essential skeleton of a character as a graph from a character image
MRF-ZOOM: A Fast Dictionary Searching Algorithm for Magnetic Resonance Fingerprinting
Face Prediction Model for an Automatic Age-invariant Face Recognition System
Understanding Neural Networks Through Deep Visualization
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Forming A Random Field via Stochastic Cliques: From Random Graphs to Fully Connected Random Fields
Saliency maps on image hierarchies
Gaussian Mixture Reduction Using Reverse Kullback-Leibler Divergence
Deep Convolutional Neural Networks for Smile Recognition
Adapting Resilient Propagation for Deep Learning
Direct high-order edge-preserving regularization for tomographic image reconstruction
Fast Template Matching by Subsampled Circulant Matrix
Projection Bank: From High-dimensional Data to Medium-length Binary Codes
A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation
Implicit Sparse Code Hashing
Compressed Dynamic Mode Decomposition for Background Modeling
Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web
Exploiting Local Structures with the Kronecker Layer in Convolutional Networks
Computational Pathology: Challenges and Promises for Tissue Analysis
B-spline Shape from Motion & Shading: An Automatic Free-form Surface Modeling for Face Reconstruction
Geometric-Algebra LMS Adaptive Filter and its Application to Rotation Estimation
Fast Binary Embedding via Circulant Downsampled Matrix -- A Data-Independent Approach
Very Efficient Training of Convolutional Neural Networks using Fast Fourier Transform and Overlap-and-Add
A semi-automatic computer-aided method for surgical template design
Preoperative Volume Determination for Pituitary Adenoma
Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation
Computer Aided Restoration of Handwritten Character Strokes
On Study of the Binarized Deep Neural Network for Image Classification
Learning Shapes by Convex Composition
Adaptive Frequency Cepstral Coefficients for Word Mispronunciation Detection
Dynamic Memory Networks for Visual and Textual Question Answering
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval
Stitching Stabilizer: Two-frame-stitching Video Stabilization for Embedded Systems
On Fast Bilateral Filtering using Fourier Kernels
Sparse Activity and Sparse Connectivity in Supervised Learning
Deep Embedding for Spatial Role Labeling
FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos
Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos
Analog Signal Processing Approach for Coarse and Fine Depth Estimation
A metric on the space of finite sets of trajectories for evaluation of multi-target tracking algorithms
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
Shaping the Future through Innovations: From Medical Imaging to Precision Medicine
Real-time Eye Gaze Direction Classification Using Convolutional Neural Network
openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit
A Deep Learning Approach to Block-based Compressed Sensing of Images
Very Deep Convolutional Networks for Text Classification
Fast and Extensible Online Multivariate Kernel Density Estimation
Convolutional Sketch Inversion
Universal Correspondence Network
cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL
Multiplierless 16-point DCT Approximation for Low-complexity Image and Video Coding
Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series
Alternating Back-Propagation for Generator Network
De-Hashing: Server-Side Context-Aware Feature Reconstruction for Mobile Visual Search
Resolution- and throughput-enhanced spectroscopy using high-throughput computational slit
Visualizing Natural Language Descriptions: A Survey
From Collective Adaptive Systems to Human Centric Computation and Back: Spatial Model Checking for Medical Imaging
Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures
Multi-modal image retrieval with random walk on multi-layer graphs
Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems
Features Fusion for Classification of Logos
Segmentation and Classification of Skin Lesions for Disease Diagnosis
Warped Convolutions: Efficient Invariance to Spatial Transformations
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
Matrix Product State for Higher-Order Tensor Compression and Classification
Context Aware Nonnegative Matrix Factorization Clustering
Image-to-Markup Generation with Coarse-to-Fine Attention
From Multiview Image Curves to 3D Drawings
Document Image Coding and Clustering for Script Discrimination
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
Neural Photo Editing with Introspective Adversarial Networks
The face-space duality hypothesis: a computational model
Low-complexity Image and Video Coding Based on an Approximate Discrete Tchebichef Transform
A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
Proposal for Automatic License and Number Plate Recognition System for Vehicle Identification
Video Depth-From-Defocus
Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input
Image Clustering without Ground Truth
Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm
Diversity Promoting Online Sampling for Streaming Video Summarization
Sliding Dictionary Based Sparse Representation For Action Recognition
Learning Deep Embeddings with Histogram Loss
Regularized Pel-Recursive Motion Estimation Using Generalized Cross-Validation and Spatial Adaptation
Detecting Moving Regions in CrowdCam Images
Towards Interconnected Virtual Reality: Opportunities, Challenges and Enablers
DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows
PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
Generative One-Class Models for Text-based Person Retrieval in Forensic Applications
Fast low-level pattern matching algorithm
NoiseOut: A Simple Way to Prune Neural Networks
Learning the Number of Neurons in Deep Networks
Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis
Deep Tensor Convolution on Multicores
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
Computational Mapping of the Ground Reflectivity with Laser Scanners
Computer Aided Detection of Oral Lesions on CT Images
Training Bit Fully Convolutional Network for Fast Semantic Segmentation
A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information
Automatic Lymphocyte Detection in H&E Images with Deep Neural Networks
Development of a Real-time Colorectal Tumor Classification System for Narrow-band Imaging zoom-videoendoscopy
A Stochastic Large Deformation Model for Computational Anatomy
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Cross-Modal Manifold Learning for Cross-modal Retrieval
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning
Active Learning and Proofreading for Delineation of Curvilinear Structures
Bayesian Nonparametric Models for Synchronous Brain-Computer Interfaces
Optimization on Product Submanifolds of Convolution Kernels
Learning Word-Like Units from Joint Audio-Visual Analysis
Pruned non-local means
Seeded Laplaican: An Eigenfunction Solution for Scribble Based Interactive Image Segmentation
Task-driven Visual Saliency and Attention-based Visual Question Answering
Automatic segmentation of trees in dynamic outdoor environments
Fast Back-Projection for Non-Line of Sight Reconstruction
A Computational Model of a Single-Photon Avalanche Diode Sensor for Transient Imaging
Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D Skeletons
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks
A 3D Object Detection and Pose Estimation Pipeline Using RGB-D Images
Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data
SurfNet: Generating 3D shape surfaces using deep residual networks
Local Patch Classification Based Framework for Single Image Super-Resolution
Robust Non-Rigid Registration With Reweighted Dual Sparsities
Cloud Radiative Effect Study Using Sky Camera
Fast Spectral Ranking for Similarity Search
Generative Adversarial Residual Pairwise Networks for One Shot Learning
Adversarial Transformation Networks: Learning to Generate Adversarial Examples
Semantic-driven Generation of Hyperlapse from $360^\circ$ Video
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection
A Computational Approach to Relative Aesthetics
Learning Important Features Through Propagating Activation Differences
Efficient Sparse Subspace Clustering by Nearest Neighbour Filtering
3D seismic data denoising using two-dimensional sparse coding scheme
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality
Fast Generation for Convolutional Autoregressive Models
Second-order Temporal Pooling for Action Recognition
A Deep Learning Perspective on the Origin of Facial Expressions
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization
Phase recovery and holographic image reconstruction using deep learning in neural networks
Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions
On Convergence and Stability of GANs
Gaze Distribution Analysis and Saliency Prediction Across Age Groups
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Adversarial Generation of Natural Language
Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks
Submanifold Sparse Convolutional Networks
Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor
Compression Fractures Detection on CT
Distributed Hierarchical Control for State Estimation With Robotic Sensor Networks
ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks
Large-Scale Plant Classification with Deep Neural Networks
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
Robotic Ironing with 3D Perception and Force/Torque Feedback in Household Environments
Using Convolutional Neural Networks in Robots with Limited Computational Resources: Detecting NAO Robots while Playing Soccer
GPGPU Acceleration of the KAZE Image Feature Extraction Algorithm
Class-specific image denoising using importance sampling
Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling
Research Opportunities and Visions for Smart and Pervasive Health
Detection and Localization of Image Forgeries using Resampling Features and Deep Learning
Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video
Data-Driven Sparse Structure Selection for Deep Neural Networks
Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
A dataset for Computer-Aided Detection of Pulmonary Embolism in CTA images
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring
Interleaved Group Convolutions for Deep Neural Networks
SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes
Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning
Speeding up the Köhler's method of contrast thresholding
Slanted Stixels: Representing San Francisco's Steepest Streets
On Optimizing Distributed Tucker Decomposition for Dense Tensors
Fast Screening Algorithm for Rotation and Scale Invariant Template Matching
Deeply-Learned Part-Aligned Representations for Person Re-Identification
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM
Statistics on the (compact) Stiefel manifold: Theory and Applications
Real-time Deep Video Deinterlacing
CNN Cascades for Segmenting Whole Slide Images of the Kidney
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?
Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification
Mutual Visibility by Robots with Persistent Memory
Semantic Video CNNs through Representation Warping
Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects
Large Batch Training of Convolutional Networks
Active Orthogonal Matching Pursuit for Sparse Subspace Clustering
Pillar Networks++: Distributed non-parametric deep and wide networks
Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
Statistical Selection of CNN-Based Audiovisual Features for Instantaneous Estimation of Human Emotional States
Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Is human face processing a feature- or pattern-based task? Evidence using a unified computational method driven by eye movements
Real-time convolutional networks for sonar image classification in low-power embedded systems
A Novel Low-Complexity Framework in Ultra-Wideband Imaging for Breast Cancer Detection
Can you tell a face from a HEVC bitstream?
Acceleration of Histogram-Based Contrast Enhancement via Selective Downsampling
Viewpoint Invariant Action Recognition using RGB-D Videos
Une véritable approche $\ell_0$ pour l'apprentissage de dictionnaire
Neural network identification of people hidden from view with a single-pixel, single-photon detector
Generative learning for deep networks
Muon Trigger for Mobile Phones
Numerical optimization for Artificial Retina Algorithm
Tensor Product Generation Networks for Deep NLP Modeling
Efficient Convolutional Neural Network For Audio Event Detection
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks
Anatomical Pattern Analysis for decoding visual stimuli in human brains
DiffuserCam: Lensless Single-exposure 3D Imaging
CAMREP- Concordia Action and Motion Repository
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression
Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks
A Line-Point Unified Solution to Relative Camera Pose Estimation
Block DCT filtering using vector processing
Deep Cropping via Attention Box Prediction and Aesthetics Assessment
Computational ghost imaging using deep learning
Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment
Whodunnit? Crime Drama as a Case for Natural Language Understanding
ReBNet: Residual Binarized Neural Network
Computationally efficient cardiac views projection using 3D Convolutional Neural Networks
Curve Reconstruction via the Global Statistics of Natural Curves
Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency
A General Neural Network Hardware Architecture on FPGA
Parametric Manifold Learning Via Sparse Multidimensional Scaling
Grammatical facial expression recognition using customized deep neural network architecture
Mobile Video Object Detection with Temporally-Aware Feature Maps
A Generalized Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles of Complex Types
Total Variation-Based Dense Depth from Multi-Camera Array
Robust Object Tracking Based on Self-adaptive Search Area
BlockDrop: Dynamic Inference Paths in Residual Networks
Efficient quantum circuit for singular value thresholding
CondenseNet: An Efficient DenseNet using Learned Group Convolutions
WSNet: Compact and Efficient Networks with Weight Sampling
Deformation estimation of an elastic object by partial observation using a neural network
TensorFlow Distributions
Budget-Aware Activity Detection with A Recurrent Policy Network
Real-time Semantic Image Segmentation via Spatial Sparsity
Human activity recognition from mobile inertial sensors using recurrence plots
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
Design Automation for Binarized Neural Networks: A Quantum Leap Opportunity?
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Tactics to Directly Map CNN graphs on Embedded FPGAs
Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians
Deformable Classifiers
Tracking objects using 3D object proposals
End-to-end weakly-supervised semantic alignment
Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography images
Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer
Benchmarking Decoupled Neural Interfaces with Synthetic Gradients
Facial emotion recognition using min-max similarity classifier
Topological Tracking of Connected Components in Image Sequences
Reducing Deep Network Complexity with Fourier Transform Methods
FOTS: Fast Oriented Text Spotting with a Unified Network
Architecture Based Classification of Leaf Images
Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture
Empirical Explorations in Training Networks with Discrete Activations
TexT - Text Extractor Tool for Handwritten Document Transcription and Annotation
Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective
EffNet: An Efficient Structure for Convolutional Neural Networks
Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks
Robust Multi-subspace Analysis Using Novel Column L0-norm Constrained Matrix Factorization
Fast and Accurate Reconstruction of Compressed Color Light Field
Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in Convolutional Networks
Dual Recurrent Attention Units for Visual Question Answering
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
Recent Advances in Efficient Computation of Deep Convolutional Neural Networks
Adviser Networks: Learning What Question to Ask for Human-In-The-Loop Viewpoint Estimation
Highly accurate model for prediction of lung nodule malignancy with CT scans
Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors
2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation
FastNet
Universal Deep Neural Network Compression
MiMatrix: A Massively Distributed Deep Learning Framework on a Petascale High-density Heterogeneous Cluster
Advertising in the IoT Era: Vision and Challenges
An Optimized Architecture for Unpaired Image-to-Image Translation
Analyzing and Mitigating the Impact of Permanent Faults on a Systolic Array Based Neural Network Accelerator
Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey
Teaching Machines to Code: Neural Markup Generation with Visual Attention
Security and Privacy Approaches in Mixed Reality: A Literature Survey
On Lyapunov exponents and adversarial perturbation
Least Square Error Method Robustness of Computation: What is not usually considered and taught
Locally Adaptive Learning Loss for Semantic Image Segmentation
Recurrent Residual Module for Fast Inference in Videos
Speeding Up the Bilateral Filter: A Joint Acceleration Way
A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)
Calcium Removal From Cardiac CT Images Using Deep Convolutional Neural Network
Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction
Classification based Grasp Detection using Spatial Transformer Network
MIS-SLAM: Real-time Large Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing
Where is my Device? - Detecting the Smart Device's Wearing Location in the Context of Active Safety for Vulnerable Road Users
Inferencing Based on Unsupervised Learning of Disentangled Representations
Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling
Towards Image Understanding from Deep Compression without Decoding
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
Local Binary Pattern Networks
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up Baselines
Fisher Pruning of Deep Nets for Facial Trait Classification
CSfM: Community-based Structure from Motion
Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications
Precision Sugarcane Monitoring Using SVM Classifier
Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching
Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator
B-DCGAN:Evaluation of Binarized DCGAN for FPGA
Fast and Robust Subspace Clustering Using Random Projections
Low-Latency Video Semantic Segmentation
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification
Building Efficient CNN Architecture for Offline Handwritten Chinese Character Recognition
Semi-supervised multi-organ segmentation via multi-planar co-training
Attention U-Net: Learning Where to Look for the Pancreas
Discovery and usage of joint attention in images
SAMI: Service-Based Arbitrated Multi-Tier Infrastructure for Mobile Cloud Computing
A study on non-destructive method for detecting Toxin in pepper using Neural networks
Computing as compression: the SP theory of intelligence
Analysis of Farthest Point Sampling for Approximating Geodesics in a Graph
GPU Accelerated Fractal Image Compression for Medical Imaging in Parallel Computing Platform
The Zen of Graduate-level Programming
OpenHEC: A Framework for Application Programmers to Design FPGA-based Systems
Developing Autonomic Properties for Distributed Pattern-Recognition Systems with ASSL: A Distributed MARF Case Study
Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling
Training Deep Networks with Structured Layers by Matrix Backpropagation
Get More With Less: Near Real-Time Image Clustering on Mobile Phones
A Distributed Deep Representation Learning Model for Big Image Data Classification
Deep Learning for Computational Chemistry
Learning Generative Models with Sinkhorn Divergences
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
The Cyborg Astrobiologist: Scouting Red Beds for Uncommon Features with Geological Significance
Unified Structured Learning for Simultaneous Human Pose Estimation and Garment Attribute Classification
Hierarchical Deep Learning Architecture For 10K Objects Classification
Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition
Real-time dynamics of lattice gauge theories with a few-qubit quantum computer
Adaptive Algorithm and Platform Selection for Visual Detection and Tracking
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory
Aggregated Wasserstein Metric and State Registration for Hidden Markov Models
The magnitude of the effect of calf muscles fatigue on postural control during bipedal quiet standing with vision depends on the eye-visual target distance
MOMCC: Market-Oriented Architecture for Mobile Cloud Computing Based on Service Oriented Architecture
Sparse Spike Coding : applications of Neuroscience to the processing of natural images
Deep Learning for Detecting Robotic Grasps
Geodesic-based Salient Object Detection
Online Unsupervised Feature Learning for Visual Tracking
Revisiting loss-specific training of filter-based MRFs for image restoration
Virtual Windshields: Merging Reality and Digital Content to Improve the Driving Experience
Complex Events Recognition under Uncertainty in a Sensor Network
Deep Convolutional Neural Fields for Depth Estimation from a Single Image
Adaptive Objectness for Object Tracking
End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
Object-Proposal Evaluation Protocol is 'Gameable'
A Multi-scale Multiple Instance Video Description Network
Modelling, Measuring and Compensating Color Weak Vision
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
Unsupervised Image Segmentation using the Deffuant-Weisbuch Model from Social Dynamics
Articulated Hand Pose Estimation Review
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos
A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset
Deep Convolution Networks for Compression Artifacts Reduction
A Comparative Study for the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization
A Fuzzy Logic Based Certain Trust Model for E-Commerce
The standing pool of genomic structural variation in a natural population of Mimulus guttatus
A Manifesto for Semantic Model Differencing
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding
Appearance-based indoor localization: A comparison of patch descriptor performance
End-to-End Training of Deep Visuomotor Policies
DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural Nets
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition
A Comparison of High-Level Design Tools for SoC-FPGA on Disparity Map Calculation Example
Multivariate Median Filters and Partial Differential Equations
A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours
Event Specific Multimodal Pattern Mining with Image-Caption Pairs
Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss
Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis
Discriminative Sparse Neighbor Approximation for Imbalanced Learning
Efficient Multi-view Performance Capture of Fine-Scale Surface Detail
Modular Tracking Framework: A Unified Approach to Registration based Tracking
Elastic Functional Coding of Riemannian Trajectories
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge
Descriptor transition tables for object retrieval using unconstrained cluttered video acquired using a consumer level handheld mobile device
The Conditional Lucas & Kanade Algorithm
Look-ahead before you leap: end-to-end active recognition by forecasting the effect of motion
Automatic Selection of the Optimal Local Feature Detector
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
Dictionary Learning for Robotic Grasp Recognition and Detection
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Detection of concealed cars in complex cargo X-ray imagery using Deep Learning
Salient Region Detection and Segmentation in Images using Dynamic Mode Decomposition
A Machine learning approach for Shape From Shading
Event-based, 6-DOF Camera Tracking from Photometric Depth Maps
Video Registration in Egocentric Vision under Day and Night Illumination Changes
Image segmentation based on histogram of depth and an application in driver distraction detection
HMD Vision-based Teleoperating UGV and UAV for Hostile Environment using Deep Learning
Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles
Fast and reliable stereopsis measurement at multiple distances with iPad
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
A Rich Source of Labels for Deep Network Models of the Primate Dorsal Visual Stream
Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
Learning a Deep Embedding Model for Zero-Shot Learning
DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
Examining the Impact of Blur on Recognition by Convolutional Networks
Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision
Superpixels: An Evaluation of the State-of-the-Art
Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling
Shape Estimation from Defocus Cue for Microscopy Images via Belief Propagation
3D tracking of water hazards with polarized stereo cameras
Temporal scale selection in time-causal scale space
A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials
Tracking using Numerous Anchor points
How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots
Real-time 3D Human Tracking for Mobile Robots with Multisensors
Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Hard Mixtures of Experts for Large Scale Weakly Supervised Vision
Proxy Templates for Inverse Compositional Photometric Bundle Adjustment
Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval
Attention-based Natural Language Person Retrieval
A Vision System for Multi-View Face Recognition
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text
Advanced Steel Microstructural Classification by Deep Learning Methods
Independent Motion Detection with Event-driven Cameras
Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets
Analysis and Modeling of 3D Indoor Scenes
Efficient Eye Typing with 9-direction Gaze Estimation
Class-Weighted Convolutional Features for Visual Instance Search
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
Vision-Based Fallen Person Detection for the Elderly
PROBE: Predictive Robust Estimation for Visual-Inertial Navigation
On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition
GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled Images
Semantic Instance Segmentation with a Discriminative Loss Function
Learning Policies for Adaptive Tracking with Deep Feature Cascades
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
Visual-textual Attention Driven Fine-grained Representation Learning
Bridge the Gap Between Group Sparse Coding and Rank Minimization via Adaptive Dictionary Learning
Capturing Localized Image Artifacts through a CNN-based Hyper-image Representation
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training
CMCGAN: A Uniform Framework for Cross-Modal Visual-Audio Mutual Generation
Visual Question Answering as a Meta Learning Task
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
Scene-Specific Pedestrian Detection Based on Parallel Vision
The Robust Manifold Defense: Adversarial Training using Generative Models
Deep Supervision with Intermediate Concepts
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations
GapFlyt: Active Vision Based Minimalist Structure-less Gap Detection For Quadrotor Flight
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
MoNet: Moments Embedding Network
Driver Hand Localization and Grasp Analysis: A Vision-based Real-time Approach
IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network
SalientDSO: Bringing Attention to Direct Sparse Odometry
A new stereo formulation not using pixel and disparity models
Cubic Range Error Model for Stereo Vision with Illuminators
Digital Limits of Government: The Failure of E-Democracy
Improving Transferability of Adversarial Examples with Input Diversity
Monocular Depth Estimation by Learning from Heterogeneous Datasets
SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data
Image Segmentation Using Subspace Representation and Sparse Decomposition
Expressway visibility estimation based on image entropy and piecewise stationary time series analysis
The secret world of shrimps: polarisation vision at its best
Automated Pattern Detection--An Algorithm for Constructing Optimally Synchronizing Multi-Regular Language Filters
Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis
Fast space-variant elliptical filtering using box splines
Evidence-Based Filters for Signal Detection: Application to Evoked Brain Responses
Constant-time filtering using shiftable kernels
Real-time Image-based 6-DOF Localization in Large-Scale Environments
Smoothed Analysis of Belief Propagation for Minimum-Cost Flow and Matching
Nonlinear Dynamic Field Embedding: On Hyperspectral Scene Visualization
Fast non parametric entropy estimation for spatial-temporal saliency method
P-HGRMS: A Parallel Hypergraph Based Root Mean Square Algorithm for Image Denoising
Optimizing Auto-correlation for Fast Target Search in Large Search Space
Designing labeled graph classifiers by exploiting the Rényi entropy of the dissimilarity representation
Algorithmic Analysis of Edge Ranking and Profiling for MTF Determination of an Imaging System
Fast Computation of PERCLOS and Saccadic Ratio
Active skeleton for bacteria modeling
A Computational Model for Amodal Completion
Unsupervised single-particle deep clustering via statistical manifold learning
Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
Neural tuning size is a key factor underlying holistic face processing
HSR: L1/2 Regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection
Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation
Cascade Learning by Optimally Partitioning
A generalized flow for multi-class and binary classification tasks: An Azure ML approach
Classification of Large-Scale Fundus Image Data Sets: A Cloud-Computing Framework
Deep Action Sequence Learning for Causal Shape Transformation
Automated Resolution Selection for Image Segmentation
Application-Driven Near-Data Processing for Similarity Search
Metaheuristic Algorithms for Convolution Neural Network
Numerical Inversion of SRNF Maps for Elastic Shape Analysis of Genus-Zero Surfaces
A Distance Function for Comparing Straight-Edge Geometric Figures
Parsimonious Inference on Convolutional Neural Networks: Learning and applying on-line kernel activation rules
RenderMap: Exploiting the Link Between Perception and Rendering for Dense Mapping
Adaptive Neural Networks for Efficient Inference
Large-Scale Evolution of Image Classifiers
High Accuracy Classification of Parkinson's Disease through Shape Analysis and Surface Fitting in $^{123}$I-Ioflupane SPECT Imaging
Espresso: Efficient Forward Propagation for BCNNs
Development of the SP machine
Improved Bilinear Pooling with CNNs
Recurrent Scale Approximation for Object Detection in CNN
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes
Distributed Deep Neural Networks over the Cloud, the Edge and End Devices
UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model
Generating Reflectance Curves from sRGB Triplets
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method
Exposing Computer Generated Images by Using Deep Convolutional Neural Networks
Regularized Evolution for Image Classifier Architecture Search
PRUNE: Dynamic and Decidable Dataflow for Signal Processing on Heterogeneous Platforms
Hardware-Efficient Guided Image Filtering For Multi-Label Problem
Notes on a New Philosophy of Empirical Science
Acceleration of the shiftable O(1) algorithm for bilateral filtering and non-local means
A Comparative Study of Human thermal face recognition based on Haar wavelet transform (HWT) and Local Binary Pattern (LBP)
Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation
YouTube-8M: A Large-Scale Video Classification Benchmark
Automatically tracking neurons in a moving and deforming brain
Partial Procedural Geometric Model Fitting for Point Clouds
Adversarially Tuned Scene Generation
Unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis
Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual grouping
Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark
Learning to Associate Words and Images Using a Large-scale Graph
Capturing natural-colour 3D models of insects for species discovery
MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
The Theory of Unified Relativity for a Biovielectroluminescence Phenomenon via Fly's Visual and Imaging System
A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations
An Even Faster and More Unifying Algorithm for Comparing Trees via Unbalanced Bipartite Matchings
Probabilistic Search for Object Segmentation and Recognition
Data Engineering for the Analysis of Semiconductor Manufacturing Data
Segmentation, Indexing, and Visualization of Extended Instructional Videos
Analysis and Interface for Instructional Video
Supporting Dynamic Ad hoc Collaboration Capabilities
The Generalized Riemann or Henstock Integral Underpinning Multivariate Data Analysis: Application to Faint Structure Finding in Price Processes
Using Stochastic Encoders to Discover Structure in Data
Invariant Stochastic Encoders
Adaptive Cluster Expansion (ACE): A Hierarchical Bayesian Network
Distributed Regression in Sensor Networks: Training Distributively with Alternating Projections
Automatic Face Recognition System Based on Local Fourier-Bessel Features
Face Verification in Polar Frequency Domain: a Biologically Motivated Approach
Multilevel Thresholding for Image Segmentation through a Fast Statistical Recursive Algorithm
A third level trigger programmable on FPGA for the gamma/hadron separation in a Cherenkov telescope using pseudo-Zernike moments and the SVM classifier
A kernel for time series based on global alignments
A stochastic-variational model for soft Mumford-Shah segmentation
Local to Global Normalization Dynamic by Nonlinear Local Interactions
N-Particle Dynamics of the Euler Equations for Planar Diffeomorphisms
Features and dimensions: Motion estimation in fly vision
Clustering fetal heart rate tracings by compression
Enhancement of Noisy Planar Nuclear Medicine Images using Mean Field Annealing
Structural Health Monitoring Using Neural Network Based Vibrational System Identification
Local Area Damage Detection in Composite Structures Using Piezoelectric Transducers
Code Similarity on High Level Programs
Efficient representation as a design principle for neural coding and computation
Theory and Applications of Two-dimensional, Null-boundary, Nine-Neighborhood, Cellular Automata Linear rules
Natural pseudo-distance and optimal matching between reduced size functions
Analytic Torsion of a Bounded Generalized Cone
Efficient Exact Inference in Planar Ising Models
Graph-based classification of multiple observation sets
Exact Histogram Specification Optimized for Structural Similarity
Gradient-based adaptive interpolation in super-resolution image restoration
Managing Distributed MARF with SNMP
Total Variation, Adaptive Total Variation and Nonconvex Smoothly Clipped Absolute Deviation Penalty for Denoising Blocky Images
Segmentation of Facial Expressions Using Semi-Definite Programming and Generalized Principal Component Analysis
The VOISE Algorithm: a Versatile Tool for Automatic Segmentation of Astronomical Images
Maximal digital straight segments and convergence of discrete geometric estimators
Combinatorial pyramids and discrete geometry for energy-minimizing segmentation
Automatic Defect Detection and Classification Technique from Image: A Special Case Using Ceramic Tiles
Efficient IRIS Recognition through Improvement of Feature Extraction and subset Selection
Multiresolution Elastic Medical Image Registration in Standard Intensity Scale
Fully Automatic 3D Reconstruction of Histological Images
Image Sampling with Quasicrystals
Automatic local Gabor Features extraction for face recognition
Side-channel attack on labeling CAPTCHAs
Fast adaptive elliptical filtering using box splines
MACH: Fast Randomized Tensor Decompositions
Low-rank Matrix Completion with Noisy Observations: a Quantitative Comparison
Parallelization of the LBG Vector Quantization Algorithm for Shared Memory Systems
Seeing Science
Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
A Model-Based Approach to Predicting Predator-Prey & Friend-Foe Relationships in Ant Colonies
Fast Alternating Linearization Methods for Minimizing the Sum of Two Convex Functions
An Unsupervised Algorithm For Learning Lie Group Transformations
Incorporating characteristics of human creativity into an evolutionary art algorithm
Features Based Text Similarity Detection
3D Skull Recognition Using 3D Matching Technique
Hybrid Medical Image Classification Using Association Rule Mining with Decision Tree Algorithm
Gradient Based Seeded Region Grow method for CT Angiographic Image Segmentation
Detection and Demarcation of Tumor using Vector Quantization in MRI images
Threshold Based Indexing of Commercial Shoe Print to Create Reference and Recovery Images
Supervised Learning of Digital image restoration based on Quantization Nearest Neighbor algorithm
Supervised Classification Performance of Multispectral Images
Scalable Large-Margin Mahalanobis Distance Metric Learning
A Unified Algorithmic Framework for Multi-Dimensional Scaling
An Offline Technique for Localization of License Plates for Indian Commercial Vehicles
Investigation and Assessment of Disorder of Ultrasound B-mode Images
A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron
Land-cover Classification and Mapping for Eastern Himalayan State Sikkim
Large Margin Boltzmann Machines and Large Margin Sigmoid Belief Networks
Development of an automated Red Light Violation Detection System (RLVDS) for Indian vehicles
A novel scheme for binarization of vehicle images using hierarchical histogram equalization technique
Extended Two-Dimensional PCA for Efficient Face Representation and Recognition
A Robust Fuzzy Clustering Technique with Spatial Neighborhood Information for Effective Medical Image Segmentation
New Clustering Algorithm for Vector Quantization using Rotation of Error Vector
SAR Image Segmentation using Vector Quantization Technique on Entropy Images
Offline Handwriting Recognition using Genetic Algorithm
Color Image Compression Based On Wavelet Packet Best Tree
Signature Region of Interest using Auto cropping
An Efficient Watermarking Algorithm to Improve Payload and Robustness without Affecting Image Perceptual Quality
Multistage Hybrid Arabic/Indian Numeral OCR System
An Efficient Vein Pattern-based Recognition System
Application Of Fuzzy System In Segmentation Of MRI Brain Tumor
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
Efficient Region-Based Image Querying
Fusion of Daubechies Wavelet Coefficients for Human Face Recognition
Registration of Brain Images using Fast Walsh Hadamard Transform
A Study on the Effectiveness of Different Patch Size and Shape for Eyes and Mouth Detection
An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network
Homotopy Perturbation Method for Image Restoration and Denoising
Automated Acanthamoeba polyphaga detection and computation of Salmonella typhimurium concentration in spatio-temporal images
Rotation Invariant Face Detection Using Wavelet, PCA and Radial Basis Function Networks
How to Extract the Geometry and Topology from Very Large 3D Segmentations
An Embarrassingly Simple Speed-Up of Belief Propagation with Robust Potentials
Visual-hint Boundary to Segment Algorithm for Image Segmentation
Axiomatic Digital Topology
Nearness to Local Subspace Algorithm for Subspace and Motion Segmentation
Fast Color Quantization Using Weighted Sort-Means Clustering
Lesion Border Detection in Dermoscopy Images
Low-Rank Matrix Approximation with Weights or Missing Data is NP-hard
Real-time Visual Tracking Using Sparse Representation
Stochastic Vector Quantisers
Harmonic Order Parameters for Characterizing Complex Particle Morphologies
Texture feature extraction in the spatial-frequency domain for content-based image retrieval
Affine-invariant geodesic geometry of deformable 3D shapes
Improving the Performance of K-Means for Color Quantization
Application of Freeman Chain Codes: An Alternative Recognition Technique for Malaysian Car Plates
Group Invariant Scattering
Support vector machines/relevance vector machine for remote sensing classification: A review
Efficient Independence-Based MAP Approach for Robust Markov Networks Structure Discovery
Smart depth of field optimization applied to a robotised view camera
A General Framework for Development of the Cortex-like Visual Object Recognition System: Waves of Spikes, Predictive Coding and Universal Dictionary of Features
Multi-task GLOH feature selection for human age estimation
Computationally efficient algorithms for statistical image processing. Implementation in R
Continuous Multiclass Labeling Approaches and Algorithms
Aorta Segmentation for Stent Simulation
Automatic Open Space Area Extraction and Change Detection from High Resolution Urban Satellite Images
Benchmarking the Quality of Diffusion-Weighted Images
"Improved FCM algorithm for Clustering on Web Usage Mining"
Off-Line Handwritten Signature Retrieval using Curvelet Transforms
Disconnected Skeleton: Shape at its Absolute Scale
A Novel Image Segmentation Enhancement Technique based on Active Contour and Topological Alignments
Nearest Prime Simplicial Complex for Object Recognition
Comparing Haar-Hilbert and Log-Gabor Based Iris Encoders on Bath Iris Image Database
Sufficient Conditions for Low-rank Matrix Recovery, Translated from Sparse Signal Recovery
Morphological Reconstruction for Word Level Script Identification
A Replica Inference Approach to Unsupervised Multi-Scale Image Segmentation
Unstructured Human Activity Detection from RGBD Images
The IHS Transformations Based Image Fusion
An Efficient Real Time Method of Fingertip Detection
Gender Recognition Based on Sift Features
A new embedding quality assessment method for manifold learning
Hierarchical Object Parsing from Structured Noisy Point Clouds
Biometric Authorization System using Gait Biometry
Design of an Optical Character Recognition System for Camera-based Handheld Devices
Learning Topic Models by Belief Propagation
Beyond pixels and regions: A non local patch means (NLPM) method for content-level restoration, enhancement, and reconstruction of degraded document images
New Method for 3D Shape Retrieval
Enhancement of Image Resolution by Binarization
A New IRIS Normalization Process For Recognition System With Cryptographic Techniques
Invariant texture analysis through Local Binary Patterns
Why We Shouldn't Forget Multicast in Name-oriented Publish/Subscribe
A Topic Modeling Toolbox Using Belief Propagation
Minutiae Extraction from Fingerprint Images - a Review
NegCut: Automatic Image Segmentation based on MRF-MAP
A New Color Feature Extraction Method Based on Dynamic Color Distribution Entropy of Neighborhoods
Organic Design of Massively Distributed Systems: A Complex Networks Perspective
On the Lagrangian Biduality of Sparsity Minimization Problems
Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models
Automatic Clustering with Single Optimal Solution
An evaluation of local shape descriptors for 3D shape retrieval
Efficient Web-based Facial Recognition System Employing 2DHOG
Regularized Robust Coding for Face Recognition
Locally Linear Embedding Clustering Algorithm for Natural Imagery
Handwritten Bangla Alphabet Recognition using an MLP Based Classifier
Video Object Tracking and Analysis for Computer Assisted Surgery
Enhancement of Images using Morphological Transformation
Single Reduct Generation Based on Relative Indiscernibility of Rough Set Theory
A Framework for Automated Cell Tracking in Phase Contrast Microscopic Videos based on Normal Velocities
Analysis of Magnification in Depth from Defocus
A New Approach to Speeding Up Topic Modeling
Principal Component Analysis-Linear Discriminant Analysis Feature Extractor for Pattern Recognition
Image segmentation by adaptive distance based on EM algorithm
A New Approach for Arabic Handwritten Postal Addresses Recognition
Automatic facial feature extraction and expression recognition based on neural network
Ubiquitous WLAN/Camera Positioning using Inverse Intensity Chromaticity Space-based Feature Detection and Matching: A Preliminary Result
Speech Recognition: Increasing Efficiency of Support Vector Machines
Morphological Filtering in Shape Spaces: Applications using Tree-Based Image Representations
Spectral Analysis of Projection Histogram for Enhancing Close matching character Recognition in Malayalam
Hajj and Umrah Event Recognition Datasets
Fuzzy - Rough Feature Selection With Π- Membership Function For Mammogram Classification
Image Filtering using All Neighbor Directional Weighted Pixels: Optimization using Particle Swarm Optimization
Real time facial expression recognition using a novel method
Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information
Leaf vein segmentation using Odd Gabor filters and morphological operations
Conditional Sparse Coding and Grouped Multivariate Regression
Modeling Images using Transformed Indian Buffet Processes
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
Improving neural networks by preventing co-adaptation of feature detectors
Polarimetric SAR Image Smoothing with Stochastic Distances
A Fast Projected Fixed-Point Algorithm for Large Graph Matching
Non-Local Euclidean Medians
Tracking Tetrahymena Pyriformis Cells using Decision Trees
Dimension Reduction by Mutual Information Feature Extraction
Hierarchical Approach for Total Variation Digital Image Inpainting
Qualitative Comparison of Community Detection Algorithms
Penalty Constraints and Kernelization of M-Estimation Based Fuzzy C-Means
A New Training Algorithm for Kanerva's Sparse Distributed Memory
Recklessly Approximate Sparse Coding
Performance Measurement and Method Analysis (PMMA) for Fingerprint Reconstruction
Approximating the Weil-Petersson Metric Geodesics on the Universal Teichmüller space by Singular Solutions
Color Image Compression Algorithm Based on the DCT Blocks
Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval
A two-stage denoising filter: the preprocessed Yaroslavsky filter
A Session Based Blind Watermarking Technique within the NROI of Retinal Fundus Images for Authentication Using DWT, Spread Spectrum and Harris Corner Detection
Performance Analysis Of Neuro Genetic Algorithm Applied On Detecting Proportion Of Components In Manhole Gas Mixture
A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm
Wavelet Based Image Coding Schemes : A Recent Survey
A Hajj And Umrah Location Classification System For Video Crowded Scenes
Image Classification and Optimized Image Reproduction
Spike Timing Dependent Competitive Learning in Recurrent Self Organizing Pulsed Neural Networks Case Study: Phoneme and Word Recognition
PlaceRaider: Virtual Theft in Physical Spaces with Smartphones
Noise Influence on the Fuzzy-Linguistic Partitioning of Iris Code Space
Approximate evaluation of marginal association probabilities with belief propagation
Sparse Modeling of Intrinsic Correspondences
Demosaicing and Superresolution for Color Filter Array via Residual Image Reconstruction and Sparse Representation
Discrete geodesic calculus in the space of viscous fluidic objects
Schrödinger Diffusion for Shape Analysis with Texture
Evaluating Discussion Boards on BlackBoard as a Collaborative Learning Tool A Students Survey and Reflections
Variational time discretization of geodesic calculus
Developing ICC Profile Using Gray Level Control In Offset Printing Process
Multi-input Multi-output Beta Wavelet Network: Modeling of Acoustic Units for Speech Recognition
Time Complexity Analysis of Binary Space Partitioning Scheme for Image Compression
3D Surface Reconstruction of Underwater Objects
NF-SAVO: Neuro-Fuzzy system for Arabic Video OCR
Exact and Stable Recovery of Rotations for Robust Synchronization
A Comparative Study of Gaussian Mixture Model and Radial Basis Function for Voice Recognition
A Non-Blind Watermarking Scheme for Gray Scale Images in Discrete Wavelet Transform Domain using Two Subbands
Sketch Recognition using Domain Classification
Matching Through Features and Features Through Matching
SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges
Compressive Schlieren Deflectometry
Stratified SIFT Matching for Human Iris Recognition
Time-Frequency Representation of Microseismic Signals using the Synchrosqueezing Transform
Lip Localization and Viseme Classification for Visual Speech Recognition
Image registration with sparse approximations in parametric dictionaries
Image Denoising Using Interquartile Range Filter with Local Averaging
Kriging Interpolation Filter to Reduce High Density Salt and Pepper Noise
Cooperative Environmental Monitoring for PTZ Visual Sensor Networks: A Payoff-based Learning Approach
A new bio-inspired method for remote sensing imagery classification
Comparision and analysis of photo image forgery detection techniques
Adaptive Temporal Compressive Sensing for Video
Learning Stable Multilevel Dictionaries for Sparse Representations
Genetic Programming for Document Segmentation and Region Classification Using Discipulus
Scale Selection of Adaptive Kernel Regression by Joint Saliency Map for Nonrigid Image Registration
Omega Model for Human Detection and Counting for application in Smart Surveillance System
Spatial Fuzzy C Means PET Image Segmentation of Neurodegenerative Disorder
Gaussian Mixture Model for Handwritten Script Identification
Statistical Texture Features based Handwritten and Printed Text Classification in South Indian Documents
Dictionary learning based image enhancement for rarity detection
Hybridization of Otsu Method and Median Filter for Color Image Segmentation
Speckle Noise Reduction in Medical Ultrasound Images
Repairing and Inpainting Damaged Images using Diffusion Tensor
Human Mood Detection For Human Computer Interaction
Image Optimization and Prediction
Novel variational model for inpainting in the wavelet domain
Image Inpainting by Kriging Interpolation Technique
Geometric operations implemented by conformal geometric algebra neural nodes
Speckle Reduction with Adaptive Stack Filters
3D model retrieval using global and local radial distances
Physeter catodon localization by sparse coding
Non-Correlated Character Recognition using Artificial Neural Network
Discriminative Training: Learning to Describe Video with Sentences, from Video Described with Sentences
Compressive Coded Aperture Keyed Exposure Imaging with Optical Flow Reconstruction
A Novel Active Contour Model for Texture Segmentation
Increasing Compression Ratio in PNG Images by k-Modulus Method for Image Transformation
Extending UML for Conceptual Modeling of Annotation of Medical Images
Major Limitations of Satellite images
Video Text Localization using Wavelet and Shearlet Transforms
Online Tracking Parameter Adaptation based on Evaluation
Visual saliency estimation by integrating features using multiple kernel learning
Bayesian Fusion of Multi-Band Images
Veni Vidi Vici, A Three-Phase Scenario For Parameter Space Analysis in Image Analysis and Visualization
High-Accuracy Total Variation for Compressed Video Sensing
Efficient binary tomographic reconstruction
Boosting in Location Space
A multi-stream hmm approach to offline handwritten arabic word recognition
The Classification Accuracy of Multiple-Metric Learning Algorithm on Multi-Sensor Fusion
Multiple-object tracking in cluttered and crowded public spaces
Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM
Personal Identification from Lip-Print Features using a Statistical Model
Singular Value Decomposition of Images from Scanned Photographic Plates
A Robust Variational Model for Positive Image Deconvolution
Linear Algorithm for Digital Euclidean Connected Skeleton
Image Restoration using Total Variation with Overlapping Group Sparsity
A novel sparsity and clustering regularization
Devnagari Handwritten Numeral Recognition using Geometric Features and Statistical Combination Classifier
Determination, Calculation and Representation of the Upper and Lower Sealing Zones During Virtual Stenting of Aneurysms
Improvement of Automatic Hemorrhages Detection Methods Using Shapes Recognition
Skin Segmentation based Elastic Bunch Graph Matching for efficient multiple Face Recognition
On Convergent Finite Difference Schemes for Variational - PDE Based Image Processing
An iterative algorithm for computed tomography image reconstruction from limited-angle projections
Smoothness-Constrained Image Recovery from Block-Based Random Projections
TOP-SPIN: TOPic discovery via Sparse Principal component INterference
Fast Tracking via Spatio-Temporal Context Learning
On a non-local spectrogram for denoising one-dimensional signals
Scientific Workflows and Provenance: Introduction and Research Opportunities
Dictionary-Learning-Based Reconstruction Method for Electron Tomography
On the Design and Analysis of Multiple View Descriptors
Color and Shape Content Based Image Classification using RBF Network and PSO Technique: A Survey
Real-time High Resolution Fusion of Depth Maps on GPU
Improving Texture Categorization with Biologically Inspired Filtering
Automatic White Blood Cell Measuring Aid for Medical Diagnosis
Feature Extraction of Human Lip Prints
An Approach: Modality Reduction and Face-Sketch Recognition
Book embeddings of Reeb graphs
From Maxout to Channel-Out: Encoding Information on Sparse Pathways
Classifiers With a Reject Option for Early Time-Series Classification
Clustering using Vector Membership: An Extension of the Fuzzy C-Means Algorithm
Rectifying Self Organizing Maps for Automatic Concept Learning from Web Images
Network In Network
Dropout improves Recurrent Neural Networks for Handwriting Recognition
Constraint Reduction using Marginal Polytope Diagrams for MAP LP Relaxations
Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
Comparative analysis of evolutionary algorithms for image enhancement
Unsupervised Feature Learning by Deep Sparse Coding
Competitive Learning with Feedforward Supervisory Signal for Pre-trained Multilayered Networks
Learning Generative Models with Visual Attention
A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain
Learning Paired-associate Images with An Unsupervised Deep Learning Architecture
Do Deep Nets Really Need to be Deep?
Spectral Networks and Locally Connected Networks on Graphs
IVSS Integration of Color Feature Extraction Techniques for Intelligent Video Search Systems
Speech Recognition Front End Without Information Loss
A Novel Scheme for Generating Secure Face Templates Using BDA
Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias
A Novel Retinal Vessel Segmentation Based On Histogram Transformation Using 2-D Morlet Wavelet and Supervised Classification
Robust Hierarchical Clustering
A Hybrid NN/HMM Modeling Technique for Online Arabic Handwriting Recognition
Brazilian License Plate Detection Using Histogram of Oriented Gradients and Sliding Windows
Experiments of Distance Measurements in a Foliage Plant Retrieval System
Study of Efficient Technique Based On 2D Tsallis Entropy For Image Thresholding
Brain Tumor Detection Based On Symmetry Information
Painting Analysis Using Wavelets and Probabilistic Topic Models
Video Compressive Sensing for Dynamic MRI
Learning Deep Face Representation
Shape-Based Plagiarism Detection for Flowchart Figures in Texts
Parallel WiSARD object tracker: a ram-based tracking system
Spectral Clustering with Jensen-type kernels and their multi-point extensions
The state of play of ASC-Inclusion: An Integrated Internet-Based Environment for Social Inclusion of Children with Autism Spectrum Conditions
Classroom Video Assessment and Retrieval via Multiple Instance Learning
Theory and Application of Shapelets to the Analysis of Surface Self-assembly Imaging
Pseudo-Zernike Based Multi-Pass Automatic Target Recognition From Multi-Channel SAR
A Compact Linear Programming Relaxation for Binary Sub-modular MRF
Real-time Decolorization using Dominant Colors
Algorithm For Multi-Hand Finger Counting: An Easy Approach
Thoughts on a Recursive Classifier Graph: a Multiclass Network for Deep Object Recognition
Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences
Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
The fshape framework for the variability analysis of functional shapes
Proximal Iteratively Reweighted Algorithm with Multiple Splitting for Nonconvex Sparsity Optimization
Human Pose Estimation from RGB Input Using Synthetic Training Data
Up and Away: A Cheap UAV Cyber-Physical Testbed (Work in Progress)
Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes using High-Resolution Neural EM Data
Image Segmentation Using Frequency Locking of Coupled Oscillators
Graph Matching: Relax at Your Own Risk
Robust Fuzzy corner detector
Semi-supervised Spectral Clustering for Classification
An enhanced neural network based approach towards object extraction
A Bi-clustering Framework for Consensus Problems
Human Face as human single identity
A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation
Cortical spatio-temporal dimensionality reduction for visual grouping
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices
Generalized Higher-Order Tensor Decomposition via Parallel ADMM
Deep Networks with Internal Selective Attention through Feedback Connections
Deep Metric Learning for Practical Person Re-Identification
Adaptive Image Denoising by Targeted Databases
Optimized Method for Iranian Road Signs Detection and recognition system
Real-Time and Efficient Method for Accuracy Enhancement of Edge Based License Plate Recognition System
New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component
A Robust and Efficient Method for Improving Accuracy of License Plate Characters Recognition
A Survey on Two Dimensional Cellular Automata and Its Application in Image Processing
Merging and Shifting of Images with Prominence Coefficient for Predictive Analysis using Combined Image
Low-rank SIFT: An Affine Invariant Feature for Place Recognition
Active Sensing as Bayes-Optimal Sequential Decision Making
Bags of Affine Subspaces for Robust Object Tracking
Unsupervised learning segmentation for dynamic speckle activity images
Object Segmentation in Images using EEG Signals
Video In Sentences Out
Real Time Fabric Defect Detection System on an Embedded DSP Platform
Recognition of Handwritten Bangla Basic Characters and Digits using Convex Hull based Feature Set
Explain Images with Multimodal Recurrent Neural Networks
Recognition of cDNA microarray image Using Feedforward artificial neural network
Computing Topology Preservation of RBF Transformations for Landmark-Based Image Registration
Refined Particle Swarm Intelligence Method for Abrupt Motion Tracking
An exact mapping between the Variational Renormalization Group and Deep Learning
Efficient Image Categorization with Sparse Fisher Vector
High Order Structure Descriptors for Scene Images
A two-pass fuzzy-geno approach to pattern classification
Building pattern recognition applications with the SPARE library
Iris Biometric System using a hybrid approach
Exact Expression For Information Distance
Higher-order MRFs based image super resolution: why not MAP?
Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling
A Short Image Series Based Scheme for Time Series Digital Image Correlation
On the Covariance of ICP-based Scan-matching Techniques
A hierarchical framework for object recognition
DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks
Entropy of Overcomplete Kernel Dictionaries
Do Convnets Learn Correspondence?
Convolutional Neural Network-based Place Recognition
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Amoeba Techniques for Shape and Texture Analysis
Deep Belief Network Training Improvement Using Elite Samples Minimizing Free Energy
Efficient and Accurate Approximations of Nonlinear Convolutional Networks
Efficient Object Localization Using Convolutional Networks
Fully Convolutional Neural Networks for Crowd Segmentation
Attentional Neural Network: Feature Selection Using Cognitive Feedback
Learning a Recurrent Visual Representation for Image Caption Generation
Visual Sentiment Prediction with Deep Convolutional Neural Networks
Understanding image representations by measuring their equivariance and equivalence
Learning to Generate Chairs, Tables and Cars with Convolutional Networks
Virtual View Networks for Object Reconstruction
An Egocentric Look at Video Photographer Identity
Image Super-Resolution Using Deep Convolutional Networks
Unsupervised Feature Learning for Dense Correspondences across Scenes
Sparse Deep Stacking Network for Image Classification
The Quadrifocal Variety
Hard to Cheat: A Turing Test based on Answering Questions about Images
LATCH: Learned Arrangements of Three Patch Codes
Visual Analytics of Image-Centric Cohort Studies in Epidemiology
Reconstruction-free action inference from compressive imagers
Automatic Objects Removal for Scene Completion
Constrained Extreme Learning Machines: A Study on Classification Cases
Parametric Image Segmentation of Humans with Structural Shape Priors
The Beauty of Capturing Faces: Rating the Quality of Digital Portraits
Sketch-a-Net that Beats Humans
Overlapping and Non-overlapping Camera Layouts for Robot Pose Estimation
Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes
Approaching unstructured search from function bilateral symmetry detection - A quantum algorithm
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
Shadow Optimization from Structured Deep Edge Detection
Fast Spectral Unmixing based on Dykstra's Alternating Projection
Object detection via a multi-region & semantic segmentation-aware CNN model
The structure of optimal parameters for image restoration problems
Exploring Models and Data for Image Question Answering
Bilevel approaches for learning of variational imaging models
COROLA: A Sequential Solution to Moving Object Detection Using Low-rank Approximation
A PCA-Based Convolutional Network
Task-Based Optimization of Computed Tomography Imaging Systems
Automatic Facial Expression Recognition Using Features of Salient Facial Patches
Robust Facial Expression Classification Using Shape and Appearance Features
Harmonic Exponential Families on Manifolds
Global Variational Method for Fingerprint Segmentation by Three-part Decomposition
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
Measuring Visibility using Atmospheric Transmission and Digital Surface Model
Affine and Regional Dynamic Time Warpng
Expresso : A user-friendly GUI for Designing, Training and Exploring Convolutional Neural Networks
Smooth and iteratively Restore: A simple and fast edge-preserving smoothing model
Inner and Inter Label Propagation: Salient Object Detection in the Wild
New characterizations of minimum spanning trees and of saliency maps based on quasi-flat zones
Texture Synthesis Using Convolutional Neural Networks
Like Partying? Your Face Says It All. Predicting the Ambiance of Places with Profile Pictures
Visual Search at Pinterest
Query by String word spotting based on character bi-gram indexing
Salient Object Detection via Augmented Hypotheses
Distributed image reconstruction for very large arrays in radio astronomy
Learning Better Encoding for Approximate Nearest Neighbor Search with Dictionary Annealing
Emulating short-term synaptic dynamics with memristive devices
Double-Base Asymmetric AdaBoost
Towards Effective Codebookless Model for Image Classification
Unsupervised Decision Forest for Data Clustering and Density Estimation
Analysis of the South Slavic Scripts by Run-Length Features of the Image Texture
Classification of Complex Wishart Matrices with a Diffusion-Reaction System guided by Stochastic Distances
Human Gender Classification: A Review
The Cumulative Distribution Transform and Linear Pattern Classification
Deep Fishing: Gradient Features from Deep Nets
Human Pose Estimation with Iterative Error Feedback
Multimodal Deep Learning for Robust RGB-D Object Recognition
A Study of Morphological Filtering Using Graph and Hypergraphs
Real-time 2D/3D Registration via CNN Regression
A Hyperelastic Two-Scale Optimization Model for Shape Matching
SynapCountJ --- a Tool for Analyzing Synaptic Densities in Neurons
Deep Learning for Single-View Instance Recognition
Multilinear Map Layer: Prediction Regularization by Structural Constraint
Agglomerative clustering and collectiveness measure via exponent generating function
Second order elastic metrics on the shape space of curves
Data Association for an Adaptive Multi-target Particle Filter Tracking System
Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns
Calculating entropy at different scales among diverse communication systems
SentiCap: Generating Image Descriptions with Sentiments
Predicting Daily Activities From Egocentric Images Using Deep Learning
Learn to Evaluate Image Perceptual Quality Blindly from Statistics of Self-similarity
Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?
Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests
Seam Puckering Objective Evaluation Method for Sewing Process
Pan-Tilt Camera and PIR Sensor Fusion Based Moving Object Detection for Mobile Security Robots
Cells in the Internet of Things
Privacy Prediction of Images Shared on Social Media Sites Using Deep Features
RATM: Recurrent Attentive Tracking Model
High-Performance and Tunable Stereo Reconstruction
Understanding symmetries in deep networks
Symmetry-invariant optimization in deep networks
Generation and Comprehension of Unambiguous Object Descriptions
Explicit Knowledge-based Reasoning for Visual Question Answering
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?
Shearlet-Based Detection of Flame Fronts
Learning Human Identity from Motion Patterns
Multimodal Skip-gram Using Convolutional Pseudowords
Deep Gaussian Conditional Random Field Network: A Model-based Deep Network for Discriminative Denoising
Solving Jigsaw Puzzles with Linear Programming
Learning Neural Network Architectures using Backpropagation
Return of Frustratingly Easy Domain Adaptation
Identifying the Absorption Bump with Deep Learning
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
Dense Human Body Correspondences Using Convolutional Networks
Stochastic gradient method with accelerated stochastic dynamics
Semi-supervised Learning for Convolutional Neural Networks via Online Graph Construction
Robust Convolutional Neural Networks under Adversarial Noise
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks
Order-Embeddings of Images and Language
Delving Deeper into Convolutional Networks for Learning Video Representations
Fast Metric Learning For Deep Neural Networks
Learning to decompose for object detection and instance segmentation
Direct Prediction of 3D Body Poses from Motion Compensated Sequences
Semantic Diversity versus Visual Diversity in Visual Dictionaries
Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets
Gradual DropIn of Layers to Train Very Deep Neural Networks
SceneNet: Understanding Real World Indoor Scenes With Synthetic Data
CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android
An open access repository of images on plant health to enable the development of mobile disease diagnostics
A Short Survey on Data Clustering Algorithms
Hierarchical Invariant Feature Learning with Marginalization for Person Re-Identification
Automated Alertness and Emotion Detection for Empathic Feedback During E-Learning
Adapting Models to Signal Degradation using Distillation
Automatic Annotation of Structured Facts in Images
A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI
Multi-Bias Non-linear Activation in Deep Neural Networks
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Marr Revisited: 2D-3D Alignment via Surface Normal Prediction
Towards Bayesian Deep Learning: A Survey
Automatic Content-aware Non-Photorealistic Rendering of Images
Trajectory Aligned Features For First Person Action Recognition
STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling
Statistics of RGBD Images
Binarized Neural Networks on the ImageNet Classification Task
Hardware-oriented Approximation of Convolutional Neural Networks
Orientation-boosted Voxel Nets for 3D Object Recognition
Video Description using Bidirectional Recurrent Neural Networks
What do different evaluation metrics tell us about saliency models?
DENSER Cities: A System for Dense Efficient Reconstructions of Cities
A simple numeric algorithm for ancient coin dies identification
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
Tracking Human-like Natural Motion Using Deep Recurrent Neural Networks
CNN-RNN: A Unified Framework for Multi-label Image Classification
Automatic Segmentation of Dynamic Objects from an Image Pair
Improving Raw Image Storage Efficiency by Exploiting Similarity
Estimating 3D Trajectories from 2D Projections via Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Analysis of the Entropy-guided Switching Trimmed Mean Deviation-based Anisotropic Diffusion filter
A New Approach in Persian Handwritten Letters Recognition Using Error Correcting Output Coding
DASC: Robust Dense Descriptor for Multi-modal and Multi-spectral Correspondence Estimation
Crafting Adversarial Input Sequences for Recurrent Neural Networks
Artistic style transfer for videos
Mysteries of Visual Experience
Deep FisherNet for Object Classification
Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training
Modeling Context in Referring Expressions
Fast and robust pushbroom hyperspectral imaging via DMD-based scanning
Accelerating the Super-Resolution Convolutional Neural Network
Identification of repeats in DNA sequences using nucleotide distribution uniformity
Global Vertices and the Noising Paradox
Autonomous Grounding of Visual Field Experience through Sensorimotor Prediction
Language free character recognition using character sketch and center of gravity shifting
An efficient iterative thresholding method for image segmentation
Identifying Metastases in Sentinel Lymph Nodes with Deep Convolutional Neural Networks
Sparse Subspace Clustering via Diffusion Process
OnionNet: Sharing Features in Cascaded Deep Classifiers
Automatic text extraction and character segmentation using maximally stable extremal regions
Solving Visual Madlibs with Multiple Cues
Multi-View Product Image Search Using Deep ConvNets Representations
Speech Signal Analysis for the Estimation of Heart Rates Under Different Emotional States
Applying Deep Learning to Basketball Trajectories
DeepDiary: Automatic Caption Generation for Lifelogging Image Streams
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
A Riemannian Network for SPD Matrix Learning
Intrinsic Light Field Images
Anomaly detection and classification for streaming data using PDEs
SenTion: A framework for Sensing Facial Expressions
Medical image denoising using convolutional denoising autoencoders
Large Angle based Skeleton Extraction for 3D Animation
VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation
Local Binary Convolutional Neural Networks
A Delay-Tolerant Potential-Field-Based Network Implementation of an Integrated Navigation System
Failure Detection for Facial Landmark Detectors
A Non-Local Conventional Approach for Noise Removal in 3D MRI
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
A Novel Approach for Shot Boundary Detection in Videos
Kullback-Leibler Penalized Sparse Discriminant Analysis for Event-Related Potential Classification
An Octree-Based Approach towards Efficient Variational Range Data Fusion
A Fast Ellipse Detector Using Projective Invariant Pruning
Learning to generalize to new compositions in image understanding
Visual Question: Predicting If a Crowd Will Agree on the Answer
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
American Sign Language fingerspelling recognition from video: Methods for unrestricted recognition and signer-independence
Multi-Class Multi-Object Tracking using Changing Point Detection
Training Deep Spiking Neural Networks using Backpropagation
Towards Transparent AI Systems: Interpreting Visual Question Answering Models
An Automated Size Recognition Technique for Acetabular Implant in Total Hip Replacement
Retrieval and Clustering from a 3D Human Database based on Body and Head Shape
Benchmarks, Performance Evaluation and Contests for 3D Shape Retrieval
Preprocessing for Automating Early Detection of Cervical Cancer
Image Splicing Detection Using Inherent Lens Radial Distortion
Neural Networks for Emotion Classification
Incremental Top-k List Comparison Approach to Robust Multi-Structure Model Fitting
Learning image transformations without training examples
Robust artificial neural networks and outlier detection. Technical report
Eclectic Extraction of Propositional Rules from Neural Networks
Linearized Additive Classifiers
Rotation, Scaling and Translation Analysis of Biometric Signature Templates
A Comparative Experiment of Several Shape Methods in Recognizing Plants
The Generalized A* Architecture
Studying Satellite Image Quality Based on the Fusion Techniques
Hand Tracking based on Hierarchical Clustering of Range Data
On B-spline framelets derived from the unitary extension principle
Online Adaptive Statistical Compressed Sensing of Gaussian Mixture Models
Automated PolyU Palmprint sample Registration and Coarse Classification
Multiscale Hybrid Non-local Means Filtering Using Modified Similarity Measure
Hiding Image in Image by Five Modulus Method for Image Steganography
Automatic Fingerprint Recognition Using Minutiae Matching Technique for the Large Fingerprint Database
Multispectral Spatial Characterization: Application to Mitosis Detection in Breast Cancer Histopathology
Speckle Reduction in Polarimetric SAR Imagery with Stochastic Distances and Nonlocal Means
Tracking of Fingertips and Centres of Palm using KINECT
Robust Noise Filtering in Image Sequences
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution
Color image denoising by chromatic edges based vector valued diffusion
k-Modulus Method for Image Transformation
Fractal-Based Detection of Microcalcification Clusters in Digital Mammograms
Sparse arrays of signatures for online character recognition
Multimodal Approach for Video Surveillance Indexing and Retrieval
Learning Features and their Transformations by Spatial and Temporal Spherical Clustering
An interactive engine for multilingual video browsing using semantic content
Influences Combination of Multi-Sensor Images on Classification Accuracy
Categorizing ancient documents
A proposition of a robust system for historical document images indexation
A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data
A New Algorithm of Speckle Filtering using Stochastic Distances
Deeply Coupled Auto-encoder Networks for Cross-view Classification
Image Search Reranking
Zero-bias autoencoders and the benefits of co-adapting features
Application of the Ring Theory in the Segmentation of Digital Images
Anisotropic Mesh Adaptation for Image Representation
Exploiting Two-Dimensional Group Sparsity in 1-Bit Compressive Sensing
Structure Tensor Based Image Interpolation Method
Active spline model: A shape based model-interactive segmentation
$l_1$-regularized Outlier Isolation and Regression
Beyond $χ^2$ Difference: Learning Optimal Metric for Boundary Detection
Training Convolutional Networks with Noisy Labels
Deep Epitomic Convolutional Neural Networks
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network
Mass Classification Method in Mammogram Using Fuzzy K-Nearest Neighbour Equality
MRF-based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
Fast, Robust and Non-convex Subspace Recovery
Incorporating Near-Infrared Information into Semantic Image Segmentation
Face Identification with Second-Order Pooling
Adaptive texture energy measure method
Persistent Homology in Sparse Regression and Its Application to Brain Morphometry
Enforcing Label and Intensity Consistency for IR Target Detection
10,000+ Times Accelerated Robust Subset Selection (ARSS)
Deeply-Supervised Nets
Solving the Maximum-Weight Connected Subgraph Problem to Optimality
Domain Adaptive Neural Networks for Object Recognition
Spatially-sparse convolutional neural networks
On The Power of Joint Wavelet-DCT Features for Multispectral Palmprint Recognition
Combining human and machine learning for morphological analysis of galaxy images
Multiple Instance Reinforcement Learning for Efficient Weakly-Supervised Detection in Images
Simple Two-Dimensional Object Tracking based on a Graph Algorithm
Event Retrieval Using Motion Barcodes
Textural Approach for Mass Abnormality Segmentation in Mammographic Images
Parsing Occluded People by Flexible Compositions
Fisher Kernel for Deep Neural Activations
Bayesian Image Restoration for Poisson Corrupted Image using a Latent Variational Method with Gaussian MRF
Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model based on Generalized Gaussian Priors
Cancer Detection with Multiple Radiologists via Soft Multiple Instance Logistic Regression and $L_1$ Regularization
Brain Tumor Detection Based on Bilateral Symmetry Information
Descriptor Ensemble: An Unsupervised Approach to Descriptor Fusion in the Homography Space
Fixed Point Algorithm Based on Quasi-Newton Method for Convex Minimization Problem with Application to Image Deblurring
Automatic video scene segmentation based on spatial-temporal clues and rhythm
Translating Videos to Natural Language Using Deep Recurrent Neural Networks
Discovering beautiful attributes for aesthetic image analysis
Efficient GPU Implementation for Single Block Orthogonal Dictionary Learning
Towards Deep Neural Network Architectures Robust to Adversarial Examples
Locally Scale-Invariant Convolutional Neural Networks
Are We Ready for Driver-less Vehicles? Security vs. Privacy- A Social Perspective
Image enhancement using the mean dynamic range maximization with logarithmic operations
Compressing Deep Convolutional Networks using Vector Quantization
Learning to Segment Moving Objects in Videos
Discovering Hidden Factors of Variation in Deep Networks
Striving for Simplicity: The All Convolutional Net
Learning Activation Functions to Improve Deep Neural Networks
Contour Detection Using Cost-Sensitive Convolutional Neural Networks
Half-CNN: A General Framework for Whole-Image Regression
Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks
Training deep neural networks with low precision multiplications
Fully Convolutional Multi-Class Multiple Instance Learning
Learning Compact Convolutional Neural Networks with Nested Dropout
Convolutional Neural Networks for joint object detection and pose estimation: A comparative study
Enhancing fractal descriptors on images by combining boundary and interior of Minkowski dilation
Texture analysis by multi-resolution fractal descriptors
Disjunctive Normal Networks
Max-Margin Object Detection
Pose and Shape Estimation with Discriminatively Learned Parts
A Class of DCT Approximations Based on the Feig-Winograd Algorithm
Deep Boosting: Layered Feature Mining for General Image Classification
Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model
Towards a Practical Architecture for the Next Generation Internet of Things
Unsupervised Fusion Weight Learning in Multiple Classifier Systems
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Reflectance Hashing for Material Recognition
A Survey on Hough Transform, Theory, Techniques and Applications
A HMAX with LLC for visual recognition
Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
Image denoising based on improved data-driven sparse representation
Large-Scale Deep Learning on the YFCC100M Dataset
Phrase-based Image Captioning
Fusion of Image Segmentation Algorithms using Consensus Clustering
Application of Independent Component Analysis Techniques in Speckle Noise Reduction of Retinal OCT Images
Spike Event Based Learning in Neural Networks
Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval
Puzzle Imaging: Using Large-scale Dimensionality Reduction Algorithms for Localization
Image Segmentation in Liquid Argon Time Projection Chamber Detector
Generating Multi-Sentence Lingual Descriptions of Indoor Scenes
Macroblock Classification Method for Video Applications Involving Motions
Deep Transfer Network: Unsupervised Domain Adaptation
Grouping and Recognition of Dot Patterns with Straight Offset Polygons
Video-Based Facial Expression Recognition Using Local Directional Binary Pattern
Band selection in RKHS for fast nonlinear unmixing of hyperspectral images
Representation Learning with Deep Extreme Learning Machines for Efficient Image Set Classification
Global 6DOF Pose Estimation from Untextured 2D City Models
Deep Hierarchical Parsing for Semantic Segmentation
Deep Convolutional Inverse Graphics Network
Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction
Convolutional Neural Network Architectures for Matching Natural Language Sentences
Diverse Landmark Sampling from Determinantal Point Processes for Scalable Manifold Learning
Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation
Single image super-resolution by approximated Heaviside functions
Sparse Code Formation with Linear Inhibition
A Dictionary-based Approach for Estimating Shape and Spatially-Varying Reflectance
LiSens --- A Scalable Architecture for Video Compressive Sensing
Metric Localization using Google Street View
Statistical Analysis of Loopy Belief Propagation in Random Fields
Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network
Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder
A Comparative Analysis of Tensor Decomposition Models Using Hyper Spectral Image
Discriminative Bayesian Dictionary Learning for Classification
Fast Optimal Transport Averaging of Neuroimaging Data
Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images
Microsoft COCO Captions: Data Collection and Evaluation Server
Fast algorithms for morphological operations using run-length encoded binary images
Robust real time face recognition and tracking on gpu using fusion of rgb and depth image
A Multiphase Image Segmentation Based on Fuzzy Membership Functions and L1-norm Fidelity
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
Efficient Scene Text Localization and Recognition with Local Character Refinement
Text Localization in Video Using Multiscale Weber's Local Descriptor
Application of Enhanced-2D-CWT in Topographic Images for Mapping Landslide Risk Areas
Local Variation as a Statistical Hypothesis Test
Deviation Based Pooling Strategies For Full Reference Image Quality Assessment
Fast Dictionary Matching for Content-based Image Retrieval
Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection
Becoming the Expert - Interactive Multi-Class Machine Teaching
Probabilistic Depth Image Registration incorporating Nonvisual Information
Hierarchical Subquery Evaluation for Active Learning on a Graph
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
An Open Source Testing Tool for Evaluating Handwriting Input Methods
Visual Madlibs: Fill in the blank Image Generation and Question Answering
Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions
A Riemannian low-rank method for optimization over semidefinite matrices with block-diagonal constraints
Visualizing and Understanding Neural Models in NLP
Recognition of Changes in SAR Images Based on Gauss-Log Ratio and MRFFCM
The Long-Short Story of Movie Description
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
Circulant temporal encoding for video retrieval and temporal alignment
Inverting Visual Representations with Convolutional Networks
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation
Towards Benchmarking Scene Background Initialization
ParseNet: Looking Wider to See Better
Using Hankel Matrices for Dynamics-based Facial Emotion Recognition and Pain Detection
Deep Convolutional Networks on Graph-Structured Data
CFORB: Circular FREAK-ORB Visual Odometry
Learning with a Wasserstein Loss
Point-wise Map Recovery and Refinement from Functional Correspondence
An Open Science Platform for the Next Generation of Data
Crowd Flow Segmentation in Compressed Domain using CRF
moco: Fast Motion Correction for Calcium Imaging
CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits
Adaptive Digital Scan Variable Pixels
Incremental RANSAC for Online Relocation in Large Dynamic Environments
Natural Scene Recognition Based on Superpixels and Deep Boltzmann Machines
Unshredding of Shredded Documents: Computational Framework and Implementation
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation
DeepMatching: Hierarchical Deformable Dense Matching
Nonnegative Matrix Factorization applied to reordered pixels of single images based on patches to achieve structured nonnegative dictionaries
Deep-Plant: Plant Identification with convolutional neural networks
Variational Inference for Background Subtraction in Infrared Imagery
Recognition of Emotions using Kinects
Semantic Pose using Deep Networks Trained on Synthetic RGB-D
Automatic 3D Liver Segmentation Using Sparse Representation of Global and Local Image Information via Level Set Formulation
Borobudur was Built Algorithmically
Mobile-Based Experience Sampling for Behaviour Research
Towards universal neural nets: Gibbs machines and ACE
Chebyshev and Conjugate Gradient Filters for Graph Image Denoising
Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation
An Approach to the Analysis of the South Slavic Medieval Labels Using Image Texture
A New Low-Rank Tensor Model for Video Completion
A Dual Fast and Slow Feature Interaction in Biologically Inspired Visual Recognition of Human Action
Analyzing structural characteristics of object category representations from their semantic-part distributions
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data
Linearized Kernel Dictionary Learning
Modelling Uncertainty in Deep Learning for Camera Relocalization
Telugu OCR Framework using Deep Learning
Deep Convolutional Features for Image Based Retrieval and Scene Categorization
New Fuzzy LBP Features for Face Recognition
Hyper-Fisher Vectors for Action Recognition
Compression of Deep Neural Networks on the Fly
Online Object Tracking with Proposal Selection
Towards Dropout Training for Convolutional Neural Networks
On Optical Flow Models for Variational Motion Estimation
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Predicting and visualizing psychological attributions with a deep neural network
Max-Pooling Dropout for Regularization of Convolutional Neural Networks
Fixation prediction with a combined model of bottom-up saliency and vanishing point
Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method
In-situ multi-scattering tomography
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views
Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding
MovieQA: Understanding Stories in Movies through Question-Answering
Neural Self Talk: Image Understanding via Continuous Questioning and Answering
Deep Relative Attributes
On non-iterative training of a neural classifier
Context Driven Label Fusion for segmentation of Subcutaneous and Visceral Fat in CT Volumes
Multiple penalized principal curves: analysis and computation
Local and global gestalt laws: A neurally based spectral approach
Transformed Residual Quantization for Approximate Nearest Neighbor Search
Deep Learning with S-shaped Rectified Linear Activation Units
Adaptive Object Detection Using Adjacency and Zoom Prediction
A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI
Part-Stacked CNN for Fine-Grained Visual Categorization
Graph entropies in texture segmentation of images
Robust Scene Text Recognition Using Sparse Coding based Features
GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation
Susceptibility of texture measures to noise: an application to lung tumor CT images
Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition
Multi-Atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT
Brain-Inspired Deep Networks for Image Aesthetics Assessment
Comparison-based Image Quality Assessment for Parameter Selection
The Image Torque Operator for Contour Processing
Adaptive Image Denoising by Mixture Adaptation
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application
PupilNet: Convolutional Neural Networks for Robust Pupil Detection
PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors
Learning Support Correlation Filters for Visual Tracking
Topological descriptors for 3D surface analysis
Person Re-Identification by Discriminative Selection in Video Ranking
An Unsupervised Method for Detection and Validation of The Optic Disc and The Fovea
Relief R-CNN : Utilizing Convolutional Features for Fast Object Detection
Pixel Recurrent Neural Networks
Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers
PersonNet: Person Re-identification with Deep Convolutional Neural Networks
Osteoporotic and Neoplastic Compression Fracture Classification on Longitudinal CT
Face Alignment by Local Deep Descriptor Regression
Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)
A Deep Learning Based Fast Image Saliency Detection Algorithm
Improving Vertebra Segmentation through Joint Vertebra-Rib Atlases
Learning Discriminative Features via Label Consistent Neural Network
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
On Feature based Delaunay Triangulation for Palmprint Recognition
Homogeneity of Cluster Ensembles
Exploiting Cyclic Symmetry in Convolutional Neural Networks
Challenges of Integrating A Priori Information Efficiently in the Discovery of Spatio-Temporal Objects in Large Databases
Image encryption with dynamic chaotic Look-Up Table
A Versatile Scene Model with Differentiable Visibility Applied to Generative Pose Estimation
Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model
Character Proposal Network for Robust Text Extraction
Embracing Error to Enable Rapid Crowdsourcing
Generating images with recurrent adversarial networks
A landmark-based algorithm for automatic pattern recognition and abnormality detection
Creating Simplified 3D Models with High Quality Textures
Correlation Hashing Network for Efficient Cross-Modal Retrieval
Implicit LOD using points ordering for processing and visualisation in Point Cloud Servers
A statistical shape space model of the palate surface trained on 3D MRI scans of the vocal tract
A Single Model Explains both Visual and Auditory Precortical Coding
FALDOI: A new minimization strategy for large displacement variational optical flow
Scalable Metric Learning via Weighted Approximate Rank Component Analysis
Technical Report: Band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem
Network Morphism
Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data
Deep Contrast Learning for Salient Object Detection
Learning a Discriminative Null Space for Person Re-identification
A non-extensive entropy feature and its application to texture classification
DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data
Summary Transfer: Exemplar-based Subset Selection for Video Summarization
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Learning Typographic Style
A comprehensive study of sparse codes on abnormality detection
A Novel Method for Extrinsic Calibration of a 2-D Laser-Rangefinder and a Camera
Graph Based Sinogram Denoising for Tomographic Reconstructions
Fourier ptychographic reconstruction using Poisson maximum likelihood and truncated Wirtinger gradient
A Neural Approach to Blind Motion Deblurring
Variable-Length Hashing
Tracking multiple moving objects in images using Markov Chain Monte Carlo
Generative Image Modeling using Style and Structure Adversarial Networks
Deep Shading: Convolutional Neural Networks for Screen-Space Shading
Controlling Explanatory Heatmap Resolution and Semantics via Decomposition Depth
Convolution in Convolution for Network in Network
Multi-Subregion Based Correlation Filter Bank for Robust Face Recognition
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces
Fast and Provably Accurate Bilateral Filtering
Human Pose Estimation using Deep Consensus Voting
Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention
A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report
On distances, paths and connections for hyperspectral image segmentation
Instance-sensitive Fully Convolutional Networks
Palmprint Recognition Using Deep Scattering Convolutional Network
Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
Deep Networks with Stochastic Depth
Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network
Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
Fourier Analysis and q-Gaussian Functions: Analytical and Numerical Results
Improving Image Captioning by Concept-based Sentence Reranking
Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints
The embedding dimension of Laplacian eigenfunction maps
Matrix Factorization-Based Clustering Of Image Features For Bandwidth-Constrained Information Retrieval
Robust and Low-Rank Representation for Fast Face Identification with Occlusions
Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching
When Do Luxury Cars Hit the Road? Findings by A Big Data Approach
Recurrent Human Pose Estimation
Eco-Strategy: Towards a New Generation Managerial Model Based on Green IT and CSR
Blind image separation based on exponentiated transmuted Weibull distribution
Ternary Weight Networks
Multilevel Thresholding Segmentation of T2 weighted Brain MRI images using Convergent Heterogeneous Particle Swarm Optimization
Going Deeper into Action Recognition: A Survey
Structured Prediction of 3D Human Pose with Deep Neural Networks
Generative Adversarial Text to Image Synthesis
Matching Handwritten Document Images
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods
Poisson multi-Bernoulli conjugate prior for multiple extended object estimation
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
Swapout: Learning an ensemble of deep architectures
Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources
Wide Residual Networks
Dense CNN Learning with Equivalent Mappings
Measuring Neural Net Robustness with Constraints
Review Networks for Caption Generation
DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies
Discrete Deep Feature Extraction: A Theory and New Architectures
Benign-Malignant Lung Nodule Classification with Geometric and Appearance Histogram Features
Achieving stable subspace clustering by post-processing generic clustering results
Stacking With Auxiliary Features
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings
k2-means for fast and accurate large scale clustering
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks
Hierarchical Question-Image Co-Attention for Visual Question Answering
Recursive Autoconvolution for Unsupervised Learning of Convolutional Neural Networks
A Crowd Monitoring Framework using Emotion Analysis of Social Media for Emergency Management in Mass Gatherings
Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet
Scene Grammars, Factor Graphs, and Belief Propagation
An Interactive Medical Image Segmentation Framework Using Iterative Refinement
Better Image Segmentation by Exploiting Dense Semantic Predictions
Unsupervised classification of children's bodies using currents
Deep neural networks are robust to weight binarization and other non-linear distortions
Systematic evaluation of CNN advances on the ImageNet
Point-wise mutual information-based video segmentation with high temporal consistency
Convolutional Neural Fabrics
The Mythos of Model Interpretability
Improved Techniques for Training GANs
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
Inverting face embeddings with convolutional neural networks
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection
Conditional Image Generation with PixelCNN Decoders
Deep Learning for Identifying Metastatic Breast Cancer
RRV: A Spatiotemporal Descriptor for Rigid Body Motion Recognition
Slack and Margin Rescaling as Convex Extensions of Supermodular Functions
Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation
Social-sparsity brain decoders: faster spatial sparsity
Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions
Tagger: Deep Unsupervised Perceptual Grouping
3D Display Calibration by Visual Pattern Analysis
Identifying individual facial expressions by deconstructing a neural network
Dynamical optical flow of saliency maps for predicting visual attention
DropNeuron: Simplifying the Structure of Deep Neural Networks
Analyzing the Behavior of Visual Question Answering Models
Fast Multi-Layer Laplacian Enhancement
Sort Story: Sorting Jumbled Images and Captions into Stories
Convex Decomposition And Efficient Shape Representation Using Deformable Convex Polytopes
Disjunctive Normal Level Set: An Efficient Parametric Implicit Method
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Learning Concept Taxonomies from Multi-modal Data
How smart does your profile image look? Estimating intelligence from social network profile images
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Cell assemblies at multiple time scales with arbitrary lag constellations
CUNet: A Compact Unsupervised Network for Image Classification
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification
CNN-LTE: a Class of 1-X Pooling Convolutional Neural Networks on Label Tree Embeddings for Audio Scene Recognition
Fast Predictive Image Registration
Multimodal Affect Recognition using Kinect
Intra-layer Nonuniform Quantization for Deep Convolutional Neural Network
City-Identification of Flickr Videos Using Semantic Acoustic Features
DNA Image Pro -- A Tool for Generating Pixel Patterns using DNA Tile Assembly
Accelerating Eulerian Fluid Simulation With Convolutional Networks
Spatio-Temporal Saliency Networks for Dynamic Saliency Prediction
Weakly supervised object detection using pseudo-strong labels
Distributed Coding of Multiview Sparse Sources with Joint Recovery
Deep Active Contours
Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
4D Cardiac Ultrasound Standard Plane Location by Spatial-Temporal Correlation
Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation from Single and Multiple Images
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Large-Scale Video Search with Efficient Temporal Voting Structure
Tracking with multi-level features
Mesh Denoising based on Normal Voting Tensor and Binary Optimization
Generic Feature Learning for Wireless Capsule Endoscopy Analysis
Fundamental Matrices from Moving Objects Using Line Motion Barcodes
Low-complexity feedback-channel-free distributed video coding using Local Rank Transform
CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss
Incremental Noising and its Fractal Behavior
A Deep Primal-Dual Network for Guided Depth Super-Resolution
A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps
SPICE: Semantic Propositional Image Caption Evaluation
Attentional Push: Augmenting Salience with Shared Attention Modeling
Semantic Image Based Geolocation Given a Map
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes
CryptoImg: Privacy Preserving Processing Over Encrypted Images
Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding
An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution
Automation of Pedestrian Tracking in a Crowded Situation
Tracking System to Automate Data Collection of Microscopic Pedestrian Traffic Flow
Delaunay Triangulation on Skeleton of Flowers for Classification
Animal Classification System: A Block Based Approach
Guided Filter based Edge-preserving Image Non-blind Deconvolution
Polysemous codes
Clearing the Skies: A deep network architecture for single-image rain removal
Tracking Algorithm for Microscopic Flow Data Collection
Comparison of several short-term traffic speed forecasting models
Image Denoising Via Collaborative Support-Agnostic Recovery
Probabilistic Saliency Estimation
Crafting a multi-task CNN for viewpoint estimation
Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation
A Deep Metric for Multimodal Registration
Color: A Crucial Factor for Aesthetic Quality Assessment in a Subjective Dataset of Paintings
Graph-Structured Representations for Visual Question Answering
GAdaBoost: Accelerating Adaboost Feature Selection with Genetic Algorithms
Markov Random Field Model-Based Salt and Pepper Noise Removal
Geometry-Based Next Frame Prediction from Monocular Video
Revealing Structure in Large Graphs: Szemerédi's Regularity Lemma and its Use in Pattern Recognition
Land Use Classification using Convolutional Neural Networks Applied to Ground-Level Images
Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution
Symmetric Non-Rigid Structure from Motion for Category-Specific Object Structure Estimation
Image-embodied Knowledge Representation Learning
Large Margin Nearest Neighbor Classification using Curved Mahalanobis Distances
Deep Learning in Multi-Layer Architectures of Dense Nuclei
Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation
Understanding and Exploiting Object Interaction Landscapes
Deep Architectures for Face Attributes
Recurrent Convolutional Networks for Pulmonary Nodule Detection in CT Imaging
MinMax Radon Barcodes for Medical Image Retrieval
Stacked Autoencoders for Medical Image Search
Real Time Fine-Grained Categorization with Accuracy and Interpretability
Adaptive Graph-based Total Variation for Tomographic Reconstructions
Mobility Map Computations for Autonomous Navigation using an RGBD Sensor
DeepGaze II: Reading fixations from deep features trained on object recognition
Compressive Imaging with Iterative Forward Models
Distributed Averaging CNN-ELM for Big Data
Learning What and Where to Draw
Content-Based Image Retrieval Using Multiresolution Analysis Of Shape-Based Classified Images
Open-Ended Visual Question-Answering
Image Segmentation Based on the Self-Balancing Mechanism in Virtual 3D Elastic Mesh
Matching of Images with Rotation Transformation Based on the Virtual Electromagnetic Interaction
Crossing the Road Without Traffic Lights: An Android-based Safety Device
The Analysis of Local Motion and Deformation in Image Sequences Inspired by Physical Electromagnetic Interaction
A Model of Virtual Carrier Immigration in Digital Images for Region Segmentation
The Virtual Electromagnetic Interaction between Digital Images for Image Matching with Shifting Transformation
Embedded real-time stereo estimation via Semi-Global Matching on the GPU
Hadamard Product for Low-rank Bilinear Pooling
RGBD-based Parameter Extraction for Door Opening Tasks with Human Assists in Nuclear Rescue
Rule Extraction Algorithm for Deep Neural Networks: A Review
Shape-based defect classification for Non Destructive Testing
Fast L1-NMF for Multiple Parametric Model Estimation
Change-point Detection Methods for Body-Worn Video
An Image Dataset of Text Patches in Everyday Scenes
Proposing Plausible Answers for Open-ended Visual Question Answering
Scalable Pooled Time Series of Big Video Data from the Deep Web
Efficient Global Indoor Localization for Micro Aerial Vehicles
A Novel Boundary Matching Algorithm for Video Temporal Error Concealment
Spatial Relationship Based Features for Indian Sign Language Recognition
Savu: A Python-based, MPI Framework for Simultaneous Processing of Multiple, N-dimensional, Large Tomography Datasets
Automated Management of Pothole related Disasters Using Image Processing and Geotagging
Judging a Book By its Cover
Discovering containment: from infants to machines
Flood-Filling Networks
Deep Convolutional Neural Network Design Patterns
Integrating Atlas and Graph Cut Methods for LV Segmentation from Cardiac Cine MRI
Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting
Real-Time Visual Place Recognition for Personal Localization on a Mobile Device
Memory-augmented Attention Modelling for Videos
Domain Adaptation with L2 constraints for classifying images from different endoscope systems
Gradients of Counterfactuals
Audio Visual Speech Recognition using Deep Recurrent Neural Networks
Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput
Show me the material evidence: Initial experiments on evaluating hypotheses from user-generated multimedia data
OctNet: Learning Deep 3D Representations at High Resolutions
Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations
Zero-Shot Visual Question Answering
Learning to detect and localize many objects from few examples
A Discriminatively Learned CNN Embedding for Person Re-identification
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification
LCNN: Lookup-based Convolutional Neural Network
A Hierarchical Approach for Generating Descriptive Image Paragraphs
Covariate conscious approach for Gait recognition based upon Zernike moment invariants
Non-Local Color Image Denoising with Convolutional Neural Networks
Statistical Learning for OCR Text Correction
Multiple-View Spectral Clustering for Group-wise Functional Community Detection
Learning Multi-level Features For Sensor-based Human Action Recognition
Distributable Consistent Multi-Object Matching
3D Menagerie: Modeling the 3D shape and pose of animals
Deep Feature Flow for Video Recognition
Multiframe Motion Coupling for Video Super Resolution
Adaptive Feature Abstraction for Translating Video to Text
Semantic Compositional Networks for Visual Captioning
InstanceCut: from Edges to Instances with MultiCut
Color Constancy with Derivative Colors
Handwriting Profiling using Generative Adversarial Networks
Kernel classification of connectomes based on earth mover's distance between graph spectra
What Is Around The Camera?
Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce
Lens Distortion Rectification using Triangulation based Interpolation
Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model
Fast Supervised Discrete Hashing and its Analysis
Adversarial Images for Variational Autoencoders
Temporal Attention-Gated Model for Robust Sequence Classification
Understanding image motion with group representations
Scribbler: Controlling Deep Image Synthesis with Sketch and Color
Semi-supervised learning of deep metrics for stereo reconstruction
Ensembles of Generative Adversarial Networks
Multi-way Particle Swarm Fusion
Message Passing Multi-Agent GANs
Highly Efficient Regression for Scalable Person Re-Identification
AI Researchers, Video Games Are Your Friends!
Cluster-Wise Ratio Tests for Fast Camera Localization
Mode Regularized Generative Adversarial Networks
Exploring the potential of combining time of flight and thermal infrared cameras for person detection
Generalized Sinkhorn iterations for regularizing inverse problems using optimal mass transport
Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Progressive Tree-like Curvilinear Structure Reconstruction with Structured Ranking Learning and Graph Algorithm
Joint Hand Detection and Rotation Estimation by Using CNN
Facial Expression Recognition using Convolutional Neural Networks: State of the Art
Feature Pyramid Networks for Object Detection
Generalized Deep Image to Image Regression
Neural Networks with Manifold Learning for Diabetic Retinopathy Detection
Recurrent Image Captioner: Describing Images with Spatial-Invariant Transformation and Attention Filtering
A Multilinear Tongue Model Derived from Speech Related MRI Data of the Human Vocal Tract
Visual Compiler: Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator
Machine Reading with Background Knowledge
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
Handwritten Signature Verification Using Hand-Worn Devices
Learning Features by Watching Objects Move
Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale
Automatic Generation of Grounded Visual Questions
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
Two-stream convolutional neural network for accurate RGB-D fingertip detection using depth and edge information
Image-Text Multi-Modal Representation Learning by Adversarial Backpropagation
Semantic Video Segmentation by Gated Recurrent Flow Propagation
Memory Efficient Multi-Scale Line Detector Architecture for Retinal Blood Vessel Segmentation
A Joint Speaker-Listener-Reinforcer Model for Referring Expressions
Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning
Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space
A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus images
A Concave Optimization Algorithm for Matching Partially Overlapping Point Sets
Demystifying Neural Style Transfer
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
Autoencoder Regularized Network For Driving Style Representation Learning
Deep Convolutional Denoising of Low-Light Images
Deep Class Aware Denoising
Unsupervised Learning of Long-Term Motion Dynamics for Videos
Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition
Efficient Image Set Classification using Linear Regression based Image Reconstruction
Light Source Point Cluster Selection Based Atmosphere Light Estimation
On Hölder projective divergences
Light Source Estimation with Analytical Path-tracing
A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe
Accurate Motion Estimation through Random Sample Aggregated Consensus
End-To-End Visual Speech Recognition With LSTMs
Image Compression with SVD : A New Quality Metric Based On Energy Ratio
Distributed methods for synchronization of orthogonal matrices over graphs
Deep Reinforcement Learning: An Overview
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
Re-ranking Person Re-identification with k-reciprocal Encoding
Transformation-Based Models of Video Sequences
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
Feature Selection based on PCA and PSO for Multimodal Medical Image Fusion using DTCWT
Deep Multitask Architecture for Integrated 2D and 3D Human Sensing
Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval
Visual Saliency Prediction Using a Mixture of Deep Neural Networks
Product Graph-based Higher Order Contextual Similarities for Inexact Subgraph Matching
Learning a time-dependent master saliency map from eye-tracking data in videos
Maritime situational awareness using adaptive multi-sensor management under hazy conditions
Deep Learning with Low Precision by Half-wave Gaussian Quantization
Printed Arabic Text Recognition using Linear and Nonlinear Regression
Multi-scale Convolutional Neural Networks for Crowd Counting
Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation
Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning
Texture Characterization by Using Shape Co-occurrence Patterns
A Morphology-aware Network for Morphological Disambiguation
Integrating Three Mechanisms of Visual Attention for Active Visual Search
Filling missing data in point clouds by merging structured and unstructured point clouds
Spectral Algorithms for Temporal Graph Cuts
3D Cell Nuclei Segmentation with Balanced Graph Partitioning
Defect detection for patterned fabric images based on GHOG and low-rank decomposition
Online Robust Principal Component Analysis with Change Point Detection
Memory Efficient Max Flow for Multi-label Submodular MRFs
Synthesis versus analysis in patch-based image priors
The Power of Sparsity in Convolutional Neural Networks
Differential Geometric Retrieval of Deep Features
Fast Resampling of 3D Point Clouds via Graphs
Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification
Lensless Photography with only an image sensor
Robust and fully automated segmentation of mandible from CT scans
Viewpoint Adaptation for Rigid Object Detection
Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment
Enabling Sparse Winograd Convolution by Native Pruning
Selective Video Object Cutout
MILD: Multi-Index hashing for Loop closure Detection
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank
Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
Adversarial Examples for Semantic Image Segmentation
Instance Flow Based Online Multiple Object Tracking
Learning across scales - A multiscale method for Convolution Neural Networks
Deep View Morphing
Removal of Salt and Pepper noise from Gray-Scale and Color Images: An Adaptive Approach
Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening
Faster Coordinate Descent via Adaptive Importance Sampling
Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations
LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network
The xDotGrid Native, Cross-Platform, High-Performance xDFS File Transfer Framework
Parallel Multiscale Autoregressive Density Estimation
End-to-End Learning of Geometry and Context for Deep Stereo Regression
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets
Guetzli: Perceptually Guided JPEG Encoder
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets
A Proximity-Aware Hierarchical Clustering of Faces
Real-Time Panoramic Tracking for Event Cameras
Learning Robust Visual-Semantic Embeddings
TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals
RoomNet: End-to-End Room Layout Estimation
Multilevel Context Representation for Improving Object Recognition
A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes
VQABQ: Visual Question Answering by Basic Questions
Object category understanding via eye fixations on freehand sketches
SORT: Second-Order Response Transform for Visual Recognition
Simple Online and Realtime Tracking with a Deep Association Metric
Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes
Robust SfM with Little Image Overlap
Recurrent and Contextual Models for Visual Question Answering
DeepVisage: Making face recognition simple yet with powerful generalization skills
Exploiting Color Name Space for Salient Object Detection
A Study on the Extraction and Analysis of a Large Set of Eye Movement Features during Reading
Discriminative Transfer Learning for General Image Restoration
Femoral ROIs and Entropy for Texture-based Detection of Osteoarthritis from High-Resolution Knee Radiographs
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective
Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination
Deep 6-DOF Tracking
Towards thinner convolutional neural networks through Gradually Global Pruning
Application of a Shallow Neural Network to Short-Term Stock Trading
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images
Concurrent Segmentation and Localization for Tracking of Surgical Instruments
Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation
Fast Predictive Multimodal Image Registration
Geodesic Distance Histogram Feature for Video Segmentation
Customizing First Person Image Through Desired Actions
Dense Multi-view 3D-reconstruction Without Dense Correspondences
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
sWSI: A Low-cost and Commercial-quality Whole Slide Imaging System on Android and iOS Smartphones
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Relative Learning from Web Images for Content-adaptive Enhancement
Supporting Navigation of Outdoor Shopping Complexes for Visually-impaired Users through Multi-modal Data Fusion
Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascade
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption
A New Pseudo-color Technique Based on Intensity Information Protection for Passive Sensor Imagery
Motion Saliency Based Automatic Delineation of Glottis Contour in High-speed Digital Images
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
Feature Selection Parallel Technique for Remotely Sensed Imagery Classification
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Zero-order Reverse Filtering
FastVentricle: Cardiac Segmentation with ENet
CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data
ShapeWorld - A new test methodology for multimodal language understanding
Deep Learning for Photoacoustic Tomography from Sparse Data
A learning-based approach for automatic image and video colorization
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
Temporal Action Localization by Structured Maximal Sums
Universal Adversarial Perturbations Against Semantic Image Segmentation
SkiMap: An Efficient Mapping Framework for Robot Navigation
A Nuclear-norm Model for Multi-Frame Super-Resolution Reconstruction from Video Clips
Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
Hierarchical Bayesian Data Fusion for Robotic Platform Navigation
Convolutional Neural Networks for Facial Expression Recognition
Multi-Task Video Captioning with Video and Entailment Generation
The loss surface of deep and wide neural networks
New region force for variational models in image segmentation and high dimensional data clustering
C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset
Deep Cross-Modal Audio-Visual Generation
(Quasi)Periodicity Quantification in Video Data, Using Topology
Saliency Benchmarking: Separating Models, Maps and Metrics
Sparse Hierachical Extrapolated Parametric Methods for Cortical Data Analysis
Obstacle Avoidance through Deep Networks based Intermediate Perception
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences
Topologically Robust 3D Shape Matching via Gradual Deflation and Inflation
SurfCut: Surfaces of Minimal Paths From Topological Structures
A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images
Query-adaptive Video Summarization via Quality-aware Relevance Estimation
The Promise of Premise: Harnessing Question Premises in Visual Question Answering
STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset
Visual Attribute Transfer through Deep Image Analogy
The Forgettable-Watcher Model for Video Question Answering
Optical Flow in Mostly Rigid Scenes
A Rural Lens on a Research Agenda for Intelligent Infrastructure
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks
High-Level Concepts for Affective Understanding of Images
Machine Learning with World Knowledge: The Position and Survey
Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks
Multi-Scale Spatially Weighted Local Histograms in O(1)
Inferring and Executing Programs for Visual Reasoning
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising
SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering
Learning to see people like people
Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine
Motion-Compensated Autonomous Scanning for Tumour Localisation using Intraoperative Ultrasound
Robust Registration of Gaussian Mixtures for Colour Transfer
Elastic and Secure Energy Forecasting in Cloud Environments
Building effective deep neural network architectures one feature at a time
A New 3D Method to Segment the Lumbar Vertebral Bodies and to Determine Bone Mineral Density and Geometry
Phase-Shifting Separable Haar Wavelets and Applications
Large-Scale Classification of Structured Objects using a CRF with Deep Class Embedding
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Dynamics Based 3D Skeletal Hand Tracking
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks
Isomorphism between Differential and Moment Invariants under Affine Transform
Accelerating Discrete Wavelet Transforms on GPUs
Matching neural paths: transfer from recognition to correspondence search
A New 3D Segmentation Technique for QCT Scans of the Lumbar Spine to Determine BMD and Vertebral Geometry
An Invariant Model of the Significance of Different Body Parts in Recognizing Different Actions
Classification of Aerial Photogrammetric 3D Point Clouds
Better Text Understanding Through Image-To-Text Transfer
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition
Dense Transformer Networks
SLAM based Quasi Dense Reconstruction For Minimally Invasive Surgery Scenes
Analysis of universal adversarial perturbations
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations
BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet
L1-norm Error Function Robustness and Outlier Regularization
Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors
Emergent Communication in a Multi-Modal, Multi-Step Referential Game
Deep Learning is Robust to Massive Label Noise
Working hard to know your neighbor's margins: Local descriptor learning loss
Teaching Machines to Describe Images via Natural Language Feedback
Shape and Positional Geometry of Multi-Object Configurations
An Effective Approach for Point Clouds Registration Based on the Hard and Soft Assignments
DiracNets: Training Very Deep Neural Networks Without Skip-Connections
Modeling Latent Attention Within Neural Networks
Early Experiences with Crowdsourcing Airway Annotations in Chest CT
DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data
Training Quantized Nets: A Deeper Understanding
Evaluating (and improving) the correspondence between deep neural networks and human representations
C-arm Tomographic Imaging Technique for Nephrolithiasis and Detection of Kidney Stones
Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs
Bicycle Detection Based On Multi-feature and Multi-frame Fusion in low-resolution traffic videos
Channel-Recurrent Autoencoding for Image Modeling
Recurrent Inference Machines for Solving Inverse Problems
Automatic Localization of Deep Stimulation Electrodes Using Trajectory-based Segmentation Approach
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI
A Fully Trainable Network with RNN-based Pooling
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
Rethinking Atrous Convolution for Semantic Image Segmentation
Deep learning with spatiotemporal consistency for nerve segmentation in ultrasound images
Optimising the topological information of the $A_\infty$-persistence groups
Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Network
Recognition of Grasp Points for Clothes Manipulation under unconstrained Conditions
Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition
Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans
Tracking Single-Cells in Overcrowded Bacterial Colonies
Multiresolution Match Kernels for Gesture Video Classification
Computer-aided implant design for the restoration of cranial defects
Large-Scale Mapping of Human Activity using Geo-Tagged Videos
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks
Scalable multimodal convolutional networks for brain tumour segmentation
Deep Semantics-Aware Photo Adjustment
Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog
Using Frame Theoretic Convolutional Gridding for Robust Synthetic Aperture Sonar Imaging
Hierarchical Attentive Recurrent Tracking
Alternative Semantic Representations for Zero-Shot Human Action Recognition
Actor-Critic Sequence Training for Image Captioning
Scale-Aware Face Detection
Automatic Face Image Quality Prediction
Color-opponent mechanisms for local hue encoding in a hierarchical framework
A Batch-Incremental Video Background Estimation Model using Weighted Low-Rank Approximation of Matrices
Automatic Trimap Generation for Image Matting
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
Deep-learning-based data page classification for holographic memory
Deep Representation Learning with Part Loss for Person Re-Identification
Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning
Copy-move Forgery Detection based on Convolutional Kernel Network
Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object Rotation
Towards lightweight convolutional neural networks for object detection
Automated Lane Detection in Crowds using Proximity Graphs
Cross-linguistic differences and similarities in image descriptions
Fast Stochastic Hierarchical Bayesian MAP for Tomographic Imaging
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
Towards Crafting Text Adversarial Samples
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search
Learning the Latent "Look": Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images
Deep Learning for Sensor-based Activity Recognition: A Survey
Two-dimensional nonseparable discrete linear canonical transform based on CM-CC-CM-CC decomposition
Data preprocessing methods for robust Fourier ptychographic microscopy
Learning Photography Aesthetics with Deep CNNs
Recognizing Abnormal Heart Sounds Using Deep Learning
Pathological OCT Retinal Layer Segmentation using Branch Residual U-shape Networks
Visual Question Answering with Memory-Augmented Networks
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
Fast Feature Fool: A data independent approach to universal adversarial perturbations
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
Hashed Binary Search Sampling for Convolutional Network Training with Large Overhead Image Patches
AirCode: Unobtrusive Physical Tags for Digital Fabrication
On Finding Maximum Cardinality Subset of Vectors with a Constraint on Normalized Squared Length of Vectors Sum
Video Question Answering via Attribute-Augmented Attention Network Learning
Scalable Full Flow with Learned Binary Descriptors
Recovering Sparse Nonnegative Signals via Non-convex Fraction Function Penalty
Local Geometry Inclusive Global Shape Representation
What Looks Good with my Sofa: Multimodal Search Engine for Interior Design
Persistent-homology-based gait recognition
OBJ2TEXT: Generating Visually Descriptive Language from Object Layouts
Towards Good Practices for Deep 3D Hand Pose Estimation
Robust Tracking and Behavioral Modeling of Movements of Biological Collectives from Ordinary Video Recordings
A new take on measuring relative nutritional density: The feasibility of using a deep neural network to assess commercially-prepared pureed food concentrations
Traffic scene recognition based on deep cnn and vlad spatial pyramids
Image Pivoting for Learning Multilingual Multimodal Representations
Liver lesion segmentation informed by joint liver segmentation
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
A Unified Joint Matrix Factorization Framework for Data Integration
Efficient Low Rank Tensor Ring Completion
TensorLayer: A Versatile Library for Efficient Deep Learning Development
Interpatient Respiratory Motion Model Transfer for Virtual Reality Simulations of Liver Punctures
Deep Residual Learning for Weakly-Supervised Relation Extraction
A Locally Adapting Technique for Boundary Detection using Image Segmentation
Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation
Zero-Shot Activity Recognition with Verb Attribute Induction
ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images
Guided Co-training for Large-Scale Multi-View Spectral Clustering
Representation Learning on Large and Small Data
Learned in Translation: Contextualized Word Vectors
Fast Preprocessing for Robust Face Sketch Synthesis
On the Importance of Consistency in Training Deep Neural Networks
Generation of High Dynamic Range Illumination from a Single Image for the Enhancement of Undesirably Illuminated Images
Action recognition by learning pose representations
Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model
Fingerprint Extraction Using Smartphone Camera
Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
Phase-error estimation and image reconstruction from digital-holography data using a Bayesian framework
Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC
Semantic Augmented Reality Environment with Material-Aware Physical Interactions
Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
Improving Speaker-Independent Lipreading with Domain-Adversarial Training
Region-Based Multiscale Spatiotemporal Saliency for Video
Parametrization and Generation of Geological Models with Generative Adversarial Networks
Reinforced Video Captioning with Entailment Rewards
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot
Privacy Preserving Face Retrieval in the Cloud for Mobile Users
Interacting with Acoustic Simulation and Fabrication
An evaluation of large-scale methods for image instance and class discovery
Convolutional Neural Networks for Font Classification
A Cost-Sensitive Visual Question-Answer Framework for Mining a Deep And-OR Object Semantics from Web Images
Artistic style transfer for videos and spherical images
Efficiently Tracking Homogeneous Regions in Multichannel Images
Deep Neural Network with l2-norm Unit for Brain Lesions Detection
Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects
Mesh-based 3D Textured Urban Mapping
Employing Weak Annotations for Medical Image Analysis Problems
Sharpness-aware Low dose CT denoising using conditional generative adversarial network
Contrast and visual saliency similarity induced index for image quality assessment
Activity Recognition based on a Magnitude-Orientation Stream Network
What does 2D geometric information really tell us about 3D face shape?
Non-linear Convolution Filters for CNN-based Learning
An Image Analysis Approach to the Calligraphy of Books
FacePoseNet: Making a Case for Landmark-Free Face Alignment
The Parallel Algorithm for the 2-D Discrete Wavelet Transform
Batch-Based Activity Recognition from Egocentric Photo-Streams
Maximum A Posteriori Estimation of Distances Between Deep Features in Still-to-Video Face Recognition
Facial Expression Recognition using Visual Saliency and Deep Learning
Imbalanced Malware Images Classification: a CNN based Approach
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
Automatic Discovery and Geotagging of Objects from Street View Imagery
Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection
Deep Structure for end-to-end inverse rendering
ScatterNet Hybrid Deep Learning (SHDL) Network For Object Classification
Neural Class-Specific Regression for face verification
Abnormal Event Detection in Videos using Generative Adversarial Nets
Glyph-aware Embedding of Chinese Characters
First and Second Order Methods for Online Convolutional Dictionary Learning
XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual Classification
Self-Supervised Learning for Stereo Matching with Self-Improving Ability
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
Squeeze-and-Excitation Networks
BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks
CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
Towards high-throughput 3D insect capture for species discovery and diagnostics
The Mating Rituals of Deep Neural Networks: Learning Compact Feature Representations through Sexual Evolutionary Synthesis
Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach
DPC-Net: Deep Pose Correction for Visual Localization
Art of singular vectors and universal adversarial perturbations
On the definition of Shape Parts: a Dominant Sets Approach
PQk-means: Billion-scale Clustering for Product-quantized Codes
A low cost non-wearable gaze detection system based on infrared image processing
Denoising Autoencoders for Overgeneralization in Neural Networks
Informed Non-convex Robust Principal Component Analysis with Features
Joint Hierarchical Category Structure Learning and Large-Scale Image Classification
A Streaming Accelerator for Deep Convolutional Neural Networks with Image and Feature Decomposition for Resource-limited System Applications
Detecting Faces Using Region-based Fully Convolutional Networks
The Multiscale Bowler-Hat Transform for Blood Vessel Enhancement in Retinal Images
DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
Kernel Cross-Correlator
Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI
MUFold-SS: Protein Secondary Structure Prediction Using Deep Inception-Inside-Inception Networks
A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks
An Adaptive Algorithm for Precise Pupil Boundary Detection using Entropy of Contour Gradients
Updating the silent speech challenge benchmark with deep learning
Multi-camera Multi-Object Tracking
Learned Features are better for Ethnicity Classification
Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
Single-pixel imaging with Morlet wavelet correlated random patterns
Can Image Retrieval help Visual Saliency Detection?
HDLTex: Hierarchical Deep Learning for Text Classification
Understanding Infographics through Textual and Visual Tag Prediction
A Read-Write Memory Network for Movie Story Understanding
Connectivity Learning in Multi-Branch Networks
Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives
Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks
Distance-based Confidence Score for Neural Network Classifiers
Recognition of Documents in Braille
Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation
Improving image generative models with human interactions
Human motion primitive discovery and recognition
Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF
Pyramidal RoR for Image Classification
Learning event representation: As sparse as possible, but not sparser
Out-of-focus Blur: Image De-blurring
Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming
Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework for ITK
Finding phonemes: improving machine lip-reading
Learning Autoencoded Radon Projections
GraphMatch: Efficient Large-Scale Graph Construction for Structure from Motion
A self-organizing neural network architecture for learning human-object interactions
Semantic keyword spotting by learning from images and speech
Deep Convolutional Neural Networks as Generic Feature Extractors
Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder
An automatic deep learning approach for coronary artery calcium segmentation
Multitask training with unlabeled data for end-to-end sign language fingerspelling recognition
Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images
Deep Hyperalignment
RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans
Microaneurysm Detection in Fundus Images Using a Two-step Convolutional Neural Networks
An Adaptive Framework for Missing Depth Inference Using Joint Bilateral Filter
Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks
A New Coherence-Penalized Minimal Path Model with Application to Retinal Vessel Centerline Delineation
Towards CT-quality Ultrasound Imaging using Deep Learning
Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks
Do Convolutional Neural Networks Learn Class Hierarchy?
Pose-based Deep Gait Recognition
Enhancing the Performance of Convolutional Neural Networks on Quality Degraded Datasets
Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings
Visual Speech Recognition Using PCA Networks and LSTMs in a Tandem GMM-HMM System
Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model
Anticipating Daily Intention using On-Wrist Motion Triggered Sensing
MR to X-Ray Projection Image Synthesis
ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection
Progressive Learning for Systematic Design of Large Neural Networks
AutoEncoder Inspired Unsupervised Feature Selection
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
Robust Photometric Stereo via Dictionary Learning
The Shape of an Image: A Study of Mapper on Images
Crop Planning using Stochastic Visual Optimization
Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networks
Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
A Generative Model for Volume Rendering
Phase Transitions in Image Denoising via Sparsely Coding Convolutional Neural Networks
SEGMENT3D: A Web-based Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem
Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation
Stochastic Conjugate Gradient Algorithm with Variance Reduction
Dual Path Networks for Multi-Person Human Pose Estimation
Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach
Regularization for Deep Learning: A Taxonomy
Automated Tumor Segmentation and Brain Mapping for the Tumor Area
Generating Natural Adversarial Examples
Updating the VESICLE-CNN Synapse Detector
Log-DenseNet: How to Sparsify a DenseNet
Smooth Neighbors on Teacher Graphs for Semi-supervised Learning
Hierarchical Representations for Efficient Architecture Search
Don't Decay the Learning Rate, Increase the Batch Size
Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications
A Classification-Based Perspective on GAN Distributions
Background Subtraction via Fast Robust Matrix Completion
Attentional Pooling for Action Recognition
A Survey on Dialogue Systems: Recent Advances and New Frontiers
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm
Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
SIMILARnet: Simultaneous Intelligent Localization and Recognition Network
Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz
Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication
What Really is Deep Learning Doing?
Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records
Commonsense LocatedNear Relation Extraction
Robust Keyframe-based Dense SLAM with an RGB-D Camera
Dynamic Zoom-in Network for Fast Object Detection in Large Images
A Correlation Based Feature Representation for First-Person Activity Recognition
Fast and Efficient Calculations of Structural Invariants of Chirality
Learning to Compare: Relation Network for Few-Shot Learning
Language-Based Image Editing with Recurrent Attentive Models
Improvements to context based self-supervised learning
Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks
High-Resolution Deep Convolutional Generative Adversarial Networks
Dependent landmark drift: robust point set registration based on the Gaussian mixture model with a statistical shape model
Learning Discriminative Affine Regions via Discriminability
An Automatic Solver for Very Large Jigsaw Puzzles Using Genetic Algorithms
Style Transfer in Text: Exploration and Evaluation
Learning Steerable Filters for Rotation Equivariant CNNs
Detection of Tooth caries in Bitewing Radiographs using Deep Learning
Self-Similarity Based Time Warping
Fully Convolutional Neural Networks for Page Segmentation of Historical Document Images
The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack
Receptive Field Block Net for Accurate and Fast Object Detection
Visual and Textual Sentiment Analysis Using Deep Fusion Convolutional Neural Networks
Generating Analytic Insights on Human Behaviour using Image Processing
Personalization of Saliency Estimation
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
On the Automatic Generation of Medical Imaging Reports
SolarisNet: A Deep Regression Network for Solar Radiation Prediction
In Defense of Product Quantization
DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem
DeepPainter: Painter Classification Using Deep Convolutional Autoencoders
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
Unsupervised Domain Adaptation with Similarity Learning
Natural and Effective Obfuscation by Head Inpainting
Transfer Learning in CNNs Using Filter-Trees
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders
Evaluating gender portrayal in Bangladeshi TV
Particle Filter Re-detection for Visual Tracking via Correlation Filters
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
PSIque: Next Sequence Prediction of Satellite Images using a Convolutional Sequence-to-Sequence Network
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction
Transfer Learning with Binary Neural Networks
Video Captioning via Hierarchical Reinforcement Learning
ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism
Neural Signatures for Licence Plate Re-identification
Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection
Progressive Neural Architecture Search
Evaluation of Alzheimer's Disease by Analysis of MR Images using Multilayer Perceptrons and Kohonen SOM Classifiers as an Alternative to the ADC Maps
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks
Multimodal Visual Concept Learning with Weakly Supervised Techniques
Semi-Global Stereo Matching with Surface Orientation Priors
Composite Quantization
Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
Learning by Asking Questions
Examining Cooperation in Visual Dialog Models
3D Semantic Trajectory Reconstruction from 3D Pixel Continuum
Multimodal Storytelling via Generative Adversarial Imitation Learning
Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks
Triagem virtual de imagens de imuno-histoquímica usando redes neurais artificiais e espectro de padrões
Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
Learning General Latent-Variable Graphical Models with Predictive Belief Propagation and Hilbert Space Embeddings
Generative Adversarial Perturbations
Tomographic Reconstruction using Global Statistical Prior
CNNs are Globally Optimal Given Multi-Layer Support
Broadcasting Convolutional Network for Visual Relational Reasoning
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing
End-to-end Learning of Deterministic Decision Trees
Stochastic reconstruction of an oolitic limestone by generative adversarial networks
Image Inpainting for High-Resolution Textures using CNN Texture Synthesis
Peephole: Predicting Network Performance Before Training
Geometry Guided Adversarial Facial Expression Synthesis
A practical guide and software for analysing pairwise comparison experiments
Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing
Learning Modality-Invariant Representations for Speech and Images
Fusing Multiple Multiband Images
Deep Quaternion Networks
Regularization and Optimization strategies in Deep Convolutional Neural Network
Multidimensional Data Tensor Sensing for RF Tomographic Imaging
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
Object Detection with an Aligned Spatial-Temporal Memory
Multi-modal Face Pose Estimation with Multi-task Manifold Deep Learning
Dynamic Weight Alignment for Convolutional Neural Networks
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Multi-shot Pedestrian Re-identification via Sequential Decision Making
Finding Competitive Network Architectures Within a Day Using UCT
An Order Preserving Bilinear Model for Person Detection in Multi-Modal Data
An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction
Interpretable Counting for Visual Question Answering
Towards Structured Analysis of Broadcast Badminton Videos
A model for interpreting social interactions in local image regions
Consensus-based Sequence Training for Video Captioning
Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture
Deep Learning Interior Tomography for Region-of-Interest Reconstruction
Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data
Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review
Automated image segmentation for detecting cell spreading for metastasizing assessments of cancer development
A Novel Approach to Skew-Detection and Correction of English Alphabets for OCR
Live Intrinsic Material Estimation
Fingerprint Distortion Rectification using Deep Convolutional Neural Networks
Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner
3D Surface-to-Structure Translation using Deep Convolutional Networks
Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary
Accelerated Training for Massive Classification via Dynamic Class Selection
Improved Style Transfer by Respecting Inter-layer Correlations
Cross-modal Embeddings for Video and Audio Retrieval
Anatomical Data Augmentation For CNN based Pixel-wise Classification
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
Unsupervised Discovery of Toxoplasma gondii Motility Phenotypes
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition
A Benchmark for Breast Ultrasound Image Segmentation (BUSIS)
FWLBP: A Scale Invariant Descriptor for Texture Classification
Focus: Querying Large Video Datasets with Low Latency and Low Cost
Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Data
A Bio-inspired Collision Detecotr for Small Quadcopter
Fix your classifier: the marginal value of training the last weight layer
Face Recognition via Centralized Coordinate Learning
3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Transfer Learning for Improving Speech Emotion Classification Accuracy
An Improved LPTC Neural Model for Background Motion Direction Estimation
Demonstrably Doing Accountability in the Internet of Things
Clustering with Deep Learning: Taxonomy and New Methods
A Classification Refinement Strategy for Semantic Segmentation
MAttNet: Modular Attention Network for Referring Expression Comprehension
A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management
Image2GIF: Generating Cinemagraphs using Recurrent Deep Q-Networks
DeepSIC: Deep Semantic Image Compression
RGB image-based data analysis via discrete Morse theory and persistent homology
Spherical CNNs
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning
Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography
Semantic White Balance: Semantic Color Constancy Using Convolutional Neural Network
APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework
Visual Interpretability for Deep Learning: a Survey
Densely Connected Bidirectional LSTM with Applications to Sentence Classification
Pose Flow: Efficient Online Pose Tracking
Fast Piecewise-Affine Motion Estimation Without Segmentation
A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography
A Systematic Analysis for State-of-the-Art 3D Lung Nodule Proposals Generation
Seeded Ising Model and Statistical Natures of Human Iris Templates
Energy-Efficient CMOS Memristive Synapses for Mixed-Signal Neuromorphic System-on-a-Chip
An Unsupervised Learning Model for Deformable Medical Image Registration
Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
Saliency-Enhanced Robust Visual Tracking
Piecewise Flat Embedding for Image Segmentation
Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence
ADC: Automated Deep Compression and Acceleration with Reinforcement Learning
Answerer in Questioner's Mind for Goal-Oriented Visual Dialogue
Deep feature compression for collaborative object detection
Temporal and Volumetric Denoising via Quantile Sparse Image (QuaSI) Prior in Optical Coherence Tomography and Beyond
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Modelling of Facial Aging and Kinship: A Survey
Web-Scale Responsive Visual Search at Bing
ISEC: Iterative over-Segmentation via Edge Clustering
A New De-blurring Technique for License Plate Images with Robust Length Estimation
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution
Efficient Sparse-Winograd Convolutional Neural Networks
Machine Learning Methods for Solving Assignment Problems in Multi-Target Tracking
Teaching Categories to Human Learners with Visual Explanations
Segmentation hiérarchique faiblement supervisée
Fusing Video and Inertial Sensor Data for Walking Person Identification
Transport-Based Pattern Theory: A Signal Transformation Approach
Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation
Liver Segmentation in Abdominal CT Images by Adaptive 3D Region Growing
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
VizWiz Grand Challenge: Answering Visual Questions from Blind People
Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Sleep-deprived Fatigue Pattern Analysis using Large-Scale Selfies from Social Med
A Twofold Siamese Network for Real-Time Object Tracking
ReHAR: Robust and Efficient Human Activity Recognition
Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing
Graph-based Image Anomaly Detection
3D Object Super-Resolution
Compressing Neural Networks using the Variational Information Bottleneck
Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images
Fast and robust misalignment correction of Fourier ptychographic microscopy
Fusion of multispectral satellite imagery using a cluster of graphics processing unit
Driving Digital Rock towards Machine Learning: predicting permeability with Gradient Boosting and Deep Neural Networks
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
Greedy stochastic algorithms for entropy-regularized optimal transport problems
Less Is More: Picking Informative Frames for Video Captioning
Path Aggregation Network for Instance Segmentation
The Contextual Loss for Image Transformation with Non-Aligned Data
2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks
Methodology to analyze the accuracy of 3D objects reconstructed with collaborative robot based monocular LSD-SLAM
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation
Learning Effective Binary Visual Representations with Deep Networks
Measuring Conflict in a Multi-Source Environment as a Normal Measure
Image Segmentation and Processing for Efficient Parking Space Analysis
Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion
Expert identification of visual primitives used by CNNs during mammogram classification
Averaging Weights Leads to Wider Optima and Better Generalization
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Targeted change detection in remote sensing images
Exploring Linear Relationship in Feature Map Subspace for ConvNets Compression
I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators
Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model
Adaptive strategy for superpixel-based region-growing image segmentation
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection
Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network
Depth-aware CNN for RGB-D Segmentation
Featureless: Bypassing feature extraction in action categorization
Live Target Detection with Deep Learning Neural Network and Unmanned Aerial Vehicle on Android Mobile Device
Diagnostic Classification Of Lung Nodules Using 3D Neural Networks
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model
Residual Codean Autoencoder for Facial Attribute Analysis
A Distance Oriented Kalman Filter Particle Swarm Optimizer Applied to Multi-Modality Image Registration
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions
IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning
Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
Fast Semantic Segmentation on Video Using Motion Vector-Based Feature Interpolation
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
Deep Learning using Rectified Linear Units (ReLU)
Automated Detection of Acute Leukemia using K-mean Clustering Algorithm
Hardware based Spatio-Temporal Neural Processing Backend for Imaging Sensors: Towards a Smart Camera
Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs
Predicting Gaze in Egocentric Video by Learning Task-dependent Attention Transition
A Face Recognition Signature Combining Patch-based Features with Soft Facial Attributes
Fast and Accurate Single Image Super-Resolution via Information Distillation Network
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
Neural Baby Talk
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification
Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions
A Fast Face Detection Method via Convolutional Neural Network
Feed-forward Uncertainty Propagation in Belief and Neural Networks
Weakly-Supervised Action Segmentation with Iterative Soft Boundary Assignment
Adversarial Network Compression
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
A real-time warning system for rear-end collision based on random forest classifier
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years
Unsupervised Textual Grounding: Linking Words to Image Concepts
Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts
Parallel Grid Pooling for Data Augmentation
On the Resistance of Neural Nets to Label Noise
A Subpixel Registration Algorithm for Low PSNR Images
Gated Fusion Network for Single Image Dehazing
End-to-End Detection and Re-identification Integrated Net for Person Search
Generative Spatiotemporal Modeling Of Neutrophil Behavior
End-to-End Learning of Motion Representation for Video Understanding
Towards Explanation of DNN-based Prediction with Guided Feature Inversion
Universal Planning Networks
Dynamic Video Segmentation Network
In-depth Question classification using Convolutional Neural Networks
Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part Affinity Fields
Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input
Image Generation from Scene Graphs
Finding beans in burgers: Deep semantic-visual embedding with localization
Ordinal Pooling Networks: For Preserving Information over Shrinking Feature Maps
A Generation Method of Immunological Memory in Clonal Selection Algorithm by using Restricted Boltzmann Machines
Composing photomosaic images using clustering based evolutionary programming
Abdominal Aortic Aneurysm Segmentation with a Small Number of Training Subjects
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
Cortex Neural Network: learning with Neural Network groups
A Fast Hierarchically Preconditioned Eigensolver Based On Multiresolution Matrix Decomposition
Parameterized Algorithms for the Matrix Completion Problem
Imagine This! Scripts to Compositions to Videos
Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation
Sentiment Transfer using Seq2Seq Adversarial Autoencoders
Learning to Extract a Video Sequence from a Single Motion-Blurred Image
A two-stage 3D Unet framework for multi-class segmentation on full resolution image
Learned Deformation Stability in Convolutional Neural Networks
Group Anomaly Detection using Deep Generative Models
Understanding Design Fundamentals: How Synthesis and Analysis Drive Creativity, Resulting in Emergence
Generalized Discriminant Analysis algorithm for feature reduction in Cyber Attack Detection System
Mapping the spatiotemporal dynamics of calcium signaling in cellular neural networks using optical flow
Fish recognition based on the combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree
Perturbation Resilience and Superiorization of Iterative Algorithms
Memory-Efficient Topic Modeling
Large-scale continuous subgraph queries on streams
Distributed optimization of deeply nested systems
Tensor-based formulation and nuclear norm regularization for multi-energy computed tomography
Medical Aid for Automatic Detection of Malaria
Particle methods enable fast and simple approximation of Sobolev gradients in image segmentation
Exploring the power of GPU's for training Polyglot language models
Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs
Single Image Super Resolution via Manifold Approximation
Comparative Evaluation of Symmetric SVD Algorithms for Real-time Face and Eye Tracking
An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability
Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo
Adapted sampling for 3D X-ray computed tomography
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
A Survey of the Trends in Facial and Expression Recognition Databases and Methods
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
Integrating Deep Features for Material Recognition
A method for locally approximating regularized iterative tomographic reconstruction methods
GPU-FV: Realtime Fisher Vector and Its Applications in Video Monitoring
Epipolar Geometry Based On Line Similarity
Deep Adaptive Network: An Efficient Deep Neural Network with Sparse Binary Connections
Pruning Filters for Efficient ConvNets
Socio-inspired ICT - Towards a socially grounded society-ICT symbiosis
Multi Circle Detection on Images Using Artificial Bee Colony (ABC) Optimization
A role for recurrent processing in object completion: neurophysiological, psychophysical and computational"evidence
Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification
Learning of Proto-object Representations via Fixations on Low Resolution
A Holistic Approach for Modeling and Synthesis of Image Processing Applications for Heterogeneous Computing Architectures
Anatomy-specific classification of medical images using deep convolutional nets
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
Deep SimNets
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Estimating Absolute-Phase Maps Using ESPIRiT and Virtual Conjugate Coils
3-D/2-D Registration of Cardiac Structures by 3-D Contrast Agent Distribution Estimation
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
Storm Detection by Visual Learning Using Satellite Images
Kernelized Weighted SUSAN based Fuzzy C-Means Clustering for Noisy Image Segmentation
Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time
Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups
ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images
The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differences
Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs
Multispectral image denoising with optimized vector non-local mean filter
Deep fusion of visual signatures for client-server facial analysis
Hierarchical Object Detection with Deep Reinforcement Learning
Measuring and modeling the perception of natural and unconstrained gaze in humans and machines
Temporal-Needle: A view and appearance invariant video descriptor
An extended Perona-Malik model based on probabilistic models
Camera-trap images segmentation using multi-layer robust principal component analysis
Deep Learning the Indus Script
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning
An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses
Fast and Accurate Inference with Adaptive Ensemble Prediction in Image Classification with Deep Neural Networks
Mining Object Parts from CNNs via Active Question-Answering
Fast PET reconstruction using Multi-scale Fully Convolutional Neural Networks
Matrix Completion via Factorizing Polynomials
Exploring Computation-Communication Tradeoffs in Camera Systems
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
Full-Network Embedding in a Multimodal Embedding Pipeline
Cross-Media Similarity Evaluation for Web Image Retrieval in the Wild
Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks
Skin Lesion Segmentation: U-Nets versus Clustering
Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy
Evolving Deep Convolutional Neural Networks for Image Classification
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking
On Usage of Autoencoders and Siamese Networks for Online Handwritten Signature Verification
Efficient Trimmed Convolutional Arithmetic Encoding for Lossless Image Compression
Recognizing Cuneiform Signs Using Graph Based Methods
LDOP: Local Directional Order Pattern for Robust Face Retrieval
Image Recognition Using Scale Recurrent Neural Networks
FPGA Implementations of 3D-SIMD Processor Architecture for Deep Neural Networks Using Relative Indexed Compressed Sparse Filter Encoding Format and Stacked Filters Stationary Flow
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
On the Computation of Kantorovich-Wasserstein Distances between 2D-Histograms by Uncapacitated Minimum Cost Flows
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory
Global registration of multiple point clouds using semidefinite programming
An Empirical Study into Annotator Agreement, Ground Truth Estimation, and Algorithm Evaluation
Numerical Methods for Coupled Reconstruction and Registration in Digital Breast Tomosynthesis
Calibration of an Articulated Camera System with Scale Factor Estimation
Stable Camera Motion Estimation Using Convex Programming
A fast eikonal equation solver using the Schrodinger wave equation
Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice
Single camera pose estimation using Bayesian filtering and Kinect motion priors
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
A Data-Driven Approach for Tag Refinement and Localization in Web Videos
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Edge-Preserving Coherence
Brain Tumor Segmentation with Deep Neural Networks
Face Search at Scale: 80 Million Gallery
Supervised Dictionary Learning and Sparse Representation-A Review
Video Inpainting of Complex Scenes
A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval
EIE: Efficient Inference Engine on Compressed Deep Neural Network
Fast Training of Triplet-based Deep Binary Embedding Networks
Adaptive foveated single-pixel imaging with dynamic super-sampling
Visual Dialog
Asynchronous approach in the plane: A deterministic polynomial algorithm
A Computer Vision Approach To Identify Einstein Rings And Arcs
Texture Classification of MR Images of the Brain in ALS using CoHOG
ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information
Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text
Multi-View Stereo with Single-View Semantic Mesh Refinement
Exploring and Exploiting Diversity for Image Segmentation
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Embedding
Multi-label Pixelwise Classification for Reconstruction of Large-scale Urban Areas
Adversarial Attacks Beyond the Image Space
LSTM Pose Machines
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
A New Target-specific Object Proposal Generation Method for Visual Tracking
Multi-label Learning with Missing Labels using Mixed Dependency Graphs
Learning a Robust Society of Tracking Parts using Co-occurrence Constraints
Information Compression, Intelligence, Computing, and Mathematics
Montblanc: GPU accelerated Radio Interferometer Measurement Equations in support of Bayesian Inference for Radio Observations
Training CNNs with Low-Rank Filters for Efficient Image Classification
NiftyNet: a deep-learning platform for medical imaging
Inducing Features of Random Fields
Clustering by compression
The Perceptron Algorithm: Image and Signal Decomposition, Compression, and Analysis by Iterative Gaussian Blurring
Similarity of Objects and the Meaning of Words
Linear versus Non-linear Acquisition of Step-Functions
A computational theory for the classification of natural biosonar targets based on a spike code
Polygon Exploration with Time-Discrete Vision
A branch-and-bound feature selection algorithm for U-shaped cost functions
The Modular Audio Recognition Framework (MARF) and its Applications: Scientific and Software Engineering Notes
Handwritten Farsi Character Recognition using Artificial Neural Network
Iterative Shrinkage Approach to Restoration of Optical Imagery
Distributed Object Medical Imaging Model
An Innovative Scheme For Effectual Fingerprint Data Compression Using Bezier Curve Representations
Biogeography based Satellite Image Classification
A Topological derivative based image segmentation for sign language recognition system using isotropic filter
Multi-camera Realtime 3D Tracking of Multiple Flying Animals
A GA based Window Selection Methodology to Enhance Window based Multi wavelet transformation and thresholding aided CT image denoising technique
Employing fuzzy intervals and loop-based methodology for designing structural signature: an application to symbol recognition
Biometric Authentication using Nonparametric Methods
Biometric Authentication using Nonparametric Methods
3-D Rigid Models from Partial Views - Global Factorization
From Social Simulation to Integrative System Design
Self-Organising Stochastic Encoders
Exact Reconstruction of the Rank Order Coding using Frames Theory
Exploratory simulation of an Intelligent Iris Verifier Distributed System
Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities for CAD Systems
Facial Expression Classification Based on Multi Artificial Neural Network and Two Dimensional Principal Component Analysis
3-Phase Recognition Approach to Pseudo 3D Building Generation from 2D Floor Plan
Automatic Application Level Set Approach in Detection Calcifications in Mammographic Image
Topology on locally finite metric spaces
A New Local Adaptive Thresholding Technique in Binarization
Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers
Divide-and-Conquer Method for L1 Norm Matrix Factorization in the Presence of Outliers and Missing Data
On-Board Visual Tracking with Unmanned Aircraft System (UAS)
Extraction of Facial Feature Points Using Cumulative Histogram
Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks
Heterogeneous Highly Parallel Implementation of Matrix Exponentiation Using GPU
Functional Currents : a new mathematical tool to model and analyse functional shapes
Face Recognition Algorithms based on Transformed Shape Features
Joint-ViVo: Selecting and Weighting Visual Words Jointly for Bag-of-Features based Tissue Classification in Medical Images
EGovernment Stage Model: Evaluating the Rate of Web Development Progress of Government Websites in Saudi Arabia
Multi-Sensor Fusion via Reduction of Dimensionality
Multislice Modularity Optimization in Community Detection and Image Segmentation
Training Support Vector Machines Using Frank-Wolfe Optimization Methods
Autonomous Navigation by Robust Scan Matching Technique
An Analysis of Gene Expression Data using Penalized Fuzzy C-Means Approach
Morphological Analusis Of The Left Ventricular Eendocardial Surface Using A Bag-Of-Features Descriptor
The State of the Art Recognize in Arabic Script through Combination of Online and Offline
Geometric tree kernels: Classification of COPD from airway tree geometry
Compressive Sensing of Sparse Tensors
Discriminative extended canonical correlation analysis for pattern set matching
Automated Thermal Face recognition based on Minutiae Extraction
Improvements to deep convolutional neural networks for LVCSR
Scan-based Compressed Terahertz Imaging and Real-Time Reconstruction via the Complex-valued Fast Block Sparse Bayesian Learning Algorithm
An Application of Backpropagation Artificial Neural Network Method for Measuring The Severity of Osteoarthritis
Dynamic Model of Facial Expression Recognition based on Eigen-face Approach
A robust Iris recognition method on adverse conditions
Generative NeuroEvolution for Deep Learning
Monte Carlo non local means: Random sampling for large-scale image filtering
Pectoral Muscles Suppression in Digital Mammograms using Hybridization of Soft Computing Methods
A Study of Image Analysis with Tangent Distance
An Identification System Using Eye Detection Based On Wavelets And Neural Networks
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
Geometry-based Adaptive Symbolic Approximation for Fast Sequence Matching on Manifolds
Collaborative Verification-Driven Engineering of Hybrid Systems
Gaussian-Chain Filters for Heavy-Tailed Noise with Application to Detecting Big Buyers and Big Sellers in Stock Market
Newton-Type Iterative Solver for Multiple View $L2$ Triangulation
Coarse-to-Fine Classification via Parametric and Nonparametric Models for Computer-Aided Diagnosis
Large-scale Supervised Hierarchical Feature Learning for Face Recognition
Novel and Tuneable Method for Skin Detection Based on Hybrid Color Space and Color Statistical Features
Toward Automated Discovery of Artistic Influence
2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers
A Fusion Approach for Efficient Human Skin Detection
Zero-Aliasing Correlation Filters for Object Recognition
Computational Baby Learning
Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction
The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classification
Skincure: An Innovative Smart Phone-Based Application To Assist In Melanoma Early Detection And Prevention
From Visual Attributes to Adjectives through Decompositional Distributional Semantics
Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation
Coupled Depth Learning
Image Denoising using Optimally Weighted Bilateral Filters: A Sure and Fast Approach
Self-Expressive Decompositions for Matrix Approximation and Clustering
Efficient Large Scale Video Classification
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
Approximate Fisher Kernels of non-iid Image Models for Image Categorization
Some like it hot - visual guidance for preference prediction
Grounding of Textual Phrases in Images by Reconstruction
Symbol Grounding Association in Multimodal Sequences with Missing Elements
Sequential Optimization for Efficient High-Quality Object Proposal Generation
Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?
Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations
A Semi-Lagrangian two-level preconditioned Newton-Krylov solver for constrained diffeomorphic image registration
Video Analysis for Body-worn Cameras in Law Enforcement
Facial expression recognition based on local region specific features and support vector machines
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory
Detecting Sarcasm in Multimodal Social Platforms
Similarity Search on Automata Processors
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
Face Shape and Reflectance Acquisition using a Multispectral Light Stage
Fast O(1) bilateral filtering using trigonometric range kernels
POCS Based Super-Resolution Image Reconstruction Using an Adaptive Regularization Parameter
Discretization of Parametrizable Signal Manifolds
Embedding of Blink Frequency in Electrooculography Signal using Difference Expansion based Reversible Watermarking Technique
The varifold representation of non-oriented shapes for diffeomorphic registration
Local image registration a comparison for bilateral registration mammography
Detection of copy-move forgery in digital images based on DCT
Discriminative Parameter Estimation for Random Walks Segmentation
Semantic Graph for Zero-Shot Learning
Natural Color Image Enhancement based on Modified Multiscale Retinex Algorithm and Performance Evaluation usingWavelet Energy
Minimizing the Number of Matching Queries for Object Retrieval
Generative Modeling of Convolutional Neural Networks
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Bi-directional Shape Correspondences (BSC): A Novel Technique for 2-d Shape Warping in Quadratic Time?
A specialized face-processing network consistent with the representational geometry of monkey face patches
Out-of-sample generalizations for supervised manifold learning for classification
Study on Sparse Representation based Classification for Biometric Verification
Node.DPWS: High performance and scalable Web Services for the IoT
Parallel Statistical Multi-resolution Estimation
Low-Level Features for Image Retrieval Based on Extraction of Directional Binary Patterns and Its Oriented Gradients Histogram
Skilled Impostor Attacks Against Fingerprint Verification Systems And Its Remedy
Autonomy Infused Teleoperation with Application to BCI Manipulation
Reduced Basis Decomposition: a Certified and Fast Lossy Data Compression Algorithm
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
User Preferences Modeling and Learning for Pleasing Photo Collage Generation
Color Constancy by Learning to Predict Chromaticity from Luminance
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure
Stereoscopic Cinema
The Multi-Strand Graph for a PTZ Tracker
End-to-End Privacy for Open Big Data Markets
Multiresolution Approach to Acceleration of Iterative Image Reconstruction for X-Ray Imaging for Security Applications
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
SPF-CellTracker: Tracking multiple cells with strongly-correlated moves using a spatial particle filter
Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
Efficient Discriminative Nonorthogonal Binary Subspace with its Application to Visual Tracking
Symbol Emergence in Robotics: A Survey
The Indian Spontaneous Expression Database for Emotion Recognition
We Are Humor Beings: Understanding and Predicting Visual Humor
Probabilistic Programming with Gaussian Process Memoization
Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images
Statistical and Computational Guarantees for the Baum-Welch Algorithm
Using Filter Banks in Convolutional Neural Networks for Texture Classification
Detection and Visualization of Endoleaks in CT Data for Monitoring of Thoracic and Abdominal Aortic Aneurysm Stents
Composable Industrial Internet Applications for Tiered Architectures
Autonomous navigation for low-altitude UAVs in urban areas
Pandora: Description of a Painting Database for Art Movement Recognition with Baselines and Perspectives
Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity
Automatic 3D liver location and segmentation via convolutional neural networks and graph cut
A Gaussian Mixture MRF for Model-Based Iterative Reconstruction with Applications to Low-Dose X-ray CT
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Multilingual Visual Sentiment Concept Matching
Training Recurrent Answering Units with Joint Loss Minimization for VQA
Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks
Deep Image Set Hashing
Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method
Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
Is a Picture Worth Ten Thousand Words in a Review Dataset?
Generalized Wishart processes for interpolation over diffusion tensor fields
Optimising The Input Window Alignment in CD-DNN Based Phoneme Recognition for Low Latency Processing
Object Boundary Detection and Classification with Image-level Labels
Incorporating prior knowledge in medical image segmentation: a survey
Large Scale SfM with the Distributed Camera Model
Approximate Policy Iteration for Budgeted Semantic Video Segmentation
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
Component-Based Distributed Framework for Coherent and Real-Time Video Dehazing
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks
OPML: A One-Pass Closed-Form Solution for Online Metric Learning
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
DOTmark - A Benchmark for Discrete Optimal Transport
Learning and Fusing Multimodal Features from and for Multi-task Facial Computing
Visual-Inertial Monocular SLAM with Map Reuse
Video Analysis of "YouTube Funnies" to Aid the Study of Human Gait and Falls - Preliminary Results and Proof of Concept
Optimal Multiple Surface Segmentation with Convex Priors in Irregularly Sampled Space
Deep Convolutional Neural Network for Inverse Problems in Imaging
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Inverting The Generator Of A Generative Adversarial Network
Inferring Restaurant Styles by Mining Crowd Sourced Photos from User-Review Websites
Relaxed Earth Mover's Distances for Chain- and Tree-connected Spaces and their use as a Loss Function in Deep Learning
Multigrid Neural Architectures
Recognition of Text Image Using Multilayer Perceptron
Parameter Compression of Recurrent Neural Networks and Degradation of Short-term Memory
Multi-Agent Cooperation and the Emergence of (Natural) Language
A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data
Assessing Uncertainties in X-ray Single-particle Three-dimensional reconstructions
Random Sampling for Fast Face Sketch Synthesis
Light Field Super-Resolution Via Graph-Based Regularization
Profiling of OCR'ed Historical Texts Revisited
UmUTracker: A versatile MATLAB program for automated particle tracking of 2D light microscopy or 3D digital holography data
A novel method for automatic localization of joint area on knee plain radiographs
Computational Model for Predicting Visual Fixations from Childhood to Adulthood
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
Deep artifact learning for compressed sensing and parallel MRI
Chain-NN: An Energy-Efficient 1D Chain Architecture for Accelerating Deep Convolutional Neural Networks
WebCaricature: a benchmark for caricature face recognition
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture
Where to put the Image in an Image Caption Generator
Hidden Two-Stream Convolutional Networks for Action Recognition
Estimation of Tissue Microstructure Using a Deep Network Inspired by a Sparse Reconstruction Framework
Reconstruction of three-dimensional porous media using generative adversarial neural networks
Toward a new approach for massive LiDAR data processing
Recovery of damped exponentials using structured low rank matrix completion
A location-aware embedding technique for accurate landmark recognition
Deep LDA-Pruned Nets for Efficient Facial Gender Classification
Accelerated Nearest Neighbor Search with Quick ADC
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
Discovery Radiomics via Evolutionary Deep Radiomic Sequencer Discovery for Pathologically-Proven Lung Cancer Detection
Image-based immersed boundary model of the aortic root
Back to RGB: 3D tracking of hands and hand-object interactions based on short-baseline stereo
Learning Convolutional Text Representations for Visual Question Answering
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI
Fiber Orientation Estimation Guided by a Deep Network
Non-Linear Phase-Shifting of Haar Wavelets for Run-Time All-Frequency Lighting
Image Segmentation by Iterative Inference from Conditional Score Estimation
Plan3D: Viewpoint and Trajectory Optimization for Aerial Multi-View Stereo Reconstruction
Deep manifold-to-manifold transforming network for action recognition
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation
Global-Local Airborne Mapping (GLAM): Reconstructing a City from Aerial Videos
Volume Calculation of CT lung Lesions based on Halton Low-discrepancy Sequences
A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images
Reconstructing the Forest of Lineage Trees of Diverse Bacterial Communities Using Bio-inspired Image Analysis
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study
Fast and accurate classification of echocardiograms using deep learning
Evolutionary Training of Sparse Artificial Neural Networks: A Network Science Perspective
Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
Residual Features and Unified Prediction Network for Single Stage Detection
HMM-based Writer Identification in Music Score Documents without Staff-Line Removal
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings
Sparse Deep Nonnegative Matrix Factorization
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT
Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
Hand2Face: Automatic Synthesis and Recognition of Hand Over Face Occlusions
Combining Keystroke Dynamics and Face Recognition for User Verification
What your Facebook Profile Picture Reveals about your Personality
Computational Motility Tracking of Calcium Dynamics in Toxoplasma gondii
Deep Learning for Passive Synthetic Aperture Radar
Spotting Separator Points at Line Terminals in Compressed Document Images for Text-line Segmentation
Visual Forecasting by Imitating Dynamics in Natural Sequences
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings
A Type II Fuzzy Entropy Based Multi-Level Image Thresholding Using Adaptive Plant Propagation Algorithm
Non-rigid image registration using fully convolutional networks with deep self-supervision
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
A Deep Structured Learning Approach Towards Automating Connectome Reconstruction from 3D Electron Micrographs
Learning to Segment Instances in Videos with Spatial Propagation Network
Vehicle Tracking in Wide Area Motion Imagery via Stochastic Progressive Association Across Multiple Frames (SPAAM)
Deep-Learnt Classification of Light Curves
Fast Barcode Retrieval for Consensus Contouring
SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control
Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network
Deep Spectral Descriptors: Learning the point-wise correspondence metric via Siamese deep neural networks
Wildbook: Crowdsourcing, computer vision, and data science for conservation
Weight Initialization of Deep Neural Networks(DNNs) using Data Statistics
Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes
Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery
Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train
Spatio-Temporal Data Mining: A Survey of Problems and Methods
No Reference Stereoscopic Video Quality Assessment Using Joint Motion and Depth Statistics
Attend and Interact: Higher-Order Object Interactions for Video Understanding
A Two-Phase Genetic Algorithm for Image Registration
Light-Head R-CNN: In Defense of Two-Stage Object Detector
Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding
Accessible Melanoma Detection using Smartphones and Mobile Image Analysis
3D-A-Nets: 3D Deep Dense Descriptor for Volumetric Shapes with Adversarial Networks
Online Product Quantization
Integrated Nanophotonics Architecture for Residue Number System Arithmetic
Automatic Spine Segmentation using Convolutional Neural Network via Redundant Generation of Class Labels for 3D Spine Modeling
Fuzzy-Based Dialectical Non-Supervised Image Classification and Clustering
Avaliação do método dialético na quantização de imagens multiespectrais
Dialectical Multispectral Classification of Diffusion-Weighted Magnetic Resonance Images as an Alternative to Apparent Diffusion Coefficients Maps to Perform Anatomical Analysis
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning
Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems
Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA
A fully automated framework for lung tumour detection, segmentation and analysis
Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks
Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization
Hyperspectral recovery from RGB images using Gaussian Processes
Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers
StressedNets: Efficient Feature Representations via Stress-induced Evolutionary Synthesis of Deep Neural Networks
Interactive Diversity Optimization of Environments
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
Deep Learning based Retinal OCT Segmentation
Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices
A Continuation Method for Discrete Optimization and its Application to Nearest Neighbor Classification
Deep Predictive Coding Network for Object Recognition
Semi-supervised multi-task learning for lung cancer diagnosis
Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet
Osteoarthritis Disease Detection System using Self Organizing Maps Method based on Ossa Manus X-Ray
Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks
Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation
Innovative Texture Database Collecting Approach and Feature Extraction Method based on Combination of Gray Tone Difference Matrixes, Local Binary Patterns,and K-means Clustering
The ARM Scalable Vector Extension
Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification
Neural Architecture Construction using EnvelopeNets
Real-time Burst Photo Selection Using a Light-Head Adversarial Network
Tensor graph convolutional neural network
Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms
Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning
Contrast-Oriented Deep Neural Networks for Salient Object Detection
Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks
Cancelable Indexing Based on Low-rank Approximation of Correlation-invariant Random Filtering for Fast and Secure Biometric Identification
EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images
Visual Tracking Using Sparse Coding and Earth Mover's Distance
Pilot Comparative Study of Different Deep Features for Palmprint Identification in Low-Quality Images
Central and peripheral vision for scene recognition: A neurocomputational modeling exploration
Comparing Distributions and Shapes using the Kernel Distance
A new variational principle for the Euclidean distance function: Linear approach to the non-linear eikonal problem
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Genomics as a Service: a Joint Computing and Networking Perspective
Source Detection in Simulated XMM-Newton Observations
Timing with the EPIC pn Camera of XMM-Newton
X-ray powerful diagnostics for highly-ionized plasmas: He-like ions
Virtual Observatory: From Concept to Implementation
Emergent Probability - A directed Scale-Free Network Approach to Lonergan's Generic Model of Development
Analysis of Three-Dimensional Protein Images
Robustness of Regional Matching Scheme over Global Matching Scheme
A Bayesian Reflection on Surfaces
Integrating E-Commerce and Data Mining: Architecture and Challenges
Inventing E-Regulation in the US, EU and East Asia: Conflicting Social Visions of the Internet & the Information Society
The Open Language Archives Community and Asian Language Resources
The Mysterious Optimality of Naive Bayes: Estimation of the Probability in the System of "Classifiers"
Prosody Based Co-analysis for Continuous Recognition of Coverbal Gestures
Technical Note: Bias and the Quantification of Stability
Types of Cost in Inductive Concept Learning
Exploiting Context When Learning to Classify
Robust Classification with Context-Sensitive Features
Manifold Learning with Geodesic Minimal Spanning Trees
An Analytical Piecewise Radial Distortion Model for Precision Camera Calibration
A Family of Simplified Geometric Distortion Models for Camera Calibration
Better Foreground Segmentation Through Graph Cuts
A Numerical Example on the Principles of Stochastic Discrimination
Three-Dimensional Face Orientation and Gaze Detection from a Single Image
Self-Organised Factorial Encoding of a Toroidal Manifold
Reverse Engineering Ontology to Conceptual Data Models
From Feature Extraction to Classification: A multidisciplinary Approach applied to Portuguese Granites
Less is More - Genetic Optimisation of Nearest Neighbour Classifiers
Clustering Techniques for Marbles Classification
Line and Word Matching in Old Documents
Gradient Vector Flow Models for Boundary Extraction in 2D Images
Semi-automatic vectorization of linear networks on rasterized cartographic maps
Geometric Models of Rolling-Shutter Cameras
Convexity Analysis of Snake Models Based on Hamiltonian Formulation
Pattern Recognition for Conditionally Independent Data
Regularity of Position Sequences
Achievable Rates for Pattern Recognition
The consistency principle for a digitization procedure. An algorithm for building normal digital spaces of continuous n-dimensional objects
Understanding physics from interconnected data
Geometric symmetry in the quadratic Fisher discriminant operating on image pixels
The `Face on Mars': a photographic approach for the search of signs of past civilizations from a macroscopic point of view, factoring long-term erosion in image reconstruction
Locally Adaptive Block Thresholding Method with Continuity Constraint
Fourier Analysis and Holographic Representations of 1D and 2D Signals
Semi-Supervised Learning -- A Statistical Physics Approach
Face Recognition using Principal Component Analysis and Log-Gabor Filters
Recognition of expression variant faces using masked log-Gabor features and Principal Component Analysis
Notes on Geometric Measure Theory Applications to Image Processing; De-noising, Segmentation, Pattern, Texture, Lines, Gestalt and Occlusion
A New Quartet Tree Heuristic for Hierarchical Clustering
An effective edge--directed frequency filter for removal of aliasing in upsampled images
Cooperative Optimization for Energy Minimization: A Case Study of Stereo Matching
Invariant template matching in systems with spatiotemporal coding: a vote for instability
Extraction of cartographic objects in high resolution satellite images for object model generation
The Hough transform estimator
Image denoising by statistical area thresholding
An active curve approach for tomographic reconstruction of binary radially symmetric objects
Recovering convex boundaries from blurred and noisy observations
Texture synthesis and nonparametric resampling of random fields
Functional dissipation microarrays for classification
Statistical Mechanics Characterization of Neuronal Mosaics
A Combinatorial Bit Bang Leading to Quaternions
Text Line Segmentation of Historical Documents: a Survey
Morphing Ensemble Kalman Filters
SiZer for time series: A new approach to the analysis of trends
Very fast watermarking by reversible contrast mapping
Bandwidth selection for kernel estimation in mixed multi-dimensional spaces
Graph rigidity, Cyclic Belief Propagation and Point Pattern Matching
Comparison and Combination of State-of-the-art Techniques for Handwritten Character Recognition: Topping the MNIST Benchmark
Image Classification Using SVMs: One-against-One Vs One-against-All
Learning View Generalization Functions
Hierarchy construction schemes within the Scale set framework
Pattern Recognition System Design with Linear Encoding for Discrete Patterns
Probabilistic Visual Secret Sharing Schemes for Gray-scale images and Color images
Automatic Text Area Segmentation in Natural Images
Implementing a Test Strategy for an Advanced Video Acquisition and Processing Architecture
Wavelet and Curvelet Moments for Image Classification: Application to Aggregate Mixture Grading
Acquisition Accuracy Evaluation in Visual Inspection Systems - a Practical Approach
Using Spatially Varying Pixels Exposures and Bayer-covered Photosensors for High Dynamic Range Imaging
Linear Time Recognition Algorithms for Topological Invariants in 3D
Notes on Convex Sets, Polytopes, Polyhedra, Combinatorial Topology, Voronoi Diagrams and Delaunay Triangulations
A New Algorithm for Interactive Structural Image Segmentation
Statistical region-based active contours with exponential family observations
Region-based active contour with noise and shape priors
DimReduction - Interactive Graphic Environment for Dimensionality Reduction
Projective Reeds-Shepp car on $S^2$ with quadratic cost
Human expert fusion for image classification
Generalized proportional conflict redistribution rule applied to Sonar imagery and Radar targets classification
Cech homology for shape recognition in the presence of occlusions
The Five Points Pose Problem : A New and Accurate Solution Adapted to any Geometric Configuration
An Image-Based Sensor System for Autonomous Rendez-Vous with Uncooperative Satellites
A 8 bits Pipeline Analog to Digital Converter Design for High Speed Camera Application
On Bounded Integer Programming
Automatic Identification and Data Extraction from 2-Dimensional Plots in Digital Documents
Supervised Dictionary Learning
Robust Near-Isometric Matching via Structured Learning of Graphical Models
Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models
Non-Negative Matrix Factorization, Convexity and Isometry
Hierarchical Bag of Paths for Kernel Based Shape Classification
Astronomical imaging: The theory of everything
Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy
3D Face Recognition with Sparse Spherical Representations
Feature Selection By KDDA For SVM-Based MultiView Face Recognition
Face Detection Using Adaboosted SVM-Based Component Classifier
Sparse Component Analysis (SCA) in Random-valued and Salt and Pepper Noise Removal
On the Dual Formulation of Boosting Algorithms
Model-Based Event Detection in Wireless Sensor Networks
A Keygraph Classification Framework for Real-Time Object Detection
Uniqueness of Low-Rank Matrix Completion by Rigidity Theory
Are Tensor Decomposition Solutions Unique? On the global convergence of HOSVD and ParaFac algorithms
Tracking using explanation-based modeling
Markov Random Field Segmentation of Brain MR Images
Building the information kernel and the problem of recognition
Boosting through Optimization of Margin Distributions
Exponential Family Graph Matching and Ranking
FaceBots: Steps Towards Enhanced Long-Term Human-Robot Interaction by Utilizing and Publishing Online Social Information
Generalized Kernel-based Visual Tracking
A statistical learning approach to color demosaicing
A New Solution to the Relative Orientation Problem using only 3 Points and the Vertical Direction
Automatic Spatially-Adaptive Balancing of Energy Terms for Image Segmentation
A Novel Two-Staged Decision Support based Threat Evaluation and Weapon Assignment Algorithm, Asset-based Dynamic Weapon Scheduling using Artificial Intelligence Techinques
Registration of Standardized Histological Images in Feature Space
Gabor wavelet analysis and the fractional Hilbert transform
Scale-Based Gaussian Coverings: Combining Intra and Inter Mixture Models in Image Segmentation
Sparsity and `Something Else': An Approach to Encrypted Image Folding
Median K-flats for hybrid linear modeling with many outliers
Adaboost with "Keypoint Presence Features" for Real-Time Vehicle Visual Detection
Visual object categorization with new keypoint-based adaBoost features
Modular Traffic Sign Recognition applied to on-vehicle real-time visual detection of American and European speed limit signs
Local and global approaches of affinity propagation clustering for large scale data
Microstructure reconstruction using entropic descriptors
Positive Semidefinite Metric Learning with Boosting
Fractional differentiation based image processing
Pigment Melanin: Pattern for Iris Recognition
Isometric Multi-Manifolds Learning
Heart Rate Variability Analysis Using Threshold of Wavelet Package Coefficients
Writer Identification Using Inexpensive Signal Processing Techniques
Regularization for Matrix Completion
Digital Mathematics Libraries: The Good, the Bad, the Ugly
Face Identification by SIFT-based Complete Graph Topology
Feature Level Fusion of Biometrics Cues: Human Identification with Doddingtons Caricature
The Influence of Intensity Standardization on Medical Image Registration
An Improved DC Recovery Method from AC Coefficients of DCT-Transformed Images
Intrinsic dimension estimation of data by principal component analysis
Feature Level Fusion of Face and Fingerprint Biometrics
Assessment Of The Wind Farm Impact On The Radar
Multibiometrics Belief Fusion
Exact feature probabilities in images with occlusion
Pattern recognition using inverse resonance filtration
On MMSE and MAP Denoising Under Sparse Representation Modeling Over a Unitary Dictionary
The Projected GSURE for Automatic Parameter Tuning in Iterative Shrinkage Methods
The Video Genome
Development of a multi-user handwriting recognition system using Tesseract open source OCR engine
Recognition of Handwritten Textual Annotations using Tesseract Open Source OCR Engine for information Just In Time (iJIT)
Development of a Multi-User Recognition Engine for Handwritten Bangla Basic Characters and Digits
Feature Level Fusion of Face and Palmprint Biometrics by Isomorphic Graph-based Improved K-Medoids Partitioning
Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition
Hashing Image Patches for Zooming
Compressed Sensing with off-axis frequency-shifting holography
Detecting the Most Unusual Part of Two and Three-dimensional Digital Images
Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets
Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition
Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition
Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine
Image Segmentation Using Weak Shape Priors
Penalized K-Nearest-Neighbor-Graph Based Metrics for Clustering
Algorithm for Sector Spectra Calculation from Images Registered by the Spectral Airglow Temperature Imager
Segmentation of Natural Images by Texture and Boundary Compression
Noise Invalidation Denoising
Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition
Application of Statistical Features in Handwritten Devnagari Character Recognition
Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition
Fuzzy Classification of Facial Component Parameters
Face Synthesis (FASY) System for Determining the Characteristics of a Face Image
Quotient Based Multiresolution Image Fusion of Thermal and Visual Images Using Daubechies Wavelet Transform for Human Face Recognition
Image Pixel Fusion for Human Face Recognition
Classification of Fused Images using Radial Basis Function Neural Network for Human Face Recognition
Classification of fused face images using multilayer perceptron neural network
Classification of Log-Polar-Visual Eigenfaces using Multilayer Perceptron
Human Face Recognition using Line Features
Improved RANSAC performance using simple, iterative minimal-set solvers
Neural Network Based Reconstruction of a 3D Object from a 2D Wireframe
Video Event Recognition for Surveillance Applications (VERSA)
Ear Identification by Fusion of Segmented Slice Regions using Invariant Features: An Experimental Manifold with Dual Fusion Approach
Image sequence interpolation using optimal control
Modeling the growth of fingerprints improves matching for adolescents
Optimally Training a Cascade Classifier
Multi-Agent Deployment for Visibility Coverage in Polygonal Environments with Holes
Nonlinear Vector Filtering for Impulsive Noise Removal from Color Images
Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images
An Improved Objective Evaluation Measure for Border Detection in Dermoscopy Images
Approximate Lesion Localization in Dermoscopy Images
3D-Mesh denoising using an improved vertex based anisotropic diffusion
Balancing clusters to reduce response time variability in large scale image search
Image Segmentation by Discounted Cumulative Ranking on Maximal Cliques
A Microwave Imaging and Enhancement Technique from Noisy Synthetic Data
Profile Based Sub-Image Search in Image Databases
Statistical Compressive Sensing of Gaussian Mixture Models
Performance Analysis of Spectral Clustering on Compressed, Incomplete and Inaccurate Measurements
Single Frame Image super Resolution using Learned Directionlets
Image Segmentation with Multidimensional Refinement Indicators
Bounded Multivariate Surfaces On Monovariate Internal Functions
The Data Replication Method for the Classification with Reject Option
Warping Peirce Quincuncial Panoramas
Modeling Image Structure with Factorized Phase-Coupled Boltzmann Machines
An Introduction to Conditional Random Fields
Generalized Tree-Based Wavelet Transform
Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces
An Effective Method of Image Retrieval using Image Mining Techniques
Automatic Image Segmentation by Dynamic Region Merging
Sparse motion segmentation using multiple six-point consistencies
TILT: Transform Invariant Low-rank Textures
A Fast Statistical Method for Multilevel Thresholding in Wavelet Domain
A Framework for Real-Time Face and Facial Feature Tracking using Optical Flow Pre-estimation and Template Tracking
Binary and nonbinary description of hypointensity in human brain MR images
SafeVchat: Detecting Obscene Content and Misbehaving Users in Online Video Chat Services
Transductive-Inductive Cluster Approximation Via Multivariate Chebyshev Inequality
Using Feature Weights to Improve Performance of Neural Networks
A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation
Guaranteeing Convergence of Iterative Skewed Voting Algorithms for Image Segmentation
Feature selection via simultaneous sparse approximation for person specific face verification
An Efficient and Integrated Algorithm for Video Enhancement in Challenging Lighting Conditions
Searching in one billion vectors: re-rank with source coding
A linear framework for region-based image segmentation and inpainting involving curvature penalization
A Trajectory UML profile For Modeling Trajectory Data: A Mobile Hospital Use Case
Detection of objects in noisy images and site percolation on square lattices
Weighted Radial Variation for Node Feature Classification
An Algorithm for Repairing Low-Quality Video Enhancement Techniques Based on Trained Filter
Submodular Decomposition Framework for Inference in Associative Markov Networks with Global Constraints
Ray-Based and Graph-Based Methods for Fiber Bundle Boundary Estimation
Adaptive mosaic image representation for image processing
SO(3)-invariant asymptotic observers for dense depth field estimation based on visual data and known camera motion
Identification of arabic word from bilingual text using character features
Automatic Extraction of Open Space Area from High Resolution Urban Satellite Imagery
A comparison of Gap statistic definitions with and without logarithm function
Improved Edge Awareness in Discontinuity Preserving Smoothing
Internal Constraints of the Trifocal Tensor
A Statistical Nonparametric Approach of Face Recognition: Combination of Eigenface & Modified k-Means Clustering
GEOMIR2K9 - A Similar Scene Finder
An Axis-Based Representation for Recognition
A Meshless Method for Variational Nonrigid 2-D Shape Registration
Curved Gabor Filters for Fingerprint Image Enhancement
Convex Approaches to Model Wavelet Sparsity Patterns
Bayesian approach for near-duplicate image detection
Content-Based Spam Filtering on Video Sharing Social Networks
Clustering with Multi-Layer Graphs: A Spectral Perspective
Who clicks there!: Anonymizing the photographer in a camera saturated society
Inferring 3D Articulated Models for Box Packaging Robot
Face Identification from Manipulated Facial Images using SIFT
Automated segmentation of the pulmonary arteries in low-dose CT by vessel tracking
Augmented Reality Implementation Methods in Mainstream Applications
Active Classification: Theory and Application to Underwater Inspection
Image denoising assessment using anisotropic stack filtering
Automatic Road Lighting System (ARLS) Model Based on Image Processing of Moving Object
Analysis and Improvement of Low Rank Representation for Subspace segmentation
Median Algorithm for Sector Spectra Calculation from Images Registered by the Spectral Airglow Temperature Imager
Learning Hypergraph Labeling for Feature Matching
Topographic Feature Extraction for Bengali and Hindi Character Images
Face Recognition using Curvelet Transform
The Chan-Vese Algorithm
On the Hilbert transform of wavelets
Diffeomorphic Metric Mapping of High Angular Resolution Diffusion Imaging based on Riemannian Structure of Orientation Distribution Functions
Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition
Real time face recognition using adaboost improved fast PCA algorithm
Undithering using linear filtering and non-linear diffusion techniques
Compressive Imaging using Approximate Message Passing and a Markov-Tree Prior
The Statistical methods of Pixel-Based Image Fusion Techniques
Advanced phase retrieval: maximum likelihood technique with sparse regularization of phase and amplitude
A Machine Learning Perspective on Predictive Coding with PAQ
Multisensor Images Fusion Based on Feature-Level
Vessel Segmentation in Medical Imaging Using a Tight-Frame Based Algorithm
ShareBoost: Efficient Multiclass Learning with Feature Sharing
Color Texture Classification Approach Based on Combination of Primitive Pattern Units and Statistical Features
Minimax hypothesis testing for curve registration
Curvature Prior for MRF-based Segmentation and Shape Inpainting
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation
MIS-Boost: Multiple Instance Selection Boosting
A Probabilistic Framework for Discriminative Dictionary Learning
Multi-Hypothesis CRF-Segmentation of Neural Tissue in Anisotropic EM Volumes
Online Robust Subspace Tracking from Partial Information
Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications
Low-rank data modeling via the Minimum Description Length principle
The Statistical Inefficiency of Sparse Coding for Images (or, One Gabor to Rule them All)
A Novel comprehensive method for real time Video Motion Detection Surveillance
Distributed Lossy Source Coding Using Real-Number Codes
Securing Biometric Images using Reversible Watermarking
Face Recognition Using Discrete Cosine Transform for Global and Local Features
Efficient Hierarchical Markov Random Fields for Object Detection on a Mobile Robot
Digital Manifolds and the Theorem of Jordan-Brouwer
A prototype system for handwritten sub-word recognition: Toward Arabic-manuscript transliteration
Multi-font Multi-size Kannada Numeral Recognition Based on Structural Features
Redundant Wavelets on Graphs and High Dimensional Data Clouds
Facial Asymmetry and Emotional Expression
Suboptimality of Nonlocal Means for Images with Sharp Edges
Efficient Adaptive Compressive Sensing Using Sparse Hierarchical Learned Dictionaries
Learning joint intensity-depth sparse representations
A United Image Force for Deformable Models and Direct Transforming Geometric Active Contorus to Snakes by Level Sets
Identifying and Analysis of Scene Mining Methods Beased on Scenes Extracted Features
Nonparametric Sparse Representation
G-Lets: Signal Processing Using Transformation Groups
Enhancing Volumetric Bouligand-Minkowski Fractal Descriptors by using Functional Data Analysis
Shape analysis using fractal dimension: a curvature based approach
Fractal Descriptors in the Fourier Domain Applied to Color Texture Analysis
Fractal and Multi-Scale Fractal Dimension analysis: a comparative study of Bouligand-Minkowski method
Variations of images to increase their visibility
Fractal Descriptors Based on Fourier Spectrum Applied to Texture Analysis
Charge migration in organic materials: Can propagating charges affect the key physical quantities controlling their motion?
RT-SLAM: A Generic and Real-Time Visual SLAM Implementation
Feature selection using nearest attributes
Examplers based image fusion features for face recognition
Resolving Implementation Ambiguity and Improving SURF
Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation
Combined Haar-Hilbert and Log-Gabor Based Iris Encoders
A better Beta for the H measure of classification performance
No-reference image quality assessment through the von Mises distribution
Segmentation of Offline Handwritten Bengali Script
A Simple Unsupervised Color Image Segmentation Method based on MRF-MAP
Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain
Multilevel Image Encryption
A new hybrid jpeg image compression scheme using symbol reduction technique
Left-Invariant Diffusion on the Motion Group in terms of the Irreducible Representations of SO(3)
Fast approximations to structured sparse coding and applications to object classification
Stable image reconstruction using total variation minimization
Perturbation of the Eigenvectors of the Graph Laplacian: Application to Image Denoising
Multi-Level Feature Descriptor for Robust Texture Classification via Locality-Constrained Collaborative Strategy
A Report on Multilinear PCA Plus Multilinear LDA to Deal with Tensorial Data: Visual Classification as An Example
Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior
An MLP based Approach for Recognition of Handwritten `Bangla' Numerals
Learning Random Kernel Approximations for Object Recognition
Substructure and Boundary Modeling for Continuous Action Recognition
Hybrid Poisson and multi-Bernoulli filters
Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer
Hybrid Generative/Discriminative Learning for Automatic Image Annotation
A stochastic algorithm for probabilistic independent component analysis
Handwritten digit Recognition using Support Vector Machine
Scilab and SIP for Image Processing
Notions of Chaotic Cryptography: Sketch of a Chaos based Cryptosystem
Clustering Using Isoperimetric Number of Trees
Reconstruction of hidden 3D shapes using diffuse reflections
A Local Approach for Identifying Clusters in Networks
Kernel Density Feature Points Estimator for Content-Based Image Retrieval
Face Expression Recognition and Analysis: The State of the Art
Validation of nonlinear PCA
Efficient Fruit Defect Detection and Glare removal Algorithm by anisotropic diffusion and 2D Gabor filter
Continuous Markov Random Fields for Robust Stereo Estimation
Multi-Level Coding Efficiency with Improved Quality for Image Compression based on AMBTC
Robust Nonnegative Matrix Factorization via $L_1$ Norm Regularization
Video In Sentences Out
Seeing Unseeability to See the Unseeable
Statistical Multiresolution Estimation for Variational Imaging: With an Application in Poisson-Biophotonics
A New Approach of Improving CFA Image for Digital Camera's
Background subtraction based on Local Shape
A 3D Segmentation Method for Retinal Optical Coherence Tomography Volume Data
Active Contour with A Tangential Component
Elimination of Glass Artifacts and Object Segmentation
OCT Segmentation Survey and Summary Reviews and a Novel 3D Segmentation Algorithm and a Proof of Concept Implementation
Image Enhancement with Statistical Estimation
DBC based Face Recognition using DWT
A novel statistical fusion rule for image fusion and its comparison in non subsampled contourlet transform domain and wavelet domain
M-FISH Karyotyping - A New Approach Based on Watershed Transform
Are visual dictionaries generalizable?
Learning Mixed Graphical Models
Locally Orderless Registration
A Brief Summary of Dictionary Learning Based Approach for Classification
A Brief Summary of Dictionary Learning Based Approach for Classification (revised)
An Unsupervised Dynamic Image Segmentation using Fuzzy Hopfield Neural Network based Genetic Algorithm
Template-Cut: A Pattern-Based Segmentation Paradigm
ICT's role in e-Governance in India and Malaysia: A Review
Dimension Reduction by Mutual Information Discriminant Analysis
Comments on "On Approximating Euclidean Metrics by Weighted t-Cost Distances in Arbitrary Dimension"
Revolvable Indoor Panoramas Using a Rectified Azimuthal Projection
Image Similarity Using Sparse Representation and Compression Distance
Blind PSF estimation and methods of deconvolution optimization
The Ultrasound Visualization Pipeline - A Survey
A Linear Approximation to the chi^2 Kernel with Geometric Convergence
Manifold Relevance Determination
Total Variation and Euler's Elastica for Supervised Learning
Learning Efficient Structured Sparse Models
Dimensionality Reduction by Local Discriminative Gaussians
Clustering by Low-Rank Doubly Stochastic Matrix Decomposition
Dynamic Domain Classification for Fractal Image Compression
A generic framework for video understanding applied to group behavior recognition
Learning Invariant Representations with Local Transformations
Deep Lambertian Networks
Learning Object Arrangements in 3D Scenes using Human Context
Differentiable Pooling for Hierarchical Feature Learning
Local Water Diffusion Phenomenon Clustering From High Angular Resolution Diffusion Imaging (HARDI)
PAC-Bayesian Majority Vote for Late Classifier Fusion
Molecular Biology at the Quantum Level: Can Modern Density Functional Theory Forge the Path?
Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks
An Innovative Skin Detection Approach Using Color Based Image Retrieval Technique
Non-Convex Rank Minimization via an Empirical Bayesian Approach
Kernelized Supervised Dictionary Learning
A Novel Approach Coloured Object Tracker with Adaptive Model and Bandwidth using Mean Shift Algorithm
Camera identification by grouping images from database, based on shared noise patterns
Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking
Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models
Designing various component analysis at will
Image Labeling on a Network: Using Social-Network Metadata for Image Classification
Polarimetric SAR Image Segmentation with B-Splines and a New Statistical Model
A Two-Stage Combined Classifier in Scale Space Texture Classification
Piecewise Linear Patch Reconstruction for Segmentation and Description of Non-smooth Image Structures
Guarantees of Augmented Trace Norm Models in Tensor Recovery
Towards a theory of statistical tree-shape analysis
Autofocus Correction of Azimuth Phase Error and Residual Range Cell Migration in Spotlight SAR Polar Format Imagery
Human Activity Learning using Object Affordances from RGB-D Videos
Contour Completion Around a Fixation Point
Improved Total Variation based Image Compressive Sensing Recovery by Nonlocal Regularization
Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Iterative graph cuts for image segmentation with a nonlinear statistical shape prior
A Unified Approach for Modeling and Recognition of Individual Actions and Group Activities
The Segmentation Fusion Method On10 Multi-Sensors
Are You Imitating Me? Unsupervised Sparse Modeling for Group Activity Analysis from a Single Video
Benchmarking recognition results on word image datasets
Short-time homomorphic wavelet estimation
Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds
On the Use of Lee's Protocol for Speckle-Reducing Techniques
Blind Image Deblurring by Spectral Properties of Convolution Operators
Visual Tracking with Similarity Matching Ratio
Hirarchical Digital Image Inpainting Using Wavelets
Detection and Classification of Viewer Age Range Smart Signs at TV Broadcast
The Pascal Triangle of a Discrete Image: Definition, Properties and Application to Shape Analysis
Combined Descriptors in Spatial Pyramid Domain for Image Classification
Super-resolution using Sparse Representations over Learned Dictionaries: Reconstruction of Brain Structure using Electron Microscopy
Blurred Image Classification based on Adaptive Dictionary
Robust Degraded Face Recognition Using Enhanced Local Frequency Descriptor and Multi-scale Competition
Learning Human Activities and Object Affordances from RGB-D Videos
Video De-fencing
Near-optimal compressed sensing guarantees for total variation minimization
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures
Contemporary Semantic Web Service Frameworks: An Overview and Comparisons
Notes on image annotation
On the Role of Contrast and Regularity in Perceptual Boundary Saliency
Image Processing using Smooth Ordering of its Patches
Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study
Epitome for Automatic Image Colorization
A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning
DBN-Based Combinatorial Resampling for Articulated Object Tracking
Nested Dictionary Learning for Hierarchical Organization of Imagery and Text
OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
Multi-Stage Multi-Task Feature Learning
Textural Approach to Palmprint Identification
Managing sparsity, time, and quality of inference in topic models
A Multiscale Framework for Challenging Discrete Optimization
Recognizing Static Signs from the Brazilian Sign Language: Comparing Large-Margin Decision Directed Acyclic Graphs, Voting Support Vector Machines and Artificial Neural Networks
Performance Evaluation of Different Techniques for texture Classification
Mugshot Identification from Manipulated Facial Images
Dimensionality Reduction and Classification Feature Using Mutual Information Applied to Hyperspectral Images: A Wrapper Strategy Algorithm Based on Minimizing the Error Probability Using the Inequality of Fano
Implementation of Radon Transformation for Electrical Impedance Tomography (EIT)
Handwritten digit recognition by bio-inspired hierarchical networks
From Bits to Images: Inversion of Local Binary Descriptors
Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms
Learning Monocular Reactive UAV Control in Cluttered Natural Environments
Tangent-based manifold approximation with locally linear models
Fourier-Bessel rotational invariant eigenimages
Deep Attribute Networks
Rate-Distortion Analysis of Multiview Coding in a DIBR Framework
Applying Dynamic Model for Multiple Manoeuvring Target Tracking Using Particle Filtering
Five Modulus Method For Image Compression
An Effective Method for Fingerprint Classification
Content based video retrieval
The Nature of Quantum States Created by One Photon Absorption: Pulsed Coherent vs. Pulsed Incoherent Light
A New Automatic Method to Adjust Parameters for Object Recognition
Secure voice based authentication for mobile devices: Vaulted Voice Verification
Artificial Neural Network Fuzzy Inference System (ANFIS) For Brain Tumor Detection
Inverting and Visualizing Features for Object Detection
Visual Objects Classification with Sliding Spatial Pyramid Matching
Sketch-to-Design: Context-based Part Assembly
Automatic landmark annotation and dense correspondence registration for 3D human facial images
On the Adaptability of Neural Network Image Super-Resolution
In Vivo Quantification of Clot Formation in Extracorporeal Circuits
High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization
Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval
A Semi-automated Statistical Algorithm for Object Separation
Classifier Fusion Method to Recognize Handwritten Kannada Numerals
PaFiMoCS: Particle Filtered Modified-CS and Applications in Visual Tracking across Illumination Change
A novel processing pipeline for optical multi-touch surfaces
A Factorized Variational Technique for Phase Unwrapping in Markov Random Fields
Lattice Particle Filters
Enhancing the retrieval performance by combing the texture and edge features
Robust subspace clustering
Wavelet-based Scale Saliency
Factorized Topic Models
Boltzmann Machines and Denoising Autoencoders for Image Denoising
Pushing Stochastic Gradient towards Second-Order Methods -- Backpropagation Learning with Transformations in Nonlinearities
Learnable Pooling Regions for Image Classification
Deep Predictive Coding Networks
Information Theoretic Learning with Infinitely Divisible Kernels
Big Neural Networks Waste Capacity
Regularized Discriminant Embedding for Visual Descriptor Learning
Zero-Shot Learning Through Cross-Modal Transfer
Convex Variational Image Restoration with Histogram Priors
Discriminative Recurrent Sparse Auto-Encoders
Learning Graphical Models of Images, Videos and Their Spatial Transformations
Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity Estimation from Facial Images
Spread spectrum compressed sensing MRI using chirp radio frequency pulses
Multi-Class Detection and Segmentation of Objects in Depth
An improvement to k-nearest neighbor classifier
Simplifying the Configuration of 802.11 Wireless Networks with Effective SNR
Correcting Camera Shake by Incremental Sparse Approximation
Multi-scale Visual Attention & Saliency Modelling with Decision Theory
Centrality-constrained graph embedding
Hybrid Image Segmentation using Discerner Cluster in FCM and Histogram Thresholding
Image Segmentation in Video Sequences: A Probabilistic Approach
Adaptive low rank and sparse decomposition of video using compressive sensing
Surveillance Video Processing Using Compressive Sensing
pROST : A Smoothed Lp-norm Robust Online Subspace Tracking Method for Realtime Background Subtraction in Video
Assessing Semantic Quality of Web Directory Structure
A Fresnelet-Based Encryption of Medical Images using Arnold Transform
A new scheme of signature extraction for iris authentication
Matching Pursuit LASSO Part II: Applications and Sparse Recovery over Batch Signals
Unsupervised edge map scoring: a statistical complexity approach
Nonparametric Basis Pursuit via Sparse Kernel-based Learning
Image restoration using sparse approximations of spatially varying blur operators in the wavelet domain
Ensemble Sparse Models for Image Analysis
Sparse Shape Reconstruction
On a link between kernel mean maps and Fraunhofer diffraction, with an application to super-resolution beyond the diffraction limit
Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning
Symmetry Based Cluster Approach for Automatic Recognition of the Epileptic Focus in Brain Using PET Scan Image : An Analysis
ALPRS - A New Approach for License Plate Recognition using the Sift Algorithm
Improving Automatic Emotion Recognition from speech using Rhythm and Temporal feature
Least-Squares FIR Models of Low-Resolution MR data for Efficient Phase-Error Compensation with Simultaneous Artefact Removal
Voxel-wise Weighted MR Image Enhancement using an Extended Neighborhood Filter
Bilateral Filter: Graph Spectral Interpretation and Extensions
Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification
Material quality assessment of silk nanofibers based on swarm intelligence
Which research in design creativity and innovation? Let us not forget the reality of companies
Methods Of Measurement The Three-Dimensional Wind Waves Spectra, Based On The Processing Of Video Images Of The Sea Surface
Cortical Surface Co-Registration based on MRI Images and Photos
Machine learning of hierarchical clustering to segment 2D and 3D images
Performance Evaluation of Edge-Directed Interpolation Methods for Images
An N-dimensional approach towards object based classification of remotely sensed imagery
An intelligent approach towards automatic shape modeling and object extraction from satellite images using cellular automata based algorithm
An investigation towards wavelet based optimization of automatic image registration techniques
Inductive Hashing on Manifolds
An Adaptive Descriptor Design for Object Recognition in the Wild
On the Convergence and Consistency of the Blurring Mean-Shift Process
How to find real-world applications for compressive sensing
A new framework for optimal classifier design
Early Detection of Alzheimer's - A Crucial Requirement
A Bag of Words Approach for Semantic Segmentation of Monitored Scenes
Classification for Big Dataset of Bioacoustic Signals Based on Human Scoring System and Artificial Neural Network
Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network
Machine learning on images using a string-distance
Mobile Network Anomaly Detection and Mitigation: The NEMESYS Approach
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Efficient Image Retargeting for High Dynamic Range Scenes
A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation
Nonnegative Tensor Factorization, Completely Positive Tensors and an Hierarchical Elimination Algorithm
Edge Detection in Radar Images Using Weibull Distribution
Matrices of forests, analysis of networks, and ranking problems
A Local Active Contour Model for Image Segmentation with Intensity Inhomogeneity
Lensless Imaging by Compressive Sensing
Robust Hyperspectral Unmixing with Correntropy based Metric
An Analysis of the Connections Between Layers of Deep Neural Networks
PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities
Statistical Denoising for single molecule fluorescence microscopic images
Emotional Expression Classification using Time-Series Kernels
Discriminative k-means clustering
Hand Gesture Recognition Based on Karhunen-Loeve Transform
Recurrent Convolutional Neural Networks for Scene Parsing
Optimization of Clustering for Clustering-based Image Denoising
The Ripple Pond: Enabling Spiking Networks to See
Learning to encode motion using spatio-temporal synchrony
Feature Learning by Multidimensional Scaling and its Applications in Object Recognition
Matching objects across the textured-smooth continuum
Live-wire 3D medical images segmentation
Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be?
Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior
Two-View Matching with View Synthesis Revisited
Multi-view in Lensless Compressive Imaging
Finite Element Based Tracking of Deforming Surfaces
Fine-Grained Visual Classification of Aircraft
New Approach of Estimating PSNR-B For De-blocked Images
A maximal-information color to gray conversion method for document images: Toward an optimal grayscale representation for document image binarization
Active Contour Models for Manifold Valued Image Segmentation
New Mathematical and Algorithmic Schemes for Pattern Classification with Application to the Identification of Writers of Important Ancient Documents
Hyperspectral Data Unmixing Using GNMF Method and Sparseness Constraint
Multilevel Threshold Based Gray Scale Image Segmentation using Cuckoo Search
A Novel Robust Method to Add Watermarks to Bitmap Images by Fading Technique
Further results on dissimilarity spaces for hyperspectral images RF-CBIR
Toward Guaranteed Illumination Models for Non-Convex Objects
Detection of Outer Rotations on 3D-Vector Fields with Iterative Geometric Correlation and its Efficiency
Anisotropic Diffusion for Details Enhancement in Multi-Exposure Image Fusion
Fast Exact Search in Hamming Space with Multi-Index Hashing
Contrast Enhancement And Brightness Preservation Using Multi- Decomposition Histogram Equalization
Modified SPLICE and its Extension to Non-Stereo Data for Noise Robust Speech Recognition
Processing stationary noise: model and parameter selection in variational methods
Automated Defect Localization via Low Rank Plus Outlier Modeling of Propagating Wavefield Data
Using a Dynamic Neural Field Model to Explore a Direct Collicular Inhibition Account of Inhibition of Return
Understanding Humans' Strategies in Maze Solving
Appearance Descriptors for Person Re-identification: a Comprehensive Review
An Adaptive GMM Approach to Background Subtraction for Application in Real Time Surveillance
Matching-Constrained Active Contours
Self-Learning for Player Localization in Sports Video
Union of Low-Rank Subspaces Detector
A Study on Unsupervised Dictionary Learning and Feature Encoding for Action Classification
Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm
Radar shadow detection in SAR images using DEM and projections
Single image super resolution in spatial and wavelet domain
Real-Time and Continuous Hand Gesture Spotting: an Approach Based on Artificial Neural Networks
Contour Manifolds and Optimal Transport
Robust Periocular Recognition By Fusing Sparse Representations of Color and Geometry Information
Recovery guarantees for exemplar-based clustering
Visual-Semantic Scene Understanding by Sharing Labels in a Context Network
SEEDS: Superpixels Extracted via Energy-Driven Sampling
Sparsity Based Poisson Denoising with Dictionary Learning
Photon counting compressive depth mapping
GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images
A novel approach for nose tip detection using smoothing by weighted median filtering applied to 3D face images in variant poses
Detection of pose orientation across single and multiple axes in case of 3D face images
A novel approach to nose-tip and eye corners detection using H-K Curvature Analysis in case of 3D images
Blind Deconvolution via Maximum Kurtosis Adaptive Filtering
Latent Fisher Discriminant Analysis
Online Algorithms for Factorization-Based Structure from Motion
Efficient Algorithms for Robust and Stable Principal Component Pursuit Problems
An Efficient Index for Visual Search in Appearance-based SLAM
Adopting level set theory based algorithms to segment human ear
Face Verification Using Boosted Cross-Image Features
Identificación y Registro Catastral de Cuerpos de Agua mediante Técnicas de Procesamiento Digital de Imagenes
Object Detection Using Keygraphs
Electricity Market Forecasting via Low-Rank Multi-Kernel Learning
Spatially Scalable Compressed Image Sensing with Hybrid Transform and Inter-layer Prediction Model
Second order scattering descriptors predict fMRI activity due to visual textures
A Novel Progressive Image Scanning and Reconstruction Scheme based on Compressed Sensing and Linear Prediction
Director Field Model of the Primary Visual Cortex for Contour Detection
A Splitting Augmented Lagrangian Method for Low Multilinear-Rank Tensor Recovery
End-to-End Text Recognition with Hybrid HMM Maxout Models
Early Fire Detection Using HEP and Space-time Analysis
Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets
From Shading to Local Shape
An Improved K-means Clustering Based Approach to Detect a DNA Structure in H&E Image of Mouse Tissue Reacted with CD4-Green Antigen
New Ways to Promote Sustainability and Social Well-Being in a Complex, Strongly Interdependent World: The FuturICT Approach
Mapping the stereotyped behaviour of freely-moving fruit flies
Fine-grained Categorization -- Short Summary of our Entry for the ImageNet Challenge 2012
Principal motion components for gesture recognition using a single-example
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Ship Detection and Segmentation using Image Correlation
RANSAC: Identification of Higher-Order Geometric Features and Applications in Humanoid Robot Soccer
Fusion of Hyperspectral and Panchromatic Images using Spectral Uumixing Results
Pseudo vs. True Defect Classification in Printed Circuits Boards using Wavelet Features
Two Dimensional Array Imaging with Beam Steered Data
Compressed Sensing SAR Imaging with Multilook Processing
Impulse Noise Removal In Speech Using Wavelets
Robust Compressed Sensing and Sparse Coding with the Difference Map
Structure-preserving color transformations using Laplacian commutativity
Reconstruction of Complex-Valued Fractional Brownian Motion Fields Based on Compressive Sampling and Its Application to PSF Interpolation in Weak Lensing Survey
Tracking Deformable Parts via Dynamic Conditional Random Fields
A Parallel Compressive Imaging Architecture for One-Shot Acquisition
Motion and audio analysis in mobile devices for remote monitoring of physical activities and user authentication
Face Recognition via Globality-Locality Preserving Projections
Biometric Signature Processing & Recognition Using Radial Basis Function Network
A new stopping criterion for the mean shift iterative algorithm
Volumetric Reconstruction Applied to Perceptual Studies of Size and Weight
Visualizing and Understanding Convolutional Networks
An Efficient Method for Recognizing the Low Quality Fingerprint Verification by Means of Cross Correlation
The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video
Describing Textures in the Wild
Periodicity Extraction using Superposition of Distance Matching Function and One-dimensional Haar Wavelet Transform
Blind Deconvolution with Non-local Sparsity Reweighting
A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement
Reflection methods for user-friendly submodular optimization
Texture descriptor combining fractal dimension and artificial crawlers
Adaptive Learning of Region-based pLSA Model for Total Scene Annotation
PANDA: Pose Aligned Networks for Deep Attribute Modeling
On Nonrigid Shape Similarity and Correspondence
A brief network analysis of Artificial Intelligence publication
Local Similarities, Global Coding: An Algorithm for Feature Coding and its Applications
On Approximate Inference for Generalized Gaussian Process Models
Hilditchs Algorithm Based Tamil Character Recognition
Cross-Domain Sparse Coding
Unobtrusive Low Cost Pupil Size Measurements using Web cameras
Shape from Texture using Locally Scaled Point Processes
Dual coordinate solvers for large-scale structural SVMs
Multi-frame denoising of high speed optical coherence tomography data using inter-frame and intra-frame priors
Scalable Object Detection using Deep Neural Networks
On the Performance of Filters for Reduction of Speckle Noise in SAR Images off the Coast of the Gulf of Guinea
Fast Neighborhood Graph Search using Cartesian Concatenation
Unsupervised learning of depth and motion
Sparse Matrix-based Random Projection for Classification
ECOC-Based Training of Neural Networks for Face Recognition
One-Shot-Learning Gesture Recognition using HOG-HOF Features
Teleoperation System Using Past Image Records Considering Narrow Communication Band
Decomposition of Optical Flow on the Sphere
Learned versus Hand-Designed Feature Representations for 3d Agglomeration
Total variation with overlapping group sparsity for image deblurring under impulse noise
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Growing Regression Forests by Classification: Applications to Object Pose Estimation
Top Down Approach to Multiple Plane Detection
Sequentially Generated Instance-Dependent Image Representations for Classification
3D Interest Point Detection via Discriminative Learning
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Finding More Relevance: Propagating Similarity on Markov Random Field for Image Retrieval
Correlation-based construction of neighborhood and edge features
Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods
Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition
Collaborative Discriminant Locality Preserving Projections With its Application to Face Recognition
Total variation regularization for manifold-valued data
A Novel Approach For Generating Face Template Using Bda
System Analysis And Design For Multimedia Retrieval Systems
Medical Image Fusion: A survey of the state of the art
Hybrid Approach to Face Recognition System using Principle component and Independent component with score based fusion process
Adaptive-Rate Compressive Sensing Using Side Information
Machine Assisted Authentication of Paper Currency: an Experiment on Indian Banknotes
ConceptVision: A Flexible Scene Classification Framework
Context-Aware Hypergraph Construction for Robust Spectral Clustering
From Kernel Machines to Ensemble Learning
Feature Selection Using Classifier in High Dimensional Data
Fast nonparametric clustering of structured time-series
Image reconstruction from few views by L0-norm optimization
Enhancement performance of road recognition system of autonomous robots in shadow scenario
Satellite image classification and segmentation using non-additive entropy
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs
Multilinear Wavelets: A Statistical Shape Space for Human Faces
Tensor Representation and Manifold Learning Methods for Remote Sensing Images
Application of the Modified Fractal Signature Method for Terrain Classification from Synthetic Aperture Radar Images
An Enhanced Method For Evaluating Automatic Video Summaries
Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification
Learning $\ell_1$-based analysis and synthesis sparsity priors using bi-level optimization
Distortion-driven Turbulence Effect Removal using Variational Model
Humanoid Robot With Vision Recognition Control System
An Analysis of Random Projections in Cancelable Biometrics
Enhancing Template Security of Face Biometrics by Using Edge Detection and Hashing
Image Block Loss Restoration Using Sparsity Pattern as Side Information
Face Verification Using Kernel Principle Component Analysis
Face Verification System based on Integral Normalized Gradient Image(INGI)
Image enhancement using fusion by wavelet transform and laplacian pyramid
Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization
Information quantity in a pixel of digital image
A Generalized Probabilistic Framework for Compact Codebook Creation
Cross-calibration of Time-of-flight and Colour Cameras
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval
Multiview Hessian regularized logistic regression for action recognition
K-Tangent Spaces on Riemannian Manifolds for Improved Pedestrian Detection
Collaborative Representation for Classification, Sparse or Non-sparse?
Illumination,Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real-Time Applications
Automated Tracking and Estimation for Control of Non-rigid Cloth
Feature Extraction of ECG Signal Using HHT Algorithm
Compressive Hyperspectral Imaging Using Progressive Total Variation
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
3D Well-composed Polyhedral Complexes
A Novel Method to Extract Rocks from Mars Images
Spontaneous expression classification in the encrypted domain
Geometric VLAD for Large Scale Image Search
Structured Sparse Method for Hyperspectral Unmixing
A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems
An Efficient Method for Face Recognition System In Various Assorted Conditions
SmartAnnotator: An Interactive Tool for Annotating RGBD Indoor Images
SRA: Fast Removal of General Multipath for ToF Sensors
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information
Selective Factor Extraction in High Dimensions
Image Retargeting by Content-Aware Synthesis
Optimized imaging using non-rigid registration
Pyramidal Fisher Motion for Multiview Gait Recognition
Compressive Pattern Matching on Multispectral Data
Expectation-Maximization Technique and Spatial-Adaptation Applied to Pel-Recursive Motion Estimation
Active Deformable Part Models
A Continuous Max-Flow Approach to General Hierarchical Multi-Labeling Problems
Enabling Automatic Certification of Online Auctions
Sparse Wavelet Representations of Spatially Varying Blurring Operators
Resolving Multi-path Interference in Time-of-Flight Imaging via Modulation Frequency Diversity and Sparse Regularization
Recognition of Handwritten MODI Numerals using Hu and Zernike features
Improving Bilayer Product Quantization for Billion-Scale Approximate Nearest Neighbors in High Dimensions
RANCOR: Non-Linear Image Registration with Total Variation Regularization
Bayesian image segmentations by Potts prior and loopy belief propagation
Shrinkage Optimized Directed Information using Pictorial Structures for Action Recognition
Recover Canonical-View Faces in the Wild with Deep Neural Networks
Online Group Feature Selection
Robust Face Recognition via Adaptive Sparse Representation
Geometric Abstraction from Noisy Image-Based 3D Reconstructions
A higher-order MRF based variational model for multiplicative noise reduction
Fast Approximate Matching of Cell-Phone Videos for Robust Background Subtraction
Linking Geographic Vocabularies through WordNet
Large Margin Image Set Representation and Classification
Find my mug: Efficient object search with a mobile robot using semantic segmentation
Maximum Margin Vector Correlation Filter
Improving weather radar by fusion and classification
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images
Structural Group Sparse Representation for Image Compressive Sensing Recovery
VSCAN: An Enhanced Video Summarization using Density-based Spatial Clustering
Rule of Three for Superresolution of Still Images with Applications to Compression and Denoising
A Continuous Max-Flow Approach to Multi-Labeling Problems under Arbitrary Region Regularization
Comparing apples to apples in the evaluation of binary coding methods
Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes
New Algorithmic Approaches to Point Constellation Recognition
Texture Based Image Segmentation of Chili Pepper X-Ray Images Using Gabor Filter
Precision Enhancement of 3D Surfaces from Multiple Compressed Depth Maps
Variational Image Segmentation Model Coupled with Image Restoration Achievements
An Overview of Face Liveness Detection
Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
Hyperspectral pan-sharpening: a variational convex constrained formulation to impose parallel level lines, solved with ADMM
Anomaly-Sensitive Dictionary Learning for Unsupervised Diagnostics of Solid Media
Cross-view Action Modeling, Learning and Recognition
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
Active Mining of Parallel Video Streams
Kronecker PCA Based Spatio-Temporal Modeling of Video for Dismount Classification
Sparsity Based Methods for Overparameterized Variational Problems
Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word Recognition
Iterative Non-Local Shrinkage Algorithm for MR Image Reconstruction
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT
Improvements and Experiments of a Compact Statistical Background Model
Multi-view Metric Learning for Multi-view Video Summarization
Supervised Dictionary Learning by a Variational Bayesian Group Sparse Nonnegative Matrix Factorization
Large Scale, Large Margin Classification using Indefinite Similarity Measures
Sampling, splines and frames on compact manifolds
Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition
Feature sampling and partitioning for visual vocabulary generation on large action classification datasets
DEM Registration and Error Analysis using ASCII values
Identifying Outliers in Large Matrices via Randomized Adaptive Compressive Sampling
A New Path to Construct Parametric Orientation Field: Sparse FOMFE Model and Compressed Sparse FOMFE Model
Deep Poselets for Human Detection
Solving QVIs for Image Restoration with Adaptive Constraint Sets
Expanding the Family of Grassmannian Kernels: An Embedding Perspective
Weakly Supervised Action Labeling in Videos Under Ordering Constraints
A Cylindrical Basis Function for Solving Partial Differential Equations on Manifolds
Homophilic Clustering by Locally Asymmetric Geometry
The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings
PAINTER: a spatio-spectral image reconstruction algorithm for optical interferometry
Online Stroke and Akshara Recognition GUI in Assamese Language Using Hidden Markov Model
Classifiers fusion method to recognize handwritten persian numerals
Classifying Fonts and Calligraphy Styles Using Complex Wavelet Transform
Offline handwritten signature identification using adaptive window positioning techniques
ARTOS -- Adaptive Real-Time Object Detection System
FAME: Face Association through Model Evolution
An SVM Based Approach for Cardiac View Planning
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations
An Enhancement Neighborhood connected Segmentation for 2D-Cellular Image
A New Approach for Super resolution by Using Web Images and FFT Based Image Registration
Recovery of Images with Missing Pixels using a Gradient Compressive Sensing Algorithm
Aggregate channel features for multi-view face detection
Hand Pointing Detection Using Live Histogram Template of Forehead Skin
Certifying the Existence of Epipolar Matrices
The U-curve optimization problem: improvements on the original algorithm and time complexity analysis
Joint Energy-based Detection and Classificationon of Multilingual Text Lines
scikit-image: Image processing in Python
Learning Structured Outputs from Partial Labels using Forest Ensemble
Novel and Fast Algorithm for Extracting License Plate Location Based on Edge Analysis
A unified framework for thermal face recognition
A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset
A Fast Hierarchical Method for Multi-script and Arbitrary Oriented Scene Text Extraction
Accurate merging of images for predictive analysis using combined image
A New Model of Array Grammar for generating Connected Patterns on an Image Neighborhood
Variational Depth from Focus Reconstruction
Methodology For Detection of QRS Pattern Using Secondary Wavelets
Adaptive Wavelet Based Identification and Extraction of PQRST Combination in Randomly Stretching ECG Sequence
Determining the Number of Clusters via Iterative Consensus Clustering
A Flexible Iterative Framework for Consensus Clustering
It is hard to see a needle in a haystack: Modeling contrast masking effect in a numerical observer
Formation of General Position by Asynchronous Mobile Robots
Multidimensional Digital Filters for Point-Target Detection in Cluttered Infrared Scenes
Spectral Unmixing of Hyperspectral Imagery using Multilayer NMF
Hashing for Similarity Search: A Survey
Human Activity Learning and Segmentation using Partially Hidden Discriminative Models
Robust Statistical Ranking: Theory and Algorithms
Parallel software implementation of recursive multidimensional digital filters for point-target detection in cluttered infrared scenes
Motion Deblurring for Plenoptic Images
Highly Accurate Multispectral Palmprint Recognition Using Statistical and Wavelet Features
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition
Action Classification with Locality-constrained Linear Coding
Learning Deep Representation for Face Alignment with Auxiliary Attributes
What makes an Image Iconic? A Fine-Grained Case Study
Unsupervised Parallel Extraction based Texture for Efficient Image Representation
Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods
GIMP and Wavelets for Medical Image Processing: Enhancing Images of the Fundus of the Eye
Unsupervised Spike Sorting Based on Discriminative Subspace Learning
Hierarchical Saliency Detection on Extended CSSD
Learn Convolutional Neural Network for Face Anti-Spoofing
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
Sparse Graph-based Transduction for Image Classification
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging
Multispectral Palmprint Recognition Using Textural Features
Text Line Identification in Tagore's Manuscript
Temporal Extension of Scale Pyramid and Spatial Pyramid Matching for Action Recognition
Riemannian Multi-Manifold Modeling
Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Forests
Multidimensional Digital Smoothing Filters for Target Detection
Group Orbit Optimization: A Unified Approach to Data Normalization
A Model of Plant Identification System Using GLCM, Lacunarity And Shen Features
Learning Invariant Color Features for Person Re-Identification
Memristive Threshold Logic Circuit Design of Fast Moving Object Detection
Hierarchical Sparse and Collaborative Low-Rank Representation for Emotion Recognition
Face Detection Using Radial Basis Functions Neural Networks With Fixed Spread
Image Denoising using New Adaptive Based Median Filters
An Aerial Image Recognition Framework using Discrimination and Redundancy Quality Measure
A unified approach for multi-object triangulation, tracking and camera calibration
Tag Relevance Fusion for Social Image Retrieval
Zero-Shot Object Recognition System based on Topic Model
Crowd Saliency Detection via Global Similarity Structure
Detection of Salient Regions in Crowded Scenes
Online interpretation of numeric sign language using 2-d skeletal model
Implicit segmentation of Kannada characters in offline handwriting recognition using hidden Markov models
A Gesture Recognition System for Detecting Behavioral Patterns of ADHD
Graph-Sparse LDA: A Topic Model with Structured Sparsity
Randomized Structural Sparsity via Constrained Block Subsampling for Improved Sensitivity of Discriminative Voxel Identification
Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy
Compositional Structure Learning for Action Understanding
Foreground-Background Segmentation Based on Codebook and Edge Detector
A Novel Visual Word Co-occurrence Model for Person Re-identification
A Framework for On-Line Devanagari Handwritten Character Recognition
Directional Bilateral Filters
A method for context-based adaptive QRS clustering in real-time
Visual Chunking: A List Prediction Framework for Region-Based Object Detection
Deep Structured learning for mass segmentation from Mammograms
Robust Piecewise-Constant Smoothing: M-Smoother Revisited
Super-resolution method using sparse regularization for point-spread function recovery
Collaborative Multi-sensor Classification via Sparsity-based Representation
Extended Dynamic Programming and Fast Multidimensional Search Algorithm for Energy Minization in Stereo and Motion
A comparison of dense region detectors for image search and fine-grained classification
An ensemble-based system for automatic screening of diabetic retinopathy
An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading
Symmetric low-rank representation for subspace clustering
Generalized Adaptive Dictionary Learning via Domain Shift Minimization
Detection of texts in natural images
Geodesic Exponential Kernels: When Curvature and Linearity Conflict
High Dynamic Range Imaging by Perceptual Logarithmic Exposure Merging
Non Binary Local Gradient Contours for Face Recognition
Simultaneous Localization, Mapping, and Manipulation for Unsupervised Object Discovery
A random algorithm for low-rank decomposition of large-scale matrices with missing entries
Large-Margin Determinantal Point Processes
Fast Mesh-Based Medical Image Registration
Stacked Quantizers for Compositional Vector Compression
Infinite Object Coating in the Amoebot Model
Applications of sampling Kantorovich operators to thermographic images for seismic engineering
3D Shape Estimation from 2D Landmarks: A Convex Relaxation Approach
Person Re-identification Based on Color Histogram and Spatial Configuration of Dominant Color Regions
Window-Based Descriptors for Arabic Handwritten Alphabet Recognition: A Comparative Study on a Novel Dataset
Gaze Stabilization for Humanoid Robots: a Comprehensive Framework
Sparse And Low Rank Decomposition Based Batch Image Alignment for Speckle Reduction of retinal OCT Images
Fully Convolutional Networks for Semantic Segmentation
A Faster Method for Tracking and Scoring Videos Corresponding to Sentences
Joint cross-domain classification and subspace learning for unsupervised adaptation
TILDE: A Temporally Invariant Learned DEtector
AlexU-Word: A New Dataset for Isolated-Word Closed-Vocabulary Offline Arabic Handwriting Recognition
Designing Deep Networks for Surface Normal Estimation
A Pooling Approach to Modelling Spatial Relations for Image Retrieval and Annotation
Sparse distributed localized gradient fused features of objects
Efficient Media Retrieval from Non-Cooperative Queries
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
Maximum Likelihood Directed Enumeration Method in Piecewise-Regular Object Recognition
Hypercolumns for Object Segmentation and Fine-grained Localization
Category-Specific Object Reconstruction from a Single Image
On the mathematic modeling of non-parametric curves based on cubic Bézier curves
Deep convolutional filter banks for texture recognition and segmentation
Image Classification and Retrieval from User-Supplied Tags
Post-acquisition image based compensation for thickness variation in microscopy section series
Patents used by NPE as an Open Information System in Web 2.0 - Two mini case studies
Bi-objective Optimization for Robust RGB-D Visual Odometry
A statistical reduced-reference method for color image quality assessment
Cross-Modal Learning via Pairwise Constraints
Articulated motion discovery using pairs of trajectories
On Rendering Synthetic Images for Training an Object Detector
Learning Face Representation from Scratch
Multiple object tracking with context awareness
Effective Face Frontalization in Unconstrained Images
Face recognition using color local binary pattern from mutually independent color channels
Non-iterative rigid 2D/3D point-set registration using semidefinite programming
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
Hashing with binary autoencoders
Group $K$-Means
Stem-Calyx Recognition of an Apple using Shape Descriptors
A Novel Technique for Grading of Dates using Shape and Texture Features
Object localization in ImageNet by looking out of the window
Super-resolution MRI Using Finite Rate of Innovation Curves
A Systematic Scheme for Measuring the Performance of the Display-Camera Channel
Online Handwritten Devanagari Stroke Recognition Using Extended Directional Features
A Modified No Search Algorithm for Fractal Image Compression
An Adaptive Neuro-Fuzzy Inference System Modeling for Grid-Adaptive Interpolation over Depth Images
Robust and Real Time Detection of Curvy Lanes (Curves) with Desired Slopes for Driving Assistance and Autonomous Vehicles
Higher dimensional homodyne filtering for suppression of incidental phase artifacts in multichannel MRI
Image enhancement in intensity projected multichannel MRI using spatially adaptive directional anisotropic diffusion
Screen Content Image Segmentation Using Least Absolute Deviation Fitting
Submodular relaxation for inference in Markov random fields
Mind the Gap: Subspace based Hierarchical Domain Adaptation
Improving resolution and depth of astronomical observations via modern mathematical methods for image analysis
A Fast Fractal Image Compression Algorithm Using Predefined Values for Contrast Scaling
Pairwise Constraint Propagation on Multi-View Data
Clustering based on the In-tree Graph Structure and Affinity Propagation
Instance Significance Guided Multiple Instance Boosting for Robust Visual Tracking
Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
DeepHash: Getting Regularization, Depth and Fine-Tuning Right
Handwritten Devanagari Script Segmentation: A non-linear Fuzzy Approach
Design of a novel convex hull based feature set for recognition of isolated handwritten Roman numerals
An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet
Globally Optimal Cell Tracking using Integer Programming
Estimating the Intrinsic Dimension of Hyperspectral Images Using an Eigen-Gap Approach
Unsupervised image segmentation by Global and local Criteria Optimization Based on Bayesian Networks
Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues
Taking a Deeper Look at Pedestrians
Unsupervised Segmentation of Multispectral Images with Cellular Automata
Accurate automatic segmentation of retina layers with emphasis on first layer
An Occlusion Reasoning Scheme for Monocular Pedestrian Tracking in Dynamic Scenes
Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals
Parallel Magnetic Resonance Imaging
Deep Transductive Semi-supervised Maximum Margin Clustering
Geodesic convolutional neural networks on Riemannian manifolds
IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering
Pairwise Rotation Hashing for High-dimensional Features
Weakly Supervised Learning for Salient Object Detection
Co-Regularized Deep Representations for Video Summarization
Gibbs-Ringing Artifact Removal Based on Local Subvoxel-shifts
Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
Quality Control in Crowdsourced Object Segmentation
Application of S-Transform on Hyper kurtosis based Modified Duo Histogram Equalized DIC images for Pre-cancer Detection
Dense Optical Flow Prediction from a Static Image
Learning Temporal Embeddings for Complex Video Analysis
Joint Multi-Leaf Segmentation, Alignment and Tracking from Fluorescence Plant Videos
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
Electron Neutrino Classification in Liquid Argon Time Projection Chamber Detector
Sequence to Sequence -- Video to Text
Modeling Representation of Videos for Anomaly Detection using Deep Learning: A Review
Higher Order Maximum Persistency and Comparison Theorems
Unsupervised Learning of Visual Representations using Videos
Empirical Evaluation of Rectified Activations in Convolutional Network
Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
Adaptive diffusion constrained total variation scheme with application to `cartoon + texture + edge' image decomposition
In Defense of the Direct Perception of Affordances
Contextual Action Recognition with R*CNN
Webly Supervised Learning of Convolutional Networks
Adaptive Nonparametric Image Parsing
Filter characteristics in image decomposition with singular spectrum analysis
DeepBox: Learning Objectness with Convolutional Networks
Learning image representations tied to ego-motion
Subset Feature Learning for Fine-Grained Category Classification
Automatic Script Identification in the Wild
A new Level-set based Protocol for Accurate Bone Segmentation from CT Imaging
Leveraging Image based Prior for Visual Place Recognition
APAC: Augmented PAttern Classification with Neural Networks
Loop-corrected belief propagation for lattice spin models
Multi-scale Volumes for Deep Object Detection and Localization
Unsupervised Object Discovery and Tracking in Video Collections
Robust Real-time Extraction of Fiducial Facial Feature Points using Haar-like Features
Learning Deconvolution Network for Semantic Segmentation
Visual Semantic Role Labeling
U-Net: Convolutional Networks for Biomedical Image Segmentation
Multi-Image Matching via Fast Alternating Minimization
Have a Look at What I See
Character-level Chinese Writer Identification using Path Signature Feature, DropStroke and Deep CNN
Convective regularization for optical flow
Image Reconstruction from Bag-of-Visual-Words
Unsupervised Visual Representation Learning by Context Prediction
Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation
Image aesthetic evaluation using paralleled deep convolution neural network
Multi-scale recognition with DAG-CNNs
Live Video Synopsis for Multiple Cameras
A Posteriori Error Control for the Binary Mumford-Shah Model
Object Modelling with a Handheld RGB-D Camera
Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos
Diffusion Methods for Classification with Pairwise Relationships
Tunnel Surface 3D Reconstruction from Unoriented Image Sequences
Smooth PARAFAC Decomposition for Tensor Completion
Recognition Confidence Analysis of Handwritten Chinese Character with CNN
Sequential Dimensionality Reduction for Extracting Localized Features
Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts
Improving Spatial Codification in Semantic Segmentation
Privacy in the Internet of Things: Threats and Challenges
Invertible Orientation Scores of 3D Images
Estimating Visual Comfort in Stereoscopic Displays Using Electroencephalography: A Proof-of-Concept
CURL: Co-trained Unsupervised Representation Learning for Image Classification
Representing data by sparse combination of contextual data points for classification
Compressive Deconvolution in Medical Ultrasound Imaging
Cross Modal Distillation for Supervision Transfer
TV News Commercials Detection using Success based Locally Weighted Kernel Combination
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
Beyond Semantic Image Segmentation : Exploring Efficient Inference in Video
Spotlight the Negatives: A Generalized Discriminative Latent Model
Iris Recognition Using Scattering Transform and Textural Features
Understanding Intra-Class Knowledge Inside CNN
Learning Structured Ordinal Measures for Video based Face Recognition
Generalized Video Deblurring for Dynamic Scenes
Multi-Type Activity Recognition in Robot-Centric Scenarios
Deep Perceptual Mapping for Thermal to Visible Face Recognition
Face Alignment Assisted by Head Pose Estimation
Unconstrained Facial Landmark Localization with Backbone-Branches Fully-Convolutional Networks
Ensemble of Hankel Matrices for Face Emotion Recognition
Untangling AdaBoost-based Cost-Sensitive Classification. Part I: Theoretical Perspective
Untangling AdaBoost-based Cost-Sensitive Classification. Part II: Empirical Analysis
A Deep Hashing Learning Network
Diagnosing State-Of-The-Art Object Proposal Methods
RBIR Based on Signature Graph
Deep Multimodal Speaker Naming
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
A Parameter-free Affinity Based Clustering
Efficient moving point handling for incremental 3D manifold reconstruction
Clustering Tree-structured Data on Manifold
Rule Of Thumb: Deep derotation for improved fingertip detection
Online Metric-Weighted Linear Representations for Robust Visual Tracking
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Banzhaf Random Forests
Towards Storytelling from Visual Lifelogging: An Overview
Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition
Efficient Face Alignment via Locality-constrained Representation for Robust Recognition
Learning 3D Deformation of Animals from 2D Images
Offline Handwritten Signature Verification - Literature Review
Collaborative Representation Classification Ensemble for Face Recognition
Cross-pose Face Recognition by Canonical Correlation Analysis
When VLAD met Hilbert
People Counting in High Density Crowds from Still Images
Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs
Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification
Multimodal Multipart Learning for Action Recognition in Depth Videos
Mobile Multi-View Object Image Search
Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples
Intensity-only optical compressive imaging using a multiply scattering material and a double phase retrieval approach
RAID: A Relation-Augmented Image Descriptor
A System for Precise End-to-End Delay Measurements in Video Communication
Visual Tracking via Nonnegative Regularization Multiple Locality Coding
Efficient Object Detection for High Resolution Images
On the Existence of Epipolar Matrices
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
Euclidean Auto Calibration of Camera Networks: Baseline Constraint Removes Scale Ambiguity
Structured Transforms for Small-Footprint Deep Learning
Building Resource Adaptive Software Systems (BRASS): Objectives and System Evaluation
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation
On the Definiteness of Earth Mover's Distance Yields and Its Relation to Set Intersection
Wavelet Frame Based Image Restoration Using Sparsity, Nonlocal and Support Prior of Frame Coefficients
TagBook: A Semantic Video Representation without Supervision for Event Detection
Temporal Dynamic Appearance Modeling for Online Multi-Person Tracking
On 1-Laplacian Elliptic Equations Modeling Magnetic Resonance Image Rician Denoising
Spatial Semantic Regularisation for Large Scale Object Detection
Using Anatomical Markers for Left Ventricular Segmentation of Long Axis Ultrasound Images
Multiresolution Search of the Rigid Motion Space for Intensity Based Registration
Filtrated Spectral Algebraic Subspace Clustering
A Novel Approach for Human Action Recognition from Silhouette Images
Sparsity-aware Possibilistic Clustering Algorithms
Beyond Spatial Pyramid Matching: Space-time Extended Descriptor for Action Recognition
A Brief Survey of Image Processing Algorithms in Electrical Capacitance Tomography
Shape Complexes in Continuous Max-Flow Hierarchical Multi-Labeling Problems
No Spare Parts: Sharing Part Detectors for Image Categorization
Content adaptive screen image scaling
Predicting popularity of online videos using Support Vector Regression
Efficient Unsupervised Temporal Segmentation of Motion Data
Order-Fractal transition in abstract paintings
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm
Objects2action: Classifying and localizing actions without any video example
Vehicle Color Recognition using Convolutional Neural Network
Aggregating Deep Convolutional Features for Image Retrieval
Generalized Regressive Motion: a Visual Cue to Collision
Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks
Defect Detection Techniques for Airbag Production Sewing Stages
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion
Hybrid One-Shot 3D Hand Pose Estimation by Exploiting Uncertainties
Scale-aware Fast R-CNN for Pedestrian Detection
Robust Subspace Clustering via Tighter Rank Approximation
VISALOGY: Answering Visual Analogy Questions
Postprocessing of Compressed Images via Sequential Denoising
Deep Recurrent Regression for Facial Landmark Detection
Estimating Target Signatures with Diverse Density
Semantic Cross-View Matching
Optimized Mission Planning for Planetary Exploration Rovers
Semantic Summarization of Egocentric Photo Stream Events
Water Detection through Spatio-Temporal Invariant Descriptors
Face Aging Effect Simulation using Hidden Factor Analysis Joint Sparse Representation
Train and Test Tightness of LP Relaxations in Structured Prediction
Image classification based on support vector machine and the fusion of complementary features
Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model
Wood Species Recognition Based on SIFT Keypoint Histogram
Radon-Nikodym approximation in application to image analysis
Enhanced Low-Rank Matrix Approximation
Pooling the Convolutional Layers in Deep ConvNets for Action Recognition
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception
LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks
A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model
Bearing fault diagnosis based on spectrum images of vibration signals
Batch-normalized Maxout Network in Network
Biologically Inspired Dynamic Textures for Probing Motion Perception
Partial Membership Latent Dirichlet Allocation
Multiple Instance Dictionary Learning using Functions of Multiple Instances
Spatially Coherent Random Forests
Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders
Hyperspectral Image Recovery via Hybrid Regularization
Traffic Sign Classification Using Deep Inception Based Convolutional Networks
Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition
Online Action Recognition based on Incremental Learning of Weighted Covariance Descriptors
Analyzing Stability of Convolutional Neural Networks in the Frequency Domain
Dynamic Belief Fusion for Object Detection
The Radon cumulative distribution transform and its application to image classification
TemplateNet for Depth-Based Object Instance Recognition
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Attention to Scale: Scale-aware Semantic Image Segmentation
Principal Autoparallel Analysis: Data Analysis in Weitzenböck Space
Discovery Radiomics via StochasticNet Sequencers for Cancer Detection
Facial Expression Detection using Patch-based Eigen-face Isomap Networks
God(s) Know(s): Developmental and Cross-Cultural Patterns in Children Drawings
A Continuous Max-Flow Approach to Cyclic Field Reconstruction
Automatic Content-Aware Color and Tone Stylization
ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks
Hand-Object Interaction and Precise Localization in Transitive Action Recognition
When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks
Feature Learning based Deep Supervised Hashing with Pairwise Labels
Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density
Facial Landmark Detection with Tweaked Convolutional Neural Networks
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images
Action Recognition using Visual Attention
Adaptive Affinity Matrix for Unsupervised Metric Learning
Unsupervised Learning of Edges
Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition
Learning Dense Convolutional Embeddings for Semantic Segmentation
Deep Reflectance Maps
Similarity-based Text Recognition by Deeply Supervised Siamese Network
Zero-Shot Learning via Joint Latent Similarity Embedding
Learning Fine-grained Features via a CNN Tree for Large-scale Classification
Jointly Learning Non-negative Projection and Dictionary with Discriminative Graph Constraints for Classification
Implementation and comparative quantitative assessment of different multispectral image pansharpening approches
Deep Neural Network for Real-Time Autonomous Indoor Navigation
Semi-Inner-Products for Convex Functionals and Their Use in Image Decomposition
Separation Surfaces in the Spectral TV Domain for Texture Decomposition
Performing Highly Accurate Predictions Through Convolutional Networks for Actual Telecommunication Challenges
Proposal Flow
Joint Training of Generic CNN-CRF Models with Stochastic Optimization
Nonlinear Local Metric Learning for Person Re-identification
Visualizing and Understanding Deep Texture Representations
Robust PCA via Nonconvex Rank Approximation
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning
Hierarchical Spatial Sum-Product Networks for Action Recognition in Still Images
Towards Predicting the Likeability of Fashion Images
Particular object retrieval with integral max-pooling of CNN activations
Collecting and Annotating the Large Continuous Action Dataset
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
Active Object Localization with Deep Reinforcement Learning
A Hierarchical Deep Temporal Model for Group Activity Recognition
Compact Bilinear Pooling
Deep Learning for Tactile Understanding From Visual and Haptic Data
Structured Depth Prediction in Challenging Monocular Video Sequences
Principled Parallel Mean-Field Inference for Discrete Random Fields
Coreset-Based Adaptive Tracking
Multimodal sparse representation learning and applications
Convolutional Clustering for Unsupervised Learning
Foveation-based Mechanisms Alleviate Adversarial Examples
face anti-spoofing based on color texture analysis
Robust Classification by Pre-conditioned LASSO and Transductive Diffusion Component Analysis
Efficient inference in occlusion-aware generative models of images
Manifold Regularized Deep Neural Networks using Adversarial Examples
Geodesics of learned representations
Feature-based Attention in Convolutional Neural Networks
QBDC: Query by dropout committee for training deep supervised architecture
First Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural Networks
A convnet for non-maximum suppression
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
Deep Metric Learning via Lifted Structured Feature Embedding
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications
ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environments
Recurrent Semi-supervised Classification and Constrained Adversarial Generation with Motion Capture Data
Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank
Recognizing Activities of Daily Living with a Wrist-mounted Camera
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
Learning visual groups from co-occurrences in space and time
Fidelity-Naturalness Evaluation of Single Image Super Resolution
Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation
Real-Time Anomaly Detection and Localization in Crowded Scenes
End-to-end Learning of Action Detection from Frame Glimpses in Videos
Fine-grained pose prediction, normalization, and recognition
Auxiliary Image Regularization for Deep CNNs with Noisy Labels
Multi-Scale Context Aggregation by Dilated Convolutions
Face Alignment Across Large Poses: A 3D Solution
Rendering refraction and reflection of eyeglasses for synthetic eye tracker images
Learning Visual Predictive Models of Physics for Playing Billiards
Real-Time Anomalous Behavior Detection and Localization in Crowded Scenes
Picking a Conveyor Clean by an Autonomously Learning Robot
Mouse Pose Estimation From Depth Images
Weakly Supervised Object Boundaries
Shape and Symmetry Induction for 3D Objects
Principal Basis Analysis in Sparse Representation
Video Tracking Using Learned Hierarchical Features
PASCAL Boundaries: A Class-Agnostic Semantic Boundary Dataset
Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry
Higher Order Conditional Random Fields in Deep Neural Networks
Tracking Motion and Proxemics using Thermal-sensor Array
Towards Automatic Image Editing: Learning to See another You
An analysis of the factors affecting keypoint stability in scale-space
TennisVid2Text: Fine-grained Descriptions for Domain Specific Videos
Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking
The Multiverse Loss for Robust Transfer Learning
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks
Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video
To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction
Variational reaction-diffusion systems for semantic segmentation
It's Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos
Learning a Pose Lexicon for Semantic Action Recognition
Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons (Extended Version)
How to Transfer? Zero-Shot Object Recognition via Hierarchical Transfer of Semantic Attributes
Person Re-identification in Appearance Impaired Scenarios
Overlay Text Extraction From TV News Broadcast
Robust video object tracking via Bayesian model averaging based feature fusion
Image Quality Assessment for Performance Evaluation of Focus Measure Operators
GAL: A Global-Attributes Assisted Labeling System for Outdoor Scenes
HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor
Detecting Engagement in Egocentric Video
Extended Object Tracking: Introduction, Overview and Applications
Comparative Deep Learning of Hybrid Representations for Image Recommendations
Cohomology of Cryo-Electron Microscopy
Deep Image Retrieval: Learning global representations for image search
Highly accurate gaze estimation using a consumer RGB-depth sensor
Forecasting Interactive Dynamics of Pedestrians with Fictitious Play
Learning A Deep $\ell_\infty$ Encoder for Hashing
Correlated and Individual Multi-Modal Deep Learning for RGB-D Object Recognition
Fusing Face and Periocular biometrics using Canonical correlation analysis
The Cityscapes Dataset for Semantic Urban Scene Understanding
A Subpath Kernel for Learning Hierarchical Image Representations
Exploiting Semantic Information and Deep Matching for Optical Flow
Joint Detection and Identification Feature Learning for Person Search
Reinterpreting the Transformation Posterior in Probabilistic Image Registration
A Novel Scene Text Detection Algorithm Based On Convolutional Neural Network
Geometric Scene Parsing with Hierarchical LSTM
Edge Detection Based Shape Identification
3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching
Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks
Infrared Colorization Using Deep Convolutional Neural Networks
Bayesian Neighbourhood Component Analysis
Machine Learning for Visual Navigation of Unmanned Ground Vehicles
Application of Multifractal Analysis to Segmentation of Water Bodies in Optical and Synthetic Aperture Radar Satellite Images
Person Re-identification in the Wild
Scene-driven Retrieval in Edited Videos using Aesthetic and Semantic Deep Features
Direction matters: hand pose estimation from local surface normals
DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
Soccer Field Localization from a Single Image
Capturing Dynamic Textured Surfaces of Moving Targets
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Active Learning for Online Recognition of Human Activities from Streaming Videos
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Semantic 3D Reconstruction with Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis
Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection
CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval
Semi-supervised learning of local structured output predictors
Application of the Second-Order Statistics for Estimation of the Pure Spectra of Individual Components from the Visible Hyperspectral Images of Their Mixture
Volumetric and Multi-View CNNs for Object Classification on 3D Data
Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention
Sweep Distortion Removal from THz Images via Blind Demodulation
Structured Matrix Recovery via the Generalized Dantzig Selector
DTM: Deformable Template Matching
Going Deeper with Contextual CNN for Hyperspectral Image Classification
Cross-stitch Networks for Multi-task Learning
Quantifying mesoscale neuroanatomy using X-ray microtomography
Learning Social Affordance for Human-Robot Interaction
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels
System Design of Internet-of-Things for Residential Smart Grid
Multi-Oriented Text Detection with Fully Convolutional Networks
On Reducing the Number of Visual Words in the Bag-of-Features Representation
Unsupervised Nonlinear Spectral Unmixing based on a Multilinear Mixing Model
DARI: Distance metric And Representation Integration for Person Verification
Long-term Temporal Convolutions for Action Recognition
Learning Temporal Regularity in Video Sequences
Bags of Local Convolutional Features for Scalable Instance Search
Radon Features and Barcodes for Medical Image Retrieval via SVM
Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering
Phase-Aligned Spectral Filtering for Decomposing Spatiotemporal Dynamics
Generating Semi-Synthetic Validation Benchmarks for Embryomics
Fully Convolutional Recurrent Network for Handwritten Chinese Text Recognition
Most Likely Separation of Intensity and Warping Effects in Image Registration
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
Non-contact hemodynamic imaging reveals the jugular venous pulse waveform
Learning Dense Correspondence via 3D-guided Cycle Consistency
Triplet Probabilistic Embedding for Face Verification and Clustering
Parts for the Whole: The DCT Norm for Extreme Visual Recovery
Tuning the work function in transition metal oxides and their heterostructures
Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering
Deep CNNs for HEp-2 Cells Classification : A Cross-specimen Analysis
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient Regularization
Scene Parsing with Integration of Parametric and Non-parametric Models
Jansen-MIDAS: a multi-level photomicrograph segmentation software based on isotropic undecimated wavelets
Labeled Multi-Bernoulli Tracking for Industrial Mobile Platform Safety
Symmetry-aware Depth Estimation using Deep Neural Networks
Miniature optical planar camera based on a wide-angle metasurface doublet corrected for monochromatic aberrations
Novelty Detection in MultiClass Scenarios with Incomplete Set of Class Labels
Visual Congruent Ads for Image Search
Convolutional Two-Stream Network Fusion for Video Action Recognition
The Mean Partition Theorem of Consensus Clustering
Synthetic Data for Text Localisation in Natural Images
Learning rotation invariant convolutional filters for texture classification
Refining Architectures of Deep Convolutional Neural Networks
Word2VisualVec: Image and Video to Sentence Matching by Visual Feature Prediction
Contextual object categorization with energy-based model
Text Flow: A Unified Text Detection System in Natural Scene Images
Bayesian Inference of Recursive Sequences of Group Activities from Tracks
Cardiac Motion Analysis by Temporal Flow Graphs
Towards Better Analysis of Deep Convolutional Neural Networks
Semi-supervised Vocabulary-informed Learning
Makeup like a superstar: Deep Localized Makeup Transfer Network
Actionness Estimation Using Hybrid Fully Convolutional Networks
Semi-supervised Dictionary Learning Based on Hilbert-Schmidt Independence Criterion
Supervised Incremental Hashing
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification
Context Encoders: Feature Learning by Inpainting
Balancing Appearance and Context in Sketch Interpretation
Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks
Once for All: a Two-flow Convolutional Neural Network for Visual Tracking
Semantic Change Detection with Hypermaps
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification
Spot On: Action Localization from Pointly-Supervised Proposals
An Accelerometer Based Calculator for Visually Impaired People Using Mobile Devices
Real-time Action Recognition with Enhanced Motion Vector CNNs
EgoSampling: Wide View Hyperlapse from Egocentric Videos
An Enhanced Deep Feature Representation for Person Re-identification
Zero-shot object prediction using semantic scene knowledge
Simultaneous Food Localization and Recognition
Detecting Violence in Video using Subclasses
Laser light-field fusion for wide-field lensfree on-chip phase contrast nanoscopy
Unsupervised Classification in Hyperspectral Imagery with Nonlocal Total Variation and Primal-Dual Hybrid Gradient Algorithm
A Probabilistic Adaptive Search System for Exploring the Face Space
Improved Dense Trajectory with Cross Streams
Convolutional Neural Networks for Attribute-based Active Authentication on Mobile Devices
Face Recognition Using Scattering Convolutional Network
Visual Relationship Detection with Language Priors
A Data-driven Approach for Human Pose Tracking Based on Spatio-temporal Pictorial Structure
Learning deep representation from coarse to fine for face alignment
Similarity Registration Problems for 2D/3D Ultrasound Calibration
Denoising and compression in wavelet domain via projection onto approximation coefficients
New wavelet-based superresolution algorithm for speckle reduction in SAR images
Denoising based on wavelets and deblurring via self-organizing map for Synthetic Aperture Radar images
Fuzzy thresholding in wavelet domain for speckle reduction in Synthetic Aperture Radar images
Video Summarization in a Multi-View Camera Network
Top-down Neural Attention by Excitation Backprop
Dimensionality reduction based on Distance Preservation to Local Mean (DPLM) for SPD matrices and its application in BCI
Modeling Context Between Objects for Referring Expression Understanding
Interactive Image Segmentation Using Constrained Dominant Sets
A Survey of Visual Analysis of Human Motion and Its Applications
Semantically Guided Depth Upsampling
Interactive Removal and Ground Truth for Difficult Shadow Scenes
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016
A study of the effect of JPG compression on adversarial images
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Towards Learning to Perceive and Reason About Liquids
PicHunt: Social Media Image Retrieval for Improved Law Enforcement
Automated X-ray Image Analysis for Cargo Security: Critical Review and Future Promise
Incremental Real-Time Multibody VSLAM with Trajectory Optimization Using Stereo Camera
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks
UnitBox: An Advanced Object Detection Network
Recoding Color Transfer as a Color Homography
Saliency Integration: An Arbitrator Model
Compartmental analysis of dynamic nuclear medicine data: regularization procedure and application to physiology
Blind Deconvolution of PET Images using Anatomical Priors
Enhanced Directional Smoothing Algorithm for Edge-Preserving Smoothing of Synthetic-Aperture Radar Images
Compressive Change Retrieval for Moving Object Detection
Multi-Model Hypothesize-and-Verify Approach for Incremental Loop Closure Verification
Signs in time: Encoding human motion as a temporal image
ShapeFit and ShapeKick for Robust, Scalable Structure from Motion
Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition
Residual CNDS
Discriminatively Trained Latent Ordinal Model for Video Classification
A combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation
Database of handwritten Arabic mathematical formulas images
Steerable Principal Components for Space-Frequency Localized Images
Convolutional Oriented Boundaries
Residual Networks of Residual Networks: Multilevel Residual Networks
Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation
3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information
DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns
Fractional Calculus In Image Processing: A Review
Approximate search with quantized sparse representations
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition
Recurrent Neural Networks to Correct Satellite Image Classification Maps
Faster Training of Very Deep Networks Via p-Norm Gates
Reasoning and Algorithm Selection Augmented Symbolic Segmentation
Self-paced Learning for Weakly Supervised Evidence Discovery in Multimedia Event Search
When was that made?
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees
The Importance of Skip Connections in Biomedical Image Segmentation
Every Filter Extracts A Specific Texture In Convolutional Neural Networks
Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field
Face Alignment In-the-Wild: A Survey
Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval
Visual place recognition using landmark distribution descriptors
Transitive Hashing Network for Heterogeneous Multimedia Retrieval
Weakly Supervised Object Localization Using Size Estimates
Star-galaxy Classification Using Deep Convolutional Neural Networks
Unconstrained Two-parallel-plane Model for Focused Plenoptic Cameras Calibration
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units
Geometry-aware Similarity Learning on SPD Manifolds for Visual Recognition
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation
An image compression and encryption scheme based on deep learning
Scene Labeling Through Knowledge-Based Rules Employing Constrained Integer Linear Programing
IM2CAD
Full Resolution Image Compression with Recurrent Neural Networks
Multi-stage Object Detection with Group Recursive Learning
Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation
How Image Degradations Affect Deep CNN-based Face Recognition?
Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness
Feedback-Controlled Sequential Lasso Screening
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks
Deep Double Sparsity Encoder: Learning to Sparsify Not Only Features But Also Parameters
Convolutional Network for Attribute-driven and Identity-preserving Human Face Generation
Searching Action Proposals via Spatial Actionness Estimation and Temporal Path Inference and Tracking
Neural Networks with Smooth Adaptive Activation Functions for Regression
Automatic Synchronization of Multi-User Photo Galleries
Ambient Sound Provides Supervision for Visual Learning
Modeling and Propagating CNNs in a Tree Structure for Visual Tracking
Scalable Compression of Deep Neural Networks
Spatio-temporal Aware Non-negative Component Representation for Action Recognition
Multi-Path Feedback Recurrent Neural Network for Scene Parsing
Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier
Using k-nearest neighbors to construct cancelable minutiae templates
Correspondence Insertion for As-Projective-As-Possible Image Stitching
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
Where is my Phone ? Personal Object Retrieval from Egocentric Images
ORBSLAM-based Endoscope Tracking and 3D Reconstruction
Tracking Completion
Real-Time Visual Tracking: Promoting the Robustness of Correlation Filter Learning
Construction of Convex Sets on Quadrilateral Ordered Tiles or Graphs with Propagation Neighborhood Operations. Dales, Concavity Structures. Application to Gray Image Analysis of Human-Readable Shapes
Low-rank Multi-view Clustering in Third-Order Tensor Space
Motion Representation with Acceleration Images
CliqueCNN: Deep Unsupervised Exemplar Learning
Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification
Measuring the Quality of Exercises
Streaming Multimedia Information Using the Features of the DVB-S Card
A Modified Cross Correlation Algorithm for Reference-free Image Alignment of Non-Circular Projections in Single-Particle Electron Microscopy
Internet of Things: Applications and Challenges in Technology and Standardization
$\ell_0$ Minimization for Wavelet Frame Based Image Restoration
Salient Local 3D Features for 3D Shape Retrieval
Face Recognition using 3D Facial Shape and Color Map Information: Comparison and Combination
Optimal Camera Placement to measure Distances Conservativly Regarding Static and Dynamic Obstacles
Hierarchical Recursive Running Median
A Multiple-Choice Test Recognition System based on the Gamera Framework
Human Identity Verification based on Heart Sounds: Recent Advances and Future Directions
Scale-Invariant Local Descriptor for Event Recognition in 1D Sensor Signals
Density Estimation and Classification via Bayesian Nonparametric Learning of Affine Subspaces
Identifying relationships between drugs and medical conditions: winning experience in the Challenge 2 of the OMOP 2010 Cup
Dictionary Learning for Deblurring and Digital Zoom
Nash Equilibria in Quantum Games
Foliage Plant Retrieval using Polar Fourier Transform, Color Moments and Vein Features
The proximal point method for a hybrid model in image restoration
Closed-Loop Learning of Visual Control Policies
Ground Metric Learning
Robust Image Analysis by L1-Norm Semi-supervised Learning
Anti-sparse coding for approximate nearest neighbor search
Local Naive Bayes Nearest Neighbor for Image Classification
Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem
Meaningful Matches in Stereovision
Improvement of BM3D Algorithm and Employment to Satellite and CFA Images Denoising
Large Scale Correlation Clustering Optimization
Supervised Generative Reconstruction: An Efficient Way To Flexibly Store and Recognize Patterns
Data Processing For Atomic Resolution EELS
Higher-Order Momentum Distributions and Locally Affine LDDMM Registration
Automatic post-picking improves particle image detection from Cryo-EM micrographs
A Reduced Reference Image Quality Measure Using Bessel K Forms Model for Tetrolet Coefficients
Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods
Zero-Temperature Limit of a Convergent Algorithm to Minimize the Bethe Free Energy
Multispectral Palmprint Recognition Using a Hybrid Feature
Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals
Jassologie : Une vision originale sur les cartes orientables
Improved Performance of Unsupervised Method by Renovated K-Means
Lie Algebrized Gaussians for Image Representation
Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise using Blind Inpainting
Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference
Kernel Reconstruction ICA for Sparse Representation
Image Classification by Feature Dimension Reduction and Graph based Ranking
Detecting Directionality in Random Fields Using the Monogenic Signal
Planning, Scheduling, and Uncertainty in the Sequence of Future Events
Merging Satellite Measurements of Rainfall Using Multi-scale Imagery Technique
Single View Depth Estimation from Examples
A new Bayesian ensemble of trees classifier for identifying multi-class labels in satellite images
Polygon Matching and Indexing Under Affine Transformations
Separating the Real from the Synthetic: Minutiae Histograms as Fingerprints of Fingerprints
Counting people from above: Airborne video based crowd analysis
Learning Visual Symbols for Parsing Human Poses in Images
Filament and Flare Detection in Hα image sequences
Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images
Deterministic Initialization of the K-Means Algorithm Using Hierarchical Clustering
Registration of Images with Outliers Using Joint Saliency Map
Domain-invariant Face Recognition using Learned Low-rank Transformation
Sparse Dictionary-based Attributes for Action Recognition and Summarization
Sign Stable Projections, Sign Cauchy Projections and Chi-Square Kernels
Image interpolation using Shearlet based iterative refinement
Spatial-Aware Dictionary Learning for Hyperspectral Image Classification
A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments
Satellite image classification methods and Landsat 5TM bands
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
Matching Demand with Supply in the Smart Grid using Agent-Based Multiunit Auction
A Unified Framework for Multi-Sensor HDR Video Reconstruction
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization
Suspicious Object Recognition Method in Video Stream Based on Visual Attention
Text recognition in both ancient and cartographic documents
GNCGCP - Graduated NonConvexity and Graduated Concavity Procedure
Image Set based Collaborative Representation for Face Recognition
Collaborative Receptive Field Learning
A Robust Framework for Moving-Object Detection and Vehicular Traffic Density Estimation
Scene Labeling with Contextual Hierarchical Models
Signal to Noise Ratio in Lensless Compressive Imaging
Patchwise Joint Sparse Tracking with Occlusion Detection
Quantile Representation for Indirect Immunofluorescence Image Classification
A Hybrid Loss for Multiclass and Structured Prediction
Leveraging Long-Term Predictions and Online-Learning in Agent-based Multiple Person Tracking
Signal Reconstruction Framework Based On Projections Onto Epigraph Set Of A Convex Cost Function (PESC)
Modeling sequential data using higher-order relational features and predictive training
Sparsity averaging for radio-interferometric imaging
Animation of 3D Human Model Using Markerless Motion Capture Applied To Sports
Real-Time Hand Shape Classification
Noise Analysis for Lensless Compressive Imaging
Hand-Eye and Robot-World Calibration by Global Polynomial Optimization
Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit
Improving Streaming Video Segmentation with Early and Mid-Level Visual Processing
A Narrative Vehicle Protection Representation for Vehicle Speed Regulator Under Driver Exhaustion -- A Study
Scalable Kernel Clustering: Approximate Kernel k-means
Sparse Coding Approach for Multi-Frame Image Super Resolution
The Algebraic Approach to Phase Retrieval and Explicit Inversion at the Identifiability Threshold
Statistical Noise Analysis in SENSE Parallel MRI
Information Theory of Matrix Completion
Vesselness via Multiple Scale Orientation Scores
Real-time Automatic Emotion Recognition from Body Gestures
Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity
Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit
A Novel Histogram Based Robust Image Registration Technique
Localization of License Plate Using Morphological Operations
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
A Novel Scheme for Intelligent Recognition of Pornographic Images
A Novel Face Recognition Method using Nearest Line Projection
A Testbed for Cross-Dataset Analysis
A Multiplierless Pruned DCT-like Transformation for Image and Video Compression that Requires 10 Additions Only
A Novel Method for the Recognition of Isolated Handwritten Arabic Characters
Low-Cost Compressive Sensing for Color Video and Depth
Hierarchical community structure in complex (social) networks
Convex Total Least Squares
On Classification with Bags, Groups and Sets
Visual Reranking with Improved Image Graph
Multiscale Fields of Patterns
Shared Representation Learning for Heterogeneous Face Recognition
Illusory Shapes via Phase Transition
A Context-aware Delayed Agglomeration Framework for Electron Microscopy Segmentation
Towards building a Crowd-Sourced Sky Map
Small Sample Learning of Superpixel Classifiers for EM Segmentation- Extended Version
Fine-grained Activity Recognition with Holistic and Pose based Features
Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science
Two-Stream Convolutional Networks for Action Recognition in Videos
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
Robust Estimation of 3D Human Poses from a Single Image
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency
Why do linear SVMs trained on HOG features perform so well?
The Secrets of Salient Object Segmentation
Acoustic Gait-based Person Identification using Hidden Markov Models
Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection
"Mental Rotation" by Optimizing Transforming Distance
Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding
A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes Recognition
Impact of Exponent Parameter Value for the Partition Matrix on the Performance of Fuzzy C Means Algorithm
PRISM: Person Re-Identification via Structured Matching
Multi-stage Multi-task feature learning via adaptive threshold
Deep Learning Face Representation by Joint Identification-Verification
Inner Product Similarity Search using Compositional Codes
Web-Scale Training for Face Identification
Early Recognition of Human Activities from First-Person Videos Using Onset Representations
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
Image Completion for View Synthesis Using Markov Random Fields and Efficient Belief Propagation
On the Convergence Rate of Decomposable Submodular Function Minimization
$ N^4 $-Fields: Neural Network Nearest Neighbor Fields for Image Transforms
Support vector machine classification of dimensionally reduced structural MRI images for dementia
3DUNDERWORLD-SLS: An Open-Source Structured-Light Scanning System for Rapid Geometry Acquisition
Face Image Classification by Pooling Raw Features
Deep Learning Multi-View Representation for Face Recognition
3D planar patch extraction from stereo using probabilistic region growing
On a new formulation of nonlocal image filters involving the relative rearrangement
Fusion Based Holistic Road Scene Understanding
Kernel Coding: General Formulation and Special Cases
Transferring Landmark Annotations for Cross-Dataset Face Alignment
Visual Passwords Using Automatic Lip Reading
Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features
Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images
Annotating Synapses in Large EM Datasets
Automatic Neuron Type Identification by Neurite Localization in the Drosophila Medulla
Depth image hand tracking from an overhead perspective using partially labeled, unbalanced data: Development and real-world testing
A theoretical contribution to the fast implementation of null linear discriminant analysis method using random matrix multiplication with scatter matrices
Ambiguity-Driven Fuzzy C-Means Clustering: How to Detect Uncertain Clustered Records
Unsupervised learning of clutter-resistant visual representations from natural videos
Concurrent Tracking of Inliers and Outliers
Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects
A Combined Method Of Fractal And GLCM Features For MRI And CT Scan Images Classification
Fingerprint Classification Based on Depth Neural Network
Subspace Alignment For Domain Adaptation
Deformable Part Models are Convolutional Neural Networks
Hyperspectral and Multispectral Image Fusion based on a Sparse Representation
Active Dictionary Learning in Sparse Representation Based Classification
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
Analyzing sparse dictionaries for online learning with kernels
1-HKUST: Object Detection in ILSVRC 2014
A non-linear learning & classification algorithm that achieves full training accuracy with stellar classification accuracy
Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images
Recent Progress in Image Deblurring
Do More Dropouts in Pool5 Feature Maps for Better Object Detection
Deep Learning Representation using Autoencoder for 3D Shape Retrieval
Two-stage Geometric Information Guided Image Reconstruction
How close are we to understanding image-based saliency?
Audio Surveillance: a Systematic Review
Understanding Deep Image Representations by Inverting Them
A Bayesian Framework for Sparse Representation-Based 3D Human Pose Estimation
Color image quality assessment measure using multivariate generalized Gaussian distribution
Robust Camera Location Estimation by Convex Programming
Simple pairs of points in digital spaces. Topology-preserving transformations of digital spaces by contracting simple pairs of points
A Clearer Picture of Blind Deconvolution
Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection
Fuzzy human motion analysis: A review
Recovering Spatiotemporal Correspondence between Deformable Objects by Exploiting Consistent Foreground Motion in Video
Orthogonal Matrix Retrieval in Cryo-Electron Microscopy
Deeply learned face representations are sparse, selective, and robust
Memory Bounded Deep Convolutional Networks
Convolutional Neural Networks at Constrained Time Cost
Reading Text in the Wild with Convolutional Neural Networks
CoMIC: Good features for detection and matching at object boundaries
Learning Multi-target Tracking with Quadratic Object Interactions
Deep Visual-Semantic Alignments for Generating Image Descriptions
Visual Causal Feature Learning
Linear optical demonstration of quantum speed-up with a single qudit
HyperSpectral classification with adaptively weighted L1-norm regularization and spatial postprocessing
Image quality assessment measure based on natural image statistics in the Tetrolet domain
Subspace based low rank and joint sparse matrix recovery
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Real-Time Grasp Detection Using Convolutional Neural Networks
Deep Domain Confusion: Maximizing for Domain Invariance
Road Detection by One-Class Color Classification: Dataset and Experiments
EgoSampling: Fast-Forward and Stereo for Egocentric Videos
A Novel Adaptive Possibilistic Clustering Algorithm
Compact Compositional Models
Machine Learning for Neuroimaging with Scikit-Learn
An Automatic Seeded Region Growing for 2D Biomedical Image Segmentation
High-level numerical simulations of noise in CCD and CMOS photosensors: review and tutorial
An Experimental Evaluation of Machine-to-Machine Coordination Middleware: Extended Version
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
A Framework for Shape Analysis via Hilbert Space Embedding
A Study of Sindhi Related and Arabic Script Adapted languages Recognition
Combining the Best of Graphical Models and ConvNets for Semantic Segmentation
Inexact Alternating Direction Method Based on Newton descent algorithm with Application to Poisson Image Deblurring
A Robust Regression Approach for Background/Foreground Segmentation
Automatic Training Data Synthesis for Handwriting Recognition Using the Structural Crossing-Over Technique
Unsupervised Learning of Spatiotemporally Coherent Metrics
Fractional Max-Pooling
Semantic Part Segmentation using Compositional Model combining Shape and Appearance
Data Representation using the Weyl Transform
Automated Objective Surgical Skill Assessment in the Operating Room Using Unstructured Tool Motion
Pooled Motion Features for First-Person Videos
Cauchy Principal Component Analysis
Score Function Features for Discriminative Learning
Fracking Deep Convolutional Image Descriptors
Automatic Discovery and Optimization of Parts for Image Classification
The local low-dimensionality of natural images
Visualizing and Comparing Convolutional Neural Networks
Mixture of Parts Revisited: Expressive Part Interactions for Pose Estimation
Fusing Color and Texture Cues to Categorize the Fruit Diseases from Images
Symmetry in Image Registration and Deformation Modeling
An Effective Semi-supervised Divisive Clustering Algorithm
A Fuzzy Based Model to Identify Printed Sinhala Characters (ICIAfS14)
Joint Deep Learning for Car Detection
Metacarpal Bones Localization in X-ray Imagery Using Particle Filter Segmentation
Rigid and Non-rigid Shape Evolutions for Shape Alignment and Recovery in Images
SHOE: Supervised Hashing with Output Embeddings
Category-Epitomes : Discriminatively Minimalist Representations for Object Categories
Optimized Projection for Sparse Representation Based Classification
Modified Fast Fractal Image Compression Algorithm in spatial domain
Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models
Iterated Support Vector Machines for Distance Metric Learning
Towards a solid solution of real-time fire and flame detection
Learning the Matching Function
Recovery of Piecewise Smooth Images from Few Fourier Samples
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection
Face frontalization for Alignment and Recognition
DeepID3: Face Recognition with Very Deep Neural Networks
Classification of Hyperspectral Imagery on Embedded Grassmannians
ORB-SLAM: a Versatile and Accurate Monocular SLAM System
A Multiple-Expert Binarization Framework for Multispectral Images
Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets
Collaborative Feature Learning from Social Media
Fast Constraint Propagation for Image Segmentation
Semantic Embedding Space for Zero-Shot Action Recognition
Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients
Generalized Inpainting Method for Hyperspectral Image Acquisition
A Fingerprint-based Access Control using Principal Component Analysis and Edge Detection
Visual Recognition by Counting Instances: A Multi-Instance Cardinality Potential Kernel
Comparison of Algorithms for Compressed Sensing of Magnetic Resonance Images
Deep Neural Networks for Anatomical Brain Segmentation
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification
An equalised global graphical model-based approach for multi-camera object tracking
Convergence of gradient based pre-training in Denoising autoencoders
Towards zero-configuration condition monitoring based on dictionary learning
Discovering Human Interactions in Videos with Limited Data Labeling
Semi-supervised Data Representation via Affinity Graph Learning
Skeleton Matching based approach for Text Localization in Scene Images
Gray-Level Image Transitions Driven by Tsallis Entropic Index
Cardiac MR Image Segmentation Techniques: an overview
Spatial Stimuli Gradient Sketch Model
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
Bi-Level Image Thresholding obtained by means of Kaniadakis Entropy
Inferring 3D Object Pose in RGB-D Images
3D Pose from Detections
Context Tricks for Cheap Semantic Segmentation
What makes for effective detection proposals?
Prediction of Search Targets From Fixations in Open-World Settings
SA-CNN: Dynamic Scene Classification using Convolutional Neural Networks
Visualizing Object Detection Features
VIP: Finding Important People in Images
Pairwise Constraint Propagation: A Survey
Visual object tracking performance measures revisited
A new network-based algorithm for human activity recognition in video
A Heat-Map-based Algorithm for Recognizing Group Activities in Videos
Study of a Robust Algorithm Applied in the Optimal Position Tuning for the Camera Lens in Automated Visual Inspection Systems
Don't Just Listen, Use Your Imagination: Leveraging Visual Common Sense for Non-Visual Tasks
Video Text Localization with an emphasis on Edge Features
Boosting of Image Denoising Algorithms
Spatio-temporal Video Parsing for Abnormality Detection
Compressive Hyperspectral Imaging with Side Information
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Discrete Wavelet Transform and Gradient Difference based approach for text localization in videos
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
Real-Time System of Hand Detection And Gesture Recognition In Cyber Presence Interactive System For E-Learning
Coercive Region-level Registration for Multi-modal Images
Concept for a CMOS Image Sensor Suited for Analog Image Pre-Processing
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
Dynamic Belief Fusion for Object Detection
Landmark-Guided Elastic Shape Analysis of Human Character Motions
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
Second Order Minimum Energy Filtering on $\operatorname{SE}_3$ with Nonlinear Measurement Equations
Activity Recognition Using A Combination of Category Components And Local Models for Video Surveillance
Group Event Detection with a Varying Number of Group Members for Video Surveillance
Improved Image Deblurring based on Salient-region Segmentation
Graphical Representation for Heterogeneous Face Recognition
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
A review of mean-shift algorithms for clustering
Joint calibration of Ensemble of Exemplar SVMs
Context Forest for efficient object detection with large mixture models
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
Anisotropic Diffusion in ITK
Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research
Learning Super-Resolution Jointly from External and Internal Examples
Do We Need More Training Data?
Jointly Learning Multiple Measures of Similarities from Triplet Comparisons
Spectral Clustering by Ellipsoid and Its Connection to Separable Nonnegative Matrix Factorization
Deep Temporal Appearance-Geometry Network for Facial Expression Recognition
Pyrcca: regularized kernel canonical correlation analysis in Python and its applications to neuroimaging
Learning to rank in person re-identification with metric ensembles
Inference of hidden structures in complex physical systems by multi-scale clustering
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
Color Image Classification via Quaternion Principal Component Analysis Network
Latent Hierarchical Model for Activity Recognition
Deep Clustered Convolutional Kernels
Linear Global Translation Estimation with Feature Tracks
Partial light field tomographic reconstruction from a fixed-camera focal stack
On the Invariance of Dictionary Learning and Sparse Representation to Projecting Data to a Discriminative Space
An Improved Image Mosaicing Algorithm for Damaged Documents
Fitting 3D Morphable Models using Local Features
Fast and Robust Fixed-Rank Matrix Recovery
Remarks on pointed digital homotopy
Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution
A model-based approach to recovering the structure of a plant from images
A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine
Stochastic Texture Difference for Scale-Dependent Data Analysis
Browserbite: Cross-Browser Testing via Image Processing
Android based Portable Hand Sign Recognition System
2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA
Diagnosing Heterogeneous Dynamics for CT Scan Images of Human Brain in Wavelet and MFDFA domain
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
Template-based Monocular 3D Shape Recovery using Laplacian Meshes
Phase and TV Based Convex Sets for Blind Deconvolution of Microscopic Images
Edge Detection: A Collection of Pixel based Approach for Colored Images
An approach to improving edge detection for facial and remotely sensed images using vector order statistics
Automatic Pollen Grain and Exine Segmentation from Microscope Images
Learning Hypergraph-regularized Attribute Predictors
A General Framework for Multi-focal Image Classification and Authentication: Application to Microscope Pollen Images
Skin Detection of Animation Characters
Wavelet based approach for tissue fractal parameter measurement: Pre cancer detection
A novel pLSA based Traffic Signs Classification System
Vehicle Local Position Estimation System
Factorization of View-Object Manifolds for Joint Object Recognition and Pose Estimation
Compressed sensing MRI using masked DCT and DFT measurements
Robust Eye Centers Localization with Zero--Crossing Encoded Image Projections
Pain Intensity Estimation by a Self--Taught Selection of Histograms of Topographical Features
Content-Based Bird Retrieval using Shape context, Color moments and Bag of Features
Transductive Multi-class and Multi-label Zero-shot Learning
CRF Learning with CNN Features for Image Segmentation
Label-Embedding for Image Classification
Beyond Short Snippets: Deep Networks for Video Classification
Real-World Font Recognition Using Deep Network and Domain Adaptation
Weakly Supervised Learning of Objects, Attributes and their Associations
The Approximation of the Dissimilarity Projection
Convex Denoising using Non-Convex Tight Frame Regularization
Graph Connectivity in Noisy Sparse Subspace Clustering
Locally Non-rigid Registration for Mobile HDR Photography
Design and Implementation of a 3D Undersea Camera System
Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
Performance measures for classification systems with rejection
A Coarse-to-Fine Model for 3D Pose Estimation and Sub-category Recognition
Appearance-Based Gaze Estimation in the Wild
A Novel Approach to Develop a New Hybrid Technique for Trademark Image Retrieval
Image Denoising Using Low Rank Minimization With Modified Noise Estimation
Background Subtraction via Generalized Fused Lasso Foreground Modeling
A spectral optical flow method for determining velocities from digital imagery
Understanding the Fisher Vector: a multimodal part model
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition
F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation
Exploiting Local Features from Deep Networks for Image Retrieval
Viewpoint distortion compensation in practical surveillance systems
Automatic Face Recognition from Video
Key-Pose Prediction in Cyclic Human Motion
Adaptive Compressive Tracking via Online Vector Boosting Feature Selection
Self-Tuned Deep Super Resolution
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
LOAD: Local Orientation Adaptive Descriptor for Texture and Material Classification
Edge Detection Based on Global and Local Parameters of the Image
Understanding and Diagnosing Visual Tracking Systems
Object Detection Networks on Convolutional Feature Maps
Online Adaptive Hidden Markov Model for Multi-Tracker Fusion
Sparse Radial Sampling LBP for Writer Identification
Person Re-identification with Correspondence Structure Learning
Depth-based hand pose estimation: methods, data, and challenges
Situational Object Boundary Detection
Semantic Motion Segmentation Using Dense CRF Formulation
WxBS: Wide Baseline Stereo Generalizations
Differential Recurrent Neural Networks for Action Recognition
Max-margin Deep Generative Models
Detection and Recognition of Malaysian Special License Plate Based On SIFT Features
SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization
Shape Representation and Classification through Pattern Spectrum and Local Binary Pattern - A Decision Level Fusion Approach
Compact CNN for Indexing Egocentric Videos
A Robust Lane Detection and Departure Warning System
Accelerating the Development of Software-Defined Network Optimization Applications Using SOL
Visual Information Retrieval in Endoscopic Video Archives
Comparative study of image registration techniques for bladder video-endoscopy
Robust hyperspectral image classification with rejection fields
Semi-Orthogonal Multilinear PCA with Relaxed Start
Efficient Image-Space Extraction and Representation of 3D Surface Topography
Bag-of-Genres for Video Genre Retrieval
Hiding Information in Noise: Fundamental Limits of Covert Wireless Communication
RBIR using Interest Regions and Binary Signatures
What Makes Kevin Spacey Look Like Kevin Spacey
Stochastic And-Or Grammars: A Unified Framework and Logic Perspective
Image Retrieval System Base on EMD Similarity Measure and S-Tree
Color Image Retrieval Using Fuzzy Measure Hamming and S-Tree
HEP-FCE Working Group on Libraries and Tools
Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation
Multilayer Structured NMF for Spectral Unmixing of Hyperspectral Images
Monocular SLAM Supported Object Recognition
Learning to track for spatio-temporal action localization
Spatial Transformer Networks
Sentence Directed Video Object Codetection
Automatic tracking of protein vesicles
What's the Point: Semantic Segmentation with Point Supervision
Describing Common Human Visual Actions in Images
Visual Learning of Arithmetic Operations
SVM and ELM: Who Wins? Object Recognition with Deep Convolutional Features from ImageNet
Learning with Group Invariant Features: A Kernel Perspective
Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework
Flowing ConvNets for Human Pose Estimation in Videos
Multiscale edge detection and parametric shape modeling for boundary delineation in optoacoustic images
License Plate Recognition System Based on Color Coding Of License Plates
Wide baseline stereo matching with convex bounded-distortion constraints
Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion
Generative Image Modeling Using Spatial LSTMs
P-CNN: Pose-based CNN Features for Action Recognition
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
Pose-Invariant 3D Face Alignment
Tree-Cut for Probabilistic Image Segmentation
Sparse Multi-layer Image Approximation: Facial Image Compression
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Combinatorial Energy Learning for Image Segmentation
Resolving Scale Ambiguity Via XSlit Aspect Ratio Analysis
Reading Scene Text in Deep Convolutional Sequences
Flow Segmentation in Dense Crowds
Leveraging the Power of Gabor Phase for Face Identification: A Block Matching Approach
Multi-path Convolutional Neural Networks for Complex Image Classification
Layered Interpretation of Street View Images
Image-based Recommendations on Styles and Substitutes
End-to-end people detection in crowded scenes
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
Post-Reconstruction Deconvolution of PET Images by Total Generalized Variation Regularization
Robust High Quality Image Guided Depth Upsampling
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Aligning where to see and what to tell: image caption with region-based attention and scene factorization
3D Reconstruction from Full-view Fisheye Camera
Target Tracking In Real Time Surveillance Cameras and Videos
R-CNN minus R
Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes
Deep CNN Ensemble with Data Augmentation for Object Detection
Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Salient Object Detection via Objectness Measure
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
Degenerate Motions in Multicamera Cluster SLAM with Non-overlapping Fields of View
Generalized Majorization-Minimization
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
Occlusion Coherence: Detecting and Localizing Occluded Faces
A note on patch-based low-rank minimization for fast image denoising
Unsupervised Semantic Parsing of Video Collections
Tell and Predict: Kernel Classifier Prediction for Unseen Visual Classes from Unstructured Text Descriptions
Automatic Channel Network Extraction from Remotely Sensed Images by Singularity Analysis
An automatic and efficient foreground object extraction scheme
Lens Factory: Automatic Lens Generation Using Off-the-shelf Components
Online Learning to Sample
Long-Range Motion Trajectories Extraction of Articulated Human Using Mesh Evolution
Multi-Cue Structure Preserving MRF for Unconstrained Video Segmentation
Discovering Characteristic Landmarks on Ancient Coins using Convolutional Networks
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision
Dictionary and Image Recovery from Incomplete and Random Measurements
Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns
Online Domain Adaptation for Multi-Object Tracking
Estimating snow cover from publicly available images
Detection of Critical Number of People in Interlocked Doors for Security Access Control by Exploiting a Microwave Transceiver-Array
Evaluating color texture descriptors under large variations of controlled lighting conditions
Socially Constrained Structural Learning for Groups Detection in Crowd
TabletGaze: Unconstrained Appearance-based Gaze Estimation in Mobile Tablets
Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations
Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance
Automatic Extraction of the Passing Strategies of Soccer Teams
Gait Assessment for Multiple Sclerosis Patients Using Microsoft Kinect
What is Holding Back Convnets for Detection?
Mountain Peak Detection in Online Social Media
A New Approach to an Old Problem: The Reconstruction of a Go Game through a Series of Photographs
Lensless Compressive Imaging
Light-field Microscopy with a Consumer Light-field Camera
Beat-Event Detection in Action Movie Franchises
Pose-Guided Human Parsing with Deep Learned Features
LCNN: Low-level Feature Embedded CNN for Salient Object Detection
Sense Beyond Expressions: Cuteness
A Generative Model for Multi-Dialect Representation
Action Recognition based on Subdivision-Fusion Model
Supervised learning of sparse context reconstruction coefficients for data representation and classification
Image tag completion by local learning
Robust Subspace Clustering via Smoothed Rank Approximation
DeepWriterID: An End-to-end Online Text-independent Writer Identification System
Improving Image Restoration with Soft-Rounding
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification
Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging
BREN: Body Reflection Essence-Neuter Model for Separation of Reflection Components
Multiple kernel multivariate performance learning using cutting plane algorithm
Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation
A Comparative Analysis of Retrieval Techniques In Content Based Image Retrieval
Shopper Analytics: a customer activity recognition system using a distributed RGB-D camera network
Validation of neural spike sorting algorithms without ground-truth information
Discrete Hashing with Deep Neural Network
Bilevel parameter learning for higher-order total variation regularisation models
Mixed Gaussian-Impulse Noise Removal from Highly Corrupted Images via Adaptive Local and Nonlocal Statistical Priors
Image Annotation Incorporating Low-Rankness, Tag and Visual Correlation and Inhomogeneous Errors
Love Thy Neighbors: Image Annotation by Exploiting Image Metadata
Action Recognition by Hierarchical Mid-level Action Elements
Domain Generalization for Object Recognition with Multi-task Autoencoders
Approximate Nearest Neighbor Fields in Video
Learning A Task-Specific Deep Architecture For Clustering
Robust Face Recognition via Multimodal Deep Face Representation
DAG-Recurrent Neural Networks For Scene Labeling
Dictionary based Approach to Edge Detection
Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging
Light Efficient Flutter Shutter
Image Classification with Rejection using Contextual Information
Learning Temporal Alignment Uncertainty for Efficient Event Detection
CNN Based Hashing for Image Retrieval
Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
Conjugate Gradient Acceleration of Non-Linear Smoothing Filters
Object Recognition from Short Videos for Robotic Perception
Co-interest Person Detection from Multiple Wearable Camera Videos
Structured Prediction with Output Embeddings for Semantic Image Annotation
HEp-2 Cell Classification: The Role of Gaussian Scale Space Theory as A Pre-processing Approach
Edge-enhancing Filters with Negative Weights
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data
Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map
A deep matrix factorization method for learning attribute representations
A reliable order-statistics-based approximate nearest neighbor search algorithm
OCR accuracy improvement on document images through a novel pre-processing approach
Fingerprint Recognition Using Translation Invariant Scattering Network
Oracle MCG: A first peek into COCO Detection Challenges
On Binary Classification with Single-Layer Convolutional Neural Networks
Learning to Divide and Conquer for Online Multi-Target Tracking
Expanded Parts Model for Semantic Description of Humans in Still Images
A Total Fractional-Order Variation Model for Image Restoration with Non-homogeneous Boundary Conditions and its Numerical Solution
Comparative Design Space Exploration of Dense and Semi-Dense SLAM
Zero-Shot Learning via Semantic Similarity Embedding
Group Membership Prediction
DenseBox: Unifying Landmark Localization with End to End Object Detection
An Improved Algorithm for Eye Corner Detection
Guiding Long-Short Term Memory for Image Caption Generation
Human and Sheep Facial Landmarks Localisation by Triplet Interpolated Features
Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture
HCLAE: High Capacity Locally Aggregating Encodings for Approximate Nearest Neighbor Search
Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search
Hand-held Video Deblurring via Efficient Fourier Aggregation
Humans Are Easily Fooled by Digital Images
Recurrent Spatial Transformer Networks
Geometry-aware Deep Transform
Learning from Synthetic Data Using a Stacked Multichannel Autoencoder
An Experimental Survey on Correlation Filter-based Tracking
Similar Handwritten Chinese Character Discrimination by Weakly Supervised Learning
Face Photo Sketch Synthesis via Larger Patch and Multiresolution Spline
Image Retrieval Based on LBP Pyramidal Multiresolution using Reversible Watermarking
On Large-Scale Retrieval: Binary or n-ary Coding?
Fusing Multi-Stream Deep Networks for Video Classification
On 3D Face Reconstruction via Cascaded Regression in Shape Space
From Facial Parts Responses to Face Detection: A Deep Learning Approach
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks
Invariants of objects and their images under surjective maps
A Dual-Source Approach for 3D Pose Estimation from a Single Image
Robust Object Tracking with a Hierarchical Ensemble Framework
Multi-Region Probabilistic Dice Similarity Coefficient using the Aitchison Distance and Bipartite Graph Matching
Learning Concept Embeddings with Combined Human-Machine Expertise
Incremental Loop Closure Verification by Guided Sampling
Self-localization Using Visual Experience Across Domains
Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection
Modeling Curiosity in a Mobile Robot for Long-Term Autonomous Exploration and Monitoring
Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling
Amodal Completion and Size Constancy in Natural Scenes
Robust video object tracking using particle filter with likelihood based feature fusion and adaptive template updating
Long-Range Trajectories from Global and Local Motion Representations
Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval
Stats-Calculus Pose Descriptor Feeding A Discrete HMM Low-latency Detection and Recognition System For 3D Skeletal Actions
Moving Object Detection in Video Using Saliency Map and Subspace Learning
A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation
General Dynamic Scene Reconstruction from Multiple View Video
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
Efficient Edge Detection on Low-Cost FPGAs
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale
Continuous and Simultaneous Gesture and Posture Recognition for Commanding a Robotic Wheelchair; Towards Spotting the Signal Patterns
Actions ~ Transformations
Compressive hyperspectral imaging via adaptive sampling and dictionary learning
A Literature Survey of various Fingerprint De-noising Techniques to justify the need of a new De-noising model based upon Pixel Component Analysis
Fast Low-Rank Matrix Learning with Nonconvex Regularization
Occlusion-Aware Human Pose Estimation with Mixtures of Sub-Trees
Trending Chic: Analyzing the Influence of Social Media on Fashion Brands
Staple: Complementary Learners for Real-Time Tracking
A Shapley Value Solution to Game Theoretic-based Feature Reduction in False Alarm Detection
Maximum Entropy Binary Encoding for Face Template Protection
Sparsifying Neural Network Connections for Face Recognition
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Learning to Point and Count
Tracking Objects with Higher Order Interactions using Delayed Column Generation
Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Window-Object Relationship Guided Representation Learning for Generic Object Detections
Yet Another Statistical Analysis of Bob Ross Paintings
Deep Residual Learning for Image Recognition
Improving Human Activity Recognition Through Ranking and Re-ranking
Learning the Correction for Multi-Path Deviations in Time-of-Flight Cameras
Deep Learning-Based Image Kernel for Inductive Transfer
Articulated Pose Estimation Using Hierarchical Exemplar-Based Models
A Person Re-Identification System For Mobile Devices
Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
Learning Deep Features for Discriminative Localization
Watch-Bot: Unsupervised Learning for Reminding Humans of Forgotten Actions
Instance-aware Semantic Segmentation via Multi-task Network Cascades
Sparse Representation of a Blur Kernel for Blind Image Restoration
Blockout: Dynamic Model Selection for Hierarchical Deep Networks
Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling
Reconstruction of Enhanced Ultrasound Images From Compressed Measurements Using Simultaneous Direction Method of Multipliers
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Deformable Distributed Multiple Detector Fusion for Multi-Person Tracking
Modeling Colors of Single Attribute Variations with Application to Food Appearance
Multistage SFM: A Coarse-to-Fine Approach for 3D Reconstruction
Neutro-Connectedness Cut
Kernel principal component analysis network for image classification
Harnessing the Deep Net Object Models for Enhancing Human Action Recognition
Spatial Phase-Sweep: Increasing temporal resolution of transient imaging using a light source array
Deep Learning for Surface Material Classification Using Haptic And Visual Information
Sparse Coding with Fast Image Alignment via Large Displacement Optical Flow
Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs
Multi-Instance Visual-Semantic Embedding
Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels
Implementation of deep learning algorithm for automatic detection of brain tumors using intraoperative IR-thermal mapping data
Mid-level Representation for Visual Recognition
A Deep Generative Deconvolutional Image Model
Convolutional Architecture Exploration for Action Recognition and Image Classification
On the Automated Synthesis of Enterprise Integration Patterns to Adapt Choreography-based Distributed Systems
Fast Acquisition for Quantitative MRI Maps: Sparse Recovery from Non-linear Measurements
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
Texture measures combination for improved meningioma classification of histopathological images
Visually Indicated Sounds
Combined statistical and model based texture features for improved image classification
Actor-Action Semantic Segmentation with Grouping Process Models
Autoencoding beyond pixels using a learned similarity metric
Discriminative Sparsity for Sonar ATR
A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection
Multi-task CNN Model for Attribute Prediction
Matrix Variate RBM and Its Applications
Low-Rank Representation over the Manifold of Curves
Memory Matters: Convolutional Recurrent Neural Network for Scene Text Recognition
Automatic 3D object detection of Proteins in Fluorescent labeled microscope images with spatial statistical analysis
Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis
Block-Diagonal Sparse Representation by Learning a Linear Combination Dictionary for Recognition
Learning to Remove Multipath Distortions in Time-of-Flight Range Images for a Robotic Arm Setup
Multicuts and Perturb & MAP for Probabilistic Graph Clustering
3D Gaze Estimation from 2D Pupil Positions on Monocular Head-Mounted Eye Trackers
Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors
Digital Image Forensics vs. Image Composition: An Indirect Arms Race
Document image classification, with a specific view on applications of patent images
A Score-level Fusion Method for Eye Movement Biometrics
The Ultimate Display
Dynamic Concept Composition for Zero-Example Event Detection
Stereo Matching by Joint Energy Minimization
$\mathbf{D^3}$: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
Studying Very Low Resolution Recognition Using Deep Networks
Face-space Action Recognition by Face-Object Interactions
Discovering Picturesque Highlights from Egocentric Vacation Videos
Deep Perceptual Mapping for Cross-Modal Face Recognition
Detecting Temporally Consistent Objects in Videos through Object Class Label Propagation
RGB-D-based Action Recognition Datasets: A Survey
Automatic 3D modelling of craniofacial form
Manifold-Kernels Comparison in MKPLS for Visual Speech Recognition
Depth and Reflection Total Variation for Single Image Dehazing
Online Event Recognition from Moving Vessel Trajectories
Unsupervised convolutional neural networks for motion estimation
A bifibrational reconstruction of Lawvere's presheaf hyperdoctrine
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization
Using compatible shape descriptor for lexicon reduction of printed Farsi subwords
Synthesis of Gaussian Trees with Correlation Sign Ambiguity: An Information Theoretic Approach
Egocentric Activity Recognition with Multimodal Fisher Vector
Fisher Motion Descriptor for Multiview Gait Recognition
Font Identification in Historical Documents Using Active Learning
Fast Integral Image Estimation at 1% measurement rate
Learning to Extract Motion from Videos in Convolutional Neural Networks
Combining Maps and Street Level Images for Building Height and Facade Estimation
DehazeNet: An End-to-End System for Single Image Haze Removal
Geo-distinctive Visual Element Matching for Location Estimation of Images
Mapping Tractography Across Subjects
What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots
Convolutional Pose Machines
Learning a low-rank shared dictionary for object classification
Transfer Learning Based on AdaBoost for Feature Selection from Multiple ConvNet Layer Features
Algorithm-Induced Prior for Image Restoration
Combining ConvNets with Hand-Crafted Features for Action Recognition Based on an HMM-SVM Classifier
Simple Online and Realtime Tracking
Learning a Deep Model for Human Action Recognition from Novel Viewpoints
Head Pose Estimation of Occluded Faces using Regularized Regression
A-expansion for multiple "hedgehog" shapes
How Far are We from Solving Pedestrian Detection?
Learning scale-variant and scale-invariant features for deep image classification
Development of an Ideal Observer that Incorporates Nuisance Parameters and Processes List-Mode Data
An ensemble diversity approach to supervised binary hashing
Appearance Based Robot and Human Activity Recognition System
Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification
Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild
Visual Tracking via Reliable Memories
Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
Sub-cortical brain structure segmentation using F-CNN's
Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization
Characterization of a Multi-User Indoor Positioning System Based on Low Cost Depth Vision (Kinect) for Monitoring Human Activity in a Smart Home
Tumour ROI Estimation in Ultrasound Images via Radon Barcodes in Patients with Locally Advanced Breast Cancer
Joint Defogging and Demosaicking
Face Recognition: Perspectives from the Real-World
A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images
Design of false color palettes for grayscale reproduction
Generating Discriminative Object Proposals via Submodular Ranking
HMM and DTW for evaluation of therapeutical gestures using kinect
Semi-supervised Learning with Explicit Relationship Regularization
Wavelet-Based Semantic Features for Hyperspectral Signature Discrimination
Image Restoration and Reconstruction using Variable Splitting and Class-adapted Image Priors
Manifolds of Projective Shapes
Convolutional Tables Ensemble: classification in microseconds
A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes
Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation
Image Restoration: A General Wavelet Frame Based Model and Its Asymptotic Analysis
Boost Picking: A Universal Method on Converting Supervised Classification to Semi-supervised Classification
Feature-Area Optimization: A Novel SAR Image Registration Method
Weighted Unsupervised Learning for 3D Object Detection
Plücker Correction Problem: Analysis and Improvements in Efficiency
Large age-gap face verification by feature injection in deep networks
Denoising and Covariance Estimation of Single Particle Cryo-EM Images
Planogram Compliance Checking Based on Detection of Recurring Patterns
Exploring the Neural Algorithm of Artistic Style
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
How Deep Neural Networks Can Improve Emotion Recognition on Video Data
Learning to Generate with Memory
A fine-grained approach to scene text script identification
Improving patch-based scene text script identification with ensembles of conjoined networks
A Low Complexity VLSI Architecture for Multi-Focus Image Fusion in DCT Domain
CNN for License Plate Motion Deblurring
Auto-JacoBin: Auto-encoder Jacobian Binary Hashing
Multimodal Emotion Recognition Using Multimodal Deep Learning
Victory Sign Biometric for Terrorists Identification
Seq-NMS for Video Object Detection
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images
Single-Image Superresolution Through Directional Representations
Content-based Video Indexing and Retrieval Using Corr-LDA
Learning Multilayer Channel Features for Pedestrian Detection
A Universal Update-pacing Framework For Visual Tracking
Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach
Keypoint Density-based Region Proposal for Fine-Grained Object Detection and Classification using Regions with Convolutional Neural Network Features
Synthesized Classifiers for Zero-Shot Learning
Flies as Ship Captains? Digital Evolution Unravels Selective Pressures to Avoid Collision in Drosophila
A Nonlinear Weighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography
Automatic segmentation of lizard spots using an active contour model
LiDAR Ground Filtering Algorithm for Urban Areas Using Scan Line Based Segmentation
PCANet: An energy perspective
Self-localization from Images with Small Overlap
Automatic learning of gait signatures for people identification
First Steps Toward Camera Model Identification with Convolutional Neural Networks
What is the right way to represent document images?
HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition
Learning deep representation of multityped objects and tasks
Saliency Detection combining Multi-layer Integration algorithm with background prior and energy function
A Feature Learning and Object Recognition Framework for Underwater Fish Images
Grading of Mammalian Cumulus Oocyte Complexes using Machine Learning for in Vitro Embryo Culture
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
Variational methods for Conditional Multimodal Deep Learning
Single Image Restoration for Participating Media Based on Prior Fusion
A Two-Stage Shape Retrieval (TSR) Method with Global and Local Features
Adaptive Visualisation System for Construction Building Information Models Using Saliency
A novel learning-based frame pooling method for Event Detection
Gaussian Process Regression for Out-of-Sample Extension
Blur Robust Optical Flow using Motion Channel
Hand Segmentation for Hand-Object Interaction from Depth map
A hybrid approach based segmentation technique for brain tumor in MRI Images
A New Method to Visualize Deep Neural Networks
Iterative Hough Forest with Histogram of Control Points for 6 DoF Object Registration from Depth Images
A regularization-based approach for unsupervised image segmentation
Discriminative models for robust image classification
Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
UTSig: A Persian Offline Signature Dataset
Learning Gaze Transitions from Depth to Improve Video Saliency Estimation
Real-time 3D scene description using Spheres, Cones and Cylinders
Optical Flow with Semantic Segmentation and Localized Layers
Robust Scene Text Recognition with Automatic Rectification
Template Adaptation for Face Verification and Identification
Pose for Action - Action for Pose
RISAS: A Novel Rotation, Illumination, Scale Invariant Appearance and Shape Feature
Saliency Detection for Improving Object Proposals
Regression-based Hypergraph Learning for Image Clustering and Classification
Visual Concept Recognition and Localization via Iterative Introspection
Diversity in Object Proposals
Automatic Discrimination of Color Retinal Images using the Bag of Words Approach
Rapid building detection using machine learning
Object Contour Detection with a Fully Convolutional Encoder-Decoder Network
Scalable Image Retrieval by Sparse Product Quantization
Modeling Time Series Similarity with Siamese Recurrent Networks
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation
Deep Fully-Connected Networks for Video Compressive Sensing
Non-linear Dimensionality Regularizer for Solving Inverse Problems
Identity Mappings in Deep Residual Networks
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Image Labeling by Assignment
Saliency Detection with Spaces of Background-based Distribution
Unsupervised Cross-Media Hashing with Structure Preservation
A Flexible Primal-Dual Toolbox
Geometric Hypergraph Learning for Visual Tracking
Transferring Learned Microcalcification Group Detection from 2D Mammography to 3D Digital Breast Tomosynthesis Using a Hierarchical Model and Scope-based Normalization Features
Large scale near-duplicate image retrieval using Triples of Adjacent Ranked Features (TARF) with embedded geometric information
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
Buried object detection using handheld WEMI with task-driven extended functions of multiple instances
Adaptive coherence estimator (ACE) for explosive hazard detection using wideband electromagnetic induction (WEMI)
Towards Automatic Wild Animal Monitoring: Identification of Animal Species in Camera-trap Images using Very Deep Convolutional Neural Networks
Segmentation from Natural Language Expressions
Modelling Temporal Information Using Discrete Fourier Transform for Video Classification
Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields
Appearance Harmonization for Single Image Shadow Removal
Beyond Sharing Weights for Deep Domain Adaptation
Frankenstein: Learning Deep Face Representations using Small Data
Action-Affect Classification and Morphing using Multi-Task Representation Learning
Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos
Input Aggregated Network for Face Video Representation
Implementation of a FPGA-Based Feature Detection and Networking System for Real-time Traffic Monitoring
Image Super-Resolution Based on Sparsity Prior via Smoothed $l_0$ Norm
Knowledge Transfer for Scene-specific Motion Prediction
Active Detection and Localization of Textureless Objects in Cluttered Environments
Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
Weakly-Supervised Semantic Segmentation using Motion Cues
Face Recognition Using Deep Multi-Pose Representations
Pixel-Level Domain Transfer
Fine-scale Surface Normal Estimation using a Single NIR Image
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
An Effective Unconstrained Correlation Filter and Its Kernelization for Face Recognition
Conditional Similarity Networks
Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics
Blind signal separation and identification of mixtures of images
Support Driven Wavelet Frame-based Image Deblurring
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
VolumeDeform: Real-time Volumetric Non-rigid Reconstruction
DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination
Hierarchy of Groups Evaluation Using Different F-score Variants
Hierarchical Gaussian Mixture Model with Objects Attached to Terminal and Non-terminal Dendrogram Nodes
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Colorful Image Colorization
Shuffle and Learn: Unsupervised Learning using Temporal Order Verification
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
Exploring Local Context for Multi-target Tracking in Wide Area Aerial Surveillance
Learning a Predictable and Generative Vector Representation for Objects
Multi-Band Image Fusion Based on Spectral Unmixing
Scalable Solution for Approximate Nearest Subspace Search
SMASH: Physics-guided Reconstruction of Collisions from Videos
Dense Image Representation with Spatial Pyramid VLAD Coding of CNN for Locally Robust Captioning
Structured Feature Learning for Pose Estimation
Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Partial Face Detection for Continuous Authentication
The Open World of Micro-Videos
Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories
Robust Uncalibrated Stereo Rectification with Constrained Geometric Distortions (USR-CGD)
A ParaBoost Stereoscopic Image Quality Assessment (PBSIQA) System
Large Scale Deep Convolutional Neural Network Features Search with Lucene
Object Boundary Guided Semantic Segmentation
Deep Convolutional Neural Networks on Cartoon Functions
DisturbLabel: Regularizing CNN on the Loss Layer
3D Keypoint Detection Based on Deep Neural Network with Sparse Autoencoder
Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking
Multidimensional Scaling on Multiple Input Distance Matrices
Dominant Codewords Selection with Topic Model for Action Recognition
Parallel Wavelet Schemes for Images
Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification
Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video
Hierarchical Bayesian Noise Inference for Robust Real-time Probabilistic Object Classification
MARLow: A Joint Multiplanar Autoregressive and Low-Rank Approach for Image Completion
Learning Covariant Feature Detectors
Unsupervised Total Variation Loss for Semi-supervised Deep Learning of Semantic Segmentation
Leveraging Visual Question Answering for Image-Caption Ranking
Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)
Classification of Human Whole-Body Motion using Hidden Markov Models
Adversarial Diversity and Hard Positive Generation
Robust SAR STAP via Kronecker Decomposition
Shape from Mixed Polarization
Deeply Exploit Depth Information for Object Detection
Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks
Fuzzy Clustering Based Segmentation Of Vertebrae in T1-Weighted Spinal MR Images
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
Unsupervised Semantic Action Discovery from Video Collections
A robust particle detection algorithm based on symmetry
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
Efficiently Creating 3D Training Data for Fine Hand Pose Estimation
On-the-fly Network Pruning for Object Detection
Real-time 3D Tracking of Articulated Tools for Robotic Surgery
Deep Neural Networks Under Stress
View Synthesis by Appearance Flow
Item Popularity Prediction in E-commerce Using Image Quality Feature Vectors
Going Deeper into First-Person Activity Recognition
Robust and Efficient Relative Pose with a Multi-camera System for Autonomous Vehicle in Highly Dynamic Environments
Deformable Parts Correlation Filters for Robust Visual Tracking
Fast Graph-Based Object Segmentation for RGB-D Images
A New Manifold Distance Measure for Visual Object Categorization
Track Extraction with Hidden Reciprocal Chain Models
Fast Semantic Image Segmentation with High Order Context and Guided Filtering
With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams
Simultaneous Surface Reflectance and Fluorescence Spectra Estimation
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild
Improving the Neural Algorithm of Artistic Style
Mono-jet Signatures of Gluphilic Scalar Dark Matter
Video2GIF: Automatic Generation of Animated GIFs from Video
Classification of Big Data with Application to Imaging Genetics
Image stitching with perspective-preserving warping
Incremental Robot Learning of New Objects with Fixed Update Time
Monocular Urban Localization using Street View
Human Action Localization with Sparse Spatial Supervision
Learning Deep Representations of Fine-grained Visual Descriptions
Relative distance features for gait recognition with Kinect
Beyond Caption To Narrative: Video Captioning With Multiple Sentences
Low-Rank Matrices on Graphs: Generalized Recovery & Applications
Scalable low dimensional manifold model in the reconstruction of noisy and incomplete hyperspectral images
Robust Image Descriptors for Real-Time Inter-Examination Retargeting in Gastrointestinal Endoscopy
Tongue contour extraction from ultrasound images based on deep neural network
A Geometric Approach to Color Image Regularization
Inter-Battery Topic Representation Learning
Fine-Grained Classification of Pedestrians in Video: Benchmark and State of the Art
Fully Convolutional Networks for Semantic Segmentation
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks
Learning shape correspondence with anisotropic convolutional neural networks
X-ray image separation via coupled dictionary learning
WAHRSIS: A Low-cost, High-resolution Whole Sky Imager With Near-Infrared Capabilities
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification
Automatic Detection of Epileptiform Discharges in the EEG
A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machines
3D Face Tracking and Texture Fusion in the Wild
Self-expressive Dictionary Learning for Dynamic 3D Reconstruction
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
Spatio-Temporal Image Boundary Extrapolation
Quickest Moving Object Detection
Natural Scene Image Segmentation Based on Multi-Layer Feature Extraction
Blind Analysis of CT Image Noise Using Residual Denoised Images
DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
Multi-Object Tracking and Identification over Sets
Video Summarization with Long Short-term Memory
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning
A single scale retinex based method for palm vein extraction
Multiple target tracking based on sets of trajectories
Discovering Causal Signals in Images
Pairwise Decomposition of Image Sequences for Active Multi-View Recognition
Domain Transfer Multi-Instance Dictionary Learning
Dense Volume-to-Volume Vascular Boundary Detection
A Feature based Approach for Video Compression
A Channelized Binning Method for Extraction of Dominant Color Pixel Value
Video Key Frame Extraction using Entropy value as Global and Local Feature
Sparse Coding and Counting for Robust Visual Tracking
Semi-supervised Zero-Shot Learning by a Clustering-based Approach
Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information
Image segmentation based on the hybrid total variation model and the K-means clustering strategy
Control of Memory, Active Perception, and Action in Minecraft
Blind Modulation Classification based on MLP and PNN
Robust Deep-Learning-Based Road-Prediction for Augmented Reality Navigation Systems
Semantic-Aware Depth Super-Resolution in Outdoor Scenes
Model-driven Simulations for Deep Convolutional Neural Networks
Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval
Towards ontology driven learning of visual concept detectors
Fast Zero-Shot Image Tagging
Texture Synthesis Using Shallow Convolutional Networks with Random Filters
A Comparative Study of Algorithms for Realtime Panoramic Video Blending
OpenSalicon: An Open Source Implementation of the Salicon Saliency Model
Multiview Rectification of Folded Documents
A Survey on Learning to Hash
Hyperspectral Subspace Identification Using SURE
Improving Deep Neural Network with Multiple Parametric Exponential Linear Units
A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment
Recurrent Fully Convolutional Networks for Video Segmentation
Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis
Comparison of 14 different families of classification algorithms on 115 binary datasets
Automatic Separation of Compound Figures in Scientific Articles
Extraction of clinical information from the non-invasive fetal electrocardiogram
Learning under Distributed Weak Supervision
Reinforcement Learning for Semantic Segmentation in Indoor Scenes
What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?
Pairwise Quantization
Integrated perception with recurrent multi-task neural networks
Optically lightweight tracking of objects around a corner
Learning deep structured network for weakly supervised change detection
Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform
Joint Recursive Monocular Filtering of Camera Motion and Disparity Map
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)
SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks
Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation
Progressive Attention Networks for Visual Attribute Prediction
Estimation of solar irradiance using ground-based whole sky imagers
DISCO Nets: DISsimilarity COefficient Networks
Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery
Rotation Invariant Angular Descriptor Via A Bandlimited Gaussian-like Kernel
Simultaneous Inpainting and Denoising by Directional Global Three-part Decomposition: Connecting Variational and Fourier Domain Based Image Processing
Implicit Tubular Surface Generation Guided by Centerline
Mutual Exclusivity Loss for Semi-Supervised Deep Learning
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications
FOMTrace: Interactive Video Segmentation By Image Graphs and Fuzzy Object Models
Alternative Technique to Asymmetry Analysis-Based Overlapping for Foot Ulcer Examination: Scalable Scanning
Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras
Segmentation of scanning electron microscopy images from natural rubber samples with gold nanoparticles using starlet wavelets
Human Centred Object Co-Segmentation
Deep Image Homography Estimation
Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance
Visual-Inertial-Semantic Scene Representation for 3-D Object Detection
DCNNs on a Diet: Sampling Strategies for Reducing the Training Set Size
Richardson-Lucy Deblurring for Moving Light Field Cameras
Efficient adaptation of complex-valued noiselet sensing matrices for compressed single-pixel imaging
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
In the Shadows, Shape Priors Shine: Using Occlusion to Improve Multi-Region Segmentation
Natural Scene Character Recognition Using Robust PCA and Sparse Representation
Watch What You Just Said: Image Captioning with Text-Conditional Attention
Free Form based active contours for image segmentation and free space perception
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach
3DFS: Deformable Dense Depth Fusion and Segmentation for Object Reconstruction from a Handheld Camera
CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration
How many faces can be recognized? Performance extrapolation for multi-class classification
Learning feed-forward one-shot learners
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
Generating Object Cluster Hierarchies for Benchmarking
A Survey of Pansharpening Methods with A New Band-Decoupled Variational Model
Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models
DualNet: Domain-Invariant Network for Visual Question Answering
Detection and Tracking of Liquids with Fully Convolutional Networks
Recognizing Surgical Activities with Recurrent Neural Networks
Multiple Instance Hyperspectral Target Characterization
Efficient 2D and 3D Facade Segmentation using Auto-Context
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
Learning to Poke by Poking: Experiential Learning of Intuitive Physics
Coupled Generative Adversarial Networks
Multipartite Ranking-Selection of Low-Dimensional Instances by Supervised Projection to High-Dimensional Space
A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
Training LDCRF model on unsegmented sequences using Connectionist Temporal Classification
Scalable image coding based on epitomes
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
Geometry in Active Learning for Binary and Multi-class Image Segmentation
A spectral-spatial fusion model for robust blood pulse waveform extraction in photoplethysmographic imaging
Multiphase Segmentation For Simultaneously Homogeneous and Textural Images
Zero-Shot Learning with Multi-Battery Factor Analysis
Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks
maskSLIC: Regional Superpixel Generation with Application to Local Pathology Characterisation in Medical Images
Fully-Convolutional Siamese Networks for Object Tracking
Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition
Noise Models in Feature-based Stereo Visual Odometry
Machine-based Multimodal Pain Assessment Tool for Infants: A Review
Keyframe-based monocular SLAM: design, survey, and future directions
Active Object Localization in Visual Situations
An Analysis System for DNA Gel Electrophoresis Images Based on Automatic Thresholding an Enhancement
Automatic Techniques for Gridding cDNA Microarray Images
Robust Deep Appearance Models
A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images
Facial Expression Classification Using Rotation Slepian-based Moment Invariants
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis
Aggressive actions and anger detection from multiple modalities using Kinect
Learning the semantic structure of objects from Web supervision
Feature Selection Library (MATLAB Toolbox)
Object Recognition and Identification Using ESM Data
On a method for Rock Classification using Textural Features and Genetic Optimization
VideoLSTM Convolves, Attends and Flows for Action Recognition
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge
Overcoming Challenges in Fixed Point Training of Deep Convolutional Networks
Siamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation
Non-Central Catadioptric Cameras Pose Estimation using 3D Lines
Screen Content Image Segmentation Using Robust Regression and Sparse Decomposition
A Photometrically Calibrated Benchmark For Monocular Visual Odometry
Action Recognition with Joint Attention on Multi-Level Deep Features
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
Augmenting Supervised Emotion Recognition with Rule-Based Decision Model
Towards an "In-the-Wild" Emotion Dataset Using a Game-based Framework
Adversarial Training For Sketch Retrieval
Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search
Hypergraph Modelling for Geometric Model Fitting
Learning a metric for class-conditional KNN
Fast Cosine Transform to increase speed-up and efficiency of Karhunen-Loeve Transform for lossy image compression
Gland Instance Segmentation by Deep Multichannel Side Supervision
Local feature hierarchy for face recognition across pose and illumination
Weakly Supervised Learning of Heterogeneous Concepts in Videos
A Variational Model for Joint Motion Estimation and Image Reconstruction
DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing
A Representation Theory Perspective on Simultaneous Alignment and Classification
End-to-end training of object class detectors for mean average precision
Improved Multi-Class Cost-Sensitive Boosting via Estimation of the Minimum-Risk Class
Hierarchical learning for DNN-based acoustic scene classification
Adaptable Precomputation for Random Walker Image Segmentation and Registration
End-to-End Learning for Image Burst Deblurring
A Real-Time Deep Learning Pedestrian Detector for Robot Navigation
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
Spatial Context based Angular Information Preserving Projection for Hyperspectral Image Classification
Person Re-identification with Hyperspectral Multi-Camera Systems --- A Pilot Study
Construction of extended 3D field of views of the internal bladder wall surface: a proof of concept
Gland Instance Segmentation by Deep Multichannel Neural Networks
Composite Kernel Local Angular Discriminant Analysis for Multi-Sensor Geospatial Image Analysis
Sparse Representation-Based Classification: Orthogonal Least Squares or Orthogonal Matching Pursuit?
End-to-end optimization of nonlinear transform codes for perceptual quality
Deep Cascaded Bi-Network for Face Hallucination
Recycle deep features for better object detection
Query-Focused Extractive Video Summarization
HeMIS: Hetero-Modal Image Segmentation
A Multi-task Deep Network for Person Re-identification
Generating Images Part by Part with Composite Generative Adversarial Networks
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods
Supervised Transformer Network for Efficient Face Detection
Dual Purpose Hashing
Person Re-identification for Real-world Surveillance Systems
On the Modeling of Error Functions as High Dimensional Landscapes for Weight Initialization in Learning Networks
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
Hashmod: A Hashing Method for Scalable 3D Object Detection
Local Multiple Directional Pattern of Palmprint Image
Haze Visibility Enhancement: A Survey and Quantitative Benchmarking
Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation
Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit detection
A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects
Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional Networks
Reasoning about Body-Parts Relations for Sign Language Recognition
Hierarchical Attention Network for Action Recognition in Videos
Prior-based Coregistration and Cosegmentation
An ensemble learning method for scene classification based on Hidden Markov Model image representation
A probabilistic patch based image representation using Conditional Random Field model for image classification
Recurrent Regression for Face Recognition
DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
Automatic Attribute Discovery with Neural Activations
Learning Aligned Cross-Modal Representations from Weakly Aligned Data
Automated quantification of one-dimensional nanostructure alignment on surfaces
Symmetry-free SDP Relaxations for Affine Subspace Clustering
Salient Object Subitizing
Semantic Image Inpainting with Deep Generative Models
Generic 3D Convolutional Fusion for image restoration
Scale Invariant Interest Points with Shearlets
Region-based semantic segmentation with end-to-end training
Joint Optical Flow and Temporally Consistent Semantic Segmentation
How scientific literature has been evolving over the time? A novel statistical approach using tracking verbal-based methods
A Continuous Optimization Approach for Efficient and Accurate Scene Flow
ATGV-Net: Accurate Depth Super-Resolution
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem
A Siamese Long Short-Term Memory Architecture for Human Re-Identification
Faceless Person Recognition; Privacy Implications in Social Media
A Nonlocal Denoising Algorithm for Manifold-Valued Images Using Second Order Statistics
Fine-To-Coarse Global Registration of RGB-D Scans
Connectionist Temporal Modeling for Weakly Supervised Action Labeling
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition
Analysis of a low memory implementation of the Orthogonal Matching Pursuit greedy strategy
Segmentation Free Object Discovery in Video
Autonomous driving challenge: To Infer the property of a dynamic object based on its motion pattern using recurrent neural network
Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation
Greedy MAXCUT Algorithms and their Information Content
Stochastic Learning of Multi-Instance Dictionary for Earth Mover's Distance based Histogram Comparison
Towards Segmenting Consumer Stereo Videos: Benchmark, Baselines and Ensembles
A Probabilistic Optimum-Path Forest Classifier for Binary Classification Problems
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
A Deep Multi-Level Network for Saliency Prediction
A max-cut approach to heterogeneity in cryo-electron microscopy
Efficient Volumetric Fusion of Airborne and Street-Side Data for Urban Reconstruction
Object Specific Deep Learning Feature and Its Application to Face Detection
Multi-instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation
Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation-Maximization
Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution
Best-Buddies Similarity - Robust Template Matching using Mutual Nearest Neighbors
Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane
Human pose estimation via Convolutional Part Heatmap Regression
Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
A Boosting Method to Face Image Super-resolution
DAiSEE: Towards User Engagement Recognition in the Wild
Object Tracking via Dynamic Feature Selection Processes
Human Body Orientation Estimation using Convolutional Neural Network
Optimizing Codes for Source Separation in Color Image Demosaicing and Compressive Video Recovery
Automated Segmentation of Retinal Layers from Optical Coherent Tomography Images Using Geodesic Distance
Ear-to-ear Capture of Facial Intrinsics
Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks
Latest Datasets and Technologies Presented in the Workshop on Grasping and Manipulation Datasets
Generating Videos with Scene Dynamics
Automatic Selection of Stochastic Watershed Hierarchies
The Role of Context Selection in Object Detection
Learning-Based View Synthesis for Light Field Cameras
Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN
Style-Transfer via Texture-Synthesis
Learning Semantic Part-Based Models from Google Images
Active Canny: Edge Detection and Recovery with Open Active Contour Models
MUG: A Parameterless No-Reference JPEG Quality Evaluator Robust to Block Size and Misalignment
Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation
A Multi-Scale Cascade Fully Convolutional Network Face Detector
Dilemma First Search for Effortless Optimization of NP-Hard Problems
Detecting Text in Natural Image with Connectionist Text Proposal Network
Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers
3D Simulation for Robot Arm Control with Deep Q-Learning
Image Decomposition Using a Robust Regression Approach
VIPLFaceNet: An Open Source Deep Face Recognition SDK
The CUDA LATCH Binary Descriptor: Because Sometimes Faster Means Better
Single-image RGB Photometric Stereo With Spatially-varying Albedo
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision
Learning Robust Features for Gait Recognition by Maximum Margin Criterion
Transport-based analysis, modeling, and learning from signal and data distributions
From the Skin-Depth Equation to the Inverse RFEC Sensor Model
A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality
Visual Stability Prediction and Its Application to Manipulation
Unbiased Sparse Subspace Clustering By Selective Pursuit
Radon-Gabor Barcodes for Medical Image Retrieval
SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks
Deep Kinematic Pose Regression
Pose from Action: Unsupervised Learning of Pose Features based on Motion
Consistent Discretization and Minimization of the L1 Norm on Manifolds
Coarse-to-fine Surgical Instrument Detection for Cataract Surgery Monitoring
Deep CTR Prediction in Display Advertising
A very fast iterative algorithm for TV-regularized image reconstruction with applications to low-dose and few-view CT
Transfer Learning for Material Classification using Convolutional Networks
Hands-Free Segmentation of Medical Volumes via Binary Inputs
Matrix Variate RBM Model with Gaussian Distributions
Detecting facial landmarks in the video based on a hybrid framework
Multi-View Constraint Propagation with Consensus Prior Knowledge
Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
PixelNet: Towards a General Pixel-level Architecture
How should we evaluate supervised hashing?
Deep Learning for Video Classification and Captioning
How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?
Walker-Independent Features for Gait Recognition from Motion Capture Data
Customized Facial Constant Positive Air Pressure (CPAP) Masks
Deep Quality: A Deep No-reference Quality Assessment System
Example-Based Image Synthesis via Randomized Patch-Matching
Real-time Human Pose Estimation from Video with Convolutional Neural Networks
A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses
Three Tiers Neighborhood Graph and Multi-graph Fusion Ranking for Multi-feature Image Retrieval: A Manifold Aspect
Perceptual uniform descriptor and Ranking on manifold: A bridge between image representation and ranking for image retrieval
Deep learning based fence segmentation and removal from an image using a video sequence
Linear Support Tensor Machine: Pedestrian Detection in Thermal Infrared Images
Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background
Swipe Mosaics from Video
Image Retrieval with Fisher Vectors of Binary Features
Tensor Based Second Order Variational Model for Image Reconstruction
House price estimation from visual and textual features
Learning convolutional neural network to maximize Pos@Top performance measure
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches
Task Specific Adversarial Cost Function
A Transportation $L^p$ Distance for Signal Analysis
Scalable Discrete Supervised Hash Learning with Asymmetric Matrix Factorization
Understanding data augmentation for classification: when to warp?
Transforming building industry and health outcomes through social data-supported design
Effective Combination of Language and Vision Through Model Composition and the R-CCA Method
A Discriminative Framework for Anomaly Detection in Large Videos
Learning to Push by Grasping: Using multiple tasks for effective learning
Structure-Aware Classification using Supervised Dictionary Learning
CNN-aware Binary Map for General Semantic Segmentation
A comparative study of complexity of handwritten Bharati characters with that of major Indian scripts
Modelling depth for nonparametric foreground segmentation using RGBD devices
Robust Moving Objects Detection in Lidar Data Exploiting Visual Cues
Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks
Cooperative Training of Descriptor and Generator Networks
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge
A CNN Cascade for Landmark Guided Semantic Part Segmentation
Latent fingerprint minutia extraction using fully convolutional network
Near-Infrared Image Dehazing Via Color Regularization
Deep Learning Algorithms for Signal Recognition in Long Perimeter Monitoring Distributed Fiber Optic Sensors
Deep Feature Consistent Variational Autoencoder
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection
Rain structure transfer using an exemplar rain image for synthetic rain image generation
Video Pixel Networks
Deep Visual Foresight for Planning Robot Motion
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints
Multi-View Representation Learning: A Survey from Shallow Methods to Deep Methods
Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy
Domain Adaptation with Soft-margin multiple feature-kernel learning beats Deep Learning for surveillance face recognition
Recognizing and Presenting the Storytelling Video Structure with Deep Multimodal Networks
Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions
Template shape estimation: correcting an asymptotic bias
Supervision via Competition: Robot Adversaries for Learning Tasks
PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI
Searching Scenes by Abstracting Things
Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning
Utilizing High-level Visual Feature for Indoor Shopping Mall Navigation
Xception: Deep Learning with Depthwise Separable Convolutions
Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)
4D Crop Monitoring: Spatio-Temporal Reconstruction for Agriculture
Boost K-Means
Visual Closed-Loop Control for Pouring Liquids
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
Zero Shot Hashing
Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare
Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval
FaceVR: Real-Time Facial Reenactment and Eye Gaze Control in Virtual Reality
Restoring STM images via Sparse Coding: noise and artifact removal
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images
Visual Place Recognition with Probabilistic Vertex Voting
Fast Training of Convolutional Neural Networks via Kernel Rescaling
Deep Fruit Detection in Orchards
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders
Recursive Diffeomorphism-Based Regression for Shape Functions
Semi-Coupled Two-Stream Fusion ConvNets for Action Recognition at Extremely Low Resolutions
Predicting the dynamics of 2d objects with a deep residual network
Video Fill in the Blank with Merging LSTMs
Towards end-to-end optimisation of functional image analysis pipelines
Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance
Improved phase-unwrapping method using geometric constraints
Are Accuracy and Robustness Correlated?
A Closed Form Solution to Multi-View Low-Rank Regression
Incremental One-Class Models for Data Classification
Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning
ARTiS: Appearance-based Action Recognition in Task Space for Real-Time Human-Robot Collaboration
Edge Based Grid Super-Imposition for Crowd Emotion Recognition
Master's Thesis : Deep Learning for Visual Recognition
Deep Identity-aware Transfer of Facial Attributes
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips
Lensless Imaging with Compressive Ultrafast Sensing
StuffNet: Using 'Stuff' to Improve Object Detection
POI: Multiple Object Tracking with High Performance Detection and Appearance Feature
Adaptive Substring Extraction and Modified Local NBNN Scoring for Binary Feature-based Local Mobile Visual Search without False Positives
Dynamic Probabilistic Network Based Human Action Recognition
Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
Model-based Outdoor Performance Capture
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields
Multitask Learning of Vegetation Biochemistry from Hyperspectral Data
Optimization on Submanifolds of Convolution Kernels in CNNs
Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks
SPiKeS: Superpixel-Keypoints Structure for Robust Visual Tracking
Theoretical Analysis of Active Contours on Graphs
Feature Sensitive Label Fusion with Random Walker for Atlas-based Image Segmentation
Automatic and Manual Segmentation of Hippocampus in Epileptic Patients MRI
A data augmentation methodology for training machine/deep learning gait recognition algorithms
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
A Learned Representation For Artistic Style
Camera Fingerprint: A New Perspective for Identifying User's Identity
Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
PATH: Person Authentication using Trace Histories
Estimating the concentration of gold nanoparticles incorporated on Natural Rubber membranes using Multi-Level Starlet Optimal Segmentation
PCM and APCM Revisited: An Uncertainty Perspective
Estimation of Bandlimited Grayscale Images From the Single Bit Observations of Pixels Affected by Additive Gaussian Noise
Volumetric Light-field Encryption at the Microscopic Scale
Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016
Single- and Multi-Task Architectures for Tool Presence Detection Challenge at M2CAI 2016
Detecting People in Artwork with CNNs
Cross-Modal Scene Networks
Compressive Holographic Video
Icon: An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures
Learnable Visual Markers
Detecting Breast Cancer using a Compressive Sensing Unmixing Algorithm
Learning Adaptive Parameter Tuning for Image Processing
Asynchronous Stochastic Block Coordinate Descent with Variance Reduction
FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras
Conditional Image Synthesis With Auxiliary Classifier GANs
Compressed Learning: A Deep Neural Network Approach
Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search
Visual Tracking via Boolean Map Representations
Joint Large-Scale Motion Estimation and Image Reconstruction
ConfocalGN : a minimalistic confocal image simulator
Bi-modal First Impressions Recognition using Temporally Ordered Deep Audio and Stochastic Visual Features
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks
Structured illumination microscopy with unknown patterns and a statistical prior
Combining Multiple Cues for Visual Madlibs Question Answering
Statistical Inverse Formulation of Optical Flow with Uncertainty Quantification
Adversarial Machine Learning at Scale
Learning Identity Mappings with Residual Gates
What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?
The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs
Learning to Act by Predicting the Future
Action2Activity: Recognizing Complex Activities from Sensor Data
Chinese/English mixed Character Segmentation as Semantic Segmentation
Hamiltonian operator for spectral shape analysis
Spatiotemporal Residual Networks for Video Action Recognition
Unsupervised Cross-Domain Image Generation
Meat adulteration detection through digital image analysis of histological cuts using LBP
Multiple Object Tracking with Kernelized Correlation Filters in Urban Mixed Traffic
The Loss Surface of Residual Networks: Ensembles and the Role of Batch Normalization
Estimating motion with principal component regression strategies
Multispectral Deep Neural Networks for Pedestrian Detection
A backward pass through a CNN using a generative model of its activations
Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest
Mahalanobis Distance for Class Averaging of Cryo-EM Images
Error concealment by means of motion refinement and regularized Bregman divergence
Variables effecting photomosaic reconstruction and ortho-rectification from aerial survey datasets
Fast Algorithm of High-resolution Microwave Imaging Using the Non-parametric Generalized Reflectivity Model
Construction Inspection through Spatial Database
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification
Learning to Navigate in Complex Environments
Leveraging Video Descriptions to Learn Video Question Answering
Least Squares Generative Adversarial Networks
Hand Gesture Recognition for Contactless Device Control in Operating Rooms
Baseline CNN structure analysis for facial expression recognition
3-D Convolutional Neural Networks for Glioblastoma Segmentation
When Saliency Meets Sentiment: Understanding How Image Content Invokes Emotion and Sentiment
Motion Estimated-Compensated Reconstruction with Preserved-Features in Free-Breathing Cardiac MRI
Scale-constrained Unsupervised Evaluation Method for Multi-scale Image Segmentation
Diversity encouraged learning of unsupervised LSTM ensemble for neural activity video prediction
One-to-Many Network for Visually Pleasing Compression Artifacts Reduction
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation
One-Shot Video Object Segmentation
Joint Network based Attention for Action Recognition
A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching
Deep Transfer Learning for Person Re-identification
Temporal Convolutional Networks for Action Segmentation and Detection
Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search
A Semi-supervised Framework for Image Captioning
Lip Reading Sentences in the Wild
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting
Aggregated Residual Transformations for Deep Neural Networks
Self-calibration-based Approach to Critical Motion Sequences of Rolling-shutter Structure from Motion
Probabilistic Fluorescence-Based Synapse Detection
Semantic Regularisation for Recurrent Image Annotation
On the Exploration of Convolutional Fusion Networks for Visual Recognition
Deep Feature Interpolation for Image Content Changes
Instance-aware Image and Sentence Matching with Selective Multimodal LSTM
SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation
Building Deep Networks on Grassmann Manifolds
Cross-Domain Face Verification: Matching ID Document and Self-Portrait Photographs
Reweighted Low-Rank Tensor Decomposition based on t-SVD and its Applications in Video Denoising
Reweighted Low-Rank Tensor Completion and its Applications in Video Recovery
Online Visual Multi-Object Tracking via Labeled Random Finite Set Filtering
End-to-End Subtitle Detection and Recognition for Videos in East Asian Languages via CNN Ensemble with Near-Human-Level Performance
Expert Gate: Lifelong Learning with a Network of Experts
Ear Recognition: More Than a Survey
Understanding Anatomy Classification Through Attentive Response Maps
Invertible Conditional GANs for image editing
Deep Outdoor Illumination Estimation
On The Stability of Video Detection and Tracking
Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution
Object Recognition with and without Objects
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Temporal Generative Adversarial Nets with Singular Value Clipping
Phrase Localization and Visual Relationship Detection with Comprehensive Image-Language Cues
Cascaded Face Alignment via Intimacy Definition Feature
Deep Learning for the Classification of Lung Nodules
ResFeats: Residual Network Based Features for Image Classification
Deep Temporal Linear Encoding Networks
Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition
Efficient Convolutional Neural Network with Binary Quantization Layer
Image-to-Image Translation with Conditional Adversarial Networks
Learning Multi-level Deep Representations for Image Emotion Classification
A Spatial and Temporal Non-Local Filter Based Data Fusion
Single-View and Multi-View Depth Fusion
Active learning with version spaces for object detection
Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading
Grad-CAM: Why did you say that?
Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning
Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model
Sar image despeckling based on nonlocal similarity sparse decomposition
Learning Joint Feature Adaptation for Zero-Shot Recognition
Fast Fourier Color Constancy
T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction
Deep Convolutional Neural Networks with Merge-and-Run Mappings
iCaRL: Incremental Classifier and Representation Learning
PoseTrack: Joint Multi-Person Pose Estimation and Tracking
Convergence Analysis of MAP based Blur Kernel Estimation
Controlling Perceptual Factors in Neural Style Transfer
The World of Fast Moving Objects
Image-based localization using LSTMs for structured feature correlation
Image Segmentation Using Overlapping Group Sparsity
Deep Restricted Boltzmann Networks
Straight to Shapes: Real-time Detection of Encoded Shapes
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Recalling Holistic Information for Semantic Segmentation
Extraction of airway trees using multiple hypothesis tracking and template matching
Comparative study of histogram distance measures for re-identification
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos
Weakly Supervised Cascaded Convolutional Networks
Learning an Invariant Hilbert Space for Domain Adaptation
Deep Video Deblurring
Neural Machine Translation with Latent Semantic of Image and Text
Directional Mean Curvature for Textured Image Demixing
Texture analysis using deterministic partially self-avoiding walk with thresholds
Convolutional Experts Constrained Local Model for Facial Landmark Detection
Real-Time Video Highlights for Yahoo Esports
SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks
Deep Deformable Registration: Enhancing Accuracy by Fully Convolutional Neural Net
Long-Term Image Boundary Prediction
Uniform Information Segmentation
Semantic Scene Completion from a Single Depth Image
Improving Fully Convolution Network for Semantic Segmentation
3D Human Pose Estimation from a Single Image via Distance Matrix Regression
Awesome Typography: Statistics-Based Text Effects Transfer
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition
Spatio-Temporal Movements in Team Sports: A Visualization approach using Motion Charts
Who's that Actor? Automatic Labelling of Actors in TV series starting from IMDB Images
Gaze Embeddings for Zero-Shot Image Classification
Hierarchical Boundary-Aware Neural Encoder for Video Captioning
Social Behavior Prediction from First Person Videos
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model
Occlusion-Aware Video Deblurring with a New Layered Blur Model
Fast Face-swap Using Convolutional Neural Networks
A Large-scale Distributed Video Parsing and Evaluation Platform
Surveillance Video Parsing with Single Frame Supervision
InterpoNet, A brain inspired neural network for optical flow dense interpolation
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
Weakly-supervised Discriminative Patch Learning via CNN for Fine-grained Recognition
Attend in groups: a weakly-supervised deep learning framework for learning from web data
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
Modeling Relationships in Referential Expressions with Compositional Modular Networks
Deep Cuboid Detection: Beyond 2D Bounding Boxes
Active Deep Learning for Classification of Hyperspectral Images
User Dependent Features in Online Signature Verification
Effective Quantization Methods for Recurrent Neural Networks
Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures
Generalized Fourier-Bessel operator and almost-periodic interpolation and approximation
Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking
CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional Variational Generation
RMPE: Regional Multi-person Pose Estimation
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Monge's Optimal Transport Distance for Image Classification
Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts
Video Captioning with Multi-Faceted Attention
Playing Doom with SLAM-Augmented Deep Reinforcement Learning
Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks
Learning Shape Abstractions by Assembling Volumetric Primitives
Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment
TorontoCity: Seeing the World with a Million Eyes
In Teacher We Trust: Learning Compressed Models for Pedestrian Detection
Guided Open Vocabulary Image Captioning with Constrained Beam Search
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Globally Consistent Multi-People Tracking using Motion Patterns
SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
Centrog Feature technique for vehicle type recognition at day and night times
Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling
Identifying and Categorizing Anomalies in Retinal Imaging Data
Action Recognition with Dynamic Image Networks
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Commonly Uncommon: Semantic Sparsity in Situation Recognition
Mining Spatio-temporal Data on Industrialization from Historical Registries
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework
Areas of Attention for Image Captioning
Semi-Automated Annotation of Discrete States in Large Video Datasets
Learning to Segment Object Proposals via Recursive Neural Networks
Word Recognition with Deep Conditional Random Fields
Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning
End-to-end Learning of Driving Models from Large-scale Video Datasets
Pyramid Scene Parsing Network
General models for rational cameras and the case of two-slit projections
Who is Mistaken?
Deep Metric Learning via Facility Location
Deep Multi-Modal Image Correspondence Learning
Local Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks
Turning an Urban Scene Video into a Cinemagraph
Cancerous Nuclei Detection and Scoring in Breast Cancer Histopathological Images
Deep Image Category Discovery using a Transferred Similarity Function
Panoramic Structure from Motion via Geometric Relationship Detection
Human-In-The-Loop Person Re-Identification
Authoring image decompositions with generative models
ROAM: a Rich Object Appearance Model with Application to Rotoscoping
Automatic Event Detection for Signal-based Surveillance
MarioQA: Answering Questions by Watching Gameplay Videos
Deep Stereo Matching with Dense CRF Priors
Video Ladder Networks
FLIC: Fast Linear Iterative Clustering with Active Search
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer
Learning Diverse Image Colorization
Core Sampling Framework for Pixel Classification
Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
Consensus Based Medical Image Segmentation Using Semi-Supervised Learning And Graph Cuts
Fusion of Range and Thermal Images for Person Detection
Saliency Driven Image Manipulation
A Matrix Splitting Method for Composite Function Minimization
Pano2Vid: Automatic Cinematography for Watching 360$^{\circ}$ Videos
Research on the Multiple Feature Fusion Image Retrieval Algorithm based on Texture Feature and Rough Set Theory
Discrete Schroedinger Transform For Texture Recognition
Complex Matrix Factorization for Face Recognition
An Efficient Algorithm for the Piecewise-Smooth Model with Approximately Explicit Solutions
AGA: Attribute Guided Augmentation
Filter sharing: Efficient learning of parameters for volumetric convolutions
Learning Video Object Segmentation from Static Images
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
Domain knowledge assisted cyst segmentation in OCT retinal images
Predicting Ground-Level Scene Layout from Aerial Imagery
A Maximum A Posteriori Estimation Framework for Robust High Dynamic Range Video Synthesis
3D Shape Segmentation with Projective Convolutional Networks
Deep TEN: Texture Encoding Network
Exploiting 2D Floorplan for Building-scale Panorama RGBD Alignment
Fast Fourier single-pixel imaging using binary illumination
ActionFlowNet: Learning Motion Representation for Action Recognition
Following Gaze Across Views
Boundary-aware Instance Segmentation
Towards an Automated Image De-fencing Algorithm Using Sparsity
Text-guided Attention Model for Image Captioning
A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing
Generalizable Features From Unsupervised Learning
Deep Supervised Hashing with Triplet Labels
Spatial Pyramid Convolutional Neural Network for Social Event Detection in Static Image
Fast Patch-based Style Transfer of Arbitrary Style
Disentangling Space and Time in Video with Hierarchical Variational Auto-encoders
Single Image Action Recognition using Semantic Body Part Actions
Astronomical image reconstruction with convolutional neural networks
Permutation-equivariant neural networks applied to dynamics prediction
Efficient phase retrieval based on dark fringe recognition with an ability of bypassing invalid fringes
Linear feature detection algorithm for astronomical surveys - I. Algorithm description
Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network
Fast-AT: Fast Automatic Thumbnail Generation using Deep Neural Networks
A fuzzy approach for segmentation of touching characters
Border-Peeling Clustering
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
Towards Score Following in Sheet Music Images
Coupling Adaptive Batch Sizes with Learning Rates
Learning Residual Images for Face Attribute Manipulation
Output Constraint Transfer for Kernelized Correlation Filter in Tracking
Deep Residual Hashing
SymbioCity: Smart Cities for Smarter Networks
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
On the crucial impact of the coupling projector-backprojector in iterative tomographic reconstruction
A Fusion Method Based on Decision Reliability Ratio for Finger Vein Verification
Microscopic Muscle Image Enhancement
Learning to predict where to look in interactive environments using deep recurrent q-learning
3D Shape Induction from 2D Views of Multiple Objects
Deep Learning on Lie Groups for Skeleton-based Action Recognition
Adversarial Deep Structural Networks for Mammographic Mass Segmentation
X-ray In-Depth Decomposition: Revealing The Latent Structures
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes
Crowd collectiveness measure via graph-based node clique learning
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images
Asynchronous Temporal Fields for Action Recognition
Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension
Binary Distance Transform to Improve Feature Extraction
Exploring Structure for Long-Term Tracking of Multiple Objects in Sports Videos
3D Human Pose Estimation = 2D Pose Estimation + Matching
End-to-End Pedestrian Collision Warning System based on a Convolutional Neural Network with Semantic Segmentation
Dynamic Action Recognition: A convolutional neural network model for temporally organized joint location data
Two decades of local binary patterns: A survey
Unsupervised Place Discovery for Visual Place Classification
Temporal Tessellation: A Unified Approach for Video Analysis
Image biomarker standardisation initiative
An Empirical Study of Language CNN for Image Captioning
Trilaminar Multiway Reconstruction Tree for Efficient Large Scale Structure from Motion
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
Learning Motion Patterns in Videos
Top-down Visual Saliency Guided by Captions
Deep Blind Compressed Sensing
Handwriting recognition using Cohort of LSTM and lexicon verification with extremely large lexicon
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics
First-Person Activity Forecasting with Online Inverse Reinforcement Learning
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
Understanding Non-optical Remote-sensed Images: Needs, Challenges and Ways Forward
Correlation Preserving Sparse Coding Over Multi-level Dictionaries for Image Denoising
Joint denoising and distortion correction of atomic scale scanning transmission electron microscopy images
YOLO9000: Better, Faster, Stronger
Extracting Sub-Exposure Images from a Single Capture Through Fourier-based Optical Modulation
An Automated CNN Recommendation System for Image Classification Tasks
End-to-End Data Visualization by Metric Learning and Coordinate Transformation
An FFT-based Synchronization Approach to Recognize Human Behaviors using STN-LFP Signal
Multivariate mixture model for myocardium segmentation combining multi-source images
FastMask: Segment Multi-scale Object Candidates in One Shot
MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification
Partial Membership Latent Dirichlet Allocation
Learning Visual N-Grams from Web Data
Generalized Intersection Kernel
Deep Learning Logo Detection with Data Expansion by Synthesising Context
A Unified Tensor-based Active Appearance Face Model
p-DLA: A Predictive System Model for Onshore Oil and Gas Pipeline Dataset Classification and Monitoring - Part 1
EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras (Extended Abstract)
The Geodesic Distance between $\mathcal{G}_I^0$ Models and its Application to Region Discrimination
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
Weakly Supervised Semantic Segmentation using Web-Crawled Videos
Retrieving Similar X-Ray Images from Big Image Data Using Radon Barcodes with Single Projections
Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
Image denoising using group sparsity residual and external nonlocal self-similarity prior
Constrained Deep Weak Supervision for Histopathology Image Segmentation
Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation
Learning a Mixture of Deep Networks for Single Image Super-Resolution
An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods
The Dem@Care Experiments and Datasets: a Technical Report
Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study
Motion Deblurring in the Wild
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics
To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
Oriented Response Networks
Sign Language Recognition Using Temporal Classification
DeepFace: Face Generation using Deep Learning
Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies
MS and PAN image fusion by combining Brovey and wavelet methods
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
A Learning-based Variable Size Part Extraction Architecture for 6D Object Pose Recovery in Depth
Multiple Instance Hybrid Estimator for Learning Target Signatures
Information Pursuit: A Bayesian Framework for Sequential Scene Parsing
Scene Graph Generation by Iterative Message Passing
Deep Learning for Logo Recognition
ChaLearn Looking at People: A Review of Events and Resources
Unsupervised Image-to-Image Translation with Generative Adversarial Networks
Full-reference image quality assessment-based B-mode ultrasound image similarity measure
Stochastic Generative Hashing
Context-aware Captions from Context-agnostic Supervision
Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications
A More General Robust Loss Function
Kähler structures on spaces of framed curves
Ordered Pooling of Optical Flow Sequences for Action Recognition
Probabilistic Diffeomorphic Registration: Representing Uncertainty
A Digital Fuzzy Edge Detector for Color Images
Joint Dictionary Learning for Example-based Image Super-resolution
Comprehension-guided referring expressions
Maximum Entropy Flow Networks
Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehicles
Learning Linear Dynamical Systems with High-Order Tensor Data for Skeleton based Action Recognition
Boosting Dictionary Learning with Error Codes
Iterative Block Tensor Singular Value Thresholding for Extraction of Low Rank Component of Image Data
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading
Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images: AutoCloud+
Hierarchical Salient Object Detection for Assisted Grasping
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection
Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation
Image Generation and Editing with Variational Info Generative AdversarialNetworks
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
3D Reconstruction of Simple Objects from A Single View Silhouette Image
Synthesizing Normalized Faces from Facial Identity Features
Compression of Deep Neural Networks for Image Instance Retrieval
Bringing Impressionism to Life with Neural Style Transfer in Come Swim
Pixel Objectness
FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos
Moving to VideoKifu: the last steps toward a fully automatic record-keeping of a Go game
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition
Synthetic to Real Adaptation with Generative Correlation Alignment Networks
High Performance Novel Skin Segmentation Algorithm for Images With Complex Background
Fast and Efficient Skin Detection for Facial Detection
Dual Recovery Network with Online Compensation for Image Super-Resolution
A Large-scale Dataset and Benchmark for Similar Trademark Retrieval
Lyrics-to-Audio Alignment by Unsupervised Discovery of Repetitive Patterns in Vowel Acoustics
DeadNet: Identifying Phototoxicity from Label-free Microscopy Images of Cells using Deep ConvNets
Perception-based energy functions in seam-cutting
Greedy Compositional Clustering for Unsupervised Learning of Hierarchical Compositional Models
Person Re-Identification via Recurrent Feature Aggregation
Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces
Learning what to look in chest X-rays with a recurrent visual attention model
Perceptually Optimized Image Rendering
Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification
DSSD : Deconvolutional Single Shot Detector
Training Group Orthogonal Neural Networks with Privileged Information
Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation
An Edge Driven Wavelet Frame Model for Image Restoration
Towards End-to-End Face Recognition through Alignment Learning
Deep Local Video Feature for Action Recognition
A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms
Recovering 3D Planar Arrangements from Videos
Case Study of a highly automated Layout Analysis and OCR of an incunabulum: 'Der Heiligen Leben' (1488)
Learning an attention model in an artificial visual system
Sparse Ternary Codes for similarity search have higher coding gain than dense binary codes
Pose Invariant Embedding for Deep Person Re-identification
Structural Connectome Validation Using Pairwise Classification
Quasi-homography warps in image stitching
Sampling Without Time: Recovering Echoes of Light via Temporal Phase Retrieval
Exploiting saliency for object segmentation from image level labels
Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted Representations
Pooling Facial Segments to Face: The Shallow and Deep Ends
Supervised Deep Sparse Coding Networks
VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
MSCM-LiFe: Multi-scale cross modal linear feature for horizon detection in maritime images
Random Forest regression for manifold-valued responses
Faceness-Net: Face Detection through Deep Facial Part Responses
When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum
SafeDrive: A Robust Lane Tracking System for Autonomous and Assisted Driving Under Limited Visibility
Scalable Nearest Neighbor Search based on kNN Graph
Self-Adaptation of Activity Recognition Systems to New Sensors
ICT Green Governance: new generation model based on Corporate Social Responsibility and Green IT
Emergence of Selective Invariance in Hierarchical Feed Forward Networks
3D Shape Retrieval via Irrelevance Filtering and Similarity Ranking (IF/SR)
Co-segmentation for Space-Time Co-located Collections
Deep Reinforcement Learning for Visual Object Tracking in Videos
Towards Adversarial Retinal Image Synthesis
A New Method for Removing the Moire' Pattern from Images
DeepNav: Learning to Navigate Large Cities
High Order Stochastic Graphlet Embedding for Graph-Based Pattern Recognition
Design, Analysis and Application of A Volumetric Convolutional Neural Network
A Kinematic Chain Space for Monocular Motion Capture
Evolving Boxes for Fast Vehicle Detection
Learning to Compose with Professional Photographs on the Web
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability
Pixel Recursive Super Resolution
FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence
Random Triangles and Polygons in the Plane
Joint 2D-3D-Semantic Data for Indoor Scene Understanding
Exploring the microstructure manifold: image texture representations applied to ultrahigh carbon steel microstructures
Towards Unsupervised Weed Scouting for Agricultural Robotics
Using Complex Wavelet Transform and Bilateral Filtering for Image Denoising
Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples
Gender-From-Iris or Gender-From-Mascara?
Detailed Surface Geometry and Albedo Recovery from RGB-D Video Under Natural Illumination
Contextually Customized Video Summaries via Natural Language
Slice-to-volume medical image registration: a survey
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure
View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network
A Deep Convolutional Neural Network for Background Subtraction
Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption
A New Point-set Registration Algorithm for Fingerprint Matching
Image Reconstruction using Matched Wavelet Estimated from Data Sensed Compressively using Partial Canonical Identity Matrix
Face Aging With Conditional Generative Adversarial Networks
Keyframe-Based Visual-Inertial Online SLAM with Relocalization
Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images
Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections
Adversarial Attacks on Neural Network Policies
Scene-adapted plug-and-play algorithm with convergence guarantees
Video Frame Synthesis using Deep Voxel Flow
Semi-Dense Visual Odometry for RGB-D Cameras Using Approximate Nearest Neighbour Fields
Monocular LSD-SLAM Integration within AR System
Backpropagation Training for Fisher Vectors within Neural Networks
Semi-Supervised Deep Learning for Monocular Depth Map Prediction
L1-regularized Reconstruction Error as Alpha Matte
Attribute-controlled face photo synthesis from simple line drawing
EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection
A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery
A clustering approach to heterogeneous change detection
ArtGAN: Artwork Synthesis with Conditional Categorical GANs
A Novel Weight-Shared Multi-Stage Network Architecture of CNNs for Scale Invariance
Underwater Optical Image Processing: A Comprehensive Review
Online People Tracking and Identification with RFID and Kinect
Estimation of the volume of the left ventricle from MRI images using deep neural networks
SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person
Efficient Algorithms for Moral Lineage Tracing
On Detecting Adversarial Perturbations
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
Learning from Ambiguously Labeled Face Images
Normalized Total Gradient: A New Measure for Multispectral Image Registration
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition
Deep Hybrid Similarity Learning for Person Re-identification
Discovering objects and their relations from entangled scene representations
Improving Text Proposals for Scene Images with Fully Convolutional Networks
Automatic Handgun Detection Alarm in Videos Using Deep Learning
The Effect of Color Space Selection on Detectability and Discriminability of Colored Objects
Adversarial Discriminative Domain Adaptation
An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation
Collaborative Deep Reinforcement Learning for Joint Object Search
3D Face Reconstruction with Geometry Details from a Single Image
Robust Shape Registration using Fuzzy Correspondences
Zoom Out-and-In Network with Recursive Training for Object Proposal
Person Search with Natural Language Description
A Survey on Deep Learning in Medical Image Analysis
Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images
From Photo Streams to Evolving Situations
The importance of stain normalization in colorectal tissue classification with convolutional networks
Reflection Separation Using Guided Annotation
An Extended Framework for Marginalized Domain Adaptation
Projection based advanced motion model for cubic mapping for 360-degree video
Visual Tracking by Reinforced Decision Making
Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation
BrnoCompSpeed: Review of Traffic Camera Calibration and Comprehensive Dataset for Monocular Speed Measurement
Crowd Sourcing Image Segmentation with iaSTAPLE
PixelNet: Representation of the pixels, by the pixels, and for the pixels
VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization
3D Reconstruction of Temples in the Special Region of Yogyakarta By Using Close-Range Photogrammetry
MomentsNet: a simple learning-free method for binary image recognition
Boosted Multiple Kernel Learning for First-Person Activity Recognition
Learning Deep Features via Congenerous Cosine Loss for Person Recognition
Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
Improving high-pass fusion method using wavelets
Continuous-Time Visual-Inertial Trajectory Estimation with Event Cameras
Sequence-based Multimodal Apprenticeship Learning For Robot Perception and Decision Making
Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning
Toward high-performance online HCCR: a CNN approach with DropDistortion, path signature and spatial stochastic max-pooling
Fast and robust curve skeletonization for real-world elongated objects
A recommender system to restore images with impulse noise
An EM Based Probabilistic Two-Dimensional CCA with Application to Face Recognition
Learning Deep NBNN Representations for Robust Place Categorization
BARCHAN: Blob Alignment for Robust CHromatographic ANalysis
Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction
Bayesian Nonparametric Feature and Policy Learning for Decision-Making
Instance Hash Segmentation
Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration
Multi-Label Segmentation via Residual-Driven Adaptive Regularization
Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network
Super-Trajectory for Video Segmentation
Billion-scale similarity search with GPUs
ShaResNet: reducing residual network parameter number by sharing weights
Deep Image Harmonization
Inertial Odometry on Handheld Smartphones
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition
Improving Object Detection with Region Similarity Learning
Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images
Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
Lossy Image Compression with Compressive Autoencoders
Making 360$^{\circ}$ Video Watchable in 2D: Learning Videography for Click Free Viewing
Learning Social Affordance Grammar from Videos: Transferring Human Interactions to Human-Robot Interactions
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
A novel image tag completion method based on convolutional neural network
Wireless Interference Identification with Convolutional Neural Networks
Unsupervised Image-to-Image Translation Networks
Depth Estimation using Modified Cost Function for Occlusion Handling
Belief Propagation in Conditional RBMs for Structured Prediction
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
Outlier Cluster Formation in Spectral Clustering
Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression
Denoising Adversarial Autoencoders
Incident Light Frequency-based Image Defogging Algorithm
Looking at Outfit to Parse Clothing
Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features
Automated Top View Registration of Broadcast Football Videos
Genetic CNN
LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
Perceiving and Reasoning About Liquids Using Fully Convolutional Networks
L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction
Reasoning About Liquids via Closed-Loop Simulation
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
Diversified Texture Synthesis with Feed-forward Networks
4-DoF Tracking for Robot Fine Manipulation Tasks
Building a Regular Decision Boundary with Deep Networks
All the people around me: face discovery in egocentric photo-streams
Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions
Non-line-of-sight tracking of people at long range
An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds
Sharing Residual Units Through Collective Tensor Factorization in Deep Neural Networks
Using Deep Learning Method for Classification: A Proposed Algorithm for the ISIC 2017 Skin Lesion Classification Challenge
X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM
Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce
Qualitative Assessment of Recurrent Human Motion
Learning from Noisy Labels with Distillation
Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos
Data Noising as Smoothing in Neural Network Language Models
A Pursuit of Temporal Accuracy in General Activity Detection
A Linear Extrinsic Calibration of Kaleidoscopic Imaging System from Single 3D Point
Deep Bayesian Active Learning with Image Data
Transformation-Grounded Image Generation Network for Novel 3D View Synthesis
QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks
End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks
UntrimmedNets for Weakly Supervised Action Recognition and Detection
Fast and Robust Detection of Fallen People from a Mobile Robot
Position Tracking for Virtual Reality Using Commodity WiFi
A New Representation of Skeleton Sequences for 3D Action Recognition
A Convolutional Neural Network Approach for Half-Pel Interpolation in Video Coding
Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks
From Depth Data to Head Pose Estimation: a Siamese approach
Negentropic Planar Symmetry Detector
Colorization as a Proxy Task for Visual Understanding
Multi-Pose Face Recognition Using Hybrid Face Features Descriptor
Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI
Improving Interpretability of Deep Neural Networks with Semantic Information
Co-occurrence Filter
GUN: Gradual Upsampling Network for single image super-resolution
Poisson multi-Bernoulli mixture filter: direct derivation and implementation
Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs
Deep Learning for Skin Lesion Classification
Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention
Improving LBP and its variants using anisotropic diffusion
Detailed, accurate, human shape estimation from clothed 3D scan sequences
Learning Background-Aware Correlation Filters for Visual Tracking
Subspace Learning in The Presence of Sparse Structured Outliers and Noise
A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization
6-DoF Object Pose from Semantic Keypoints
Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction
Visual end-effector tracking using a 3D model-aided particle filter for humanoid robot platforms
In Search of a Dataset for Handwritten Optical Music Recognition: Introducing MUSCIMA++
Skin lesion segmentation based on preprocessing, thresholding and neural networks
Face Recognition using Multi-Modal Low-Rank Dictionary Learning
Source Camera Identification Based On Content-Adaptive Fusion Network
Large Margin Object Tracking with Circulant Feature Maps
A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Transfer Learning for Melanoma Detection: Participation in ISIC 2017 Skin Lesion Classification Challenge
A Hybrid Supervised-unsupervised Method on Image Topic Visualization with Convolutional Neural Network and LDA
Illuminant Estimation using Ensembles of Multivariate Regression Trees
Convolutional Low-Resolution Fine-Grained Classification
End-to-end optimization of goal-driven and visually grounded dialogue systems
Global and Local Information Based Deep Network for Skin Lesion Segmentation
Steganographic Generative Adversarial Networks
Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition
Segmented and Directional Impact Detection for Parked Vehicles using Mobile Devices
SVDNet for Pedestrian Retrieval
Learning Robust Hash Codes for Multiple Instance Image Retrieval
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images
DropRegion Training of Inception Font Network for High-Performance Chinese Font Recognition
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images
Semi-Supervised Deep Learning for Fully Convolutional Networks
PSF field learning based on Optimal Transport Distances
Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction
Deep Tensor Encoding
Direct Monocular Odometry Using Points and Lines
Zero-Shot Learning by Generating Pseudo Feature Representations
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
Detecting Oriented Text in Natural Images by Linking Segments
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Mask R-CNN
Multi-style Generative Network for Real-time Transfer
Active Decision Boundary Annotation with Deep Generative Models
Cross-modal Deep Metric Learning with Multi-task Regularization
Proposal Flow: Semantic Correspondences from Object Proposals
Improving Person Re-identification by Attribute and Identity Learning
License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding
Knowledge Transfer for Melanoma Screening with Deep Learning
Deep Photo Style Transfer
Video Frame Interpolation via Adaptive Convolution
Deeply-Supervised CNN for Prostate Segmentation
Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image
Predicting Deeper into the Future of Semantic Segmentation
Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks
Cross-View Image Matching for Geo-localization in Urban Environments
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
Perspective: Energy Landscapes for Machine Learning
Changing Fashion Cultures
Recurrent Multimodal Interaction for Referring Image Segmentation
Image-based Localization using Hourglass Networks
Nonlinear Spectral Image Fusion
Content-based similar document image retrieval using fusion of CNN features
Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs
Saliency-guided video classification via adaptively weighted learning
Quality Resilient Deep Neural Networks
Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling
Semi-Automatic Segmentation and Ultrasonic Characterization of Solid Breast Lesions
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition
Medical Image Retrieval using Deep Convolutional Neural Network
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
Local Deep Neural Networks for Age and Gender Classification
Improving the Accuracy of the CogniLearn System for Cognitive Behavior Assessment
Sketch-based Face Editing in Video Using Identity Deformation Transfer
Open Vocabulary Scene Parsing
Multivariate Regression with Gross Errors on Manifold-valued Data
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
A Visual Measure of Changes to Weighted Self-Organizing Map Patterns
Scaling the Scattering Transform: Deep Hybrid Networks
LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks
Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video
Transfer learning for music classification and regression tasks
Coherent Online Video Style Transfer
Graph Regularized Tensor Sparse Coding for Image Representation
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting
Evaluation of Classifiers for Image Segmentation: Applications for Eucalypt Forest Inventory
Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs
Robust Depth-based Person Re-identification
L2-constrained Softmax Loss for Discriminative Face Verification
Two-Stream RNN/CNN for Action Recognition in 3D Videos
Perception Driven Texture Generation
Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks
Click Here: Human-Localized Keypoints as Guidance for Viewpoint Estimation
LabelBank: Revisiting Global Perspectives for Semantic Segmentation
Learning with Privileged Information for Multi-Label Classification
Who's Better? Who's Best? Pairwise Deep Ranking for Skill Determination
On Convergence Property of Implicit Self-paced Objective
Image Restoration using Autoencoding Priors
A Geometric Framework for Stochastic Shape Analysis
Flow-Guided Feature Aggregation for Video Object Detection
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Google Map Aided Visual Navigation for UAVs in GPS-denied Environment
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
Detecting Human Interventions on the Landscape: KAZE Features, Poisson Point Processes, and a Construction Dataset
Learning High Dynamic Range from Outdoor Panoramas
Smartphone Based Colorimetric Detection via Machine Learning
SeGAN: Segmenting and Generating the Invisible
DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
Planecell: Representing the 3D Space with Planes
Efficient optimization for Hierarchically-structured Interacting Segments (HINTS)
Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis
Geometric Affordances from a Single Example via the Interaction Tensor
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention
Deep 3D Face Identification
Unsupervised Holistic Image Generation from Key Local Patches
Novel Framework for Spectral Clustering using Topological Node Features(TNF)
A Hybrid Data Association Framework for Robust Online Multi-Object Tracking
Single Image Super Resolution - When Model Adaptation Matters
Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos
InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image
Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Efficient Asymmetric Co-Tracking using Uncertainty Sampling
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly
Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition
Efficient Version-Space Reduction for Visual Tracking
Randomness in Deconvolutional Networks for Visual Representation
Restoration of Images with Wavefront Aberrations
Geometric Loss Functions for Camera Pose Regression with Deep Learning
A Comparison of Directional Distances for Hand Pose Estimation
Convolutional neural networks for segmentation and object detection of human semen
Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points
Block-Matching Convolutional Neural Network for Image Denoising
The 2017 DAVIS Challenge on Video Object Segmentation
Hierarchical Surface Prediction for 3D Object Reconstruction
Cascaded Segmentation-Detection Networks for Word-Level Text Spotting
Guided Proofreading of Automatic Segmentations for Connectomics
Simultaneous Feature Aggregating and Hashing for Large-scale Image Search
A Branch-and-Bound Algorithm for Checkerboard Extraction in Camera-Laser Calibration
Pose2Instance: Harnessing Keypoints for Person Instance Segmentation
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
Joint Regression and Ranking for Image Enhancement
Investigating Human Factors in Image Forgery Detection
Incremental Tube Construction for Human Action Detection
On the Relation between Color Image Denoising and Classification
The UMCD Dataset
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild
Automatic Breast Ultrasound Image Segmentation: A Survey
Convolutional Neural Networks for Page Segmentation of Historical Document Images
Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification
Action Representation Using Classifier Decision Boundaries
Beyond triplet loss: a deep quadruplet network for person re-identification
Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation
A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection
Online Hashing
Semantically-Guided Video Object Segmentation
"RAPID" Regions-of-Interest Detection In Big Histopathological Images
Supervised Deep Hashing for Hierarchical Labeled Data
Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
Semi-Latent GAN: Learning to generate and modify facial images from attributes
Hand3D: Hand Pose Estimation using 3D Neural Network
It Takes (Only) Two: Adversarial Generator-Encoder Networks
Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation
Pixelwise Instance Segmentation with a Dynamically Instantiated Network
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification
BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis
Quaternion Based Camera Pose Estimation From Matched Feature Points
Deep Affordance-grounded Sensorimotor Object Recognition
R-Clustering for Egocentric Video Segmentation
Learning Human Motion Models for Long-term Predictions
ActionVLAD: Learning spatio-temporal aggregation for action classification
Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of many temporal dimensions
Loss Max-Pooling for Semantic Image Segmentation
Weakly-Supervised Spatial Context Networks
Semantically Consistent Regularization for Zero-Shot Recognition
A semidiscrete version of the Citti-Petitot-Sarti model as a plausible model for anthropomorphic image reconstruction and pattern recognition
Detecting Visual Relationships with Deep Relational Networks
Deep Multimodal Representation Learning from Temporal Data
EAST: An Efficient and Accurate Scene Text Detector
Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering
Online Video Deblurring via Dynamic Temporal Blending Network
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
Forecasting Human Dynamics from Static Images
Learning Detection with Diverse Proposals
Attention-based Extraction of Structured Information from Street View Imagery
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification
Instance-Level Salient Object Segmentation
Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization
Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease
Object proposal generation applying the distance dependent Chinese restaurant process
Attention-Set based Metric Learning for Video Face Recognition
Optimal Threshold Design for Quanta Image Sensor
What's in a Question: Using Visual Questions as a Form of Supervision
Deep Reinforcement Learning-based Image Captioning with Embedding Reward
Asymmetric Feature Maps with Application to Sketch Based Retrieval
Tractable Clustering of Data on the Curve Manifold
2D-3D Pose Consistency-based Conditional Random Fields for 3D Human Pose Estimation
Interspecies Knowledge Transfer for Facial Keypoint Detection
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
Learning to Estimate Pose by Watching Videos
Recognizing Activities of Daily Living from Egocentric Images
Neural Face Editing with Intrinsic Image Disentangling
Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization
Applying High-Resolution Visible Imagery to Satellite Melt Pond Fraction Retrieval: A Neural Network Approach
Dataset Augmentation for Pose and Lighting Invariant Face Recognition
Quantum Biometrics with Retinal Photon Counting
Parallel Multi Channel Convolution using General Matrix Multiplication
Deep Structured Learning for Facial Action Unit Intensity Estimation
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks
A Quadratic Penalty Method for Hypergraph Matching
A Comprehensive Review of Smart Wheelchairs: Past, Present and Future
Trigger for the SoLid Reactor Antineutrino Experiment
CT Image Reconstruction in a Low Dimensional Manifold
Video Object Segmentation using Supervoxel-Based Gerrymandering
Robust Optical Flow Estimation in Rainy Scenes
Image Fusion With Cosparse Analysis Operator
Light Field Blind Motion Deblurring
Annotating Object Instances with a Polygon-RNN
Illuminant Spectra-based Source Separation Using Flash Photography
Learning to Fly by Crashing
OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images
Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems
FSITM: A Feature Similarity Index For Tone-Mapped Images
Skeleton Boxes: Solving skeleton based action detection with a single deep convolutional neural network
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn
Design of low-cost, compact and weather-proof whole sky imagers for high-dynamic-range captures
Unsupervised Creation of Parameterized Avatars
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring
Learning Video Object Segmentation with Visual Memory
Network Dissection: Quantifying Interpretability of Deep Visual Representations
Learn to Model Motion from Blurry Footages
Generative Face Completion
HPatches: A benchmark and evaluation of handcrafted and learned local descriptors
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections
Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging
End-to-end representation learning for Correlation Filter based tracking
Understanding the Mechanisms of Deep Transfer Learning for Medical Images
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
Training object class detectors with click supervision
Temporal Action Detection with Structured Segment Networks
Identifying First-person Camera Wearers in Third-person Videos
NormFace: L2 Hypersphere Embedding for Face Verification
Multiple Reflection Symmetry Detection via Linear-Directional Kernel Density Estimation
Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion
PQTable: Non-exhaustive Fast Search for Product-quantized Codes using Hash Tables
Scatteract: Automated extraction of data from scatter plots
SREFI: Synthesis of Realistic Example Face Images
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
Deep Learning based Isolated Arabic Scene Character Recognition
Deep Learning for Medical Image Processing: Overview, Challenges and Future
Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition
Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning
Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration
Exploiting Multi-layer Graph Factorization for Multi-attributed Graph Matching
Unified Framework for Automated Person Re-identification and Camera Network Topology Inference in Camera Networks
Dense 3D Facial Reconstruction from a Single Depth Image in Unconstrained Environment
Monocular Visual Odometry with a Rolling Shutter Camera
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method
Measuring the Accuracy of Object Detectors and Trackers
Detecting and Recognizing Human-Object Interactions
Paying Attention to Descriptions Generated by Image Captioning Models
A Context Aware and Video-Based Risk Descriptor for Cyclists
Towards a quality metric for dense light fields
Skeleton-based Action Recognition with Convolutional Neural Networks
Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering
Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation
Arabidopsis roots segmentation based on morphological operations and CRFs
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
Multi-View Dynamic Facial Action Unit Detection
Spatio-temporal Person Retrieval via Natural Language Queries
Anisotropic twicing for single particle reconstruction using autocorrelation analysis
SphereFace: Deep Hypersphere Embedding for Face Recognition
AutoDIAL: Automatic DomaIn Alignment Layers
A Faster Patch Ordering Method for Image Denoising
Misdirected Registration Uncertainty
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network
Compact Descriptors for Video Analysis: the Emerging MPEG Standard
A Generalization of Convolutional Neural Networks to Graph-Structured Data
Face Identification and Clustering
No More Discrimination: Cross City Adaptation of Road Scene Segmenters
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
Automatic Real-time Background Cut for Portrait Videos
Active Collaborative Ensemble Tracking
A new image compression by gradient Haar wavelet
Improving Small Object Proposals for Company Logo Detection
Unbiased Shape Compactness for Segmentation
A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation
Understanding People Flow in Transportation Hubs
Deep Multi-view Models for Glitch Classification
The Pose Knows: Video Forecasting by Generating Pose Futures
Effective scaling registration approach by imposing the emphasis on the scale factor
Indoor Frame Recovery from Refined Line Segments
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification
Sub-Pixel Registration of Wavelet-Encoded Images
Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach
Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting
Regularized Residual Quantization: a multi-layer sparse dictionary learning approach
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
Dense-Captioning Events in Videos
Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks
Investigation of Different Skeleton Features for CNN-based 3D Action Recognition
Transfer Learning by Ranking for Weakly Supervised Object Annotation
Active Image-based Modeling with a Toy Drone
Out-of-focus: Learning Depth from Image Bokeh for Robotic Perception
Cascaded Boundary Regression for Temporal Action Detection
Marine Animal Classification with Correntropy Loss Based Multi-view Learning
Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
Weakly-supervised Visual Grounding of Phrases with Linguistic Structures
Learning to Estimate 3D Hand Pose from Single RGB Images
Gabor Convolutional Networks
Am I Done? Predicting Action Progress in Videos
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis
Action Tubelet Detector for Spatio-Temporal Action Localization
Edge-based Component-Trees for Multi-Channel Image Segmentation
Characterizing and Improving Stability in Neural Style Transfer
TALL: Temporal Activity Localization via Language Query
Phase Congruency Parameter Optimization for Enhanced Detection of Image Features for both Natural and Medical Applications
Part-based Deep Hashing for Large-scale Person Re-identification
Unified Embedding and Metric Learning for Zero-Exemplar Event Detection
Detecting Adversarial Samples Using Density Ratio Estimates
Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild
Deep Patch Learning for Weakly Supervised Object Classification and Discovery
Sparse Representation-based Open Set Recognition
Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis
A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions
Context-Aware Trajectory Prediction
Multimodal Affect Analysis for Product Feedback Assessment
Automatic Recognition of Mammal Genera on Camera-Trap Images using Multi-Layer Robust Principal Component Analysis and Mixture Neural Networks
Deep Descriptor Transforming for Image Co-Localization
Scene Text Eraser
Video Processing for Barycenter Trajectory Identification in Diving
Multi Resolution LSTM For Long Term Prediction In Neural Activity Video
Geometric GAN
Learning non-maximum suppression
Konzept für Bildanalysen in Hochdurchsatz-Systemen am Beispiel des Zebrabärblings
Real-Time User-Guided Image Colorization with Learned Deep Priors
Deep Spatio-temporal Manifold Network for Action Recognition
Contour Detection from Deep Patch-level Boundary Prediction
Convolutional Dictionary Learning via Local Processing
Model Complexity-Accuracy Trade-off for a Convolutional Neural Network
Adaptive Regularization of Some Inverse Problems in Image Analysis
Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation
Learning Deep Networks from Noisy Labels with Dropout Regularization
Signal reconstruction via operator guiding
Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features
4d isip: 4d implicit surface interest point detection
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
SCNet: Learning Semantic Correspondence
A Generative Model of People in Clothing
Incremental Learning Through Deep Adaptation
Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect
An Optimal Dimensionality Multi-shell Sampling Scheme with Accurate and Efficient Transforms for Diffusion MRI
Transfer Learning for Cross-Dataset Recognition: A Survey
View-Invariant Template Matching Using Homography Constraints
Adaptive Feature Representation for Visual Tracking
External Prior Guided Internal Prior Learning for Real Noisy Image Denoising
Self-Committee Approach for Image Restoration Problems using Convolutional Neural Network
Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation
Deep neural networks on graph signals for brain imaging analysis
Gland Segmentation in Histopathology Images Using Random Forest Guided Boundary Construction
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling
Compressed Sensing for Scalable Robotic Tactile Skins
View-invariant Gait Recognition through Genetic Template Segmentation
A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots
Handwritten Urdu Character Recognition using 1-Dimensional BLSTM Classifier
WordFence: Text Detection in Natural Images with Border Awareness
Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers
Motion-Compensated Temporal Filtering for Critically-Sampled Wavelet-Encoded Images
Volumetric Super-Resolution of Multispectral Data
Real-Time Adaptive Image Compression
Learning a Hierarchical Latent-Variable Model of 3D Shapes
One Shot Joint Colocalization and Cosegmentation
PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification
A deep level set method for image segmentation
Deep Diagnostics: Applying Convolutional Neural Networks for Vessels Defects Detection
Bayer Demosaicking Using Optimized Mean Curvature over RGB channels
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
Detecting Cyber-Physical Attacks in Additive Manufacturing using Digital Audio Signing
Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization
Learning Texture Manifolds with the Periodic Spatial GAN
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
Model-based Catheter Segmentation in MRI-images
Deep-LK for Efficient Adaptive Object Tracking
Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature
Prediction of Sea Surface Temperature using Long Short-Term Memory
Hyperspectral Band Selection Using Unsupervised Non-Linear Deep Auto Encoder to Train External Classifiers
The Kinetics Human Action Video Dataset
Classification revisited: a web of knowledge
What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
A New 3D Segmentation Methodology for Lumbar Vertebral Bodies for the Measurement of BMD and Geometry
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation
Multiple-Human Parsing in the Wild
PixColor: Pixel Recursive Colorization
Recurrent Scene Parsing with Perspective Understanding in the Loop
End-to-end Planning of Fixed Millimeter-Wave Networks
Forecasting Hands and Objects in Future Frames
Critical Contours: An Invariant Linking Image Flow with Salient Surface Organization
Stabilizing Adversarial Nets With Prediction Methods
Incorporating Depth into both CNN and CRF for Indoor Semantic Segmentation
Generative Partition Networks for Multi-Person Pose Estimation
The Do's and Don'ts for CNN-based Face Verification
Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network
Semantic Softmax Loss for Zero-Shot Learning
Robust Localized Multi-view Subspace Clustering
TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation
Regularizing deep networks using efficient layerwise adversarial training
Stabilizing GAN Training with Multiple Random Projections
Improving Fine-Grained Visual Classification using Pairwise Confusion
Learning multiple visual domains with residual adapters
Multiple Images Recovery Using a Single Affine Transformation
Patchnet: Interpretable Neural Networks for Image Classification
Visual Semantic Planning using Deep Successor Representations
Universal Style Transfer via Feature Transforms
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data
Correlation Alignment by Riemannian Metric for Domain Adaptation
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
How hard can it be? Estimating the difficulty of visual search in an image
Sequence Summarization Using Order-constrained Kernelized Feature Subspaces
Deep Rotation Equivariant Network
Continual Learning with Deep Generative Replay
Stochastic Sequential Neural Networks with Structured Inference
Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-in-the-Blank Image Captioning
The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
Unsupervised Learning Layers for Video Analysis
Visual Servoing from Deep Neural Networks
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation
Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation
Classification of Quantitative Light-Induced Fluorescence Images Using Convolutional Neural Network
Pose Guided Person Image Generation
Unsupervised Feature Learning for Writer Identification and Writer Retrieval
Algorithmic clothing: hybrid recommendation, from street-style-to-shop
Predicting Human Interaction via Relative Attention Model
Zero-Shot Learning with Generative Latent Prototype Model
Learning Robust Features with Incremental Auto-Encoders
Classification regions of deep neural networks
Bayesian GAN
Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data
A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
LiDAR-Camera Calibration using 3D-3D Point correspondences
Global hard thresholding algorithms for joint sparse image representation and denoising
Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection
Vocabulary-informed Extreme Value Learning
Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval
Care about you: towards large-scale human-centric visual relationship detection
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising
Conditional CycleGAN for Attribute Guided Face Image Generation
Data Driven Coded Aperture Design for Depth Recovery
Ensemble of Part Detectors for Simultaneous Classification and Localization
On the Power Spectral Density Applied to the Analysis of Old Canvases
Pose-Aware Person Recognition
Towards Visual Ego-motion Learning in Robots
Feature Incay for Representation Regularization
Learning to Generate Chairs with Generative Adversarial Nets
Robust Tracking Using Region Proposal Networks
Decorrelation of Neutral Vector Variables: Theory and Applications
Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks
Saliency Revisited: Analysis of Mouse Movements versus Fixations
Interpreting and Extending The Guided Filter Via Cyclic Coordinate Descent
End-to-end Active Object Tracking via Reinforcement Learning
Nighttime sky/cloud image segmentation
Jeffrey's prior sampling of deep sigmoidal networks
Multi-View Task-Driven Recognition in Visual Sensor Networks
Addressing Ambiguity in Multi-target Tracking by Hierarchical Strategy
PCM-TV-TFV: A Novel Two Stage Framework for Image Reconstruction from Fourier Data
Generic Tubelet Proposals for Action Localization
Morphological Error Detection in 3D Segmentations
Efficient, sparse representation of manifold distance matrices for classical scaling
Weakly supervised 3D Reconstruction with Adversarial Constraint
Naturally Combined Shape-Color Moment Invariants under Affine Transformations
Effective Target Aware Visual Navigation for UAVs
Class Specific Feature Selection for Interval Valued Data Through Interval K-Means Clustering
Deep Supervised Discrete Hashing
Neuron Segmentation Using Deep Complete Bipartite Networks
EvaluationNet: Can Human Skill be Evaluated by Deep Networks?
Representation Learning by Rotating Your Faces
U-Phylogeny: Undirected Provenance Graph Construction in the Wild
Putting a Face to the Voice: Fusing Audio and Visual Signals Across a Video to Determine Speakers
Superhuman Accuracy on the SNEMI3D Connectomics Challenge
Depth Structure Preserving Scene Image Generation
Deep Mutual Learning
Fader Networks: Manipulating Images by Sliding Attributes
Machine Assisted Analysis of Vowel Length Contrasts in Wolof
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks
Integrated Deep and Shallow Networks for Salient Object Detection
SAR Image Despeckling Using a Convolutional
Rank Persistence: Assessing the Temporal Performance of Real-World Person Re-Identification
Image Restoration from Patch-based Compressed Sensing Measurement
Dynamic Steerable Blocks in Deep Residual Networks
Dual-reference Face Retrieval
Temporal Action Labeling using Action Sets
One-Sided Unsupervised Domain Mapping
Multi-Class Model Fitting by Energy Minimization and Mode-Seeking
Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering
Learning Person Trajectory Representations for Team Activity Analysis
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
Graph-Cut RANSAC
Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG
Where and Who? Automatic Semantic-Aware Person Composition
Brain Intelligence: Go Beyond Artificial Intelligence
Learning Structured Semantic Embeddings for Visual Recognition
3D Pathfinding and Collision Avoidance Using Uneven Search-space Quantization and Visual Cone Search
Visual attention models for scene text recognition
Geometric Multi-Model Fitting with a Convex Relaxation Algorithm
Hyperplane Clustering Via Dual Principal Component Pursuit
A Minimal Solution for Two-view Focal-length Estimation using Two Affine Correspondences
Understanding and Eliminating the Large-kernel Effect in Blind Deconvolution
Face Alignment Using K-Cluster Regression Forests With Weighted Splitting
StreetStyle: Exploring world-wide clothing styles from millions of photos
Imposing Hard Constraints on Deep Networks: Promises and Limitations
Unsupervised Place Discovery for Place-Specific Change Classifier
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks
Learning to Represent Mechanics via Long-term Extrapolation and Interpolation
Synthesizing Filamentary Structured Images with GANs
Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network
CoMaL Tracking: Tracking Points at the Object Boundaries
Low-shot learning with large-scale diffusion
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network
Active Learning for Structured Prediction from Partially Labeled Data
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Image Captioning with Object Detection and Localization
Sliced Wasserstein Generative Models
Structured Light Phase Measuring Profilometry Pattern Design for Binary Spatial Light Modulators
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
Face Detection through Scale-Friendly Deep Convolutional Networks
Class-specific Poisson denoising by patch-based importance sampling
DCCO: Towards Deformable Continuous Convolution Operators
Unsupervised learning of object frames by dense equivariant image labelling
Collaborative Summarization of Topic-Related Videos
Diversity-aware Multi-Video Summarization
Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopy
Exploring Convolutional Networks for End-to-End Visual Servoing
Generate Identity-Preserving Faces by Generative Adversarial Networks
Segmentation of nearly isotropic overlapped tracks in photomicrographs using successive erosions as watershed markers
Few-Shot Image Recognition by Predicting Parameters from Activations
Exploring the similarity of medical imaging classification problems
Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition
Image Crowd Counting Using Convolutional Neural Network and Markov Random Field
Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images
Transferring a Semantic Representation for Person Re-Identification and Search
SmoothGrad: removing noise by adding noise
Subspace Clustering via Optimal Direction Search
Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking
Contrast Enhancement Estimation for Digital Image Forensics
Long-Term Video Interpolation with Bidirectional Predictive Network
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
Text Extraction From Texture Images Using Masked Signal Decomposition
Indirect Image Registration with Large Diffeomorphic Deformations
Video Imagination from a Single Image with Transformation Generation
The "something something" video database for learning and evaluating visual common sense
Action Search: Learning to Search for Human Activities in Untrimmed Videos
AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces
Saliency detection by aggregating complementary background template with optimization framework
Photo-realistic Facial Texture Transfer
SalProp: Salient object proposals via aggregated edge cues
Arabian Horse Identification Benchmark Dataset
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Hierarchical Label Inference for Video Classification
Distance weighted discrimination of face images for gender classification
The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge
Self-ensembling for visual domain adaptation
Dimensionality Reduction using Similarity-induced Embeddings
Tversky loss function for image segmentation using 3D fully convolutional deep networks
Exploring Content-based Artwork Recommendation with Metadata and Visual Features
An Entropy-based Pruning Method for CNN Compression
Histograms of Gaussian normal distribution for feature matching in clutter scenes
Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging
Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition
Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets
Dualing GANs
Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture
SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning
A comparative study of breast surface reconstruction for aesthetic outcome assessment
Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
Compact Tensor Pooling for Visual Question Answering
Comicolorization: Semi-Automatic Manga Colorization
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
GM-Net: Learning Features with More Efficiency
MEC: Memory-efficient Convolution for Deep Neural Network
Learnable pooling with Context Gating for video classification
cGAN-based Manga Colorization Using a Single Training Image
Scalable Online Convolutional Sparse Coding
Two-Stream Convolutional Networks for Dynamic Texture Synthesis
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks
Shape recognition of volcanic ash by simple convolutional neural network
Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet Decomposition
Pixels to Graphs by Associative Embedding
Single Classifier-based Passive System for Source Printer Classification using Local Texture Features
Learning Spatial-Aware Regressions for Visual Tracking
Fractal dimension analysis for automatic morphological galaxy classification
Sampling Matters in Deep Embedding Learning
Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs
Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds
On Detection of Faint Edges in Noisy Images
Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks
Encoding Video and Label Priors for Multi-label Video Classification on YouTube-8M dataset
Self-Learning Phase Boundaries by Active Contours
Photometric Stereo by Hemispherical Metric Embedding
Robust Video-Based Eye Tracking Using Recursive Estimation of Pupil Characteristics
End-to-end Learning of Image based Lane-Change Decision
YoTube: Searching Action Proposal via Recurrent and Static Regression Networks
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
Multi-Label Learning with Label Enhancement
Learning to Map Vehicles into Bird's Eye View
Group Synchronization on Grids
Detecting Small Signs from Large Images
Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification
VoxCeleb: a large-scale speaker identification dataset
Dense Non-rigid Structure-from-Motion Made Easy - A Spatial-Temporal Smoothness based Solution
Hierarchical Model for Long-term Video Prediction
Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain
Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis
Approximate Reflection Symmetry in a Point Set: Theory and Algorithm with an Application
Cross-Country Skiing Gears Classification using Deep Learning
Training a Fully Convolutional Neural Network to Route Integrated Circuits
Super-Resolution via Deep Learning
Perceptual Adversarial Networks for Image-to-Image Transformation
Yes-Net: An effective Detector Based on Global Information
A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches
The YouTube-8M Kaggle Competition: Challenges and Methods
Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification
Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
Real-time Distracted Driver Posture Classification
Flow-free Video Object Segmentation
R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
Iterative Spectral Clustering for Unsupervised Object Localization
Robust Face Tracking using Multiple Appearance Models and Graph Relational Learning
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
Multiple VLAD encoding of CNNs for image classification
Adversarial Image Alignment and Interpolation
Better than Real: Complex-valued Neural Nets for MRI Fingerprinting
Image Companding and Inverse Halftoning using Deep Convolutional Neural Networks
Deep GrabCut for Object Selection
Where to Play: Retrieval of Video Segments using Natural-Language Queries
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference
Pedestrian Alignment Network for Large-scale Person Re-identification
End-to-End Learning of Video Super-Resolution with Motion Compensation
Geometric calibration of Colour and Stereo Surface Imaging System of ESA's Trace Gas Orbiter
Appearance invariance in convolutional networks with neighborhood similarity
Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter
Aggregating Frame-level Features for Large-Scale Video Classification
Selective Deep Convolutional Features for Image Retrieval
One-Shot Fine-Grained Instance Retrieval
Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition
Face Recognition with Machine Learning in OpenCV_ Fusion of the results with the Localization Data of an Acoustic Camera for Speaker Identification
Conditional generation of multi-modal data using constrained embedding space mapping
The Candidate Multi-Cut for Cell Segmentation
Skeleton-aided Articulated Motion Generation
Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments
R-PHOC: Segmentation-Free Word Spotting using CNN
Benchmarking Denoising Algorithms with Real Photographs
Robust Multi-Image HDR Reconstruction for the Modulo Camera
Generative diffeomorphic atlas construction from brain and spinal cord MRI data
AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
Sensor Analytics in Basketball
SSGAN: Secure Steganography Based on Generative Adversarial Networks
CNN features are also great at unsupervised classification
Weighted Low Rank Approximation for Background Estimation Problems
Tensor-Train Recurrent Neural Networks for Video Classification
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
Zero-Shot Deep Domain Adaptation
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
Automatic Classification of Bright Retinal Lesions via Deep Network Features
A spatiotemporal model with visual attention for video classification
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification
A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising
The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation
Learning Efficient Image Representation for Person Re-Identification
Learning Representations and Generative Models for 3D Point Clouds
Self Adversarial Training for Human Pose Estimation
Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing
Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study
A Human and Group Behaviour Simulation Evaluation Framework utilising Composition and Video Analysis
Anisotropic Diffusion-based Kernel Matrix Model for Face Liveness Detection
Synthesis-based Robust Low Resolution Face Recognition
Improving speaker turn embedding by crossmodal transfer learning from face embedding
Scale-Regularized Filter Learning
An Analysis of Human-centered Geolocation
Enhanced Deep Residual Networks for Single Image Super-Resolution
Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms
Automatic Construction of Real-World Datasets for 3D Object Localization using Two Cameras
Foot anthropometry device and single object image thresholding
Underwater object classification using scattering transform of sonar signals
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
Adversarial training and dilated convolutions for brain MRI segmentation
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
Hierarchical Deep Recurrent Architecture for Video Understanding
Obstacle detection test in real-word traffic contexts for the purposes of motorcycle autonomous emergency braking (MAEB)
Machine Learning in Appearance-based Robot Self-localization
Machine Learning for RealisticBall Detection in RoboCup SPL
Adversarial Dropout for Supervised and Semi-supervised Learning
Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations
Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition
Two-pixel polarimetric camera by compressive sensing
Contour and Centreline Tracking of Vessels from Angiograms using the Classical Image Processing Techniques
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
Large-scale Multiview 3D Hand Pose Dataset
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion
Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling
Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks
Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks
Merge or Not? Learning to Group Faces via Imitation Learning
Query-Aware Sparse Coding for Multi-Video Summarization
Deep Learning with Topological Signatures
Large-scale Video Classification guided by Batch Normalized LSTM Translator
Stable Distribution Alignment Using the Dual of the Adversarial Distance
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting
Guiding InfoGAN with Semi-Supervision
Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding
The Reversible Residual Network: Backpropagation Without Storing Activations
Modified Alpha-Rooting Color Image Enhancement Method On The Two-Side 2-D Quaternion Discrete Fourier Transform And The 2-D Discrete Fourier Transform
RED: Reinforced Encoder-Decoder Networks for Action Anticipation
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
Generative Adversarial Network based on Resnet for Conditional Image Restoration
Chinese Typography Transfer
Expected exponential loss for gaze-based video and volume ground truth annotation
Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions
Non-Linear Subspace Clustering with Learned Low-Rank Kernels
MoCoGAN: Decomposing Motion and Content for Video Generation
"Maximizing rigidity" revisited: a convex programming approach for generic 3D shape reconstruction from multiple perspective views
Designing Effective Inter-Pixel Information Flow for Natural Image Matting
Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy
Aesthetic-Driven Image Enhancement by Adversarial Learning
Houdini: Fooling Deep Structured Prediction Models
Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
Hybrid PS-V Technique: A Novel Sensor Fusion Approach for Fast Mobile Eye-Tracking with Sensor-Shift Aware Correction
Discriminative Transformation Learning for Fuzzy Sparse Subspace Clustering
Pruning Convolutional Neural Networks for Image Instance Retrieval
APE-GAN: Adversarial Perturbation Elimination with GAN
Order-Free RNN with Visual Attention for Multi-Label Classification
Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks (MSNets) with Backward Skip Connections for Semantic Segmentation
Transitioning between Convolutional and Fully Connected Layers in Neural Networks
Optimizing the Latent Space of Generative Networks
A Novel Deep Learning Architecture for Testis Histology Image Classification
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
Recognizing and Curating Photo Albums via Event-Specific Image Importance
Face Alignment Robust to Pose, Expressions and Occlusions
Drone-based Object Counting by Spatially Regularized Regional Proposal Network
Orthogonal and Idempotent Transformations for Learning Deep Neural Networks
Closed-form Solution for IMU based LSD-SLAM Point Cloud Conversion into the Scaled 3D World Environment
Detecting Parts for Action Localization
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model
Discriminative convolutional Fisher vector network for action recognition
Channel Pruning for Accelerating Very Deep Neural Networks
Deformable Part-based Fully Convolutional Network for Object Detection
Domain-adversarial neural networks to address the appearance variability of histopathology images
Deformable Registration through Learning of Context-Specific Metric Aggregation
Shape Generation using Spatially Partitioned Point Clouds
Pose-Invariant Face Alignment with a Single CNN
STag: A Stable Fiducial Marker System
Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks
DenseNet for Dense Flow
Automatic Segmentation of Retinal Vasculature
Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
Semantic Segmentation with Reverse Attention
Deep Layer Aggregation
An All-in-One Network for Dehazing and Beyond
Video Object Segmentation using Tracked Object Proposals
Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture
Temporal Convolution Based Action Proposal: Submission to ActivityNet 2017
A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics
Neural Person Search Machines
Recurrent Neural Networks for Online Video Popularity Prediction
Neuron Pruning for Compressing Deep Networks using Maxout Architectures
Retinal Microaneurysms Detection using Local Convergence Index Features
Multi-kernel learning of deep convolutional features for action recognition
A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification
Memory-Efficient Implementation of DenseNets
Confidence estimation in Deep Neural networks via density modelling
Automatic Curation of Golf Highlights using Multimodal Excitement Features
PatchShuffle Regularization
Deep Networks for Compressed Image Sensing
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module
Clinical Patient Tracking in the Presence of Transient and Permanent Occlusions via Geodesic Feature
Eyemotion: Classifying facial expressions in VR using eye-tracking cameras
Spatio-temporal Human Action Localisation and Instance Segmentation in Temporally Untrimmed Videos
SAR Image Colorization: Converting Single-Polarization to Fully Polarimetric Using Deep Neural Networks
Person Re-identification Using Visual Attention
Group-wise Deep Co-saliency Detection
Semantic 3D Occupancy Mapping through Efficient High Order CRFs
Contrastive-center loss for deep neural networks
Wavelet Convolutional Neural Networks for Texture Classification
Synthesizing Robust Adversarial Examples
Toward Geometric Deep SLAM
Generative OpenMax for Multi-Class Open Set Classification
LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction
Delineation of line patterns in images using B-COSFIRE filters
Detecting Semantic Parts on Partially Occluded Objects
Improving Robustness of Feature Representations to Image Deformations using Powered Convolution in CNNs
Motion-Appearance Interactive Encoding for Object Segmentation in Unconstrained Videos
Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns
Spatiotemporal Modeling for Crowd Counting in Videos
Residual Conv-Deconv Grid Network for Semantic Segmentation
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Fast Deep Matting for Portrait Animation on Mobile Phone
Graph-Based Classification of Omnidirectional Images
RankIQA: Learning from Rankings for No-reference Image Quality Assessment
Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network
Maximum entropy based non-negative optoacoustic tomographic image reconstruction
SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set
A Guided Spatial Transformer Network for Histology Cell Differentiation
Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models
Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition
Context-Aware Single-Shot Detector
Ultra-low-power Wireless Streaming Cameras
Exploiting Web Images for Weakly Supervised Object Detection
A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data
Food Ingredients Recognition through Multi-label Learning
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
STN-OCR: A single Neural Network for Text Detection and Text Recognition
Handwritten character recognition using some (anti)-diagonal structural features
Building Detection from Satellite Images on a Global Scale
Efficient Deformable Shape Correspondence via Kernel Matching
Learning from Video and Text via Large-Scale Discriminative Clustering
Object Detection of Satellite Images Using Multi-Channel Higher-order Local Autocorrelation
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization
Localizing Actions from Video Labels and Pseudo-Annotations
Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions
A weighting strategy for Active Shape Models
The WILDTRACK Multi-Camera Person Dataset
FontCode: Embedding Information in Text Documents using Glyph Perturbation
Weakly-supervised learning of visual relations
Deep Feature Consistent Deep Image Transformations: Downscaling, Decolorization and HDR Tone Mapping
Synthetic Database for Evaluation of General, Fundamental Biometric Principles
Improved Adversarial Systems for 3D Object Generation and Reconstruction
Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results
Discover and Learn New Objects from Documentaries
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
Scalable and Effective Deep CCA via Soft Decorrelation
Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles
2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation
Deep Domain Adaptation by Geodesic Distance Minimization
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN
Spatially variant PSF modeling in confocal macroscopy
Feature Extraction via Recurrent Random Deep Ensembles and its Application in Gruop-level Happiness Estimation
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
Spatio-Temporal Action Detection with Cascade Proposal and Location Anticipation
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
Material Editing Using a Physically Based Rendering Network
Image Denoising via CNNs: An Adversarial Approach
Model-based learning of local image features for unsupervised texture segmentation
Tensorial Recurrent Neural Networks for Longitudinal Data Analysis
Video Object Segmentation with Re-identification
CREST: Convolutional Residual Learning for Visual Tracking
Self-Supervised Learning for Spinal MRIs
Momo: Monocular Motion Estimation on Manifolds
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Dense Piecewise Planar RGB-D SLAM for Indoor Environments
Kernalised Multi-resolution Convnet for Visual Tracking
Joint Transmission Map Estimation and Dehazing using Deep Networks
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching
A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models
Controllable Generative Adversarial Network
Exact Tensor Completion from Sparsely Corrupted Observations via Convex Optimization
OmniArt: Multi-task Deep Learning for Artistic Data Analysis
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks
Latent tree models
PIVO: Probabilistic Inertial-Visual Odometry for Occlusion-Robust Navigation
Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks
An Energy Minimization Approach to 3D Non-Rigid Deformable Surface Estimation Using RGBD Data
Predicting Human Activities Using Stochastic Grammar
Semantic Instance Labeling Leveraging Hierarchical Segmentation
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
Dual Quadrics from Object Detection BoundingBoxes as Landmark Representations in SLAM
ORGB: Offset Correction in RGB Color Space for Illumination-Robust Image Processing
Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning
A Unified View-Graph Selection Framework for Structure from Motion
Deep MR to CT Synthesis using Unpaired Data
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Recent Developments and Future Challenges in Medical Mixed Reality
Image reconstruction with imperfect forward models and applications in deblurring
Unsupervised Representation Learning by Sorting Sequences
Automatic Spatially-aware Fashion Concept Discovery
CASSL: Curriculum Accelerated Self-Supervised Learning
A Latent Variable Model for Two-Dimensional Canonical Correlation Analysis and its Variational Inference
Localizing Moments in Video with Natural Language
Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
Better Together: Joint Reasoning for Non-rigid 3D Reconstruction with Specularities and Shading
Accelerated Image Reconstruction for Nonlinear Diffractive Imaging
Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting
Query-guided Regression Network with Context Policy for Phrase Grounding
Video Frame Interpolation via Adaptive Separable Convolution
Optimizing Region Selection for Weakly Supervised Object Detection
Interactively Transferring CNN Patterns for Part Localization
Interpreting CNN Knowledge via an Explanatory Graph
Detecting Noteheads in Handwritten Scores with ConvNets and Bounding Box Regression
Manifold Constrained Low-Rank Decomposition
End-to-end learning potentials for structured attribute prediction
Intensity Video Guided 4D Fusion for Improved Highly Dynamic 3D Reconstruction
Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions
Identity-Aware Textual-Visual Matching with Latent Co-attention
A Solution for Crime Scene Reconstruction using Time-of-Flight Cameras
Structured Attentions for Visual Question Answering
Learning for Active 3D Mapping
Two-Phase Learning for Weakly Supervised Object Localization
Learning to segment on tiny datasets: a new shape model
Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos
MemNet: A Persistent Memory Network for Image Restoration
Learning a CNN-based End-to-End Controller for a Formula SAE Racecar
Graph Classification with 2D Convolutional Neural Networks
Image Quality Assessment Techniques Show Improved Training and Evaluation of Autoencoder Generative Adversarial Networks
Real-Time Visual Localisation in a Tagged Environment
An Adaptive Cluster-based Wiener Filter for Speckle Reduction of OCT Skin Images
Multibiometric Secure System Based on Deep Learning
What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics
Unconstrained Face Detection and Open-Set Face Recognition Challenge
Temporal Context Network for Activity Localization in Videos
Learning a Repression Network for Precise Vehicle Search
An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data
An Unsupervised Game-Theoretic Approach to Saliency Detection
Fast Scene Understanding for Autonomous Driving
An Error Detection and Correction Framework for Connectomics
Human Skin Detection Using RGB, HSV and YCbCr Color Models
What Actions are Needed for Understanding Human Actions in Videos?
Sequential Dual Deep Learning with Shape and Texture Features for Sketch Recognition
Deep Face Feature for Face Alignment
Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition
Learning to Disambiguate by Asking Discriminative Questions
Multi-dimensional Gated Recurrent Units for Automated Anatomical Landmark Localization
SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion
SUBIC: A supervised, structured binary code for image search
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References
Modality-bridge Transfer Learning for Medical Image Classification
Writer Identification and Verification from Intra-variable Individual Handwriting
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel
Iterative Deep Convolutional Encoder-Decoder Network for Medical Image Segmentation
Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1
Face Parsing via a Fully-Convolutional Continuous CRF Neural Network
Calipso: Physics-based Image and Video Editing through CAD Model Proxies
Flower Categorization using Deep Convolutional Neural Networks
Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation
Kill Two Birds With One Stone: Boosting Both Object Detection Accuracy and Speed With adaptive Patch-of-Interest Composition
Recurrent Filter Learning for Visual Tracking
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals
Visual Graph Mining
Context-based Normalization of Histological Stains using Deep Convolutional Features
Fast-Forward Video Based on Semantic Extraction
Divide and Fuse: A Re-ranking Approach for Person Re-identification
Deep Object-Centric Representations for Generalizable Robot Learning
An ELU Network with Total Variation for Image Denoising
Situation Recognition with Graph Neural Networks
Image Augmentation using Radial Transform for Training Deep Neural Networks
Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation
Pathological Pulmonary Lobe Segmentation from CT Images using Progressive Holistically Nested Neural Networks and Random Walker
Improved Regularization of Convolutional Neural Networks with Cutout
Sequence-to-Label Script Identification for Multilingual OCR
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis
Learning Graph While Training: An Evolving Graph Convolutional Neural Network
Language Identification Using Deep Convolutional Recurrent Neural Networks
GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion
A Generalised Directional Laplacian Distribution: Estimation, Mixture Models and Audio Source Separation
Random Erasing Data Augmentation
Stacked Deconvolutional Network for Semantic Segmentation
ConvNet Architecture Search for Spatiotemporal Feature Learning
Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study
Deep Binary Reconstruction for Cross-modal Hashing
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
Robust Registration and Geometry Estimation from Unstructured Facial Scans
PixelNN: Example-based Image Synthesis
Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning
Eigen Evolution Pooling for Human Action Recognition
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
What does a convolutional neural network recognize in the moon?
High Voltage Insulator Surface Evaluation Using Image Processing
A Brief Survey of Deep Reinforcement Learning
Teaching UAVs to Race Using Sim4CV
Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks
Shapelet-based Sparse Representation for Landcover Classification of Hyperspectral Images
More cat than cute? Interpretable Prediction of Adjective-Noun Pairs
Distantly Supervised Road Segmentation
Learning Spread-out Local Feature Descriptors
PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection
Towards Automatic Construction of Diverse, High-quality Image Dataset
Sparsity Invariant CNNs
ProbFlow: Joint Optical Flow and Uncertainty Estimation
Tags2Parts: Discovering Semantic Regions from Shape Tags
A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition
Representation Learning by Learning to Count
Seeing Through Noise: Visually Driven Speaker Separation and Enhancement
Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots
Pose Estimation using Local Structure-Specific Shape and Appearance Context
Exploiting Convolution Filter Patterns for Transfer Learning
Incremental Learning of Object Detectors without Catastrophic Forgetting
Fast single image super-resolution based on sigmoid transformation
Application of a Convolutional Neural Network for image classification to the analysis of collisions in High Energy Physics
Single Reference Image based Scene Relighting via Material Guided Filtering
3D Morphable Models as Spatial Transformer Networks
SPARCNN: SPAtially Related Convolutional Neural Networks
Objective Classes for Micro-Facial Expression Recognition
A wavelet frame coefficient total variational model for image restoration
Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Evaluation of Deep Learning on an Abstract Image Classification Dataset
Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications
Multi-task Self-Supervised Visual Learning
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras
RaspiReader: An Open Source Fingerprint Reader Facilitating Spoof Detection
Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariance
3D Object Reconstruction from a Single Depth View with Adversarial Learning
Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling
An IoT Real-Time Biometric Authentication System Based on ECG Fiducial Extracted Features Using Discrete Cosine Transform
Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
Automatic Dataset Augmentation
Digital image splicing detection based on Markov features in QDCT and QWT domain
A Compromise Principle in Deep Monocular Depth Estimation
Stylizing Face Images via Multiple Exemplars
Open-World Visual Recognition Using Knowledge Graphs
Deep Learning Sparse Ternary Projections for Compressed Sensing of Images
Curriculum Learning for Multi-Task Classification of Visual Attributes
Multi-view Low-rank Sparse Subspace Clustering
Autoencoder with recurrent neural networks for video forgery detection
Study of Clear Sky Models for Singapore
Semantic Texture for Robust Dense Tracking
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
Analyzing Cloud Optical Properties Using Sky Cameras
Learning a 3D descriptor for cross-source point cloud registration from synthetic data
A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling
Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks
Convolutional Sparse Coding with Overlapping Group Norms
Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraints
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network
Joint Maximum Purity Forest with Application to Image Super-Resolution
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks - Preliminary Results
Efficient Convolutional Network Learning using Parametric Log based Dual-Tree Wavelet ScatterNet
End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design
Action Classification and Highlighting in Videos
Learning a Generative Adversarial Network for High Resolution Artwork Synthesis
Video Summarization with Attention-Based Encoder-Decoder Networks
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition
ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose Estimation
Automatic Semantic Style Transfer using Deep Convolutional Neural Networks and Soft Masks
Inferring Human Activities Using Robust Privileged Probabilistic Learning
Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Context Based Visual Content Verification
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
Facial 3D Model Registration Under Occlusions With SensiblePoints-based Reinforced Hypothesis Refinement
Deep Learning-Guided Image Reconstruction from Incomplete Data
Simulated Annealing for JPEG Quantization
Detection of Moving Object in Dynamic Background Using Gaussian Max-Pooling and Segmentation Constrained RPCA
A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders
Blind Stereo Image Quality Assessment Inspired by Brain Sensory-Motor Fusion
Hyperspectral Light Field Stereo Matching
To Learn or Not to Learn Features for Deformable Registration?
Hierarchical loss for classification
A Nonparametric Model for Multimodal Collaborative Activities Summarization
A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras
Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision
Inhomogeneous Hypergraph Clustering with Applications
Visualizing and Improving Scattering Networks
Predicting Visual Features from Text for Image and Video Caption Retrieval
Subspace Segmentation by Successive Approximations: A Method for Low-Rank and High-Rank Data with Missing Entries
Fine-tuning deep CNN models on specific MS COCO categories
Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data
PageNet: Page Boundary Extraction in Historical Handwritten Documents
Using Cross-Model EgoSupervision to Learn Cooperative Basketball Intention
Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model
Group-level Emotion Recognition using Transfer Learning from Face Identification
Blind image deblurring using class-adapted image priors
Detecting animals in African Savanna with UAVs and the crowds
Deep learning from crowds
Automatic Document Image Binarization using Bayesian Optimization
Cross-Domain Image Retrieval with Attention Modeling
Soft Proposal Networks for Weakly Supervised Object Localization
Clustering of Data with Missing Entries using Non-convex Fusion Penalties
Synthetic Medical Images from Dual Generative Adversarial Networks
Polar Transformer Networks
Blended e-Learning Training (BeLT): Enhancing Railway Station Controller Knowledge
Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images
Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)
Integrating Specialized Classifiers Based on Continuous Time Markov Chain
Improving Sonar Image Patch Matching via Deep Learning
FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction
Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks
Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling
Multi-modal Conditional Attention Fusion for Dimensional Emotion Prediction
Adaptive Real-Time Removal of Impulse Noise in Medical Images
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Scalable Annotation of Fine-Grained Categories Without Experts
DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets
Deep Subspace Clustering Networks
Learning to Segment Breast Biopsy Whole Slide Images
Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional Neural Networks
Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition
Calibration of depth cameras using denoised depth images
Completion of High Order Tensor Data with Missing Entries via Tensor-train Decomposition
Method to Detect Eye Position Noise from Video-Oculography when Detection of Pupil or Corneal Reflection Position Fails
Improving Heterogeneous Face Recognition with Conditional Adversarial Networks
Learning a Dilated Residual Network for SAR Image Despeckling
Image Processing Operations Identification via Convolutional Neural Network
Joint Calibration of Panoramic Camera and Lidar Based on Supervised Learning
How to Train Triplet Networks with 100K Identities?
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation
Optimal Transport for Deep Joint Transfer Learning
A Product Shape Congruity Measure via Entropy in Shape Scale Space
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Deep multi-frame face super-resolution
3D Densely Convolutional Networks for Volumetric Segmentation
Recurrent neural networks based Indic word-wise script identification using character-wise training
Fused Text Segmentation Networks for Multi-oriented Scene Text Detection
Stack-Captioning: Coarse-to-Fine Learning for Image Captioning
Recovering Homography from Camera Captured Documents using Convolutional Neural Networks
Real-Time Multiple Object Tracking - A Study on the Importance of Speed
Capturing the contributions of the semantic web to the IoT: a unifying vision
Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification
Learning Gating ConvNet for Two-Stream based Methods in Action Recognition
Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering
Adversarial Discriminative Heterogeneous Face Recognition
Joint Dictionaries for Zero-Shot Learning
Deep Mean-Shift Priors for Image Restoration
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers
ExprGAN: Facial Expression Editing with Controllable Expression Intensity
A Deep Cascade Network for Unaligned Face Attribute Classification
End-to-End United Video Dehazing and Detection
Constant Space Complexity Environment Representation for Vision-based Navigation
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
Meta Networks for Neural Style Transfer
Densely tracking sequences of 3D face scans
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction
An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping
Subspace Clustering using Ensembles of $K$-Subspaces
Food Recognition using Fusion of Classifiers based on CNNs
One-Shot Visual Imitation Learning via Meta-Learning
On Coordinate Minimization of Convex Piecewise-Affine Functions
ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks
Multi-scale Deep Learning Architectures for Person Re-identification
Masquer Hunter: Adversarial Occlusion-aware Face Detection
Cystoid macular edema segmentation of Optical Coherence Tomography images using fully convolutional neural networks and fully connected CRFs
Embedding Deep Networks into Visual Explanations
NIMA: Neural Image Assessment
Long-Term Ensemble Learning of Visual Place Classifiers
An Improved Fatigue Detection System Based on Behavioral Characteristics of Driver
Neural Affine Grayscale Image Denoising
Organizing Multimedia Data in Video Surveillance Systems Based on Face Verification with Convolutional Neural Networks
Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution from a Blurred Image Sequence
Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks
Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition
Direction-Aware Semi-Dense SLAM
StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
Beyond SIFT using Binary features for Loop Closure Detection
Microscopy Cell Segmentation via Adversarial Neural Networks
Continuous Multimodal Emotion Recognition Approach for AVEC 2017
Depression Scale Recognition from Audio, Visual and Text Analysis
Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
Adaptive compressed 3D imaging based on wavelet trees and Hadamard multiplexing with a single photon counting detector
Towards CNN map representation and compression for camera relocalisation
Multi-Person Pose Estimation via Column Generation
LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation
Target-adaptive CNN-based pansharpening
Rotation Adaptive Visual Object Tracking with Motion Consistency
Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players
When is a Convolutional Filter Easy To Learn?
Look Wider to Match Image Patches with Convolutional Neural Networks
Exploring Human-like Attention Supervision in Visual Question Answering
Human Action Forecasting by Learning Task Grammars
Automatic Leaf Extraction from Outdoor Images
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning
Image operator learning coupled with CNN classification and its application to staff line removal
Learning to Detect Violent Videos using Convolutional Long Short-Term Memory
Curriculum Learning of Visual Attribute Clusters for Multi-Task Classification
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
Latent Embeddings for Collective Activity Recognition
UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning
Estimated Depth Map Helps Image Classification
SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes
Temporal Multimodal Fusion for Video Emotion Classification in the Wild
Convolutional neural networks that teach microscopes how to image
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
Efficient Column Generation for Cell Detection and Segmentation
Class-Splitting Generative Adversarial Networks
Virtual Blood Vessels in Complex Background using Stereo X-ray Images
Novel Evaluation Metrics for Seam Carving based Image Retargeting
Happy Travelers Take Big Pictures: A Psychological Study with Machine Learning and Big Data
Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks
Demography-based Facial Retouching Detection using Subclass Supervised Sparse Autoencoder
SwGridNet: A Deep Convolutional Neural Network based on Grid Topology for Image Classification
Real-time 3D Shape Instantiation from Single Fluoroscopy Projection for Fenestrated Stent Graft Deployment
On Encoding Temporal Evolution for Real-time Action Prediction
MR Acquisition-Invariant Representation Learning
A semi-automated segmentation method for detection of pulmonary embolism in True-FISP MRI sequences
Semi-Supervised Hierarchical Semantic Object Parsing
A Generic Regression Framework for Pose Recognition on Color and Depth Images
Domain Adaptation from Synthesis to Reality in Single-model Detector for Video Smoke Detection
Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification
Survey of Recent Advances in Visual Question Answering
3D Camouflaging Object using RGB-D Sensors
Pose-driven Deep Convolutional Model for Person Re-identification
3D Textured Model Encryption via 3D Lu Chaotic Mapping
Deep Sparse Subspace Clustering
Variational Reflectance Estimation from Multi-view Images
Camera-Aware Multi-Resolution Analysis (CAMRA) for Raw Sensor Data Compression
Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks
Learning to Inpaint for Image Compression
Learning Multi-grid Generative ConvNets by Minimal Contrastive Divergence
Learning to Label Affordances from Simulated and Real Data
Multi-layer Visualization for Medical Mixed Reality
Region-Based Image Retrieval Revisited
Augmented Robust PCA For Foreground-Background Separation on Noisy, Moving Camera Video
Signature Verification Approach using Fusion of Hybrid Texture Features
Light field super resolution through controlled micro-shifts of light field sensor
FoodNet: Recognizing Foods Using Ensemble of Deep Networks
ANSAC: Adaptive Non-minimal Sample and Consensus
Photorealistic Style Transfer with Screened Poisson Equation
X-View: Graph-Based Semantic Multi-View Localization
Deep Competitive Pathway Networks
Robust Photometric Stereo Using Learned Image and Gradient Dictionaries
Unsupervised Domain Adaptation with Copula Models
PCANet-II: When PCANet Meets the Second Order Pooling
Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze
Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis
Robust Surface Reconstruction from Gradients via Adaptive Dictionary Regularization
DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning
Image Dehazing using Bilinear Composition Loss Function
Adaptive Smoothing in fMRI Data Processing Neural Networks
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring
Conditional Chromatic Filtering for Restoring Pansharpened Images
A Study of Cross-domain Generative Models applied to Cartoon Series
Neural Color Transfer between Images
End-to-end Learning for 3D Facial Animation from Raw Waveforms of Speech
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
A concatenating framework of shortcut convolutional neural networks
Resolution limits on visual speech recognition
Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks
Speaker-independent machine lip-reading with speaker-dependent viseme classifiers
Wide and deep volumetric residual networks for volumetric image classification
Adaptive Measurement Network for CS Image Reconstruction
Deep learning for source camera identification on mobile devices
Spinal cord gray matter segmentation using deep dilated convolutions
BodyDigitizer: An Open Source Photogrammetry-based 3D Body Scanner
Effective Image Differencing with ConvNets for Real-time Transient Hunting
Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment
Context Embedding Networks
Plane-extraction from depth-data using a Gaussian mixture regression model
Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation
Online Photometric Calibration for Auto Exposure Video for Realtime Visual Odometry and SLAM
Detecting the Moment of Completion: Temporal Models for Localising Action Completion
Real-Time Illegal Parking Detection System Based on Deep Learning
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks
A New Spectral Clustering Algorithm
Micro-Expression Spotting: A Benchmark
On Matching Skulls to Digital Face Images: A Preliminary Approach
Visual Servoing of Unmanned Surface Vehicle from Small Tethered Unmanned Aerial Vehicle
Island Loss for Learning Discriminative Features in Facial Expression Recognition
iVQA: Inverse Visual Question Answering
Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN
AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition
DocEmul: a Toolkit to Generate Structured Historical Documents
Detect to Track and Track to Detect
Deep learning in remote sensing: a review
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
Image retargeting via Beltrami representation
Deep Semantic Abstractions of Everyday Human Activities: On Commonsense Representations of Human Interactions
Local Radon Descriptors for Image Search
Recognizing Daily Activities from Egocentric Photo-Streams
GUIDES - Geospatial Urban Infrastructure Data Engineering Solutions
Joint Image Filtering with Deep Convolutional Networks
Self-Taught Support Vector Machine
Residual Connections Encourage Iterative Inference
Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution
Recent Advances in Zero-shot Recognition
Object Classification in Images of Neoclassical Artifacts Using Deep Learning
Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)
Automatic Detection and Uncertainty Quantification of Landmarks on Elastic Curves
Improving Shadow Suppression for Illumination Robust Face Recognition
An adaptive thresholding approach for automatic optic disk segmentation
K-means clustering for efficient and robust registration of multi-view point sets
CNNComparator: Comparative Analytics of Convolutional Neural Networks
A multi-branch convolutional neural network for detecting double JPEG compression
What is (missing or wrong) in the scene? A Hybrid Deep Boltzmann Machine For Contextualized Scene Modeling
A Survey on Optical Character Recognition System
Vehicle classification based on convolutional networks applied to FM-CW radar signals
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks
Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network
Gradient-free Policy Architecture Search and Adaptation
Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Superpixels Based Marker Tracking Vs. Hue Thresholding In Rodent Biomechanics Application
Scene Parsing with Global Context Embedding
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification
Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection
Image Restoration by Iterative Denoising and Backward Projections
VisDA: The Visual Domain Adaptation Challenge
Improved Search in Hamming Space using Deep Multi-Index Hashing
Generative Adversarial Networks: An Overview
Deep Self-taught Learning for Remote Sensing Image Classification
Sea Level Anomaly Prediction using Recurrent Neural Networks
Combining Multiple Views for Visual Speech Recognition
Dress like a Star: Retrieving Fashion Products from Videos
Interpretable Transformations with Encoder-Decoder Networks
Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks
Superpixel Based Segmentation and Classification of Polyps in Wireless Capsule Endoscopy
Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition
Generalized linear mixing model accounting for endmember variability
Learning Discrete Weights Using the Local Reparameterization Trick
An efficient deep learning hashing neural network for mobile visual search
Image Disguise based on Generative Model
Deep Neural Network Approximation using Tensor Sketching
Feedback-prop: Convolutional Neural Network Inference under Partial Evidence
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100$\times$ speed-up
An iterative closest point method for measuring the level of similarity of 3d log scans in wood industry
Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net
Investigating the feature collection for semantic segmentation via single skip connection
Amorphous Dynamic Partial Reconfiguration with Flexible Boundaries to Remove Fragmentation
The ETH-MAV Team in the MBZ International Robotics Challenge
Listening to the World Improves Speech Command Recognition
Fully Context-Aware Video Prediction
Max-Margin Invariant Features from Transformed Unlabeled Data
One pixel attack for fooling deep neural networks
Compressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background Separation
Supervised Classification: Quite a Brief Overview
LOOP Descriptor: Local Optimal Oriented Pattern
GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks
Optimal Shrinkage of Singular Values Under Random Data Contamination
Lip2AudSpec: Speech reconstruction from silent lip movements video
Dynamic Routing Between Capsules
How far did we get in face spoofing detection?
Image Compression: Sparse Coding vs. Bottleneck Autoencoders
Deep Learning for Accelerated Ultrasound Imaging
Dual Skipping Networks
Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images
Object Recognition by Using Multi-level Feature Point Extraction
A Novel Approach to Artistic Textual Visualization via GAN
Synthetic Iris Presentation Attack using iDCGAN
Examining CNN Representations with respect to Dataset Bias
High-Precision Localization Using Ground Texture
Multilinear Class-Specific Discriminant Analysis
On Pre-Trained Image Features and Synthetic Images for Deep Learning
A Saak Transform Approach to Efficient, Scalable and Robust Handwritten Digits Recognition
Cascade Region Proposal and Global Context for Deep Object Detection
Open Set Logo Detection and Retrieval
Learning to solve inverse problems using Wasserstein loss
The loss surface and expressivity of deep convolutional neural networks
Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks
On the Taut String Interpretation of the One-dimensional Rudin-Osher-Fatemi Model: A New Proof, a Fundamental Estimate and Some Applications
Denoising random forests
Continuous Authentication Using One-class Classifiers and their Fusion
An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
Deep word embeddings for visual speech recognition
Spatio-temporal interaction model for crowd video analysis
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
Deep Hashing with Triplet Quantization Loss
Optimal Resource Allocation in Distributed Broadband Wireless Communication Systems
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation
A multi-layer network based on Sparse Ternary Codes for universal vector compression
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection
Common Representation Learning Using Step-based Correlation Multi-Modal CNN
Countering Adversarial Images using Input Transformations
Accelerated Sparse Subspace Clustering
Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation
A multitask deep learning model for real-time deployment in embedded systems
Improving Object Localization with Fitness NMS and Bounded IoU Loss
Learning deep features for source color laser printer identification based on cascaded learning
Query-free Clothing Retrieval via Implicit Relevance Feedback
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning
Complex-valued image denosing based on group-wise complex-domain sparsity
Understanding and Predicting The Attractiveness of Human Action Shot
Statistical evaluation of visual quality metrics for image denoising
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Automatic Query Image Disambiguation for Content-Based Image Retrieval
A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision
Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection
Motion Artifact Detection in Confocal Laser Endomicroscopy Images
End-to-end Flow Correlation Tracking with Spatial-temporal Attention
Distributed Unmixing of Hyperspectral Data With Sparsity Constraint
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor
Adversarial Dropout Regularization
Simultaneous Joint and Object Trajectory Templates for Human Activity Recognition from 3-D Data
End-to-End Video Classification with Knowledge Graphs
Active Learning for Visual Question Answering: An Empirical Study
Towards Reverse-Engineering Black-Box Neural Networks
Radical analysis network for zero-shot learning in printed Chinese character recognition
Optimal transport maps for distribution preserving operations on latent spaces of Generative Models
Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data
Characterizing Sparse Connectivity Patterns in Neural Networks
Alpha-expansion is Exact on Stable Instances
Image Segmentation of Multi-Shaped Overlapping Objects
Challenges in Disentangling Independent Factors of Variation
Unconstrained Scene Text and Video Text Recognition for Arabic Script
Fine-tuning CNN Image Retrieval with No Human Annotation
Few-Shot Adversarial Domain Adaptation
Moonshine: Distilling with Cheap Convolutions
Compression-aware Training of Deep Networks
Latent hypernet: Exploring all Layers from Convolutional Neural Networks
Recurrent Autoregressive Networks for Online Multi-Object Tracking
A New Hybrid-parameter Recurrent Neural Networks for Online Handwritten Chinese Character Recognition
Revealing structure components of the retina by deep learning networks
Learning Sparse Visual Representations with Leaky Capped Norm Regularizers
Offline signature authenticity verification through unambiguously connected skeleton segments
Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Fast camera focus estimation for gaze-based focus control
Toward Depth Estimation Using Mask-Based Lensless Cameras
Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning
Breast density classification with deep convolutional neural networks
Self-Supervised Intrinsic Image Decomposition
A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation
Saliency Prediction for Mobile User Interfaces
Robotic Tactile Perception of Object Properties: A Review
Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?
CARLA: An Open Urban Driving Simulator
EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
Longitudinal Study of Child Face Recognition
DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition
Towards ECDSA key derivation from deep embeddings for novel Blockchain applications
3D Randomized Connection Network with Graph-based Label Inference
D-PCN: Parallel Convolutional Networks for Image Recognition via a Discriminator
Robust Image Registration via Empirical Mode Decomposition
High-Order Attention Models for Visual Question Answering
An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network
Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
Denoising Imaging Polarimetry by an Adapted BM3D Method
Modeling Human Categorization of Natural Images Using Deep Feature Representations
Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants
Saliency-based Sequential Image Attention with Multiset Prediction
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Loss Functions for Multiset Prediction
C-WSL: Count-guided Weakly Supervised Localization
Velocity variations at Columbia Glacier captured by particle filtering of oblique time-lapse images
Deep Epitome for Unravelling Generalized Hamming Network: A Fuzzy Logic Interpretation of Deep Learning
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design
Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes
Modal Regression based Atomic Representation for Robust Face Recognition
Occlusion Aware Unsupervised Learning of Optical Flow
Priming Neural Networks
Learning Deeply Supervised Visual Descriptors for Dense Monocular Reconstruction
HandSeg: A Dataset for Hand Segmentation from Depth Images
Less-forgetful Learning for Domain Expansion in Deep Neural Networks
Learning to Find Good Correspondences
Natural Language Guided Visual Relationship Detection
Deep Matching Autoencoders
Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN
Grounded Objects and Interactions for Video Captioning
3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network
Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Training a network to attend like human drivers saves it from common but misleading loss functions
Using KL-divergence to focus Deep Visual Explanation
Chinese Typeface Transformation with Hierarchical Adversarial Network
Grounding Visual Explanations (Extended Abstract)
Detecting hip fractures with radiologist-level performance using deep neural networks
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
Efficient Diverse Ensemble for Discriminative Co-Tracking
Learning to Play Othello with Deep Neural Networks
Deep Local Binary Patterns
Superpixels Based Segmentation and SVM Based Classification Method to Distinguish Five Diseases from Normal Regions in Wireless Capsule Endoscopy
Depth Assisted Full Resolution Network for Single Image-based View Synthesis
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments
Neural Motifs: Scene Graph Parsing with Global Context
Multiresolution and Hierarchical Analysis of Astronomical Spectroscopic Cubes using 3D Discrete Wavelet Transform
ADVISE: Symbolism and External Knowledge for Decoding Advertisements
Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection
Integrating Disparate Sources of Experts for Robust Image Denoising
Learning SO(3) Equivariant Representations with Spherical CNNs
A Color Quantization Optimization Approach for Image Representation Learning
DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
A novel total variation model based on kernel functions and its application
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning
Kill Two Birds with One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement
MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Frontal Face Images
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space
Non-line-of-sight Imaging with Partial Occluders and Surface Normals
Spectral-Spatial Feature Extraction and Classification by ANN Supervised with Center Loss in Hyperspectral Imagery
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
Stochastic metamorphosis with template uncertainties
MegDet: A Large Mini-Batch Object Detector
Optical Character Recognition (OCR) for Telugu: Database, Algorithm and Application
Face Attention Network: An Effective Face Detector for the Occluded Faces
Verifying Neural Networks with Mixed Integer Programming
Memory Based Online Learning of Deep Representations from Video Streams
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)
Pixel-wise object tracking
Robust Seed Mask Generation for Interactive Image Segmentation
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images
Virtual Adversarial Ladder Networks For Semi-supervised Learning
Neural 3D Mesh Renderer
Residual Parameter Transfer for Deep Domain Adaptation
Discussion among Different Methods of Updating Model Filter in Object Tracking
Functional Map of the World
Autoencoder Node Saliency: Selecting Relevant Latent Representations
SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes
Aperture Supervision for Monocular Depth Estimation
WAYLA - Generating Images from Eye Movements
Dynamic High Resolution Deformable Articulated Tracking
Relating Input Concepts to Convolutional Neural Network Decisions
Identifying Most Walkable Direction for Navigation in an Outdoor Environment
Video Semantic Object Segmentation by Self-Adaptation of DCNN
An Analysis of Scale Invariance in Object Detection - SNIP
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks
Neuron-level Selective Context Aggregation for Scene Segmentation
Conditional Image-Text Embedding Networks
VITON: An Image-based Virtual Try-on Network
Multiple component decomposition from millimeter single-channel data
Frustum PointNets for 3D Object Detection from RGB-D Data
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval
Temporal Relational Reasoning in Videos
Adversarial Feature Augmentation for Unsupervised Domain Adaptation
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
Contextual Based Image Inpainting: Infer, Match and Translate
Regularization of Deep Neural Networks with Spectral Dropout
Unsupervised End-to-end Learning for Deformable Medical Image Registration
Self-Reinforced Cascaded Regression for Face Alignment
Robust Visual SLAM with Point and Line Features
Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline
3D Based Landmark Tracker Using Superpixels Based Segmentation for Neuroscience and Biomechanics Studies
Visual Speech Enhancement
A Dictionary Approach to Identifying Transient RFI
Real-Time Seamless Single Shot 6D Object Pose Prediction
Wasserstein Introspective Neural Networks
Feature Selective Networks for Object Detection
Supervised Hashing with End-to-End Binary Deep Neural Network
CatGAN: Coupled Adversarial Transfer for Domain Generation
For Your Eyes Only: Learning to Summarize First-Person Videos
End-to-End Deep HDR Imaging with Large Foreground Motions
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty
Distance to Center of Mass Encoding for Instance Segmentation
Efficient and Invariant Convolutional Neural Networks for Dense Prediction
Video Enhancement with Task-Oriented Flow
Deep Extreme Cut: From Extreme Points to Object Segmentation
Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery
Geometric robustness of deep networks: analysis and improvement
Convolutional Image Captioning
Real-Time Capable Micro-Doppler Signature Decomposition of Walking Human Limbs
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
Structure-Aware and Temporally Coherent 3D Human Pose Estimation
Unsupervised 3D Reconstruction from a Single Image via Adversarial Learning
In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks
Semantically Consistent Image Completion with Fine-grained Details
Automatic Color Image Segmentation Using a Square Elemental Region-Based Seeded Region Growing and Merging Method
Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images
Personalized and Occupational-aware Age Progression by Generative Adversarial Networks
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
MAVOT: Memory-Augmented Video Object Tracking
Depth Map Completion by Jointly Exploiting Blurry Color Images and Sparse Depth Maps
Query-Adaptive R-CNN for Open-Vocabulary Object Detection and Retrieval
DeepDeblur: Fast one-step blurry face images restoration
Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams
FCLT - A Fully-Correlational Long-Term Tracker
Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting
On the Robustness of Semantic Segmentation Models to Adversarial Attacks
Tensor Completion Algorithms in Big Data Analytics
Restricting Greed in Training of Generative Adversarial Network
Multi-stream 3D FCN with Multi-scale Deep Supervision for Multi-modality Isointense Infant Brain MR Image Segmentation
Tracking for Half an Hour
Learning Less is More - 6D Camera Localization via 3D Surface Regression
Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Between-class Learning for Image Classification
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
Camera Style Adaptation for Person Re-identification
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning
Learning Face Age Progression: A Pyramid Architecture of GANs
Learning to Segment Every Thing
An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations
Entropy-difference based stereo error detection
A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry
FearNet: Brain-Inspired Model for Incremental Learning
Deep-Person: Learning Discriminative Deep Features for Person Re-Identification
Do Convolutional Neural Networks act as Compositional Nearest Neighbors?
Road Extraction by Deep Residual U-Net
An Amateur Drone Surveillance System Based on Cognitive Internet of Things
Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes
Blind estimation of white Gaussian noise variance in highly textured images
Saliency Weighted Convolutional Features for Instance Search
DeepSkeleton: Skeleton Map for 3D Human Pose Regression
Sparse Photometric 3D Face Reconstruction Guided by Morphable Models
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
Occlusion-aware Hand Pose Estimation Using Hierarchical Mixture Density Network
Joint Blind Motion Deblurring and Depth Estimation of Light Field
Deep Image Prior
Saccade Sequence Prediction: Beyond Static Saliency Maps
A fast nonconvex Compressed Sensing algorithm for highly low-sampled MR images reconstruction
Properties on n-dimensional convolution for image deconvolution
A Closer Look at Spatiotemporal Convolutions for Action Recognition
ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene
A novel graph structure for salient object detection based on divergence background and compact foreground
Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks
Radially-Distorted Conjugate Translations
Improving Video Generation for Multi-functional Applications
Auxiliary Guided Autoregressive Variational Autoencoders
Relation Networks for Object Detection
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification
Graph Distillation for Action Detection with Privileged Information
Blind Gain and Phase Calibration via Sparse Spectral Methods
Label Efficient Learning of Transferable Representations across Domains and Tasks
Video retrieval based on deep convolutional neural network
Distance-based Camera Network Topology Inference for Person Re-identification
Delineation of Skin Strata in Reflectance Confocal Microscopy Images using Recurrent Convolutional Networks with Toeplitz Attention
3D Facial Action Units Recognition for Emotional Expression
Deformable Shape Completion with Graph Convolutional Autoencoders
GANosaic: Mosaic Creation with Generative Texture Manifolds
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning
Precision Learning: Towards Use of Known Operators in Neural Networks
Single-Shot Object Detection with Enriched Semantics
Image to Image Translation for Domain Adaptation
Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks
Multi-Content GAN for Few-Shot Font Style Transfer
Towards understanding feedback from supermassive black holes using convolutional neural networks
Lecture video indexing using boosted margin maximizing neural networks
Taming Adversarial Domain Transfer with Structural Constraints for Image Enhancement
From Pixels to Object Sequences: Recurrent Semantic Instance Segmentation
Compressed Video Action Recognition
GAGAN: Geometry-Aware Generative Adversarial Networks
Low-Rank Tensor Completion by Truncated Nuclear Norm Regularization
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
A Deep Learning Approach to Drone Monitoring
Data Dropout in Arbitrary Basis for Deep Network Regularization
Composition-aided Sketch-realistic Portrait Generation
Deep Learning Can Reverse Photon Migration for Diffuse Optical Tomography
Deep Sampling Networks
FSSD: Feature Fusion Single Shot Multibox Detector
GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB
SOT for MOT
A Generalized Motion Pattern and FCN based approach for retinal fluid detection and segmentation
Structured Deep Neural Network Pruning via Matrix Pivoting
Iterative Deep Learning for Network Topology Extraction
A Perceptual Measure for Deep Single Image Camera Calibration
SfSNet : Learning Shape, Reflectance and Illuminance of Faces in the Wild
Long-Term Visual Object Tracking Benchmark
A+D-Net: Shadow Detection with Adversarial Shadow Attenuation
Imagine it for me: Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Zone-based Keyword Spotting in Bangla and Devanagari Documents
Adversarial Attribute-Image Person Re-identification
Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks
Joint Embedding and Classification for SAR Target Recognition
Manifold-valued Image Generation with Wasserstein Adversarial Networks
Deep learning for semantic segmentation of remote sensing images with rich spectral content
Deep Learning for automatic sale receipt understanding
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion
Can CNNs Construct Highly Accurate Model Efficiently with Limited Training Samples?
Fully-Convolutional Measurement Network for Compressive Sensing Image Reconstruction
Color Face Recognition using High-Dimension Quaternion-based Adaptive Representation
Open Evaluation Tool for Layout Analysis of Document Images
An Ensemble of Deep Convolutional Neural Networks for Alzheimer's Disease Detection and Classification
Avaliação da doença de Alzheimer pela análise multiespectral de imagens DW-MR por redes RBF como alternativa aos mapas ADC
Automated Pruning for Deep Neural Network Compression
R-FCN-3000 at 30fps: Decoupling Detection and Classification
Towards Recovery of Conditional Vectors from Conditional Generative Adversarial Networks
Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
Blind Image Deblurring Using Row-Column Sparse Representations
Learning Latent Super-Events to Detect Multiple Activities in Videos
Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering
What's in my closet?: Image classification using fuzzy logic
Learning Semantic Concepts and Order for Image and Sentence Matching
Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis
Lung Nodule Classification by the Combination of Fusion Classifier and Cascaded Convolutional Neural Networks
Stretching Domain Adaptation: How far is too far?
Joint 3D Proposal Generation and Object Detection from View Aggregation
From Lifestyle Vlogs to Everyday Interactions
Top-down Flow Transformer Networks
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition
Adversarial Examples that Fool Detectors
Take it in your stride: Do we need striding in CNNs?
Consistent Multiple Graph Matching with Multi-layer Random Walks Synchronization
Using SVDD in SimpleMKL for 3D-Shapes Filtering
Creating Capsule Wardrobes from Fashion Images
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
Per-Pixel Feedback for improving Semantic Segmentation
MoDL: Model Based Deep Learning Architecture for Inverse Problems
Learned Perceptual Image Enhancement
Multi-Scale Video Frame-Synthesis Network with Transitive Consistency Loss
Exploiting Modern Hardware for High-Dimensional Nearest Neighbor Search
Chaining Identity Mapping Modules for Image Denoising
Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints
CycleGAN, a Master of Steganography
Compact Hash Code Learning with Binary Deep Neural Network
Direct and Real-Time Cardiovascular Risk Prediction
Weaving Multi-scale Context for Single Shot Detector
Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning
Minimal Solvers for Monocular Rolling Shutter Compensation under Ackermann Motion
Transformational Sparse Coding
IQA: Visual Question Answering in Interactive Environments
Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
A Deep Recurrent Framework for Cleaning Motion Capture Data
Visual aesthetic analysis using deep neural network: model and techniques to increase accuracy without transfer learning
Single-Shot Multi-Person 3D Body Pose Estimation From Monocular RGB Input
FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection
The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images
Domain Adaptation Using Adversarial Learning for Autonomous Navigation
Identifying the Mislabeled Training Samples of ECG Signals using Machine Learning
Unsupervised Feature Learning for Audio Analysis
Using a single RGB frame for real time 3D hand pose estimation in the wild
Generalized Zero-Shot Learning via Synthesized Examples
MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
StrassenNets: Deep learning with a multiplication budget
A GRU-based Encoder-Decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition
Eye In-Painting with Exemplar Generative Adversarial Networks
Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications
Learning Compressible 360° Video Isomers
Im2Flow: Motion Hallucination from Static Images for Action Recognition
Direction-aware Spatial Context Features for Shadow Detection
Benchmarking Single Image Dehazing and Beyond
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation
3D Object Classification via Spherical Projections
Data Distillation: Towards Omni-Supervised Learning
Image Registration for the Alignment of Digitized Historical Documents
Fingerprint Spoof Buster
Camera Calibration for Daylight Specular-Point Locus
Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View
Transfer Adversarial Hashing for Hamming Space Retrieval
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Stochastic Low-Rank Bandits
Learning Disentangling and Fusing Networks for Face Completion Under Structured Occlusions
GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data
Symbol detection in online handwritten graphics using Faster R-CNN
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels
Weakly Supervised Action Localization by Sparse Temporal Pooling Network
Extreme 3D Face Reconstruction: Seeing Through Occlusions
Robust Estimation of Similarity Transformation for Visual Object Tracking with Correlation Filters
Pointwise Convolutional Neural Networks
SEE: Towards Semi-Supervised End-to-End Scene Text Recognition
RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment
Transfer Learning for OCRopus Model Training on Early Printed Books
Pre-training Attention Mechanisms
Unsupervised Domain Adaptation for 3D Keypoint Prediction from a Single Depth Scan
A novel nonconvex approach to recover the low-tubal-rank tensor data: when t-SVD meets PSSV
Impression Network for Video Object Detection
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
Visual Explanations from Hadamard Product in Multimodal Deep Networks
Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video
Space-Filling Curve Indices as Acceleration Structure for Exemplar-Based Inpainting
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples
Super-Resolution with Deep Adaptive Image Resampling
Guiding human gaze with convolutional neural networks
Multi-point Vibration Measurement for Mode Identification of Bridge Structures using Video-based Motion Magnification
Objects that Sound
Hierarchical Cross Network for Person Re-identification
Comparison of fingerprint authentication algorithms for small imaging sensors
Learning Fixation Point Strategy for Object Detection and Classification
On the Evaluation of Video Keyframe Summaries using User Ground Truth
ComboGAN: Unrestrained Scalability for Image Domain Translation
Scale-Space Anisotropic Total Variation for Limited Angle Tomography
Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks
Adversarial Examples: Attacks and Defenses for Deep Learning
Real-time 3D Reconstruction on Construction Site using Visual SLAM and UAV
Real-time deep hair matting on mobile devices
Y-net: 3D intracranial artery segmentation using a convolutional autoencoder
Deep Regression Forests for Age Estimation
Hyperparameters Optimization in Deep Convolutional Neural Network / Bayesian Approach with Gaussian Process Prior
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Learning Sight from Sound: Ambient Sound Provides Supervision for Visual Learning
LVreID: Person Re-Identification with Long Sequence Videos
Lost in Time: Temporal Analytics for Long-Term Video Surveillance
On the Diversity of Realistic Image Synthesis
Incremental Adversarial Domain Adaptation for Continually Changing Environments
Accurate 3D Reconstruction of Dynamic Scenes from Monocular Image Sequences with Severe Occlusions
Attribute CNNs for Word Spotting in Handwritten Documents
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning
Learning to Act Properly: Predicting and Explaining Affordances from Images
Image Segmentation to Distinguish Between Overlapping Human Chromosomes
Deep metric learning for multi-labelled radiographs
Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth
Enhance Visual Recognition under Adverse Conditions via Deep Networks
Context-Aware Semantic Inpainting
Exploring Models and Data for Remote Sensing Image Caption Generation
Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning
Human Action Recognition: Pose-based Attention draws focus to Hands
Learning Intelligent Dialogs for Bounding Box Annotation
A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading
Unifying Map and Landmark Based Representations for Visual Navigation
Smart, Sparse Contours to Represent and Edit Images
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks
Recurrent Pixel Embedding for Instance Grouping
CSGNet: Neural Shape Parser for Constructive Solid Geometry
Deep Hashing with Category Mask for Fast Video Retrieval
Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging - with data from the Osteoarthritis Initiative
Evaluation of PPG Biometrics for Authentication in different states
Denoising of image gradients and total generalized variation denoising
Boundary-sensitive Network for Portrait Segmentation
Aerial Spectral Super-Resolution using Conditional Adversarial Networks
Combining Weakly and Webly Supervised Learning for Classifying Food Images
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
Use of Generative Adversarial Network for Cross-Domain Change Detection
RIDI: Robust IMU Double Integration
Deep Blind Image Inpainting
Deep Meta Learning for Real-Time Visual Tracking based on Target-Specific Feature Space
Segmenting Sky Pixels in Images
Detect-and-Track: Efficient Pose Estimation in Videos
Aircraft Fuselage Defect Detection using Deep Neural Networks
Large-Scale 3D Scene Classification With Multi-View Volumetric CNN
Audio to Body Dynamics
RaspiReader: Open Source Fingerprint Reader
Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge
Eventness: Object Detection on Spectrograms for Temporal Localization of Audio Events
Efficient Parallel Connected Components Labeling with a Coarse-to-fine Strategy
Siamese LSTM based Fiber Structural Similarity Network (FS2Net) for Rotation Invariant Brain Tractography Segmentation
A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
Visualizing the Loss Landscape of Neural Nets
Rapid Adaptation with Conditionally Shifted Neurons
Learning Deep and Compact Models for Gesture Recognition
Estimation under group actions: recovering orbits from invariants
Polyp detection inside the capsule endoscopy: an approach for power consumption reduction
Dense Fully Convolutional Network for Skin Lesion Segmentation
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
Deformable GANs for Pose-based Human Image Generation
A Unified Method for First and Third Person Action Recognition
Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation
Context aware saliency map generation using semantic segmentation
Interactive Video Object Segmentation in the Wild
Deep Stacked Networks with Residual Polishing for Image Inpainting
Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition
SenseNet: 3D Objects Database and Tactile Simulator
Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imaging
Image denoising through bivariate shrinkage function in framelet domain
Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras
Optimal Bayesian Transfer Learning
Panoptic Segmentation
Recovery of Point Clouds on Surfaces: Application to Image Reconstruction
Recovery of Noisy Points on Band-limited Surfaces: Kernel Methods Re-explained
Instance Embedding Transfer to Unsupervised Video Object Segmentation
Joint convolutional neural pyramid for depth map super-resolution
Spot the Difference by Object Detection
3D Face Reconstruction with Region Based Best Fit Blending Using Mobile Phone for Virtual Reality Based Social Media
ICFVR 2017: 3rd International Competition on Finger Vein Recognition
PixelLink: Detecting Scene Text via Instance Segmentation
SmartTennisTV: Automatic indexing of tennis videos
IMU2Face: Real-time Gesture-driven Facial Reenactment
Quantifying Translation-Invariance in Convolutional Neural Networks
Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data
Deep Cross Polarimetric Thermal-to-visible Face Recognition
Object Referring in Videos with Language and Human Gaze
Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning
Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies
Deep learning for word-level handwritten Indic script identification
Efficient Image Evidence Analysis of CNN Classification Results
Gatekeeping Algorithms with Human Ethical Bias: The ethics of algorithms in archives, libraries and society
3D-DETNet: a Single Stage Video-Based Vehicle Detector
Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
A First Step in the Co-Evolution of Blockchain and Ontologies: Towards Engineering an Ontology of Governance at the Blockchain Protocol Level
Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding
Deep Crisp Boundaries: From Boundaries to Higher-level Tasks
Bridging the Gap: Simultaneous Fine Tuning for Data Re-Balancing
Boundary Optimizing Network (BON)
Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks
TextBoxes++: A Single-Shot Oriented Scene Text Detector
DeepStyle: Multimodal Search Engine for Fashion and Interior Design
EBIC: an artificial intelligence-based parallel biclustering algorithm for pattern discovery
Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network
Unsupervised Despeckling
Inferring a Third Spatial Dimension from 2D Histological Images
Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition
Segment-based Methods for Facial Attribute Detection from Partial Faces
From Superpixel to Human Shape Modelling for Carried Object Detection
Cortical-inspired image reconstruction via sub-Riemannian geometry and hypoelliptic diffusion
Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction
Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks
How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?
Generative Single Image Reflection Separation
QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds
Conditional Probability Models for Deep Image Compression
Deep saliency: What is learnt by a deep network about saliency?
Size-to-depth: A New Perspective for Single Image Depth Estimation
Non-Parametric Transformation Networks
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly
Classification of histopathological breast cancer images using iterative VMD aided Zernike moments & textural signatures
Circular Antenna Array Design for Breast Cancer Detection
Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis
Deep Multi-Spectral Registration Using Invariant Descriptor Learning
Long-term Visual Localization using Semantically Segmented Images
Autonomous Driving in Reality with Reinforcement Learning and Image Translation
Benchmark Visual Question Answer Models by using Focus Map
Re-ID done right: towards good practices for person re-identification
Cahn--Hilliard inpainting with the double obstacle potential
Semi-supervised FusedGAN for Conditional Image Generation
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Additive Margin Softmax for Face Verification
Multi-View Stereo 3D Edge Reconstruction
Faster gaze prediction with dense networks and Fisher pruning
Sparsely Connected Convolutional Networks
Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction
Visualization of Hyperspectral Images Using Moving Least Squares
Structured Inhomogeneous Density Map Learning for Crowd Counting
DeepISP: Learning End-to-End Image Processing Pipeline
Boundary-based Image Forgery Detection by Fast Shallow CNN
PU-Net: Point Cloud Upsampling Network
Deep joint rain and haze removal from single images
Dense Recurrent Neural Networks for Scene Labeling
Towards Automated Tuberculosis detection using Deep Learning
E-swish: Adjusting Activations to Different Network Depths
Fluorescence Microscopy Image Segmentation Using Convolutional Neural Network With Generative Adversarial Networks
DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks
Low-level Active Visual Navigation: Increasing robustness of vision-based localization using potential fields
Vehicle Detection in Aerial Images
Numerical Coordinate Regression with Convolutional Neural Networks
Novel digital tissue phenotypic signatures of distant metastasis in colorectal cancer
Survey on Emotional Body Gesture Recognition
Statistically Motivated Second Order Pooling
Side Information for Face Completion: a Robust PCA Approach
Towards Low-Latency and Ultra-Reliable Virtual Reality
Human Activity Recognition for Mobile Robot
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
PointCNN
Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks
Deep Structured Energy-Based Image Inpainting
Near-lossless L-infinity constrained Multi-rate Image Decompression via Deep Neural Network
Unsupervised learning from videos using temporal coherency deep networks
DVQA: Understanding Data Visualizations via Question Answering
Class label autoencoder for zero-shot learning
Dual Asymmetric Deep Hashing Learning
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process
Self-Learning to Detect and Segment Cysts in Lung CT Images without Manual Annotation
Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery
Generating Handwritten Chinese Characters using CycleGAN
Cloud Detection From RGB Color Remote Sensing Images With Deep Pyramid Networks
Weakly Supervised Object Detection with Pointwise Mutual Information
3D Scanning: A Comprehensive Survey
Deflecting Adversarial Attacks with Pixel Deflection
Efficient Hierarchical Graph-Based Segmentation of RGBD Videos
A Two-point Method for PTZ Camera Calibration in Sports
Ear Recognition With Score-Level Fusion Based On CMC In Long-Wave Infrared Spectrum
A Multi-Biometrics for Twins Identification Based Speech and Ear
Fine-grained Visual Categorization using PAIRS: Pose and Appearance Integration for Recognizing Subcategories
Interactive Deep Colorization With Simultaneous Global and Local Inputs
Interactive Generative Adversarial Networks for Facial Expression Generation in Dyadic Interactions
Meshed Up: Learnt Error Correction in 3D Reconstructions
Improved Training of Generative Adversarial Networks Using Representative Features
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks
Local Visual Microphones: Improved Sound Extraction from Silent Video
Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings
Learning-based Image Reconstruction via Parallel Proximal Algorithm
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding
Denoising Arterial Spin Labeling Cerebral Blood Flow Images Using Deep Learning
Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks
Object Detection in Videos by Short and Long Range Object Linking
Open3D: A Modern Library for 3D Data Processing
Structured Memory based Deep Model to Detect as well as Characterize Novel Inputs
Sliding Line Point Regression for Shape Robust Scene Text Detection
Malaria Detection Using Image Processing and Machine Learning
SegDenseNet: Iris Segmentation for Pre and Post Cataract Surgery
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
Netizen-Style Commenting on Fashion Photos: Dataset and Diversity Measures
A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video
An Infinitesimal Probabilistic Model for Principal Component Analysis of Manifold Valued Data
Robust 3D Human Motion Reconstruction Via Dynamic Template Construction
From Benedict Cumberbatch to Sherlock Holmes: Character Identification in TV series without a Script
Counting Cells in Time-Lapse Microscopy using Deep Neural Networks
Improved Image Segmentation via Cost Minimization of Multiple Hypotheses
Cross-domain CNN for Hyperspectral Image Classification
Deep Learning with Data Dependent Implicit Activation Function
Full Image Recover for Block-Based Compressive Sensing
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations
Automatic Safety Helmet Wearing Detection
Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters
3D Object Dense Reconstruction from a Single Depth View
A Fusion of Appearance based CNNs and Temporal evolution of Skeleton with LSTM for Daily Living Action Recognition
DensePose: Dense Human Pose Estimation In The Wild
A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs
Complex Network Classification with Convolutional Neural Network
Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming
Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach
No Modes left behind: Capturing the data distribution effectively using GANs
Intriguing Properties of Randomly Weighted Networks: Generalizing While Learning Next to Nothing
Multi-attention Recurrent Network for Human Communication Comprehension
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification
Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation
Image Posterization Using Fuzzy Logic and Bilateral Filter
End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning
Object Detection and Sorting by Using a Global Texture-Shape 3D Feature Descriptor
Efficient Video Object Segmentation via Network Modulation
Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques
Face Destylization
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces
Zero-Shot Kernel Learning
Task-Aware Compressed Sensing with Generative Adversarial Networks
Adversarial Vulnerability of Neural Networks Increases With Input Dimension
A Method for Restoring the Training Set Distribution in an Image Classifier
Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional Neural Networks
Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
Real-time Prediction of Intermediate-Horizon Automotive Collision Risk
Scale-recurrent Network for Deep Image Deblurring
Geometry-Contrastive Generative Adversarial Network for Facial Expression Synthesis
Learning Image Representations by Completing Damaged Jigsaw Puzzles
The steerable graph Laplacian and its application to filtering image data-sets
Attribute-Guided Network for Cross-Modal Zero-Shot Hashing
Orthogonally Regularized Deep Networks For Image Super-resolution
On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction
DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision
Smile detection in the wild based on transfer learning
A High-Performance HOG Extractor on FPGA
Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation
Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network
IONet: Learning to Cure the Curse of Drift in Inertial Odometry
Describing Semantic Representations of Brain Activity Evoked by Visual Stimuli
The Heart of an Image: Quantum Superposition and Entanglement in Visual Perception
Scalable Meta-Learning for Bayesian Optimization
Generative Adversarial Networks using Adaptive Convolution
Feature Based Framework to Detect Diseases, Tumor, and Bleeding in Wireless Capsule Endoscopy
A machine learning approach to reconstruction of heart surface potentials from body surface potentials
Spectral Image Visualization Using Generative Adversarial Networks
Outlier Detection for Robust Multi-dimensional Scaling
SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images
Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors
On the Generalizability of Linear and Non-Linear Region of Interest-Based Multivariate Regression Models for fMRI Data
Stochastic Deconvolutional Neural Network Ensemble Training on Generative Pseudo-Adversarial Networks
Learning One Convolutional Layer with Overlapping Patches
Bitewing Radiography Semantic Segmentation Base on Conditional Generative Adversarial Nets
Spatially adaptive image compression using a tiled deep network
SCK: A sparse coding based key-point detector
Learning to score the figure skating sports videos
Peekaboo - Where are the Objects? Structure Adjusting Superpixels
Archetypal Analysis for Sparse Representation-based Hyperspectral Sub-pixel Quantification
From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting
Practical Issues of Action-conditioned Next Image Prediction
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
Deep Private-Feature Extraction
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly
Unsupervised Deep Domain Adaptation for Pedestrian Detection
Slice Sampling Particle Belief Propagation
Temporally Object-based Video Co-Segmentation
Shapes Characterization on Address Event Representation Using Histograms of Oriented Events and an Extended LBP Approach
A Two-Stage Method for Text Line Detection in Historical Documents
Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification
Local Contrast Learning
Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification
Coverless information hiding based on Generative Model
Collaborative Learning for Weakly Supervised Object Detection
Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction
2-gram-based Phonetic Feature Generation for Convolutional Neural Network in Assessment of Trademark Similarity
Supervised classification of Dermatological diseases by Deep neural networks
FlipDial: A Generative Model for Two-Way Visual Dialogue
Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms
Object Detection with Mask-based Feature Encoding
Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline
Subspace Support Vector Data Description
Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms
Image Retargetability
Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections
Automatic localization and decoding of honeybee markers using deep convolutional neural networks
Barista - a Graphical Tool for Designing and Training Deep Neural Networks
Single-Perspective Warps in Natural Image Stitching
BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks
Semantic Scene Completion Combining Colour and Depth: preliminary experiments
Learning via social awareness: improving sketch representations with facial feedback
Paraphrasing Complex Network: Network Compression via Factor Transfer
M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance
Recursive Chaining of Reversible Image-to-image Translators For Face Aging
The Multiscale Bowler-Hat Transform for Vessel Enhancement in 3D Biomedical Images
Two Is Harder To Recognize Than Tom: the Challenge of Visual Numerosity for Deep Learning
Learning Privacy Preserving Encodings through Adversarial Training
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests
Deep Learning for Lip Reading using Audio-Visual Information for Urdu Language
Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models
cGANs with Projection Discriminator
A Bio-inspired Redundant Sensing Architecture
Inverting The Generator Of A Generative Adversarial Network (II)
IBeaconMap: Automated Indoor Space Representation for Beacon-Based Wayfinding
Joint Estimation of Room Geometry and Modes with Compressed Sensing
A complete hand-drawn sketch vectorization framework
Real-Time 3D Shape of Micro-Details
Visual-Only Recognition of Normal, Whispered and Silent Speech
Fast 5DOF Needle Tracking in iOCT
A Closed-form Solution to Photorealistic Image Stylization
Image Forensics: Detecting duplication of scientific images with manipulation-invariant image similarity
Weighted Linear Discriminant Analysis based on Class Saliency Information
Deep Residual Network for Joint Demosaicing and Super-Resolution
Simultaneous Compression and Quantization: A Joint Approach for Efficient Unsupervised Hashing
Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition
Learning Representative Temporal Features for Action Recognition
Divide, Denoise, and Defend against Adversarial Attacks
Online Action Detection in Untrimmed, Streaming Videos
Global Pose Estimation with an Attention-based Recurrent Network
Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach
Learning to Abstain via Curve Optimization
Latent RANSAC
Novel View Synthesis for Large-scale Scene using Adversarial Loss
Composite Optimization by Nonconvex Majorization-Minimization
Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
i-RevNet: Deep Invertible Networks
Camera-based vehicle velocity estimation from monocular video
Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices
Devon: Deformable Volume Network for Learning Optical Flow
Density-aware Single Image De-raining using a Multi-stream Dense Network
Angle constrained path to cluster multiple manifolds
Conditional Adversarial Synthesis of 3D Facial Action Units
Binary Constrained Deep Hashing Network for Image Retrieval without Human Intervention
Learning to Play with Intrinsically-Motivated Self-Aware Agents
Load Balanced GANs for Multi-view Face Image Synthesis
Spatial Morphing Kernel Regression For Feature Interpolation
Learning Image Conditioned Label Space for Multilabel Classification
Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells
Batch Normalization and the impact of batch structure on the behavior of deep convolution networks
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Building Efficient ConvNets using Redundant Feature Pruning
Learning Multiple Categories on Deep Convolution Networks
Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background
Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network
Lossless Image Compression Algorithm for Wireless Capsule Endoscopy by Content-Based Classification of Image Blocks
Reversible Image Watermarking for Health Informatics Systems Using Distortion Compensation in Wavelet Domain
Segmentation of Bleeding Regions in Wireless Capsule Endoscopy Images an Approach for inside Capsule Video Summarization
Semantic Segmentation Refinement by Monte Carlo Region Growing of High Confidence Detections
Liver segmentation in CT images using three dimensional to two dimensional fully convolutional network
Low complexity convolutional neural network for vessel segmentation in portable retinal diagnostic devices
Detecting Small, Densely Distributed Objects with Filter-Amplifier Networks and Loss Boosting
Multi-Sensor Integration for Indoor 3D Reconstruction
Where's YOUR focus: Personalized Attention
Adversarial Learning for Semi-Supervised Semantic Segmentation
Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation
Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise
MagnifyMe: Aiding Cross Resolution Face Recognition via Identity Aware Synthesis
Discriminative Label Consistent Domain Adaptation
Harmonious Attention Network for Person Re-Identification
Adaptive specular reflection detection and inpainting in colonoscopy video frames
6D Pose Estimation using an Improved Method based on Point Pair Features
An Approach to Vehicle Trajectory Prediction Using Automatically Generated Traffic Maps
Interactive Image Manipulation with Natural Language Instruction Commands
Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation
Deep learning in radiology: an overview of the concepts and a survey of the state of the art
Spatially Constrained Location Prior for Scene Parsing
Constrained Image Generation Using Binarized Neural Networks with Decision Procedures
Free-breathing cardiac MRI using bandlimited manifold modelling
A Dataset To Evaluate The Representations Learned By Video Prediction Models
Building Instance Classification Using Street View Images
Seeing Small Faces from Robust Anchor's Perspective
Attention-Aware Generative Adversarial Networks (ATA-GANs)
PBGen: Partial Binarization of Deconvolution-Based Generators for Edge Intelligence
Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network
Depth Masked Discriminative Correlation Filter
2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition
DropLasso: A robust variant of Lasso for single cell RNA-seq data
Using Curvilinear Features in Focus for Registering a Single Image to a 3D Object
A Resilient Image Matching Method with an Affine Invariant Feature Detector and Descriptor
How (Not) To Train Your Neural Network Using the Information Bottleneck Principle
Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition
Adversarial Active Learning for Deep Networks: a Margin Based Approach
Real-World Repetition Estimation by Div, Grad and Curl
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Neural Stereoscopic Image Style Transfer
Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
Tell Me Where to Look: Guided Attention Inference Network
Networking the Boids is More Robust Against Adversarial Learning
Neural Aesthetic Image Reviewer
Joint Event Detection and Description in Continuous Video Streams
A Model for Medical Diagnosis Based on Plantar Pressure
Learning to Adapt Structured Output Space for Semantic Segmentation
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image
Fine-grained wound tissue analysis using deep neural network
A Simple Method to improve Initialization Robustness for Active Contours driven by Local Region Fitting Energy
Novelty Detection with GAN
Stereoscopic Neural Style Transfer
A Feature Clustering Approach Based on Histogram of Oriented Optical Flow and Superpixels
Invariant properties of a locally salient dither pattern with a spatial-chromatic histogram
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Ring loss: Convex Feature Normalization for Face Recognition
A Class-Incremental Learning Method Based on One Class Support Vector Machine
DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images
Five-point Fundamental Matrix Estimation for Uncalibrated Cameras
Detecting Volcano Deformation in InSAR using Deep learning
Fibres of Failure: Classifying errors in predictive processes
MAGAN: Aligning Biological Manifolds
A General Pipeline for 3D Detection of Vehicles
Image Dataset for Visual Objects Classification in 3D Printing
Left ventricle segmentation By modelling uncertainty in prediction of deep convolutional neural networks and adaptive thresholding inference
An Intelligent Intersection
Natural data structure extracted from neighborhood-similarity graphs
The 2018 DAVIS Challenge on Video Object Segmentation
Semi-parametric Topological Memory for Navigation
Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders
Aspl{ü}nd's metric defined in the Logarithmic Image Processing (LIP) framework for colour and multivariate images
Deep Unsupervised Intrinsic Image Decomposition by Siamese Training
Quantum distance-based classifier with constant size memory, distributed knowledge and state recycling
Multi-Instance Dynamic Ordinal Random Fields for Weakly-supervised Facial Behavior Analysis
Tree Species Identification from Bark Images Using Convolutional Neural Networks
Multimodal Registration of Retinal Images Using Domain-Specific Landmarks and Vessel Enhancement
Hashing with Mutual Information
High-Dynamic-Range Imaging for Cloud Segmentation
Enhancement of land-use change modeling using convolutional neural networks and convolutional denoising autoencoders
Real-Time Deep Learning Method for Abandoned Luggage Detection in Video
Deep Bayesian Active Semi-Supervised Learning
Unsupervised Learning of Face Representations
Training Deep Learning based Denoisers without Ground Truth Data
Egocentric Basketball Motion Planning from a Single First-Person Image
Deep Continuous Clustering
LSTD: A Low-Shot Transfer Detector for Object Detection
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Relocalization, Global Optimization and Map Merging for Monocular Visual-Inertial SLAM
Beyond Context: Exploring Semantic Similarity for Tiny Face Detection
Local Distance Metric Learning for Nearest Neighbor Algorithm
Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation
Spectral reflectance estimation from one RGB image using self-interreflections in a concave object
Using Visual Saliency to Improve Human Detection with Convolutional Networks
Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis
A generalized parametric 3D shape representation for articulated pose estimation
ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing
M3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal} satellite data fusion
Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks
Occupancy Map Prediction Using Generative and Fully Convolutional Networks for Vehicle Navigation
The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera
Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising
A Non-Technical Survey on Deep Convolutional Neural Network Architectures
Fully Convolutional Grasp Detection Network with Oriented Anchor Box
Depth Information Guided Crowd Counting for Complex Crowd Scenes
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content
Personalized Attention-Aware Exposure Control Using Reinforcement Learning
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
Learning monocular visual odometry with dense 3D mapping from dense 3D flow
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification
Comparison of various image fusion methods for impervious surface classification from VNREDSat-1
Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry
PI-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and Lines
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification
Pyramid Person Matching Network for Person Re-identification
Object cosegmentation using deep Siamese network
Multi-Channel Pyramid Person Matching Network for Person Re-Identification
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
3D Human Pose Estimation in RGBD Images for Robotic Task Learning
CNN-Based Automatic Urinary Particles Recognition
Deep Back-Projection Networks For Super-Resolution
HENet:A Highly Efficient Convolutional Neural Networks Optimized for Accuracy, Speed and Storage
RTSeg: Real-time Semantic Segmentation Comparative Study
A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images
Rethinking Feature Distribution for Loss Functions in Image Classification
Robustness of control point configurations for homography and planar pose estimation
Preserving Semantic Relations for Zero-Shot Learning
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation
Generalization in Metric Learning: Should the Embedding Layer be the Embedding Layer?
Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer
Deep Semantic Face Deblurring
Learning a Discriminative Prior for Blind Image Deblurring
Fusing Hierarchical Convolutional Features for Human Body Segmentation and Clothing Fashion Classification
Robust Landmark Detection for Alignment of Mouse Brain Section Images
Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble
Construction of neural networks for realization of localized deep learning
Breast Tumor Classification Based on Decision Information Genes and Inverse Projection Sparse Representation
Local Kernels that Approximate Bayesian Regularization and Proximal Operators
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation
ShuffleSeg: Real-time Semantic Segmentation Network
Fire detection in a still image using colour information
Sample-Relaxed Two-Dimensional Color Principal Component Analysis for Face Recognition and Image Reconstruction
Learning to Localize Sound Source in Visual Scenes
Knowledge Aided Consistency for Weakly Supervised Phrase Grounding
Deeply supervised neural network with short connections for retinal vessel segmentation
Deep Dictionary Learning: A PARametric NETwork Approach
Cascade context encoder for improved inpainting
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
Learning Local Distortion Visibility From Image Quality Data-sets
Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification
Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation
SO-Net: Self-Organizing Network for Point Cloud Analysis
Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network
Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings
Dissimilarity-based representation for radiomics applications
Correction by Projection: Denoising Images with Generative Adversarial Networks
Learning to Maintain Natural Image Statistics
Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling
Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features
Testing Deep Neural Networks
Low Rank Variation Dictionary and Inverse Projection Group Sparse Representation Model for Breast Tumor Classification
A Learning-Based Visual Saliency Fusion Model for High Dynamic Range Video (LBVS-HDR)
3D Video Quality Assessment
LCANet: End-to-End Lipreading with Cascaded Attention-CTC
A Multi-Modal Approach to Infer Image Affect
Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects
EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching
LivDet 2017 Fingerprint Liveness Detection Competition 2017
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks
Face-MagNet: Magnifying Feature Maps to Detect Small Faces
Knowledge-based Recurrent Attentive Neural Network for Traffic Sign Detection
Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
Image Colorization with Generative Adversarial Networks
Approximate Query Matching for Image Retrieval
Improving Object Counting with Heatmap Regulation
Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild
Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction
Object Detection in Video with Spatiotemporal Sampling Networks
Facelet-Bank for Fast Portrait Manipulation
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation
VEGAC: Visual Saliency-based Age, Gender, and Facial Expression Classification Using Convolutional Neural Networks
What Catches the Eye? Visualizing and Understanding Deep Saliency Models
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
Local Spectral Graph Convolution for Point Set Feature Learning
Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multimodal MR Image Using Convolutional Neural Network
Pseudo Mask Augmented Object Detection
Learned Iterative Decoding for Lossy Image Compression Systems
Deep Structure Inference Network for Facial Action Unit Recognition
Studying Invariances of Trained Convolutional Neural Networks
Deep Co-Training for Semi-Supervised Image Recognition
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
Deep Multiple Instance Learning for Zero-shot Image Tagging
Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Images
A dataset and architecture for visual reasoning with a working memory
A constant-ratio approximation algorithm for a class of hub-and-spoke network design problems and metric labeling problems: star metric case
Patchwise object tracking via structural local sparse appearance model
Triplet-Center Loss for Multi-View 3D Object Retrieval
Synchronisation of Partial Multi-Matchings via Non-negative Factorisations
Learning deep structured active contours end-to-end
Faces as Lighting Probes via Unsupervised Deep Highlight Extraction
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing
Deep Component Analysis via Alternating Direction Neural Networks
Learning to Segment via Cut-and-Paste
Learning to Cluster for Proposal-Free Instance Segmentation
Weakly Supervised Salient Object Detection Using Image Labels
Learning Unsupervised Visual Grounding Through Semantic Self-Supervision
MergeNet: A Deep Net Architecture for Small Obstacle Discovery
SeqFace: Make full use of sequence information for face recognition
Convolutional Point-set Representation: A Convolutional Bridge Between a Densely Annotated Image and 3D Face Alignment
A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents
Efficient and accurate inversion of multiple scattering with deep learning
Line Artist: A Multiple Style Sketch to Painting Synthesis Scheme
Ratio-Preserving Half-Cylindrical Warps for Natural Image Stitching
Sdf-GAN: Semi-supervised Depth Fusion with Multi-scale Adversarial Networks
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
Attention-GAN for Object Transfiguration in Wild Images
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Alive Caricature from 2D to 3D
Weakly Supervised Object Localization on grocery shelves using simple FCN and Synthetic Dataset
A Mixture of Views Network with Applications to the Classification of Breast Microcalcifications
Asymmetric kernel in Gaussian Processes for learning target variance
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition
Zero-Shot Detection
Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition
Unveiling the invisible - mathematical methods for restoring and interpreting illuminated manuscripts
Adaptive Polar Active Contour for Segmentation and Tracking in Ultrasound Videos
DYAN: A Dynamical Atoms Network for Video Prediction
A Temporally-Aware Interpolation Network for Video Frame Inpainting
Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization
Text Detection and Recognition in images: A survey
Flex-Convolution (Deep Learning Beyond Grid-Worlds)
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
Adaptive Co-weighting Deep Convolutional Features For Object Retrieval
Are you eligible? Predicting adulthood from face images via class specific mean autoencoder
Patch-Based Image Inpainting with Generative Adversarial Networks
An Improved Evaluation Framework for Generative Adversarial Networks
Actor and Action Video Segmentation from a Sentence
Learning Category-Specific Mesh Reconstruction from Image Collections
Thermal to Visible Synthesis of Face Images using Multiple Regions
Dynamic Sampling Convolutional Neural Networks
Robust Depth Estimation from Auto Bracketed Images
Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions
Patch-based Fake Fingerprint Detection Using a Fully Convolutional Neural Network with a Small Number of Parameters and an Optimal Threshold
End-to-End Fingerprints Liveness Detection using Convolutional Networks with Gram module
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
A Cascaded Convolutional Neural Network for Single Image Dehazing
Adversarial Defense based on Structure-to-Signal Autoencoders
Video Object Segmentation with Language Referring Expressions
A Unified Framework for Multi-View Multi-Class Object Pose Estimation
Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data
Dichromatic Gray Pixel for Camera-agnostic Color Constancy
Found a good match: should I keep searching? - Accuracy and Performance in Iris Matching Using 1-to-First Search
Densely Connected Pyramid Dehazing Network
PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction
A Smoke Removal Method for Laparoscopic Images
Group Sparsity Residual with Non-Local Samples for Image Denoising
Clustering-driven Deep Embedding with Pairwise Constraints
Towards Universal Representation for Unseen Action Recognition
Branched Generative Adversarial Networks for Multi-Scale Image Manifold Learning
KonIQ-10k: Towards an ecologically valid and large-scale IQA database
Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings
Generalized Scene Reconstruction
Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligence
Fictitious GAN: Training GANs with Historical Models
Pyramid Stereo Matching Network
Object Detection for Comics using Manga109 Annotations
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries
Region-filtering Correlation Tracking
An Incremental Boolean Tensor Factorization approach to model Change Patterns of Objects in Images
Pose-Driven Deep Models for Person Re-Identification
A Deep Error Correction Network for Compressed Sensing MRI
Learning Deep Context-Network Architectures for Image Annotation
Geometric and Physical Constraints for Head Plane Crowd Density Estimation in Videos
Audio-Visual Event Localization in Unconstrained Videos
Learning Shape-from-Shading for Deformable Surfaces
Pattern Analysis with Layered Self-Organizing Maps
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
Realtime Time Synchronized Event-based Stereo
A Single-shot Camera-Projector Calibration System For Imperfect Planar Targets
Comparing Generative Adversarial Network Techniques for Image Creation and Modification
Multi-Level Factorisation Net for Person Re-Identification
Importance Weighted Adversarial Nets for Partial Domain Adaptation
Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture
P2P-NET: Bidirectional Point Displacement Net for Shape Transform
Deep Depth Completion of a Single RGB-D Image
Deep Faster Detection of Faint Edges in Noisy Images
REST: Real-to-Synthetic Transform for Illumination Invariant Camera Localization
Image Set Classification for Low Resolution Surveillance
On Regularized Losses for Weakly-supervised CNN Segmentation
One-Shot Segmentation in Clutter
Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
BAGAN: Data Augmentation with Balancing GAN
A multilayer backpropagation saliency detection algorithm and its applications
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification
Attributes as Operators
WebSeg: Learning Semantic Segmentation from Web Searches
Three Birds One Stone: A Unified Framework for Salient Object Segmentation, Edge Detection and Skeleton Extraction
A Divide-and-Conquer Approach to Compressed Sensing MRI
Image Semantic Transformation: Faster, Lighter and Stronger
Image-based deep learning for classification of noise transients in gravitational wave detectors
Recent Developments from Attribute Profiles for Remote Sensing Image Classification
A Framework for Evaluating 6-DOF Object Trackers
Efficient parametrization of multi-domain deep neural networks
Point Convolutional Neural Networks by Extension Operators
Event-based Dynamic Face Detection and Tracking Based on Activity
Adaptive Affinity Field for Semantic Segmentation
Structural inpainting
ClickBAIT-v2: Training an Object Detector in Real-Time
Referring Relationships
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
Automatic Stroke Lesions Segmentation in Diffusion-Weighted MRI
3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation
The HAM10000 Dataset: A Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions
The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples
Objects Localisation from Motion with Constraints
ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes
Stochastic Variational Inference with Gradient Linearization
Person re-identification with fusion of hand-crafted and deep pose-based body region features
Motion Guided LIDAR-camera Autocalibration and Accelerated Depth Super Resolution
Pose2Seg: Human Instance Segmentation Without Detection
End-to-End Multi-Task Learning with Attention
Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy
Deep Photometric Stereo on a Sunny Day
Features for Multi-Target Multi-Camera Tracking and Re-Identification
Memory Warps for Learning Long-Term Online Video Representations
Learning to Look around Objects for Top-View Representations of Outdoor Scenes
Simplifying transforms for general elastic metrics on the space of plane curves
Motion-Appearance Co-Memory Networks for Video Question Answering
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation
Mining on Manifolds: Metric Learning without Labels
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
Bag of Recurrence Patterns Representation for Time-Series Classification
MaskRNN: Instance Level Video Object Segmentation
Generative Modeling using the Sliced Wasserstein Distance
The Price is Right: Predicting Prices with Product Images
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
Task-Driven Super Resolution: Object Detection in Low-resolution Images
Transductive Unbiased Embedding for Zero-Shot Learning
DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer
Joint Optimization Framework for Learning with Noisy Labels
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition
Cross-modal Deep Variational Hand Pose Estimation
Scalable Deep Learning Logo Detection
Reconstruction Network for Video Captioning
Regularizing RNNs for Caption Generation by Reconstructing The Past with The Present
Predicting Future Instance Segmentations by Forecasting Convolutional Features
Hierarchical Transfer Convolutional Neural Networks for Image Classification
Adversarial Attacks and Defences Competition
Tagging like Humans: Diverse and Distinct Image Annotation
DeepIM: Deep Iterative Matching for 6D Pose Estimation
Webly Supervised Learning for Skin Lesion Classification
Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks
One-Two-One Networks for Compression Artifacts Reduction in Remote Sensing
Real-time Progressive 3D Semantic Segmentation for Indoor Scene
EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery
Differential Attention for Visual Question Answering
Attention-based Ensemble for Deep Metric Learning
Bridging the Gap Between 2D and 3D Organ Segmentation
SyncGAN: Synchronize the Latent Space of Cross-modal Generative Adversarial Networks
A Vehicle Detection Approach using Deep Learning Methodologies
Exploring to learn visual saliency: The RL-IAC approach
Regional Priority Based Anomaly Detection using Autoencoders
A Review on Image Texture Analysis Methods
Transferable Pedestrian Motion Prediction Models at Intersections
Semantic Adversarial Examples
Multilayer Complex Network Descriptors for Color-Texture Characterization
Generalizability vs. Robustness: Adversarial Examples for Medical Imaging
CompNet: Complementary Segmentation Network for Brain MRI Extraction
Predictions of short-term driving intention using recurrent neural network on sequential data
Learning Intrinsic Image Decomposition from Watching the World
Learning Descriptor Networks for 3D Shape Synthesis and Analysis
Updating the generator in PPGN-h with gradients flowing through the encoder
3D Registration of Curves and Surfaces using Local Differential Information
DeepMVS: Learning Multi-view Stereopsis
Interactive Hand Pose Estimation: Boosting accuracy in localizing extended finger joints
Confidence from Invariance to Image Transformations
Hierarchical Novelty Detection for Visual Object Recognition
Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering
Left-Right Comparative Recurrent Model for Stereo Matching
Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning
Deep Appearance Maps
PhaseNet for Video Frame Interpolation
Learning to Guide Decoding for Image Captioning
When will you do what? - Anticipating Temporal Occurrences of Activities
Towards whole-body CT Bone Segmentation
Unsupervised Learning of Sequence Representations by Autoencoders
Training VAEs Under Structured Residuals
Transferring Common-Sense Knowledge for Object Detection
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior
Normalized Cut Loss for Weakly-supervised CNN Segmentation
Patch-based Face Recognition using a Hierarchical Multi-label Matcher
Unsupervised Semantic-based Aggregation of Deep Convolutional Features
Representing Videos based on Scene Layouts for Recognizing Agent-in-Place Actions
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
Density Adaptive Point Set Registration
Stochastic Adversarial Video Prediction
Semi-Supervised Deep Metrics for Image Registration
StainGAN: Stain Style Transfer for Digital Histological Images
Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Learning Strict Identity Mappings in Deep Residual Networks
Learning to Separate Object Sounds by Watching Unlabeled Video
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space
Guess Where? Actor-Supervision for Spatiotemporal Action Localization
Multi-level Activation for Segmentation of Hierarchically-nested Classes
High-dimension Tensor Completion via Gradient-based Optimization Under Tensor-train Format
Regularizing Deep Networks by Modeling and Predicting Label Structure
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
Noise-resistant Deep Learning for Object Classification in 3D Point Clouds Using a Point Pair Descriptor
Question Type Guided Attention in Visual Question Answering
Motion Segmentation by Exploiting Complementary Geometric Models
OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions
Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Auto-Encoders
Mix and match networks: encoder-decoder alignment for zero-pair image translation
Ensemble Manifold Segmentation for Model Distillation and Semi-supervised Learning
Automatic Prediction of Building Age from Photographs
Cross-Domain Image Matching with Deep Feature Maps
Extracting Scientific Figures with Distantly Supervised Neural Networks
MVSNet: Depth Inference for Unstructured Multi-view Stereo
Learning a Text-Video Embedding from Incomplete and Heterogeneous Data
Statistical transformer networks: learning shape and appearance models via self supervision
Efficient No-Reference Quality Assessment and Classification Model for Contrast Distorted Images
Estimation of Camera Locations in Highly Corrupted Scenarios: All About that Base, No Shape Trouble
Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound
Dimensionality's Blessing: Clustering Images by Underlying Distribution
OATM: Occlusion Aware Template Matching by Consensus Set Maximization
Learning-based Video Motion Magnification
Detecting Multi-Oriented Text with Corner-based Region Proposals
Estimating Depth from RGB and Sparse Sensing
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
Photometric Stereo in Participating Media Considering Shape-Dependent Forward Scatter
Semantic Edge Detection with Diverse Deep Supervision
A Fully Progressive Approach to Single-Image Super-Resolution
Bringing Alive Blurred Moments!
Generative Adversarial Networks for Extreme Learned Image Compression
Vision as an Interlingua: Learning Multilingual Semantic Embeddings of Untranscribed Speech
Attribute-Centered Loss for Soft-Biometrics Guided Face Sketch-Photo Recognition
$\mathcal{G}$-Distillation: Reducing Overconfident Errors on Novel Samples
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
Towards Deep Cellular Phenotyping in Placental Histology
Recurrent Neural Networks for Person Re-identification Revisited
Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning
Modular Generative Adversarial Networks
Graphical Generative Adversarial Networks
RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces
Exploring Disentangled Feature Representation Beyond Face Identification
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction
Audio-Visual Scene Analysis with Self-Supervised Multisensory Features
Graph Matching with Anchor Nodes: A Learning Approach
Nonlinear 3D Face Morphable Model
Multi-Scale Generalized Plane Match for Optical Flow
Demoiréing of Camera-Captured Screen Images Using Deep Convolutional Neural Network
ExFuse: Enhancing Feature Fusion for Semantic Segmentation
Attention Cropping: A Novel Data Augmentation Method for Real-world Plant Species Identification
Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning
Fusing Saliency Maps with Region Proposals for Unsupervised Object Localization
Offline Object Extraction from Dynamic Occupancy Grid Map Sequences
VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning
Projection image-to-image translation in hybrid X-ray/MR imaging
Edge-based LBP description of surfaces with colorimetric patterns
QuadricSLAM: Constrained Dual Quadrics from Object Detections as Landmarks in Semantic SLAM
Seed-Point Detection of Clumped Convex Objects by Short-Range Attractive Long-Range Repulsive Particle Clustering
Detail-Preserving Pooling in Deep Networks
Ranking Generative Adversarial Networks: Subjective Control over Semantic Image Attributes
Beamformed Fingerprint Learning for Accurate Millimeter Wave Positioning
The Conversation: Deep Audio-Visual Speech Enhancement
Text2Colors: Guiding Image Colorization through Text-Driven Palette Generation
Multi-scale Neural Networks for Retinal Blood Vessels Segmentation
View Extrapolation of Human Body from a Single Image
Clustering via Boundary Erosion
STAIR Actions: A Video Dataset of Everyday Home Actions
Zero-Shot Object Detection
Image Correction via Deep Reciprocating HDR Transformation
Iterative fully convolutional neural networks for automatic vertebra segmentation
Multi-Label Wireless Interference Identification with Convolutional Neural Networks
Unsupervised Discovery of Object Landmarks as Structural Representations
Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning
CubeNet: Equivariance to 3D Rotation and Translation
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
DLL: A Blazing Fast Deep Neural Network Library
Generative Visual Rationales
Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking
Towards integrating spatial localization in convolutional neural networks for brain image segmentation
Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR
Personalized Classifier for Food Image Recognition
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning
A Variational U-Net for Conditional Appearance and Shape Generation
A Hybrid Model for Identity Obfuscation by Face Replacement
Deep Motion Boundary Detection
Precise Temporal Action Localization by Evolving Temporal Proposals
Learning Deep Sketch Abstraction
MSnet: Mutual Suppression Network for Disentangled Video Representations
Spline Error Weighting for Robust Visual-Inertial Fusion
BodyNet: Volumetric Inference of 3D Human Body Shapes
Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation
Pose estimation of a single circle using default intrinsic calibration
A New Vision of the Coma Cluster: Conference Summary
Survey on Various Gesture Recognition Techniques for Interfacing Machines Based on Ambient Intelligence
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
Prototypical Priors: From Improving Classification to Zero-Shot Learning
CAD2RL: Real Single-Image Flight without a Single Real Image
Dense Associative Memory is Robust to Adversarial Inputs
Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning
Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios
Pushing the envelope in deep visual recognition for mobile platforms
Visual Concepts and Compositional Voting
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation
Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts
Hypothesize and Bound: A Computational Focus of Attention Mechanism for Simultaneous 3D Shape Reconstruction, Pose Estimation and Classification from a Single 2D Image
Scalable Visibility Color Map Construction in Spatial Databases
Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Distributed-memory large deformation diffeomorphic 3D image registration
GPU Acclerated Automated Feature Extraction from Satellite Images
SENNS: Sparse Extraction Neural NetworkS for Feature Extraction
Proposal for the creation of a research facility for the development of the SP machine
Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
Remote sensing of forests using discrete return airborne LiDAR
FOCAN: A Fog-supported Smart City Network Architecture for Management of Applications in the Internet of Everything Environments
On-Chip Communication Network for Efficient Training of Deep Convolutional Networks on Heterogeneous Manycore Systems
Trade-off between angular resolution and straylight contamination in CMB anisotropy experiments. I. Pattern simulations
Robust Combining of Disparate Classifiers through Order Statistics
Image Compression with Iterated Function Systems, Finite Automata and Zerotrees: Grand Unification
Contextual Normalization Applied to Aircraft Gas Turbine Engine Diagnosis
Fast Verification of Convexity of Piecewise-linear Surfaces
Swarming around Shellfish Larvae
Artificial Ant Colonies in Digital Image Habitats - A Mass Behaviour Effect Study on Pattern Recognition
Image Colour Segmentation by Genetic Algorithms
Face Recognition Based on Polar Frequency Features
A Better Alternative to Piecewise Linear Time Series Segmentation
Self-Replication and Self-Assembly for Manufacturing
Life Under Your Feet: An End-to-End Soil Ecology Sensor Network, Database, Web Server, and Analysis Service
Classical and Quantum Causality in Quantum Field Theory. Or, "The Quantum Universe"
The entropy of keys derived from laser speckle
Classification of curves in 2D and 3D via affine integral signatures
Automatic Generation of the Axial Lines of Urban Environments to Capture What We Perceive
A Novel Clustering Algorithm Based Upon Games on Evolving Network
A Theoretical Analysis of Joint Manifolds
A new approach for digit recognition based on hand gesture analysis
Non-quadratic convex regularized reconstruction of MR images from spiral acquisitions
Maximin affinity learning of image segmentation
Behavior and performance of the deep belief networks on image classification
A Novel Feature Extraction for Robust EMG Pattern Recognition
An Explicit Nonlinear Mapping for Manifold Learning
Message-Passing Algorithms: Reparameterizations and Splittings
Handwritten Bangla Basic and Compound character recognition using MLP and SVM classifier
Securing Interactive Sessions Using Mobile Device through Visual Channel and Visual Inspection
Nonlinear Filter Based Image Denoising Using AMF Approach
Object-image correspondence for curves under finite and affine cameras
Regularized Richardson-Lucy Algorithm for Sparse Reconstruction of Poissonian Images
A New Approach to Lung Image Segmentation using Fuzzy Possibilistic C-Means Algorithm
Scalable Tensor Factorizations for Incomplete Data
Classification of LULC Change Detection using Remotely Sensed Data for Coimbatore City, Tamilnadu, India
Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance
Proliferating cell nuclear antigen (PCNA) allows the automatic identification of follicles in microscopic images of human ovarian tissue
Comparative Study of Statistical Skin Detection Algorithms for Sub-Continental Human Images
Penalty Decomposition Methods for $L0$-Norm Minimization
Weighted Attribute Fusion Model for Face Recognition
Deep Self-Taught Learning for Handwritten Character Recognition
Statistical mechanics of digital halftoning
Characterizing Structure Through Shape Matching and Applications to Self Assembly
Statistical Compressed Sensing of Gaussian Mixture Models
A novel super resolution reconstruction of low reoslution images progressively using dct and zonal filter based denoising
On Democracy in Peer-to-Peer systems
Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition
High Performance Human Face Recognition using Independent High Intensity Gabor Wavelet Responses: A Statistical Approach
Arithmetic and Frequency Filtering Methods of Pixel-Based Image Fusion Techniques
A Distributed Mincut/Maxflow Algorithm Combining Path Augmentation and Push-Relabel
On Partial Opimality by Auxiliary Submodular Problems
Progressive versus Random Projections for Compressive Capture of Images, Lightfields and Higher Dimensional Visual Signals
A robust, low-cost approach to Face Detection and Face Recognition
Iris Recognition Based on LBP and Combined LVQ Classifier
Multidimensional counting grids: Inferring word order from disordered bags of words
A Co-Prime Blur Scheme for Data Security in Video Surveillance
Feature Extraction Methods for Color Image Similarity
Discrimination of English to other Indian languages (Kannada and Hindi) for OCR system
Mesh Learning for Classifying Cognitive Processes
Spatial And Spectral Quality Evaluation Based On Edges Regions Of Satellite Image Fusion
Automated Training and Maintenance through Kinect
A Novel Metric Approach Evaluation For The Spatial Enhancement Of Pan-Sharpened Images
An Online Character Recognition System to Convert Grantha Script to Malayalam
Visual Exploration of Simulated and Measured Blood Flow
FCM Based Blood Vessel Segmentation Method for Retinal Images
Hessian Schatten-Norm Regularization for Linear Inverse Problems
The Biometric Menagerie - A Fuzzy and Inconsistent Concept
Efficient Solution to the 3D Problem of Automatic Wall Paintings Reassembly
Sampling and Reconstruction of Spatial Fields using Mobile Sensors
Segmentation of ultrasound images of thyroid nodule for assisting fine needle aspiration cytology
New Edge Detection Technique based on the Shannon Entropy in Gray Level Images
G-invariant Persistent Homology
Kernel Estimation from Salient Structure for Robust Motion Deblurring
Similarity of Polygonal Curves in the Presence of Outliers
Pituitary Adenoma Volumetry with 3D Slicer
Discrete moving frames and discrete integrable systems
High-precision camera distortion measurements with a "calibration harp"
Multiscale Discriminant Saliency for Visual Attention
Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters
Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling
Local Structure Matching Driven by Joint-Saliency-Structure Adaptive Kernel Regression
An Optical Watermarking Solution for Color Personal Identification Pictures
Fast Matching by 2 Lines of Code for Large Scale Face Recognition Systems
On the convergence of the IRLS algorithm in Non-Local Patch Regression
Optical Flow Sensing and the Inverse Perception Problem for Flying Bats
Shape Reconstruction and Recognition with Isolated Non-directional Cues
Multi-q Pattern Classification of Polarization Curves
Geometric primitive feature extraction - concepts, algorithms, and applications
Indexing Medical Images based on Collaborative Experts Reports
Distributed Bayesian inference for consistent labeling of tracked objects in non-overlapping camera networks
Highly Scalable, Parallel and Distributed AdaBoost Algorithm using Light Weight Threads and Web Services on a Network of Multi-Core Machines
Comparing Edge Detection Methods based on Stochastic Entropies and Distances for PolSAR Imagery
Symmetries in LDDMM with higher order momentum distributions
Free Instrument for Movement Measure
Mammogram Edge Detection Using Hybrid Soft Computing Methods
Regularized Discrete Optimal Transport
Automatic Mammogram image Breast Region Extraction and Removal of Pectoral Muscle
Minutiae Based Thermal Face Recognition using Blood Perfusion Data
Saying What You're Looking For: Linguistics Meets Video Search
An Image-Based Fluid Surface Pattern Model
PCG-Cut: Graph Driven Segmentation of the Prostate Central Gland
Minimax rates in permutation estimation for feature matching
Quality Assessment of Pixel-Level ImageFusion Using Fuzzy Logic
Performing edge detection by difference of Gaussians using q-Gaussian kernels
Template-Based Active Contours
An adaptive block based integrated LDP,GLCM,and Morphological features for Face Recognition
A Gabor block based Kernel Discriminative Common Vector (KDCV) approach using cosine kernels for Human Face Recognition
A Face Recognition approach based on entropy estimate of the nonlinear DCT features in the Logarithm Domain together with Kernel Entropy Component Analysis
Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image
High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model
Deep Belief Networks for Image Denoising
Intriguing properties of neural networks
Combining persistent homology and invariance groups for shape comparison
An Empirical Evaluation of Similarity Measures for Time Series Classification
Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification
Graph matching: relax or not?
Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method
Sparse Principal Component Analysis via Rotation and Truncation
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching
Coherent Multi-Sentence Video Description with Variable Level of Detail
Beyond L2-Loss Functions for Learning Sparse Models
MBIS: Multivariate Bayesian Image Segmentation Tool
PCANet: A Simple Deep Learning Baseline for Image Classification?
Quadratization of Symmetric Pseudo-Boolean Functions
High-Speed Tracking with Kernelized Correlation Filters
MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source
Group-based Sparse Representation for Image Restoration
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
Multi-ellipses detection on images inspired by collective animal behavior
Fast algorithm for Multiple-Circle detection on images using Learning Automata
BiofilmQuant: A Computer-Assisted Tool for Dental Biofilm Quantification
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
Near-optimal Keypoint Sampling for Fast Pathological Lung Segmentation
Optimally Stabilized PET Image Denoising Using Trilateral Filtering
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection
Kernel Nonnegative Matrix Factorization Without the Curse of the Pre-image - Application to Unmixing Hyperspectral Images
Probabilistic Group Testing under Sum Observations: A Parallelizable 2-Approximation for Entropy Loss
Efficient On-the-fly Category Retrieval using ConvNets and GPUs
Novel and Automatic Parking Inventory System Based on Pattern Recognition and Directional Chain Code
Dissimilarity-based Sparse Subset Selection
"Your click decides your fate": Leveraging clickstream patterns from MOOC videos to infer students' information processing & attrition behavior
Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis
A Fast and Accurate Unconstrained Face Detector
Automatic Removal of Marginal Annotations in Printed Text Document
A brief survey on deep belief networks and introducing a new object oriented toolbox (DeeBNet)
Robust 3D face recognition in presence of pose and partial occlusions or missing parts
Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition
Feature Learning from Incomplete EEG with Denoising Autoencoder
Mumford-Shah and Potts Regularization for Manifold-Valued Data with Applications to DTI and Q-Ball Imaging
Direct Processing of Document Images in Compressed Domain
Canonical Polyadic Decomposition with Auxiliary Information for Brain Computer Interface
Detecting Figures and Part Labels in Patents: Competition-Based Development of Image Processing Algorithms
Data Assimilation of Satellite Fire Detection in Coupled Atmosphere-Fire Simulation by WRF-SFIRE
Towards a Visual Turing Challenge
On Chord and Sagitta in ${\mathbb Z}^2$: An Analysis towards Fast and Robust Circular Arc Detection
Power-Law Graph Cuts
Parallax Effect Free Mosaicing of Underwater Video Sequence Based on Texture Features
Multi-modal Image Registration for Correlative Microscopy
A Comparative Study of Techniques of Distant Reconstruction of Displacement Fields by using DISTRESS Simulator
Combining contextual and local edges for line segment extraction in cluttered images
From Captions to Visual Concepts and Back
SIRF: Simultaneous Image Registration and Fusion in A Unified Framework
Visual Noise from Natural Scene Statistics Reveals Human Scene Category Representations
Persistent Evidence of Local Image Properties in Generic ConvNets
Understanding Trajectory Behavior: A Motion Pattern Approach
The Effect of Wedge Tip Angles on Stress Intensity Factors in the Contact Problem between Tilted Wedge and a Half Plane with an Edge Crack Using Digital Image Correlation
Improved 8-point Approximate DCT for Image and Video Compression Requiring Only 14 Additions
Sketch and Validate for Big Data Clustering
A General Preprocessing Method for Improved Performance of Epipolar Geometry Estimation Algorithms
Graphical Potential Games
Language Models for Image Captioning: The Quirks and What Works
Machine learning based data mining for Milky Way filamentary structures reconstruction
Autoencoding the Retrieval Relevance of Medical Images
DCTNet : A Simple Learning-free Approach for Face Recognition
Towards Good Practices for Very Deep Two-Stream ConvNets
Fast Segmentation of Left Ventricle in CT Images by Explicit Shape Regression using Random Pixel Difference Features
Tracking Randomly Moving Objects on Edge Box Proposals
A Visual Embedding for the Unsupervised Extraction of Abstract Semantics
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
Fast Single Image Super-Resolution
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Diverse Large-Scale ITS Dataset Created from Continuous Learning for Real-Time Vehicle Detection
Text-Attentional Convolutional Neural Networks for Scene Text Detection
Dual Principal Component Pursuit
A Picture is Worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Pixels
Layer-Specific Adaptive Learning Rates for Deep Networks
Toward Long Distance, Sub-diffraction Imaging Using Coherent Camera Arrays
FireCaffe: near-linear acceleration of deep neural network training on compute clusters
Image-Based Correction of Continuous and Discontinuous Non-Planar Axial Distortion in Serial Section Microscopy
Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism
A Century of Portraits: A Visual Historical Record of American High School Yearbooks
Parkinson's disease patient rehabilitation using gaming platforms: lessons learnt
A Light CNN for Deep Face Representation with Noisy Labels
Experimental robustness of Fourier Ptychography phase retrieval algorithms
Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning
Sherlock: Scalable Fact Learning in Images
Adversarial Manipulation of Deep Representations
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
Super-Resolution with Deep Convolutional Sufficient Statistics
Density Modeling of Images using a Generalized Normalization Transformation
Unsupervised Learning of Visual Structure using Predictive Generative Networks
An Immersive Telepresence System using RGB-D Sensors and Head Mounted Display
Pushing the Boundaries of Boundary Detection using Deep Learning
The Limitations of Deep Learning in Adversarial Settings
Fine-Grain Annotation of Cricket Videos
LocNet: Improving Localization Accuracy for Object Detection
Design of Kernels in Convolutional Neural Networks for Image Classification
Building Machines That Learn and Think Like People
Waterdrop Stereo
Image Captioning with Deep Bidirectional LSTMs
Face Image Analysis using AAM, Gabor, LBP and WD features for Gender, Age, Expression and Ethnicity Classification
GIFT: A Real-time and Scalable 3D Shape Search Engine
One-class classifiers based on entropic spanning graphs
Filling in the details: Perceiving from low fidelity images
Evolutionary Projection Selection for Radon Barcodes
Deep Aesthetic Quality Assessment with Semantic Information
End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks
Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks
A Framework for Human Pose Estimation in Videos
Deep Learning for Saliency Prediction in Natural Video
A Novel Method to Study Bottom-up Visual Saliency and its Neural Mechanism
Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification
Detailed Garment Recovery from a Single-View Image
Facial Expression Recognition Using a Hybrid CNN-SIFT Aggregator
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
Automated Selection of Uniform Regions for CT Image Quality Detection
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction
Towards Bayesian Deep Learning: A Framework and Some Existing Methods
Densely Connected Convolutional Networks
Curvature Integration in a 5D Kernel for Extracting Vessel Connections in Retinal Images
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On Derivation Languages of Flat Splicing Systems
What's in a Name?
Fence - An Efficient Parser with Ambiguity Support for Model-Driven Language Specification
On Even Linear Indexed Languages with a Reduction to the Learning of Context-Free Languages
Multi-Level Languages are Generalized Arrows
Pumping Lemma for Higher-order Languages
Concept Generation in Language Evolution
A Perfect Model for Bounded Verification
Visual Representation of 3D Language Constructs Specified by Generic Depictions
Human languages order information efficiently
Generation and analysis of lamplighter programs
Principal ideal languages and synchronizing automata
Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation
Design and Implementation of a Reversible Object-Oriented Programming Language
A Syntactic Neural Model for General-Purpose Code Generation
On Store Languages of Language Acceptors
Automatic Generation of Language-Independent Features for Cross-Lingual Classification
Rational stochastic languages
Regular Languages are Church-Rosser Congruential
Invisible pushdown languages
Transliteration in Any Language with Surrogate Languages
Construction of rational expression from tree automata using a generalization of Arden's Lemma
Randomness of formal languages via automatic martingales
Formal Properties of XML Grammars and Languages
Deleting Powers in Words
Dyck-based characterizations of Indexed Languages
Basic Classes of Grammars with Prohibition
Varieties of Unranked Tree Languages
The MMT API: A Generic MKM System
A prototype Malayalam to Sign Language Automatic Translator
Translating into Free Word Order Languages
Circular Languages Generated by Complete Splicing Systems and Pure Unitary Languages
A Tool for Model-Based Language Specification
Quantum, Stochastic, and Pseudo Stochastic Languages with Few States
A Constraint-Satisfaction Parser for Context-Free Grammars
Engineering Tagging Languages for DSLs
Adapting the Core Language Engine to French and Spanish
Exploiting Similarities among Languages for Machine Translation
Generic Results for Concatenation Hierarchies
On context-free languages of scattered words
Inclusion of regular and linear languages in group languages
User Reviews and Language: How Language Influences Ratings
ManyDSL: A Host for Many Languages
Modeling of languages for tensor manipulation
Learning Algorithm for Relation-Substitutable Context-Free Languages
Generic Description of Well-Scoped, Well-Typed Syntaxes
Computational Representation of Linguistic Structures using Domain-Specific Languages
Chart-driven Connectionist Categorial Parsing of Spoken Korean
Parsing of part-of-speech tagged Assamese Texts
On the Structure and Complexity of Rational Sets of Regular Languages
A Survey and Classification of Controlled Natural Languages
Decidability of regular language genus computation
A Domain Specific Transformation Language
Precise but Natural Specification for Robot Tasks
An undecidable property of context-free languages
Finite Orbits of Language Operations
Finitely generated ideal languages and synchronizing automata
On the structure and syntactic complexity of generalized definite languages
Regular Boardgames
Random Words in a (Weighted) Regular Language: a Free Energy Approach
Coqatoo: Generating Natural Language Versions of Coq Proofs
From Syntactic Theories to Interpreters: A Specification Language and Its Compilation
On the Complexity of Quantum Languages
Immunity and Pseudorandomness of Context-Free Languages
Cone types and geodesic languages for lamplighter groups and Thompson's group F
Piecewise excluding geodesic languages
Rewriting Preserving Recognizability of Finite Tree Languages
Contextual Information and Specific Language Models for Spoken Language Understanding
Automata and Reduced Words in the Free Group
Abstraction Level Taxonomy of Programming Language Frameworks
Existential Rule Languages with Finite Chase: Complexity and Expressiveness
An assessment of orthographic similarity measures for several African languages
An approach to computing downward closures
Interactive Grounded Language Acquisition and Generalization in a 2D World
Natural Language Statistical Features of LSTM-generated Texts
A General Architecture for Heterogeneous Language Engineering and Projectional Editor Support
Synthetic Data for Neural Machine Translation of Spoken-Dialects
Analyzing and Improving Statistical Language Models for Speech Recognition
Beyond $ω$BS-regular Languages: $ω$T-regular Expressions and Counter-Check Automata
A Model-Driven Parser Generator, from Abstract Syntax Trees to Abstract Syntax Graphs
A General Architecture for Language Engineering (GATE) - a new approach to Language Engineering R&D
Methods and Tools for Building the Catalan WordNet
Operational Semantics and Type Soundness of Quantum Programming Language LanQ
A Domain-Specific Language for Programming in the Tile Assembly Model
Extensible Pattern Matching in an Extensible Language
Probabilistic and Geometric Languages in the Context of the Principle of Least Action
Attributes as Semantic Units between Natural Language and Visual Recognition
Teaching natural language to computers
A Paradigm for Situated and Goal-Driven Language Learning
GuStL - An Experimental Guarded States Language
Dual Language Models for Code Mixed Speech Recognition
Semantics of Programming Languages: A Tool-Oriented Approach
The Concurrent Language Aldwych
Stemmer for Serbian language
The distributed Language Hello White Paper
Can Machines Think in Radio Language?
A modifiction of the CSP algorithm for infinite languages
The Role of the Gricean Maxims in the Generation of Referring Expressions
Towards rule-based visual programming of generic visual systems
Formalization of simplification for context-free grammars
Unsupervised Language Acquisition
From Regular to Strictly Locally Testable Languages
Implementation of nlization framework for verbs, pronouns and determiners with eugene
Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling
Deciding Whether a Regular Language is Generated by a Splicing System
Ordered Monoids: Languages and Relations
General-Purpose Visual Language and Information System with Case-Studies in Developing Business Applications
Patterns of Language - A Population Model for Language Structure
Towards Generic Refactoring
Learning to Order Facts for Discourse Planning in Natural Language Generation
Splicing systems and the Chomsky hierarchy
A Neural Knowledge Language Model
A Generative Parser with a Discriminative Recognition Algorithm
Language Detection For Short Text Messages In Social Media
The ModelCC Model-Based Parser Generator
Specifying Logic Programs in Controlled Natural Language
Notions of Equivalence in Software Design
Descriptional complexity of bounded context-free languages
The Magic Number Problem for Subregular Language Families
On reverse-engineering the KUKA Robot Language
Power of Randomization in Automata on Infinite Strings
Engineering Delta Modeling Languages
POLYGLOT-NER: Massive Multilingual Named Entity Recognition
Hindi to English Transfer Based Machine Translation System
Syntactic complexity of regular ideals
Classifying Syntactic Regularities for Hundreds of Languages
MatLM: a Matrix Formulation for Probabilistic Language Models
Some Subclasses of Linear Languages based on Nondeterministic Linear Automata
The probabilistic analysis of language acquisition: Theoretical, computational, and experimental analysis
Translating Nondeterministic Functional Language based on Attribute Grammars into Java
Using graph transformation algorithms to generate natural language equivalents of icons expressing medical concepts
Extensible type checker for parser generation
The morphospace of language networks
Applying Explanation-based Learning to Control and Speeding-up Natural Language Generation
Julia: A Fast Dynamic Language for Technical Computing
The Cyan Language
LSTM based Conversation Models
Bidirectional American Sign Language to English Translation
Microservices: a Language-based Approach
Modelling Word Burstiness in Natural Language: A Generalised Polya Process for Document Language Models in Information Retrieval
MyProLang - My Programming Language: A Template-Driven Automatic Natural Programming Language
Binary equality sets are generated by two words
Synthetic Language Generation and Model Validation in BEAST2
Tradeoffs in Metaprogramming
Learning rational stochastic languages
Using Inverse lambda and Generalization to Translate English to Formal Languages
The Chomsky-Schützenberger Theorem for Quantitative Context-Free Languages
Using Duality in Circuit Complexity
Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention
A pragmatic theory of generic language
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation
Primitive words and roots of words
Pseudorandom Generators Against Advised Context-Free Languages
Controlling Linguistic Style Aspects in Neural Language Generation
Language Modeling with Generative AdversarialNetworks
Completeness of Compositional Translation for Context-Free Grammars
Co-evolution of Language and of the Language Acquisition Device
Temporal Phylogenetic Networks and Logic Programming
Fuzzy L languages
Simulation of Quantum Error Correcting Code
How to Evaluate Controlled Natural Languages
Multi-valued Action Languages in CLP(FD)
Strategical languages of infinite words
Quantum Finite Automata and Probabilistic Reversible Automata: R-trivial Idempotent Languages
Further Results on Languages of Membrane Structures
Conjugacy growth series and languages in groups
On sets of numbers rationally represented in a rational base number system
Rhythmic generation of infinite trees and languages
Pumping lemma and Ogden lemma for displacement context-free grammars
Linear Context-Free Tree Languages and Inverse Homomorphisms
Towards an algebraic characterization of rational word functions
Formal Specification and Integration of Distributed Security Policies
On Finite-Index Indexed Grammars and Their Restrictions
Probabilistic Typology: Deep Generative Models of Vowel Inventories
Natural Language Processing: State of The Art, Current Trends and Challenges
Counterfactual Language Model Adaptation for Suggesting Phrases
Tools and resources for Romanian text-to-speech and speech-to-text applications
Language Modelling For Task-Oriented Domains
Quotient Complexity of Regular Languages
Reversible Language Extensions and their Application in Debugging
Multipass automata and group word problems
On Infinite Words Determined by Indexed Languages
Design Guidelines for Domain Specific Languages
Language Recognition using Random Indexing
Beyond Word-based Language Model in Statistical Machine Translation
Many Languages, One Parser
A Bayesian Model of Multilingual Unsupervised Semantic Role Induction
Two Discourse Driven Language Models for Semantics
Learning Lexical Entries for Robotic Commands using Crowdsourcing
Statistical Machine Translation for Indian Languages: Mission Hindi
Statistical Machine Translation for Indian Languages: Mission Hindi 2
A Multichannel Convolutional Neural Network For Cross-language Dialog State Tracking
The Word Problem of $\mathbb{Z}^n$ Is a Multiple Context-Free Language
Listen, Interact and Talk: Learning to Speak via Interaction
Learning with Latent Language
Language: The missing selection pressure
Query learning of derived $ω$-tree languages in polynomial time
A Formal Comparison of Visual Web Wrapper Generators
Marciani Normal Form of context-free grammars
Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical Inference
Parameterized Neural Network Language Models for Information Retrieval
Restrictions on Tree Adjoining Languages
A Generalized Language Model as the Combination of Skipped n-grams and Modified Kneser-Ney Smoothing
Applications of L systems to group theory
A Categorical Model for a Quantum Circuit Description Language (Extended Abstract)
Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata
Generating Sentence Planning Variations for Story Telling
Composable Languages for Bioinformatics: The NYoSh experiment
The complexity of conservative valued CSPs
A Study of Language Usage Evolution in Open Source Software
An Autoencoder Approach to Learning Bilingual Word Representations
Grounded Language Learning in a Simulated 3D World
Sentence Object Notation: Multilingual sentence notation based on Wordnet
Backward and Forward Language Modeling for Constrained Sentence Generation
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation
RDF2PT: Generating Brazilian Portuguese Texts from RDF Data
On the Size Complexity of Non-Returning Context-Free PC Grammar Systems
Descriptional Complexity of Three-Nonterminal Scattered Context Grammars: An Improvement
Guided Grammar Convergence. Full Case Study Report. Generated by converge::Guided
Morphological Analyzer and Generator for Russian and Ukrainian Languages
Learning to Generate Compositional Color Descriptions
Dynamic Entity Representations in Neural Language Models
CLARE: A Contextual Reasoning and Cooperative Response Framework for the Core Language Engine
Pluggable AOP: Designing Aspect Mechanisms for Third-party Composition
Information Flow Analysis for a Dynamically Typed Functional Language with Staged Metaprogramming
On the effect of the IO-substitution on the Parikh image of semilinear AFLs
Distinguishability Operations and Closures on Regular Languages
Rank diversity of languages: Generic behavior in computational linguistics
Generating Domain-Specific Transformation Languages for Component & Connector Architecture Descriptions
Document Context Language Models
The Language of Search
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network
Language-Based Image Editing with Recurrent Attentive Models
A Tool for Collecting Domain Dependent Sortal Constraints From Corpora
Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition
Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
Representing Real Numbers in a Generalized Numeration Systems
Extended Lambek calculi and first-order linear logic
PRoMoTo 2013 proceedings
Conjugacy languages in groups
Modeling languages from graph networks
MATLAB based language for generating randomized multiple choice questions
Language Models with Pre-Trained (GloVe) Word Embeddings
Compiling Purely Functional Structured Programs
A Survey of Neural Network Techniques for Feature Extraction from Text
The ModelCC Model-Driven Parser Generator
Generative Software Development
Multi-lingual neural title generation for e-Commerce browse pages
Fibred Computational Effects
Compiling Language Definitions: The ASF+SDF Compiler
Context-Free Language Theory Formalization
Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks
A framework for lexical representation
Speech Recognition by Composition of Weighted Finite Automata
Full abstraction for nominal general references
GADT meet Subtyping
On periodic points of free inverse monoid endomorphisms
GADTs meet subtyping
Generalized Eilenberg Theorem I: Local Varieties of Languages
A Binary Data Stream Scripting Language
Forkable Regular Expressions
An investigation into language complexity of World-of-Warcraft game-external texts
A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
Early Experience with ASDL in lcc
Logic Engines as Interactors
Adapting general-purpose speech recognition engine output for domain-specific natural language question answering
Intelligent Voice Prosthesis: Converting Icons into Natural Language Sentences
Storage of Natural Language Sentences in a Hopfield Network
Handling Defeasibilities in Action Domains
An Abstract Programming System
A Language-theoretic View on Guidelines and Consistency Rules of UML
Mean-payoff Automaton Expressions
Descriptional Complexity of the Languages KaL: Automata, Monoids and Varieties
A dichotomy theorem for conservative general-valued CSPs
Aging in language dynamics
A DSL for Mapping Abstract Syntax Models to Concrete Syntax Models in ModelCC
Semilinearity and Context-Freeness of Languages Accepted by Valence Automata
One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling
New Results on the Minimum Amount of Useful Space
Why It's Nice to be Quoted: Quasiquoting for Prolog
Separating Regular Languages with First-Order Logic
Technical Report: Towards a Universal Code Formatter through Machine Learning
Processing Natural Language About Ongoing Actions
SMPOST: Parts of Speech Tagger for Code-Mixed Indic Social Media Text
Computing the longest common prefix of a context-free language in polynomial time
Long-Short Range Context Neural Networks for Language Modeling
On the widths of regular and context free languages, with an application to information flow
A categorical foundation for structured reversible flowchart languages: Soundness and adequacy
Gender Aware Spoken Language Translation Applied to English-Arabic
Unpaired Image Captioning by Language Pivoting
Adversarial Generation of Natural Language
On Generalization of Definitional Equivalence to Languages with Non-Disjoint Signatures
Modelling Concurrent Behaviors in the Process Specification Language
Symbolic Languages and Ars Combinatoria
Self-assembling interactive modules: A research programme
Monitorability of $ω$-regular languages
Language Without Words: A Pointillist Model for Natural Language Processing
On Formal Reasoning on the Semantics of PLC using Coq
The genus of regular languages
Architecture and Behavior Modeling of Cyber-Physical Systems with MontiArcAutomaton
A Domain-Specific Language and Editor for Parallel Particle Methods
Least Generalizations and Greatest Specializations of Sets of Clauses
Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution
Quotient complexity of ideal languages
Phonetic based SoundEx & ShapeEx algorithm for Sindhi Spell Checker System
Lolisa: Formal Syntax and Semantics for a Subset of the Solidity Programming Language
Initial Semantics for Reduction Rules
Two-level, Many-Paths Generation
Constructing a Natural Language Inference Dataset using Generative Neural Networks
Tagset Design and Inflected Languages
Bayesian Grammar Induction for Language Modeling
Better Language Models with Model Merging
The Theoretical Status of Ontologies in Natural Language Processing
"I'm sorry Dave, I'm afraid I can't do that": Linguistics, Statistics, and Natural Language Processing circa 2001
Similarity-Based Supervisory Control of Discrete Event Systems
Sequential products in effect categories
Closures in Formal Languages and Kuratowski's Theorem
Application of Generalised sequential crossover of languages to generalised splicing
Linear-Logic Based Analysis of Constraint Handling Rules with Disjunction
Considering a resource-light approach to learning verb valencies
Bounded Counter Languages
Symbolic Representation of Algorithmic Game Semantics
Sentence Compression in Spanish driven by Discourse Segmentation and Language Models
Some proof theoretical remarks on quantification in ordinary language
Information content versus word length in natural language: A reply to Ferrer-i-Cancho and Moscoso del Prado Martin [arXiv:1209.1751]
Eliminating Network Protocol Vulnerabilities Through Abstraction and Systems Language Design
Operads, quasiorders, and regular languages
Learning Language from a Large (Unannotated) Corpus
Modeling multi-stage decision optimization problems
Phrase Based Language Model For Statistical Machine Translation
Varieties of Languages in a Category
Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
Numerically Grounded Language Models for Semantic Error Correction
Dependently Typed Programming based on Automated Theorem Proving
Towards Efficient Abstractions for Concurrent Consensus
A Fibrational Approach to Automata Theory
Dynamic Bayesian Ontology Languages
IVOA recommendation: Parameter Description Language Version 1.0
Rationality for subclasses of 321-avoiding permutations
Nominal LCF: A Language for Generic Proof
Generalizing and Hybridizing Count-based and Neural Language Models
Annotation Methodologies for Vision and Language Dataset Creation
Robust Natural Language Processing - Combining Reasoning, Cognitive Semantics and Construction Grammar for Spatial Language
A Framework for Extending microKanren with Constraints
Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing
Synthesising Sign Language from semantics, approaching "from the target and back"
Sentence Correction Based on Large-scale Language Modelling
Low-Rank RNN Adaptation for Context-Aware Language Modeling
Unsupervised Morphological Expansion of Small Datasets for Improving Word Embeddings
Label Languages of 8-directional Array P System
Biomedical term normalization of EHRs with UMLS
Neural Program Search: Solving Programming Tasks from Description and Examples
CAESAR: Context Awareness Enabled Summary-Attentive Reader
Remarks on formal languages and the model theory of monoids
Understanding Editing Behaviors in Multilingual Wikipedia
Software Infrastructure for Natural Language Processing
Next Generation Language Resources using GRID
Quantum Physics and Human Language
The C Object System: Using C as a High-Level Object-Oriented Language
Proceedings 5th International Workshop on Logical Frameworks and Meta-languages: Theory and Practice
SignsWorld; Deeping Into the Silence World and Hearing Its Signs (State of the Art)
The essence of component-based design and coordination
Acronym recognition and processing in 22 languages
Architecture of an Ontology-Based Domain-Specific Natural Language Question Answering System
INAUT, a Controlled Language for the French Coast Pilot Books Instructions nautiques
Permutations of context-free, ET0L and indexed languages
Explaining Violation Traces with Finite State Natural Language Generation Models
A New Skill Based Robot Programming Language Using UML/P Statecharts
Toward an Energy Efficient Language and Compiler for (Partially) Reversible Algorithms
Dependent Types for Multi-Rate Flows in Synchronous Programming
Understanding Grounded Language Learning Agents
Some apparently disjoint aims and requirements for grammar development environments: the case of natural language generation
On Descriptive Complexity, Language Complexity, and GB
Nez: practical open grammar language
Generalizing input-driven languages: theoretical and practical benefits
A Flexible Pragmatics-driven Language Generator for Animated Agents
Dialogue as Discourse: Controlling Global Properties of Scripted Dialogue
A Context-aware Natural Language Generator for Dialogue Systems
Morphological Inflection Generation Using Character Sequence to Sequence Learning
Conciseness through Aggregation in Text Generation
Differentially Private Distributed Learning for Language Modeling Tasks
Colored operads, series on colored operads, and combinatorial generating systems
Tactical Generation in a Free Constituent Order Language
Generating Natural Language Inference Chains
Model-based generation of natural language specifications
Initiality for Typed Syntax and Semantics
A Corrective Training Algorithm for Adaptive Learning in Bag Generation
An Information Structural Approach to Spoken Language Generation
C++ Templates as Partial Evaluation
Language Diversity of Measured Quantum Processes
Non-redundant random generation from weighted context-free languages
Generative Knowledge Transfer for Neural Language Models
Recognising and Generating Terms using Derivatives of Parsing Expression Grammars
Language Generation with Recurrent Generative Adversarial Networks without Pre-training
Formal Languages in Dynamical Systems
A Framework for Natural Language Interfaces to Temporal Databases
Regular Ideal Languages and Their Boolean Combinations
On the Formal Semantics of Speech-Act Based Communication in an Agent-Oriented Programming Language
Efficiently Computing Edit Distance to Dyck Language
Towards More Security in Data Exchange: Defining Unparsers with Context-Sensitive Encoders for Context-Free Grammars
Human language reveals a universal positivity bias
Design of an intermediate representation for query languages
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification
On computational complexity of Set Automata
Finite-State Approximation of Phrase-Structure Grammars
An Iterative Algorithm to Build Chinese Language Models
Hybrid language processing in the Spoken Language Translator
Design and Implementation of a Computational Lexicon for Turkish
Estimation of English and non-English Language Use on the WWW
Towards a query language for annotation graphs
Monadic Datalog and the Expressive Power of Languages for Web Information Extraction
A Framework for Interoperability
Effects of Language Modeling on Speech-driven Question Answering
ECA-LP / ECA-RuleML: A Homogeneous Event-Condition-Action Logic Programming Language
Rational semigroup automata
Entropy sensitivity of languages defined by infinite automata, via Markov chains with forbidden transitions
Rewriting Logic Semantics of a Plan Execution Language
Rewriting Constraint Models with Metamodels
A Type System for Tom
Programming Discrete Physical Systems
Reflection-based language support for the heterogeneous capture and restoration of running computations
Theory of Atomata
Program Equivalence in Linear Contexts
Saying Hello World with Epsilon - A Solution to the 2011 Instructive Case
Query Language for Complex Similarity Queries
A 10-dimensional Phonetic-prosodic Space and its Stochastic Structure (A framework for probabilistic modeling of spoken languages and their phonology)
PROOFTOOL: a GUI for the GAPT Framework
DGT-TM: A freely Available Translation Memory in 22 Languages
Verifiable Source Code Documentation in Controlled Natural Language
Analysis Tool for UNL-Based Knowledge Representation
On the Computation of Distances for Probabilistic Context-Free Grammars
Representing and Reasoning about Game Strategies
Gaussian Tree Constraints Applied to Acoustic Linguistic Functional Data
IFC Inside: Retrofitting Languages with Dynamic Information Flow Control (Extended Version)
The role of concurrency in an evolutionary view of programming abstractions
Multilingual Image Description with Neural Sequence Models
A Multilingual FrameNet-based Grammar and Lexicon for Controlled Natural Language
Correction of Noisy Sentences using a Monolingual Corpus
Toward verbalizing ontologies in isiZulu
Black-box Integration of Heterogeneous Modeling Languages for Cyber-Physical Systems
Convolutional Neural Networks over Tree Structures for Programming Language Processing
MontiCore: Modular Development of Textual Domain Specific Languages
Computing downward closures for stacked counter automata
Convolutional Neural Network Architectures for Matching Natural Language Sentences
A Survey of Current Datasets for Vision and Language Research
Spin Glass Models of Syntax and Language Evolution
On the Expressiveness of Joining
Neural Language Correction with Character-Based Attention
WordNet2Vec: Corpora Agnostic Word Vectorization Method
Visualizing Natural Language Descriptions: A Survey
Designing a semantic model for a wide-spectrum language with concurrency
A Language-theoretic View on Network Protocols
False-Friend Detection and Entity Matching via Unsupervised Transliteration
An Empirical Study of Language CNN for Image Captioning
Most Complex Non-Returning Regular Languages
What can the programming language Rust do for astrophysics?
A Morphology-aware Network for Morphological Disambiguation
Word forms - not just their lengths- are optimized for efficient communication
Cross-lingual and cross-domain discourse segmentation of entire documents
Minimal Forbidden Factors of Circular Words
Grounding Spatio-Semantic Referring Expressions for Human-Robot Interaction
Fluency-Guided Cross-Lingual Image Captioning
A New Semantic Theory of Natural Language
Auto Analysis of Customer Feedback using CNN and GRU Network
PyFml - a Textual Language For Feature Modeling
PTL-separability and closures for WQOs on words
Canonizable Partial Order Generators and Regular Slice Languages
The SQL++ Query Language: Configurable, Unifying and Semi-structured
An Efficient Generation Algorithm for Lexicalist MT
Segregatory Coordination and Ellipsis in Text Generation
Infinite Correlation in Measured Quantum Processes
Anisimov's Theorem for inverse semigroups
Generalized LR parsing and the shuffle operator
Natural Language Generation in Healthcare: Brief Review
A Survey of Paraphrasing and Textual Entailment Methods
Grammatical Aspects for Language Descriptions
Liquidsoap: a High-Level Programming Language for Multimedia Streaming
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
SceneSeer: 3D Scene Design with Natural Language
Affect-LM: A Neural Language Model for Customizable Affective Text Generation
Generating Realtime Motion Plans from Attribute-Based Natural Language Instructions Using Dynamic Constraint Mapping
Harvesting Creative Templates for Generating Stylistically Varied Restaurant Reviews
Cost and dimension of words of zero topological entropy
Text2Action: Generative Adversarial Synthesis from Language to Action
On the Design of Generic Static Analyzers for Modern Imperative Languages
Toward an example-based machine translation from written text to ASL using virtual agent animation
Bisimulation of Labelled State-to-Function Transition Systems Coalgebraically
Improving the Performance of English-Tamil Statistical Machine Translation System using Source-Side Pre-Processing
Approximate N-Gram Markov Model for Natural Language Generation
Linguistics Computation, Automatic Model Generation, and Intensions
Default Handling in Incremental Generation
Modeling informational novelty in a conversational system with a hybrid statistical and grammar-based approach to natural language generation
The Design and Algorithms of a Verification Condition Generator
A Model-Driven Probabilistic Parser Generator
Modelling homogeneous generative meta-programming
The Code2Text Challenge: Text Generation in Source Code Libraries
Hierarchical Text Generation and Planning for Strategic Dialogue
Domain and Language Independent Feature Extraction for Statistical Text Categorization
General Game Management Agent
Church: a language for generative models
Unified Form Language: A domain-specific language for weak formulations of partial differential equations
Language-based Games
Augur: a Modeling Language for Data-Parallel Probabilistic Inference
Generating Natural Language Descriptions from OWL Ontologies: the NaturalOWL System
Efficient Editor Generation for Compositional DSLs in Eclipse
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
On Varieties of Ordered Automata
Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings
Expressiveness and Closure Properties for Quantitative Languages
Clustering Web Search Results For Effective Arabic Language Browsing
Semantic Folding Theory And its Application in Semantic Fingerprinting
Evolutionary forces in language change
The KIT Motion-Language Dataset
Rank dynamics of word usage at multiple scales
Towards Understanding Generics in Mainstream OOP
Beginner's Luck: A Language for Property-Based Generators
N-Gram Cluster Identification During Empirical Knowledge Representation Generation
Three Generative, Lexicalised Models for Statistical Parsing
Parsing and Generation with Tabulation and Compilation
An Analysis of Lambek's Production Machines
Regular geodesic normal forms in virtually abelian groups
A c*-algebraic framework for quantum groups
Typesafe Modeling in Text Mining
Sentiment Analysis: A Survey
Java Generics are Turing Complete
Multimodal Semantic Simulations of Linguistically Underspecified Motion Events
Generating Memorable Mnemonic Encodings of Numbers
Characterisation of (Sub)sequential Rational Functions over a General Class Monoids
Towards Hybrid Intensional Programming with JLucid, Objective Lucid, and General Imperative Compiler Framework in the GIPSY
Markov semigroups, monoids, and groups
Corpora Preparation and Stopword List Generation for Arabic data in Social Network
On the Expressive Power of Multiple Heads in CHR
Toward an architecture for quantum programming
Flavor: A Language for Media Representation
Constraint-based verification of abstract models of multitreaded programs
Ciliate Gene Unscrambling with Fewer Templates
Lightweight Time Modeling in Timed Creol
IDL-Expressions: A Formalism for Representing and Parsing Finite Languages in Natural Language Processing
Step-Indexed Normalization for a Language with General Recursion
BarQL: Collaborating Through Change
A Swiss Pocket Knife for Computability
Implementation of an Automatic Sign Language Lexical Annotation Framework based on Propositional Dynamic Logic
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Learning in the Rational Speech Acts Model
ModelWizard: Toward Interactive Model Construction
Coalgebraic Characterizations of Context-Free Languages
System and Methods for Converting Speech to SQL
From Clarity to Efficiency for Distributed Algorithms
Talking about the Moving Image: A Declarative Model for Image Schema Based Embodied Perception Grounding and Language Generation
Translation into any natural language of the error messages generated by any computer program
Around Context-Free Grammars - a Normal Form, a Representation Theorem, and a Regular Approximation
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
Bangla Word Clustering Based on Tri-gram, 4-gram and 5-gram Language Model
Long-Range Correlation Underlying Childhood Language and Generative Models
Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks
Off-line Optimization for Earley-style HPSG Processing
Improving the Efficiency of a Generation Algorithm for Shake and Bake Machine Translation Using Head-Driven Phrase Structure Grammar
Letting the Cat out of the Bag: Generation for Shake-and-Bake MT
Collocational Grammar
Higher-Order Coloured Unification and Natural Language Semantics
Two Sources of Control over the Generation of Software Instructions
Language Trees and Zipping
Generalization of automatic sequences for numeration systems on a regular language
Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation
Real numbers having ultimately periodic representations in abstract numeration systems
Test Collections for Patent-to-Patent Retrieval and Patent Map Generation in NTCIR-4 Workshop
Mixing the Objective Caml and C# Programming Models in the .Net Framework
Getting More From Your Multicore: Exploiting OpenMP From An Open Source Numerical Scripting Language
Mechanized semantics
Groups with poly-context-free word problem
Minimalist Grammars and Minimalist Categorial Grammars, definitions toward inclusion of generated languages
On Varieties of Automata Enriched with an Algebraic Structure (Extended Abstract)
Complexity of Problems of Commutative Grammars
Regular realizability problems and models of a generalized nondeterminism
Controlled Natural Language Generation from a Multilingual FrameNet-based Grammar
An Approach for Text Steganography Based on Markov Chains
Language to Logical Form with Neural Attention
Weight Computation of Regular Tree Languages
Swift: Compiled Inference for Probabilistic Programming Languages
Probabilistic call by push value
Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
Quantifiers on languages and codensity monads
Data-driven Natural Language Generation: Paving the Road to Success
Grammar induction for mildly context sensitive languages using variational Bayesian inference
Initiality for Typed Syntax and Semantics
Expressiveness and Closure Properties for Quantitative Languages
Test Case Generation for Object-Oriented Imperative Languages in CLP
FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text
A Descriptive Characterization of Tree-Adjoining Languages (Full Version)
A Topos Foundation for Theories of Physics: I. Formal Languages for Physics
BiLingual Information Retrieval System for English and Tamil
A Comparative Study of the Usability of Two Object-oriented Concurrent Programming Languages
Language learning from positive evidence, reconsidered: A simplicity-based approach
Toric grammars: a new statistical approach to natural language modeling
Opinion Mining In Hindi Language: A Survey
A Reduction from Valued CSP to Min Cost Homomorphism Problem for Digraphs
Applied Choreographies
Several types of types in programming languages
A Polya Urn Document Language Model for Improved Information Retrieval
An Incremental Learner for Language-Based Anomaly Detection in XML
The Schützenberger product for syntactic spaces
A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity
From Query to Usable Code: An Analysis of Stack Overflow Code Snippets
Canonical Completeness in Lattice-Based Languages for Attribute-Based Access Control
A Lambda Calculus for Transfinite Arrays: Unifying Arrays and Streams
A Language for Generic Programming in the Large
Characterizations of one-way general quantum finite automata
Code Generator Composition for Model-Driven Engineering of Robotics Component & Connector Systems
Machine Learning of Phonologically Conditioned Noun Declensions For Tamil Morphological Generators
Egyptian Dialect Stopword List Generation from Social Network Data
Semantic Refinement GRU-based Neural Language Generation for Spoken Dialogue Systems
Automatic Generation of Natural Language Explanations
Learning an Executable Neural Semantic Parser
Role of Morphology Injection in Statistical Machine Translation
Multi-Stage Programs are Generalized Arrows
Large Aperiodic Semigroups
Separating regular languages with two quantifier alternations
Generating Multilingual Parallel Corpus Using Subtitles
Amalia -- A Unified Platform for Parsing and Generation
Trainable Methods for Surface Natural Language Generation
A Planning based Framework for Essay Generation
Multi-domain Neural Network Language Generation for Spoken Dialogue Systems
Fuzzy Sets Across the Natural Language Generation Pipeline
Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data
Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder
Adversarial Ranking for Language Generation
Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks
Steering Output Style and Topic in Neural Response Generation
Estimating Performance of Pipelined Spoken Language Translation Systems
Improving Language Models by Clustering Training Sentences
Adnominal adjectives, code-switching and lexicalized TAG
Constraining Lexical Selection Across Languages Using TAGs
Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies
Phonetic Ambiguity : Approaches, Touchstones, Pitfalls and New Approaches
Building and Refining Abstract Planning Cases by Change of Representation Language
Resolving Part-of-Speech Ambiguity in the Greek Language Using Learning Techniques
Introduction to the GiNaC Framework for Symbolic Computation within the C++ Programming Language
Assertion checker for the C programming language based on computations over event traces
Modelling Semantic Association and Conceptual Inheritance for Semantic Analysis
Ownership Confinement Ensures Representation Independence for Object-Oriented Programs
Building an Open Language Archives Community on the OAI Foundation
The Athena Data Dictionary and Description Language
Unsupervised Topic Adaptation for Lecture Speech Retrieval
A survey of topological work at CEOL
Quantum finite multitape automata
Linear-algebraic lambda-calculus
Algebraic characterization of logically defined tree languages
Language structure in the n-object naming game
Orbits of linear maps and regular languages
Tiling-Recognizable Two-Dimensional Languages: From Non-Determinism to Determinism through Unambiguity
It Is NL-complete to Decide Whether a Hairpin Completion of Regular Languages Is Regular
On Conditional Decomposability
Recognizing Bangla Grammar using Predictive Parser
A System-Level Semantics
A Query Language for Formal Mathematical Libraries
Enumerating regular expressions and their languages
Incomplete Transition Complexity of Basic Operations on Finite Languages
A Graphical Language for Proof Strategies
A Graphical Language for Real-Time Critical Robot Commands
Development of a Hindi Lemmatizer
Coordination Control of Discrete-Event Systems Revisited
Graphical law beneath each written natural language
Effective Quotation: relating approaches to language-integrated query
On Combinatorial Generation of Prefix Normal Words
More ties than we thought
Regression analysis in quantum language
Logics with rigidly guarded data tests
Transfer Learning for Speech and Language Processing
High-level GPU programming in Julia
A deep language model for software code
A Programming Language With a POMDP Inside
L-FLAT: Logtalk Toolkit for Formal Languages and Automata Theory
FrameNet CNL: a Knowledge Representation and Information Extraction Language
Proceedings 13th International Workshop on Foundations of Coordination Languages and Self-Adaptive Systems
Identifying an Honest ${\rm EXP}^{\rm NP}$ Oracle Among Many
Loo.py: From Fortran to performance via transformation and substitution rules
Bengali to Assamese Statistical Machine Translation using Moses (Corpus Based)
Syntactic Monoids in a Category
Towards correct-by-construction product variants of a software product line: GFML, a formal language for feature modules
Learning Regular Languages over Large Ordered Alphabets
A Hybrid Model for Enhancing Lexical Statistical Machine Translation (SMT)
Solution sets for equations over free groups are EDT0L languages
Symmetry and Universality in Language Change
RIPL: An Efficient Image Processing DSL for FPGAs
A Framework for Analyzing Stochastic Jumps in Finance based on Belief and Knowledge
Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning
Proceedings 14th International Workshop on Foundations of Coordination Languages and Self-Adaptive Systems
Why Just Boogie? Translating Between Intermediate Verification Languages
A Factorized Recurrent Neural Network based architecture for medium to large vocabulary Language Modelling
Language recognition power and succintness of affine automata
Corpus analysis without prior linguistic knowledge - unsupervised mining of phrases and subphrase structure
An Implementation and Analysis of a Kernel Network Stack in Go with the CSP Style
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning
The Use of ICT to preserve Australian Indigenous Culture and Language - a Preliminary Proposal using the Activity Theory Framework
Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks
Enabling Medical Translation for Low-Resource Languages
Efficient Implementation of a Higher-Order Language with Built-In AD
Attention-based Memory Selection Recurrent Network for Language Modeling
Sentence-level dialects identification in the greater China region
Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features
DAWT: Densely Annotated Wikipedia Texts across multiple languages
The Formal Semantics of Rascal Light
Automated Hate Speech Detection and the Problem of Offensive Language
Topically Driven Neural Language Model
Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
A Verified Compiler for Probability Density Functions
Rule-Based Spanish Morphological Analyzer Built From Spell Checking Lexicon
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
Simplicity: A New Language for Blockchains
Large-scale Cloze Test Dataset Designed by Teachers
A superpolynomial lower bound for the size of non-deterministic complement of an unambiguous automaton
Efficient Representation for Natural Language Processing via Kernelized Hashcodes
An Encoder-Decoder Framework Translating Natural Language to Database Queries
Review of Design of Speech Recognition and Text Analytics based Digital Banking Customer Interface and Future Directions of Technology Adoption
Analyzing Language Learned by an Active Question Answering Agent
Generating Python Code From Object-Z Specifications
Q#: Enabling scalable quantum computing and development with a high-level domain-specific language
A Factoid Question Answering System for Vietnamese
Generating Bilingual Pragmatic Color References
Colorless green recurrent networks dream hierarchically
Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs
LR(1) Parser Generation System: LR(1) Error Recovery, Oracles, and Generic Tokens
On Learning More Appropriate Selectional Restrictions
NPtool, a detector of English noun phrases
Handling Sparse Data by Successive Abstraction
Semi-Automatic Acquisition of Domain-Specific Translation Lexicons
Numeration systems on a regular language
A Matter of Opinion: Sentiment Analysis and Business Intelligence (position paper)
A type-based termination criterion for dependently-typed higher-order rewrite systems
Einsteins Arbeiten in Bezug auf die moderne Kosmologie
A Feynman Diagram Analyser DIANA
Regular geodesic languages and the falsification by fellow traveler property
Mathematics as an Exact and Precise Language of Nature
Some Insight into Many Constituent Dynamics
Iterators, Recursors and Interaction Nets
Counting Finite Languages by Total Word Length
Displacement Calculus
On the Descriptional Complexity of Limited Propagating Lindenmayer Systems
Free inductive K-semialgebras
From indexed grammars to generating functions
Kolmogorov complexity as a language
On uncountable hypersimple unidimensional theories
Partial actions and automata
Some Properties of Brzozowski Derivatives of Regular Expressions
Concept-oriented programming: from classes to concepts and from inheritance to inclusion
Theory of Programs
Two Results on Discontinuous Input Processing
On nonpermutational transformation semigroups with an application to syntactic complexity
A Second-Order Formulation of Non-Termination
Incorporating User Interaction into Imperative Languages
Operators for Space and Time in BeSpaceD
X575: writing rengas with web services
A Linear Acceleration Theorem for 2D Cellular Automata on all Complete Neighborhoods
The Algorithmic Inflection of Russian and Generation of Grammatically Correct Text
A Topological proof that $O_2$ is $2$-MCFL
Clickbait Identification using Neural Networks
Learning and analyzing vector encoding of symbolic representations
K-vec: A New Approach for Aligning Parallel Texts
Lexikoneintraege fuer deutsche Adverbien (Dictionary Entries for German Adverbs)
Concurrent Lexicalized Dependency Parsing: The ParseTalk Model
Parsing Using Linearly Ordered Phonological Rules
Free-ordered CUG on Chemical Abstract Machine
Robust stochastic parsing using the inside-outside algorithm
Literal Movement Grammars
A Computational Treatment of HPSG Lexical Rules as Covariation in Lexical Entries
GLR-Parsing of Word Lattices Using a Beam Search Method
Building Natural-Language Generation Systems
Parsing for Semidirectional Lambek Grammar is NP-Complete
An Efficient Compiler for Weighted Rewrite Rules
Learning Micro-Planning Rules for Preventative Expressions
Machine Transliteration
Learning Parse and Translation Decisions From Examples With Rich Context
An Information Extraction Core System for Real World German Text Processing
A General, Sound and Efficient Natural Language Parsing Algorithm based on Syntactic Constraints Propagation
WebScript -- A Scripting Language for the Web
Construction of regular languages and recognizability of polynomials
Semantic robust parsing for noun extraction from natural language queries
Exploiting Diversity in Natural Language Processing: Combining Parsers
Distributive Computability
Fast Recompilation of Object Oriented Modules
Linguistic-Mathematical Statistics in Rebus, Lyrics, Juridical Texts, Fancies and Paradoxes
Representation Theory of Finite Semigroups, Semigroup Radicals and Formal Language Theory
Extending the Lambda Calculus to Express Randomized and Quantumized Algorithms
Theorem proving support in programming language semantics
Generating models for temporal representations
Compiling ER Specifications into Declarative Programs
DSL development based on target meta-models. Using AST transformations for automating semantic analysis in a textual DSL framework
On Measuring Non-Recursive Trade-Offs
Mathematics, Recursion, and Universals in Human Languages
Typing rule-based transformations over topological collections
Developing a New Approach for Arabic Morphological Analysis and Generation
On the capabilities of grammars, automata, and transducers controlled by monoids
Ghost free Massive Gravity in the Stückelberg language
CoInDiVinE: Parallel Distributed Model Checker for Component-Based Systems
Wreath Products of Forest Algebras, with Applications to Tree Logics
Input Scheme for Hindi Using Phonetic Mapping
On the transition reduction problem for finite automata
Functional Package Management with Guix
Language Modeling with Power Low Rank Ensembles
Natural Language Feature Selection via Cooccurrence
$L$-Primitive Words in Submonoids
Distributed Graph Automata
EF+EX Forest Algebras
Thompson's group F is 1-counter graph automatic
$\mathbb{N}$-algebraicity of zeta functions of sofic-Dyck shifts
Comparative Analysis of Classic Garbage-Collection Algorithms for a Lisp-like Language
Reasoning About Pragmatics with Neural Listeners and Speakers
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
A Modular Formalization of Reversibility for Concurrent Models and Languages
A program logic for higher-order procedural variables and non-local jumps
Extensible Validation Framework for DSLs using MontiCore on the Example of Coding Guidelines
Meta-Modeling Semantics of UML
A Simple and Efficient Method To Generate Word Sense Representations
Graph Grammars, Insertion Lie Algebras, and Quantum Field Theory
A Next-Generation Data Language Proposal
Recognize Foreign Low-Frequency Words with Similar Pairs
Feature-based Decipherment for Large Vocabulary Machine Translation
Confluent Orthogonal Drawings of Syntax Diagrams
Recurrent Neural Network Grammars
Unsupervised Ranking Model for Entity Coreference Resolution
Schützenberger Products in a Category
Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model
Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey
On the Herbrand content of LK
Partial Derivatives for Context-Free Languages: From $μ$-Regular Expressions to Pushdown Automata
Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling
ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge
Semi-supervised Multitask Learning for Sequence Labeling
Parameterized Complexity of CSP for Infinite Constraint Languages
QuantumOptics.jl: A Julia framework for simulating open quantum systems
Bayesian Sparsification of Recurrent Neural Networks
Deep Transfer in Reinforcement Learning by Language Grounding
Constructive completeness and non-discrete languages
WHY: Natural Explanations from a Robot Navigator
Locally Nameless Permutation Types
Inducing Regular Grammars Using Recurrent Neural Networks
Système de traduction automatique statistique Anglais-Arabe
A Short Survey on Sense-Annotated Corpora for Diverse Languages and Resources
A generic characterization of Pol(C)
Almost Sure Productivity
Open Programming Language Interpreters
Design and Implementation of a Tactical Generator for Turkish, a Free Constituent Order Language
Complex Systems: a Physicist's Viewpoint
Inductive-data-type Systems
Recasting results in equivariant geometry: affine cosets, observable subgroups and existence of good quotients
Using Built-In Domain-Specific Modeling Support to Guide Model-Based Test Generation
Monolingual Probabilistic Programming Using Generalized Coroutines
k-Colorability is Graph Automaton Recognizable
Linguistic Descriptions for Automatic Generation of Textual Short-Term Weather Forecasts on Real Prediction Data
Generating Sentences from a Continuous Space
Avatar-independent scripting for real-time gesture animation
A Software Package for Chemically Inspired Graph Transformation
Neural Text Generation from Structured Data with Application to the Biography Domain
Language as a Latent Variable: Discrete Generative Models for Sentence Compression
Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting
Weighted Regular Tree Grammars with Storage
Towards a Java Subtyping Operad
Adaptive Convolutional Filter Generation for Natural Language Understanding
A Memory-Based Approach to Learning Shallow Natural Language Patterns
Forgetting Exceptions is Harmful in Language Learning
On the accuracy of language trees
Proceedings Eight Workshop on Structural Operational Semantics 2011
A Rhetorical Analysis Approach to Natural Language Processing
A Vision for Online Verification-Validation
Modular, Fully-abstract Compilation by Approximate Back-translation
Language Oriented Modularity: From Theory to Practice
Automated code generation for discontinuous Galerkin methods
Synthesizing Novel Pairs of Image and Text
PGPG: An Automatic Generator of Pipeline Design for Programmable GRAPE Systems
Zipf's law is a consequence of coherent language production
Ma(r)king concessions in English and German
Generating Multilingual Personalized Descriptions of Museum Exhibits - The M-PIRO Project
The Generation of Textual Entailment with NLML in an Intelligent Dialogue system for Language Learning CSIEC
The C++0x "Concepts" Effort
Gibbs Sampling in Open-Universe Stochastic Languages
Capture-Avoiding and Hygienic Program Transformations (incl. Proofs)
Visualization of Constraint Handling Rules
Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language
Modeling meaning: computational interpreting and understanding of natural language fragments
Running Probabilistic Programs Backwards
Robot Language Learning, Generation, and Comprehension
Answer-Type Modification without Tears: Prompt-Passing Style Translation for Typed Delimited-Control Operators
No positive cone in a free product is regular
Reference-Aware Language Models
SEA: String Executability Analysis by Abstract Interpretation
ShapeWorld - A new test methodology for multimodal language understanding
Control Improvisation
Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation
Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media
A monadic solution to the Cartwright-Felleisen-Wadler conjecture
Referenceless Quality Estimation for Natural Language Generation
A Novel Way of Identifying Cyber Predators
A General Path-Based Representation for Predicting Program Properties
Connectivity in Bag Generation
Emphatic generation: employing the theory of semantic emphasis for text generation
A generation algorithm for f-structure representations
Incremental Parser Generation for Tree Adjoining Grammars
Generative Adversarial Nets for Multiple Text Corpora
Towards the quantification of the semantic information encoded in written language
A visual programming language for drawing and executing flowcharts
Interrupt Timed Automata: verification and expressiveness
The power of linear programming for general-valued CSPs
Just-in-Time Static Type Checking for Dynamic Languages
Where in the World are You? Geolocation and Language Identification in Twitter
Characterizing classes of regular languages using prefix codes of bounded synchronization delay
Semantic Similarity from Natural Language and Ontology Analysis
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Representing Hybrid Automata by Action Language Modulo Theories
Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English
Network Controllability in the IFG Relates to Controlled Language Variability and Susceptibility to TMS
ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network
Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation
Stone duality, topological algebra, and recognition
Generating Configurable Hardware from Parallel Patterns
An error correcting parser for context free grammars that takes less than cubic time
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
Multiple Context-Free Tree Grammars: Lexicalization and Characterization
Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance
User-Defined Operators Including Name Binding for New Language Constructs
Towards History-based Grammars: Using Richer Models for Probabilistic Parsing
NL Understanding with a Grammar of Constructions
Learning Syntactic Rules and Tags with Genetic Algorithms for Information Retrieval and Filtering: An Empirical Basis for Grammatical Rules
Using Single Layer Networks for Discrete, Sequential Data: An Example from Natural Language Processing
Language identification of controlled systems: Modelling, control and anomaly detection
Factorization of Language Models through Backing-Off Lattices
Model Checking Linear Logic Specifications
Constraint-based automatic verification of abstract models of multithreaded programs
PageRank without hyperlinks: Structural re-ranking using links induced by language models
The Hole Argument for Covariant Theories
A triangle-based logic for affine-invariant querying of spatial and spatio-temporal data
A Generalized Streaming Model for Concurrent Computing
Solving the TTC 2011 Reengineering Case with MOLA and Higher-Order Transformations
Descriptive complexity for pictures languages (extended abstract)
Elaborating Intersection and Union Types
FST Based Morphological Analyzer for Hindi Language
Stochastic Context-Free Grammars, Regular Languages, and Newton's Method
SYNTAGMA. A Linguistic Approach to Parsing
$\mathcal C$-graph automatic groups
RProtoBuf: Efficient Cross-Language Data Serialization in R
Classical and quantum realtime alternating automata
Object Oriented Analysis using Natural Language Processing concepts: A Review
Tailoring the MontiArcAutomaton Component & Connector ADL for Generative Development
Crowd-sourcing NLG Data: Pictures Elicit Better Data
Boundary-based MWE segmentation with text partitioning
MontiWeb - Modular Development of Web Information Systems
MontiCore: A Framework for the Development of Textual Domain Specific Languages
Voice based self help System: User Experience Vs Accuracy
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Reduction of Nondeterministic Tree Automata
Toward Mention Detection Robustness with Recurrent Neural Networks
Improving Automated Patent Claim Parsing: Dataset, System, and Experiments
Determining the Characteristic Vocabulary for a Specialized Dictionary using Word2vec and a Directed Crawler
Adaptable Symbol Table Management by Meta Modeling and Generation of Symbol Table Infrastructures
Certification of Prefixed Tableau Proofs for Modal Logic
The Best of Both Worlds: Linear Functional Programming without Compromise
Why Can't You Behave? Non-termination Analysis of Direct Recursive Rules with Constraints
Liveness Verification and Synthesis: New Algorithms for Recursive Programs
Solutions of twisted word equations, EDT0L languages, and context-free groups
Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
Detecting English Writing Styles For Non Native Speakers
Building Morphological Chains for Agglutinative Languages
Where to Play: Retrieval of Video Segments using Natural-Language Queries
The Power of Constraint Grammars Revisited
CodeSum: Translate Program Language to Natural Language
Morphology Generation for Statistical Machine Translation
Combining Representation Learning with Logic for Language Processing
Bidirectional Attention for SQL Generation
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering
Counterexamples for Robotic Planning Explained in Structured Language
Synthesizing Bijective Lenses
Emergent Parsing and Generation with Generalized Chart
A Chart Generator for Shake and Bake Machine Translation
Building Knowledge Bases for the Generation of Software Documentation
On Torsion-Free Semigroups Generated by Invertible Reversible Mealy Automata
Towards Music Captioning: Generating Music Playlist Descriptions
A Geometric Method to Obtain the Generation Probability of a Sentence
Deep Recurrent Generative Decoder for Abstractive Text Summarization
Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition
Generating Sentences by Editing Prototypes
Source-side Prediction for Neural Headline Generation
Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types
The PITA System: Tabling and Answer Subsumption for Reasoning under Uncertainty
Where to put the Image in an Image Caption Generator
Generating One-Anaphoric Expressions: Where Does the Decision Lie?
A Logic-based Approach to Generatively Defined Discriminative Modeling
Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation
A Generalized Quantifier Concept in Computational Complexity Theory
The equality problem for infinite words generated by primitive morphisms
An Analysis of General Fuzzy Logic and Fuzzy Reasoning Method
Automatic Test Data Generation and Model Checking with CHR
The lamplighter group $\mathbb{Z}_3\wr\mathbb{Z}$ generated by a bireversible automaton
An Application of the Generalized Rectangular Fuzzy Model to Critical Thinking Assessment
On elementary equivalence of rings with a finitely generated additive group
Event Representations for Automated Story Generation with Deep Neural Nets
Order-Planning Neural Text Generation From Structured Data
Automatic Generation of Benchmarks for Entity Recognition and Linking
MojiTalk: Generating Emotional Responses at Scale
Image Captioning at Will: A Versatile Scheme for Effectively Injecting Sentiments into Image Descriptions
Visual definition of procedures for automatic virtual scene generation
Learning Fault-tolerant Speech Parsing with SCREEN
Observability and Decentralized Control of Fuzzy Discrete Event Systems
Modules over relative monads for syntax and semantics
An MML-based tool for evaluating the complexity of (stochastic) logic theories
Checking generalized debates with small space and randomness
Deverbal semantics and the Montagovian generative lexicon
Synthesis from Formal Partial Abstractions
Building a Fine-Grained Entity Typing System Overnight for a New X (X = Language, Domain, Genre)
End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering
The cognitive roots of regularization in language
An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning
Proof-Relevant Logical Relations for Name Generation
Emergence of Algorithmic Languages in Genetic Systems
Automated tone transcription
Parsing English with a Link Grammar
Apportioning Development Effort in a Probabilistic LR Parsing System through Evaluation
Efficient Algorithms for Parsing the DOP Model
Metrics for Evaluating Dialogue Strategies in a Spoken Language System
A Portable Algorithm for Mapping Bitext Correspondence
Some Properties of Preposition and Subordinate Conjunction Attachments
Conditions on Consistency of Probabilistic Tree Adjoining Grammars
A Splitting Set Theorem for Epistemic Specifications
ATLAS: A flexible and extensible architecture for linguistic annotation
Fault Detection using Immune-Based Systems and Formal Language Algorithms
Disjunctive Logic Programs with Inheritance
Ellogon: A New Text Engineering Platform
Measuring the Functional Load of Phonological Contrasts
Supervisory Control of Fuzzy Discrete Event Systems
Checking modes of HAL programs
On the freeze quantifier in Constraint LTL: decidability and complexity
Towards a quantum evolutionary scheme: violating Bell's inequalities in language
Detecting palindromes, patterns, and borders in regular languages
A Formal Foundation for XrML
Discovering Global Patterns in Linguistic Networks through Spectral Analysis: A Case Study of the Consonant Inventories
A Type System Theory for Higher-Order Intensional Logic Support for Variable Bindings in Hybrid Intensional-Imperative Programs in GIPSY
Lazy mixin modules and disciplined effects
The Complexity of Translation Membership for Macro Tree Transducers
Review and Analysis of The Issues of Unified Modeling Language for Visualizing, Specifying, Constructing and Documenting the Artifacts of a Software-Intensive System
Interlanguages and synchronic models of computation
Extended Computation Tree Logic
On the Iterated Hairpin Completion
Isomorphism of regular trees and words
Quantum-Like Uncertain Conditionals for Text Analysis
Translation of Pronominal Anaphora between English and Spanish: Discrepancies and Evaluation
Formal Model Engineering for Embedded Systems Using Real-Time Maude
An Abstract Semantics for Inference of Types and Effects in a Multi-Tier Web Language
Tight bounds for the space complexity of nonregular language recognition by real-time machines
Abstract Diagnosis for Timed Concurrent Constraint programs
Using the DiaSpec design language and compiler to develop robotics systems
Implementation of the Domain-Specific Language EasyTime using a LISA Compiler Generator
The Power of Centralized PC Systems of Pushdown Automata
Mining the Web for the Voice of the Herd to Track Stock Market Bubbles
Induction by Coinduction and Control Operators in Call-by-Name
Semantic Measures for the Comparison of Units of Language, Concepts or Instances from Text and Knowledge Base Analysis
Cross-lingual Pseudo-Projected Expectation Regularization for Weakly Supervised Learning
Relative Expressive Power of Navigational Querying on Graphs
The Obvious Solution to Semantic Mapping -- Ask an Expert
Linear usage of state
Quantum Non-Objectivity from Performativity of Quantum Phenomena
A Deep Architecture for Semantic Parsing
Online Stroke and Akshara Recognition GUI in Assamese Language Using Hidden Markov Model
Contrastive Unsupervised Word Alignment with Non-Local Features
Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language
A type-theoretical approach to Universal Grammar
Adding Partial Functions to Constraint Logic Programming with Sets
Semi-supervised Bootstrapping approach for Named Entity Recognition
Extracting Temporal and Causal Relations between Events
Quipper: A Scalable Quantum Programming Language
Auto Spell Suggestion for High Quality Speech Synthesis in Hindi
Basis Identification for Automatic Creation of Pronunciation Lexicon for Proper Names
Constrained Expressions and their Derivatives
Verification of Information Flow Properties under Rational Observation
CD2Alloy: Class Diagrams Analysis Using Alloy Revisited
System Model-Based Definition of Modeling Language Semantics
Translating Videos to Natural Language Using Deep Recurrent Neural Networks
The Timestamp of Timed Automata
An Upper Bound on the Complexity of Recognizable Tree Languages
An Intermediate Language and Estimator for Automated Design Space Exploration on FPGAs
Language Emptiness of Continuous-Time Parametric Timed Automata
A complex network approach to stylometry
Augmenting Agent Platforms to Facilitate Conversation Reasoning
Structural Complexity of Multi-Valued Partial Functions Computed by Nondeterministic Pushdown Automata
Program Synthesis using Natural Language
A Generalised Quantifier Theory of Natural Language in Categorical Compositional Distributional Semantics with Bialgebras
Authorship Attribution Using a Neural Network Language Model
A Survey on Domain-Specific Languages for Machine Learning in Big Data
Semantics of Higher-Order Quantum Computation via Geometry of Interaction
NESTML: a modeling language for spiking neurons
Bi-Text Alignment of Movie Subtitles for Spoken English-Arabic Statistical Machine Translation
Character-Level Language Modeling with Hierarchical Recurrent Neural Networks
The ACPATH Metric: Precise Estimation of the Number of Acyclic Paths in C-like Languages
Piecewise Latent Variables for Neural Variational Text Processing
From signatures to monads in UniMath
A Simple Approach to Multilingual Polarity Classification in Twitter
A Review of Methodologies for Natural-Language-Facilitated Human-Robot Cooperation
Type- and Content-Driven Synthesis of SQL Queries from Natural Language
Weighted Operator Precedence Languages
Native Language Identification using Stacked Generalization
From Modal to Multimodal Ambiguities: a Classification Approach
From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing
A Teacher-Student Framework for Zero-Resource Neural Machine Translation
Candidate sentence selection for language learning exercises: from a comprehensive framework to an empirical evaluation
Linear Parsing Expression Grammars
Outfix-guided insertion
Adversarial Examples for Evaluating Reading Comprehension Systems
Guiding Reinforcement Learning Exploration Using Natural Language
Fooling Vision and Language Models Despite Localization and Attention Mechanism
Natural Language Aggregate Query over RDF Data
Correctness of Speculative Optimizations with Dynamic Deoptimization
Interactive Robot Learning of Gestures, Language and Affordances
Code Completion with Neural Attention and Pointer Networks
Interactive Reinforcement Learning for Object Grounding via Self-Talking
Arrows for Parallel Computation
Stochastic Learning of Nonstationary Kernels for Natural Language Modeling
Comparing the power of advice strings: a notion of complexity for infinite words
Vietnamese Open Information Extraction
Learning Families of Formal Languages from Positive and Negative Information
Examining the Tip of the Iceberg: A Data Set for Idiom Translation
Annotation Artifacts in Natural Language Inference Data
Co-occurrence of the Benford-like and Zipf Laws Arising from the Texts Representing Human and Artificial Languages
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation
Compositional Obverter Communication Learning From Raw Visual Input
A Categorical Approach to Syntactic Monoids
An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems
Some Novel Applications of Explanation-Based Learning to Parsing Lexicalized Tree-Adjoining Grammars
An Efficient Algorithm for Surface Generation
Generic rules and non-constituent coordination
Noun Phrase Reference in Japanese-to-English Machine Translation
Best-First Surface Realization
Determining Internal and External Indices for Chart Generation
Dynamics of text generation with realistic Zipf distribution
Generating a 3D Simulation of a Car Accident from a Written Description in Natural Language: the CarSim System
A Generic Analysis Environment for Curry Programs
Geometric Algebra Techniques for General Relativity
Evolving XSLT stylesheets
Language recognition by generalized quantum finite automata with unbounded error (abstract & poster)
Star-free geodesic languages for groups
On Two Infinite Families of Pairing Bijections
OntoVerbal: a Generic Tool and Practical Application to SNOMED CT
Generate Image Descriptions based on Deep RNN and Memory Cells for Images Features
Creating a Real-Time, Reproducible Event Dataset
Exploration of Proximity Heuristics in Length Normalization
Transition-Based Generation from Abstract Meaning Representations
Disjunctive Datalog with Existential Quantifiers: Semantics, Decidability, and Complexity Issues
Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands
Building a Generation Knowledge Source using Internet-Accessible Newswire
Mixing representation levels: The hybrid approach to automatic text generation
A Novel Approach to Dropped Pronoun Translation
A Bayesian Model for Generative Transition-based Dependency Parsing
Latent Predictor Networks for Code Generation
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation
Structure and enumeration theorems for hereditary properties in finite relational languages
Continuous multilinguality with language vectors
Hygienic Source-Code Generation Using Functors
A Principled Framework for Constructing Natural Language Interfaces To Temporal Databases
Exploiting Term Hiding to Reduce Run-time Checking Overhead
Tagging and Morphological Disambiguation of Turkish Text
Solutions of the quantum dynamical Yang-Baxter equation and dynamical quantum groups
Morphological annotation of Korean with Directly Maintainable Resources
DBMSs Should Talk Back Too
Web Based Cross Language Plagiarism Detection
Acquiring Word-Meaning Mappings for Natural Language Interfaces
A CONVERT compiler of REC for PDP-8
Statistical Laws Governing Fluctuations in Word Use from Word Birth to Word Death
Towards a Tool-based Development Methodology for Pervasive Computing Applications
Gender identity and lexical variation in social media
Interacting via the Heap in the Presence of Recursion
An efficient way to assemble finite element matrices in vector languages
Identifying Bengali Multiword Expressions using Semantic Clustering
Exploring the power of GPU's for training Polyglot language models
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
Joining Transition Systems of Records: Some Congruency and Language-Theoretic Results
The Algebra of Recursive Graph Transformation Language UnCAL: Complete Axiomatisation and Iteration Categorical Semantics
Echoes of power: Language effects and power differences in social interaction
Language of physics, language of math: Disciplinary culture and dynamic epistemology
An End-to-End Neural Network for Polyphonic Piano Music Transcription
On the State Complexity of the Shuffle of Regular Languages
A multi-paradigm language for reactive synthesis
Matrix Product Operators, Matrix Product States, and ab initio Density Matrix Renormalization Group algorithms
Dependency resolution and semantic mining using Tree Adjoining Grammars for Tamil Language
The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGA
Sequence to Sequence Networks for Roman-Urdu to Urdu Transliteration
Topic Compositional Neural Language Model
Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
Compiling Diderot: From Tensor Calculus to C
Automatic Generation of Technical Documentation
Has a Consensus NL Generation Architecture Appeared, and is it Psycholinguistically Plausible?
Textual Economy through Close Coupling of Syntax and Semantics
Bootstrapping Lexical Choice via Multiple-Sequence Alignment
Aspects of enumeration and generation with a string automata representation
Non-redundant random generation algorithms for weighted context-free languages
Formal Model-Driven Engineering: Generating Data and Behavioural Components
Towards a Coalgebraic Chomsky Hierarchy
Text to 3D Scene Generation with Rich Lexical Grounding
Character-based Neural Machine Translation
Sequence Level Training with Recurrent Neural Networks
Modeling Context in Referring Expressions
Simple Image Description Generator via a Linear Phrase-Based Approach
A Hierarchical Neural Autoencoder for Paragraphs and Documents
Industrial Experiences with a Formal DSL Semantics to Check the Correctness of DSL Artifacts
Natural Language Generation as Planning under Uncertainty Using Reinforcement Learning
Generating machine-executable plans from end-user's natural-language instructions
A Hierarchical Approach for Generating Descriptive Image Paragraphs
Joint Copying and Restricted Generation for Paraphrase
Context-aware Captions from Context-agnostic Supervision
Long-Term Memory Networks for Question Answering
A parallel corpus of Python functions and documentation strings for automated code documentation and code generation
OBJ2TEXT: Generating Visually Descriptive Language from Object Layouts
Generalized Results on Monoids as Memory
A Chinese Dataset with Negative Full Forms for General Abbreviation Prediction
Interactive Image Manipulation with Natural Language Instruction Commands
Neural Baby Talk
The Dissecting Power of Regular Languages
Capturing CFLs with Tree Adjoining Grammars
Generating Precondition Expressions in Instructional Text
Specifying Intonation from Context for Speech Synthesis
Efficient Normal-Form Parsing for Combinatory Categorial Grammar
Mental State Adjectives: the Perspective of Generative Lexicon
A Theory of Parallelism and the Case of VP Ellipsis
Applying Natural Language Generation to Indicative Summarization
On probabilistic analog automata
Adapting a general parser to a sublanguage
The Problem of Classical Limit in Quantum Cosmology: The effective action language
Isomorphism property in nonstandard extensions of ZFC universe
Operads in Higher-Dimensional Category Theory
Exponential sums over definable subsets of finite fields
The cognitive homunculus: do tunable languages-of-thought convey adaptive advantage?
Quantum Automata and Quantum Grammars
The holographic principle and the language of genes
Transport in molecular states language: Generalized quantum master equation approach
An Order on Sets of Tilings Corresponding to an Order on Languages
BagPack: A general framework to represent semantic relations
Computational Power of P Systems with Small Size Insertion and Deletion Rules
Dire n'est pas concevoir
Nondeterministic fuzzy automata
Palindromic richness for languages invariant under more symmetries
Further developments in generating type-safe messaging
Preparing Korean Data for the Shared Task on Parsing Morphologically Rich Languages
General Purpose Textual Sentiment Analysis and Emotion Detection Tools
Generation, Implementation and Appraisal of an N-gram based Stemming Algorithm
Looking at Vector Space and Language Models for IR using Density Matrices
Automated Code Generation for Lattice Quantum Chromodynamics and beyond
Maximally Permissive Coordination Supervisory Control -- Towards Necessary and Sufficient Conditions
Robust Named Entity Recognition in Idiosyncratic Domains
The Generalized Smallest Grammar Problem
A Foundational View on Integration Problems
Neural Sentence Ordering
Feasibility of Post-Editing Speech Transcriptions with a Mismatched Crowd
Puzzles in modern biology. II. Language, cancer and the recursive processes of evolutionary innovation
Generating captions without looking beyond objects
A Hybrid Convolutional Variational Autoencoder for Text Generation
Word Affect Intensities
Unlabeled Data for Morphological Generation With Character-Based Sequence-to-Sequence Models
Generic Approach to Certified Static Checking of Module-like Constructs
Programmable Agents
On the Definition of Word Hyperbolic Groups
Some equivalences for Martin's Axiom in asymmetric topology
A generalized small model property for languages which force the infinity
Naive Philosophical Foundations
The Bivariate Normal Copula
Multi-dimensional sets recognizable in all abstract numeration systems
Fixed points avoiding Abelian $k$-powers
dup -- Explicit un-sharing in Haskell
Lambda Dependency-Based Compositional Semantics
Cauchy filters from Pelant's games
Relatively Complete Counterexamples for Higher-Order Programs
Henselian valued fields and inp-minimality
A Gentle Introduction to a Beautiful Theorem of Molien
On the letter frequencies and entropy of written Marathi
Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue
Generating Video Descriptions with Topic Guidance
x-area
A more reasonable proof of Cobham's theorem
Solution sets for equations over free groups are EDT0L languages -- ICALP 2015 version
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals
Ontology as a Source for Rule Generation
Generating Context-Appropriate Word Orders in Turkish
Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions
Generalizing Case Frames Using a Thesaurus and the MDL Principle
Example-Based Optimization of Surface-Generation Tables
Knowledge Acquisition for Content Selection
Tailored Patient Information: Some Issues and Questions
Machine Learning of User Profiles: Representational Issues
Decidable Reasoning in Terminological Knowledge Representation Systems
A Machine-Learning Approach to Estimating the Referential Properties of Japanese Noun Phrases
Generalized Strong Preservation by Abstract Interpretation
Jartege: a Tool for Random Generation of Unit Tests for Java Classes
On tractability and congruence distributivity
Parsimony Principles for Software Components and Metalanguages
Reasoning in Abella about Structural Operational Semantics Specifications
Optimal Control of Infinite Horizon Partially Observable Decision Processes Modeled As Generators of Probabilistic Regular Languages
Pattern matching in compilers
Toward General Analysis of Recursive Probability Models
Probabilistic State-Dependent Grammars for Plan Recognition
A type theoretical framework for natural language semantics: the Montagovian generative lexicon
From bounded affine types to automatic timing analysis
A Generic Analysis Server System for Functional Logic Programs
Context-Free Groups and Bass-Serre Theory
Structural Induction Principles for Functional Programmers
Sign Language Gibberish for syntactic parsing evaluation
Persistent Topology of Syntax
Industrial Experiences with a Formal DSL Semantics to Check Correctness of DSL Transformations
Hybrid SRL with Optimization Modulo Theories
Query Containment for Highly Expressive Datalog Fragments
Phrase-based Image Captioning
Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion
Utilização de Grafos e Matriz de Similaridade na Sumarização Automática de Documentos Baseada em Extração de Frases
Compilation as a Typed EDSL-to-EDSL Transformation
Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings
Context-aware Natural Language Generation with Recurrent Neural Networks
Q-WordNet PPV: Simple, Robust and (almost) Unsupervised Generation of Polarity Lexicons for Multiple Languages
Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
A Module-System Discipline for Model-Driven Software Development
Neural Attribute Machines for Program Generation
Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext
Automatic Summarization of Online Debates
Context Generation from Formal Specifications for C Analysis Tools
Unified Pragmatic Models for Generating and Following Instructions
A Flexible Approach to Automated RNN Architecture Generation
Describing Semantic Representations of Brain Activity Evoked by Visual Stimuli
Language properties and Grammar of Parallel and Series Parallel Languages
An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery
The Control System Modeling Language
Generic modes of consensus formation in stochastic language dynamics
Acquiring Correct Knowledge for Natural Language Generation
Strictly convergent analytic structures
Programming with models: writing statistical algorithms for general model structures with NIMBLE
On the role of simplicity in science
Deep API Learning
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
Attention-based Natural Language Person Retrieval
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates
Error-tolerant Finite State Recognition with Applications to Morphological Analysis and Spelling Correction
Interfacing Constraint-Based Grammars and Generation Algorithms
Constraint-Based Categorial Grammar
Classifying Cue Phrases in Text and Speech Using Machine Learning
Grammar Specialization through Entropy Thresholds
Multiset-Valued Linear Index Grammars: Imposing Dominance Constraints on Derivations
DPOCL: A Principled Approach to Discourse Planning
Decision Lists for Lexical Ambiguity Resolution: Application to Accent Restoration in Spanish and French
Combining Knowledge Sources to Reorder N-Best Speech Hypothesis Lists
The Acquisition of a Lexicon from Paired Phoneme Sequences and Semantic Representations
Recovering From Parser Failures: A Hybrid Statistical/Symbolic Approach
Minimal Change and Bounded Incremental Parsing
Determining Determiner Sequencing: A Syntactic Analysis for English
Focus on ``only" and ``Not"
Memoization of Coroutined Constraints
CRYSTAL: Inducing a Conceptual Dictionary
User-Defined Nonmonotonicity in Unification-Based Formalisms
Co-Indexing Labelled DRSs to Represent and Reason with Ambiguities
Bridging as Coercive Accommodation
Countability and Number in Japanese-to-English Machine Translation
Fast Parsing using Pruning and Grammar Specialization
Efficient Tabular LR Parsing
A Simple Transformation for Offline-Parsable Grammars and its Termination Properties
Coordination as a Direct Process
Computing Optimal Descriptions for Optimality Theory Grammars with Context-Free Position Structures
Maximizing Top-down Constraints for Unification-based Systems
GramCheck: A Grammar and Style Checker
Parallel Replacement in Finite State Calculus
Centering theory and the Italian pronominal system
Evaluating Multilingual Gisting of Web Pages
PARADISE: A Framework for Evaluating Spoken Dialogue Agents
Tracking Initiative in Collaborative Dialogue Interactions
Sense Tagging: Semantic Tagging with a Lexicon
Tagging Grammatical Functions
Generating Coherent Messages in Real-time Decision Support: Exploiting Discourse Theory for Discourse Practice
Expectations in Incremental Discourse Processing
Fast Context-Free Parsing Requires Fast Boolean Matrix Multiplication
Off-line Parsability and the Well-foundedness of Subsumption
Variation and Synthetic Speech
Cognitive scale-free networks as a model for intermittency in human natural language
Context-free multilanguages
Transducers from Rewrite Rules with Backreferences
Detecting Sub-Topic Correspondence through Bipartite Term Clustering
Stacking classifiers for anti-spam filtering of e-mail
CHR as grammar formalism. A first report
Unique Pattern Matching in Strings
Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment
Part-of-Speech Tagging with Minimal Lexicalization
DAB Content Annotation and Receiver Hardware Control with XML
Higher-Order Concurrent Win32 Programming
RRL: A Rich Representation Language for the Description of Agent Behaviour in NECA
A Tutorial on the Expectation-Maximization Algorithm Including Maximum-Likelihood Estimation and EM Training of Probabilistic Context-Free Grammars
Canonical Abstract Syntax Trees
Efficient Compression of Prolog Programs
Toward Functionality Oriented Programming
A Study on Learnability for Rigid Lambek Grammars
Feasible Depth
The Cotton, Simon-Mars and Cotton-York Tensors in Stationary Spacetimes
From 2D conformal to 4D self-dual theories: quaternionic analyticity
A note on Context Sensitive languages and Word Problems
A finite basis theorem for residually finite, congruence meet-semidistributive varieties
Computational Theory of Biological Function I
Quantum Finite One-Counter Automata
Structuring quantum effects: superoperators as arrows
Forbidden lists (NP and CSP for combinatorialists)
A Formal Model of Dictionary Structure and Content
A proposal to a generalised splicing with a self assembly approach
Grammatic -- a tool for grammar definition reuse and modularity
Inseparability and Strong Hypotheses for Disjoint NP Pairs
Lecture notes on the Ein-Popa extension result
Variable binding, symmetric monoidal closed theories, and bigraphs
Non-Markovian quantum trajectories: an exact result
On the Number of Membranes in Unary P Systems
Non-Deterministic Kleene Coalgebras
Small NFAs from Regular Expressions: Some Experimental Results
Yacc is dead
Enumerating Finitary Processes
A Logic Programming Approach for Formal Verification of NetBill Security and Transactions Protocol
Materials to the Russian-Bulgarian Comparative Dictionary "EAD"
Universal Algebra and Mathematical Logic
Construction du lexique LGLex à partir des tables du Lexique-Grammaire des verbes du grec moderne
Counting systems and the First Hilbert problem
On Algorithms and Extensions of Coordination Control of Discrete-Event Systems
An Improved Proof-Theoretic Compilation of Logic Programs
Universal minimal flow in the language of near filters and its applications
Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories
Ultimate periodicity of b-recognisable sets : a quasilinear procedure
Real-Time Vector Automata
Arabizi Detection and Conversion to Arabic
Part of Speech Tagging of Marathi Text Using Trigram Method
Deadlock detection in linear recursive programs
Synchronous Context-Free Grammars and Optimal Linear Parsing Strategies
Cross-lingual Annotation Projection for Semantic Roles
Generating abbreviations using Google Books library
The Foundational Cryptography Framework
Four Ways from Universal to Particular: How Chomsky's Language-Acquisition Faculty is Not Selectionist
Slice Sampling for Probabilistic Programming
Mathematics and language
Shortest paths in one-counter systems
On the Systematic Design of Privacy Policies and Privacy Architectures
Extending DLR with Labelled Tuples, Projections, Functional Dependencies and Objectification (full version)
Decidability of the Membership Problem for $2\times 2$ integer matrices
An extensible formal semantics for UML activity diagrams
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
Exploring Segment Representations for Neural Segmentation Models
Embedded SML using the MLton compiler
Fault Localization in Web Applications via Model Finding
IVOA Recommendation: IVOA Astronomical Data Query Language Version 2.00
Towards a Query Language for the Web of Data (A Vision Paper)
Automata theory in nominal sets
Modeling Algorithms in SystemC and ACL2
Roles in Software Development using Domain Specific Modeling Languages
Probabilistic Models for High-Order Projective Dependency Parsing
Automata and Graph Compression
Robustly Leveraging Prior Knowledge in Text Classification
A Framework for Comparing Groups of Documents
Improving distant supervision using inference learning
On bordered theories for Khovanov homology
Syntax and Semantics of Abstract Binding Trees
Affine computation and affine automaton
QuotationFinder - Searching for Quotations and Allusions in Greek and Latin Texts and Establishing the Degree to Which a Quotation or Allusion Matches Its Source
A System for Probabilistic Linking of Thesauri and Classification Systems
LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues
Experiments in Linear Template Combination using Genetic Algorithms
Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection
PerSum: Novel Systems for Document Summarization in Persian
sk_p: a neural program corrector for MOOCs
Read, Tag, and Parse All at Once, or Fully-neural Dependency Parsing
Solving the Wastewater Treatment Plant Problem with SMT
An Enumeration of the Supercharacter Theories of $C_p \times C_2 \times C_2$ for Prime $p$
Existence of Hierarchies and Human's Pursuit of Top Hierarchy Lead to Power Law
Very Deep Convolutional Networks for End-to-End Speech Recognition
Developing a Practical Reactive Synthesis Tool: Experience and Lessons Learned
An Inductive Proof Method for Simulation-based Compiler Correctness
QEX: a framework for lattice field theories
Exploring Different Dimensions of Attention for Uncertainty Detection
Re-evaluating Automatic Metrics for Image Captioning
Verifying Heaps' law using Google Books Ngram data
Leveraging Cognitive Features for Sentiment Analysis
Training Language Models Using Target-Propagation
Hybrid Dialog State Tracker with ASR Features
Context-Aware Prediction of Derivational Word-forms
Construction of a Japanese Word Similarity Dataset
Simplifying the Bible and Wikipedia Using Statistical Machine Translation
Representing Sentences as Low-Rank Subspaces
Data Augmentation for Low-Resource Neural Machine Translation
Nemo/Hecke: Computer Algebra and Number Theory Packages for the Julia Programming Language
Multiscale sequence modeling with a learned dictionary
Representation Learning for Grounded Spatial Reasoning
A Polynomial Time Match Test for Large Classes of Extended Regular Expressions
Some Improvements in Fuzzy Turing Machines
Fast calculation of entropy with Zhang's estimator
Hierarchically-Attentive RNN for Album Summarization and Storytelling
LSTM Network for Inflected Abbreviation Expansion
V1: A Visual Query Language for Property Graphs
SLING: A framework for frame semantic parsing
Simulating Action Dynamics with Neural Process Networks
What's in a game? A theory of game models
Sentiment Classification using Images and Label Embeddings
The Zero Resource Speech Challenge 2017
Detecting Hate Speech in Social Media
Variational Attention for Sequence-to-Sequence Models
Restrictions on Potential Automatic Structures on Thompson's Group F
Using reinforcement learning to learn how to play text-based games
Tamil Open-Source Landscape - Opportunities and Challenges
The Beta-Bernoulli process and algebraic effects
Proceedings 14th International Conference on Quantum Physics and Logic
Natural Language to Structured Query Generation via Meta-Learning
Translating Questions into Answers using DBPedia n-triples
Tracing sharing in an imperative pure calculus
Text Segmentation as a Supervised Learning Task
Generalized Counters and Reversal Complexity
Reformulation of QCD in the language of general relativity
Exact generation of acyclic deterministic finite automata
Extending the Interaction Nets Calculus by Generic Rules
Interpreting canonical tensor model in minisuperspace
Generating Conceptual Metaphors from Proposition Stores
On the subword complexity of the fixed point of $a \rightarrow aab$, $b \rightarrow b$, and generalizations
Generating Politically-Relevant Event Data
Latent Variable Dialogue Models and their Diversity
Trainable Referring Expression Generation using Overspecification Preferences
Low-Resource Neural Headline Generation
On finitely generated submonoids of free groups
Priority Union and Generalization in Discourse Grammars
Generating Multilingual Documents from a Knowledge Base: The TECHDOC Project
Evaluating Discourse Processing Algorithms
From Regular to Context Free to Mildly Context Sensitive Tree Rewriting Systems: The Path of Child Language Acquisition
Ellipsis and Higher-Order Unification
Presenting Punctuation
Application-driven automatic subgrammar extraction
Dimension in Complexity Classes
Multimodal Meaning Representation for Generic Dialogue Systems Architectures
Do Goedel's incompleteness theorems set absolute limits on the ability of the brain to express and communicate mental concepts verifiably?
The rationality of Sol manifolds
A Language-Based Approach for Improving the Robustness of Network Application Protocol Implementations
Malware Detection using Attribute-Automata to parse Abstract Behavioral Descriptions
Mutation of Directed Graphs -- Corresponding Regular Expressions and Complexity of Their Generation
On Planning with Preferences in HTN
Les entités spatiales dans la langue : étude descriptive, formelle et expérimentale de la catégorisation
HMC: Verifying Functional Programs Using Abstract Interpreters
Comparison of Two Context-Free Rewriting Systems with Simple Context-Checking Mechanisms
Automated Verification of Practical Garbage Collectors
A Generic Scheme for Qualified Constraint Functional Logic Progamming
Selected Operations, Algorithms, and Applications of n-Tape Weighted Finite-State Machines
Poplar: A Java Extension for Evolvable Component Integration
Implementing Equational Constraints in a Functional Language
Question Answering in a Natural Language Understanding System Based on Object-Oriented Semantics
ACME vs PDDL: support for dynamic reconfiguration of software architectures
Automatic Generation of OWL Ontology from XML Data Source
A Framework for Concurrent Imperative Programming
A Domain Specific Language for kinematic models and fast implementations of robot dynamics algorithms
Probabilistic Description Logics
Towards the Fully Automatic Merging of Lexical Resources: A Step Forward
Relative Observability of Discrete-Event Systems and its Supremal Sublanguages
Modal Specifications for Probabilistic Timed Systems
Abstract Interpretation as a Programming Language
Symmetric Groups and Quotient Complexity of Boolean Operations
Natural Language Processing in Biomedicine: A Unified System Architecture Overview
Real Time Strategy Language
Query shredding: Efficient relational evaluation of queries over nested multisets (extended version)
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing
Julia: A Fresh Approach to Numerical Computing
An Abstract Interpretation-based Model of Tracing Just-In-Time Compilation
Dimensionality on Summarization
DNN-based Speech Synthesis for Indian Languages from ASCII text
IVOA Recommendation: Table Access Protocol Version 1.0
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
MontiArcAutomaton: Modeling Architecture and Behavior of Robotic Systems
Lexical Normalisation of Twitter Data
MontiArc - Architectural Modeling of Interactive Distributed and Cyber-Physical Systems
Defining UML Family Members Using Prefaces
Scaling laws in human speech, decreasing emergence of new words and a generalized model
$gen$CNN: A Convolutional Architecture for Word Sequence Prediction
Compression and the origins of Zipf's law of abbreviation
TreatJS: Higher-Order Contracts for JavaScript
A Simple and Practical Linear Algebra Library Interface with Static Size Checking
Describtion of normal basis of boundary algebras and factor languages of small growth
TheanoLM - An Extensible Toolkit for Neural Network Language Modeling
Declarative Machine Learning - A Classification of Basic Properties and Types
Natural Language Generation enhances human decision-making with uncertain information
Conditional Generation and Snapshot Learning in Neural Dialogue Systems
Criticality in Formal Languages and Statistical Physics
The Almost Equivalence by Asymptotic Probabilities for Regular Languages and Its Computational Complexities
DiffSharp: An AD Library for .NET Languages
Landmark-based consonant voicing detection on multilingual corpora
Generalisation in Named Entity Recognition: A Quantitative Analysis
Part of Speech Based Term Weighting for Information Retrieval
On Natural Language Generation of Formal Argumentation
An efficient algorithm to decide periodicity of b-recognisable sets using LSDF convention
A Constructor-Based Reachability Logic for Rewrite Theories
Stream Graphs and Link Streams for the Modeling of Interactions over Time
Measurement Context Extraction from Text: Discovering Opportunities and Gaps in Earth Science
A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue Modeling
Programmatic Control of a Compiler for Generating High-performance Spatial Hardware
Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding
A Deep Network Model for Paraphrase Detection in Short Text Messages
PronouncUR: An Urdu Pronunciation Lexicon Generator
RankME: Reliable Human Ratings for Natural Language Generation
Insights into End-to-End Learning Scheme for Language Identification
Robustly Safe Compilation or, Efficient, Provably Secure Compilation
Evaluating historical text normalization systems: How well do they generalize?
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text
How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks
Finite generating sets of relatively hyperbolic groups and applications to geodesic languages
The emerging field of language dynamics
Interfacing Interpreted and Compiled Languages to Support Applications on a Massively Parallel Network of Workstations (MP-NOW)
Transfer in a Connectionist Model of the Acquisition of Morphology
The Complexity of Quantified Constraint Satisfaction: Collapsibility, Sink Algebras, and the Three-Element Case
Definition and Implementation of a Points-To Analysis for C-like Languages
Beyond Zipf's law: Modeling the structure of human language
Using the General Intensional Programming System (GIPSY) for Evaluation of Higher-Order Intensional Logic (HOIL) Expressions
Parikh Images of Regular Languages: Complexity and Applications
Formal-language-theoretic Optimal Path Planning For Accommodation of Amortized Uncertainties and Dynamic Effects
Quantitative Languages Defined by Functional Automata
Feedback Generation for Performance Problems in Introductory Programming Assignments
Determinization of fuzzy automata by means of the degrees of language inclusion
Language Models for Image Captioning: The Quirks and What Works
Computational Complexity of the Minimum Cost Homomorphism Problem on Three-Element Domains
Alternation in Quantum Programming: From Superposition of Data to Superposition of Programs
Elaborating Evaluation-Order Polymorphism
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation
Human and Machine Judgements for Russian Semantic Relatedness
Call-by-value, call-by-name and the vectorial behaviour of the algebraic λ-calculus
Language Edit Distance & Maximum Likelihood Parsing of Stochastic Grammars: Faster Algorithms & Connection to Fundamental Graph Problems
Stepwise Debugging of Answer-Set Programs
Using NLU in Context for Question Answering: Improving on Facebook's bAbI Tasks
Indexed Languages and Unification Grammars
Quantum Computers and Quantum Computer Languages: Quantum Assembly Language and Quantum C Language
Super-Languages: Developing Languages and Applications with XMF (Second Edition)
Ezhil: A Tamil Programming Language
The Complexity of General-Valued CSPs
Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change
Story Generation from Sequence of Independent Short Descriptions
Automatic generation of analysis class diagrams from use case specifications
Toward Controlled Generation of Text
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
Text Generation Based on Generative Adversarial Nets with Latent Variable
QINL: Query-integrated Languages
Two-Dimensional Pattern Languages
Language Approximation With One-Counter Automata
Semi-Supervised QA with Generative Domain-Adaptive Nets
Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation
Language Segmentation
A Flexible Shallow Approach to Text Generation
First-order Complete and Computationally Complete Query Languages for Spatio-Temporal Databases
Learning to generalize to new compositions in image understanding
Generative Deep Neural Networks for Dialogue: A Short Review
Automatic Generation of Grounded Visual Questions
Generating Code with Polymorphic let: A Ballad of Value Restriction, Copying and Sharing
Neural Question Generation from Text: A Preliminary Study
Automatized Generation of Alphabets of Symbols
Neural Text Generation: Past, Present and Beyond
Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning
The limits of SDP relaxations for general-valued CSPs
Self-Organizing Machine Translation: Example-Driven Induction of Transfer Functions
A Complete and Recursive Feature Theory
Focusing for Pronoun Resolution in English Discourse: An Implementation
Syntactic Analysis Of Natural Language Using Linguistic Rules And Corpus-based Patterns
Classifier Assignment by Corpus-based Approach
Formalization and Parsing of Typed Unification-Based ID/LP Grammars
Different Issues in the Design of a Lemmatizer/Tagger for Basque
A Note on the Complexity of Restricted Attribute-Value Grammars
Collaborating on Referring Expressions
Statistical Decision-Tree Models for Parsing
The Use of Knowledge Preconditions in Language Processing
How Part-of-Speech Tags Affect Text Retrieval and Filtering Performance
Error-tolerant Tree Matching
A Data-Oriented Approach to Semantic Interpretation
Cue Phrase Classification Using Machine Learning
Stochastic Attribute-Value Grammars
Integrating HMM-Based Speech Recognition With Direct Manipulation In A Multimodal Korean Natural Language Interface
Memory-Based Learning: Using Similarity for Smoothing
A Corpus-Based Approach for Building Semantic Lexicons
On aligning trees
Probabilistic Constraint Logic Programming
On the use of expectations for detecting and repairing human-machine miscommunication
Topic Graph Generation for Query Navigation: Use of Frequency Classes for Topic Extraction
Group Theory and Grammatical Description
Letter to Sound Rules for Accented Lexicon Compression
A Variant of Earley Parsing
Statistical Inference and Probabilistic Modelling for Constraint-Based NLP
Numeration systems on a regular language: Arithmetic operations, Recognizability and Formal power series
Many uses, many annotations for large speech corpora: Switchboard and TDT as case studies
Sequence-Based Abstract Interpretation of Prolog
Coaxing Confidences from an Old Friend: Probabilistic Classifications from Transformation Rule Lists
Building Multi-Platform User Interfaces with UIML
A Multi-Step Process for Generating Multi-Platform User Interfaces using UIML
A Refinement Calculus for Logic Programs
Mostly-Unsupervised Statistical Segmentation of Japanese Kanji Sequences
A Method for Open-Vocabulary Speech-Driven Text Retrieval
McRunjob: A High Energy Physics Workflow Planner for Grid Production Processing
Local-search techniques for propositional logic extended with cardinality constraints
Running C++ models undet the Swarm environment
Zipf's law and the creation of musical context
On the Theory of Structural Subtyping
A knowledge-based approach to semi-automatic annotation of multimedia documents via user adaptation
A Systematic Aspect-Oriented Refactoring and Testing Strategy, and its Application to JHotDraw
Weighted Automata in Text and Speech Processing
Integration of the DOLCE top-level ontology into the OntoSpec methodology
ACD Term Rewriting
On the logical definability of certain graph and poset languages
Navigating multilingual news collections using automatically extracted information
Languages, Algorithms, Procedures, Calculi, and Metalogic
Programming Complex Systems
Degrees of freedom of tongue movements in speech may be constrained by biomechanics
Algebraic Geometry over Free Metabelian Lie Algebra II: Finite Field Case
Efficient Solution of Language Equations Using Partitioned Representations
The Expressional Limits of Formal Languages in the Notion of Observation
Coherence thresholds in models of language change and evolution: the effects of noise, dynamics and network of interactions
Research report: State complexity of operations on two-way quantum finite automata
Concept-Oriented Model and Query Language
On the Borel Inseparability of Game Tree Languages
As time goes by: Constraint Handling Rules - A survey of CHR research from 1998 to 2007
Structure Theorem and Strict Alternation Hierarchy for FO^2 on Words
Testing the Equivalence of Regular Languages
A unifying approach to picture grammars
Deterministic Consistency: A Programming Model for Shared Memory Parallelism
Synthesis of AMBA AHB from Formal Specification
Simplifying Parallelization of Scientific Codes by a Function-Centric Approach in Python
Recognition of Handwritten Textual Annotations using Tesseract Open Source OCR Engine for information Just In Time (iJIT)
Abstract Certification of Global Non-Interference in Rewriting Logic
Avoiding another Green Elephant - A Proposal for the Next Generation HLA based on the Model Driven Architecture
On the Implementation of GNU Prolog
Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs
Experimental Support for a Categorical Compositional Distributional Model of Meaning
Discovering Knowledge using a Constraint-based Language
Proceedings 10th International Workshop on the Foundations of Coordination Languages and Software Architectures
Efficient and Correct Stencil Computation via Pattern Matching and Static Typing
Building-Blocks for Performance Oriented DSLs
A DSEL for Studying and Explaining Causation
Resumption-based big-step and small-step interpreters for While with interactive I/O
Genetic Algorithm (GA) in Feature Selection for CRF Based Manipuri Multiword Expression (MWE) Identification
Programming errors in traversal programs over structured data
Search Combinators
Parameter Learning in PRISM Programs with Continuous Random Variables
Analysing Temporally Annotated Corpora with CAVaT
A model-driven approach for processing complex events
Non-definability of languages by generalized first-order formulas over (N,+)
Derivatives of Approximate Regular Expressions
Implementation of EasyTime Formal Semantics using a LISA Compiler Generator
A model of competition among more than two languages
Adversarial Evaluation for Models of Natural Language
OGCOSMO: An auxiliary tool for the study of the Universe within hierarchical scenario of structure formation
Managing Complex Structured Data In a Fast Evolving Environment
Natural Language Processing - A Survey
Annotating Answer-Set Programs in LANA?
Extraction of domain-specific bilingual lexicon from comparable corpora: compositional translation and ranking
Deriving program transformations by demonstration
Towards a Semantic-based Approach for Modeling Regulatory Documents in Building Industry
Probabilistic Frame Induction
A Domain-Specific Language for Rich Motor Skill Architectures
Practical Inlining of Functions with Free Variables
The Size-Change Termination Principle for Constructor Based Languages
A Grammatical Inference Approach to Language-Based Anomaly Detection in XML
Interplay between Point-Group Symmetries and the Choice of the Bloch Basis in Multiband Models
Patterns for computational effects arising from a monad or a comonad
Hybrid Automated Reasoning Tools: from Black-box to Clear-box Integration
On the Semantics of Gringo
A Study of Successive Over-relaxation Method Parallelization Over Modern HPC Languages
Towards a Generic Framework for the Development of Unicode Based Digital Sindhi Dictionaries
ARSENAL: Automatic Requirements Specification Extraction from Natural Language
Aspect-Based Opinion Extraction from Customer reviews
Complete Separation of the 3 Tiers - Divide and Conquer
A preliminary study of Croatian Language Syllable Networks
TATI -- A Logo-like interface for microworlds and simulations for physics teaching in Second Life
Dynamic Choreographies - Safe Runtime Updates of Distributed Applications
A Proposed Framework for Development of a Visualizer Based on Memory Transfer Language (MTL)
Encoding the structure of many-body localization with matrix product operators
Online interpretation of numeric sign language using 2-d skeletal model
Patterns in the English Language: Phonological Networks, Percolation and Assembly Models
Transforming while/do/for/foreach-Loops into Recursive Methods
Statistically Significant Detection of Linguistic Change
Opinion mining of text documents written in Macedonian language
Towards a Fully Abstract Compiler Using Micro-Policies: Secure Compilation for Mutually Distrustful Components
Semi-supervised Sequence Learning
Order-Embeddings of Images and Language
Regularizing RNNs by Stabilizing Activations
Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding
An Interference-Free Programming Model for Network Objects
SIMPL: A DSL for Automatic Specialization of Inference Algorithms
On Modular and Fully-Abstract Compilation -- Technical Appendix
Morphological Priors for Probabilistic Neural Word Embeddings
Resolving Out-of-Vocabulary Words with Bilingual Embeddings in Machine Translation
COREALMLIB: An ALM Library Translated from the Component Library
Declarative Event-Based Workflow as Distributed Dynamic Condition Response Graphs
Extending Object-Oriented Languages by Declarative Specifications of Complex Objects using Answer-Set Programming
Decentralized Supervisory Control of Discrete Event Systems for Bisimulation Equivalence
Locality Optimization for Data Parallel Programs
Formal Verification of a C Value Analysis Based on Abstract Interpretation
Seeing What You're Told: Sentence-Guided Activity Recognition In Video
A Representation Theorem for Second-Order Functionals
Domain Theory for Modeling OOP: A Summary
On the density of certain languages with $p^2$ letters
Controversy and Sentiment in Online News
Games for Active XML Revisited
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
Learning to Understand Phrases by Embedding the Dictionary
Constraining application behaviour by generating languages
Context-Free Path Queries on RDF Graphs
Parsing Expression Grammars Made Practical
Emerging Dimension Weights in a Conceptual Spaces Model of Concept Combination
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)
Knowledge Transfer with Medical Language Embeddings
A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation
Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs
Two-Finger Keyboard Layout Problem: An Application On Turkish Language
Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention
Exploiting N-Best Hypotheses to Improve an SMT Approach to Grammatical Error Correction
Implementing graph grammars for intelligence analysis in OCaml
Set-Theoretic Types for Polymorphic Variants
The Role of CNL and AMR in Scalable Abstractive Summarization for Multilingual Media Monitoring
Pragmatic factors in image description: the case of negations
NN-grams: Unifying neural network and n-gram language models for Speech Recognition
Predicting the Relative Difficulty of Single Sentences With and Without Surrounding Context
Tie-breaker: Using language models to quantify gender bias in sports journalism
Cantor meets Scott: Semantic Foundations for Probabilistic Networks
Multimodal Attention for Neural Machine Translation
Neural Machine Transliteration: Preliminary Results
The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)
Automatic semigroups vs automaton semigroups
Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks
Bidirectional LSTM-CRF for Clinical Concept Extraction
Reasoning with Memory Augmented Neural Networks for Language Comprehension
What Do Recurrent Neural Network Grammars Learn About Syntax?
Coherent Dialogue with Attention-based Language Models
Bidirectional LSTM-CRF for Clinical Concept Extraction
Single-Pass, Adaptive Natural Language Filtering: Measuring Value in User Generated Comments on Large-Scale, Social Media News Forums
Lazy Automata Techniques for WS1S
User Assistance Characteristics of the USE Model Checking Tool
Validating and describing linked data portals using shapes
An Empirical Evaluation of Zero Resource Acoustic Unit Discovery
Printed Arabic Text Recognition using Linear and Nonlinear Regression
Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables
McFSM: Globally Taming Complex Systems
Coping with Construals in Broad-Coverage Semantic Annotation of Adpositions
Multimodal Language Specification for Human Adaptive Mechatronics
Well-Behaved Model Transformations with Model Subtyping
Interacting Conceptual Spaces I : Grammatical Composition of Concepts
Crowdsourcing Universal Part-Of-Speech Tags for Code-Switching
Studying the Prevalence of Exception Handling Anti-Patterns
The Proof of CSP Dichotomy Conjecture
Semi-supervised sequence tagging with bidirectional language models
Logical Parsing from Natural Language Based on a Neural Translation Model
Subregular Complexity and Deep Learning
Local Monotonic Attention Mechanism for End-to-End Speech and Language Processing
Predicting Causes of Reformulation in Intelligent Assistants
Spherical Paragraph Model
SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies
From Type Spaces to Probability Frames and Back, via Language
Input-Driven Double-Head Pushdown Automata
Code Staging in GNU Guix
Translating Domain-Specific Expressions in Knowledge Bases with Neural Machine Translation
KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition
Data Innovation for International Development: An overview of natural language processing for qualitative data analysis
Unwritten Languages Demand Attention Too! Word Discovery with Encoder-Decoder Models
Low-resource bilingual lexicon extraction using graph based word embeddings
Reversible Computation in Term Rewriting
On the insertion of n-powers
Natural Language Guided Visual Relationship Detection
Grammatical facial expression recognition using customized deep neural network architecture
Style Transfer in Text: Exploration and Evaluation
HoME: a Household Multimodal Environment
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling
Neural Machine Translation by Generating Multiple Linguistic Factors
Translating Pro-Drop Languages with Reconstruction Models
Grounded Language Understanding for Manipulation Instructions Using GAN-Based Classification
Discrete Autoencoders for Sequence Models
EMME: a formal tool for ECMAScript Memory Model Evaluation
SparseMAP: Differentiable Sparse Structured Inference
A wide-spectrum language for verification of programs on weak memory models
Object Captioning and Retrieval with Natural Language
Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition
Guide Me: Interacting with Deep Networks
A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification
Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering
Programmatically Interpretable Reinforcement Learning
Restricting the Weak-Generative Capacity of Synchronous Tree-Adjoining Grammars
Corpus-Driven Knowledge Acquisition for Discourse Analysis
Learning Unification-Based Natural Language Grammars
Orthographic Structuring of Human Speech and Texts: Linguistic Application of Recurrence Quantification Analysis
The Weaves Reconfigurable Programming Framework
The DLV System for Knowledge Representation and Reasoning
Beyond word frequency: Bursts, lulls, and scaling in the temporal distributions of words
Introduction to the Report "Interlanguages and Synchronic Models of Computation."
Malagasy Dialects and the Peopling of Madagascar
Repetitive Reduction Patterns in Lambda Calculus with letrec (Work in Progress)
QIS-XML: An Extensible Markup Language for Quantum Information Science
Hamming Approximation of NP Witnesses
Reconsidering Written Language
A Core Calculus for Provenance
Category-Theoretic Quantitative Compositional Distributional Models of Natural Language Semantics
Answering SPARQL queries modulo RDF Schema with paths
Context-based Word Acquisition for Situated Dialogue in a Virtual World
Enabling FPGAs for the Masses
From Captions to Visual Concepts and Back
The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories
Left-corner Methods for Syntactic Modeling with Universal Structural Constraints
WNetKAT: A Weighted SDN Programming and Verification Language
Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL
Sentiment of Emojis
Graded Entailment for Compositional Distributional Semantics
CVC Verilog Compiler -- Fast Complex Language Compilers Can be Simple
The power of Sherali-Adams relaxations for general-valued CSPs
HoTTSQL: Proving Query Rewrites with Univalent SQL Semantics
The infochemical core
Kannada Spell Checker with Sandhi Splitter
From narrative descriptions to MedDRA: automagically encoding adverse drug reactions
Tinkering Under the Hood: Interactive Zero-Shot Learning with Net Surgery
Fast Domain Adaptation for Neural Machine Translation
Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task
A modeling and simulation language for biological cells with coupled mechanical and chemical processes
Petri Automata
Automated Phrase Mining from Massive Text Corpora
Dialectometric analysis of language variation in Twitter
Automatic Text Summarization Approaches to Speed up Topic Model Learning Process
Fine-graind Image Classification via Combining Vision and Language
Simulations and Antichains for Efficient Handling of Finite Automata
Extracting Formal Models from Normative Texts
Effective Spoken Language Labeling with Deep Recurrent Neural Networks
Revisiting Elementary Denotational Semantics
Cynical Selection of Language Model Training Data
Empower Sequence Labeling with Task-Aware Neural Language Model
Software Engineering Modeling Applied to English Verb Classification (and Poetry)
Improved Twitter Sentiment Analysis Using Naive Bayes and Custom Language Model
TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social Aspects of Internet Slang
Video-based Sign Language Recognition without Temporal Segmentation
Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings
Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition
Generalized geometries and kinematics for Quantum Gravity
Asymptotic topology
Linguistic Paradoxes and Tautologies
Orbifolds and stable homotopy groups
A Generalized Composition of Quadratic Forms based on Quadratic Pairs
Engel conditions and symmetric tensors
A method for recursively generating sequential rational approximations to $\sqrt[n]{k}$
Abstract Representations and Frequent Pattern Discovery
The Generic Model of Computation
On partial and generic uniqueness of block term tensor decompositions
Generalized geometry, T-duality, and renormalization group flow
Image processing using miniKanren
Autonomization of Monoidal Categories
Value Automata with Filters
Dirac matrices as elements of superalgebraic matrix algebra
Procedural and Non-Procedural Implementation of Search Strategies in Control Network Programming
Presentations of Topological Full Groups by Generators and Relations
Algebraic and Nori fundamental gerbes
On level-transitivity and exponential growth
Modeling documents with Generative Adversarial Networks
The null-geodesic flow near horizons
Eliminating Field Quantifiers in Strongly Dependent Henselian Fields
To Infinity and Beyond
Functorial Semantics for Relational Theories
Explicit equations for exterior square of the general linear group
Generalizing the Paige-Tarjan Algorithm by Abstract Interpretation
Automated Generation of User Guidance by Combining Computation and Deduction
Using Neural Generative Models to Release Synthetic Twitter Corpora with Reduced Stylometric Identifiability of Users
Faithful (meta-)encodings of programmable strategies into term rewriting systems
Achieving Fluency and Coherency in Task-oriented Dialog
Syntactic-Head-Driven Generation
Permutations generated by a depth 2 and infinite stack in series are algebraic
Chinese Song Iambics Generation with Neural Attention-based Model
Chinese Poetry Generation with Planning based Neural Network
PAWS: A Tool for the Analysis of Weighted Systems
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?
Tensor Product Generation Networks for Deep NLP Modeling
Language-integrated provenance in Haskell
Query-driven Procedures for Hybrid MKNF Knowledge Bases
Creating Textual Language Dialects Using Aspect-like Techniques
Quotient Complexity of Star-Free Languages
Weak $ω$-Regular Trace Languages
Fly out-smarts man
LanideNN: Multilingual Language Identification on Character Window
Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data
Combined-Semantics Equivalence Is Decidable for a Practical Class of Conjunctive Queries
A Primer on Resurgent Transseries and Their Asymptotics
Generalized Hadamard Product and the Derivatives of Spectral Functions
A generic tool to generate a lexicon for NLP from Lexicon-Grammar tables
A Formal Comparison of Approaches to Datatype-Generic Programming
Towards a Logic-Based Unifying Framework for Computing
Political Speech Generation
Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
Neural Net Models for Open-Domain Discourse Coherence
How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset
Neural Models for Key Phrase Detection and Question Generation
Generative Bridging Network in Neural Sequence Prediction
Translating Phrases in Neural Machine Translation
Generating Natural Adversarial Examples
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions
Imagine This! Scripts to Compositions to Videos
Free differential algebras and generic 2D dilatonic (super)gravities
Generating Images from Captions with Attention
A generalized Goulden-Jackson cluster method and lattice path enumeration
Towards an Accurate Mathematical Model of Generic Nominally-Typed OOP
Neural Personalized Response Generation as Domain Adaptation
Texygen: A Benchmarking Platform for Text Generation Models
Automatic Generation of Sparse Tensor Kernels with Workspaces
Review of Charniak's "Statistical Language Learning"
Birth, survival and death of languages by Monte Carlo simulation
Generalisation of language and knowledge models for corpus analysis
Efficient Separability of Regular Languages by Subsequences and Suffixes
Representation of (Left) Ideal Regular Languages by Synchronizing Automata
Abductive Equivalential Translation and its application to Natural Language Database Interfacing
Vagueness of Linguistic variable
Expressivity of Time-Varying Graphs and the Power of Waiting in Dynamic Networks
Soft Contract Verification for Higher-Order Stateful Programs
A Second-Order Approach to Complex Event Recognition
Best-first Model Merging for Hidden Markov Model Induction
Three studies of grammar-based surface-syntactic parsing of unrestricted English text. A summary and orientation
A Consistency-Based Model for Belief Change: Preliminary Report
Event Driven Computations for Relational Query Language
Stochastic model for the vocabulary growth in natural languages
Taming the Infinite Chase: Query Answering under Expressive Integrity Constraints
Text to Multi-level MindMaps: A Novel Method for Hierarchical Visual Abstraction of Natural Language Text
Subtyping in Java is a Fractal
Automatic Parallelization: Executing Sequential Programs on a Task-Based Parallel Runtime
Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging
Paraconsistency and Word Puzzles
Generating Natural Questions About an Image
Learning Visual Reasoning Without Strong Priors
A general formal memory framework in Coq for verifying the properties of programs based on higher-order logic theorem proving with increased automation, consistency, and reusability
Abstract Generation based on Rhetorical Structure Extraction
Stochastic phonological grammars and acceptability
Linguistic Reflection in Java
Extending the code generation capabilities of the Together CASE tool to support Data Definition languages
Algebraic Geometry over Free Groups: Lifting Solutions into Generic Points
MUDOS-NG: Multi-document Summaries Using N-gram Graphs (Tech Report)
Generating Stack-based Access Control Policies
Automated generation and symbolic manipulation of tensor product finite elements
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
Median-Based Generation of Synthetic Speech Durations using a Non-Parametric Approach
Polymonadic Programming
Deep Visual-Semantic Alignments for Generating Image Descriptions
Visual Madlibs: Fill in the blank Image Generation and Question Answering
A New Foundation for Finitary Corecursion
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Generating reversible circuits from higher-order functional programs
Generating Visual Explanations
Redundancy-free Verbalization of Individuals for Ontology Validation
Generating Simulations of Motion Events from Verbal Descriptions
Hybrid Static/Dynamic Schedules for Tiled Polyhedral Programs
Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks
Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization
Multi-Task Video Captioning with Video and Entailment Generation
Depression and Self-Harm Risk Assessment in Online Forums
MoNoise: Modeling Noise Using a Modular Normalization System
Learning Phrase Embeddings from Paraphrases with GRUs
A generalized parsing framework for Abstract Grammars
Generative Interest Estimation for Document Recommendations
A New Foundation for Finitary Corecursion and Iterative Algebras
Syntax-Directed Variational Autoencoder for Structured Data
An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
Not just about size - A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications
Well-Typed Languages are Sound
One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis
Syntactic Complexity of Star-Free Languages
General Logic-Systems and Consequence Operators
Random Generation and Approximate Counting of Combinatorial Structures
The Quest for Optimal Sorting Networks: Efficient Generation of Two-Layer Prefixes
Random Generation and Enumeration of Accessible Determinisitic Real-time Pushdown Automata
Closures and generating sets related to combinations of structures
PyCells for an Open Semiconductor Industry
Generalized Sampling in Julia
Optimized Automatic Code Generation for Geometric Algebra Based Algorithms with Ray Tracing Application
AMR-to-text generation as a Traveling Salesman Problem
Non-coordinates basis in General Relativity and Cartan's structure equations
No information can be conveyed by certain events: The case of the clever widows of Fornicalia and the Stobon Oracle
Introduction to the CoNLL-2000 Shared Task: Chunking
Introduction to the CoNLL-2001 Shared Task: Clause Identification
A Probabilistic Model of Machine Translation
Multi-document Biography Summarization
On the structure of linear-time reducibility
Problems of the Strategy of Regions
String Partons and Multiple Quantisation
The abelian and non-abelian Josephson effect and pseudo-goldstone bosons
On certain higher dimensional analogues of vertex algebras
Automatic Quotients of Free Groups
Groups, periodic planes and hyperbolic buildings
Adaptive Quadrilateral Mesh in Curved Domains
Causality Principle
Consciousness in Physics
Analise dinamica da tendencia para o equilibrio num modelo simples: a Segunda Lei de Newton e a Segunda Lei da Termodinamica
Finite automata models of quantized systems: conceptual status and outlook
The social aspects of quantum entanglement
Proof nets for display logic
Mathematics as the language of physics
Language of Boolean functions its Grammar and Machine
Adversary lower bounds for nonadaptive quantum algorithms
Diagrammatics for Soergel categories
On the Jacobian of the harmonic moment map
Sequences close to periodic
On the Representation of Finite Automata
Capacity Bounded Grammars and Petri Nets
Statechart Verification with iState
Specifying Data Objects with Initial Algebras
Deterministic Autopoietic Automata
The Morphisms With Unstackable Image Words
OCamlJIT 2.0 - Faster Objective Caml
Pushing undecidability of the isolation problem for probabilistic automata
Generalized Post Embedding Problems
Cell decomposition for semi-affine structures on p-adic fields
Solving the TTC 2011 Compiler Optimization Case with GrGen.NET
The rigidity of periodic frameworks as graphs on a fixed torus
Periodic Rigidity on a Variable Torus Using Inductive Constructions
Verification Condition Generation and Variable Conditions in Smallfoot
An effective characterization of the alternation hierarchy in two-variable logic
Deciding Word Problems of Semigroups using Finite State Automata
The enumeration of three pattern classes
The Cerny conjecture for automata respecting intervals of a directed graph
A Robust Specification Theory for Modal Event-Clock Automata
Several AES Variants under VHDL language In FPGA
On the Number of Unbordered Factors
Fixed points of endomorphisms of trace monoids
Quantum motor and future
Rational Subsets and Submonoids of Wreath Products
Note on Undecidability of Bisimilarity for Second-Order Pushdown Processes
Fibre bundle formulation of time-dependent mechanics
Soergel Calculus
Inverse semigroups with rational word problem are finite
A remark on the discriminant of Hill's equation and Herglotz functions
Quantum Entanglement and Decoherence: Beyond Particle Models. A Farewell to Quantum Mechanics's Weirdness
Targeting HIV-related Medication Side Effects and Sentiment Using Twitter Data
How the Voynich Manuscript was created
Subcompletions of representable relation algebras
Spin-density and Vorticity Contribution to the Cosmological Background
Axiomatizing Analog Algorithms
Derived-Term Automata of Multitape Rational Expressions (Long version)
Invitation to Algorithmic Uses of Inclusion-Exclusion
On the logical strength of the automorphism groups of free nilpotent groups
A new approach to cross-bifix-free sets
Monoidify! Monoids as a Design Principle for Efficient MapReduce Algorithms
A New Heuristic Synchronizing Algorithm
An R Implementation of the Polya-Aeppli Distribution
Unraveling simplicity in elementary cellular automata
Computable Axiomatizability of Elementary Classes
Proof Systems and Models for the First-Order Primal Logic
Partial Derivative Automaton for Regular Expressions with Shuffle
Rust-Bio - a fast and safe bioinformatics library
Thinking Required
Comparing Weakest Precondition and Weakest Liberal Precondition
Tangles and Connectivity in Graphs
Pseudo-local Theories: A Functional Class Proposal
Symbolic Tensor Calculus -- Functional and Dynamic Approach
Automatic Theorem Proving in Walnut
A Step from Probabilistic Programming to Cognitive Architectures
Relative exchangeability with equivalence relations
A Modular Structural Operational Semantics for Delimited Continuations
Unsupervised Neural Hidden Markov Models
Decision problems on unary probabilistic and quantum automata
Morphisms on infinite alphabets, countable states automata and regular sequences
Social Media Argumentation Mining: The Quest for Deliberateness in Raucousness
Smooth contractible threefolds with hyperbolic $\mathbb{G}_{m}$-actions via ps-divisors
Monadic Second Order Logic with Measure and Category Quantifiers
Vanishing theorems for perverse sheaves on abelian varieties, revisited
Deriving Generic Bounds for Time-Series Constraints Based on Regular Expressions Characteristics
Responsive Graphical User Interface (ReGUI) and its Implementation in MATLAB
A Survey of Distant Supervision Methods using PGMs
Coherence for braided and symmetric pseudomonoids
Automatic Mapping of French Discourse Connectives to PDTB Discourse Relations
AMR Parsing using Stack-LSTMs
Owl: A General-Purpose Numerical Library in OCaml
The Trees of Hanoi
Uniqueness of Schrödinger flow on manifolds
Local finiteness for Green's relations in semigroup varieties
One loop QED corrections to the process $ γγ\rightarrowμ^+μ^-γ$
Formal specification of the FlexRay protocol using FocusST
The BMM symmetrising trace conjecture for groups $G_4,\,G_5,\,G_6,\,G_7,\,G_8$
Mittens: An Extension of GloVe for Learning Domain-Specialized Representations
Network Traffic Anomaly Detection Using Recurrent Neural Networks
MaskGAN: Better Text Generation via Filling in the______
A simple branching model that reproduces language family and language population distributions
Pumping lemmas for linear and nonlinear context-free languages
Formalization of the pumping lemma for context-free languages
Natural Language Understanding with Distributed Representation
Natural Language Processing using Hadoop and KOSHIK
Processing XML for Domain Specific Languages
Language classification from bilingual word embedding graphs
On the Similarities Between Native, Non-native and Translated Texts
Discriminating Similar Languages: Evaluations and Explorations
Dialog Context Language Modeling with Recurrent Neural Networks
Predicting Native Language from Gaze
Visual Reasoning with Natural Language
Parsing with Typed Feature Structures
Learning Parse and Translation Decisions From Examples With Rich Context
From truth to computability II
Index wiki database: design and experiments
Completeness for Flat Modal Fixpoint Logics
Unbounded-error quantum computation with small space bounds
Reduced Ordered Binary Decision Diagram with Implied Literals: A New knowledge Compilation Approach
Furthering Baseline Core Lucid Standard Specification in the Context of the History of Lucid, Intensional Programming, and Context-Aware Computing
Specifying and Staging Mixed-Initiative Dialogs with Program Generation and Transformation
Optimal Coalition Structures in Cooperative Graph Games
QIRAL: A High Level Language for Lattice QCD Code Generation
Compression as a universal principle of animal behavior
Ghost: A Uniform and General-Purpose Proxy Implementation
Efficient Runtime Monitoring with Metric Temporal Logic: A Case Study in the Android Operating System
Towards Unsupervised Learning of Temporal Relations between Events
Venture: a higher-order probabilistic programming platform with programmable inference
Simple and Effective Type Check Removal through Lazy Basic Block Versioning
Multilingual Relation Extraction using Compositional Universal Schema
Image Captioning with Deep Bidirectional LSTMs
Improving Quality of Hierarchical Clustering for Large Data Series
Albanian Sign Language (AlbSL) Number Recognition from Both Hand's Gestures Acquired by Kinect Sensors
Cutoff for Extensions of Massive Gravity and Bi-Gravity
ImageCL: An Image Processing Language for Performance Portability on Heterogeneous Systems
Dynamic Choreographies: Theory And Implementation
Dynamic Structural Operational Semantics
Building Efficient Query Engines in a High-Level Language
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning
Adaptive Lock-Free Data Structures in Haskell: A General Method for Concurrent Implementation Swapping
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
The Neural Network Pushdown Automaton: Model, Stack and Learning Simulations
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
Relationship Maintenance in Software Language Repositories
A symbolic description of punning riddles and its computer implementation
Deriving Procedural and Warning Instructions from Device and Environment Models
Possessive Pronouns as Determiners in Japanese-to-English Machine Translation
Learning Features that Predict Cue Usage
Learning Correlations between Linguistic Indicators and Semantic Constraints: Reuse of Context-Dependent Descriptions of Entities
Character design for soccer commmentary
Strategic polymorphism requires just two combinators!
Anusaaraka: Machine Translation in Stages
Symmetric Space Cartan Connections and Gravity in Three and Four Dimensions
Optimising Code Generation with haggies
Vcache: Caching Dynamic Documents
Implementing Multi-Periodic Critical Systems: from Design to Code Generation
Logical Step-Indexed Logical Relations
A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data
The weighted words collector
General Bindings and Alpha-Equivalence in Nominal Isabelle
A framework for automated PDE-constrained optimisation
Checking Computations of Formal Method Tools - A Secondary Toolchain for ProB
The classical umbral calculus, and the flow of a Drinfeld module
Show and Tell: A Neural Image Caption Generator
A Generative Model of Words and Relationships from Multiple Sources
SentiCap: Generating Image Descriptions with Sentiments
A dynamical definition of f.g. virtually free groups
The generalised word problem in hyperbolic and relatively hyperbolic groups
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
DenseCap: Fully Convolutional Localization Networks for Dense Captioning
Limits to Verification and Validation of Agentic Behavior
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation
Extending Term Subsumption systems for Uncertainty Management
Learning to Predict from Textual Data
Finitely Axiomatized Set Theory: a nonclassical first-order theory implying ZF
HEPMath 1.4: A Mathematica Package for Semi-Automatic Computations in High Energy Physics
A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution
Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
Improving Trajectory Modelling for DNN-based Speech Synthesis by using Stacked Bottleneck Features and Minimum Generation Error Training
Empath: Understanding Topic Signals in Large-Scale Text
Weighted Pushdown Systems with Indexed Weight Domains
On Improving Informativity and Grammaticality for Multi-Sentence Compression
Abstract Program Slicing: an Abstract Interpretation-based approach to Program Slicing
A Proof Strategy Language and Proof Script Generation for Isabelle/HOL
Morphology Generation for Statistical Machine Translation using Deep Learning Techniques
Proposing Plausible Answers for Open-ended Visual Question Answering
Content Selection in Data-to-Text Systems: A Survey
Can Active Memory Replace Attention?
Knowledge Questions from Knowledge Graphs
Generating Sentiment Lexicons for German Twitter
A Joint Speaker-Listener-Reinforcer Model for Referring Expressions
Model Theory and Proof Theory of Coalgebraic Predicate Logic
Intersection Types and Counting
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Random vector generation of a semantic space
Learning to Generate Reviews and Discovering Sentiment
Machine Comprehension by Text-to-Text Neural Question Generation
A Unification Algorithm for GP 2 (Long Version)
Text Summarization using Abstract Meaning Representation
The E2E Dataset: New Challenges For End-to-End Generation
Understanding State Preferences With Text As Data: Introducing the UN General Debate Corpus
On expansions of non-abelian free groups by cosets of a finite index subgroup
Modeling Target-Side Inflection in Neural Machine Translation
Deriving Law-Abiding Instances
What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016
Automating Direct Speech Variations in Stories and Games
Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples
Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards
Mining Precision Interfaces From Query Logs
Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
A Family of Software Product Lines in Educational Technologies
Joint Event Detection and Description in Continuous Video Streams
Generating Contradictory, Neutral, and Entailing Sentences
Algorithmic Differentiation for Domain Specific Languages
Constant delay algorithms for regular document spanners
Actor-Critic based Training Framework for Abstractive Summarization
Neural Sketch Learning for Conditional Program Generation
Computer-Simulation des Wettbewerbs zwischen Sprachen
Higher-Order Operator Precedence Languages
Perpetual Adaptation of Software to Hardware: An Extensible Architecture for Providing Code Optimization as a Central System Service
Automatic Generation of CHR Constraint Solvers
Random Sentences from a Generalized Phrase-Structure Grammar Interpreter
Embeddability and Stresses of Graphs
Improved evolutionary generation of XSLT stylesheets
A toolkit for a generative lexicon
The Question of Expressiveness in the Generation of Referring Expressions
FPGA Based Assembling of Facial Components for Human Face Construction
Factor frequencies in generalized Thue-Morse words
Generalized Schrieffer-Wolff Formalism for Dissipative Systems
The finiteness of a group generated by a 2-letter invertible-reversible Mealy automaton is decidable
The boundary is mixed
Extentability of Automorphisms of Generic Substructures
Analyzer and generator for Pali
Connected reversible Mealy automata of prime size cannot generate infinite Burnside groups
Topic Sensitive Neural Headline Generation
Generating random braids
Socially-Informed Timeline Generation for Complex Events
Abstractive Meeting Summarization UsingDependency Graph Fusion
DroidGen: Constraint-based and Data-Driven Policy Generation for Android
Learning to generate one-sentence biographies from Wikidata
Symbolic and Numerical Analysis in General Relativity with Open Source Computer Algebra Systems
Generic expansion and Skolemization in NSOP_1 theories
Generating Appealing Brand Names
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method
About compression of vocabulary in computer oriented languages
Ogden's Lemma for Regular Tree Languages
Weighted Automata and Recurrence Equations for Regular Languages
Computing with Equations
Syntactic Complexity of Ideal and Closed Languages
Syntactic Complexity of Prefix-, Suffix-, Bifix-, and Factor-Free Regular Languages
Rust for functional programmers
Annotating Cognates and Etymological Origin in Turkic Languages
Tree Automata and Tree Grammars
Problems with the use of Web search engines to find results in foreign languages
On the State Complexity of the Reverse of R- and J-trivial Regular Languages
Modeling Language Variability
Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering
An Unsupervised Approach for Mapping between Vector Spaces
Microplanning with Communicative Intentions: The SPUD System
Be Your Own Prada: Fashion Synthesis with Structural Coherence
Analyse spectrale des textes: détection automatique des frontières de langue et de discours
Mathematical Properties of Dynamic Systems and the Foundations of Quantum Theory
Iterative Plan Construction for the Workflow Satisfiability Problem
Experiments with Three Approaches to Recognizing Lexical Entailment
Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
FabULous Interoperability for ML and a Linear Language
Modelling Requirements for Content Recommendation Systems
Training IBM Watson using Automatically Generated Question-Answer Pairs
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation
Generating Different Story Tellings from Semantic Representations of Narrative
Principles and Implementation of Deductive Parsing
A Plan-Based Model for Response Generation in Collaborative Task-Oriented Dialogues
An implemented model of punning riddles
Inducing Probabilistic Grammars by Bayesian Model Merging
Integrating Gricean and Attentional Constraints
With raised eyebrows or the eyebrows raised ? A Neural Network Approach to Grammar Checking for Definiteness
New Methods, Current Trends and Software Infrastructure for NLP
Grapheme-to-Phoneme Conversion using Multiple Unbounded Overlapping Chunks
Three New Probabilistic Models for Dependency Parsing: An Exploration
Towards a single proposal is spelling correction
Money and Goldstone modes
Using Local Optimality Criteria for Efficient Information Retrieval with Redundant Information Filters
Computation in an algebra of test selection criteria
Axiomatizing Causal Reasoning
A Classification Approach to Word Prediction
Collecting Graphical Abstract Views of Mercury Program Executions
Abductive reasoning with temporal information
Soundness, Idempotence and Commutativity of Set-Sharing
Towards Solving the Interdisciplinary Language Barrier Problem
Non-Termination Inference of Logic Programs
An Anthological Review of Research Utilizing MontyLingua, a Python-Based End-to-End Text Processor
On the Higher-Order Derivatives of Spectral Functions: Two Special Cases
The model completion of the theory of modules over finitely generated commutative algebras
Computation in Finitary Stochastic and Quantum Processes
Using Synchronic and Diachronic Relations for Summarizing Multiple Documents Describing Evolving Events
Getting More From Your Multicore: Exploiting OpenMP for Astronomy
CLAIRLIB Documentation v1.03
Measurements and confluence in quantum lambda calculi with explicit qubits
SP2Bench: A SPARQL Performance Benchmark
An Object-Oriented and Fast Lexicon for Semantic Generation
Interpretations of the Web of Data
The Usefulness of Multilevel Hash Tables with Multiple Hash Functions in Large Databases
Hybrid Rules with Well-Founded Semantics
Towards Multimodal Content Representation
Automatic Modular Abstractions for Template Numerical Constraints
Transformations of Logic Programs on Infinite Lists
Universal Numeric Segmented Display
Mantis: Predicting System Performance through Program Analysis and Modeling
Groups defined by automata
Rational subsets of groups
An ER-based Framework for Declarative Web Programming
Inverse problems of symbolic dynamics
A graphical environment to express the semantics of control systems
Solving the TTC 2011 Compiler Optimization Task with metatools
An Accurate Arabic Root-Based Lemmatizer for Information Retrieval Purposes
Towards A Generic Formal Framework for Access Control Systems
Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations
Towards a Generic Trace for Rule Based Constraint Reasoning
Traductor Writing System Web
Bisimulation of Labeled State-to-Function Transition Systems of Stochastic Process Languages
Generating events with style
SMCHR: Satisfiability Modulo Constraint Handling Rules
Automating rule generation for grammar checkers
Shape from sound: toward new tools for quantum gravity
Static Analysis for Regular Expression Denial-of-Service Attacks
A Language for Planning with Statistics
DLOLIS-A: Description Logic based Text Ontology Learning
MirrorShard: Proof by Computational Reflection with Verified Hints
Infinite probability computation by cyclic explanation graphs
On Sound Compilation of Reals
Abstract interpretation-based approaches to Security - A Survey on Abstract Non-Interference and its Challenging Applications
Propagating Regular Counting Constraints
Waterfall: Primitives Generation on the Fly
One Quantifier Alternation in First-Order Logic with Modular Predicates
On Coinductive Equivalences for Higher-Order Probabilistic Functional Programs (Long Version)
Opacity with Orwellian Observers and Intransitive Non-interference
Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming
Classifying Fonts and Calligraphy Styles Using Complex Wavelet Transform
Axiomatizing Causal Reasoning
On the Complexity of Optimization Problems based on Compiled NNF Representations
Control Improvisation
FIFTH system for general-purpose connectionist computation
Novel symmetries in an interacting N = 2 supersymmetric quantum mechanical model
Mechanically Verified Calculational Abstract Interpretation
Discriminative Segmental Cascades for Feature-Rich Phone Recognition
EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding
Vector Reachability Problem in $\mathrm{SL}(2,\mathbb{Z})$
On insertion-deletion systems over relational words
Data optimizations for constraint automata
Inference in Probabilistic Logic Programs using Lifted Explanations
Measuring Machine Intelligence Through Visual Question Answering
An Extension of Parikh's Theorem beyond Idempotence
GPU Scripting and Code Generation with PyCUDA
Automated, Credible Autocoding of An Unmanned Aggressive Maneuvering Car Controller
Recursive Neural Networks Can Learn Logical Semantics
A Requirements Modeling Language for the Component Behavior of Cyber Physical Robotics Systems
Multi-Platform Generative Development of Component & Connector Systems using Model and Code Libraries
Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews
Modeling Compositionality with Multiplicative Recurrent Neural Networks
Video (language) modeling: a baseline for generative models of natural videos
Statistical laws in linguistics
Combined Top-down and Bottom-up Approach to Multilevel Supervisory Control
Can JSP Code be Generated Using XML Tags?
Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models
Smoothing parameter estimation framework for IBM word alignment models
Resource Constrained Structured Prediction
Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning
Variational Neural Discourse Relation Recognizer
Sequence-to-Sequence Learning as Beam-Search Optimization
Intelligent audit code generation from free text in the context of neurosurgery
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
Equation Parsing: Mapping Sentences to Grounded Equations
Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations
Unsupervised Pretraining for Sequence to Sequence Learning
Bootstrapping incremental dialogue systems: using linguistic knowledge to learn from minimal data
Definition Modeling: Learning to define word embeddings in natural language
Usability Investigation on the Localization of Text CAPTCHAs: Take Chinese Characters as a Case Study
Context-aware Sentiment Word Identification: sentiword2vec
Effects of Stop Words Elimination for Arabic Information Retrieval: A Comparative Study
Specialization of Generic Array Accesses After Inlining
Game-theoretic Model of Computation
Custom Hypergraph Categories via Generalized Relations
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
Precision Interfaces
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
Understanding Task Design Trade-offs in Crowdsourced Paraphrase Collection
People on Drugs: Credibility of User Statements in Health Communities
Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
Fast-Slow Recurrent Neural Networks
Controllable Invariance through Adversarial Feature Learning
Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity
Zero-Shot Relation Extraction via Reading Comprehension
CharManteau: Character Embedding Models For Portmanteau Creation
The Influence of Feature Representation of Text on the Performance of Document Classification
Program Completionin the Input Language of GRINGO
Mimicking Word Embeddings using Subword RNNs
A New Modal Framework for Epistemic Logic
Online Deception Detection Refueled by Real World Data Collection
Safety Verification of Phaser Programs
Active Learning of Input Grammars
PVSC-DTM: A domain-specific language and matrix-free stencil code for investigating electronic properties of Dirac and topological materials
Semantic Preserving Embeddings for Generalized Graphs
AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms
HUMOR: A Crowd-Annotated Spanish Corpus for Humor Analysis
A Novel Approach to Artistic Textual Visualization via GAN
A Survey on Dialogue Systems: Recent Advances and New Frontiers
Learning Robust Dialog Policies in Noisy Environments
On tractable query evaluation for SPARQL
Byte-Level Recursive Convolutional Auto-Encoder for Text
Deep Generative Model for Joint Alignment and Word Representation
Gaussian meta-embeddings for efficient scoring of a heavy-tailed PLDA model
A Feature-Rich Vietnamese Named-Entity Recognition Model
Generic Zero-Cost Reuse for Dependent Types
Scene Graph Parsing as Dependency Parsing
Reconstruction Network for Video Captioning
The simple essence of automatic differentiation (Differentiable functional programming made easy)
Where Defaults Don't Help: the Case of the German Plural System
Isometric Lineation in English Texts: An Empirical and Mathematical Examination of its Character and Consequences
A Machine-Independent Debugger--Revisited
After Compilers and Operating Systems : The Third Advance in Application Support
On Spatial Conjunction as Second-Order Logic
The Self-Organization of Speech Sounds
Extending Prolog with Incomplete Fuzzy Information
The spinning electron: Hidrodynamical formulation, and quantum limit, of the Barut-Zanghi theory
A Topos Foundation for Theories of Physics: III. The Representation of Physical Quantities With Arrows
Design and Implementation of a Tracer Driver: Easy and Efficient Dynamic Analyses of Constraint Logic Programs
A two-level logic approach to reasoning about computations
A Homogeneous Reaction Rule Language for Complex Event Processing
Grounded Symbols in the Brain Computational Foundations for Perceptual Symbol System
URSA: A System for Uniform Reduction to SAT
Annotated English
On minimising automata with errors
Incremental dimension reduction of tensors with random index
Extended Initiality for Typed Abstract Syntax
Developing Embodied Multisensory Dialogue Agents
Modeling Languages: metrics and assessing tools
Discovering Basic Emotion Sets via Semantic Clustering on a Twitter Corpus
Sets in homotopy type theory
Development of Marathi Part of Speech Tagger Using Statistical Approach
A Model Approach to Build Basic Ontology
Latent semantics of action verbs reflect phonetic parameters of intensity and emotional content
Analysis of Timed and Long-Run Objectives for Markov Automata
Speech earthquakes: scaling and universality in human voice
The Hebrew Bible as Data: Laboratory - Sharing - Experiences
Compositional Distributional Semantics with Compact Closed Categories and Frobenius Algebras
VQA: Visual Question Answering
Similarity of symbol frequency distributions with heavy tails
Inferring Parametric Energy Consumption Functions at Different Software Levels: ISA vs. LLVM IR
TGIF: A New Dataset and Benchmark on Animated GIF Description
Well-Definedness and Efficient Inference for Probabilistic Logic Programming under the Distribution Semantics
C Language Extensions for Hybrid CPU/GPU Programming with StarPU
Complexity Classifications for logic-based Argumentation
Understanding Rulelog Computations in Silk
A Boolean Algebraic Approach to Semiproper Iterations
Towards Composable Concurrency Abstractions
Enhancing R with Advanced Compilation Tools and Methods
Improving Term Frequency Normalization for Multi-topical Documents, and Application to Language Modeling Approaches
Describing Videos by Exploiting Temporal Structure
Incremental Computation with Names
Towards Practical Graph-Based Verification for an Object-Oriented Concurrency Model
Effectiveness of Structural Restrictions for Hybrid CSPs
Edit Distance for Pushdown Automata
A Fast Compiler for NetKAT
Deep Learning Applied to Image and Text Matching
Linguistic neighbourhoods: explaining cultural borders on Wikipedia through multilingual co-editing activity
The Commutativity Problem of the MapReduce Framework: A Transducer-based Approach
Visual Question Answering: A Survey of Methods and Datasets
On Prefix Normal Words and Prefix Normal Forms
A Devil's Advocate against Termination of Direct Recursion
On Delay and Regret Determinization of Max-Plus Automata
Exact Affine Counter Automata
High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data
The possibility of constructing a relativistic space of information states based on the theory of complexity and analogies with physical space-time
Reflection calculus and conservativity spectra
An Automated Text Categorization Framework based on Hyperparameter Optimization
A Concurrency-Agnostic Protocol for Multi-Paradigm Concurrent Debugging Tools
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
An Interactive Tool for Natural Language Processing on Clinical Text
Reliability and Fault-Tolerance by Choreographic Design
Knowledge gaps in the early growth of semantic networks
Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars
Learning Continuous User Representations through Hybrid Filtering with doc2vec
SentiPers: A Sentiment Analysis Corpus for Persian
Empirical observations of ultraslow diffusion driven by the fractional dynamics in languages: Dynamical statistical properties of word counts of already popular words
Smart Contracts Software Metrics: a First Study
Joint Training for Neural Machine Translation Models with Monolingual Data
Meta-F*: Metaprogramming and Tactics in an Effectful Program Verifier
A View-based Programmable Architecture for Controlling and Integrating Decentralized Data
A Systematic Review of Automated Grammar Checking in English Language
From Symmetric Pattern-Matching to Quantum Control (Extended Version)
Algebraic Aspects of the Fractional Quantum Hall Effect
Building the access pointers to a computation environment
On the generalized dining philosophers problem
Probabilistic Parsing Strategies
Analysis of Equality Relationships for Imperative Programs
Differential Forms, Hopf Algebra and General Relativity I
The future of spin networks
Some brane theoretic no-hair results (and their field theory duals)
Conceptual issues in combining general relativity and quantum theory
Generalized Kahler Geometry from supersymmetric sigma models
Uniform first-order definitions in finitely generated fields
Law of Excluded Quantum Gambling Strategies
Applying Test-Paradigms in a Generic Tutoring System Concept for Web-based Learning
An Approach to Programming Based on Concepts
Analytic aspects of the shuffle product
Overview of some general results in combinatorial enumeration
On Theta-palindromic Richness
Turing Machines on Graphs and Inescapable Groups
L-systems in Geometric Modeling
Which finitely generated Abelian groups admit isomorphic Cayley graphs?
Applications in Enumerative Combinatorics of Infinite Weighted Automata and Graphs
Pre-processing of Domain Ontology Graph Generation System in Punjabi
A Diversity-Promoting Objective Function for Neural Conversation Models
Unifying Ghost-Free Lorentz-Invariant Lagrangians
Equations for generalized n-point information with extreme and not extreme approximations in the free Fock space
Generating Chinese Classical Poems with RNN Encoder-Decoder
Classical field theories from Hamiltonian constraint: Symmetries and conservation laws
Mutual Transformation of Information and Knowledge
N=1 vacua in Exceptional Generalized Geometry
A Semantic Approach to Summarization
A Generic Numbering System based on Catalan Families of Combinatorial Objects
A connected 3-state reversible Mealy automaton cannot generate an infinite Burnside group
Survey on Combinatorial Register Allocation and Instruction Scheduling
Compositional Invariant Generation via Linear Recurrence Analysis
Contact spectral invariants and persistence
Uniform generation in trace monoids
An Ensemble method for Content Selection for Data-to-text Systems
Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets
Knapsack in graph groups, HNN-extensions and amalgamated products
Fortran code for generating random probability vectors, unitaries, and quantum states
Representing Strategic Games and Their Equilibria in Many-Valued Logics
Topic Modeling Using Distributed Word Embeddings
Dataflow matrix machines as programmable, dynamically expandable, self-referential generalized recurrent neural networks
On a Topic Model for Sentences
Semantic Parsing with Semi-Supervised Sequential Autoencoders
Generalization Bounds for Weighted Automata
Morphological Inflection Generation with Hard Monotonic Attention
Adversarial Evaluation of Dialogue Models
AMR-to-text Generation with Synchronous Node Replacement Grammar
Post-edit Analysis of Collective Biography Generation
Feature Generation for Robust Semantic Role Labeling
Abstract Syntax Networks for Code Generation and Semantic Parsing
A Generative Model of a Pronunciation Lexicon for Hindi
Music generation with variational recurrent autoencoder supported by history
Quadratic automaton algebras and intermediate growth
Projective tensor product of protoquantum spaces
A Joint Model for Question Answering and Question Generation
Neural Machine Translation with Gumbel-Greedy Decoding
Explainable Entity-based Recommendations with Knowledge Graphs
Mekler's construction and generalized stability
Generating Query Suggestions to Support Task-Based Search
Robust Speech Recognition Using Generative Adversarial Networks
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation
Query-Based Abstractive Summarization Using Neural Networks
A Very Short Self-Interpreter
On The Liniar Time Complexity of Finite Languages
A Shrinking Lemma for Indexed Languages
An OLAC Extension for Dravidian Languages
A note on decidability of cellularity
Complexity in Prefix-Free Regular Languages
Towards Nominal Formal Languages
Challenges in Kurdish Text Processing
Reset Complexity of Ideal Languages
Lexpresso: a Controlled Natural Language
Typing Regular Path Query Languages for Data Graphs
Syntax and semantics of the weak consistency model specification language cat
Permutations of context-free and indexed languages
The While language
Discriminating between similar languages in Twitter using label propagation
Regular Separability of Parikh Automata
Designing a pi-based Programming Language in the .NET framework: CLR interoperability from the Programmer's point of view
Anonymous Variables in Imperative Languages
On the Generation of Test Data for Prolog by Partial Evaluation
Lazy Model Expansion: Interleaving Grounding with Search
Foam: A General-Purpose Cellular Monte Carlo Event Generator
Individual and Domain Adaptation in Sentence Planning for Dialogue
Generalized Cayley Graphs and Cellular Automata over them
A Comparison of Mechanisms for Integrating Handwritten and Generated Code for Object-Oriented Programming Languages
Phoneme-level speech and natural language intergration for agglutinative languages
Language Access: An Information Based Approach
Simulation of language competition by physicists
Flux: FunctionaL Updates for XML (extended report)
Agent Based Models of Language Competition: Macroscopic descriptions and Order-Disorder transitions
Deciding Regularity of Hairpin Completions of Regular Languages in Polynomial Time
On the Properties of Language Classes Defined by Bounded Reaction Automata
Operator Precedence ω-languages
Reconstructing Native Language Typology from Foreign Language Usage
A Module System for Domain-Specific Languages
Commutative Languages and their Composition by Consensual Methods
Cross-lingual Dataless Classification for Languages with Small Wikipedia Presence
Separability by Piecewise Testable Languages is PTime-Complete
Weakly and Strongly Irreversible Regular Languages
Improved Text Language Identification for the South African Languages
Multilingual Speech Recognition With A Single End-To-End Model
A Deep Generative Framework for Paraphrase Generation
A Script Language for Data Integration in Database
NLOMJ--Natural Language Object Model in Java
Language embeddings that preserve staging and safety
A FORTRAN coded regular expression Compiler for IBM 1130 Computing System
An Intuitive Automated Modelling Interface for Systems Biology
Fuzzy Modeling and Natural Language Processing for Panini's Sanskrit Grammar
tym: Typed Matlab
A Proof of the Pumping Lemma for Context-Free Languages Through Pushdown Automata
The separation problem for regular languages by piecewise testable languages
Towards Structural Natural Language Formalization: Mapping Discourse to Controlled Natural Language
Morphological Analysis of the Bishnupriya Manipuri Language using Finite State Transducers
Survey:Natural Language Parsing For Indian Languages
Pattern Languages as Media for the Creative Society
Are Style Guides Controlled Languages? The Case of Koenig & Bauer AG
Embedded Controlled Languages
Modeling Language Variability
Replacing ANSI C with other modern programming languages
Regular realizability problems and regular languages
Bottom Up Quotients and Residuals for Tree Languages
Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism
Restricted deterministic Watson-Crick automata
The Controlled Natural Language of Randall Munroe's Thing Explainer
Complexity of Left-Ideal, Suffix-Closed and Suffix-Free Regular Languages
Comparative Study Of Data Mining Query Languages
Tree Notation: an antifragile program notation
Deep Investigation of Cross-Language Plagiarism Detection Methods
Regularity of non context-free languages over a singleton terminal alphabet
Open-Set Language Identification
Composition by Conversation
Phylogenetics of Indo-European Language families via an Algebro-Geometric Analysis of their Syntactic Structures
The Frobenius problem for homomorphic embeddings of languages into the integers
The WiLI benchmark dataset for written language identification
Neural Lattice Language Models
Meta-Learning a Dynamical Language Model
A Topos Foundation for Theories of Physics: II. Daseinisation and the Liberation of Quantum Theory
Classical and quantum computation with small space bounds (PhD thesis)
A Synthesis of the Procedural and Declarative Styles of Interactive Theorem Proving
Flowchart Programs, Regular Expressions, and Decidability of Polynomial Growth-Rate
Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions
Use of Modality and Negation in Semantically-Informed Syntactic MT
Automagically encoding Adverse Drug Reactions in MedDRA
Race, Religion and the City: Twitter Word Frequency Patterns Reveal Dominant Demographic Dimensions in the United States
An Introduction to Programming for Bioscientists: A Python-based Primer
An Analysis of Introductory Programming Courses at UK Universities
SYSTRAN's Pure Neural Machine Translation Systems
Description Languages for Consistency Management Scenarios Based on Examples from the Industry Automation Domain
On the Hierarchy of Block Deterministic Languages
Topological Entropy of Formal Languages
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
Jointly Modeling Embedding and Translation to Bridge Video and Language
The Stochastic Processes Generation in OpenModelica
Phrase-based Image Captioning with Hierarchical LSTM Model
Alexander Stratifications of Character Varieties
Integration Of Visual Inter-word Constraints And Linguistic Knowledge In Degraded Text Recognition
Intention-based Segmentation: Human Reliability and Correlation with Linguistic Cues
An Integrated Heuristic Scheme for Partial Parse Evaluation
Phoneme Recognition Using Acoustic Events
The Role of Cognitive Modeling in Achieving Communicative Intentions
Automated Postediting of Documents
Principle Based Semantics for HPSG
Multi-Dimensional Inheritance
Algorithms for Analysing the Temporal Structure of Discourse
Splitting the Reference Time: Temporal Anaphora and Quantification in DRT
Principle Based Semantics for HPSG
NLG vs. Templates
The intersection of Finite State Automata and Definite Clause Grammars
Response Generation in Collaborative Negotiation
A Symbolic and Surgical Acquisition of Terms through Variation
Indefeasible Semantics and Defeasible Pragmatics
Comparative Ellipsis and Variable Binding
A Compositional Treatment of Polysemous Arguments in Categorial Grammar
Parsing with Typed Feature Structures
Text Windows and Phrases Differing by Discipline, Location in Document, and Syntactic Structure
Multi-level post-processing for Korean character recognition using morphological analysis and linguistic evaluation
Processing Metonymy: a Domain-Model Heuristic Graph Traversal Approach
Focus and Higher-Order Unification
Two Questions about Data-Oriented Parsing
Isolated-Word Confusion Metrics and the PGPfone Alphabet
Generating Information-Sharing Subdialogues in Expert-User Consultation
Charts, Interaction-Free Grammars, and the Compact Representation of Ambiguity
Attaching Multiple Prepositional Phrases: Generalized Backed-off Estimation
"I don't believe in word senses"
Foreground and Background Lexicons and Word Sense Disambiguation for Information Extraction
Anchoring a Lexicalized Tree-Adjoining Grammar for Discourse
Improving Data Driven Wordclass Tagging by System Combination
Beyond the Zipf-Mandelbrot law in quantitative linguistics
Intermittency and scale-free networks: a dynamical model for human language complexity
Time-dependent Density-Matrix Renormalization-Group Methods
The descriptive complexity approach to LOGCFL
Name Strategy: Its Existence and Implications
Supervised Grammar Induction Using Training Data with Limited Constituent Information
A Real World Implementation of Answer Extraction
Planning with Incomplete Information
The (Lazy) Functional Side of Logic Programming
How to Evaluate your Question Answering System Every Day and Still Get Real Work Done
Using a Diathesis Model for Semantic Parsing
Processing Self Corrections in a speech to speech system
Automatic Debugging Support for UML Designs
Multi-dimensional Type Theory: Rules, Categories, and Combinators for Syntax and Semantics
A Constrained Object Model for Configuration Based Workflow Composition
Mapping DEVS Models onto UML Models
Removing Redundant Arguments Automatically
Improving Term Extraction with Terminological Resources
Raisonnement stratifié à base de normes pour inférer les causes dans un corpus textuel
DepAnn - An Annotation Tool for Dependency Treebanks
SASE: Complex Event Processing over Streams
A Static Analyzer for Large Safety-Critical Software
Speeding up Domain Wall Fermion Algorithms using QCDLAB
Duality and an Operator Realization for the Fermi-Bose Transmutation in 3+1 Dimensions
Graph-Based Logic and Sketches 1: The General Framework
Some Combinatorics behind Proofs
Set theory is interpretable in the automorphism group of a free group
Analysis and Synthesis of the Distribution of Consonants over Languages: A Complex Network Approach
Non-equilibrium dynamics of language games on complex networks
Ordering dynamics with two non-excluding options: Bilingualism in language competition
Languages of Quantum Information Theory
On parallel composition of zero-knowledge proofs with black-box quantum simulators
Quantum-like Representation of Macroscopic Configurations
Success and failure of programming environments - report on the design and use of a graphic abstract syntax tree editor
Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming
TCHR: a framework for tabled CLP
Indirect Object Representation and Access by Means of Concepts
An application of the Deutsch-Josza algorithm to formal languages and the word problem in groups
Ensuring Spreadsheet Integrity with Model Master
A Logic Programming Framework for Combinational Circuit Synthesis
Conception et Evaluation de XQuery dans une architecture de médiation "Tout-XML"
Unfolding in CHR
Quantum Feedback Control: How to use Verification Theorems and Viscosity Solutions to Find Optimal Protocols
The Prolog Interface to the Unstructured Information Management Architecture
Binding bigraphs as symmetric monoidal closed theories
Automatic Modular Abstractions for Linear Constraints
A Spectral Algorithm for Learning Hidden Markov Models
Filtering Microarray Correlations by Statistical Literature Analysis Yields Potential Hypotheses for Lactation Research
Automatic Summarization System coupled with a Question-Answering System (QAAS)
Diagrams for Symmetric Product Orbifolds
Normalized Web Distance and Word Similarity
Multidimensional Generalized Automatic Sequences and Shape-symmetric Morphic Words
N-tuple Zipf Analysis and Modeling for Language, Computer Program and DNA
Wild Card Queries for Searching Resources on the Web
Multiple Retrieval Models and Regression Models for Prior Art Search
Automatic modular abstractions for template numerical constraints
On equations over sets of integers
Algebraic Linear Orderings
Development of a multi-user handwriting recognition system using Tesseract open source OCR engine
Development of a Multi-User Recognition Engine for Handwritten Bangla Basic Characters and Digits
Rankers over Infinite Words
Network analysis of a corpus of undeciphered Indus civilization inscriptions indicates syntactic organization
Using Soft Constraints To Learn Semantic Models Of Descriptions Of Shapes
Video Event Recognition for Surveillance Applications (VERSA)
Pushdown Control-Flow Analysis of Higher-Order Programs
Propositional Dynamic Logic for Message-Passing Systems
Accepting Hybrid Networks of Evolutionary Processors with Special Topologies and Small Communication
Runtime-Flexible Multi-dimensional Arrays and Views for C++98 and C++0x
Quivers of monoids with basic algebras
What can we say about nature?
Measuring Performance of Continuous-Time Stochastic Processes using Timed Automata
Symmetry-Aware Predicate Abstraction for Shared-Variable Concurrent Programs (Extended Technical Report)
XMLlab : multimedia publication of simulations applets using XML and Scilab
JavaCtx: Seamless Toolchain Integration for Context-Oriented Programming
Simple, Decidable Type Inference with Subtyping
Faire levier sur les architectures logicielles pour guider et vérifier le développement d'applications SCC
Compiling Causal Theories to Successor State Axioms and STRIPS-Like Systems
On the origin of ambiguity in efficient communication
ForOpenCL: Transformations Exploiting Array Syntax in Fortran for Accelerator Programming
(Co-)Inductive semantics for Constraint Handling Rules
Cross-moments computation for stochastic context-free grammars
Leveraging Software Architectures to Guide and Verify the Development of Sense/Compute/Control Applications
A Probabilistic Approach to Pronunciation by Analogy
Groups whose geodesics are locally testable
Confidence Estimation in Structured Prediction
Simulations of Dense Stellar Systems with the AMUSE Software Toolkit
Program Understanding: A Reengineering Case for the Transformation Tool Contest
Ramified Structural Recursion and Corecursion
An OpenCL implementation for the solution of TDSE on GPU and CPU architectures
Parametric Compositional Data Types
AD in Fortran, Part 1: Design
Automated Feedback Generation for Introductory Programming Assignments
Towards Real-Time Summarization of Scheduled Events from Twitter Streams
TopSig: Topology Preserving Document Signatures
Automatic Generation of C-code or PLD Circuits under SFC Graphical Environment
Refining Inductive Types
Model Driven Mutation Applied to Adaptative Systems Testing
Model Checking Stochastic Branching Processes
Time Warp on the Go (Updated Version)
Software Verification and Graph Similarity for Automated Evaluation of Students' Assignments
On the origin of long-range correlations in texts
Towards Algorithmic Synthesis of Synchronization for Shared-Memory Concurrent Programs
Info-Computationalism and Philosophical Aspects of Research in Information Sciences
Alan Turing's Legacy: Info-Computational Philosophy of Nature
Exploiting First-Order Regression in Inductive Policy Selection
On the specification of operations on the rational behaviour of systems
Turing machines based on unsharp quantum logic
Underapproximation of Procedure Summaries for Integer Programs
Reversible Christoffel factorizations
WiSANCloud: a set of UML-based specifications for the integration of Wireless Sensor and Actor Networks (WSANs) with the Cloud Computing
On the Use of Underspecified Data-Type Semantics for Type Safety in Low-Level Code
Development of an Astrophysical Specific Language for Big Data Computation
Continuous Time Bayesian Networks
Toward the Automatic Generation of a Semantic VRML Model from Unorganized 3D Point Clouds
Inferring Informational Goals from Free-Text Queries: A Bayesian Approach
Silent Transitions in Automata with Storage
Bilingual Terminology Extraction Using Multi-level Termhood
Effect of Query Formation on Web Search Engine Results
Revisiting the Equivalence Problem for Finite Multitape Automata
The Case for Explicit Coupling Constraints
Refining SCJ Mission Specifications into Parallel Handler Designs
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi
An Effect System for Algebraic Effects and Handlers
Unfolding for CHR programs
Reading Stockholm Riots 2013 in social media by text-mining
Arbitrary Sequence RAMs
A Preadapted Universal Switch Distribution for Testing Hilberg's Conjecture
StreaMon: a data-plane programming abstraction for Software-defined Stream Monitoring
When Equivalence and Bisimulation Join Forces in Probabilistic Automata
Query Segmentation for Relevance Ranking in Web Search
A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons
Static Application-Level Race Detection in STM Haskell using Contracts
Word Emdeddings through Hellinger PCA
Towards A Domain-specific Language For Pick-And-Place Applications
Subsumption Checking in Conjunctive Coalgebraic Fixpoint Logics
Classical realizability and arithmetical formulæ
Querying Geometric Figures Using a Controlled Language, Ontological Graphs and Dependency Lattices
Executable Refinement Types
Decentralized Supervisory Control with Communicating Supervisors Based on Top-Down Coordination Control
Lexicon Infused Phrase Embeddings for Named Entity Resolution
Performance of Python runtimes on a non-numeric scientific code
SPEEDY: An Eclipse-based IDE for invariant inference
Open induction in a bounded arithmetic for TC^0
Initial Comparison of Linguistic Networks Measures for Parallel Texts
An Expert System for Automatic Reading of A Text Written in Standard Arabic
Coordinate System Selection for Minimum Error Rate Training in Statistical Machine Translation
Interprocedural Reachability for Flat Integer Programs
Large Code Base Change Ripple Management in C++: My thoughts on how a new Boost C++ Library could help
Probabilistic Alias Analysis for Parallel Programming in SSA Forms
Loo.py: transformation-based code generation for GPUs and CPUs
KNET: A General Framework for Learning Word Embedding using Morphological Knowledge
Algebras of Open Dynamical Systems on the Operad of Wiring Diagrams
A Tentative Role for FOXP2 in the Evolution of Dual Processing Modes and Generative Abilities
What Java Developers Know About Compatibility, And Why This Matters
A Hoare-like logic of asserted single-pass instruction sequences
Finite Automata for the Sub- and Superword Closure of CFLs: Descriptional and Computational Complexity
Representations of categories of G-maps
On-the-fly Probabilistic Model Checking
A Semantic Web of Know-How: Linked Data for Community-Centric Tasks
The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation
CIDEr: Consensus-based Image Description Evaluation
Hard to Cheat: A Turing Test based on Answering Questions about Images
RDF Validation Requirements - Evaluation and Logical Underpinning
Modelling and Verifying an Object-Oriented Concurrency Model in GROOVE
Exposing ambiguities in a relation-extraction gold standard with crowdsourcing
Generating Navigable Semantic Maps from Social Sciences Corpora
Neural CRF Parsing
An Open Challenge Problem Repository for Systems Supporting Binders
Translating Hierarchical Block Diagrams into Composite Predicate Transformers
Towards Patterns for Heaps and Imperative Lambdas
Explicit Knowledge-based Reasoning for Visual Question Answering
Interprocedural Type Specialization of JavaScript Programs Without Type Analysis
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Resource theories of knowledge
Linearly Typed Dyadic Group Sessions for Building Multiparty Sessions
Parallelizing Word2Vec in Shared and Distributed Memory
Expressive Completeness of Existential Rule Languages for Ontology-based Query Answering
Robsut Wrod Reocginiton via semi-Character Recurrent Neural Network
Temporal Attention Model for Neural Machine Translation
Probabilistic Data Analysis with Probabilistic Programming
Kepler's Differential Equations
Computational Aspects of Reordering Plans
Reasoning about Actions with Temporal Answer Sets
Random Context and Semi-Conditional Insertion-Deletion Systems
Automatic case acquisition from texts for process-oriented case-based reasoning
Conceptual Understanding of Computer Program Execution: Application to C++
Evolution in a Changing Environment
Lexical State Analyzer
The infinite random simplicial complex
Thue's 1914 paper: a translation
Development of a language and its enacting engine for the unified discovery of heterogeneous services
Formal Specification Language Based IaaS Cloud Workload Regression Analysis
Exercise: +-1 bug and center of an array problem
Handling non-compositionality in multilingual CNLs
The Links Have It: Infobox Generation by Summarization over Linked Entities
Jabalin: a Comprehensive Computational Model of Modern Standard Arabic Verbal Morphology Based on Traditional Arabic Prosody
A Pragmatic Interpretation of Quantum Logic
Weak and Nested Class Memory Automata
Pruning, Pushdown Exception-Flow Analysis
Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets
Toward Refactoring of DMARF and GIPSY Case Studies -- a Team 12 SOEN6471-S14 Project Report
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Exploring Cultures through Pattern Mining - Practices from Generative Beauty Workshops
End-To-End Memory Networks
On the Stability of Online Language Features: How Much Text do you Need to know a Person?
Compositional Vector Space Models for Knowledge Base Completion
Parsing Linear Context-Free Rewriting Systems with Fast Matrix Multiplication
Learning to Transduce with Unbounded Memory
How Scale Affects Structure in Java Programs
Applying Deep Learning to Answer Selection: A Study and An Open Task
Image Representations and New Domains in Neural Image Captioning
A High-Level Modeling Language for the Efficient Design, Implementation, and Testing of Android Applications
Word, graph and manifold embedding from Markov processes
Automatic Dialect Detection in Arabic Broadcast Speech
Probabilistic Output Analysis by Program Manipulation
Distribution-based Bisimulation and Bisimulation Metric in Probabilistic Automata
Nonparametric Bayesian Storyline Detection from Microtexts
Sound and Complete Bidirectional Typechecking for Higher-Rank Polymorphism with Existentials and Indexed Types
Contextual LSTM (CLSTM) models for Large scale NLP tasks
Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning
Session Types in a Linearly Typed Multi-Threaded Lambda-Calculus
Image Captioning with Semantic Attention
Symbolic Reachability Analysis of B through ProB and LTSmin
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Recurrent Neural Network Encoder with Attention for Community Question Answering
Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community
Improving Image Captioning by Concept-based Sentence Reranking
Machine Learning Techniques with Ontology for Subjective Answer Evaluation
Topological language for RNA
Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing
Attention Correctness in Neural Image Captioning
Dependency Parsing as Head Selection
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change
Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
Fair Simulation for Nondeterministic and Probabilistic Buechi Automata: a Coalgebraic Perspective
Automatic Pronunciation Generation by Utilizing a Semi-supervised Deep Neural Networks
Summarizing Decisions in Spoken Meetings
Lifted Rule Injection for Relation Embeddings
Automatic Generation of Probabilistic Programming from Time Series Data
Modelling movement for collective adaptive systems with CARMA
Removing Unnecessary Variables from Horn Clause Verification Conditions
Neural Discourse Modeling of Conversations
The Actias system: supervised multi-strategy learning paradigm using categorical logic
Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
Persistent Contextual Values as Inter-Process Layers
Pre-Translation for Neural Machine Translation
Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research
Dependent Types in Haskell: Theory and Practice
Distraction-Based Neural Networks for Document Summarization
Ordinal Common-sense Inference
Balotage in Argentina 2015, a sentiment analysis of tweets
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents
Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text
Ontology Driven Disease Incidence Detection on Twitter
Time Series Structure Discovery via Probabilistic Program Synthesis
Neural Machine Translation with Latent Semantic of Image and Text
Revisiting the Futamura Projections: A Diagrammatic Approach
Flu Detector: Estimating influenza-like illness rates from online user-generated content
Text-guided Attention Model for Image Captioning
Recurrent Image Captioner: Describing Images with Spatial-Invariant Transformation and Attention Filtering
Reducing Nondeterministic Tree Automata by Adding Transitions
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
Understanding Neural Networks through Representation Erasure
A Typeful Integration of SQL into Curry
A Convenient Category for Higher-Order Probability Theory
RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
Deep Probabilistic Programming
Pushing for weighted tree automata
EasyInterface: A toolkit for rapid development of GUIs for research prototype tools
Structured Attention Networks
Iterative Multi-document Neural Attention for Multiple Answer Prediction
Trainable Greedy Decoding for Neural Machine Translation
Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization
Automatic Rule Extraction from Long Short Term Memory Networks
Learning Concept Embeddings for Efficient Bag-of-Concepts Densification
On the Boundary between Decidability and Undecidability of Asynchronous Session Subtyping
Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning
Vocabulary Alignment in Openly Specified Interactions
VQABQ: Visual Question Answering by Basic Questions
Métodos de Otimização Combinatória Aplicados ao Problema de Compressão MultiFrases
Learning to Predict: A Fast Re-constructive Method to Generate Multimodal Embeddings
Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks
A Transition-Based Directed Acyclic Graph Parser for UCCA
Automatic Measurement of Pre-aspiration
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction
A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification
HiFrames: High Performance Data Frames in a Scripting Language
Cross-domain Semantic Parsing via Paraphrasing
A Semantic QA-Based Approach for Text Summarization Evaluation
Universality of Confluent, Self-Loop Deterministic Partially Ordered NFAs is Hard
An expressive completeness theorem for coalgebraic modal mu-calculi
The Forgettable-Watcher Model for Video Question Answering
Data Readiness Levels
Variations of Checking Stack Automata: Obtaining Unexpected Decidability Properties
Latent Intention Dialogue Models
Emergent Communication in a Multi-Modal, Multi-Step Referential Game
Deep learning for extracting protein-protein interactions from biomedical literature
Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks
A Mention-Ranking Model for Abstract Anaphora Resolution
Compositional Hoare-style Reasoning about Hybrid CSP in the Duration Calculus
Actor-Critic Sequence Training for Image Captioning
Introducing libeemd: A program package for performing the ensemble empirical mode decomposition
Complexity Metric for Code-Mixed Social Media Text
New theories of relativistic hydrodynamics in the LHC era
Recalling a Witness: Foundations and Applications of Monotonic State
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
Towards Crafting Text Adversarial Samples
Computerized Adaptive Testing Simulation Through the Package catsim
CUNI System for the WMT17 Multimodal Translation Task
High-risk learning: acquiring new word vectors from tiny data
Why We Need New Evaluation Metrics for NLG
Split and Rephrase
Hierarchical Embeddings for Hypernymy Detection and Directionality
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set
Enforcing Constraints on Outputs with Unconstrained Inference
Tensor Networks in a Nutshell
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
An Investigation into the Pedagogical Features of Documents
e-QRAQ: A Multi-turn Reasoning Dataset and Simulator with Explanations
Learning to Paraphrase for Question Answering
Dyck Words, Lattice Paths, and Abelian Borders
Nonmalleable Information Flow: Technical Report
Investigating how well contextual features are captured by bi-directional recurrent neural network models
Trace-Based Run-time Analysis of Message-Passing Go Programs
Affective Neural Response Generation
Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis
Improving Opinion-Target Extraction with Character-Level Word Embeddings
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts
Learning Distributions of Meant Color
Semantic keyword spotting by learning from images and speech
Self-adaptive static analysis
Verb Pattern: A Probabilistic Semantic Representation on Verbs
A software framework for pipelined arithmetic algorithms in field programmable gate arrays
Conceptual Text Summarizer: A new model in continuous vector space
A Model-Based Approach to Security Analysis for Cyber-Physical Systems
Towards Neural Machine Translation with Partially Aligned Corpora
Non-Autoregressive Neural Machine Translation
Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection
Bayesian Paragraph Vectors
Towards Automated ICD Coding Using Deep Learning
Commonsense LocatedNear Relation Extraction
Weakly-supervised Semantic Parsing with Abstract Examples
Automatically Extracting Action Graphs from Materials Science Synthesis Procedures
Event Representations with Tensor-based Compositions
Application of Natural Language Processing to Determine User Satisfaction in Public Services
The Intersection Problem for Finite Monoids
The Impact of an AirBnb Host's Listing Description 'Sentiment' and Length On Occupancy Rates
Acronym Disambiguation: A Domain Independent Approach
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks
Studying tidal effects in planetary systems with Posidonius. A N-body simulator written in Rust
AWE-CM Vectors: Augmenting Word Embeddings with a Clinical Metathesaurus
Strong Disorder Real-Space Renormalization for the Many-Body-Localized phase of random Majorana models
Recursive Programs for Document Spanners
Mapping to Declarative Knowledge for Word Problem Solving
Unifying Theories of Time with Generalised Reactive Processes
Investigating the Working of Text Classifiers
Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering
Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications
Call-by-name Gradual Type Theory
An Energy-aware Mutation Testing Framework for EAST-ADL Architectural Models
Formal Verification of Spacecraft Control Programs Using a Metalanguage for State Transformers
Code Reuse With Transformation Objects
Unbounded Software Model Checking with Incremental SAT-Solving
Sentence Boundary Detection for French with Subword-Level Information Vectors and Convolutional Neural Networks
Global-scale phylogenetic linguistic inference from lexical resources
A Type-Based Complexity Analysis of Object Oriented Programs
Learning Approximate Inference Networks for Structured Prediction
Experiments with Neural Networks for Small and Large Scale Authorship Verification
Actor and Action Video Segmentation from a Sentence
Aggression-annotated Corpus of Hindi-English Code-mixed Data
Executable Operational Semantics of Solidity
Machine Learning of Generic and User-Focused Summarization
The Rough Guide to Constraint Propagation
Finite-State Non-Concatenative Morphotactics
Soft Scheduling
Organizing Encyclopedic Knowledge based on the Web and its Application to Question Answering
An object evaluator to generate flexible applications
A Sequential Model for Multi-Class Classification
The Use of Classifiers in Sequential Inference
Unsupervised Learning of Morphology without Morphemes
Type Inference for Guarded Recursive Data Types
A Generalized Two-Phase Analysis of Knowledge Flows in Security Protocols
Formalizing typical crosscutting concerns
Automatically generating Feynman rules for improved lattice field theories
Gamma-ray bursts and the sociology of science
Symmetries and the Antibracket
Spin(7) holonomy manifold and Superconnection
Five dimensional 2-branes from special Lagrangian wrapped M5-branes
Recursive method to obtain the parametric representation of a generic Feynman diagram
First-order supersymmetric sigma models and target space geometry
On N=8 attractors
Generalized Riemann-Hilbert Transmission and Boundary Value Problems, Fredholm Pairs and Bordisms
Representations of marked quivers
Geodesics in the braid group on three strands
Braid semistatistics and doubly regular R-matrix
Parallel transport of $Hom$-complexes and the Lovasz conjecture
Localization properties of highly singular generalized functions
LevelScheme: A level scheme drawing and scientific figure preparation system for Mathematica
First Order Calculi with Values in Right--Universal Bimodules
Structured psychosocial stress and therapeutic failure
Quantum Stochastic Generators
Corrections to the generalized vector dominance due to diffractive rho_3 production
Generalized Cauchy identities, trees and multidimensional Brownian motions. Part II: Combinatorial differential calculus
Two physical characteristics of numerical apparent horizons
On the Kummer construction
Downfolded Self-Energy of Many-Electron Systems
Optimizing Binary Code Produced by Valgrind (Project Report on Virtual Execution Environments Course - AVExe)
Competition and fragmentation: a simple model generating lognormal-like distributions
Bounds for the discrete correlation of infinite sequences on k symbols and generalized Rudin-Shapiro sequences
Creating modular and reusable DSL textual syntax definitions with Grammatic/ANTLR
Time as an Illusion
About raising and handling exceptions
A standard transformation from XML to RDF via XSLT
General combination rules for qualitative and quantitative beliefs
Towards a General Definition of Biometric Systems
Towards a Semantic Preservation System
Towards a Holographic Description of Inflation and Generation of Fluctuations from Thermodynamics
Nonlinear Realization of Spontaneously Broken N=1 Supersymmetry Revisited
Construction of minimal DFAs from biological motifs
Simulation vs. Equivalence
Gravitational Chern-Simons and the adiabatic limit
Infinity in computable probability
The GHZ/W-calculus contains rational arithmetic
Generalized Communicating P Systems Working in Fair Sequential Model
Dynamical generalizations of the Lagrange spectrum
Generative Prior Knowledge for Discriminative Classification
Strictness of the Collapsible Pushdown Hierarchy
On a Formulation of Qubits in Quantum Field Theory
Fixed points of endomorphisms of virtually free groups
Constructive version of Boolean algebra
Rule-weighted and terminal-weighted context-free grammars have identical expressivity
A Characterization of Cellular Automata Generated by Idempotents on the Full Shift
BADREX: In situ expansion and coreference of biomedical abbreviations using dynamic regular expressions
Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
The three-state toric homogeneous Markov chain model has Markov degree two
Identification of Fertile Translations in Medical Comparable Corpora: a Morpho-Compositional Approach
Mixed integrals and related inequalities
Binary Tree Arithmetic with Generalized Constructors
Towards an Application of Update Propagation on Logic Programs Representing Java Source Code
Two SVDs produce more focal deep learning representations
Explaining Zipf's Law via Mental Lexicon
On the toric ideal of a matroid
Nonstandard techniques and nowhere differentiable functions I: A dense family of generalized blancmange functions
Kleene Algebras and Semimodules for Energy Problems
Quantum flights
Online Classification Using a Voted RDA Method
A characterization of those automata that structurally generate finite groups
On generalization of reversible second-order cellular automata
Biquaternion formulation of relativistic tensor dynamics
Generating Music from Literature
Parsing using a grammar of word association vectors
General dynamic recovery for compensating CSP
Generalized version of the support vector machine for binary classification problems: supporting hyperplane machine
Lax functors and coalgebraic weak bisimulation
Recognizable Series on Hypergraphs
Application of Methods for Syntax Analysis of Context-Free Languages to Query Evaluation of Logic Programs
Similarity density of the Thue-Morse word with overlap-free infinite binary words
Distributive Laws and Decidable Properties of SOS Specifications
Decidability of the Clark's Completion Semantics for Monadic Programs and Queries
Towards the Ontology Web Search Engine
Implementing generating functions to obtain power indices with coalition configuration
Scattering Equations and Feynman Diagrams
Irreducible decompositions and stationary states of quantum channels
A novel code generation methodology for block diagram modeler and simulators Scicos and VSS
Words containing all permutations of a family of factors
Implementing a Small Parsing Virtual Machine on Embedded Systems
On generalized Van-Benthem-type characterizations
On the length of fully commutative elements
New Improved Massive Gravity and Three Dimensional Spacetimes of Constant Curvature and Constant Torsion
Construction of the fermionic vacuum and of fermionic operators of creation and annihilation in the theory of algebraic spinors
Bounding quantification in parametric expansions of Presburger arithmetic
Algebraic semantics for hybrid logics
Labeling Topics with Images using Neural Networks
Topological Sigma Models On Supermanifolds
A Step-indexed Semantic Model of Types for the Call-by-Name Lambda Calculus
Metaprogramming Applied to Numerical Problems
Transchromatic generalized character maps
Explicit left orders on free groups extending the lexicographic order on free monoids
Efficient Generation of Correctness Certificates for the Abstract Domain of Polyhedra
From Declarative Model to Solution: Scheduling Scenario Synthesis
Categorical Semantics for Functional Reactive Programming with Temporal Recursion and Corecursion
CLP(H): Constraint Logic Programming for Hedges
Uniform Interpolation for Coalgebraic Fixpoint Logic
Quantum families of invertible maps and related problems
A Formal Study on Backward Compatible Dynamic Software Updates
On Quantum Generalizations of Information-Theoretic Measures and their Contribution to Distributional Semantics
A note on the avoidability of binary patterns with variables and reversals
Symmetry in Cartan language for geometric theories of gravity
A Neural Attention Model for Abstractive Sentence Summarization
Sports highlights generation based on acoustic events detection: A rugby case study
Generating News Headlines with Recurrent Neural Networks
Video captioning with recurrent networks based on frame- and video-level features and visual content classification
Syntax-Semantics Interaction Parsing Strategies. Inside SYNTAGMA
Inflation and the Measurement Problem
Model Checking : A Co-algebraic Approach
Neural Network-Based Abstract Generation for Opinions and Arguments
Focused Meeting Summarization via Unsupervised Relation Extraction
Personalized Emphasis Framing for Persuasive Message Generation
Controlling Output Length in Neural Encoder-Decoders
Generalization of metric classification algorithms for sequences classification and labelling
Presenting a New Dataset for the Timeline Generation Problem
Classical field theories from Hamiltonian constraint: Local symmetries and static gauge fields
Synthesizing invariants by solving solvable loops
Propositions in Linear Multirole Logic as Multiparty Session Types
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
Automatic Wikipedia Link Generation Based On Interlanguage Links
Use Generalized Representations, But Do Not Forget Surface Features
Quantization of noncompact coverings
A Generic Online Parallel Learning Framework for Large Margin Models
Fibonacci words in hyperbolic Pascal triangles
Synchronizing non-deterministic finite automata
Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation
Scavenger 0.1: A Theorem Prover Based on Conflict Resolution
Analysing Data-To-Text Generation Benchmarks
pix2code: Generating Code from a Graphical User Interface Screenshot
A General-Purpose Tagger with Convolutional Neural Networks
Toward uniform random generation in 1-safe Petri nets
Compositions of Functions and Permutations Specified by Minimal Reaction Systems
A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
The Generalized Nagell-Ljunggren Problem: Powers with Repetitive Representations
Extractive Summarization using Deep Learning
Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator
Paradigm Completion for Derivational Morphology
WOAH: Preliminaries to Zero-shot Ontology Learning for Conversational Agents
Confluence and Convergence in Probabilistically Terminating Reduction Systems
Nominal C-Unification
Novel Uses of Category Theory in Modeling OOP
Learning to Remember Translation History with a Continuous Cache
"How Was Your Weekend?" A Generative Model of Phatic Conversation
An Introduction to Image Synthesis with Generative Adversarial Nets
Secure Web Access Control Algorithm
Bounded Context Switching for Valence Systems
Simple Models for Word Formation in English Slang
Self-referencing cellular automata: A model of the evolution of information control in biological systems
An analogue to Dixon's theorem for automaton groups
Comprehension-guided referring expressions
Adversarial Learning for Neural Dialogue Generation
Liveness-Driven Random Program Generation
Quantum Analogical Modeling: A General Quantum Computing Algorithm for Predicting Language Behavior
Consistent perturbations in an imperfect fluid
Generalizing determinization from automata to coalgebras
Unsolvability Cores in Classification Problems
Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs
A Multi-scale Multiple Instance Video Description Network
Graph Logics with Rational Relations
Comparing the writing style of real and artificial papers
A Probabilistic Generative Grammar for Semantic Parsing
Revisiting Parametricity: Inductives and Uniformity of Propositions
Generating and Estimating Nonverbal Alphabets for Situated and Multimodal Communications
Application Software, Domain-Specific Languages, and Language Design Assistants
The Open Language Archives Community and Asian Language Resources
Competition of Languages and their Hamming Distance
Hubs in Languages: Scale Free Networks of Synonyms
Swapping Lemmas for Regular and Context-Free Languages
Quotient Complexity of Closed Languages
The State of the Art: Ontology Web-Based Languages: XML Based
Finitary languages
Applying static code analysis to firewall policies for the purpose of anomaly detection
Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning
Contextual Analysis for Middle Eastern Languages with Hidden Markov Models
Modelling the Evolution of Programming Languages
Separating regular languages by piecewise testable and unambiguous languages
On GitHub's Programming Languages
Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL
Capturing divergence in dependency trees to improve syntactic projection
Sharing Network Parameters for Crosslingual Named Entity Recognition
KU-ISPL Language Recognition System for NIST 2015 i-Vector Machine Learning Challenge
KS_JU@DPIL-FIRE2016:Detecting Paraphrases in Indian Languages Using Multinomial Logistic Regression Model
Improving Document Clustering by Eliminating Unnatural Language
Naturalizing a Programming Language via Interactive Learning
A Study on Neural Network Language Modeling
The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages
Towards Language-Universal End-to-End Speech Recognition
Tracking Typological Traits of Uralic Languages in Distributed Language Representations
Emerging Language Spaces Learned From Massively Multilingual Corpora
The JHU Speech LOREHLT 2017 System: Cross-Language Transfer for Situation-Frame Detection
Automatic Identification of Closely-related Indian Languages: Resources and Experiments
Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems
Cameleon language Part 1: Processor
Integration of Heterogeneous Modeling Languages via Extensible and Composable Language Components
Deletion Operations on Deterministic Families of Automata
A natural language interface to a graph-based bibliographic information retrieval system
Long Text Generation via Adversarial Training with Leaked Information
Deformations of calibrated D-branes in flux generalized complex manifolds
Algorithmic Detection of Computer Generated Text
Learning to Start for Sequence to Sequence Architecture
Computational Model to Generate Case-Inflected Forms of Masculine Nouns for Word Search in Sanskrit E-Text
Topic Aware Neural Response Generation
Boundary-Seeking Generative Adversarial Networks
A Unified Query-based Generative Model for Question Generation and Question Answering
Neural Text Generation: A Practical Guide
End-to-end Adversarial Learning for Generative Conversational Agents
Une grammaire formelle du créole martiniquais pour la génération automatique
General three-state model with biased population replacement: Analytical solution and application to language dynamics
Implementing Support for Pointers to Private Data in a General-Purpose Secure Multi-Party Compiler
Profunctor Optics: Modular Data Accessors
Live Multi-language Development and Runtime Environments
Generating efficient belief models for task-oriented dialogues
Automatic Generation of Constraint Propagation Algorithms for Small Finite Domains
Complexity of Nested Circumscription and Nested Abnormality Theories
GraXML - Modular Geometric Modeler
General Scheme for Perfect Quantum Network Coding with Free Classical Communication
On a coordinate independent description of string worldsheet theory
Stochastic Simulation of Process Calculi for Biology
A Generation-based Text Steganography Method using SQL Queries
From Design to Implementation: an Automated, Credible Autocoding Chain for Control Systems
Formal Ontology Learning on Factual IS-A Corpus in English using Description Logics
Modularity Aspects of Disjunctive Stable Models
A Spatial Data Model for Moving Object Databases
Coherent Multi-Sentence Video Description with Variable Level of Detail
Kevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use
Towards a General Framework for Actual Causation Using CP-logic
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
Practical Run-time Checking via Unobtrusive Property Caching
A Method for Modeling Co-Occurrence Propensity of Clinical Codes with Application to ICD-10-PCS Auto-Coding
Type-Directed Synthesis of Products
Constructive Galois Connections: Taming the Galois Connection Framework for Mechanized Metatheory
A Focused Dynamic Attention Model for Visual Question Answering
Patterns and Rewrite Rules for Systematic Code Generation (From High-Level Functional Patterns to High-Performance OpenCL Code)
Distance structures for generalized metric spaces
Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks
Dynamic Witnesses for Static Type Errors (or, Ill-Typed Programs Usually Go Wrong)
A new selection strategy for selective cluster ensemble based on Diversity and Independency
A Theory of Available-by-Design Communicating Systems
Word and Document Embeddings based on Neural Network Approaches
Reproducing and learning new algebraic operations on word embeddings using genetic programming
Visual-textual Attention Driven Fine-grained Representation Learning
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]
A domain-specific language for the hybridization and static condensation of finite element methods
Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
DATR Theories and DATR Models
Trading off Completeness for Efficiency --- The \textsc{ParseTalk} Performance Grammar Approach to Real-World Text Parsing
An Annotation Scheme for Free Word Order Languages
A complexity measure for diachronic Chinese phonology
Message-Passing Protocols for Real-World Parsing -- An Object-Oriented Model and its Preliminary Evaluation
C++ programming language for an abstract massively parallel SIMD architecture
File mapping Rule-based DBMS and Natural Language Processing
Lexical Base as a Compressed Language Model of the World (on the material of the Ukrainian language)
G-automata, counter languages and the Chomsky hierarchy
Simulation for competition of languages with an ageing sexual population
Phase transition in a sexual age-structured model of learning foreign languages
A universal model for languages and cities, and their lifetimes
Analytical approach to bit-string models of language evolution
On the Length of the Wadge Hierarchy of Omega Context Free Languages
How applicable is Python as first computer language for teaching programming in a pre-university educational environment, from a teacher's point of view?
Closures in Formal Languages: Concatenation, Separation, and Algorithms
REC language is a live on IBM1130 simulator, EL lenguaje REC esta vivo en el simulador de la IBM 1130
A Concurrent Language with a Uniform Treatment of Regions and Locks
Inverse Star, Borders, and Palstars
Combinatorial Characterization of Formal Languages
Contents of COMP6411 Summer 2010 Final Reports on Comparative Studies of Programming Languages
T2Script Programming Language
Logic Characterization of Floyd Languages
Lucretia - a type system for objects in languages with reflection
Universal Witnesses for State Complexity of Boolean Operations and Concatenation Combined with Star
Unambiguous Tree Languages Are Topologically Harder Than Deterministic Ones
Verbalizing Ontologies in Controlled Baltic Languages
Minimal Nondeterministic Finite Automata and Atoms of Regular Languages
Topological dynamics and recognition of languages
Stemmers for Tamil Language: Performance Analysis
A State of the Art of Word Sense Induction: A Way Towards Word Sense Disambiguation for Under-Resourced Languages
Properties of phoneme N -grams across the world's language families
Monoid automata for displacement context-free languages
Using Scripting Languages to Teach Programming
Graph Spectral Properties of Deterministic Finite Automata
Temporal Analysis of Language through Neural Language Models
Neural Mechanism of Language
A Complete Refinement Procedure for Regular Separability of Context-Free Languages
Languages for Mobile Agents
A Primer on Neural Network Models for Natural Language Processing
Formalization of context-free language theory
On the construction of fully interpreted formal languages which posses their truth predicates
A Semisupervised Approach for Language Identification based on Ladder Networks
Filtrations of Formal Languages by Arithmetic Progressions
Apricot - An Object-Oriented Modeling Language for Hybrid Systems
Designing a Pattern Language For Surviving Earthquakes
CRF-based Named Entity Recognition @ICON 2013
An implementation of Apertium based Assamese morphological analyzer
Comparative Studies of Six Programming Languages
A Survey on Operational State Complexity
Security and Privacy Policy Languages: A Survey, Categorization and Gap Identification
Trans-gram, Fast Cross-lingual Word-embeddings
Programming Language Features for Refinement
One-Shot Neural Cross-Lingual Transfer for Paradigm Completion
Reversible Languages Having Finitely Many Reduced Automata
Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English
Cross-lingual, Character-Level Neural Morphological Tagging
Lower Bounds on Regular Expression Size
Exploring the Naturalness of Buggy Code with Recurrent Neural Networks
A Language for Function Signature Representations
Modelling Concurrency with Comtraces and Generalized Comtraces
On Generating *-Sound Nets with Substitution
Learning a Recurrent Visual Representation for Image Caption Generation
Generalized complex geometry of pure backgrounds in ten and eleven dimensions
Generating Multi-Sentence Lingual Descriptions of Indoor Scenes
Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test
Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems
Towards Automatic Generation of Entertaining Dialogues in Chinese Crosstalks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Neural Response Generation with Dynamic Vocabularies
Learning Deep Generative Models of Graphs
Text Analysis Tools in Spoken Language Processing
Anusaaraka: Overcoming the Language Barrier in India
Using a hierarchy of Domain Specific Languages in complex software systems design
Formal Languages and Infinite Groups
Formal semantics of language and the Richard-Berry paradox
Length of the Shortest Word in the Intersection of Regular Languages
JSC : A JavaScript Object System
The FC-rank of a context-free language
Natural Language Understanding Based on Semantic Relations between Sentences
The Green Language
Hyperbolic tilings and formal language theory
Separation Property for wB- and wS-regular Languages
Syntax and analytic semantics of LISA
A perspective on the advancement of natural language processing tasks via topological analysis of complex networks
On the Complexity of L-reachability
Commutative positive varieties of languages
Towards a theory of word order. Comment on "Dependency distance: a new perspective on syntactic patterns in natural language" by Haitao Liu et al
Linking Types for Multi-Language Software: Have Your Cake and Eat It Too
A Universal Semantic Space
From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings
Automatic Optimization of Hardware Accelerators for Image Processing
Formal Modelling, Testing and Verification of HSA Memory Models using Event-B
Why informatics and general science need a conjoint basic definition of information
The Speech-Language Interface in the Spoken Language Translator
Incorporating "Unconscious Reanalysis" into an Incremental, Monotonic Parser
A State-Transition Grammar for Data-Oriented Parsing
Monte Carlo simulation of the rise and the fall of languages
Exploiting Syntactic Structure for Natural Language Modeling
Two-parameter Model of Word Length "Language - Genre"
Universal Model for Paraphrasing -- Using Transformation Based on a Defined Criteria --
Monads for natural language semantics
Extending Dublin Core Metadata to Support the Description and Discovery of Language Resources
Exploiting multilingual nomenclatures and language-independent text features as an interlingua for cross-lingual text analysis applications
Canonical decomposition of catenation of factorial languages
Complex networks and human language
The DFAs of Finitely Different Languages
IST is more than an algorithm to prove ZFC theorems
Hairdressing in groups: a survey of combings and formal languages
Microscopic and Macroscopic Simulation of Competition between Languages
Monte Carlo simulation of survival for minority languages
Probabilities to accept languages by quantum finite automata
Translating a first-order modal language to relational algebra
Language simulation after a conquest
Decision Problems For Convex Languages
Weak Mso with the Unbounding Quantifier
State complexity of orthogonal catenation
Algebraic properties of structured context-free languages: old approaches and novel developments
On Languages Accepted by P/T Systems Composed of joins
LXG Compiler - Design and Implementation
Exploring Language-Independent Emotional Acoustic Features via Feature Selection
Comparative Studies of 10 Programming Languages within 10 Diverse Criteria -- a Team 7 COMP6411-S10 Term Report
Strategic programming on graph rewriting systems
Context-free ordinals
Parameterized Regular Expressions and their Languages
Self-Adjusting Stack Machines
Language understanding as a step towards human level intelligence - automatizing the construction of the initial dictionary from example sentences
Language Acquisition in Computers
Infinite Synchronizing Words for Probabilistic Automata (Erratum)
Universal Witnesses for State Complexity of Basic Operations Combined with Reversal
Finite Automata with Time-Delay Blocks (Extended Version)
In the Maze of Data Languages
Maximal Syntactic Complexity of Regular Languages Implies Maximal Quotient Complexities of Atoms
Estimating Confusions in the ASR Channel for Improved Topic-based Language Model Adaptation
On the Topological Complexity of omega-Languages of Non-Deterministic Petri Nets
Deciding the Borel complexity of regular tree languages
Martta: A C++ Language Workbench
Sign Language Lexical Recognition With Propositional Dynamic Logic
Breadth-first serialisation of trees and rational languages
Complexity of checking whether two automata are synchronized by the same language
Proceedings 14th International Conference on Automata and Formal Languages
Hidden Markov Model Based Part of Speech Tagger for Sinhala Language
Unknown Words Analysis in POS tagging of Sinhala Language
Scanning and Parsing Languages with Ambiguities and Constraints: The Lamb and Fence Algorithms
A language model based approach towards large scale and lightweight language identification systems
Hierarchical Character-Word Models for Language Identification
Demographic Dialectal Variation in Social Media: A Case Study of African-American English
Extension of hidden markov model for recognizing large vocabulary of sign language
Maximally Atomic Languages
Translation Of Telugu-Marathi and Vice-Versa using Rule Based Machine Translation
Templet: a Markup Language for Concurrent Programming
Separated by an Un-common Language: Towards Judgment Language Informed Vector Space Modeling
The Gremlin Graph Traversal Machine and Language
Liberating language research from dogmas of the 20th century
Quick Brown Fox in Formal Languages
From quantum foundations via natural language meaning to a theory of everything
An automata characterisation for multiple context-free languages
Inter-language Collaboration in an Object-oriented Virtual Machine
A Chomsky-Schützenberger representation for weighted multiple context-free languages
PMI Matrix Approximations with Applications to Neural Language Modeling
Building a robust sentiment lexicon with (almost) no resource
Industrial Experience Report on the Formal Specification of a Packet Filtering Language Using the K Framework
Towards a Theory of Complexity of Regular Languages
Monte Carlo Action Programming
Some connections between universal algebra and logics for trees
Cross-lingual Abstract Meaning Representation Parsing
Found in Translation: Reconstructing Phylogenetic Language Trees from Translations
Uncountable realtime probabilistic classes
On the decidability of $k$-Block determinism
Gated-Attention Architectures for Task-Oriented Language Grounding
Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones
Proceedings 15th International Conference on Automata and Formal Languages
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages
Language as a matrix product state
FunTAL: Reasonably Mixing a Functional Language with Assembly
Classical Control, Quantum Circuits and Linear Logic in Enriched Category Theory
One for All: Towards Language Independent Named Entity Linking
Universal Neural Machine Translation for Extremely Low Resource Languages
Unambiguous languages exhaust the index hierarchy
2D Transonic Hydrodynamics in General Relativity
Introductory Overview of Modern Cosmology
Velocity Curves for Stars in Disk Galaxies: A case for Nearly Newtonian Dynamics
Physics of glassy systems
On J. Goodman's comment to "Language Trees and Zipping"
On an Application of Relative Entropy
Twistor theory and the four-dimensional Quantum Hall effect of Zhang and Hu
A hybrid model for chaotic front dynamics: From semiconductors to water tanks
Hydrodynamic Formulation of the Hubbard Model
The energy spectrum symmetry of Heisenberg model in Fock space
Translating near-synonyms: Possibilities and preferences in the interlingua
Inducing a Semantically Annotated Lexicon via EM-Based Clustering
Noun Phrase Recognition by System Combination
Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies
Approximation and Exactness in Finite State Optimality Theory
Security Policy Consistency
Incremental construction of minimal acyclic finite-state automata
Exploiting auxiliary distributions in stochastic unification-based grammars
Semantic interpretation of temporal information by abductive inference
Type Arithmetics: Computation based on the theory of types
Object-oriented tools for advanced applications
Logic, Individuals and Concepts
The Role of Conceptual Relations in Word Sense Disambiguation
Anaphora and Discourse Structure
Stereotypical Reasoning: Logical Properties
Schedulers for Rule-based Constraint Programming
Open Network Handles Implemented in DNS
Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization
Temporal logic with predicate abstraction
Improved Inference for Checking Annotations
Explaining Constraint Programming
Knowledge Flow Analysis for Security Protocols
On Typechecking Top-Down XML Tranformations: Fixed Input or Output Schemas
Tarski's influence on computer science
Interroger un corpus par le sens
Transformation de Fourier-Mukai sur les Surfaces Hyperkählériennes
Particle Spectrum Created Through Bubble Nucleation
Vacuum Spacetimes with Future Trapped Surfaces
Perturbative Analysis of Bianchi IX using Ashtekar Formalism
On the degenerate phase boundaries
Spacetime G-structures I: Topological Defects
Isocurvature Perturbations in Quintessence Cosmologies
An extension principle for the Einstein-Vlasov system in spherical symmetry
A quantum-like description of the planetary systems
A Dodecalogue of Basic Didactics from Applications of Abstract Differential Geometry to Quantum Gravity
Strings, gravity and particle physics
Dill: An Algorithm and a Symbolic Software Package for Doing Classical Supersymmetry Calculations
A simple solution to color confinement
Skyrme Model Language in the Theory of Nucleons and Nuclei
SHERPA 1.alpha, a proof-of-concept version
Classical integrable lattice models through quantum group related formalism
Computing the BRST Operator Used in Quantization of Gauge Theories
Five Lectures on the Jet Methods in Field Theory
WBase: a C package to reduce tensor products of Lie algebra representations. Description and new developments
Current Algebra and Bosonization in Three Dimensions
Quantum Field Theories on Algebraic Curves
Expanding and contracting universes in third quantized string cosmology
Superembeddings, Non-Linear Supersymmetry and 5-branes
Probability distributions in statistical ensembles with conserved charges
CT-duality as a local property of the world-sheet
Topological D-Branes and Commutative Algebra
Completeness proof of functional logic, a formalism with variable-binding nonlogical symbols
Affine type A crystal structure on tensor products of rectangles, Demazure characters, and nilpotent varieties
Active Libraries: Rethinking the roles of compilers and libraries
Uniform Versions of Infinitary Properties in Banach Spaces
Hochschild DGLAs and torsion algebras
Sets and Their Sizes
A note on Newtonian, Lagrangian and Hamiltonian dynamical systems in Riemannian manifolds
Symplectic operad geometry and graph homology
Lie-Rinehart algebras, descent, and quantization
Can we express every transfinite concept constructively?
Is the Halting probability a Dedekind real number?
State Complexity and the Monoid of Transformations of a Finite Set
Invariant and evolutionary properties of the skew-symmetric differential forms
Covers of the multiplicative group of an algebraically closed field of characteristic zero
Countable and Full Exchange Rings
Short introduction to Nonstandard Analysis
Variations of a Coin-Removal Problem
Limits and the system of near-numbers
PA is instantiationally complete, but algorithmically incomplete: An alternative interpretation of Goedelian incompleteness under Church's Thesis that links formal logic and computability
Small Valdivia compact spaces
Kontsevich's formula and the WDVV equations in tropical geometry
An Introduction to Zoli Numbers
Some metric properties of automorphisms of groups
Combinatorial aspects of code loops
Quantum and Braided Integrals
Nested quasicrystalline discretisations of the line
Coupled Map Networks as Communication Schemes
The normal dual congruences and the dual Bianchi lattice
Mechanical interpretation of existence theorems in a nonlinear Dirichlet problem
Analise Termodinamica da aceleracao de uma massa
Estabelecimento do Conceito de Temperatura como uma grandeza derivada da Energia e da Entropia
The Best Possible Unification for any Collection of Physical Theories
Heat transmission in Relativity
Introduction to Quantum Cryptography
Fast Computation of Voigt Functions via Fourier Transforms
Do reductionist cures select for holistic diseases? Adaptive chronic infection, structured stress, and medical magic bullets
Structured psychosocial stress and the US obesity epidemic
Cucker-Smale Flocking under Hierarchical Leadership
Quantum Counting
Why Quantum Mechanics is Hard to Understand
Designing optimum CP maps for quantum teleportation
On Almost Periodicity Criteria for Morphic Sequences in Some Particular Cases
Continuous selections and sigma-spaces
The Holographic Interpretation of Hawking Radiation
O-minimal cohomology: finiteness and invariance results
Test Functions Space in Noncommutative Quantum Field Theory
An Abstract Interpolation Problem and the Extension Theory of Hermitian Operators
On the quantization of conjugacy classes
Advanced Compact Thermal Modeling by using VHDL-AMS
UML 2.0 - Overview and Perspectives in SoC Design
Unified Modeling of Complex Real-Time Control Systems
The immune system: look who's talking
Can a Computer Laugh ?
Application of Tuncay's language teacher model to business-customer relations
McKay correspondence for Landau-Ginzburg models
Q-systems as cluster algebras
Survey of Technologies for Web Application Development
Software graphs and programmer awareness
Classical Enhancement of Quantum Error-Correcting Codes
Feature Unification in TAG Derivation Trees
Data-Oblivious Stream Productivity
A Process Algebra Software Engineering Environment
On multi F-nomial coefficients and Inversion formula for F-nomial coefficients
$σ$-continuity and related forcings
Mean asymptotic behaviour of radix-rational sequences and dilation equations (Extended version)
Development of simulation package 'ELSES' for extra-large-scale electronic-structure calculation
On the Stability of Electrostatic Orbits
CoZo+ - A Content Zoning Engine for textual documents
Radiative corrections to muon decay in leading and next to leading approximation for electron spectrum
Classification de modules aux différences filtrés isogradués
Geometry of splice-quotient singularities
Quantum vacuum and accelerated expansion
Catalan numbers and relations
Bridge Theory: Oltre la Frontiera Quantistica
A Semantics-Aware Editing Environment for Prolog in Eclipse
On the lattice of sub-pseudovarieties of DA
Some Remarks on the Toeplitz Corona problem
A protocol for instruction stream processing
Considerations on Construction Ontologies
Non-regularity of floor(alpha + log_k(n))
Symbolic Script Programming for Java
Proceedings International Workshop on The Complexity of Simple Programs
Advances in the Design and Implementation of a Multi-Tier Architecture in the GIPSY Environment
A Noisy-Channel Model for Document Compression
Syndeticity and independent substitutions
Documenting Spreadsheets with Pseudo-Code: an Exercise with Cash-Flow and Loans
The congruence subgroup property for $Aut F_2$: A group-theoretic proof of Asada's theorem
Using R for data analysis and graphing in an introductory physics laboratory
Geometric and topological aspects of Type IIB D-branes
Discussion on Supervisory Control by Solving Automata Equation
Is Ramsey's theorem omega-automatic?
Combinatorial cubic surfaces and reconstruction theorems
Examples of non-compact quantum group actions
Point Processes Modeling of Time Series Exhibiting Power-Law Statistics
Unification and Emergence in Physics: the Problem of Articulation
Proceedings Ninth International Workshop on Reduction Strategies in Rewriting and Programming
Diffusive wavelets on groups and homogeneous spaces
An Improved Algorithm for Generating Database Transactions from Relational Algebra Specifications
Categorical Models for a Semantically Linear Lambda-calculus
On Linear Information Systems
Courant algebroids: Cohomology and Matched Pairs
Selected issues on justification of holographic approach to QCD
On The Structure Of The Chan-Paton Factors For D-Branes In Type II Orientifolds
Partition theorems from creatures and idempotent ultrafilters
Molecular Programming Pseudo-code Representation to Molecular Electronics
Preorientations of the derived motivic multiplicative group
Parametrizing Program Analysis by Lifting to Cardinal Power Domains
Loomis--Sikorski Theorem and Stone Duality for Effect Algebras with Internal State
Turing Automata and Graph Machines
A Framework for Constraint-Based Deployment and Autonomic Management of Distributed Applications
A Middleware Framework for Constraint-Based Deployment and Autonomic Management of Distributed Applications
$\aleph_0$-categorical strongly minimal compact complex manifolds
State Elimination Ordering Strategies: Some Experimental Results
Finite-State Complexity and the Size of Transducers
A view of canonical extension
On Second-Order Monadic Monoidal and Groupoidal Quantifiers
On the nature of financial leverage
The Wigner Distribution
The $z$-Transform and Automata-Recognizable Systems of Nonhomogeneous Linear Recurrence Equations over Semirings
Catalan structures and Catalan pairs
The semantic mapping of words and co-words in contexts
A Categorical Outlook on Cellular Automata
Negative bases and automata
Nominal Unification Revisited
Contracts for Abstract Processes in Service Composition
On Brlek-Reutenauer conjecture
Weak mu-equality is decidable
Descent and forms of tensor categories
Computing Semi-algebraic Invariants for Polynomial Dynamical Systems
Memory Reduction via Delayed Simulation
Automatic Synthesis of Switching Controllers for Linear Hybrid Automata
Reinforcement learning in signaling game
Central limit theorems for additive functionals of ergodic Markov diffusions processes
Geometric Semigroup Theory
Extensional Higher-Order Logic Programming
Unification of some classical and quantum ideas
Quantification in ordinary language
Combinatorics on words in information security: Unavoidable regularities in the construction of multicollision attacks on iterated hash functions
Geometric grid classes of permutations
Galois subfields of inertially split division algebras
RedAlert: Determinacy Inference for Prolog
Geometrical view of quantum entanglement
Groupoid-theoretical methods in the mapping class groups of surfaces
Equational theories of profinite structures
Solving the TTC 2011 Reengineering Case with GrGen.NET
An Entertaining Example of Using the Concepts of Context-Free Grammar and Pushdown Automation
A proof of Reidemeister-Singer's theorem by Cerf's methods
The quasi-Hopf analogue of $u_q(sl_2)$
An order-theoretic analysis of interpretations among propositional deductive systems
Functional Logic Programming with Generalized Circular Coinduction
CAT(0) geometry for the Thompson Group
Enumeration of edges in some lattices of paths
On Shift Spaces with Algebraic Structure
Enumeration of saturated chains in Dyck lattices
Computing Accurate Age and Distance Factors in Cosmology
A simplified framework for first-order languages and its formalization in Mizar
Matrix elements of unstable states
Subword Complexity and k-Synchronization
Least periods of k-automatic sequences
Credal nets under epistemic irrelevance
Approximating Weak Bisimilarity of Basic Parallel Processes
Coordination Level Modeling and Analysis of Parallel Programs using Petri Nets
Borel* Sets in the Generalised Baire Space
A characterization of $p$-automatic sequences as columns of linear cellular automata
Diagrammatic confluence for Constraint Handling Rules
Artex is AnotheR TEXt summarizer
An interactive programme for Steiner trees
Quantum families of maps
Poisson-Lie Sigma Models on Drinfel'd double
BoA: a versatile software for bolometer data reduction
On optimum left-to-right strategies for active context-free games
Towards a Theory of Glue
Partial Orders for Efficient BMC of Concurrent Software
On the canonical connection for smooth envelopes
On the least number of palindromes contained in an infinite word
A Semantic Matching Energy Function for Learning with Multi-relational Data
On Families in Differential Geometry
Density Ratio Hidden Markov Models
Nonexistence of the final first integral in the Zipoy-Voorhees space-time
Compactness of powers of ω
A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection
KRAB Algorithm - A Revised Algorithm for Incremental Call Graph Generation
Collapsible Pushdown Graphs of Level 2 are Tree-Automatic
An efficient way to perform the assembly of finite element matrices in Matlab and Octave
The use of the teleparallelism connection in continuum mechanics
Relative dimension of morphisms and dimension for algebraic stacks
A Babuška-Aziz type proof of the circumradius condition
Nonlinear parabolic problems in Musielak--Orlicz spaces
In-in formalism on tunneling background: multi-dimensional quantum mechanics
Syntactic Complexity of Circular Semi-Flower Automata
Linear Dependent Types for Domain Specific Program Analysis (Extended Abstract)
Integrating Datalog and Constraint Solving
Proceedings Machines, Computations and Universality 2013
Analysing Quality of English-Hindi Machine Translation Engine Outputs Using Bayesian Classification
Saturation of Concurrent Collapsible Pushdown Systems
A reduction of proof complexity to computational complexity for $AC^0[p]$ Frege systems
Fidelity susceptibility and Loschmidt echo for generic paths in a three spin interacting transverse Ising model
Expressiveness of Visibly Pushdown Transducers
Deciding $k$CFA is complete for EXPTIME
Non viability of hyperbolic quantum mechanics as a theory of Nature
A Simple Method to Produce Algorithmic MIDI Music based on Randomness, Simple Probabilities and Multi-Threading
The semantic marriage of monads and effects
TBX goes TEI -- Implementing a TBX basic extension for the Text Encoding Initiative guidelines
Reset thresholds of automata with two cycle lengths
Quantum Turing automata
Type-amalgamation properties and polygroupoids in stable theories
Memory-only selection of dictionary PINs
Classification theory for accessible categories
Generating Synchronizing Automata with Large Reset Lengths
Multi-borders classification
A heuristic prover for real inequalities
Do we really need to write documentation for a system? CASE tool add-ons: generator+editor for a precise documentation
Keeping a Crowd Safe: On the Complexity of Parameterized Verification (Corrected version)
Phynance
Functional Bandits
Towards an Efficient Prolog System by Code Introspection
Grammars with two-sided contexts
Bisimulation Equivalence of First-Order Grammars
Multi-layered graph-based multi-document summarization model
Quantitative model-checking of controlled discrete-time Markov processes
Proceedings 9th Workshop on Quantum Physics and Logic
A Framework to Synergize Partial Order Reduction with State Interpolation
Optimizing Component Combination in a Multi-Indexing Paragraph Retrieval System
Be Careful When Assuming the Obvious: Commentary on "The placement of the head that minimizes online memory: a complex systems approach"
Subset seed automaton
Forcing a countable structure to belong to the ground model
Kazama-Suzuki Models of N=2 Superconformal Field Theory and Manin triples
Symbolic Solving of Extended Regular Expression Inequalities
Principles for Verification Tools: Separation Logic
Orbit automata as a new tool to attack the order problem in automaton groups
Variations on the Stochastic Shortest Path Problem
The complexity of satisfaction problems in reverse mathematics
Metamorphosis of Fuzzy Regular Expressions to Fuzzy Automata using the Follow Automata
Certification of programs with computational effects
Peetre-Slovák's theorem revisited
QANUS: An Open-source Question-Answering Platform
Foundational Extensible Corecursion
Disaster Monitoring with Wikipedia and Online Social Networking Sites: Structured Data and Linked Data Fragments to the Rescue?
Hamiltonian cosmology in bigravity and massive gravity
A general framework for quantum macroscopicity in terms of coherence
Synchronizing delay for binary uniform morphisms
Part I: Vector Analysis of Spinors
Initial non-repetitive complexity of infinite words
Interface Between Market and Science
Machine Learning for Machine Data from a CATI Network
OmniGraph: Rich Representation and Graph Kernel Learning
Ordered Tree-Pushdown Systems
Characteristic Formulae for Session Types (extended version)
Fast k-best Sentence Compression
Minimizing Regret in Discounted-Sum Games
Proceedings 12th International Workshop on Quantum Physics and Logic
Selective inference in regression models with groups of variables
The Complexity of Interaction (Long Version)
The realization of the wave function collapse in the linguistic interpretation of quantum mechanics
On small profinite groups
Multi-Field Structural Decomposition for Question Answering
Eilenberg--Moore Monoids and Backtracking Monad Transformers
Supervised and Unsupervised Ensembling for Knowledge Base Population
Derivative-Based Diagnosis of Regular Expression Ambiguity
Probabilistic Resource Analysis by Program Transformation
Unique Parallel Decomposition for the Pi-calculus
Sex, drugs, and violence
Higher-Order Kullback-Leibler Aggregation of Markov Chains
Aligning Packed Dependency Trees: a theory of composition for distributional semantics
On sequentially h-complete groups
Topological Considerations for Tuning and Fingering Stringed Instruments
The Cubical Homology of Trace Monoids
Approximated maximum likelihood estimation in multifractal random walks
Abstracting Path Conditions
Proof nets for the Lambek-Grishin calculus
Compactness of $ω^λ$ for $λ$ singular
Quasi-Hamiltonian bookkeeping of WZNW defects
Torus Invariant Curves
State BCK-algebras and State-Morphism BCK-algebras
On Semantic Word Cloud Representation
Expectation values of quantum powers < r^a > using quantum defects for Li O Na Mg using symbolic Mathematics, and new tools such as Topbase to produce quantum defects of these elements
COINs change leaders - Lessons Learned from a Distributed Course
A Secure and Comparable Text Encryption Algorithm
What are symmetries of nonlinear PDEs and what are they themselves?
Parameterization of temperature and spectral distortions in future CMB experiments
Framed 4-valent Graph Minor Theory I: Intoduction. A Planarity Criterion and Linkless Embeddability
Periodic configurations of subshifts on groups
Standard protocol complexes for the immediate snapshot read/write model
Second order symmetry operators
Free amalgamation and automorphism groups
Monad Transformers for Backtracking Search
On the relation between continuous functions in two different metric spaces
A Bengali HMM Based Speech Synthesis System
Towards an Error Correction Memory to Enhance Technical Texts Authoring in LELIE
Supergravity Actions with Integral Forms
Dynamic Component Composition
Recurrent Neural Network Regularization
Minimal and maximal constituents of twisted Foulkes characters
A Note on Semantics (with an Emphasis on UML)
Using Answer Set Programming for pattern mining
The role of homology in fluid vortices I: non-relativistic flow
Unsupervised Domain Adaptation with Feature Embeddings
Tensor calculus with open-source software: the SageManifolds project
Bayesian Optimisation for Machine Translation
Dagger Geometry As Banach Algebraic Geometry
Rewriting Higher-Order Stack Trees
String Corrected Spacetimes and SU(N)-Structure Manifolds
On the topology of rational T-varieties of complexity one
Business Rule Mining from Spreadsheets
Discrete Temporal Constraint Satisfaction Problems
Deep Recurrent Neural Networks for Acoustic Modelling
Unsupervised Dependency Parsing: Let's Use Supervised Parsers
Homing Vector Automata
Inferring Program Transformations from Type Transformations for Partitioning of Ordered Sets
Some aspects of holographic W-gravity
Parikh matrices and Parikh Rewriting Systems
Topic Stability over Noisy Sources
Continued fraction expansions in connection with the metric Mahler measure
IllinoisSL: A JAVA Library for Structured Prediction
Sentiment Uncertainty and Spam in Twitter Streams and Its Implications for General Purpose Realtime Sentiment Analysis
Extended Conditional Independence and Applications in Causal Inference
A detailed analysis of mathematics of entanglement in Non-Hermitian systems in real eigenvalue regime
Dipole Codes Attractively Encode Glue Functions
On Rings of Differential Rota-Baxter Operators
Simple Baseline for Visual Question Answering
Turbulent Thermal Diffusion: A Way to Concentrate Dust in Protoplanetary Discs
Stack Exchange Tagger
Almost Continuous Transformations of Software and Higher-order Dataflow Programming
Fusion of Array Operations at Runtime
Improved Query Topic Models via Pseudo-Relevant Pólya Document Models
Epistemological Consequences of the Incompleteness Theorems
Variations of the Similarity Function of TextRank for Automated Summarization
Automatic Generation of Formula Simplifiers based on Conditional Rewrite Rules
On Equivalence and Uniformisation Problems for Finite Transducers
Approximate Relational Hoare Logic for Continuous Random Samplings
Prediction of Infinite Words with Automata
Measuring cones and other thick subsets in free groups
Predicate Gradual Logic and Linguistics
A Persona-Based Neural Conversation Model
Neural Summarization by Extracting Sentences and Words
Linear Distances between Markov Chains
Measuring the speed of light with electric and magnetic pendulum
Markov Chains and Unambiguous Büchi Automata
Derived-term Automata for Extended Weighted Rational Expressions
On model architecture for a children's speech recognition interactive dialog system
Construction of Non-expandable Non-overlapping Sets of Pictures
Matrix Factorization using Window Sampling and Negative Sampling for Improved Word Representations
On the Commutative Algebra of Categories
N= 4 Supersymmetric Quantum Mechanical Model: Novel Symmetries
Formalization of Phase Ordering
Toward Word Embedding for Personalized Information Retrieval
Enforcing Termination of Interprocedural Analysis
Learning for Biomedical Information Extraction: Methodological Review of Recent Advances
Query Answering with Transitive and Linear-Ordered Data
Turchin's Relation for Call-by-Name Computations: A Formal Approach
Experiments with Synchronizing Automata
Neural Machine Translation with Recurrent Attention Modeling
On the structure of formal balls of the balanced quasi-metric domain of words
Incremental Learning for Fully Unsupervised Word Segmentation Using Penalized Likelihood and Model Selection
Neural Machine Translation with Supervised Attention
Constructing Orthogonal Latin Squares from Linear Cellular Automata
Nonsymbolic Text Representation
Notes on Pure Dataflow Matrix Machines: Programming with Self-referential Matrix Transformations
Modelling Sentence Pairs with Tree-structured Attentive Encoder
Augmented Index and Quantum Streaming Algorithms for DYCK(2)
Iterative Refinement for Machine Translation
How Document Pre-processing affects Keyphrase Extraction Performance
Higher-Order Linearisability
A Compare-Aggregate Model for Matching Text Sequences
AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification
Differentiable Programs with Neural Libraries
Subgroups of Quantum Groups
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs
Coalgebraic trace semantics via forgetful logics
Static Analysis of Communicating Processes using Symbolic Transducers
Neural Document Embeddings for Intensive Care Patient Mortality Prediction
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering
TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning
Automatic Labelling of Topics with Neural Embeddings
Domain specialization: a post-training domain adaptation for Neural Machine Translation
Nondeterministic unitary OBDDs
Proceedings 13th International Conference on Quantum Physics and Logic
Modeling news spread as an SIR process over temporal networks
Bernstein-Zelevinsky derivatives: a Hecke algebra approach
A storm is Coming: A Modern Probabilistic Model Checker
Bisimulation Metrics for Weighted Automata
Quantum Computing with Variable Complex Plane. Light Beam Guide Implementation
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
Reinforcement Learning for Transition-Based Mention Detection
Permissive Supervisor Synthesis for Markov Decision Processes through Learning
Semi-Supervised Affective Meaning Lexicon Expansion Using Semantic and Distributed Word Representations
Enriched Duality in Double Categories: V-categories and V-cocategories
Embedded Collaborative Filtering for "Cold Start" Prediction
Persian Wordnet Construction using Supervised Learning
Does Neural Machine Translation Benefit from Larger Context?
Bandit Structured Prediction for Neural Sequence-to-Sequence Learning
Lexical Features in Coreference Resolution: To be Used With Caution
Algebraically Closed Fields with a Generic Multiplicative Character
Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings
Learning Topic-Sensitive Word Representations
Speech-Based Visual Question Answering
A Versatile, Sound Tool for Simplifying Definitions
Yet Another Introduction to Dark Matter
Dynamic Compositional Neural Networks over Tree Structure
A Neural Framework for Generalized Topic Models
Implementing the sine transform of fermionic modes as a tensor network
Hilbert series for twisted commutative algebras
AutoWIG: Automatic Generation of Python Bindings for C++ Libraries
Dataflow Matrix Machines as a Model of Computations with Linear Streams
Computer aided synthesis: a game theoretic approach
Prosodic Event Recognition using Convolutional Neural Networks with Context Information
Ortoedres amb longitud d'arestes enteres / Cuboids with integer length edges
trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R
An Overview of Multi-Task Learning in Deep Neural Networks
A Mixture Model for Learning Multi-Sense Word Embeddings
The Moore and the Myhill Property For Strongly Irreducible Subshifts Of Finite Type Over Group Sets
On the ghost issue of extended quasidilaton
Parareal Algorithm Implementation and Simulation in Julia
DE-PACRR: Exploring Layers Inside the PACRR Model
Vertical almost-toric systems
Multiple Range-Restricted Bidirectional Gated Recurrent Units with Attention for Relation Classification
Context Aware Document Embedding
Data-Driven Loop Invariant Inference with Automatic Feature Synthesis
On the physical interpretation of the Dirac wavefunction
Auxiliary Objectives for Neural Error Detection Models
Machine Translation at Booking.com: Journey and Lessons Learned
Combining Thesaurus Knowledge and Probabilistic Topic Models
Domain Aware Neural Dialog System
CRF Autoencoder for Unsupervised Dependency Parsing
Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models
Neural Translation of Musical Style
A Measure for Dialog Complexity and its Application in Streamlining Service Operations
Orbifold equivalence: structure and new examples
Finite-state Strategies in Delay Games
Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier
Learning to Explain Non-Standard English Words and Phrases
Towards coarse graining of discrete Lorentzian quantum gravity
Event Identification as a Decision Process with Non-linear Representation of Text
Group Sparse CNNs for Question Classification with Answer Sets
Confidence through Attention
Querying Best Paths in Graph Databases
Clickbait Detection in Tweets Using Self-attentive Network
Aligning Script Events with Narrative Texts
RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and CNN
MatchPy: A Pattern Matching Library
Embedding-Based Speaker Adaptive Training of Deep Neural Networks
Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
Deciding Confluence and Normal Form Properties of Ground Term Rewrite Systems Efficiently
Weakly 2-randoms and 1-generics in Scott sets
First Results from Using Game Refinement Measure and Learning Coefficient in Scrabble
An introduction to approximate computing
Eliminating the unit constant in the Lambek calculus with brackets
A Uniform Framework for Timed Automata and Beyond
Robert Sheckley\s Answerer for two orthogonal projections
A framework for on-line calibration of LINAC devices
Heterogeneous continuous time random walks
Attention networks for image-to-text
Enhanced Characterness for Text Detection in the Wild
Avoiding Echo-Responses in a Retrieval-Based Conversation System
Structured Optimal Transport
HotFlip: White-Box Adversarial Examples for NLP
Stable regularity for relational structures
Learning Feature Representations for Keyphrase Extraction
Trading the Twitter Sentiment with Reinforcement Learning
Lifelong Learning for Sentiment Classification
Pointlike sets for varieties determined by groups
Evaluating neural network explanation methods using hybrid documents and morphological prediction
Investigations on Knowledge Base Embedding for Relation Prediction and Extraction
Non-Projective Dependency Parsing via Latent Heads Representation (LHR)
Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks
Implicit Argument Prediction with Event Knowledge
STRIPStream: Integrating Symbolic Planners and Blackbox Samplers
Efficient Mendler-Style Lambda-Encodings in Cedille
Semantical Equivalence of the Control Flow Graph and the Program Dependence Graph
Bi-interpretability of a Free Monoid with the Arithmetic and Applications
Corpus Statistics in Text Classification of Online Data
Defeasible Reasoning in SROEL: from Rational Entailment to Rational Closure
Deep Communicating Agents for Abstractive Summarization
Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond
Automatic Generation of Chinese Short Product Titles for Mobile Display
Training Tips for the Transformer Model
Higher-Order Bounded Model Checking
Emotion Orientated Recommendation System for Hiroshima Tourist by Fuzzy Petri Net
A Dynamic Approach to Characterizing Termination of General Logic Programs
Topological sigma-models with H-flux and twisted generalized complex manifolds
Optimized Generation of Data-Path from C Codes for FPGAs
On generic properties of finitely presented monoids and semigroups
Towards a Generic Framework to Generate Explanatory Traces of Constraint Solving and Rule-Based Reasoning
Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video
Boltzmann samplers for random generation of lambda terms
On Periodicity and Complexity of Generalized Pseudostandard Words
The Influence of the Generator's License on Generated Artifacts
The Interaction of Memory and Attention in Novel Word Generalization: A Computational Investigation
Generalized Metrics
Generalized Entropies and the Similarity of Texts
SimTensor: A synthetic tensor data generator
Generative and Discriminative Text Classification with Recurrent Neural Networks
Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition
A Conditional Variational Framework for Dialog Generation
Flexible and Creative Chinese Poetry Generation Using Neural Memory
Adversarial Feature Matching for Text Generation
DTATG: An Automatic Title Generator based on Dependency Trees
RubyStar: A Non-Task-Oriented Mixture Model Dialog System
ACtuAL: Actor-Critic Under Adversarial Learning
On the Automatic Generation of Medical Imaging Reports
Table-to-text Generation by Structure-aware Seq2seq Learning
A Syntactic Approach to Domain-Specific Automatic Question Generation
Exploration on Generating Traditional Chinese Medicine Prescription from Symptoms with an End-to-End method
Generic HKT geometries in the harmonic superspace approach
Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data
CoT: Cooperative Training for Generative Modeling
GRAMPAL: A Morphological Processor for Spanish implemented in Prolog
MBT: A Memory-Based Part of Speech Tagger-Generator
OT SIMPLE - a construction-kit approach to Optimality Theory implementation
Competition-Induced Preferential Attachment
Artificial Sequences and Complexity Measures
An Algebraic Programming Style for Numerical Software and its Optimization
Rewriting Calculus: Foundations and Applications
Making Abstract Domains Condensing
Generic and Efficient Program Monitoring by trace analysis
Practical Semantic Analysis of Web Sites and Documents
Decidability of Type-checking in the Calculus of Algebraic Constructions with Size Annotations
Generative Unbinding of Names
A multiphysics and multiscale software environment for modeling astrophysical systems
Provenance Traces
Type-Safe Feature-Oriented Product Lines
Fractal Dimension for Fractal Structures
An extensible web interface for databases and its application to storing biochemical data
Amortised Resource Analysis with Separation Logic
Regular Functions, Cost Register Automata, and Generalized Min-Cost Problems
Pertinent Information retrieval based on Possibilistic Bayesian network : origin and possibilistic perspective
Disease processes as hybrid dynamical systems
Identification of Literary Movements Using Complex Networks to Represent Texts
Perfect orderings on Bratteli diagrams II: general Bratteli diagrams
Inference of Field-Sensitive Reachability and Cyclicity
Evidence and plausibility in neighborhood structures
Semantic Stability in Social Tagging Streams
Heterogeneous Programming with Single Operation Multiple Data
A Theory of Formal Synthesis via Inductive Learning
Transforming Wikipedia into an Ontology-based Information Retrieval Search Engine for Local Experts using a Third-Party Taxonomy
Knowledge Base Population using Semantic Label Propagation
Interprocedural Data Flow Analysis in Soot using Value Contexts
Interface Reconciliation in Kahn Process Networks using CSP and SAT
Using Generic Summarization to Improve Music Information Retrieval Tasks
Differentiation with stratification: a principle of theoretical physics in the tradition of the memory art
Modular Acquisition and Stimulation System for Timestamp-Driven Neuroscience Experiments
A Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN) for Unsupervised Discovery of Linguistic Units and Generation of High Quality Features
A Neural Conversational Model
Boosting Java Performance using GPGPUs
A square root map on Sturmian words
Towards Energy Consumption Verification via Static Analysis
Infant directed speech is consistent with teaching
Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
The Applied Pi Calculus: Mobile Values, New Names, and Secure Communication
Neural Architecture Search with Reinforcement Learning
Learning a Static Analyzer from Data
Old Content and Modern Tools - Searching Named Entities in a Finnish OCRed Historical Newspaper Collection 1771-1910
A covariant Hamiltonian tetrad approach to numerical relativity
Web-based Semantic Similarity for Emotion Recognition in Web Objects
An Agglomeration Law for Sorting Networks and its Application in Functional Programming
Beam Search Strategies for Neural Machine Translation
Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple Time-scale Recurrent Models
Applied Type System: An Approach to Practical Programming with Theorem-Proving
Domains for Higher-Order Games
SNMP for Common Lisp
Rank-1 Constrained Multichannel Wiener Filter for Speech Recognition in Noisy Environments
Verification of Asynchronous Systems with an Unspecified Component
Learning to Prove Safety over Parameterised Concurrent Systems (Full Version)
How Deterministic are Good-For-Games Automata?
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings
Strategy Preserving Compilation for Parallel Functional Code
On modeling vagueness and uncertainty in data-to-text systems through fuzzy sets
Joint Sentiment/Topic Modeling on Text Data Using Boosted Restricted Boltzmann Machine
Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases
Field redefinitions in theories beyond Einstein gravity using the language of differential forms
A Characterization for Decidable Separability by Piecewise Testable Languages
Gröbner methods for representations of combinatorial categories
A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents
Language is Physical
Decision Problems for Recognizable Languages of Infinite Pictures
Forward and Backward Application of Symbolic Tree Transducers
A survey of methods to ease the development of highly multilingual text mining applications
A Comparative Study of Programming Languages in Rosetta Code
A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks
Japanese Discourse and the Process of Centering
On the Notion of Proposition in Classical and Quantum Mechanics
A Strongly Grounded Stable Model Semantics for Full Propositional Language
Secondary implementation of interactive engagement teaching techniques: Choices and challenges in a Gulf Arab context
Understanding Zipf's law of word frequencies through sample-space collapse in sentence formation
Semi-galois Categories I: The Classical Eilenberg Variety Theory
GeoTextTagger: High-Precision Location Tagging of Textual Documents using a Natural Language Processing Approach
On Grothendieck's construction of Teichmüller space
Robust algorithms with polynomial loss for near-unanimity CSPs
The complexity of Boolean surjective general-valued CSPs
Mining actionable information from security forums: the case of malicious IP addresses
Functional Dynamics II : Syntactic Structure
THE GRISHCHUK-ZELDOVICH EFFECT IN THE OPEN UNIVERSE
Spin-Coefficient Form of the New Laws of Black-Hole Dynamics
Differential Forms and Wave Equations for General Relativity
Open String BRST Cohomology for Generalized Complex Branes
A Model of Classical and Quantum Measurement
Java Physics Generator and Analysis Modules
Regularity conditions via generalized interiority notions in convex optimization: new achievements and their relation to some classical statements
Deriving the Probabilistic Capacity of General Run-Length Sets Using Generating Functions
Number Theories
General Relativity and Weyl Frames
MDA-based ATL transformation to generate MVC 2 web models
Designing a CPU model: from a pseudo-formal document to fast code
Variable types for meaning assembly: a logical syntax for generic noun phrases introduced by most
PerfXplain: Debugging MapReduce Job Performance
A new approach of designing Multi-Agent Systems
Automated Word Puzzle Generation via Topic Dictionaries
Entailment in Probability of Thresholded Generalizations
Reduce Meaningless Words for Joint Chinese Word Segmentation and Part-of-speech Tagging
Structured Generative Models of Natural Source Code
Sequence to Sequence -- Video to Text
Positive Alexander Duality for Pursuit and Evasion
Abstract Learning Frameworks for Synthesis
A Hiking Trip Through the Orders of Magnitude: Deriving Efficient Generators for Closed Simply-Typed Lambda Terms and Normal Forms
A Generalized Kahn Principle for Abstract Asynchronous Networks
On the algebraicity of generalized power series
What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment
Generating Weather Forecast Texts with Case Based Reasoning
On the Uniform Random Generation of Non Deterministic Automata Up to Isomorphism
Beyond Caption To Narrative: Video Captioning With Multiple Sentences
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Generative Choreography using Deep Learning
An Attentional Neural Conversation Model with Improved Specificity
Deep Reinforcement Learning for Dialogue Generation
Neural Machine Translation with External Phrase Memory
Geometrothermodynamics for Black holes and de Sitter Space
Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
Generating Code Summaries Using the Power of the Crowd
Deep Active Learning for Dialogue Generation
A Note on One Less Known Class of Generated Residual Implications
Abstractive Headline Generation for Spoken Content by Attentive Recurrent Neural Networks with ASR Error Modeling
Hierarchical Recurrent Attention Network for Response Generation
ParseIT: A Question-Answer based Tool to Learn Parsing Techniques
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
Assigning personality/identity to a chatting machine for coherent conversation generation
Challenges in Data-to-Document Generation
Data Sets: Word Embeddings Learned from Tweets and General Data
Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders
Generative Adversarial Network for Abstractive Text Summarization
Generating High-Quality Query Suggestion Candidates for Task-Based Search
Learning Hyperedge Replacement Grammars for Graph Generation
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Image Generation from Scene Graphs
Groups with Context-Free Co-Word Problem and Embeddings into Thompson's Group $V$
High-Level Why-Not Explanations using Ontologies
Structured Query Language for Virtual Observatory
Spelling Correction in Agglutinative Languages
Using Chinese Text Processing Technique for the Processing of Sanskrit Based Indian Languages: Maximum Resource Utilization and Maximum Compatibility
Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean
A Semantics-based Communication System for Dysphasic Subjects
Temporal Meaning Representations in a Natural Language Front-End
The OLAC Metadata Set and Controlled Vocabularies
Seven Dimensions of Portability for Language Documentation and Description
The Study of the Application of a Keywords-based Chatbot System on the Teaching of Foreign Languages
Logic-Based Specification Languages for Intelligent Software Agents
The UPLNC Compiler: Design and Implementation
Mapping the Object-Role Modeling language ORM2 into Description Logic language DLRifd
Demographic growth and the distribution of language sizes
An omega-Power of a Finitary Language Which is a Borel Set of Infinite Rank
Non-Deterministic Communication Complexity of Regular Languages
The Mob core language and abstract machine (rev 0.2)
Dynamic Complexity of Formal Languages
Ontology-Based Annotation of Multimedia Language Data for the Semantic Web
On Recognizable Tree Languages Beyond the Borel Hierarchy
Complexity of countable categoricity in finite languages
On the Hairpin Incompletion
The power of linear programming for valued CSPs: a constructive characterization
The fundamentals of relations language mathematics
On the complexity of learning a language: An improvement of Block's algorithm
Benchmarking Usability and Performance of Multicore Languages
Describing groups using first-order language
Mashup of Meta-Languages and its Implementation in the Kermeta Language Workbench
Cornell SPF: Cornell Semantic Parsing Framework
Towards The Development of a Bishnupriya Manipuri Corpus
Probabilistic Programming Concepts
An efficiency dependency parser using hybrid approach for tamil language
On Upper and Lower Bounds on the Length of Alternating Towers
Azhary: An Arabic Lexical Ontology
Toward a new language of legal drafting
Larger-Context Language Modelling
Systematically Deriving Domain-Specific Transformation Languages
The languages of actions, formal grammars and qualitive modeling of companies
What to do about non-standard (or non-canonical) language in NLP
The non-abelian squares are not context-free
Operational characterization of scattered MCFLs -- Technical Report
MontiCore: a Framework for Compositional Development of Domain Specific Languages
Integrated Definition of Abstract and Concrete Syntax for Textual Languages
Index problems for game automata
An Automata Theoretic Approach to the Zero-One Law for Regular Languages: Algorithmic and Logical Aspects
Node Selection Query Languages for Trees
A Survey of the State of the Art in Data Mining and Integration Query Languages
Cross-Lingual Morphological Tagging for Low-Resource Languages
Labeling of Query Words using Conditional Random Field
Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks
Multi-task Recurrent Model for True Multilingual Speech Recognition
Foreign-language Reviews: Help or Hindrance?
KU-ISPL Speaker Recognition Systems under Language mismatch condition for NIST 2016 Speaker Recognition Evaluation
Comparison of Modified Kneser-Ney and Witten-Bell Smoothing Techniques in Statistical Language Model of Bahasa Indonesia
Do Neural Nets Learn Statistical Laws behind Natural Language?
Learning Language Representations for Typology Prediction
Neural Language Modeling by Jointly Learning Syntax and Lexicon
Phonemic and Graphemic Multilingual CTC Based Speech Recognition
A Class of Automatic Sequences
The language (and series) of Hammersley-type processes
Unboundedness problems for languages of vector addition systems
Multilingual bottleneck features for subword modeling in zero-resource languages
An Empirical Comparison of Probability Models for Dependency Grammar
Multiresolution analysis of electronic structure: semicardinal and wavelet bases
On Ising and dimer models in two and three dimensions
Constraint Programming viewed as Rule-based Programming
Constraint Exploration and Envelope of Simulation Trajectories
The similarity metric
Using Tree Automata and Regular Expressions to Manipulate Hierarchically Structured Data
An Improved k-Nearest Neighbor Algorithm for Text Categorization
Summarization from Medical Documents: A Survey
A Knowledge-Based Approach for Selecting Information Sources
Classdesc and Graphcode: support for scientific programming in C++
Field Theory of the Electron, Spin and Zitterbewegung
Supersymmetry without Supersymmetry
Heisenberg Honeycombs Solve Veneziano Puzzle
Towards a necessary change in the mathematical principles of natural philosophy
Numerical Methods as an Integrated Part of Physics Education
The Interpretation of Quantum Mechanics: Many Worlds or Many Words?
Counting, Fanout, and the Complexity of Quantum ACC
Using RDF to Model the Structure and Process of Systems
A case study of the difficulty of quantifier elimination in constraint databases: the alibi query in moving object databases
The large deviation approach to statistical mechanics
The Complexity of Enriched Mu-Calculi
On external presentations of infinite graphs
Some Remarks on the Model Theory of Epistemic Plausibility Models
Automatic Music Composition using Answer Set Programming
The physical language of molecular codes: A rate-distortion approach to the evolution and emergence of biological codes
The Conceptual Integration Modeling Framework: Abstracting from the Multidimensional Model
Complexity of Non-Monotonic Logics
Proceedings International Workshop on Strategies in Rewriting, Proving, and Programming
A Context-theoretic Framework for Compositionality in Distributional Semantics
Recovering Quantum Logic within an Extended Classical Framework
Some works of Furtwängler and Vandiver revisited and Fermat's last theorem
Counting Homomorphisms and Partition Functions
OBDD-based Universal Planning for Synchronized Agents in Non-Deterministic Domains
Higher Order Programming to Mine Knowledge for a Modern Medical Expert System
A Scalable Video Search Engine Based on Audio Content Indexing and Topic Segmentation
Constraint Satisfaction Tractability from Semi-lattice Operations on Infinite Sets
On accuracy of mathematical languages used to deal with the Riemann zeta function and the Dirichlet eta function
Fuzzy Time in LTL
You had me at hello: How phrasing affects memorability
OCR Context-Sensitive Error Correction Based on Google Web 1T 5-Gram Data Set
Finding Structure in Text, Genome and Other Symbolic Sequences
First steps in synthetic guarded domain theory: step-indexing in the topos of trees
1 Billion Pages = 1 Million Dollars? Mining the Web to Play "Who Wants to be a Millionaire?"
Complex networks analysis of language complexity
Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts
A practical approach to ontology-enabled control systems for astronomical instrumentation
A quantum linguistic characterization of the reverse relation between confidence interval and hypothesis testing
Removing Dynamic Type Tests with Context-Driven Basic Block Versioning
Continuous Speech Recognition Based on Deterministic Finite Automata Machine using Utterance and Pitch Verification
Telling Breaking News Stories from Wikipedia with Social Multimedia: A Case Study of the 2014 Winter Olympics
Pagination: It's what you say, not how long it takes to say it
Lifted Variable Elimination for Probabilistic Logic Programming
Benchmarking Named Entity Disambiguation approaches for Streaming Graphs
Towards a Visual Turing Challenge
Efficient reduction of Kappa models by static inspection of the rule-set
A Canonical Form for Weighted Automata and Applications to Approximate Minimization
A domain-level DNA strand displacement reaction enumerator allowing arbitrary non-pseudoknotted secondary structures
Effect-Dependent Transformations for Concurrent Programs
Telemedicine as a special case of Machine Translation
From F to DOT: Type Soundness Proofs with Definitional Interpreters
Symbol Grounding Association in Multimodal Sequences with Missing Elements
Compositional Memory for Visual Question Answering
The Mechanism of Additive Composition
Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC
Living in Parallel Realities -- Co-Existing Schema Versions with a Bidirectional Database Evolution Language
Expressibility in the Lambda Calculus with mu
Selection and Influence in Cultural Dynamics
Proof Pad: A New Development Environment for ACL2
Why is combinatorial communication rare in the natural world, and why is language an exception to this trend?
Regular Combinators for String Transformations
Conversational Sensing
On The Reachability Problem for Recursive Hybrid Automata with One and Two Players
Human Communication Systems Evolve by Cultural Selection
Information topology identifies emergent model classes
Solving 3-Color Parity Games in $ O(n^2) $ Time
Identifying missing dictionary entries with frequency-conserving context models
Bounding linear head reduction and visible interaction through skeletons
Photonics design tool for advanced CMOS nodes
A Critical Review of Recurrent Neural Networks for Sequence Learning
WordRank: Learning Word Embeddings via Robust Ranking
Leveraging Word Embeddings for Spoken Document Summarization
Ordering Interrogative Questions for Effective Requirements Engineering: The W6H Pattern
On Practical SMT-Based Type Error Localization
Reasoning about Entailment with Neural Attention
Topic segmentation via community detection in complex networks
Kauffman's adjacent possible in word order evolution
An Iterative Deep Learning Framework for Unsupervised Discovery of Speech Features and Linguistic Units with Applications on Spoken Term Detection
The Exception that Improves the Rule
Dialog state tracking, a machine reading approach using Memory Network
A Novel Projected Two Binary Variables Formulation for Unit Commitment Problem
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema
VideoMCC: a New Benchmark for Video Comprehension
Enriching Linked Datasets with New Object Properties
Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge
CFGs-2-NLU: Sequence-to-Sequence Learning for Mapping Utterances to Semantics and Pragmatics
Extending OMNeT++ Towards a Platform for the Design of Future In-Vehicle Network Architectures
A Study of Factuality, Objectivity and Relevance: Three Desiderata in Large-Scale Information Retrieval?
Scalable Machine Translation in Memory Constrained Environments
A Practical Approach to Interval Refinement for math.h/cmath Functions
Fencing off Go: Liveness and Safety for Channel-based Programming (extended version)
Neuro-Symbolic Program Synthesis
On Proving Confluence Modulo Equivalence for Constraint Handling Rules
Learning to Distill: The Essence Vector Modeling Framework
Domain Adaptation for Named Entity Recognition in Online Media with Word Embeddings
Tracking the World State with Recurrent Entity Networks
Learning to Hash-tag Videos with Tag2Vec
Stream Fusion, to Completeness
Variations on Variants
Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences
Minimal theory of quasidilaton massive gravity
checkmate: Fast Argument Checks for Defensive R Programming
Disruptive Behavior Disorder (DBD) Rating Scale for Georgian Population
Loyalty in Online Communities
Symbol Grounding via Chaining of Morphisms
Joint Probabilistic Linear Discriminant Analysis
Loop Quasi-Invariant Chunk Motion by peeling with statement composition
Stream Processing using Grammars and Regular Expressions
Community Identity and User Engagement in a Multi-Community Landscape
Gated Recurrent Neural Tensor Network
Accelerating Innovation Through Analogy Mining
Synthesis of Data Completion Scripts using Finite Tree Automata
Information-gain computation
Truly Sub-cubic Algorithms for Language Edit Distance and RNA Folding via Fast Bounded-Difference Min-Plus Product
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition
Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities
Learning to Blame: Localizing Novice Type Errors with Data-Driven Diagnosis
Linear-time Temporal Logic with Event Freezing Functions
A Deep Reinforcement Learning Chatbot
Horndeski extension of the minimal theory of quasidilaton massive gravity
Robustness Analysis of Visual QA Models by Basic Questions
The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings
The Semantics of Transactions and Weak Memory in x86, Power, ARMv8, and C++
$A^{4}NT$: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation
Internalising Interaction Protocols as First-Class Programming Elements in Multi Agent Systems
Political Polarization in Social Media: Analysis of the "Twitter Political Field" in Japan
Improving the Accuracy of Pre-trained Word Embeddings for Sentiment Analysis
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce
Visual Features for Context-Aware Speech Recognition
Grounding Referring Expressions in Images by Variational Context
Towards a science of human stories: using sentiment analysis and emotional arcs to understand the building blocks of complex social systems
Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities
Comparative Opinion Mining: A Review
A Deep Reinforcement Learning Chatbot (Short Version)
Continuous Space Reordering Models for Phrase-based MT
Matching Long Text Documents via Graph Convolutional Networks
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Using RuleBuilder to graphically define and visualize BioNetGen-language patterns and reaction rules
eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing
What we talk about when we talk about monads
Detecting Malicious PowerShell Commands using Deep Neural Networks
Most Complex Deterministic Union-Free Regular Languages
Proactive Empirical Assessment of New Language Feature Adoption via Automated Refactoring: The Case of Java 8 Default Methods
Construction of a Bilingual Dictionary Intermediated by a Third Language
Rapid Development of Morphological Descriptions for Full Language Processing Systems
A fast partial parse of natural language sentences using a connectionist method
Constraint Logic Programming for Natural Language Processing
MAXIMUM LIKELIHOOD AND MINIMUM ENTROPY IDENTIFICATION OF GRAMMARS
New Techniques for Context Modeling
Cluster Expansions and Iterative Scaling for Maximum Entropy Language Models
Natural language processing: she needs something old and something new (maybe something borrowed and something blue, too)
Using Terminological Knowledge Representation Languages to Manage Linguistic Resources
Head Automata for Speech Translation
Learning Translation Rules From A Bilingual Corpus
Incorporating POS Tagging into Language Modeling
Multilingual phonological analysis and speech synthesis
What is word sense disambiguation good for?
Annotation Style Guide for the Blinker Project
LuaJava - A Scripting Tool for Java
Language Identification With Confidence Limits
Comparative Analysis of Five XML Query Languages
NLTK: The Natural Language Toolkit
Using the DIFF Command for Natural Language Processing
Corpus structure, language models, and ad hoc information retrieval
Linguistically Grounded Models of Language Change
Self-similarity in the taxonomic classification of human languages
The Ising Model for Changes in Word Ordering Rule in Natural Language
Non-equilibrium and Irreversible Simulation of Competition among Languages
On the class of languages recognizable by 1-way quantum finite automata
Using conceptual metaphor and functional grammar to explore how language used in physics affects student learning
Do language change rates depend on population size?
The physics of randomness and regularities for languages (lifetimes, family trees, and the second languages); in terms of random matrices
ECOLANG - Communications Language for Ecological Simulations Network
Generalised sequential crossover of words and languages
Variable elimination for building interpreters
Parikh's Theorem: A simple and direct automaton construction
Comparative study of the Pros and Cons of Programming languages Java, Scala, C++, Haskell, VB .NET, AspectJ, Perl, Ruby, PHP & Scheme - a Team 11 COMP6411-S10 Term Report
Comparative Studies of 10 Programming Languages within 10 Diverse Criteria - a Team 10 COMP6411-S10 Term Report
CAL: A Language for Aggregating Functional and Extrafunctional Constraints in Streaming Networks
QuECT: A New Quantum Programming Paradigm
Why is language well-designed for communication? (Commentary on Christiansen and Chater: 'Language as shaped by the brain')
Language Support for Declarative Future Commitments
A Note on Undecidability of Observation Consistency for Non-Regular Languages
Algebraic Characterization of the Class of Languages recognized by Measure Only Quantum Automata
Developing a model for a text database indexed pedagogically for teaching the Arabic language
Indexed realizability for bounded-time programming with references and type fixpoints
Adaptation of pedagogical resources description standard (LOM) with the specificity of Arabic language
Universal Numeric Segment Display for Indian Scheduled Languages: an Architectural View
Evaluation of Computational Grammar Formalisms for Indian Languages
Rewrite Closure and CF Hedge Automata
A connection between concurrency and language theory
EasyTime++: A case study of incremental domain-specific language development
Quantum finite automata and linear context-free languages: a decidable problem
Improving the quality of Gujarati-Hindi Machine Translation through part-of-speech tagging and stemmer-assisted transliteration
Acceptance conditions for omega-languages and the Borel hierarchy
Description Logics based Formalization of Wh-Queries
The existential fragment of S1S over element and successor is the co-Buchi languages
Evaluation and Ranking of Machine Translated Output in Hindi Language using Precision and Recall Oriented Metrics
Annotated imports
A Structural Query System for Han Characters
"Translation can't change a name": Using Multilingual Data for Named Entity Recognition
Turkish Text Retrieval Experiments Using Lemur Toolkit
Supervised learning model for parsing Arabic language
Topological Entropy of Formal Languages
Language discrimination and clustering via a neural network approach
One model, two languages: training bilingual parsers with harmonized treebanks
Logic for Unambiguous Context-Free Languages
Towards Reversible Computation in Erlang
Statistical Sign Language Machine Translation: from English written text to American Sign Language Gloss
Machine Translation Systems in India
Invitation to Ezhil: A Tamil Programming Language for Early Computer-Science Education
Syntactic Complexity of Suffix-Free Languages
A Unified Deep Neural Network for Speaker and Language Recognition
The Prose Storyboard Language: A Tool for Annotating and Directing Movies
On the star-height of factor counting languages and their relationship to Rees zero-matrix semigroups
Learning Executable Semantic Parsers for Natural Language Understanding
Natural Language Semantics and Computability
A Free Energy Foundation of Semantic Similarity in Automata and Languages
Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
The word entropy of natural languages
An Unsupervised Probability Model for Speech-to-Translation Alignment of Low-Resource Languages
Statistical Properties of European Languages and Voynich Manuscript Analysis
Improving Neural Language Models with a Continuous Cache
Incorporating Language Level Information into Acoustic Models
LIDE: Language Identification from Text Documents
Fixing the Infix: Unsupervised Discovery of Root-and-Pattern Morphology
Languages of Play: Towards semantic foundations for game interfaces
Specifying Graph Languages with Type Graphs
RankPL: A Qualitative Probabilistic Programming Language
Understanding Abuse: A Typology of Abusive Language Detection Subtasks
Including Dialects and Language Varieties in Author Profiling
A Simple Language Model based on PMI Matrix Approximations
Syllable-level Neural Language Model for Agglutinative Language
MTIL17: English to Indian Langauge Statistical Machine Translation
Input-to-Output Gate to Improve RNN Language Models
CalcuList: a Functional Language Extended with Imperative Features
Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models
Termination analysis of logic programs using acceptability with general term orders
Easy and Hard Constraint Ranking in OT: Algorithms and Complexity
Acceptability with general orderings
Model-Based Software Engineering and Ada: Synergy for the Development of Safety-Critical Systems
Analysis of Titles and Readers For Title Generation Centered on the Readers
Towards Automated Generation of Scripted Dialogue: Some Time-Honoured Strategies
Statistical Machine Translation by Generalized Parsing
Probabilistic Automata for Computing with Words
Dimensionally Democratic Calculus and Principles of Polydimensional Physics
Gauge and General Relativity
Toda Lattice Hierarchy and Generalized String Equations
A Generalized Information Formula as the Bridge between Shannon and Popper
Fourier-Based Spectral Analysis with Adaptive Resolution
Stacks in canonical RNA pseudoknot structures
On (Omega-)Regular Model Checking
General Relativistic Rotation Curves in a Post-Newtonian Light
Unfolding Mixed-Symmetry Fields in AdS and the BMV Conjecture: I. General Formalism
A D.C. Programming Approach to the Sparse Generalized Eigenvalue Problem
Time and symmetry in models of economic markets
Limits of relatively hyperbolic groups and Lyndon's completions
Dynamically Generated Interfaces in XML Based Architecture
Context-free pairs of groups I: Context-free pairs and graphs
Graphes, moyennabilité et bas du spectre de variétés topologiquement infinies
Semantic annotation of requirements for automatic UML class diagram generation
Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis
Generalized sequential tree-reweighted message passing
Subalgebras of FA-presentable algebras
General-Purpose MCMC Inference over Relational Structures
N=2 vacua in Generalized Geometry
General relativistic statistical mechanics
A General Framework for the Derivation of Regular Expressions
The Tree Width of Separation Logic with Recursive Definitions
A Generic Scheme and Properties of Bidirectional Transformations
Inflated Cauchy Filters - A Way to Construct the Completion of a General Uniform Space
Time-dependent Hierarchical Dirichlet Model for Timeline Generation
Generalizers: New Metaobjects for Generalized Dispatch
Explain Images with Multimodal Recurrent Neural Networks
A Novel Design of a Parallel Machine Learnt Generational Garbage Collector
Spacetime and observer space symmetries in the language of Cartan geometry
On End-to-End Program Generation from User Intention by Deep Neural Networks
Generative Concatenative Nets Jointly Learn to Write and Classify Reviews
Neural Variational Inference for Text Processing
Neural Headline Generation with Sentence-wise Optimization
Growing Graphs with Hyperedge Replacement Graph Grammars
Context Gates for Neural Machine Translation
The Generalized A* Architecture
A Sufficient Condition for Hanna Neumann Property of Submonoids of a Free Monoid
Generating Extractive Summaries of Scientific Paradigms
Extended Recommendation Framework: Generating the Text of a User Review as a Personalized Summary
Neural Responding Machine for Short-Text Conversation
The geometry of generalized force matching in coarse-graining and related information metrics
Aligning where to see and what to tell: image caption with region-based attention and scene factorization
Synchronization of Bernoulli sequences on shared letters
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
A Generative Model for Multi-Dialect Representation
Neural Generative Question Answering
Horizon Shells and BMS-like Soldering Transformations
Argumentation Mining in User-Generated Web Discourse
Generalized minimum dominating set and application in automatic text summarization
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
Which Learning Algorithms Can Generalize Identity-Based Rules to Novel Inputs?
Programming Patterns in Dataflow Matrix Machines and Generalized Recurrent Neural Nets
Open Information Extraction
Neural Contextual Conversation Learning with Labeled Question-Answering Pairs
An Actor-Critic Algorithm for Sequence Prediction
SPICE: Semantic Propositional Image Caption Evaluation
Neural Paraphrase Generation with Stacked Residual LSTM Networks
Neural Machine Translation Advised by Statistical Machine Translation
Precondition Inference for Peephole Optimizations in LLVM
Environmental Bisimulations for Delimited-Control Operators with Dynamic Prompt Generation
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models
Join irreducible semigroups
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
Improved Training of Wasserstein GANs
Generating Representative Executions [Extended Abstract]
Learning Latent Representations for Speech Generation and Transformation
Deep Keyphrase Generation
Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs
Artificial Error Generation with Machine Translation and Syntactic Patterns
Detecting and Explaining Causes From Text For a Time Series Event
Scene Graph Generation from Objects, Phrases and Region Captions
Neural Rating Regression with Abstractive Tips Generation for Recommendation
Community Targeted Spam: A Middle Ground Between General Spam and Spear Phishing
Look-ahead Attention for Generation in Neural Machine Translation
A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders
Variant-Based Decidable Satisfiability in Initial Algebras with Predicates
Einstein-Gauss-Bonnet theory of gravity : The Gauss-Bonnet-Katz boundary term
Code Attention: Translating Code to Comments by Exploiting Domain Features
Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams
Generalizing inference systems by coaxioms
Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics
Generating Wikipedia by Summarizing Long Sequences
Semi-Amortized Variational Autoencoders
First Order Generative Adversarial Networks
General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms
Chern-Weil theorem, Lovelock Lagrangians in critical dimensions and boundary terms in gravity actions
Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering
Speech waveform synthesis from MFCC sequences with generative adversarial networks
Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks
Scalable Factorized Hierarchical Variational Autoencoder Training
Typed Generic Traversal With Term Rewriting Strategies
Hermitian versus holomorphic complex and quaternionic generalized supersymmetries of the M-theory. A classification
Functor is to Lens as Applicative is to Biplate: Introducing Multiplate
Generic Fibrational Induction
Scalar torsion and a new symmetry of general relativity
A Case for Dynamic Reverse-code Generation to Debug Non-deterministic Programs
Generating Abstractive Summaries from Meeting Transcripts
Generating Candidate Busy Beaver Machines (Or How to Build the Zany Zoo)
Session Types as Generic Process Types
The JRC-Acquis: A multilingual aligned parallel corpus with 20+ languages
Language, Emotions, and Cultures: Emotional Sapir-Whorf Hypothesis
A practical approach to language complexity: a Wikipedia case study
First-Order Quantifiers and the Syntactic Monoid of Height Fragments of Picture Languages
Schemas for Unordered XML on a DIME
Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages
The statistical trade-off between word order and word structure - large-scale evidence for the principle of least effort
Ambiguity in language networks
Defining relations on graphs: how hard is it in the presence of node partitions?
Probabilistic Modelling of Morphologically Rich Languages
News Across Languages - Cross-Lingual Document Similarity and Event Tracking
Language Models of Spoken Dutch
A Resource-Light Method for Cross-Lingual Semantic Textual Similarity
Essential equivalence of the GENERIC and Steepest Entropy Ascent models of dissipation for non-equilibrium thermodynamics
Generalized Homogeneous Polynomials for Efficient Template-Based Nonlinear Invariant Synthesis
Inversion Polynomials for Permutations Avoiding Consecutive Patterns
Recurrent Topic-Transition GAN for Visual Paragraph Generation
Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning
Offline Specialisation in Prolog Using a Hand-Written Compiler Generator
Reasoning about Algebraic Data Types with Abstractions
Generalized Graph Pattern Matching
Complexity of Suffix-Free Regular Languages
The Difficulties of Learning Logic Programs with Cut
On Modular Termination Proofs of General Logic Programs
Semantic filtering by inference on domain knowledge in spoken dialogue systems
Reverse Engineering Ontology to Conceptual Data Models
Termination Analysis of General Logic Programs for Moded Queries: A Dynamic Approach
A Generic Global Constraint based on MDDs
Modelling general relativistic perfect fluids in field theoretic language
Reconstruction of Black Hole Metric Perturbations from Weyl Curvature II: The Regge-Wheeler gauge
The Information Geometry of Space and Time
New Tools for Fermion Masses from Extra Dimensions
ON THE EXTENDED POINCARE POLYNOMIAL
Fat Euclidean Gravity with Small Cosmological Constant
Notes on Certain (0,2) Correlation Functions
Supersymmetric AdS Backgrounds in String and M-theory
Notes on certain other (0,2) correlation functions
Combing nilpotent and polycyclic groups
Gluing theorems for complete anti-self-dual spaces
Algebraic orbifold quantum products
A general intersection formula for Lagrangian cycles
Effective JSJ Decompositions
Saturated chains in composition posets
Algebraic G-functions associated to matrices over a group-ring
Matrix Graph Grammars
Combining generic judgments with recursive definitions
Cosmological Radar Ranging in an Expanding Universe
Logical Reasoning for Higher-Order Functions with Local State
Theory of Zipf's Law and of General Power Law Distributions with Gibrat's law of Proportional Growth
UNL-French deconversion as transfer & generation from an interlingua with possible quality enhancement through offline human interaction
Algebraic mechanics as an accessible toy model demonstrating entropy generation from reversible microscopic dynamics
A Generalized Carpenter's Rule Theorem for Self-Touching Linkages
Avoiding Squares and Overlaps Over the Natural Numbers
Coherence for rewriting 2-theories
Termination Prediction for General Logic Programs
Braided Categorical Quantum Mechanics I
Time-Varying Autoregressions in Speech: Detection Theory and Applications
Covariant star product on symplectic and Poisson spacetime manifolds
Automatic Generation of Proof Tactics for Finite-Valued Logics
Computing Critical Pairs in 2-Dimensional Rewriting Systems
On Testing Constraint Programs
Advances in Modeling of Scanning Charged-Particle-Microscopy Images
Semihyperrings Characterized by Their Hyperideals
The General Vector Addition System Reachability Problem by Presburger Inductive Invariants
Weighted random generation of context-free languages: Analysis of collisions in random urn occupancy models
Termination Casts: A Flexible Approach to Termination with General Recursion
Generalized Thue-Morse words and palindromic richness
From automatic structures to automatic groups
Measuring Intelligence through Games
Complex dynamics in learning complicated games
On a Generalization of Zaslavsky's Theorem for Hyperplane Arrangements
C++ Standard Template Library by template specialized containers
A MDA approach for defining WS-Policy semantic non-functional properties
Initial Semantics for Strengthened Signatures
Structured general corecursion and coinductive graphs [extended abstract]
Agent-time Epistemics and Coordination
The Complexity of Monotone Hybrid Logics over Linear Frames and the Natural Numbers
Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part I: template-based generic programming
Interactive visualization of a thin disc around a Schwarzschild black hole
Utilizing Static Analysis and Code Generation to Accelerate Neural Networks
Completely reducible sets
Linear-use CPS translations in the Enriched Effect Calculus
Fractional Laplacian on the torus
Quantum field theory on affine bundles
Extending the logical update view with transaction support
Generalized Counting Constraint Satisfaction Problems With Determinantal Circuits
One-variable word equations in linear time
An Empirical Study of Path Feasibility Queries
Design and implementation of the NaI (Tl)CsI (Na) detectors output signal generator
Almost local generation of EPR entanglement in non-equilibrium
Mean-payoff Games with Partial Observation
Sailfish: a flexible multi-GPU implementation of the lattice Boltzmann method
Dimensions in non-Archimedean geometries
Synchronizing weighted automata
Profinite automata
Factor Complexity of S-adic sequences generated by the Arnoux-Rauzy-Poincaré Algorithm
News-Based Group Modeling and Forecasting
A new approach to the $2$-regularity of the $\ell$-abelian complexity of $2$-automatic sequences
Kahler: An Implementation of Discrete Exterior Calculus on Hermitian Manifolds
Program Synthesis and Linear Operator Semantics
OMP2HMPP: HMPP Source Code Generation from Programs with Pragma Extensions
Parameterized Complexity of CTL: A Generalization of Courcelle's Theorem
Freeness of automata groups vs boundary dynamics
Contract-Based General-Purpose GPU Programming
Rational growth in the Heisenberg group
Generalized quantum gravity condensates for homogeneous geometries and cosmology
Yet Another Way of Building Exact Polyhedral Model for Weakly Dynamic Affine Programs
On the structure of Schnyder woods on orientable surfaces
Decomposing Nekrasov Decomposition
Top-down Tree Long Short-Term Memory Networks
Generation and Comprehension of Unambiguous Object Descriptions
Bootstrapping Ternary Relation Extractors
Aspect-based Opinion Summarization with Convolutional Neural Networks
Revisiting Summarization Evaluation for Scientific Articles
Learning to Generate Posters of Scientific Papers
Generating Concurrency Checks Automatically
Learning Joint Representations of Videos and Sentences with Web Image Search
Geometric-Algebra Adaptive Filters
Separable determination in Banach spaces
Unsupervised, Efficient and Semantic Expertise Retrieval
Inducing Probabilistic Programs by Bayesian Program Merging
Visualization and Analysis of Frames in Collections of Messages: Content Analysis and the Measurement of Meaning
Generating Sequences With Recurrent Neural Networks
A comparison of linear and non-linear calibrations for speaker recognition
Efficient and Generalized Decentralized Monitoring of Regular Languages
GSOS for non-deterministic processes with quantitative aspects
ProvGen: generating synthetic PROV graphs with predictable structure
The tangent bundle exponential map and locally autoparallel coordinates for general connections with application to Finslerian geometries
A Tale of Three Runtimes
Approximating solution structure of the Weighted Sentence Alignment problem
Palindromic sequences generated from marked morphisms
OMP2MPI: Automatic MPI code generation from OpenMP programs
Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs
Analysis of Carries in Signed Digit Expansions
Reader-Aware Multi-Document Summarization via Sparse Coding
The Long-Short Story of Movie Description
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Requirement Tracing using Term Extraction
Generation of Multimedia Artifacts: An Extractive Summarization-based Approach
A unifying framework for ghost-free Lorentz-invariant Lagrangian field theories
GCC-Plugin for Automated Accelerator Generation and Integration on Hybrid FPGA-SoCs
Weak Gravity Conjecture in AdS/CFT
Extending Hybrid CSP with Probability and Stochasticity
Efficient Compilation to Event-Driven Task Programs
TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions
Three-dimensional Boltzmann-Hydro code for core-collapse in massive stars II. The Implementation of moving-mesh for neutron star kicks
The subdivision of large simplicial cones in Normaliz
Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)
Smart Reply: Automated Response Suggestion for Email
A Generic Logic for Proving Linearizability (Extended Version)
Generic and Effective Specification of Structural Test Objectives
The basic $dd^{\mathcal{J}}$-lemma
HyperNetworks
Sequential decision problems, dependent types and generic solutions
Generating the Functions with Regular Graphs under Composition
A Surrogate-based Generic Classifier for Chinese TV Series Reviews
Veracity Computing from Lexical Cues and Perceived Certainty Trends
A Simple, Fast Diverse Decoding Algorithm for Neural Generation
Automatically generating features for learning program analysis heuristics
Learning to Decode for Future Success
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
Extensional Semantics for Higher-Order Logic Programs with Negation
CommAI: Evaluating the first steps towards a useful general AI
Intersection Types and Counting
Anisotropic stellar models admitting conformal motion
Systematic Mapping Study of Template-based Code Generation
Multirole Logic (Extended Abstract)
A practical approach to dialogue response generation in closed domains
A Neural Parametric Singing Synthesizer
Deep Reinforcement Learning-based Image Captioning with Embedding Reward
Get To The Point: Summarization with Pointer-Generator Networks
Incremental learning of high-level concepts by imitation
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search
Paying Attention to Descriptions Generated by Image Captioning Models
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Learning to Ask: Neural Question Generation for Reading Comprehension
$\star$-Liftings for Differential Privacy
Inferring and Executing Programs for Visual Reasoning
Utility of general and specific word embeddings for classifying translational stages of research
Image Captioning with Object Detection and Localization
A Generative Model of Group Conversation
Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability
A Temporal Tree Decomposition for Generating Temporal Graphs
A Verified Certificate Checker for Floating-Point Error Bounds
Efficient Vector Representation for Documents through Corruption
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
Crowdsourcing Multiple Choice Science Questions
Effective Inference for Generative Neural Parsing
SenGen: Sentence Generating Neural Variational Topic Model
Merge decompositions, two-sided Krohn-Rhodes, and aperiodic pointlikes
Narrative Variations in a Virtual Storyteller
Algorithms and Architecture for Real-time Recommendations at News UK
An Algebraic Glimpse at Bunched Implications and Separation Logic
Deconvolutional Latent-Variable Model for Text Sequence Matching
Is space a word, too?
Geometric Computing with Chain Complexes: Design and Features of a Julia Package
Reasoning about Divergences for Relaxations of Differential Privacy
Oversampling for Imbalanced Learning Based on K-Means and SMOTE
Symmetries, Holography and Quantum Phase Transition in Two-dimensional Dilaton AdS Gravity
DLVM: A modern compiler infrastructure for deep learning systems
A General Neural Network Hardware Architecture on FPGA
Question Asking as Program Generation
Data-Driven Feedback Generation for Introductory Programming Exercises
Production Ready Chatbots: Generate if not Retrieve
Document Generation with Hierarchical Latent Tree Models
From CFT to Ramond super-quantum curves
Improving Generalization Performance by Switching from Adam to SGD
HGum: Messaging Framework for Hardware Accelerators
Generalizing Gillespie's direct method to enable network-free simulations
Improving Variational Encoder-Decoders in Dialogue Generation
Validation and Topic-driven Ranking for Biomedical Hypothesis Generation Systems
Teaching Machines to Code: Neural Markup Generation with Visual Attention
Neural Voice Cloning with a Few Samples
Deep Feed-forward Sequential Memory Networks for Speech Synthesis
Syzygies of secant ideals of Plücker-embedded Grassmannians are generated in bounded degree
Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes
The Density of Linear-time Properties
MemGEN: Memory is All You Need
Symbolic Reasoning for Automatic Signal Placement (Extended Version)
A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis
Learning Topics using Semantic Locality
General Relativity and Weyl Geometry
Knowledge Engineering for Planning-Based Hypothesis Generation
Hierarchical Latent Semantic Mapping for Automated Topic Generation
Generalized Topic Modeling
Arbitrarily exhaustive hypergraph generation of 4-, 6-, 8-, 16-, and 32-dimensional quantum contextual sets
Mining Android App Usages for Generating Actionable GUI-based Execution Scenarios
Hamilton Geometry - Phase Space Geometry from Modified Dispersion Relations
Natural Language Interfaces to Databases - An Introduction
Abstract Machine for Typed Feature Structures
Designing Statistical Language Learners: Experiments on Noun Compounds
Specialized Language Models using Dialogue Predictions
Translation Methodology in the Spoken Language Translator: An Evaluation
Evaluating Parsing Schemes with Entropy Indicators
Context as a Spurious Concept
The Open Language Archives Community: An infrastructure for distributed archiving of language resources
LERIL : Collaborative Effort for Creating Lexical Resources
NLML--a Markup Language to Describe the Unlimited English Grammar
A Framework for Creating Natural Language User Interfaces for Action-Based Applications
Scaling relations for diversity of languages
Model of World; her cities, languages and countries
Language Diversity across the Consonant Inventories: A Study in the Framework of Complex Networks
A database approach to information retrieval: The remarkable relationship between language models and region models
Quotient Complexity of Bifix-, Factor-, and Subword-Free Regular Languages
The complexity of conservative finite-valued CSPs
Generalising tractable VCSPs defined by symmetric tournament pair multimorphisms
Comparing Selected Criteria of Programming Languages Java, PHP, C++, Perl, Haskell, AspectJ, Ruby, COBOL, Bash Scripts and Scheme Revision 1.0 - a Team CPLgroup COMP6411-S10 Term Report
Improving Web Page Readability by Plain Language
Naming Game on Adaptive Weighted Networks
Use Pronunciation by Analogy for text to speech system in Persian language
Do Software Languages Engineers Evaluate their Languages?
Star-Free Languages are Church-Rosser Congruential
UNL Based Bangla Natural Text Conversion - Predicate Preserving Parser Approach
Automatic Segmentation of Manipuri (Meiteilon) Word into Syllabic Units
SL: a "quick and dirty" but working intermediate language for SVP systems
3rd grade English language learners making sense of sound
GNU epsilon - an extensible programming language
The Buffered π-Calculus: A Model for Concurrent Languages
Syntactic Analysis Based on Morphological Characteristic Features of the Romanian Language
Joint Space Neural Probabilistic Language Model for Statistical Machine Translation
Analytic solution of a model of language competition with bilingualism and interlinguistic similarity
A study for the effect of the Emphaticness and language and dialect for Voice Onset Time (VOT) in Modern Standard Arabic (MSA)
A mathematical theory of truth and an application to the regress problem
Log-space counter is useful for unary languages by help of a constant-size quantum register
Unary languages recognized by two-way one-counter automata
Complexity measurement of natural and artificial languages
Multilinguals and Wikipedia Editing
Spelling Error Trends and Patterns in Sindhi
Metamorphic Domain-Specific Languages: A Journey Into the Shapes of a Language
Debates with small transparent quantum verifiers
Assamese-English Bilingual Machine Translation
On Detecting Noun-Adjective Agreement Errors in Bulgarian Language Using GATE
Network Motifs Analysis of Croatian Literature
Cross-language Wikipedia Editing of Okinawa, Japan
Zero-One Law for Regular Languages and Semigroups with Zero
There is no fast lunch: an examination of the running speed of evolutionary algorithms in several languages
Deriving a Simple Gradual Security Language
On Word and Frontier Languages of Unsafe Higher-Order Grammars
Design and Implementation of Probabilistic Programming Language Anglican
A Temporal Description Logic for Reasoning about Actions and Plans
On Separation by Locally Testable and Locally Threshold Testable Languages
Natural Language Web Interface for Database (NLWIDB)
A novel datatype architecture support for programming languages
Evaluating Indirect Strategies for Chinese-Spanish Statistical Machine Translation
Java Modular Extension for Operator Overloading
Analyzing the Language of Food on Social Media
Polarity detection movie reviews in hindi language
A Unified Mathematical Language for Medicine and Science
Variability within Modeling Language Definitions
On $k$-piecewise testability (preliminary report)
Recurrent-Neural-Network for Language Detection on Twitter Code-Switching Corpus
On Distributed Density in Tuple-based Coordination Languages
On measuring linguistic intelligence
Character-Aware Neural Language Models
Dependency length minimization: Puzzles and Promises
The "handedness" of language: Directional symmetry breaking of sign usage in words
Sentiment/Subjectivity Analysis Survey for Languages other than English
CPL: A Core Language for Cloud Computing -- Technical Report
On the notion of "von Neumann vicious circle" coined by John Backus
Regular Language Distance and Entropy
Piecewise Testable Languages and Nondeterministic Automata
Extension Complexity of Formal Languages
Complexity of Prefix-Convex Regular Languages
Morphological Constraints for Phrase Pivot Statistical Machine Translation
Towards a continuous modeling of natural language domains
Neural Machine Translation with Pivot Languages
Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation
Cross-Lingual Predicate Mapping Between Linked Data Ontologies
A Lazy Language Needs a Lazy Type System: Introducing Polymorphic Contexts
Machine Translation Approaches and Survey for Indian Languages
Language competition in a population of migrating agents
Emergence of Grounded Compositional Language in Multi-Agent Populations
Complexity of Infimal Observable Superlanguages
Knowledge Rich Natural Language Queries over Structured Biological Databases
Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages
Joint PoS Tagging and Stemming for Agglutinative Languages
On the relation between dependency distance, crossing dependencies, and parsing. Comment on "Dependency distance: a new perspective on syntactic patterns in natural languages" by Haitao Liu et al
Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog
Multilingual Hierarchical Attention Networks for Document Classification
Incremental Parametric Syntax for Multi-Language Transformation
All that is English may be Hindi: Enhancing language identification through automatic ranking of likeliness of word borrowing in social media
Vector Space Model as Cognitive Space for Text Classification
Improving Language Modelling with Noise-contrastive estimation
Adding successor: A transfer theorem for separation and covering
Person Re-Identification with Vision and Language
Syntactic and Semantic Features For Code-Switching Factored Language Models
Indowordnets help in Indian Language Machine Translation
Object Referring in Visual Scene with Spoken Language
Layer by layer - Combining Monads
An analysis of incorporating an external language model into a sequence-to-sequence model
Effective Extensible Programming: Unleashing Julia on GPUs
Analyzing Roles of Classifiers and Code-Mixed factors for Sentiment Identification
Context Models for OOV Word Translation in Low-Resource Languages
Software Fault Isolation for Robust Compilation
Emotions are Universal: Learning Sentiment Based Representations of Resource-Poor Languages using Siamese Networks
Learning Word Association Norms Using Tree Cut Pair Models
A Domain Specific Language for Performance Portable Molecular Dynamics Algorithms
Induction of Decision Trees based on Generalized Graph Queries
Time, Tense and Aspect in Natural Language Database Interfaces
The Effect of Native Language on Internet Usage
Unsupervised Language Acquisition: Theory and Practice
Parsing Transformative LR(1) Languages
There Exist some Omega-Powers of Any Borel Rank
Bounded Underapproximations
PTaCL: A Language for Attribute-Based Access Control in Open Systems
The Biological Origin of Linguistic Diversity
Punjabi Language Interface to Database: a brief review
UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval
The Machine that Builds Itself: How the Strengths of Lisp Family Languages Facilitate Building Complex and Flexible Bioinformatic Models
XTQ: A Declarative Functional XML Query Language
Towards Using Machine Translation Techniques to Induce Multilingual Lexica of Discourse Markers
Automating Abstract Interpretation of Abstract Machines
Complexity of regular bifix-free languages
Interactive Natural Language Acquisition in a Multi-modal Recurrent Neural Architecture
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
Tortoise: Interactive System Configuration Repair
First Programming Language: Visual or Textual?
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
How Does Bug-Handling Effort Differ Among Different Programming Languages?
NOOP: A Domain-Theoretic Model of Nominally-Typed OOP
Memoization in Constraint Logic Programming
SPANISH 1992 (S92): corpus-based analysis of present-day Spanish for medical purposes
Compact Representations by Finite-State Transducers
Japanese word sense disambiguation based on examples of synonyms
Parsing a Flexible Word Order Language
Acquiring a Lexicon from Unsegmented Speech
Mapping Scrambled Korean Sentences into English Using Synchronous TAGs
Identifying Word Translations in Non-Parallel Texts
CLiFF Notes: Research in the Language, Information and Computation Laboratory of the University of Pennsylvania
Restricted Parallelism in Object-Oriented Lexical Parsing
Synchronous Models of Language
Assigning Grammatical Relations with a Back-off Model
Proof Nets and the Complexity of Processing Center-Embedded Constructions
Primitive Part-of-Speech Tagging using Word Length and Sentential Structure
Question Answering System Using Syntactic Information
A Monitoring Language for Run Time and Post-Mortem Behavior Analysis and Visualization
A TCSP-like decidable constraint language generalising existing cardinal direction relations
f2mma: FORTRAN to Mathematica translator
Challenging the principle of compositionality in interpreting natural language texts
hepawk, Version 1.6: A Language for Scanning High Energy Physics Events
A context-free and a 1-counter geodesic language for a Baumslag-Solitar group
Modal languages for topology: expressivity and definability
A new model for competition between many languages
Competition of languages in the presence of a barrier
The class of languages recognizable by 1-way quantum finite automata is not closed under union
International Standard for a Linguistic Annotation Framework
Compositional Semantics Grounded in Commonsense Metaphysics
The Challenges of Hardware Synthesis from C-Like Languages
Minimum de Bruijn Sequence in a Language with Forbidden Substrings
Finding the growth rate of a regular language in polynomial time
Investigation of the Zipf-plot of the extinct Meroitic language
Reasoning About a Service-oriented Programming Paradigm
A Metamodel of Unit Testing for Object-Oriented Programming Languages
Numerical values of the growth rates of power-free languages
Decidability and Shortest Strings in Formal Languages
Shuffling and Unshuffling
Proceedings IFIP Working Conference on Domain-Specific Languages
Implementing Continuation based language in GCC
Functional Programming and Security
Live-Musikprogrammierung in Haskell
Haskell_#: Coordinating Functional Processes
Compactified Horizontal Visibility Graph for the Language Network
PENCIL: Towards a Platform-Neutral Compute Intermediate Language for DSLs
Live music programming in Haskell
Propositional Encoding of Constraints over Tree-Shaped Data
Dialogue System: A Brief Review
Infinitary Axiomatization of the Equational Theory of Context-Free Languages
Pattern Matching via Choice Existential Quantifications in Imperative Languages
A Short Introduction to NILE
Towards a Robot Perception Specification Language
A Text to Speech (TTS) System with English to Punjabi Conversion
Cognitive Systems and Question Answering
Standards for language resources in ISO -- Looking back at 13 fruitful years
OCR Error Correction Using Character Correction and Feature-Based Word Classification
PR2: A Language Independent Unsupervised Tool for Personality Recognition from Text
From algebra to logic: there and back again -- the story of a hierarchy
Modeling Hybrid Systems in Hy-tccp
Declaratively solving Google Code Jam problems with Picat
Tightening the Complexity of Equivalence Problems for Commutative Grammars
The omega-inequality problem for concatenation hierarchies of star-free languages
A Short Note on Infinite Union/Intersection of Omega Regular Languages
Siamese convolutional networks based on phonetic features for cognate identification
A Supervised Authorship Attribution Framework for Bengali Language
Language Classes Associated with Automata Over Matrix Groups
Introduction: Cognitive Issues in Natural Language Processing
Proceedings 14th International Workshop Quantitative Aspects of Programming Languages and Systems
Regular Languages of Words over Countable Linear Orderings
Beyond-Regular Typestate
It is undecidable if two regular tree languages can be separated by a deterministic tree-walking automaton
A Tidy Data Model for Natural Language Processing using cleanNLP
Extending Functional Languages with High-Level Exception Handling
BKTreebank: Building a Vietnamese Dependency Treebank
ALL-IN-1: Short Text Classification with One Model for All Languages
Proving Parikh's theorem using Chomsky-Schutzenberger theorem
SufiSent - Universal Sentence Representations Using Suffix Encodings
An elementary method for the fidel codification of texts written in Romanian language (O metodă elementară pentru codificarea fidelă a textelor scrise în limba română)
Fast, Flexible, Polyglot Instrumentation Support for Debuggers and other Tools
Process Physics: From Quantum Foam to General Relativity
A Treatise on Quantum Clifford Algebras
The Geometry of Consistency: Decohering Histories in Generalized Quantum Theory
Reconstruction of Protein-Protein Interaction Pathways by Mining Subject-Verb-Objects Intermediates
Semantic Composition and Decomposition: From Recognition to Generation
gMark: Schema-Driven Generation of Graphs and Queries
On the Generative Power of Omega-Grammars and Omega-Automata
Scalable Text Mining with Sparse Generative Models
Learning Generative Models with Sinkhorn Divergences
Effective potential for classical field theories subject to stochastic noise
Multifractal Structure of the Harmonic Measure of Diffusion Limited Aggregates
SYNTAX: A computer program to compress a sequence and to estimate its information content
Hierarchical Mean-Field Theories in Quantum Statistical Mechanics
Microscopic activity patterns in the Naming Game
Architectural Considerations for Conversational Systems -- The Verbmobil/INTARC Experience
E-RES: A System for Reasoning about Actions, Events and Observations
Noun-phrase co-occurrence statistics for semi-automatic semantic lexicon construction
Interactive Timetabling
Higher-Order Pattern Complement and the Strict Lambda-Calculus
Prototyping CLP(FD) Tracers: a Trace Model and an Experimental Validation Environment
Composing Programs in a Rewriting Logic for Declarative Programming
Optimal Ordered Problem Solver
Cooperation between Pronoun and Reference Resolution for Unrestricted Texts
A General Framework For Lazy Functional Logic Programming With Algebraic Polymorphic Types
Propositional Computability Logic II
On Generalized Records and Spatial Conjunction in Role Logic
Thematic Annotation: extracting concepts out of documents
An Improved Non-Termination Criterion for Binary Constraint Logic Programs
Towards a diagrammatic modeling of the LinBox C++ linear algebra library
Using phonetic constraints in acoustic-to-articulatory inversion
Octave-GTK: A GTK binding for GNU Octave
Termination and Confluence of Higher-Order Rewrite Systems
Reusing processes and documenting processes: toward an integrated framework
Acronym-Meaning Extraction from Corpora Using Multi-Tape Weighted Finite-State Machines
ExSched: Solving Constraint Satisfaction Problems with the Spreadsheet Paradigm
Some Issues on Incremental Abstraction-Carrying Code
Polydimensional Relativity, a Classical Generalization of the Automorphism Invariance Principle
Classical Histories in Hamiltonian Systems
Discrete Quantum Causal Dynamics
Questioning the Equivalence Principle
Sakharov's induced gravity: a modern perspective
Quantum mechanics without spacetime II : noncommutative geometry and the free point particle
Spin networks, quantum automata and link invariants
The Duality of Time Dilation and Velocity
Baryon Chiral Perturbation Theory in Manifestly Lorentz Invariant Form
Electromagnetic Form Factors and the Localization of Quark Orbital Angular Momentum in the Proton
Introduction to the functional RG and applications to gauge theories
On T-duality for open strings in general abelian and nonabelian gauge field backgrounds
Generalized Lorentzian Triangulations and the Calogero Hamiltonian
Torsion and nonmetricity in the stringy geometry
Superfield description of 5D supergravity on general warped geometry
Open strings in Lie groups and associative products
Axiomatic classical (prequantum) field theory. Jet formalism
Automatic structures, rational growth and geometrically finite hyperbolic groups
Randomness and semigenericity
Gromov compactness theorem for stable curves
Toric Prevarieties and Subtorus Actions
On Nichols algebras of low dimension
Growth of maps, distortion in groups and symplectic geometry
Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps
Multiple Saddle Connections on Flat Surfaces and Principal Boundary of the Moduli Spaces of Quadratic Differentials
The Elementary Theory of the Frobenius Automorphisms
Enumerating Segmented Patterns in Compositions and Encoding by Restricted Permutations
On the counting of holomorphic discs in toric Fano manifolds
Stable bundles on hypercomplex surfaces
On Exact Solvability of Anharmonic Oscillators in Large Dimensions
Quantum Harmonic Analysis and Geometric Invariants
Quantum Circuits with Mixed States
Use of Mathematical Logical Concepts in Quantum Mechanics: An Example
Teleportation of an arbitrary mixture of diagonal states of multiqubits via classical correlation and classical communication
General-Purpose Computing on a Semantic Network Substrate
A Generic Deployment Framework for Grid Computing and Distributed Applications
Une sémantique observationnelle du modèle des boîtes pour la résolution de programmes logiques (version étendue)
Fubini-Griffiths-Harris rigidity and Lie algebra cohomology
The Effective Field Theory of Inflation
Harvesting graphics power for MD simulations
Spectral properties of entanglement witnesses
Observational semantics of the Prolog Resolution Box Model
Automating Renormalization of Quantum Field Theories
Hierarchy wave functions--from conformal correlators to Tao-Thouless states
Proof mining in ${\mathbb R}$-trees and hyperbolic spaces
Glimpses of the Octonions and Quaternions History and Todays Applications in Quantum Physics
The molecular asymmetric rigid rotor Hamiltonian as an exactly solvable model
Superpolynomial speedups based on almost any quantum circuit
Concept-Oriented Programming
A Non-Termination Criterion for Binary Constraint Logic Programs
Simulating the All-Order Strong Coupling Expansion I: Ising Model Demo
Representation theory of mv-algebras
Maximum Entropy Rate of Markov Sources for Systems With Non-regular Constraints
Automated Induction for Complex Data Structures
Is quantum field theory a generalization of quantum mechanics?
Genealogical trees from genetic distances
Non-Confluent NLC Graph Grammar Inference by Compressing Disjoint Subgraphs
Better Quality in Synthesis through Quantitative Objectives
The Structure of First-Order Causality
The valence bond solid in quasicrystals
From Requirements to code: an Architecture-centric Approach for producing Quality Systems
Studying Maximum Information Leakage Using Karush-Kuhn-Tucker Conditions
On the homology of locally compact spaces with ends
On the Capacity of Constrained Systems
Effective Theories and Modifications of Gravity
Polytool: polynomial interpretations as a basis for termination analysis of Logic programs
The fundamental importance of discourse in theoretical physics
Refinement and Verification of Real-Time Systems
Developing Experimental Models for NASA Missions with ASSL
The Automatic Synthesis of Linear Ranking Functions: The Complete Unabridged Version
A General Simulation Framework for Supply Chain Modeling: State of the Art and Case Study
The bounds of the set of equivalent resistances of n equal resistors combined in series and in parallel
Electronic Geometry Textbook: A Geometric Textbook Knowledge Management System
YAPA: A generic tool for computing intruder knowledge
C Library for Simulated Evolution of Biological Networks
Sur les espaces test pour la moyennabilité
Automatic Probabilistic Program Verification through Random Variable Abstraction
Two refreshing views of Fluctuation Theorems through Kinematics Elements and Exponential Martingale
Van Wijngaarden grammars, metamorphism and K-ary malwares
Histories and observables in covariant field theory
Categorical Quantum Circuits
Development in the Scattering Matrix Theory: From Spin-Orbit-Coupling Affected Shot Noise to Quantum Pumping
Smooth infinite words over $n$-letter alphabets having same remainder when divided by $n$
Bisimulations for fuzzy transition systems
A Machine Checked Model of Idempotent MGU Axioms For Lists of Equational Constraints
Powermonads and Tensors of Unranked Effects
Finite-lattice form factors in free-fermion models
Picturing classical and quantum Bayesian inference
Generic Programming of Reusable, High Performance Container Types using Automatic Type Hierarchy Inference and Bidirectional Antichain Typing
A Tool for the Certification of PLCs based on a Coq Semantics for Sequential Function Charts
Almost overlap-free words and the word problem for the free Burnside semigroup satisfying x^2=x^3
Reduction of fuzzy automata by means of fuzzy quasi-orders
A Spatial-Epistemic Logic for Reasoning about Security Protocols
GRASP and path-relinking for Coalition Structure Generation
Time Fractional Schrödinger Equation; Fox's H-functions and the Effective Potential
Computing generalized inverses using LU factorization of matrix product
A Coinductive Calculus for Asynchronous Side-effecting Processes
Differential geometric formulation of the Cauchy Navier equations
Laminations in the language of leaves
MadGraph 5 : Going Beyond
Generalizing Boolean Satisfiability I: Background and Survey of Existing Work
Scalar Field Theory on a Causal Set in Histories Form
Experimenting with Transitive Verbs in a DisCoCat
Causal categories: relativistically interacting processes
The emergence of gauge invariance: the stay-at-home gauge versus local-global duality
Monoids and Maximal Codes
Geometric Path Integrals. A Language for Multiscale Biology and Systems Robustness
Maximum Segment Sum, Monadically (distilled tutorial, with solutions)
Gauge and Integrable Theories in Loop Spaces
Multi-level Contextual Type Theory
Graded CTL Model Checking for Test Generation
The Newtonian Limit of Geometrostatics
Hyper-relativistic mechanics and superluminal particles
Synthesising Graphical Theories
Bounded Satisfiability for PCTL
The phenomenological approach to modeling the dark energy
Reliable Generation of High-Performance Matrix Algebra
FreeFem++, a tool to solve PDEs numerically
Timing and Code Size Optimization on Achieving Full Parallelism in Uniform Nested Loops
A Domain-Specific Compiler for Linear Algebra Operations
MadAnalysis 5, a user-friendly framework for collider phenomenology
On hybrid models of quantum finite automata
Latent Topic Models for Hypertext
A Generic Library for Stencil Computations
A Simple Optimum-Time FSSP Algorithm for Multi-Dimensional Cellular Automata
Topology Inspired Problems for Cellular Automata, and a Counterexample in Topology
Using Program Synthesis for Social Recommendations
Ranking Functions for Linear-Constraint Loops
What properties of numbers are needed to model accelerated observers in relativity?
Bisimilarity of Probabilistic Pushdown Automata
Modelling an Automatic Proof Generator for Functional Dependency Rules Using Colored Petri Net
Efficient Instantiation of Parameterised Boolean Equation Systems to Parity Games
Summarizing Reviews with Variable-length Syntactic Patterns and Topic Models
Application-tailored Linear Algebra Algorithms: A search-based Approach
Relational Foundations For Functorial Data Migration
Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors
Approximation of grammar-based compression via recompression
Remarks on local symmetry invariance in perturbative algebraic quantum field theory
α-concave functions and a functional extension of mixed volumes
Pushdown Exception-Flow Analysis of Object-Oriented Programs
End to End Verification and Validation with SPIN
Work in Progress: Enabling robot device discovery through robot device descriptions
Automatic Equivalence Proofs for Non-deterministic Coalgebras
DBI Galileon in the Effective Field Theory of Inflation: Orthogonal non-Gaussianities and constraints from the Trispectrum
Dynamic Ising Model: Reconstruction of Evolutionary Trees
Libsharp - spherical harmonic transforms revisited
Novel discrete symmetries in the general N = 2 supersymmetric quantum mechanical model
Generalized geometry applied to 4d-supergravity
Formal Representation of the SS-DB Benchmark and Experimental Evaluation in EXTASCID
Rule-Based Semantic Tagging. An Application Undergoing Dictionary Glosses
Algebraic Net Class Rewriting Systems, Syntax and Semantics for Knowledge Representation and Automated Problem Solving
Priced Timed Petri Nets
Consistency conditions from generalized-unitarity
Efficient algorithms for discrete Gabor transforms on a nonseparable lattice
Complete Decoupling Limit of Ghost-free Massive Gravity
Cost-Aware Automatic Program Repair
A Unifying Approach to Decide Relations for Timed Automata and their Game Characterization
Lp theory for outer measures and two themes of Lennart Carleson united
A "q-deformed" generalization of the Hosszu-Gluskin theorem
Touch-enabled Programming for the Lab of Things
Transport Equations for Oscillating Neutrinos
Automaton semigroup constructions
Groups and Semigroups Defined by Colorings of Synchronizing Automata
Thread-Based Obfuscation through Control-Flow Mangling
Random Generation of Nondeterministic Finite-State Tree Automata
Implicit Sensitive Text Summarization based on Data Conveyed by Connectives
A Simple and Scalable Static Analysis for Bound Analysis and Amortized Complexity Analysis
Algebraic Properties of Valued Constraint Satisfaction Problem
A geometric approach to (semi)-groups defined by automata via dual transducers
Scalable and Robust Construction of Topical Hierarchies
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm
Cooperating distributed context-free hexagonal array grammar systems with permitting contexts
A Vernacular for Coherent Logic
Pattern Recognition in Narrative: Tracking Emotional Expression in Context
What drives the time evolution of the spacetime geometry?
Altitude Training: Strong Bounds for Single-Layer Dropout
A New Model of Array Grammar for generating Connected Patterns on an Image Neighborhood
The RD53 Collaboration's SystemVerilog-UVM Simulation Framework and its General Applicability to Design of Advanced Pixel Readout Chips
Improved Undecidability Results for Reachability Games on Recursive Timed Automata
GPGPU Computing
Modeling Creativity: Case Studies in Python
Hyperspaces in topological Categories
GR uniqueness and deformations
Notes on Noise Contrastive Estimation and Negative Sampling
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs
Membership Function Assignment for Elements of Single OWL Ontology
Program Logics for Homogeneous Generative Run-Time Meta-Programming
Hardware Counted Profile-Guided Optimization
Generalized Gross-Pitaevskii equation adapted to the $U(5)\supset SO(5)\supset SO(3)$ symmetry for spin-2 condensates
Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation
An axiomatic approach to free amalgamation
Proving Termination of Graph Transformation Systems using Weighted Type Graphs over Semirings
FELIX-1.0: A finite element solver for the time dependent generator coordinate method with the Gaussian overlap approximation
Refinement Type Inference via Horn Constraint Optimization
Algebraic approach to quantum theory: a finite-dimensional guide
Strange Work in Strange Places: Quantum Field Theory in Curved Space
Distinguishing Hidden Markov Chains
JSKETCH: Sketching for Java
Parallel Triangles Counting Using Pipelining
Matrix Schubert varieties and Gaussian conditional independence models
LSTM-based Deep Learning Models for Non-factoid Answer Selection
Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves
Decidability of multiset, set and numerically decipherable directed figure codes
Categorical semiotics
Row-less Universal Schema
A Retraction Theorem for Distributed Synthesis
Ozy: A General Orchestration Container
Cross-Domain Entity Resolution in Social Media
De-Conflated Semantic Representations
Optimal steering of a linear stochastic system to a final probability distribution, Part III
Monetary economics from econophysics perspective
BPS counting for knots and combinatorics on words
Quantum counter automata
A Computational Model for the Direct Execution of General Specifications with Multi-way Constraints
Arrangements of Submanifolds and the Tangent Bundle Complement
IR divergences and kinetic equation in de Sitter space. (Poincare patch; Principal series)
Sharp metric obstructions for quasi-Einstein metrics
A Framework for Automated and Certified Refinement Steps
Single Time-Stamped Tries for Retroactive Call Subsumption
The Rank and Hanna Neumann Property of Some Submonoids of a Free Monoid
High-Performance Astrophysical Simulations and Analysis with Python
On the lattice structure of probability spaces in quantum mechanics
Kappa-Minkowski spacetime: mathematical formalism and applications in Planck scale physics
The finiteness problem for automaton semigroups is undecidable
Corpus-based Web Document Summarization using Statistical and Linguistic Approach
Automatic Structuring Of Semantic Web Services An Approach
Automata with Generalized Rabin Pairs for Probabilistic Model Checking and LTL Synthesis
The Skin In The Game Heuristic for Protection Against Tail Events
Average expansion rate and light propagation in a cosmological Tardis spacetime
Realizability of hypergraphs and Ramsey link theory
On the Synchronization Rate for e-machines
Abelian networks III. The critical group
The automatic solution of partial differential equations using a global spectral method
Concept-Oriented Programming: References, Classes and Inheritance Revisited
The Role of Emotions in Propagating Brands in Social Networks
A practical framework for infinite-dimensional linear algebra
Similarity-based matching meets Malware Diversity
Budget Imbalance Criteria for Auctions: A Formalized Theorem
Construction of Vietnamese SentiWordNet by using Vietnamese Dictionary
RAND-WALK: A Latent Variable Model Approach to Word Embeddings
Monomial right ideals and the Hilbert series of noncommutative modules
Deep Feelings: A Massive Cross-Lingual Study on the Relation between Emotions and Virality
Fast, Multicore-Scalable, Low-Fragmentation Memory Allocation through Large Virtual Memory and Global Data Structures
Microsoft COCO Captions: Data Collection and Evaluation Server
TRIQS: A Toolbox for Research on Interacting Quantum Systems
DuQuad: an inexact (augmented) dual first order algorithm for quadratic programming
A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution
Factoriality and the Pin-Reutenauer procedure
Summarization of Films and Documentaries Based on Subtitles and Scripts
Necessary Condition for Local Distinguishability of Maximally Entangled States: Beyond Orthogonality Preservation
On the accuracy of self-normalized log-linear models
Skip-Thought Vectors
pyMOR - Generic Algorithms and Interfaces for Model Order Reduction
Attention-Based Models for Speech Recognition
Unsupervised Semantic Parsing of Video Collections
Replication and Generalization of PRECISE
Bounded Determinization of Timed Automata with Silent Transitions
Semantics-based Automated Web Testing
Geometric Arbitrage and Spectral Theory
TransG : A Generative Mixture Model for Knowledge Graph Embedding
Generalized Euler characteristic in power-bounded T-convex valued fields
Sparse Tensor Algebra as a Parallel Programming Model
Algorithmic decidability of Engel's property for automaton groups
Regarding the `Hole Argument' and the `Problem of Time'
Mined Semantic Analysis: A New Concept Space Model for Semantic Representation of Textual Data
Finite Countermodel Based Verification for Program Transformation (A Case Study)
On Cube Tilings of Tori and Classification of Perfect Codes in the Maximum Metric
Test-Driven Development of ontologies (extended version)
Weyl gravity and Cartan geometry
Synthesis of models for order-sorted first-order theories using linear algebra and constraint solving
Analyzing Walter Skeat's Forty-Five Parallel Extracts of William Langland's Piers Plowman
Dataflow Graphs as Matrices and Programming with Higher-order Matrix Elements
Research Project: Text Engineering Tool for Ontological Scientometry
Optimal cosmic microwave background map-making in the presence of cross-correlated noise
A Dichotomy for First-Order Reducts of Unary Structures
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings
Algebraic Databases
Higher-Order Recursion Abstraction: How to Make Ackermann, Knuth and Conway Look Like a Bunch of Primitives, Figuratively Speaking
Petrarch 2 : Petrarcher
ANTS2 package: simulation and experimental data processing for Anger camera type detectors
A Quantum Computational Semantics for Epistemic Logical Operators. Part II: Semantics
Ultradense Word Embeddings by Orthogonal Transformation
Automated Clustering and Program Repair for Introductory Programming Assignments
Neural Discourse Relation Recognition with Semantic Memory
The Quench Action
Constructive canonicity for lattice-based fixed point logics
openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit
Deciding Maxmin Reachability in Half-Blind Stochastic Games
Learning Moore Machines from Input-Output Traces
Variational Neural Machine Translation
BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings
Review Networks for Caption Generation
Building an Evaluation Scale using Item Response Theory
Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution
Rationalizing Neural Predictions
Watch What You Just Said: Image Captioning with Text-Conditional Attention
Query-Focused Opinion Summarization for User-Generated Content
Goldstone origin of black hole hair from supertranslations and criticality
SelQA: A New Benchmark for Selection-based Question Answering
Generation and Pruning of Pronunciation Variants to Improve ASR Accuracy
PRESAGE: Protecting Structured Address Generation against Soft Errors
Coalgebraic Trace Semantics for Buechi and Parity Automata
Domain Adaptation for Neural Networks by Parameter Augmentation
Linear dynamical systems on graphs
Uniqueness of Normal Forms for Shallow Term Rewrite Systems
Generic Statistical Relational Entity Resolution in Knowledge Graphs
Lexical Based Semantic Orientation of Online Customer Reviews and Blogs
An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
Opinion Mining in Online Reviews About Distance Education Programs
Noetherian Quasi-Polish Spaces
Harder-Narasimhan theory for linear codes
Image-to-Markup Generation with Coarse-to-Fine Attention
Select-Additive Learning: Improving Generalization in Multimodal Sentiment Analysis
Distributed Processing of Generalized Graph-Pattern Queries in SPARQL 1.1
One Sentence One Model for Neural Machine Translation
Text Network Exploration via Heterogeneous Web of Topics
Summarizing Situational and Topical Information During Crises
A General Framework for Content-enhanced Network Representation Learning
Notions of Anonymous Existence in Martin-Löf Type Theory
Interactive Attention for Neural Machine Translation
Deep Amortized Inference for Probabilistic Programs
A Theme-Rewriting Approach for Generating Algebra Word Problems
Professor Forcing: A New Algorithm for Training Recurrent Networks
Detecting Context Dependent Messages in a Conversational Environment
A Perspicuous Description of the Schwarzschild Black Hole Geodesics
Generic Construction of Efficient Matrix Product Operators
Leveraging Video Descriptions to Learn Video Question Answering
Hopf images in locally compact quantum groups
Statistical Learning for OCR Text Correction
Learning Generic Sentence Representations Using Convolutional Neural Networks
Semantic Compositional Networks for Visual Captioning
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Ergodicity of the Liouville system implies the Chowla conjecture
Generalized Shared Control versus Classical Shared Control: Illustrative Examples
Geometrical thermodynamics and P-V criticality of the black holes with power-law Maxwell field
Areas of Attention for Image Captioning
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
On Nonlinear Prices in Timed Automata
A Context-aware Attention Network for Interactive Question Answering
Analogue Stochastic Gravity in Strongly-Interacting Bose-Einstein Condensates
STRIPS Planning in Infinite Domains
Parallel Graph Rewriting with Overlapping Rules
Discontinuous Homomorphisms of $C(X)$ with $2^{\aleph_0}>\aleph_2$
Weighted omega-Restricted One Counter Automata
$L_{\infty}$ Algebras and Field Theory
Contextually Customized Video Summaries via Natural Language
From Formalised State Machines to Implementations of Robotic Controllers
A Knowledge-Grounded Neural Conversation Model
Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings
Consistent Alignment of Word Embedding Models
Two strings at Hamming distance 1 cannot be both quasiperiodic
Chiral Higher Spin Gravity
Supervised Typing of Big Graphs using Semantic Embeddings
Recurrent and Contextual Models for Visual Question Answering
Tacotron: Towards End-to-End Speech Synthesis
Diagrammatic Semantics for Digital Circuits
Injective Schur Modules
Symbolic Computation and Automated Reasoning for Program Analysis
Unfolding and Shrinking Neural Machine Translation Ensembles
Register automata with linear arithmetic
RaPro: A Novel 5G Rapid Prototyping System Architecture
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
Extractive Summarization: Limits, Compression, Generalized Model and Heuristics
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data
Scientific Article Summarization Using Citation-Context and Article's Discourse Structure
Adversarial Neural Machine Translation
Deep Text Classification Can be Fooled
Diversity driven Attention Model for Query-based Abstractive Summarization
Entity Linking with people entity on Wikipedia
Chunk-Based Bi-Scale Decoder for Neural Machine Translation
Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks
Machine Learning with World Knowledge: The Position and Survey
Sympiler: Transforming Sparse Matrix Codes by Decoupling Symbolic Analysis
Better Text Understanding Through Image-To-Text Transfer
Studies of a general flat space/boson star transition model in a box through a language similar to holographic superconductors
Biased Importance Sampling for Deep Neural Network Training
A simple neural network module for relational reasoning
A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform
Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic
Nonlinear probability. A theory with incompatible stochastic variables
Multiresolution Match Kernels for Gesture Video Classification
Talking Drums: Generating drum grooves with neural networks
Neural Sequence Model Training via $α$-divergence Minimization
Applying the Polyhedral Model to Tile Time Loops in Devito
Pairwise Well-Formed Modes and Transformations
Predicting the Quality of Short Narratives from Social Media
Kleene Algebra Modulo Theories
Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings
Introduction of Curvilinear Coordinates into Numerical Analysis
Atiyah-Floer Conjecture: a Formulation, a Strategy to Prove and Generalizations
Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop
Towards Semantic Query Segmentation
Fragile fate of driven-dissipative XY phase in two dimensions
Graviton multi-point amplitudes for higher-derivative gravity in anti-de Sitter space
Enterprise to Computer: Star Trek chatbot
Generative Statistical Models with Self-Emergent Grammar of Chord Sequences
Veamy: an extensible object-oriented C++ library for the virtual element method
Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding
TraceDiff: Debugging Unexpected Code Behavior Using Trace Divergences
Effect of strength of gravitational field on the rate of chemical reactions
M2D: Monolog to Dialog Generation for Conversational Story Telling
Scheduling Constraint Based Abstraction Refinement for Multi-Threaded Program Verification
Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events
Automatically Generating Commit Messages from Diffs using Neural Machine Translation
Interactive Attention Networks for Aspect-Level Sentiment Classification
Automata as $p$-adic Dynamical Systems
Sentiment Polarity Detection for Software Development
StarSpace: Embed All The Things!
On the Generation of Initial Contexts for Effective Deadlock Detection
A Rule-Based Approach to Analyzing Database Schema Objects with Datalog
Subjective Simulation as a Notion of Morphism for Composing Concurrent Resources
Mitigating the Impact of Speech Recognition Errors on Chatbot using Sequence-to-Sequence Model
Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality
A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification
Synchronizing Data Words for Register Automata
Describing Natural Images Containing Novel Objects with Knowledge Guided Assitance
Enhancing Inductive Entailment Proofs in Separation Logic with Lemma Synthesis
Deep Triphone Embedding Improves Phoneme Recognition
Testing the limits of unsupervised learning for semantic similarity
InterpNET: Neural Introspection for Interpretable Deep Learning
Espresso: Brewing Java For More Non-Volatility with Non-volatile Memory
General purpose graphics-processing-unit implementation of cosmological domain wall network evolution
Generalized End-to-End Loss for Speaker Verification
Evaluation of Automatic Video Captioning Using Direct Assessment
Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
On polarization of vector light beams: origin of Berry phase
Automated Migration of Hierarchical Data to Relational Tables using Programming-by-Example
Faithful to the Original: Fact Aware Neural Abstractive Summarization
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System
Grounded Objects and Interactions for Video Captioning
Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models
Convolutional Image Captioning
Teleparallel theories of gravity as analogue of non-linear electrodynamics
Hybrid Oracle: Making Use of Ambiguity in Transition-based Chinese Dependency Parsing
Will humans even write code in 2040 and what would that mean for extreme heterogeneity in computing?
Learning by Asking Questions
Multimodal Storytelling via Generative Adversarial Imitation Learning
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient
Bubble-Flip---A New Generation Algorithm for Prefix Normal Words
Mixed tête-à-tête twists as monodromies associated with holomorphic function germs
Exploring Models and Data for Remote Sensing Image Caption Generation
Monitoring Data Minimisation
Convergence of Pascal-Like Triangles in Parry-Bertrand Numeration Systems
Variational Recurrent Neural Machine Translation
Adversarial Learning for Chinese NER from Crowd Annotations
Size vs. Structure in Training Corpora for Word Embedding Models: Araneum Russicum Maximum and Russian National Corpus
Improving Review Representations with User Attention and Product Attention for Sentiment Classification
PCOT: Cache Oblivious Tiling of Polyhedral Programs
A Renormalization Group Procedure for Fiber Bundle Models
Diverse Beam Search for Increased Novelty in Abstractive Summarization
Enhance word representation for out-of-vocabulary on Ubuntu dialogue corpus
Formal Ontology Learning from English IS-A Sentences
Policy Gradients for Contextual Bandits
Black-hole kicks from numerical-relativity surrogate models
MDroid+: A Mutation Testing Framework for Android
Grammar-based Compression of Unranked Trees
Implementing distributed λ-calculus interpreter
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
Reducing Lambda Terms with Traversals
Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
A Non-Technical Survey on Deep Convolutional Neural Network Architectures
From $\mathcal{N}{=}\,4$ Galilean superparticle to three-dimensional non-relativistic $\mathcal{N}{=}\,4$ superfields
Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts
Challenges in Discriminating Profanity from Hate Speech
Controlling Decoding for More Abstractive Summaries with Copy-Based Networks
Adversarial Generalized Method of Moments
Word sense induction using word embeddings and community detection in complex networks
Context is Everything: Finding Meaning Statistically in Semantic Spaces
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples
Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder
A Generation Method of Immunological Memory in Clonal Selection Algorithm by using Restricted Boltzmann Machines
A GPU-based WFST Decoder with Exact Lattice Generation
A Hierarchical Latent Structure for Variational Conversation Modeling
Generating Clues for Gender based Occupation De-biasing in Text
Implementing Turing Machines in Dynamic Field Architectures
Association schemes on general measure spaces and zero-dimensional Abelian groups
Microtask crowdsourcing for disease mention annotation in PubMed abstracts
Learning Syntactic Program Transformations from Examples
PCT and Beyond: Towards a Computational Framework for `Intelligent' Communicative Systems
Visual Dialog
On Higher Order Query Languages which on Relational Databases Collapse to Second Order Logic
On the Behavior of Convolutional Nets for Feature Extraction
Empirical Analysis on Comparing the Performance of Alpha Miner Algorithm in SQL Query Language and NoSQL Column-Oriented Databases Using Apache Phoenix
Topological Origin of Chiral Symmetry Breaking in QCD and in Gravity
Modular Labelled Sequent Calculi for Abstract Separation Logics
LARGE SCALE PERTURBATIONS IN THE OPEN UNIVERSE
Enumeration of Rota-Baxter Words
General Logic-Systems that Determine Significant Collections of Consequence Operators
Heavy ion event generator HYDJET++ (HYDrodynamics plus JETs)
The tractability of CSP classes defined by forbidden patterns
Off-line test selection with test purposes for non-deterministic timed automata
Aperiodic pseudorandom number generators based on infinite words
Synthesis of Sequential Extended Regular Expressions for Verification
A Dynamic Axiomatic Approach to First-Price Auctions
Comparator Circuits over Finite Bounded Posets
Automatic Generation of Minimal Cut Sets
Postulation of generic lines and one double line in $\PP^n$ in view of generic lines and one multiple linear space
Drawing and Recognizing Chinese Characters with Recurrent Neural Network
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
Generating Descriptions with Grounded and Co-Referenced People
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
Senx: Sound Patch Generation for Security Vulnerabilities
Scale Up Event Extraction Learning via Automatic Training Data Generation
The Generalized Matrix Chain Algorithm
Memory-Based Lexical Acquisition and Processing
A Learning Approach to Natural Language Understanding
TDL--- A Type Description Language for Constraint-Based Grammars
Lexicalization and Grammar Development
Parsing Free Word-Order Languages in Polynomial Time
Complexity of Scrambling: A New Twist to the Competence - Performance Distinction
Grouping Words Using Statistical Context
A specification language for Lexical Functional Grammars
Compositionality for Presuppositions over Tableaux
A Pattern Matching method for finding Noun and Proper Noun Translations from Noisy Parallel Corpora
Measuring semantic complexity
Unification-Based Glossing
A Grammar Formalism and Cross-Serial Dependencies
Clustered Language Models with Context-Equivalent States
Morphological Cues for Lexical Semantics
Linguistic Structure as Composition and Perturbation
Compositional Semantics in Verbmobil
Multilingual Text Analysis for Text-to-Speech Synthesis
Instructions for Temporal Annotation of Scheduling Dialogs
Domain Adaptation with Clustered Language Models
Developing a hybrid NP parser
A Comparative Study of the Application of Different Learning Techniques to Natural Language Interfaces
Aggregate and mixed-order Markov models for statistical language processing
A Flexible POS tagger Using an Automatically Acquired Language Model
Towards an Improved Performance Measure for Language Models
Graph Interpolation Grammars: a Rule-based Approach to the Incremental Parsing of Natural Languages
An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars
Language as an Evolving Word Web
Practical experiments with regular approximation of context-free languages
Resolution of Verb Ellipsis in Japanese Sentence using Surface Expressions and Examples
Recognition Performance of a Structured Language Model
Structured Language Modeling for Speech Recognition
Ranking suspected answers to natural language questions using predictive annotation
Compiling Language Models from a Linguistically Motivated Unification Grammar
Combining semantic and syntactic structure for language modeling
Creating Annotation Tools with the Annotation Graph Toolkit
Grid-Enabling Natural Language Engineering By Stealth
Bayesian Information Extraction Network
A Logic for Reasoning about Digital Rights
Reactive Programming in Standard ML
Perspective alignment in spatial language
Conscious Intelligent Systems - Part II - Mind, Thought, Language and Understanding
The Paraldor Project
A language theoretic analysis of combings
Statistical Mechanical Approach to Human Language
Modelling linguistic taxonomic dynamics
Language Time Series Analysis
On the Development of Text Input Method - Lessons Learned
Aggregation Languages for Moving Object and Places of Interest Data
A Prolog-based Environment for Reasoning about Programming Languages (Extended abstract)
A resource-based Korean morphological annotation system
Meaning and Form in a Language Computer Simulation
A Fast Algorithm and Datalog Inexpressibility for Temporal Reasoning
Event Synchronization by Lightweight Message Passing
Influence of geography on language competition
Constructing word similarities in Meroitic as an aid to decipherment
Mechanized semantics for the Clight subset of the C language
The Structure of Phonological Networks Across Multiple Languages
Representing a P-complete problem by small trellis automata
The computational complexity of universality problems for prefixes, suffixes, factors, and subwords of regular languages
A Bayesian Model for Discovering Typological Implications
Standards for Language Resources
Language Models for Handwritten Short Message Services
Towards the Safe Programming of Wireless Sensor Networks
On possible growth of Toeplitz languages
Compiling Signal Processing Code embedded in Haskell via LLVM
Operational State Complexity of Deterministic Unranked Tree Automata
Nondeterministic State Complexity for Suffix-Free Regular Languages
Knowledge Recognition Algorithm enables P = NP
Product closure of some second-order modal logics
Precedence Automata and Languages
Models of quantum computation and quantum programming languages
Restarting Automata with Auxiliary Symbols and Small Lookahead
Borel Hierarchy and Omega Context Free Languages
Around Dot-depth One
On Understanding and Machine Understanding
Codeco: A Grammar Notation for Controlled Natural Language in Predictive Editors
Bounded Parikh Automata
On Pansiot Words Avoiding 3-Repetitions
A decidable characterization of locally testable tree languages
Du TAL au TIL
A Lexical Analysis Tool with Ambiguity Support
An Annotation Scheme for Reichenbach's Verbal Tense Structure
Basic completion strategies as another application of the Maude strategy language
Arabic Language Learning Assisted by Computer, based on Automatic Speech Recognition
Modular Type-Safety Proofs using Dependant Types
Piecewise testable tree languages
Formal languages analysed by quantum walks
Detecting English Writing Styles For Non-native Speakers
On complexity of regular realizability problems
An Evaluation of Arabic Language Learning Websites
Determining token sequence mistakes in responses to questions with open text answer
Predicate Exchangeability and Language Invariance in Pure Inductive Logic
AstraKahn: A Coordination Language for Streaming Networks
Formalisation of the lambda aleph Runtime
Rule Based Stemmer in Urdu
A Domain-Specific Language for Discrete Mathematics
A decidable class of (nominal) omega-regular languages over an infinite alphabet
The (Nested) Word Problem
Nominal Regular Expressions for Languages over Infinite Alphabets. Extended Abstract
Most Complex Regular Right-Ideal Languages
OCCA: A unified approach to multi-threading languages
A Lemma Based Evaluator for Semitic Language Text Summarization Systems
The First Parallel Multilingual Corpus of Persian: Toward a Persian BLARK
$\mathrm{Pal}^k$ Is Linear Recognizable Online
Complexity of Atoms, Combinatorially
Antescofo Intermediate Representation
Operations on Automata with All States Final
An HMM Based Named Entity Recognition System for Indian Languages: The JU System at ICON 2013
Quality Estimation Of Machine Translation Outputs Through Stemming
Pushdown automata, lambda-graph systems and C*-algebras
The expressive power of quantum walks in terms of language acceptance
Context-Free Grammars with Storage
Uniform definability of henselian valuation rings in the Macintyre language
Un résumeur à base de graphes, indépéndant de la langue
On the Effect of Human-Computer Interfaces on Language Expression
Opportunities for a Truffle-based Golo Interpreter
The Mysteries of Lisp -- I: The Way to S-expression Lisp
Response to Liu, Xu, and Liang (2015) and Ferrer-i-Cancho and Gómez-Rodríguez (2015) on Dependency Length Minimization
Non-regular unary language and parallel communicating Watson-Crick automata systems
Prediction-Adaptation-Correction Recurrent Neural Networks for Low-Resource Language Speech Recognition
Polysemy in Controlled Natural Language Texts
A new TAG Formalism for Tamil and Parser Analytics
Talk&Learn: Improving Conversation Experience and Creating Opportunities for Foreign Language Learning
An Introduction to Quantum Programming in Quipper
A Literature Review: Stemming Algorithms for Indian Languages
On the state complexity of closures and interiors of regular languages with subwords and superwords
Polymorphic Types in ACL2
Approaches to Interpreter Composition
Dafny: Statically Verifying Functional Correctness
Bricklayer: An Authentic Introduction to the Functional Programming Language SML
Multilingual Open Relation Extraction Using Cross-lingual Projection
A Reference Interpreter for the Graph Programming Language GP 2
Gap Analysis of Natural Language Processing Systems with respect to Linguistic Modality
Meta-Packages: Painless Domain Specific Languages
Learning language through pictures
Parsing Natural Language Sentences by Semi-supervised Methods
On not testing the foreign-language effect: A comment on Costa, Foucart, Arnon, Aparici, and Apesteguia (2014)
Word sense disambiguation: a survey
Online Representation Learning in Recurrent Neural Language Models
Programs as proofs
On the Complexity of Flanked Finite State Automata
Gibberish Semantics: How Good is Russian Twitter in Word Semantic Similarity Task?
A short proof that $O_2$ is an MCFL
Derivatives for Enhanced Regular Expressions
Towards Multi-Agent Communication-Based Language Learning
A Decomposable Attention Model for Natural Language Inference
Domain Specific Language for Modular Knitting Pattern Definitions: Purl
Dependency Language Models for Transition-based Dependency Parsing
Syntax-based Attention Model for Natural Language Inference
Behavioural Prototypes
Characterizing the Language of Online Communities and its Relation to Community Reception
Collaborative Learning for Language and Speaker Recognition
AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline
Type checking through unification
Comparing 1D and 2D Real Time on Cellular Automata
Orthographic Syllable as basic unit for SMT between Related Languages
VoxML: A Visualization Modeling Language
From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning
Faster decoding for subword level Phrase-based SMT between related languages
Quantitative Entropy Study of Language Complexity
Domain-Specific Languages of Mathematics: Presenting Mathematical Analysis Using Functional Programming
On the computational power of affine automata
Performance Improvements of Probabilistic Transcript-adapted ASR with Recurrent Neural Network and Language-specific Constraints
Regular Separability of One Counter Automata
Design and Implementation of Concurrent C0
Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data
UsingWord Embedding for Cross-Language Plagiarism Detection
A case study on using speech-to-translation alignments for language documentation
A Visual Representation of Wittgenstein's Tractatus Logico-Philosophicus
Global Entity Ranking Across Multiple Languages
Dynamic Bernoulli Embeddings for Language Evolution
Learning Joint Multilingual Sentence Representations with Neural Machine Translation
Improving Context Aware Language Models
280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification
Dynamics of core of language vocabulary
Who's to say what's funny? A computer using Language Models and Deep Learning, That's Who!
In Search of Effectful Dependent Types
Computational Models of Tutor Feedback in Language Acquisition
Space-Bounded OTMs and REG$^{\infty}$
Native Language Identification on Text and Speech
Tensor Fusion Network for Multimodal Sentiment Analysis
Character-level Intra Attention Network for Natural Language Inference
Self-organized Hierarchical Softmax
From Reversible Programs to Univalent Universes and Back
LangPro: Natural Language Theorem Prover
Towards an Arabic-English Machine-Translation Based on Semantic Web
A Survey of Machine Learning for Big Code and Naturalness
Elementary number-theoretical statements proved by Language Theory
Using Deep Convolutional Networks for Gesture Recognition in American Sign Language
Fine-tuned Language Models for Text Classification
E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text
Reusing Weights in Subword-aware Neural Language Models
Language Identification of Bengali-English Code-Mixed data using Character & Phonetic based LSTM Models
Word Problem Languages for Free Inverse Monoids
An Analysis of Neural Language Modeling at Multiple Scales
Leveraging translations for speech transcription in low-resource settings
Composing DTI Visualizations with End-user Programming
Some Bibliographical References on Intonation and Intonational Meaning
Lexical Functions and Machine Translation
Distributional Part-of-Speech Tagging
LexGram - a practical categorial grammar formalism -
Memoization of Top Down Parsing
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
The Logic Programming Paradigm and Prolog
Synchronization from a Categorical Perspective
In memoriam Maurice Gross
The Parikh functions of sparse context-free languages are quasi-polynomials
Programming languages with algorithmically parallelizing problem
CREOLE: a Universal Language for Creating, Requesting, Updating and Deleting Resources
Scattered context-free linear orderings
On religion and language evolutions seen through mathematical and agent based models
Notes on Electronic Lexicography
From Mathematics to Abstract Machine: A formal derivation of an executable Krivine machine
The state complexity of star-complement-star
Indus script corpora, archaeo-metallurgy and Meluhha (Mleccha)
Taxonomy and synthesis of Web services querying languages
A Pointillism Approach for Natural Language Processing of Social Media
One-counter verifiers for decidable languages
Concrete Semantics of Programs with Non-Deterministic and Random Inputs
A Note on Kolmogorov-Uspensky Machines
On disjunction of equations in the semigroup language with no constants
Hard Asymptotic Sets for One-Dimensional Cellular Automata
The Geometry of Orbifolds via Lie Groupoids
The ins and outs of iteration in Mezzo
Internal and external dynamics in language: Evidence from verb regularity in a historical corpus of English
A Transfer Theorem for the Separation Problem
Multilingual Schema Matching for Wikipedia Infoboxes
Unicode in Domain-Specific Programming Languages for Modeling & Simulation: ScalaTion as a Case Study
The freeness problem over matrix semigroups and bounded languages
Security Type Systems as Recursive Predicates
Incorporating Semi-supervised Features into Discontinuous Easy-First Constituent Parsing
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Language, Twitter and Academic Conferences
Why Bother With Syntax?
A Logical Approach to Event Handling in Imperative Languages
Parallels of human language in the behavior of bottlenose dolphins
Simplified Boardgames
Learning Python Code Suggestion with a Sparse Pointer Network
Syntactic Structures of Regular Languages
On the Upward/Downward Closures of Petri Nets
A Higher-Order Abstract Syntax Approach to the Verified Compilation of Functional Programs
Frustratingly Short Attention Spans in Neural Language Modeling
Second order conservative languages with a Maltsev polymorphism also have a majority polymorphism
Two Dichotomy Theorems
LEPOR: An Augmented Machine Translation Evaluation Metric
Predicting language diversity with complex network
Abstracting Definitional Interpreters
Improved bounds for testing Dyck languages
On h-Lexicalized Restarting Automata
Büchi VASS recognise w-languages that are Sigma^1_1 - complete
Boundedness in languages of infinite words
MMCR4NLP: Multilingual Multiway Corpora Repository for Natural Language Processing
Cross-Language Question Re-Ranking
Language Modeling for Code-Switched Data: Challenges and Approaches
Language Distribution Prediction based on Batch Markov Monte Carlo Simulation with Migration
The process of purely event-driven programs
Contrastive Learning of Emoji-based Representations for Resource-Poor Languages
The Very Idea of Dynamic Semantics
GEMINI: A Natural Language System for Spoken-Language Understanding
Having Your Cake and Eating It Too: Autonomy and Interaction in a Model of Sentence Processing
An Extended Clustering Algorithm for Statistical Language Models
Multilingual Sentence Categorization according to Language
A Note on Zipf's Law, Natural Languages, and Noncoding DNA regions
A Dynamic Approach to Rhythm in Language: Toward a Temporal Phonology
Attempto - From Specifications in Controlled Natural Language towards Executable Specifications
Building Probabilistic Models for Natural Language
Natural Language Processing: Structure and Complexity
Centering in Japanese Discourse
Maximum Entropy Modeling Toolkit
Exploiting Context to Identify Lexical Atoms -- A Statistical View of Linguistic Context
Natural Language Dialogue Service for Appointment Scheduling Agents
Adjunction As Substitution: An Algebraic Formulation of Regular, Context-Free and Tree Adjoining Languages
Semantics and Conversations for an Agent Communication Language
Universal Object Oriented Languages and Computer Algebra
Cross-Language Information Retrieval for Technical Documents
Applying Machine Translation to Two-Stage Cross-Language Information Retrieval
Extension Language Automation of Embedded System Debugging
Automated Debugging In Java Using OCL And JDI
Probabilistic top-down parsing and language modeling
A New Approach to Formal Language Theory by Kolmogorov Complexity
An Algorithm for Aligning Sentences in Bilingual Corpora Using Lexical Information
XPath-Logic and XPathLog: A Logic-Programming Style XML Data Manipulation Language
Delimited continuations in natural language: quantification and polarity sensitivity
Foundations of Modern Language Resource Archives
Automatic Identification of Document Translations in Large Multilingual Document Collections
Extending an Information Extraction tool set to Central and Eastern European languages
Bit-strings and other modifications of Viviane model for language competition
Physics of randomness and regularities for cities, languages, and their lifetimes and family trees
Indo-European languages tree by Levenshtein distance
A computer simulation of language families
Cross Comparison of Synonym Graphs in A Multi Linguistic Context
Topology and Ambiguity in Omega Context Free Languages
Syntax diagrams as a formalism for representation of syntactic relations of formal languages
How to turn a scripting language into a domain specific language for computer algebra
Universal Complex Structures in Written Language
NP Datalog: a Logic Language for Expressing NP Search and Optimization Problems
Measures of lexical distance between languages
Object-oriented Programming Laws for Annotated Java Programs
Extending scientific computing system with structural quantum programming capabilities
Cross-Lingual Adaptation using Structural Correspondence Learning
Hierarchical states in the Compositional Interchange Format
Tree Languages Defined in First-Order Logic with One Quantifier Alternation
Reducing the Number of Annotations in a Verification-oriented Imperative Language
Beating the Productivity Checker Using Embedded Languages
Languages of Dot-depth One over Infinite Words
A Comparative Case Study of Code Reuse With Language Oriented Programming
Selective Memoization
Neutral evolution: A null model for language dynamics
A Concise Query Language with Search and Transform Operations for Corpora with Multiple Levels of Annotation
Positivity of the English language
Toward a Motor Theory of Sign Language Perception
Fault detection system for Arabic language
Performance of the Google Desktop, Arabic Google Desktop and Peer to Peer Application in Arabic Language
New developments in parsing Mizar
A static cost analysis for a higher-order language
A Joint Model of Language and Perception for Grounded Attribute Learning
On logical hierarchies within FO^2-definable languages
Partially-commutative context-free languages
Languages cool as they expand: Allometric scaling and the decreasing need for new words
Binary Patterns in Binary Cube-Free Words: Avoidability and Growth
PyPLN: a Distributed Platform for Natural Language Processing
Hypergraph Automata: A Theoretical Model for Patterned Self-assembly
A Multilingual Semantic Wiki Based on Attempto Controlled English and Grammatical Framework
Uniformly defining valuation rings in Henselian valued fields with finite or pseudo-finite residue fields
How fast can we make interpreted Python?
An introduction to the Europe Media Monitor family of applications
Computing in Operations Research using Julia
Proceedings 5th Workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software
An Application of Answer Set Programming to the Field of Second Language Acquisition
Structured Approach to Web Development
A Linear Logic Programming Language for Concurrent Programming over Graph Structures
Compositional Morphology for Word Representations and Language Modelling
Simplifying Nondeterministic Finite Cover Automata
More Structural Characterizations of Some Subregular Language Families by Biautomata
Computerization of African languages-French dictionaries
Cross-Language Personal Name Mapping
Report on the First Workshop On the Globalization of Modeling Languages
A Probabilistic Translation Method for Dictionary-based Cross-lingual Information Retrieval in Agglutinative Languages
Magic coins are useful for small-space quantum machines
Modeling Hybrid Systems in the Concurrent Constraint Paradigm
Hierarchy of Scales in Language Dynamics
A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and Instant Message
Experience Report: Developing the Servo Web Browser Engine using Rust
Prior Polarity Lexical Resources for the Italian Language
Watson-Crick Quantum Finite Automata
One-Tape Turing Machine Variants and Language Recognition
Natural Language Object Retrieval
A declarative extension of parsing expression grammars for recognizing most programming languages
A Compositional Approach to Language Modeling
Transfer Learning for Low-Resource Neural Machine Translation
Uncountable classical and quantum complexity classes
Managing Schema Evolution in NoSQL Data Stores
Frameworks for Reasoning about Syntax that Utilize Quotation and Evaluation
Lifted Variable Elimination: Decoupling the Operators from the Constraint Language
Pragmatic Neural Language Modelling in Machine Translation
A Fundamental Scale of Descriptions for Analyzing Information Content of Communication Systems
A Solution to Yamakami's Problem on Advised Context-free Languages
Regular realizability problems and context-free languages
Quotient Complexities of Atoms in Regular Ideal Languages
Learning Models for Following Natural Language Directions in Unknown Environments
Pairs of Languages Closed under Shuffle Projection
Leveraging Twitter for Low-Resource Conversational Speech Language Modeling
Reasoning in complex environments with the SelectScript declarative language
PJAIT Systems for the IWSLT 2015 Evaluation Campaign Enhanced by Comparable Corpora
Learning Natural Language Inference with LSTM
Contrastive Entropy: A new evaluation metric for unnormalized language models
Proceedings Eighth International Workshop on Programming Language Approaches to Concurrency- and Communication-cEntric Software
Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages
Right Ideals of a Ring and Sublanguages of Science
The complexity of downward closure comparisons
Learning Language Games through Interaction
Aristotle's square of opposition in the light of Hilbert's epsilon and tau quantifiers
2-tape 1-way Quantum Finite State Automata
Representing Strategies
Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
Object Capabilities and Lightweight Affinity in Scala: Implementation, Formalization, and Soundness
Latent Tree Language Model
Nationalism, Immigration and the Dynamics of Language Evolution
Challenges of Computational Processing of Code-Switching
A Coordination Language for Databases
Measuring Asymmetric Opinions on Online Social Interrelationship with Language and Network Features
A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference
Structural Correspondence Learning for Cross-lingual Sentiment Classification with One-to-many Mappings
Learn Quantum Mechanics with Haskell
Automata theory on sliding windows
Parent Oriented Teacher Selection Causes Language Diversity
Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
Building a Syllable Database to Solve the Problem of Khmer Word Segmentation
Machine Learning Based Source Code Classification Using Syntax Oriented Features
Proceedings Tenth Workshop on Programming Language Approaches to Concurrency- and Communication-cEntric Software
DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning
Cross-lingual Distillation for Text Classification
Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach
One-step and Two-step Classification for Abusive Language Detection on Twitter
Preliminary Exploration of Formula Embedding for Mathematical Information Retrieval: can mathematical formulae be embedded like a natural language?
Massively Multilingual Neural Grapheme-to-Phoneme Conversion
Language Identification Using Deep Convolutional Recurrent Neural Networks
Cross-Lingual Dependency Parsing for Closely Related Languages - Helsinki's Submission to VarDial 2017
Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation
Natural Language Inference over Interaction Space
Minimal Dependency Translation: a Framework for Computer-Assisted Translation for Under-Resourced Languages
Word Translation Without Parallel Data
Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
Natural Language Inference with External Knowledge
Slim Embedding Layers for Recurrent Neural Language Models
Killing Two Birds with One Stone -- Querying Property Graphs using SPARQL via GREMLINATOR
Geospatial distributions reflect rates of evolution of features of language
JU_KS@SAIL_CodeMixed-2017: Sentiment Analysis for Indian Code Mixed Social Media Texts
Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages
On the difficulty of a distributional semantics of spoken language
Sentiment Analysis of Code-Mixed Languages leveraging Resource Rich Languages
SO(5) as a Critical Dynamical Symmetry in the SU(4) Model of High-Temperature Superconductivity
A Bootstrap Approach to Automatically Generating Lexical Transfer Rules
A General Framework for Automatic Termination Analysis of Logic Programs
Symmetries and conservation laws in histories-based generalized quantum mechanics
Symmetries and conservation laws in histories-based theories
Phases of massive gravity
Generando entrelazamiento en cadenas XY - (Generating entanglement in XY chains)
Method of Squared Eigenfunction Potentials in Integrable Hierarchies of KP Type
Generalized non-supersymmetric flux vacua
Synechococcus as a "singing" bacterium: biology inspired by micro-engineered acoustic streaming devices
On Generation of Firewall Log Status Reporter (SRr) Using Perl
A Generic Scheme for Qualified Logic Programming
On the computation of non-perturbative effective potentials in the string theory landscape -- IIB/F-theory perspective
Generalized Householder Transformations for the Complex Symmetric Eigenvalue Problem
Generic solution of the heterogeneity-induced competing risk problem in survival analysis
Automated Attack Planning
High-level robot programming based on CAD: dealing with unpredictable environments
Intrinsic-Correlation Quantum Key Generation
Generalized Biwords for Bitext Compression and Translation Spotting
Conceptual Preconditions of Overcoming of Relativistic Intentions in Modern Philosophy of Science
A unifying description of dark energy
A Logic Programming Playground for Lambda Terms, Combinators, Types and Tree-based Arithmetic Computations
A Program Logic for Verifying Secure Routing Protocols
Model-checking Quantitative Alternating-time Temporal Logic on One-counter Game Models
Vortex Formation and Evolution in Planet Harboring Disks under Thermal Relaxation
A Verified Information-Flow Architecture
Reachability Analysis of Reversal-bounded Automata on Series-Parallel Graphs
Fitting Bayesian item response models in Stata and Stan
Towards Spectral Geometric Methods for Euclidean Quantum Gravity
Operational calculus on programming spaces
A Generic Approach to Flow-Sensitive Polymorphic Effects (Extended Version)
Question Answering and Question Generation as Dual Tasks
Adversarially Regularized Autoencoders
Topology Analysis of International Networks Based on Debates in the United Nations
ChimpCheck: Property-Based Randomized Test Generation for Interactive Apps
Lattice Operations on Terms over Similar Signatures
Flexible End-to-End Dialogue System for Knowledge Grounded Conversation
Automaton Semigroups and Groups: on the Undecidability of Problems Related to Freeness and Finiteness
Fooling End-to-end Speaker Verification by Adversarial Examples
Invariant Generation for Multi-Path Loops with Polynomial Assignments
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
Assertion-based QA with Question-Aware Open Information Extraction
Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models
Waveform Modeling and Generation Using Hierarchical Recurrent Neural Networks for Speech Bandwidth Extension
Interpreting DNN output layer activations: A strategy to cope with unseen data in speech recognition
Automatic Generation of Precise and Useful Commutativity Conditions (Extended Version)
Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers
Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition
Concepts for astronomical data accessibility and analysis via relational database
Security Policy Specification Using a Graphical Approach
The role of robust semantic analysis in spoken language dialogue systems
The Interpolation Theory of Radial Basis Functions
Checking Finite State Machine Conformance when there are Distributed Observations
Generating Exact- and Ranked Partially-Matched Answers to Questions in Advertisements
On Periodically Iterated Morphisms
Myhill-Nerode methods for hypergraphs
Predicate Logic as a Modeling Language: Modeling and Solving some Machine Learning and Data Mining Problems with IDP3
Communication complexity of promise problems and their applications to finite automata
Unifying Class-Based Representation Formalisms
Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4
ACL2 Meets the GPU: Formalizing a CUDA-based Parallelizable All-Pairs Shortest Path Algorithm in ACL2
DMARF AND GIPSY High Level Architecture and Requirements Analysis
A Big Data Analyzer for Large Trace Logs
Inferring Energy Bounds via Static Program Analysis and Evolutionary Modeling of Basic Blocks
High level implementation of geometric multigrid solvers for finite element problems: applications in atmospheric modelling
Machine Translation Evaluation: A Survey
A Novel Framework to Expedite Systematic Reviews by Automatically Building Information Extraction Training Corpora
The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain
A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns
How to do lexical quality estimation of a large OCRed historical Finnish newspaper collection with scarce resources
ImageJ2: ImageJ for the next generation of scientific image data
Interconnected Linguistic Architecture
Social Media Text Processing and Semantic Analysis for Smart Cities
Quality-Efficiency Trade-offs in Machine Learning for Text Processing
Learning to Organize Knowledge with N-Gram Machines
Obtaining the Non-relativistic Quantum Mechanics from Quantum Field Theory: Issues, Folklores and Facts
Conversational AI: The Science Behind the Alexa Prize
Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains
A Comparison of Word Embeddings for the Biomedical Natural Language Processing
Towards Runtime Monitoring of Node.js and Its Application to the Internet of Things
Symmetry Breaking and Adaptation: Evidence from a Toy Model of a Virus
Restrictions on a geometrical language in gravity
Spot-like Structures of Neutron Star Surface Magnetic Fields
Grad-Shafranov Approach To Axisymmetric Stationary Flows In Astrophysics
APECS - The Atacama Pathfinder Experiment Control System
Regular unimodal systems and factors of finite automata
Riemannian theory of Hamiltonian chaos and Lyapunov exponents
Diffusion Limited Aggregation and Iterated Conformal Maps
On the low energy properies of fermions with singular interactions
Global symmetries of quantum Hall systems: lattice description
N-species stochastic models with boundaries and quadratic algebras
Object orientation and visualization of physics in two dimensions
Geometrical Properties of Cumulant Expansions
Time-dependent linear response of an inhomogeneous Bose superfluid: Microscopic theory and connection to current-density functional theory
Simplest random K-satisfiability problem
Singularity Formation and Collapse in the Attractive Gross-Pitaevskii Equation
Thermodynamic Formalism of the Harmonic Measure of Diffusion Limited Aggregates: Phase Transition and Converged $f(α)$
Accelerated growth of networks
Kondo effect in coupled quantum dots: a Non-crossing approximation study
The Functional Schrödinger Picture Approach to Many-Particle Systems
Irreversibility time scale
Restoring Coherence Lost to a Slow Interacting Mesoscopic Bath
Magnon condensation into Q-ball in 3He-B
Cue Phrase Classification Using Machine Learning
Computing Declarative Prosodic Morphology
Deriving Abstract Semantics for Forward Analysis of Normal Logic Programs
A Probabilistic Approach to Lexical Semantic Knowledge Acquisition and S tructural Disambiguation
Empirically Evaluating an Adaptable Spoken Dialogue System
DRAFT : Task System and Item Architecture (TSIA)
A Second Step Towards Complexity-Theoretic Analogs of Rice's Theorem
Requirements of Text Processing Lexicons
ACLP: Integrating Abduction and Constraint Solving
Optimal Belief Revision
Naive Bayes and Exemplar-Based approaches to Word Sense Disambiguation Revisited
Polynomial-time Computation via Local Inference Relations
An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Personal E-mail Messages
Measuring efficiency in high-accuracy, broad-coverage statistical parsing
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
On-the-fly Query-Based Debugging with Examples
The Existential Theory of Equations with Rational Constraints in Free Groups is PSPACE-Complete
Iterative Residual Rescaling: An Analysis and Generalization of LSI
Inference of termination conditions for numerical loops
Looking Under the Hood : Tools for Diagnosing your Question Answering Engine
Inference of termination conditions for numerical loops in Prolog
On termination of meta-programs
Mragyati : A System for Keyword-based Searching in Databases
Fast Context-Free Grammar Parsing Requires Fast Boolean Matrix Multiplication
A Backward Analysis for Constraint Logic Programs
Using parametric set constraints for locating errors in CLP programs
Conformal Geometry, Euclidean Space and Geometric Algebra
Monitoring and Debugging Concurrent and Distributed Object-Oriented Systems
A Probabilistic Method for Analyzing Japanese Anaphora Integrating Zero Pronoun Detection and Resolution
Thinking, Learning, and Autonomous Problem Solving
On Applying Or-Parallelism and Tabling to Logic Programs
A Cross-media Retrieval System for Lecture Videos
cTI: A constraint-based termination inference tool for ISO-Prolog
Schedulers and Redundancy for a Class of Constraint Propagation Rules
Incompleteness of States w.r.t. Traces in Model Checking
Interactive visualization of higher dimensional data in a multiview environment
Learning Hybrid Algorithms for Vehicle Routing Problems
A Public Reference Implementation of the RAP Anaphora Resolution Algorithm
A New Approach to Draw Detection by Move Repetition in Computer Chess Programming
Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities
Extending Design by Contract for Aspect-Oriented Programming
Mapping Fusion and Synchronized Hyperedge Replacement into Logic Programming
Efficient Multiclass Implementations of L1-Regularized Maximum Entropy
Proving or Disproving likely Invariants with Constraint Reasoning
On Algorithms and Complexity for Sets with Cardinality Constraints
Solving Partial Order Constraints for LPO Termination
Incremental copying garbage collection for WAM-based Prolog systems
Fast Frequent Querying with Lazy Control Flow Compilation
Reasoning About Knowledge of Unawareness
On the Design of Agent-Based Systems using UML and Extensions
Modeling the Dynamics of Social Networks
10^(10^6) Worlds and Beyond: Efficient Representation and Processing of Incomplete Information
Lexical Adaptation of Link Grammar to the Biomedical Sublanguage: a Comparative Evaluation of Three Approaches
On the confluence of lambda-calculus with conditional rewriting
Partial Evaluation for Program Comprehension
Applying Part-of-Seech Enhanced LSA to Automatic Essay Grading
Un modèle générique d'organisation de corpus en ligne: application à la FReeBank
The Reaction RuleML Classification of the Event / Action / State Processing and Reasoning Space
A Web-based Tool Combining Different Type Analyses
Pre-Requirement Specification Traceability: Bridging the Complexity Gap through Capabilities
Domain Directed Dialogs for Decision Processes
Symbolic Methods to Enhance the Precision of Numerical Abstract Domains
On the alternative description of complex holomorphic and Lorentz geometries in four dimensions
The Use of Computer Algebra in Maxwell's Theory
The Covariant Approach to LRS Perfect Fluid Spacetime Geometries
Quantum Prediction Algorithms
Quantum gravity effects in the CGHS model of collapse to a black hole
The Angular Resolution of Space-Based Gravitational Wave Detectors
Symmetries of homogeneous cosmologies
Black Hole Thermodynamics without a Black Hole?
Surface terms and the Gauss-Bonnet Hamiltonian
Acoustic Wormholes
Kerr-Sen dilaton-axion black hole lensing in the strong deflection limit
Inferring the intensity of Poisson processes at the limit of the detector sensitivity (with a case study on gravitational wave burst search)
Lattice DIS Structure Functions
The Taming of QCD by Fortran 90
Perturbative renormalization of the first two moments of non-singlet quark distributions with overlap fermions
Can a pseudo-symmetry solve the cosmological constant problem?
$1/N_c$ Expansion for Excited Baryons
From kaons to neutrinos: quantum mechanics of particle oscillations
Spin effects in tau-lepton pair production at LHC
A simple explanation of the non-appearance of physical gluons and quarks
Dynamics of the scalar field in 5-dimensional Kaluza-Klein theory
Quark Imaging in the Proton Via Quantum Phase-Space Distributions
JaxoDraw: A graphical user interface for drawing Feynman diagrams
Simulation of long-baseline neutrino oscillation experiments with GLoBES
RunMC - an object-oriented analysis framework for Monte Carlo simulation of high-energy particle collisions
Chern-Simons Perturbation Theory
A path-integral approach to polynomial invariants of links
Symmetries and String Field Theory in D=2
On braided tensor categories
Dispersionless Toda hierarchy and two-dimensional string theory
On quantum group SL_q(2)
The Quantum Gauge Principle
Coarse Grainings and Irreversibility in Quantum Field Theory
Gaudin Model, KZ Equation, and Isomonodromic Problem on Torus
Equivalence between a bosonic theory and a massive non-local Thirring model at Finite Temperature
D-branes on Nonabelian Threefold Quotient Singularities
Towards SO(2,10)-Invariant M-Theory: Multilagrangian Fields
D3-branes on partial resolutions of abelian quotient singularities of Calabi-Yau threefolds
Discrete Torsion and Gerbes II
Quasi-local structure of p-form theory
Noncompact Heisenberg spin magnets from high-energy QCD: I. Baxter Q-operator and Separation of Variables
D-brane probes on G2 Orbifolds
CFT Description of Identity String Field: Toward Derivation of the VSFT Action
On the Relation Between Fock and Schroedinger Representations for a Scalar Field
A universal symmetry structure in open string theory
Superfield Approach to (Non-)local Symmetries for One-Form Abelian Gauge Theory
From Branes to Branes
Twistors, special relativity, conformal symmetry and minimal coupling - a review
Hypermultiplets and hypercomplex geometry from 6 to 3 dimensions
Covariant One-Loop Amplitudes in D=11
Fermions in the harmonic potential and string theory
The Ricci Curvature of Half-flat Manifolds
Combinatorial problems about free groups and algebras
The Theory of Ultralogics Part II
Quantum Homology of fibrations over $S^2$
On an ambiguity in the concept of partial and total derivatives in classical analysis
Idempotent functional analysis: an algebraic approach
Factorization of nonlinear heat equation posed on Riemann manifold
A classical approach to TQFT's
Virtual moduli cycles and Gromov-Witten invariants of noncompact symplectic manifolds
Subrepresentations of Kronecker representations
A graphic generalization of arithmetic
Weak Omega Categories I
Discrete Baker Transformation and Cellular Automata
Sheaves and D-modules in integral geometry
How can we escape Thomae's relations?
The planar Tree Lagrange Inversion Formula
Analytic cell decomposition and analytic motivic integration
Lie algebroids and Cartan's method of equivalence
Integration and conjugacy in knot theory
A gerbe for the elliptic gamma function
Full field algebras, operads and tensor categories
Chen-Ruan cohomology of ADE singularities
Solving One-Variable Equations in Free Groups
A cohomological interpretation of Brion's formula
The loop problem for monoids and semigroups
Real closed fields with nonstandard and standard analytic structure
The physical heritage of Sir W.R. Hamilton
The mathematical role of (commutative and noncommutative) infinitesimal random walks over (commutative and noncommutative) riemannian manifolds in Quantum Physics
ChaNoXity: The Nonlinear Dynamics of Nature
$q$-analogue of modified KP hierarchy and its quasi-classical limit
Social network from communities of electronic mail
Limited-Diffraction Solutions to Maxwell and to Schroedinger Equations
The Network Solution For Electron Identification in a Wide Momentum Region with a TRD
Nonlinear relaxation field in charged systems under high electric fields
A Segunda Lei da Termodinamica na formulacao da Lei de Hooke
Applications of geometric algebra to black holes and Hawking radiation
The Indefinite Logarithm, Logarithmic Units, and the Nature of Entropy
Topology Induced Coarsening in Language Games
Inconsistencies of Neutrino and Quark Conjectures and their Negative Environmental Implications
Fusion of q-tensor operators: quasi-Hopf-algebraic point of view
Multibraces on the Hochschild complex
Deformation quantization of Poisson manifolds, I
Evolutionary ecology in-silico: Does mathematical modelling help in understanding the "generic" trends?
Parametrized Stochastic Grammars for RNA Secondary Structure Prediction
Mixed-Cultures and Alcoholic Fermentations
Electron structure, Zitterbewegung, and the new non-linear Dirac-like equation
Optimal Copying of One Quantum Bit
Non-local quantum evolution of entangled ensemble states in neural nets and its significance for brain function and a theory of consciousness
Pseudo-forces in quantum mechanics
Spin and Electron Structure
A relativistically invariant mass operator
Quantum Search on Bounded-Error Inputs
Quantum Computational Logics. A Survey
Statistical Structures Underlying Quantum Mechanics and Social Science
Compiling gate networks on an Ising quantum computer
Distributed measurement-based quantum computation
A Pragmatic Interpretation of Quantum Logic
Quantum automata, braid group and link polynomials
Quantum Predicative Programming
Adaptive strategies for graph state growth in the presence of monitored errors
(1+1)-Dirac particle with position-dependent mass in complexified Lorentz scalar interactions: effectively PT-symmetric
On the impossibility of extracting classical randomness using a quantum computer
Quantum Walks, Quantum Gates and Quantum Computers
A Topos Foundation for Theories of Physics: IV. Categories of Systems
An architecture-based dependability modeling framework using AADL
Are We Typical?
Avoiding Rotated Bitboards with Direct Lookup
Birationality of étale morphisms via surgery
Field theoretic description of the abelian and non-abelian Josephson effect
Diagnostic tools for 3D unstructured oceanographic data
The Common Origin of Linear and Nonlinear Chiral Multiplets in N=4 Mechanics
t-J model then and now: A personal perspective from the pioneering times
A Robust Linguistic Platform for Efficient and Domain specific Web Content Analysis
Some Observations for Mean-Field Spin Glass Models
An Architecture Framework for Complex Data Warehouses
Removing Manually-Generated Boilerplate from Electronic Texts: Experiments with Project Gutenberg e-Books
Spatial Aggregation: Data Model and Implementation
Vacuum driven accelerated expansion
Efficient Divide-and-Conquer Implementations Of Symmetric FSAs
Optimal quantum adversary lower bounds for ordered search
On the interaction between sharing and linearity
Nonstandard Higgs Decays with Visible and Missing Energy
FORAY-GEN: Automatic Generation of Affine Functions for Memory Optimizations
SWI-Prolog and the Web
On the embedding of spacetime in five-dimensional Weyl spaces
Quivers, Geometric Invariant Theory, and Moduli of Linear Dynamical Systems
Axiomatizing rational power series
QIS-XML: A metadata specification for Quantum Information Science
Matrix Hamiltonians with an algebraic guarantee of unbroken PT-symmetry
Call-by-value Termination in the Untyped lambda-calculus
Between conjecture and memento: shaping a collective emotional perception of the future
Spreadsheet Assurance by "Control Around" is a Viable Alternative to the Traditional Approach
Equation of Motion for the Quantum Characteristic Functions
Python - All a Scientist Needs
Breaking Out of the Cell: On The Benefits of a New Spreadsheet User-Interaction Paradigm
On group theory for quantum gates and quantum coherence
An Introduction to Topos Physics
The elliptic GL(n) dynamical quantum group as an h-Hopf algebroid
Integration I(d) of Nonstationary Time Series: Stationary and nonstationary increments
Optimization of Enzymatic Biochemical Logic for Noise Reduction and Scalability: How Many Biocomputing Gates Can Be Interconnected in a Circuit?
Information filtering based on wiki index database
The Complexity of Coverage
On d-dimensional d-Semimetrics and Simplex-Type Inequalities for High-Dimensional Sine Functions
Noncommutative gravity, a `no strings attached' quantum-classical duality, and the cosmological constant puzzle
Managing conflicts between users in Wikipedia
Small overlap monoids II: automatic structures and normal forms
The Correspondence Analysis Platform for Uncovering Deep Structure in Data and Information
CPBVP: A Constraint-Programming Framework for Bounded Program Verification
DescribeX: A Framework for Exploring and Querying XML Web Collections
Agent-based model of competition in a social structure
Executable Set Theory and Arithmetic Encodings in Prolog
Text Modeling using Unsupervised Topic Models and Concept Hierarchies
A Complete Grammar for Decomposing a Family of Graphs into 3-connected Components
Electromagnetic Fields Produced by Moving Sources in a Curved Beam Pipe
On finite-index extensions of subgroups of free groups
Morphic and Automatic Words: Maximal Blocks and Diophantine Approximation
Algorithmic derivation of Dyson-Schwinger Equations
Extending Cantor Paradox
Quantum theta functions and Gabor frames for modulation spaces
Arithmetic gravity and Yang-Mills theory: An approach to adelic physics via algebraic spaces
The ADAPT Tool: From AADL Architectural Models to Stochastic Petri Nets through Model Transformation
Effective Theory of Braid Excitations of Quantum Geometry in terms of Feynman Diagrams
Tricritical O(n) models in two dimensions
Equivariant Quantizations of Symmetric Algebras
Clifford Algebra with Mathematica
Quantum Curves and D-Modules
Assembling Actor-based Mind-Maps from Text Stream
Black Holes, AdS, and CFTs
Blasting through lattice calculations using CUDA
Symbolic model checking of tense logics on rational Kripke models
Coalgebraic Automata Theory: Basic Results
Stability structures, motivic Donaldson-Thomas invariants and cluster transformations
Quantum Symmetries and Marginal Deformations
A polytime proof of correctness of the Rabin-Miller algorithm from Fermat's little theorem
Probabilistic SVM/GMM Classifier for Speaker-Independent Vowel Recognition in Continues Speech
On Poncelet's maps
TAUOLA, TAUOLA universal interface PHOTOS and MC-TESTER: Status Report
Beyond Language Equivalence on Visibly Pushdown Automata
Graphical Reasoning in Compact Closed Categories for Quantum Computation
Compatibility of (co)actions and localizations
Polynomial Size Analysis of First-Order Shapely Functions
Towards a human proof of Gessel's conjecture
ImageSpace: An Environment for Image Ontology Management
Symbolic Computing with Incremental Mindmaps to Manage and Mine Data Streams - Some Applications
Convergence, Strong Law of Large Numbers, and Measurement Theory in the Language of Fuzzy Variables
Footprints in Local Reasoning
Temporal Platonic Metaphysics
Better Termination for Prolog with Constraints
Rfuzzy framework
Fuzzy Chemical Abstract Machines
The Cox ring of an algebraic variety with torus action
Mathematical Model for Transformation of Sentences from Active Voice to Passive Voice
On the embedding of spacetime in higher-dimensional spaces with torsion
Discrete concavity and the half-plane property
Induction of High-level Behaviors from Problem-solving Traces using Machine Learning Tools
Fermi liquid theory for SU(N) Kondo model
On expressive power and class invariance
On Kurosh problem in varieties of algebras
A Concrete View of Rule 110 Computation
ModelTalk: A Framework for Developing Domain Specific Executable Models
XML Data Integrity Based on Concatenated Hash Function
Reasoning About Knowledge of Unawareness Revisited
Interacting Quantum Observables: Categorical Algebra and Diagrammatics
Information processing in convex operational theories
RIOT: I/O-Efficient Numerical Computing without SQL
Using Ellipsoidal Domains to Analyze Control Systems Software
Analyse en dépendances à l'aide des grammaires d'interaction
Prograde rotation of protoplanets by accretion of pebbles in a gaseous environment
Composition and Inversion of Schema Mappings
Saturated fusion systems as idempotents in the double Burnside ring
Thermodynamics as a nonequilibrium path integral
Euclidean Jordan Algebras, Hidden Actions, and $J$-Kepler Problems
A Tighter Bound for the Determinization of Visibly Pushdown Automata
Decomposable functors and the exponential principle, II
Hybrid modeling of plasmas
Untangling Phase and Time in Monophonic Sounds
Inferring Information from Feature Diagrams to Product Line Economic Models
Quiver Chern-Simons Theories, D3-branes and Lorentzian Lie 3-algebras
Gesture Recognition with a Focus on Important Actions by Using a Path Searching Method in Weighted Graph
Axiomatisability problems for S-posets
Noncommutative gauge theory using covariant star product defined between Lie-valued differential forms
Named Models in Coalgebraic Hybrid Logic
RapidMind: Portability across Architectures and its Limitations
Weighted Logics for Nested Words and Algebraic Formal Power Series
Extensional and Intensional Strategies
Integrating Interval Constraints into Logic Programming
Towards Parameterized Regular Type Inference Using Set Constraints
A PCP Characterization of AM
Self-Organized Criticality in Solar Physics and Astrophysics
S-Program Calculus
Asymptotic principal values and regularization methods for correlation functions with reflective boundary conditions
Dust of Dark Energy
Recognition of handwritten Roman Numerals using Tesseract open source OCR engine
More on Dimension-4 Proton Decay Problem in F-theory -- Spectral Surface, Discriminant Locus and Monodromy
Finite Optimal Control for Time-Bounded Reachability in CTMDPs and Continuous-Time Markov Games
Notations Around the World: Census and Exploitation
Informal Concepts in Machines
Implementation of the Six Channel Redundancy to achieve fault tolerance in testing of satellites
A Performance Comparison of CUDA and OpenCL
Clark-Ocone type formula for non-semimartingales with finite quadratic variation
A Tree Logic with Graded Paths and Nominals
Products of Weighted Logic Programs
A Framework for Constraint-Based Deployment and Autonomic Management of Distributed Applications (Extended Abstract)
Dynamical systems theory for nonlinear evolution equations
Optimal Time-Abstract Schedulers for CTMDPs and Markov Games
Circuit Design Methods for Quantum Separator (QS) and Systems to Use Its Output
Communicative Competencies and the Structuration of Expectations: The creative tension between Habermas' critical theory and Luhmann's social systems theory
Runtime Analysis of Probabilistic Programs with Unbounded Recursion
Supervisory Control Synthesis of Discrete-Event Systems using Coordination Scheme
Single Parameter Combinatorial Auctions with Partially Public Valuations
A Declarative Semantics for CLP with Qualification and Proximity
Symmetric categorial grammar: residuation and Galois connections
On the Count of Trees
A Transformation-based Implementation for CLP with Qualification and Proximity
Multi-Agent Only-Knowing Revisited
Simplifying Negative Goals Using Typed Existence Properties
Digital image exploration at Maui Community College
Hochschild-Kohomologien von Observablenalgebren in der Klassischen Feldtheorie
Towards Quality of Service and Resource Aware Robotic Systems through Model-Driven Software Development
Curved infinity-algebras and their characteristic classes
Implementing Lego Agents Using Jason
A probabilistic top-down parser for minimalist grammars
Libpsht - algorithms for efficient spherical harmonic transforms
An Exploration of OpenCL for a Numerical Relativity Application
A non-expert view on Turing machines, Proof Verifiers, and Mental reasoning
On Selective Unboundedness of VASS
Probabilistic regular graphs
Stabilizing knowledge through standards - A perspective for the humanities
On Three Alternative Characterizations of Combined Traces
Asymptotic safety: a simple example
A Calculus of Consistent Component-based Software Updates
Software correlators as testbeds for RFI algorithms
Loops under Strategies ... Continued
Infinite dimensional manifolds from a new point of view
Automata and temporal logic over arbitrary linear time
Modelling the Spatial Dynamics of Culture Spreading in the Presence of Cultural Strongholds
Relating Church-Style and Curry-Style Subtyping
An automaton over data words that captures EMSO logic
An Algebra of Synchronous Scheduling Interfaces
The BinProlog Experience: Architecture and Implementation Choices for Continuation Passing Prolog and First-Class Logic Engines
Büchi Automata can have Smaller Quotients
The YAP Prolog System
Comparative Study on DFD to UML Diagrams Transformations
Geometric picture of quantum discord for two-qubit quantum states
EPS Confidentiality and Integrity mechanisms Algorithmic Approach
Class Schema Evolution for Persistent Object-Oriented Software: Model, Empirical Study, and Automated Support
Achievable Sets in Z^n
U(N) Based Transformations in N-Squared Dimensions
A Paradoxical Property of the Monkey Book
Fitting Ranked English and Spanish Letter Frequency Distribution in U.S. and Mexican Presidential Speeches
An Empirical Study of Real-World SPARQL Queries
On Friedmann-Lemaître-Robertson-Walker cosmologies in non-standard gravity
The Critical Exponent is Computable for Automatic Sequences
Ectoplasm with an Edge
Partial-Order Planning with Concurrent Interacting Actions
Parameterized complexity results for 1-safe Petri nets
Nonplanar Integrability: Beyond the SU(2) Sector
From Causal Models To Counterfactual Structures
The Hirzebruch--Riemann--Roch theorem in true genus-0 quantum K-theory
Understanding Code Patterns - Analysis, Interpretation & Measurement
Green-Schwarz Mechanism in Heterotic (2,0) Gauged Linear Sigma Models: Torsion and NS5 Branes
Abstraction Super-structuring Normal Forms: Towards a Theory of Structural Induction
Improvements for Free
Decidable Problems for Probabilistic Automata on Infinite Words
Edit wars in Wikipedia
libCreme: An optimization library for evaluating convex-roof entanglement measures
Actual Causation in CP-logic
Computational wave optics library for C++: CWO++ library
Innocent strategies as presheaves and interactive equivalences for CCS
A Process Algebra for Supervisory Coordination
Uniform Labeled Transition Systems for Nondeterministic, Probabilistic, and Stochastic Process Calculi
Low-energy effective field theory for finite-temperature relativistic superfluids
Infinite permutations vs. infinite words
ATP and Presentation Service for Mizar Formalizations
Constraint-Based Deadlock Checking of High-Level Specifications
Nested Hoare Triples and Frame Rules for Higher-order Store
Innocent strategies as presheaves and interactive equivalences for CCS (expanded version)
Pushdown Abstractions of JavaScript
Complexity of Model Checking Recursion Schemes for Fragments of the Modal Mu-Calculus
Extreme value distributions and Renormalization Group
Memoryless computation: new results, constructions, and extensions
Mathematics : The Language of Science
Scikit-learn: Machine Learning in Python
LTL to Büchi Automata Translation: Fast and More Deterministic
Abstracting Runtime Heaps for Program Understanding
PSDF: Particle Stream Data Format for N-Body Simulations
Mathematical and computational modeling for describing the basic behavior of free radicals and antioxidants within epithelial cells
Transporting Functions across Ornaments
A Transformation-based Implementation for CLP with Qualification and Proximity
Bosonic Loop Diagrams as Perturbative Solutions of the Classical Field Equations in $φ^4$-Theory
First-Order Model Checking on Generalisations of Pushdown Graphs
Generating a Performance Stochastic Model from UML Specifications
A family of weakly universal cellular automata in the hyperbolic plane with two states
Subtyping for F-Bounded Quantifiers and Equirecursive Types (Extended Version)
Model-Checking the Higher-Dimensional Modal mu-Calculus
GPGPU Processing in CUDA Architecture
Computer-Assisted Program Reasoning Based on a Relational Semantics of Programs
Holographic Higgs Phases
Training Restricted Boltzmann Machines on Word Observations
Applying SMT Solvers to the Test Template Framework
Exact Gap Computation for Code Coverage Metrics in ISO-C
The Horse Raced Past: Gardenpath Processing in Dynamical Systems
AD in Fortran, Part 2: Implementation via Prepreprocessor
Discovering Algorithms with Matrix Code
Inflationary perturbation theory is geometrical optics in phase space
Strong convergence for reduced free products (Remarks on a result by Paul Skoufranis)
Synchronizing Automata on Quasi Eulerian Digraph
Probabilistic Similarity Logic
Exploring Text Virality in Social Networks
Asynchronous Games over Tree Architectures
Trace Spaces: an Efficient New Technique for State-Space Reduction
Attribute Exploration of Gene Regulatory Processes
The failure of the law of brevity in two New World primates. Statistical caveats
Some remarks on the genesis of scalar-tensor theories
The Linguistic Interpretation of Quantum Mechanics
Deterministic Automata for the (F,G)-fragment of LTL
A New MHD Code with Adaptive Mesh Refinement and Parallelization for Astrophysics
Algebraic points on meromorphic curves
Securing SQLJ Source Codes from Business Logic Disclosure by Data Hiding Obfuscation
Brane Induced Gravity: From a No-Go to a No-Ghost Theorem
Old and New Reductions of Dispersionless Toda Hierarchy
CyberChair: A Web-Based Groupware Application to Facilitate the Paper Reviewing Process
Gibbs Sampling in Factorized Continuous-Time Markov Processes
Succinct Representations for Abstract Interpretation
The Machian contribution of the Universe to geodetic precession, frame dragging and gravitational clock effect
A generic framework for video understanding applied to group behavior recognition
Fast computation of gradient and sentitivity in 13C metabolic flux analysis instationary experiments using the adjoint method
Keyphrase Based Arabic Summarizer (KPAS)
Semantic Networks of Interests in Online NSSI Communities
Horava-Lifshitz theory as a Fermionic Aether in Ashtekar gravity
Applying Deep Belief Networks to Word Sense Disambiguation
UPPAAL-SMC: Statistical Model Checking for Priced Timed Automata
Nonparametric Bayesian Logic
Planning in POMDPs Using Multiplicity Automata
On the Relationship between LTL Normal Forms and Buechi Automata
Black Holes as Critical Point of Quantum Phase Transition
Solving Stochastic Büchi Games on Infinite Arenas with a Finite Attractor
Assume-Guarantee Abstraction Refinement for Probabilistic Systems
Testing the concept of integral approach to derivatives within the smoothed particle hydrodynamics technique in astrophysical scenarios
Basic Data Analysis and More - A Guided Tour Using Python
Query Optimization Over Web Services Using A Mixed Approach
Probabilistic Monads, Domains and Classical Information
Proof of Brlek-Reutenauer conjecture
A new operation on partially ordered sets
Forward Analysis for WSTS, Part II: Complete WSTS
Finite Bisimulations for Switched Linear Systems
Hardness of approximation for quantum problems
Tensor networks and the numerical renormalization group
Codensity and the ultrafilter monad
Cognitive Bias for Universal Algorithmic Intelligence
Optimal Weighting of Multi-View Data with Low Dimensional Hidden States
Movie Popularity Classification based on Inherent Movie Attributes using C4.5,PART and Correlation Coefficient
Fast Packed String Matching for Short Patterns
Proceedings 8th International Workshop on Quantum Physics and Logic
Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses
Annotation of Logic Programs for Independent AND-Parallelism by Partial Evaluation
Elaborating Inductive Definitions
Transition-Based Dependency Parsing With Pluggable Classifiers
A Rewriting View of Simple Typing
On the Performance Potential of Connection Fault-Tolerant Commit Processing in Mobile Environment
Deterministic Compression with Uncertain Priors
Formal Semantics of Heterogeneous CUDA-C: A Modular Approach with Applications
Uniform Strategies
Query-focused Multi-document Summarization: Combining a Novel Topic Model with Graph-based Semi-supervised Learning
Higher Massey products in the cohomology of mild pro-p-groups
Web-Based Question Answering: A Decision-Making Perspective
Complexity fits the fittest
Killing Spinors -- Beyond Supergravity
Leading Questions in an Extended Standard Model
Cellular automata between sofic tree shifts
Inductive Policy Selection for First-Order MDPs
Analysis of Influence of Internet Retail Service Quality (IRSQ) to Consumer Online Shopping Satisfaction at www.kebanaran.com
A Dual Number Approach for Numerical Calculation of Velocity and Acceleration in the Spherical 4R Mechanism
API Blender: A Uniform Interface to Social Platform APIs
A Complete Calculus for Possibilistic Logic Programming with Fuzzy Propositional Variables
Design Pattern-Based Extension of Class Hierarchies to Support Runtime Invariant Checks
International collaboration clusters in Africa
Transfer Topic Modeling with Ease and Scalability
Causal Theories: A Categorical Perspective on Bayesian Networks
Probabilistic Latent Semantic Analysis
Quantifying Opacity
Dynamical Local Chirality and Chiral Symmetry Breaking
Sémantique des déterminants dans un cadre richement typé
Displaying Asynchronous Reactions to a Document: Two Goals and a Design
Object Recognition with Imperfect Perception and Redundant Description
A cellular basis of the $q$-Brauer algebra related with Murphy bases of the Hecke algebras
Formation of photon spheres in boson stars with a nonminimally coupled field
Ending-based Strategies for Part-of-speech Tagging
The Automated Mapping of Plans for Plan Recognition
Order effects in dynamic semantics
Towards an Updatable Strategy Logic
The Complexity of Synthesizing Uniform Strategies
Decision problems for word-hyperbolic semigroups
Probabilistic Topic and Syntax Modeling with Part-of-Speech LDA
Types and forgetfulness in categorical linguistics and quantum mechanics
From propositional to first-order monitoring
Optimal control of a bioreactor for biofuel production
A Federated CloudNet Architecture: The PIP and the VNP Role
#Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtag
Design for a Darwinian Brain: Part 2. Cognitive Architecture
MATAWS: A Multimodal Approach for Automatic WS Semantic Annotation
PySLHA: a Pythonic interface to SUSY Les Houches Accord data
The homology of $\mathrm{tmf}$
Natural Tuning: Towards A Proof of Concept
A construction of integrated vertex operator in the pure spinor sigma-model in AdS5xS5
Random close packing fractions of lognormal distributions of hard spheres
Automatic Abstraction in SMT-Based Unbounded Software Model Checking
Recurrent Convolutional Neural Networks for Discourse Compositionality
Effective Translation of LTL to Deterministic Rabin Automata: Beyond the (F,G)-Fragment
Discriminative Training: Learning to Describe Video with Sentences, from Video Described with Sentences
Next generation input-output data format for HEP using Google's protocol buffers
Additive invariants in o-minimal valued fields
Verifying Time Complexity of Deterministic Turing Machines
Bayesian Structured Prediction Using Gaussian Processes
Pushdown Systems for Monotone Frameworks
Speaker Independent Continuous Speech to Text Converter for Mobile Application
Algebraic Meta-Theory of Processes with Data
Towards a General Framework for Formal Reasoning about Java Bytecode Transformation
A Novel Architecture for Relevant Blog Page Identifcation
BayesOpt: A Library for Bayesian optimization with Robotics Applications
Exploring the Boundaries of Monad Tensorability on Set
Blazes: Coordination Analysis for Distributed Programs
Using Self-Organizing Maps for Sentiment Analysis
Representing Code History with Development Environment Events
Abelian complexity function of the Tribonacci word
Coroutining Folds with Hyperfunctions
Abstraction and Learning for Infinite-State Compositional Verification
Interactive proofs for BQP via self-tested graph states
Domain-Specific Sentiment Word Extraction by Seed Expansion and Pattern Generation
Madeup: A Mobile Development Environment for Programming 3-D Models
An analogue of Cobham's theorem for graph directed iterated function systems
Kramers-Wannier Duality of Statistical Mechanics Applied to the Boolean Satisfiability Problem of Computer Science
IMSuite: A Benchmark Suite for Simulating Distributed Algorithms
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition
Context unification is in PSPACE
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
Distributional semantics beyond words: Supervised learning of analogy and paraphrase
The optimality of attaching unlinked labels to unlinked meanings
Monadic second-order definable graph orderings
Metric axioms: a structural study
Lazy Probabilistic Model Checking without Determinisation
The Bose-Hubbard model is QMA-complete
An NMF solution for the Flowgraphs case at the TTC 2013
An NMF solution for the Petri Nets to State Charts case study at the TTC 2013
dS/CFT at uniform energy density and a de Sitter "bluewall"
Building An Information System for a Distributed Testbed
Algorithmic Diversity for Software Security
Automatic continuity for isometry groups
On the Proxy Identity Crisis
Very general monomial valuations of $\mathbb{P}^2$ and a Nagata type conjecture
Can recursive neural tensor networks learn logical reasoning?
Which abelian tensor categories are geometric?
Speech Recognition Front End Without Information Loss
Testing for Synchronization
Optimization Of Cross Domain Sentiment Analysis Using Sentiwordnet
Content Modeling Using Latent Permutations
First-Order Stable Model Semantics and First-Order Loop Formulas
Real solutions of a problem in enumerative geometry
Ribbon graphs and bialgebra of Lagrangian subspaces
Automatic Aggregation by Joint Modeling of Aspects and Values
Chasing diagrams in cryptography
Adding modular predicates to first-order fragments
Consciousness results when communication modifies the form of self-estimated fitness
Service-Fingerprinting mittels Fuzzing
Singular cohomology from supersymmetric field theories
Learning Soft Linear Constraints with Application to Citation Field Extraction
Privacy Failures in Encrypted Messaging Services: Apple iMessage and Beyond
Subset Synchronization and Careful Synchronization of Binary Finite Automata
JSAI: Designing a Sound, Configurable, and Efficient Static Analyzer for JavaScript
Multiagent Conflict Resolution for a Specification Network of Discrete-Event Coordinating Agents
Bracing Heterogeneous Distributed Systems via Built-in Frameworks
Topologies of Stochastic Markov Models: Computational Aspects
DeepWalk: Online Learning of Social Representations
Automatic Segmentation of Broadcast News Audio using Self Similarity Matrix
Emotion Analysis Platform on Chinese Microblog
Toward Synthesis of Network Updates
Towards Verifying Safety Properties of Real-Time Probabilistic Systems
Conditional convex orders and measurable martingale couplings
Extending the range of real time density matrix renormalization group simulations
Open Question Answering with Weakly Supervised Embedding Models
Linking Geographic Vocabularies through WordNet
The square of opposition in orthomodular logic
Lipschitz Robustness of Finite-state Transducers
Uniform Hyperbolicity for Szegő Cocycles and Applications to Random CMV Matrices and the Ising Model
The Coulomb Branch Formula for Quiver Moduli Spaces
Towards Verification of Constituent Systems through Automated Proof
A sheaf-theoretic perspective on sampling
Kaggle LSHTC4 Winning Solution
KR$^3$: An Architecture for Knowledge Representation and Reasoning in Robotics
On the Local Theory of Billiards in Polygons
Structure and modeling of the network of two-Chinese-character compound words in the Japanese language
Tabling, Rational Terms, and Coinduction Finally Together!
Query Rewriting and Optimization for Ontological Databases
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting
A Well-Founded Semantics for FOL-Programs
Minimum Model Semantics for Extensional Higher-order Logic Programming with Negation
Analysis and Transformation Tools for Constrained Horn Clause Verification
Pengines: Web Logic Programming Made Easy
Higher Dimensional Modal Logic
The Impact of Disjunction on Reasoning under Existential Rules: Research Summary
Toeplitz operators defined by sesquilinear forms: Fock space case
Probability Logic for Harsanyi Type Spaces
Constraints on Natural Supersymmetry from Electroweak Precision Tests
Hybrid Type-Logical Grammars, First-Order Linear Logic and the Descriptive Inadequacy of Lambda Grammars
On the design of an expert help system for computer algebra systems
Using Local Alignments for Relation Recognition
On structural completeness vs almost structural completeness problem: A discriminator varieties case study
PaPy: Parallel and Distributed Data-processing Pipelines in Python
Sliced Slices: Separating Data and Control Influences
A Latent Space Analysis of Editor Lifecycles in Wikipedia
Graph- versus Vector-Based Analysis of a Consensus Protocol
Between quantum logic and concurrency
SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation
Quantum Mechanics: Harbinger of a Non-Commutative Probability Theory?
Proactive Quality Guidance for Model Evolution in Model Libraries
Deterministic Timed Finite State Machines: Equivalence Checking and Expressive Power
The proposal of a novel software testing framework
Chiral algebras of class S
Teaching Parallel Programming Using Java
T^σ_ρ(G) Theories and Their Hilbert Series
Delocalization of boundary states in disordered topological insulators
An exact mapping between the Variational Renormalization Group and Deep Learning
Memory Networks
Convolution, Separation and Concurrency
Type Targeted Testing
A stronger null hypothesis for crossing dependencies
Amplitudes, Form Factors and the Dilatation Operator in $\mathcal{N}=4$ SYM Theory
A Framework for On-Line Devanagari Handwritten Character Recognition
Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles
Observers and Splitting Structures in Relativistic Electrodynamics
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
A Hybrid Recurrent Neural Network For Music Transcription
Non-crossing dependencies: least effort, not grammar
The Classification of Dirac Homogeneous Spaces
Exploiting Correlations for Expensive Predicate Evaluation
Generalizing the Liveness Based Points-to Analysis
Electrical properties of polar membranes
LIVEIA: A Light-based Immersive Visualization Environment for Imaginative Actualization
Minimum Probabilistic Finite State Learning Problem on Finite Data Sets: Complexity, Solution and Approximations
A study of the interface usability issues of mobile learning applications for smart phones from the users perspective
Lifting Term Rewriting Derivations in Constructor Systems by Using Generators
A Dataset for Movie Description
Online Handwritten Devanagari Stroke Recognition Using Extended Directional Features
Quantifying Prosodic Variability in Middle English Alliterative Poetry
Privacy by Design: On the Conformance Between Protocols and Architectures
Spatial Interpolants
Learning Invariants using Decision Trees
Static Analysis of File-Processing Programs using File Format Specifications
A Note on the Uniform Kan Condition in Nominal Cubical Sets
Wise Computing: Towards Endowing System Development with True Wisdom
Exploring Models and Data for Image Question Answering
Improved Relation Extraction with Feature-Rich Compositional Embedding Models
Distant Supervision for Entity Linking
Location Prediction of Social Images via Generative Model
RNAiFold 2.0: A web server and software to design custom and Rfam-based RNA molecules
Descent via Tannaka duality
Local Integrand Representations of All Two-Loop Amplitudes in Planar SYM
Parameterized Linear Temporal Logics Meet Costs: Still not Costlier than LTL (full version)
Model categorical Koszul-Tate resolution for algebras over differential operators
Effective thermodynamics of isolated entangled squeezed and coherent states
Unbiased Monte Carlo for the age of tensor networks
Unfolding-based Partial Order Reduction
Describing Multimedia Content using Attention-based Encoder--Decoder Networks
Learning to Mine Chinese Coordinate Terms Using the Web
Idempotents in intensional type theory
A Lower Bound on Supporting Predecessor Search in $k$ sorted Arrays
Knapsack and subset sum problems in nilpotent, polycyclic, and co-context-free groups
How to Generate a Good Word Embedding?
Creating an Artificial World with a New Kind of Cellular Automata
Practical Selection of SVM Supervised Parameters with Different Feature Representations for Vowel Recognition
Robust speech recognition using consensus function based on multi-layer networks
Revisiting the combinatorics of the 2D Ising model
CRISNER: A Practically Efficient Reasoner for Qualitative Preferences
Importing SMT and Connection proofs as expansion trees
Batch Normalized Recurrent Neural Networks
Automata, reduced words, and Garside shadows in Coxeter groups
Algebraic and Logical Methods in Quantum Computation
Mapping Unseen Words to Task-Trained Embedding Spaces
Towards Meaningful Maps of Polish Case Law
Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks
A 'Gibbs-Newton' Technique for Enhanced Inference of Multivariate Polya Parameters and Topic Models
Attention with Intention for a Neural Network Conversation Model
Modular Responsive Web Design using Element Queries
Train and Test Tightness of LP Relaxations in Structured Prediction
Integrating a large-scale testing campaign in the CK framework
A disembodied developmental robotic agent called Samu Bátfai
USFD: Twitter NER with Drift Compensation and Linked Data
Stückelberg Formulation of Holography
Optimised determinisation and completion of finite tree automata
Context-Free Commutative Grammars with Integer Counters and Resets
Transforming Platform-Independent to Platform-Specific Component and Connector Software Architecture Models
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
Combining Neural Networks and Log-linear Models to Improve Relation Extraction
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval
Quantum Walks with Gremlin
Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets
The Automatic Statistician: A Relational Perspective
Quantifier Alternation for Infinite Words
Machine Learning Sentiment Prediction based on Hybrid Document Representation
Proof Relevant Corecursive Resolution
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection
Corruption and Wealth: Unveiling a national prosperity syndrome in Europe
Refactoring Delta-Oriented Product Lines to achieve Monotonicity
Structural Multi-type Sequent Calculus for Inquisitive Logic
Space-Efficient Latent Contracts
SymNet: scalable symbolic execution for modern networks
Relative Coobservability in Decentralized Supervisory Control of Discrete-Event Systems
Video Description using Bidirectional Recurrent Neural Networks
Multi-Oriented Text Detection with Fully Convolutional Networks
A parallel repetition theorem for all entangled games
StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation
Proof-relevant $π$-calculus: a constructive account of concurrency and causality
Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
Its All on the Square- The Importance of the Sum of Squares and Making the General Linear Model Simple
Cygrid: A fast Cython-powered convolution-based gridding module for Python
Why and How to Pay Different Attention to Phrase Alignments of Different Intensities
Visualization of Jacques Lacan's Registers of the Psychoanalytic Field, and Discovery of Metaphor and of Metonymy. Analytical Case Study of Edgar Allan Poe's "The Purloined Letter"
Termination Analysis of Probabilistic Programs through Positivstellensatz's
Teaching Data Science
Entities as topic labels: Improving topic interpretability and evaluability combining Entity Linking and Labeled LDA
The Power of Arc Consistency for CSPs Defined by Partially-Ordered Forbidden Patterns
Space-Efficient Error Reduction for Unitary Quantum Computations
Analyzing Timed Systems Using Tree Automata
Distance Metric Learning for Aspect Phrase Grouping
Keyphrase Extraction using Sequential Labeling
A Physical Metaphor to Study Semantic Drift
Solving General Arithmetic Word Problems
Scattering amplitudes over finite fields and multivariate functional reconstruction
Multi-task Domain Adaptation for Sequence Tagging
Coends and the tensor product of $\mathcal{C}$-modules
Self-Similarity Breeds Resilience
Practical optimal experiment design with probabilistic programs
SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification
Scaling Bounded Model Checking By Transforming Programs With Arrays
Symbolic Abstract Contract Synthesis in a Rewriting Framework
Social Networks Analysis in Discovering the Narrative Structure of Literary Fiction
Comprehensive online Atomic Database Management System (DBMS) with Highly Qualified Computing Capabilities
A statistical learning algorithm for word segmentation
Feature-Aware Verification
IVOA Recommendation: IVOA Registry Interfaces Version 1.0
Product Review Summarization based on Facet Identification and Sentence Clustering
Compressed Membership for NFA (DFA) with Compressed Labels is in NP (P)
Complexity of random smooth functions on the high-dimensional sphere
Improved Maximum Entropy Analysis with an Extended Search Space
Thermal right-handed neutrino production rate in the non-relativistic regime
Degree of arbitrariness directly from moving frames
Facing Complexity: Prediction vs. Adaptation
A note on $\aleph_α$-saturated o-minimal expansions of real closed fields
Computing Datalog Rewritings beyond Horn Ontologies
Interview with Warren Wiscombe on scientific programing and his contributions to atmospheric science tool making
Imprecise Meanings as a Cause of Uncertainty in Medical Knowledge-Based Systems
Machine Learning, Clustering, and Polymorphy
Algebraic semantics for a modal logic close to S1
An Improving Method for Loop Unrolling
Pattern Language for Good Old Future From Japanese Culture
First experiences with the Intel MIC architecture at LRZ
Towards an Abstract Domain for Resource Analysis of Logic Programs Using Sized Types
Counter-Strategy Guided Refinement of GR(1) Temporal Logic Specifications
Acyclic, connected and tree sets
Automatic Labeling for Entity Extraction in Cyber Security
Approximated Symbolic Computations over Hybrid Automata
Crowdsourcing a Word-Emotion Association Lexicon
Dyck path triangulations and extendability
VHDL Modeling of Intrusion Detection & Prevention System (IDPS) A Neural Network Approach
Modelling the Lexicon in Unsupervised Part of Speech Induction
Synthesizing Finite-state Protocols from Scenarios and Requirements
Integrated STEM in Elementary Grades Using Distributed Agent-based Computation
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
A machine-compiled macroevolutionary history of Phanerozoic life
Session Types for Broadcasting
On Reachability for Unidirectional Channel Systems Extended with Regular Tests
Parametric Strategy Iteration
Late-time Structure of the Bunch-Davies De Sitter Wavefunction
VideoSET: Video Summary Evaluation through Text
Scalable Topical Phrase Mining from Text Corpora
A Model-Based Approach to Impact Analysis Using Model Differencing
Verifying Procedural Programs via Constrained Rewriting Induction
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
Introducing Molly: Distributed Memory Parallelization with LLVM
Variability Modeling for Customizable SaaS Applications
Towards a Family-based Analysis of Applicability Conditions in Architectural Delta Models
Ability of stabilizer quantum error correction to protect itself from its own imperfection
Collaborative Deep Learning for Recommender Systems
LTL Parameter Synthesis of Parametric Timed Automata
A Binary Schema and Computational Algorithms to Process Vowel-based Euphonic Conjunctions for Word Searches
Voting for Deceptive Opinion Spam Detection
Matrix-Product-State Algorithm for Finite Fractional Quantum Hall Systems
Analyzing Conflict Freedom For Multi-threaded Programs With Time Annotations
IPMACC: Open Source OpenACC to CUDA/OpenCL Translator
Synthesizing Modular Invariants for Synchronous Code
Speeding up bootstrap computations: a vectorized implementation for statistics based on sample moments
PaREM: A Novel Approach for Parallel Regular Expression Matching
Context-Dependent Fine-Grained Entity Type Tagging
Alternative statistical methods for cytogenetic radiation biological dosimetry
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
On the Formal Semantics of the Cognitive Middleware AWDRAT
Elliptic multiple zeta values and one-loop superstring amplitudes
N-gram-Based Low-Dimensional Representation for Document Classification
Score Function Features for Discriminative Learning
Improving zero-shot learning by mitigating the hubness problem
Monads need not be endofunctors
Toward Refactoring of DMARF and GIPSY Case Studies -- a Team 10 SOEN6471-S14 Project Report
Multivariate-from-Univariate MCMC Sampler: R Package MfUSampler
Relation between firing statistics of spiking neuron with instantaneous feedback and without feedback
Holographic Thermal Relaxation in Superfluid Turbulence
Combining k-Induction with Continuously-Refined Invariants
Implicitization of rational hypersurfaces via linear syzygies: a practical overview
Ordering-sensitive and Semantic-aware Topic Modeling
Compressed Tree Canonization
F0 Modeling In Hmm-Based Speech Synthesis System Using Deep Belief Network
Scalable Bayesian Optimization Using Deep Neural Networks
Evaluating QoS Parameters for IPTV Distribution in Heterogeneous Networks
Unified vector space mapping for knowledge representation systems
Author Name Disambiguation by Using Deep Neural Network
The NLP Engine: A Universal Turing Machine for NLP
Hilbert-Post completeness for the state and the exception effects
Combining Probabilistic, Causal, and Normative Reasoning in CP-logic
Bethe Projections for Non-Local Inference
What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
Maximum a Posteriori Adaptation of Network Parameters in Deep Models
Structured Prediction of Sequences and Trees using Infinite Contexts
Timed pushdown automata revisited
Fractional charge and spin errors in self-consistent Green's function theory
Variability Abstractions: Trading Precision for Speed in Family-Based Analyses (Extended Version)
GeomRDF: A Geodata Converter with a Fine-Grained Structured Representation of Geometry in the Web
Text Segmentation based on Semantic Word Embeddings
One-dimensional F-definable sets in F((t))
Goldstone Inflation
A Secure Intelligent Decision Support System for Prescribing Medication
Relating tensor structures on representations of general linear and symmetric groups
Transferring Knowledge from a RNN to a DNN
Identifying seasonal stars in Kaurna astronomical traditions
Learning about probabilistic inference and forecasting by playing with multivariate normal distributions
Verification of Generalized Inconsistency-Aware Knowledge and Action Bases (Extended Version)
Refining Existential Properties in Separation Logic Analyses
Thermodynamics of stochastic Turing machines
Visualizing and Understanding Neural Models in NLP
Minimal theory of massive gravity
Abstractive Multi-Document Summarization via Phrase Selection and Merging
OMP2HMPP: Compiler Framework for Energy Performance Trade-off Analysis of Automatically Generated Codes
Emotion Analysis of Songs Based on Lyrical and Audio Features
Anomalous Dimensions of Heavy Operators from Magnon Energies
BRST symmetry for Regge-Teitelboim based minisuperspace models
Multi-weighted Automata and MSO Logic
Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists
Multi-domain Dialog State Tracking using Recurrent Neural Networks
Semantical conditions for the definability of functions and relations
Mixed Logical and Probabilistic Reasoning for Planning and Explanation Generation in Robotics
Significance of Maximum Spectral Amplitude in Sub-bands for Spectral Envelope Estimation and Its Application to Statistical Parametric Speech Synthesis
Learning to Discover Key Moments in Social Media Streams
Hardy's inequality for fractional powers of the sublaplacian on the Heisenberg group
Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs
Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms
A Mood-based Genre Classification of Television Content
Approximated solutions to Born-Infeld dynamics
An Automatic Machine Translation Evaluation Metric Based on Dependency Parsing Model
Hyper-Fit: Fitting Linear Models to Multidimensional Data with Multivariate Gaussian Uncertainties
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Removing Biases from Trainable MT Metrics by Using Self-Training
A Game of Attribute Decomposition for Software Architecture Design
Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Memetics and Neural Models of Conspiracy Theories
Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections
Demography-based adaptive network model reproduces the spatial organization of human linguistic groups
Visualizing NLP annotations for Crowdsourcing
Formal conjugacy growth in acylindrically hyperbolic groups
Morphisms, Symbolic sequences, and their Standard Forms
Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning
Sampled Weighted Min-Hashing for Large-Scale Topic Mining
Improved Twitter Sentiment Prediction through Cluster-then-Predict Model
Equivalence of two Fixed-Point Semantics for Definitional Higher-Order Logic Programs
Relational reasoning via probabilistic coupling
Twitter Sentiment Analysis
Hydrodynamics, resurgence and trans-asymptotics
Black hole entropy in the Chern-Simons-like theories of gravity and Lorentz-diffeomorphism Noether charge
Latency Analysis of an Aerial Video Tracking System Using Fiacre and Tina
Counterterms in Massive Gravity Theory
Hybrid architecture for satellite data processing workflow management
An encoding of array verification problems into array-free Horn clauses
Beyond Aztec Castles: Toric Cascades in the $dP_3$ Quiver
Klasifikasi Komponen Argumen Secara Otomatis pada Dokumen Teks berbentuk Esai Argumentatif
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
A weighted pair graph representation for reconstructibility of Boolean control networks
Neural Self Talk: Image Understanding via Continuous Questioning and Answering
Joint Image-Text News Topic Detection and Tracking with And-Or Graph Representation
A Bayesian approach to the g-formula
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
Closed Systems of Invertible Maps
Automatic Inference of Specifications in the K Framework
Online Keyword Spotting with a Character-Level Recurrent Neural Network
Automaton semigroups: new construction results and examples of non-automaton semigroups
Exactly solvable spin chain models corresponding to BDI class of topological superconductors
Environmental Noise Embeddings for Robust Speech Recognition
Generalizing Prototype Theory: A Formal Quantum Framework
Nmag micromagnetic simulation tool - software engineering lessons learned
Certified Context-Free Parsing: A formalisation of Valiant's Algorithm in Agda
Extracting Keyword for Disambiguating Name Based on the Overlap Principle
Synthesizing a Lego Forklift Controller in GR(1): A Case Study
Physical Version of Singularity Resolution in the Observable Universe
Sequence Classification with Neural Conditional Random Fields
Swivel: Improving Embeddings by Noticing What's Missing
Value Iteration Networks
Spoofing detection under noisy conditions: a preliminary investigation and an initial database
A Convolutional Attention Network for Extreme Summarization of Source Code
A wild model of linear arithmetic and discretely ordered modules
Fermat's Last Theorem and Catalan's Conjecture in Weak Exponential Arithmetics
Attentive Pooling Networks
Stochastic orders and the frog model
Prompt Delay
Simulation of Effective Subshifts by Two-dimensional Subshifts of Finite Type
Effect of quantified irreducibility on the computability of subshift entropy
A fine-grained approach to scene text script identification
Can one quantum bit separate any pair of words with zero-error?
Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec Approach
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification
Verified compilation of space-efficient reversible circuits
Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code
Calibrar: an R package for fitting complex ecological models
Sieve-based Coreference Resolution in the Biomedical Domain
A Computationally Efficient Framework for Automatic Inertial Sensor Calibration
Tree-to-Sequence Attentional Neural Machine Translation
Harnessing Deep Neural Networks with Logic Rules
Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition
What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets
Adaptive Computation Time for Recurrent Neural Networks
Recurrent Batch Normalization
Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence and Vector Cosine in VSMs
Homing Vector Automata
Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding
Higher Order Recurrent Neural Networks
Response Selection with Topic Clues for Retrieval-based Chatbots
Common-Description Learning: A Framework for Learning Algorithms and Generating Subproblems from Few Examples
Shift-preserving maps on $ω^*$
Synthesizing Probabilistic Invariants via Doob's Decomposition
Unsupervised Semantic Action Discovery from Video Collections
Movie Description
Epistemic Extension of Godel Logic
Well-Posed Models of Memristive Devices
Tweet Acts: A Speech Act Classifier for Twitter
A Multi-Smartwatch System for Assessing Speech Characteristics of People with Dysarthria in Group Settings
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
The Symbolic Interior Point Method
Stacking With Auxiliary Features
Does Multimodality Help Human and Machine for Translation and Image Captioning?
Hierarchical Question-Image Co-Attention for Visual Question Answering
Neural Network Translation Models for Grammatical Error Correction
Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network
SCJ-Circus: a refinement-oriented formal notation for Safety-Critical Java
Supervised Syntax-based Alignment between English Sentences and Abstract Meaning Representation Graphs
Model Checking Flat Freeze LTL on One-Counter Automata
Understanding User Instructions by Utilizing Open Knowledge for Service Robots
Linguistic Input Features Improve Neural Machine Translation
Scattering with partial information
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
A framework for detecting fraudulent activities in edo state tax collection system using investigative data mining
Neural Associative Memory for Dual-Sequence Modeling
QSWalk: a Mathematica package for quantum stochastic walks on arbitrary graphs
Sense Embedding Learning for Word Sense Induction
Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon
Full-Time Supervision based Bidirectional RNN for Factoid Question Answering
On the dependent conjunction and implication
Uncertainty in Neural Network Word Embedding: Exploration of Threshold for Similarity
DualNet: Domain-Invariant Network for Visual Question Answering
Comparing the hierarchy of keywords in on-line news portals
Uncalibrated 3D Room Reconstruction from Sound
Command injection attacks, continuations, and the Lambek calculus
A Curriculum Learning Method for Improved Noise Robustness in Automatic Speech Recognition
Inferring Logical Forms From Denotations
Some comments on the reliability of NOAA's Storm Events Database
Real-Time Synthesis is Hard!
Leveraging Semantic Web Search and Browse Sessions for Multi-Turn Spoken Dialog Systems
Learning Concept Taxonomies from Multi-modal Data
A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems
Lower Bounds for Alternating Online State Complexity
Moving Toward High Precision Dynamical Modelling in Hidden Markov Models
Representation learning for very short texts using weighted word embedding aggregation
Temporal Topic Analysis with Endogenous and Exogenous Processes
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
Guided Alignment Training for Topic-Aware Neural Machine Translation
Equivariant classification of $b^m$-symplectic surfaces and Nambu structures
JUDE: An Ultraviolet Imaging Telescope Pipeline
A Maturity Model for Public Administration as Open Translation Data Providers
SCOR: Software-defined Constrained Optimal Routing Platform for SDN
LTL-based Verification of Reconfigurable Workflows
Non-elementary classes of representable posets
Large Alphabet Source Coding using Independent Component Analysis
Topology of scrambled simplices
A Compendium of Chameleon Constraints
Hairy Black Holes in a Box
Devito: Towards a generic Finite Difference DSL using Symbolic Python
Distant Supervision for Relation Extraction beyond the Sentence Boundary
Long-Term Trends in the Public Perception of Artificial Intelligence
A Dynamical Boundary for Anti-de Sitter Space
Nonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor Processes
Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems
A Framework for Algebraic Characterizations in Recursive Analysis
Online Segment to Segment Neural Transduction
Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis
Learning Sentence Representation with Guidance of Human Attention
Knapsack problem for automaton groups
Seismic collapse prediction of frame structures by means of genetic algorithms
Divide-and-Conquer based Ensemble to Spot Emotions in Speech using MFCC and Random Forest
Neural Structural Correspondence Learning for Domain Adaptation
Computational linking theory
Quantifying moral foundations from various topics on Twitter conversations
A Chain-Detection Algorithm for Two-Dimensional Grids
SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods
Automatic sequences, generalised polynomials, and nilmanifolds
Reasoning in the Bernays-Schoenfinkel-Ramsey Fragment of Separation Logic
Stylometric Analysis of Early Modern Period English Plays
From Interacting Particles to Equilibrium Statistical Ensembles
From Event-B to Verified C via HLL
Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks
Tracing where IoT data are collected and aggregated
Word Embeddings to Enhance Twitter Gang Member Profile Identification
Ex Machina: Personal Attacks Seen at Scale
Matrix Semigroup Freeness Problems in $\mathrm{SL}(2,\mathbb{Z})$
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
The Deep Journey from Content to Collaborative Filtering
Compositional Reasoning for Shared-variable Concurrent Programs
HPVM: A Portable Virtual Instruction Set for Heterogeneous Parallel Systems
Mechanically Proving Determinacy of Hierarchical Block Diagram Translations
Words or Characters? Fine-grained Gating for Reading Comprehension
Neural Machine Translation with Reconstruction
DeepCoder: Learning to Write Programs
Keyphrase Annotation with Graph Co-Ranking
The Neural Noisy Channel
Policy-Compliant Path Diversity and Bisection Bandwidth
Semantics of Information
Binomial Checkpointing for Arbitrary Programs with No User Annotation
Evolving the Incremental λ Calculus into a Model of Forward Automatic Differentiation (AD)
Neural Networks Models for Entity Discovery and Linking
Linguistically Regularized LSTMs for Sentiment Classification
Knowledge Enhanced Hybrid Neural Network for Text Matching
SimDoc: Topic Sequence Alignment based Document Similarity Framework
Zero-Shot Visual Question Answering
Fast Non-Parametric Tests of Relative Dependency and Similarity
Using SyGuS to Synthesize Reactive Motion Plans
Adaptive Feature Abstraction for Translating Video to Text
On Sub-Propositional Fragments of Modal Logic
Nonabelian Higgs models: paving the way for asymptotic freedom
Improving Multi-Document Summarization via Text Classification
Dynamic Reductions for Model Checking Concurrent Software
On the Complexity of the Word Problem for Automaton Semigroups and Automaton Groups
Dialogue Learning With Human-In-The-Loop
NewsQA: A Machine Comprehension Dataset
Computer Assisted Composition with Recurrent Neural Networks
On Coreferring Text-extracted Event Descriptions with the aid of Ontological Reasoning
Design Automation and Design Space Exploration for Quantum Computers
Using Discourse Signals for Robust Instructor Intervention Prediction
CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews
Bilayer Linearized Tensor Renormalization Group Approach for Thermal Tensor Networks
Comparison of max-plus automata and joint spectral radius of tropical matrices
Lecture Notes on Mathematical Methods of Classical Physics
Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors
You Are What You Eat... Listen to, Watch, and Read
Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization
A Multilinear Tongue Model Derived from Speech Related MRI Data of the Human Vocal Tract
Complementary mode analyses between sub- and super-diffusions
AFLUX: The LUX materials search API for the AFLOW data repositories
Complete reducibility of subgroups of reductive algebraic groups over nonperfect fields 3
Neural Multi-Source Morphological Reinflection
Temporal Tessellation: A Unified Approach for Video Analysis
Top-down Visual Saliency Guided by Captions
Handwriting recognition using Cohort of LSTM and lexicon verification with extremely large lexicon
Jointly Extracting Relations with Class Ties via Effective Deep Ranking
Accelerated Convolutions for Efficient Multi-Scale Time to Contact Computation in Julia
Quantum corrections for the cubic Galileon in the covariant language
The Star Product in Interacting Quantum Field Theory
Synthesis of Tongue Motion and Acoustics from Text using a Multimodal Articulatory Database
Einstein-Podolsky-Rosen Paradox in Quantum Diagrams
Shortcut Sequence Tagging
A Topological Perspective on Interacting Algebraic Theories
Replication issues in syntax-based aspect extraction for opinion mining
Task-Specific Attentive Pooling of Phrase Alignments Contributes to Sentence Matching
Games with Costs and Delays
Towards Decoding as Continuous Optimization in Neural Machine Translation
Lie-Butcher series, Geometry, Algebra and Computation
A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue
Degree of sequentiality of weighted automata
Faster Algorithms for Weighted Recursive State Machines
BPS Algebras, Genus Zero, and the Heterotic Monster
A Joint Framework for Argumentative Text Analysis Incorporating Domain Knowledge
From LTL and Limit-Deterministic Büchi Automata to Deterministic Parity Automata
Learn&Fuzz: Machine Learning for Input Fuzzing
Deep Reinforcement Learning: An Overview
The Python-based Simulations of Chemistry Framework (PySCF)
Auto-Documenation for Software Development
An Intermediate Level of Abstraction for Computational Systems Chemistry
Opinion Recommendation using Neural Memory Model
A Hybrid Approach For Hindi-English Machine Translation
Multi-task memory networks for category-specific aspect and opinion terms co-extraction
Proving linearizability using forward simulations
Quantitative aspects of linear and affine closed lambda terms
A set-theoretical approach for ABox reasoning services (Extended Version)
Automated Identification of Drug-Drug Interactions in Pediatric Congestive Heart Failure Patients
On expansions of $(\mathbf{Z},+,0)$
soc2seq: Social Embedding meets Conversation Model
Synthesizing Imperative Programs from Examples Guided by Static Analysis
Task-driven Visual Saliency and Attention-based Visual Question Answering
Distributed Representation of Subgraphs
Causal Discovery Using Proxy Variables
Unsupervised Sequence Classification using Sequential Output Statistics
The Hardness of Solving Simple Word Equations
Synchronization Problems in Automata without Non-trivial Cycles
Scaffolding Networks: Incremental Learning and Teaching Through Questioning
Charged String Tensor Networks
Sound-Word2Vec: Learning Word Representations Grounded in Sounds
QT2S: A System for Monitoring Road Traffic via Fine Grounding of Tweets
DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks
A New Proof of the Nešetřil-Rödl Theorem
Wearing Many (Social) Hats: How Different are Your Different Social Network Personae?
Green's Relations in Finite Transformation Semigroups
On the Importance of Super-Gaussian Speech Priors for Machine-Learning Based Speech Enhancement
A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models
End-to-end optimization of goal-driven and visually grounded dialogue systems
TokTrack: A Complete Token Provenance and Change Tracking Dataset for the English Wikipedia
Supervisor Synthesis of POMDP based on Automata Learning
Bootstrapping a Lexicon for Emotional Arousal in Software Engineering
Is This a Joke? Detecting Humor in Spanish Tweets
Learning Similarity Functions for Pronunciation Variations
Automatic Argumentative-Zoning Using Word2vec
Building a Neural Machine Translation System Using Only Synthetic Parallel Data
Combining Lexical and Syntactic Features for Detecting Content-dense Texts in News
Ontology based Scene Creation for the Development of Automated Vehicles
Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF
Weakly Supervised Dense Video Captioning
Fostering User Engagement: Rhetorical Devices for Applause Generation Learnt from TED Talks
Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
Real-time On-Demand Crowd-powered Entity Extraction
Mobile Keyboard Input Decoding with Finite-State Transducers
A Search for Improved Performance in Regular Expressions
Room for improvement in automatic image description: an error analysis
How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?
RACE: Large-scale ReAding Comprehension Dataset From Examinations
Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption
Answering Complex Questions Using Open Information Extraction
CALF: Categorical Automata Learning Framework
Global Relation Embedding for Relation Extraction
Neural System Combination for Machine Translation
Making Neural Programming Architectures Generalize via Recursion
Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation
Learning Automata with Side-Effects
Improved Algorithms for Computing the Cycle of Minimum Cost-to-Time Ratio in Directed Graphs
Busy Beaver Scores and Alphabet Size
Finite-state Strategies in Delay Games (full version)
Non-linear Associative-Commutative Many-to-One Pattern Matching with Sequence Variables
Coherent extension of partial automorphisms, free amalgamation, and automorphism groups
Deep Speaker: an End-to-End Neural Speaker Embedding System
Ramsey properties and extending partial automorphisms for classes of finite structures
Learning Distributed Representations of Texts and Entities from Knowledge Base
Emptiness Problems for Distributed Automata
CHAM: action recognition using convolutional hierarchical attention model
DeepTingle
Generalized Jacobi identities and Jacobi elements of the group ring of the symmetric group
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
A Deep Reinforced Model for Abstractive Summarization
Higher-Order Constrained Horn Clauses and Refinement Types
State Complexity of Reversals of Deterministic Finite Automata with Output
A Regularized Framework for Sparse and Structured Neural Attention
On-the-fly Operation Batching in Dynamic Computation Graphs
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Automatic sequences and generalised polynomials
Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models
Detecting and Explaining Crisis
Lock-step simulation is child's play
An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis
Finding Root Causes of Floating Point Error with Herbgrind
NetSciEd: Network Science and Education for the Interconnected World
Discovering Discrete Latent Topics with Neural Variational Inference
Modeling Latent Attention Within Neural Networks
Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora
Joint Text Embedding for Personalized Content-based Recommendation
Measuring Offensive Speech in Online Political Discourse
Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization
Avoiding Discrimination through Causal Reasoning
Collaborative Summarization of Topic-Related Videos
Trimming and Improving Skip-thought Vectors
Neural Domain Adaptation for Biomedical Question Answering
Failure-Directed Program Trimming (Extended Version)
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation
Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder
Detecting Large Concept Extensions for Conceptual Analysis
Signal Machine And Cellular Automaton Time-Optimal Quasi-Solutions Of The Firing Squad/Mob Synchronisation Problem On Connected Graphs
Improving text classification with vectors of reduced precision
Graph-based Neural Multi-Document Summarization
A Study of Concurrency Bugs and Advanced Development Support for Actor-based Programs
Personalization in Goal-Oriented Dialog
Mixing for suspension flows over skew-translations and time-changes of quasi-abelian filiform nilflows
A Steganographic Design Paradigm for General Steganographic Objectives
An Approach for Weakly-Supervised Deep Information Retrieval
Checking Linearizability of Concurrent Priority Queues
Causal Consistency of Structural Equation Models
Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation
Tensor-Train Recurrent Neural Networks for Video Classification
Segal-type models of higher categories
Event Schema Induction using Tensor Factorization with Back-off
InferSpark: Statistical Inference at Scale
Geometrization of the Real Number System
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously
Loop Representation of Wigner's Little Groups
Is writing style predictive of scientific fraud?
On Repair with Probabilistic Attribute Grammars
The Reach-Avoid Problem for Constant-Rate Multi-Mode Systems
Learning Features from Co-occurrences: A Theoretical Analysis
Parsing with Traces: An $O(n^4)$ Algorithm and a Structural Representation
Bayesian Optimization for Probabilistic Programs
Developing a concept-level knowledge base for sentiment analysis in Singlish
Cross-genre Document Retrieval: Matching between Conversational and Formal Writings
MPIgnite: An MPI-Like Language and Prototype Implementation for Apache Spark
Visual Question Answering with Memory-Augmented Networks
graph2vec: Learning Distributed Representations of Graphs
Ask Me Anything: A Conversational Interface to Augment Information Security Workers
Solving the social choice problem under equality constraints
An Error-Oriented Approach to Word Embedding Pre-Training
A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations
A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events
Analysing Errors of Open Information Extraction Systems
Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms
Integrating Lexical and Temporal Signals in Neural Ranking Models for Searching Social Media Streams
A calibration method for estimating critical cavitation loads from below in 3D nonlinear elasticity
Knotted solutions, from electromagnetism to fluid dynamics
Process Description, Behavior, and Control
Adapting Sequence Models for Sentence Correction
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations
Distinct Squares in Circular Words
Constraint metric approximations and equations in groups
Dynamic Data Selection for Neural Machine Translation
Graph-based Features for Automatic Online Abuse Detection
Neural Machine Translation with Word Predictions
ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References
What matters in a transferable neural network model for relation classification in the biomedical domain?
Gold Standard Online Debates Summaries and First Experiments Towards Automatic Summarization of Online Debate Data
Sequence-to-Label Script Identification for Multilingual OCR
A Generalised Directional Laplacian Distribution: Estimation, Mixture Models and Audio Source Separation
Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects
Constructing Words with High Distinct Square Densities
Classification of Radiology Reports Using Neural Attention Models
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
CloudScan - A configuration-free invoice analysis system using recurrent neural networks
Supervised Speech Separation Based on Deep Learning: An Overview
Automated adjoints of coupled PDE-ODE systems
Using Optimal Ratio Mask as Training Target for Supervised Speech Separation
Abstractness, specificity, and complexity in software design
LTL to Deterministic Emerson-Lei Automata
AppTechMiner: Mining Applications and Techniques from Scientific Articles
On Uniquely Closable and Uniquely Typable Skeletons of Lambda Terms
On the decidability of the existence of polyhedral invariants in transition systems
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Extending Coinductive Logic Programming with Co-Facts
Erlang Code Evolution Control
Synthesizing Coupling Proofs of Differential Privacy
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification
Paraphrasing verbal metonymy through computational methods
MetaLDA: a Topic Model that Efficiently Incorporates Meta information
Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection
An Improvement on LSB Matching and LSB Matching Revisited Steganography Methods
Neural Optimizer Search with Reinforcement Learning
The holographic dual of the Penrose transform
"Let me convince you to buy my product ... ": A Case Study of an Automated Persuasive System for Fashion Products
Identifying Restaurant Features via Sentiment Analysis on Yelp Reviews
Region-Based Image Retrieval Revisited
Predicting Disease-Gene Associations using Cross-Document Graph-based Features
Knapsack Problems for Wreath Products
Pointless Continuous Spatial Surface Reconstruction
Thread-Modular Static Analysis for Relaxed Memory Models
Training an adaptive dialogue policy for interactive learning of visually grounded word meanings
Fully Automated Fact Checking Using External Sources
CrySL: Validating Correct Usage of Cryptographic APIs
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection
DimReader: Using auto-differentiation to explain non-linear projections
Borel functors, interpretations, and strong conceptual completeness for $\mathcal L_{ω_1ω}$
The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environments
Clickbait detection using word embeddings
A Language Hierarchy and Kitchens-Type Theorem for Self-Similar Groups
Programmable and scalable radio-frequency pulse sequence generator for multi-qubit quantum information experiments
DisSent: Sentence Representation Learning from Explicit Discourse Relations
Average Stack Cost of Buechi Pushdown Automata
Dark Energy after GW170817 and GRB170817A
Robust Hyperproperty Preservation for Secure Compilation (Extended Abstract)
Content Based Document Recommender using Deep Learning
An MCMC Algorithm for Estimating the Reduced RUM
ROS and Buzz: consensus-based behaviors for heterogeneous teams
Trace norm regularization and faster inference for embedded speech recognition RNNs
Minimal Synthesis of String To String Functions From Examples
Socialbots supporting human rights
Iterations of Multifunctions for Graph Theory: Bipartite Graphs and Filters
Refounding legitimacy towards Aethogenesis
Multi-label Dataless Text Classification with Topic Modeling
A Theory of Slicing for Probabilistic Control-Flow Graphs
Self-referential basis of undecidable dynamics: from The Liar Paradox and The Halting Problem to The Edge of Chaos
Extractive Multi-document Summarization Using Multilayer Networks
Neural Variational Inference and Learning in Undirected Graphical Models
An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks
Document Context Neural Machine Translation with Memory Networks
YEDDA: A Lightweight Collaborative Text Span Annotation Tool
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems
QuickEdit: Editing Text & Translations by Crossing Words Out
Dynamic Fusion Networks for Machine Reading Comprehension
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Human and Machine Speaker Recognition Based on Short Trivial Events
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
Crowdsourcing Question-Answer Meaning Representations
An Abstractive approach to Question Answering
Speech Dereverberation with Context-aware Recurrent Neural Networks
Abstract Interpretation of Binary Code with Memory Accesses using Polyhedra
FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
Program Synthesis using Conflict-Driven Learning
Relational Symbolic Execution
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models
SPINE: SParse Interpretable Neural Embeddings
Continuous Semantic Topic Embedding Model Using Variational Autoencoder
A Formal Specification Framework for Smart Grid Components
Designing Secure Ethereum Smart Contracts: A Finite State Machine Based Approach
TensorFlow Distributions
Embedding Words as Distributions with a Bayesian Skip-gram Model
Video Captioning via Hierarchical Reinforcement Learning
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
FlagIt: A System for Minimally Supervised Human Trafficking Indicator Mining
FPGA with Improved Routability and Robustness in 130nm CMOS with Open-Source CAD Targetability
Atiyah-Patodi-Singer index theorem for domain-wall fermion Dirac operator
Tracing a Loose Wordhood for Chinese Input Method Engine
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
"Oh Tanenbaum, oh Tanenbaum...": Technical Foundations of Xmas 4.0 Research
Multilingual Topic Models
Model-Based Clustering of Time-Evolving Networks through Temporal Exponential-Family Random Graph Models
Sheaf-Theoretic Stratification Learning
Neural Network Multitask Learning for Traffic Flow Forecasting
Semi-automatic definite description annotation: a first report
On the Semantics of Intensionality and Intensional Recursion
Rewriting in Free Hypergraph Categories
CNN Is All You Need
Tensor network states in time-bin quantum optics
Object-Oriented Theorem Proving (OOTP): First Thoughts
Identifying emergency stages in Facebook posts of police departments with convolutional and recurrent neural networks and support vector machines
Potentiality, Actuality and Non-Separability in Quantum and Classical Physics: Res Potentiae in the Macroscopic World
A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
Neural Program Synthesis with Priority Queue Training
Using probabilistic programs as proposals
Building a Conversational Agent Overnight with Dialogue Self-Play
Cobra: A Framework for Cost Based Rewriting of Database Applications
An Iterative Closest Point Method for Unsupervised Word Translation
Integrating planning for task-completion dialogue policy learning
Survey on Emotional Body Gesture Recognition
Diagrammatic Reasoning beyond Clifford+T Quantum Mechanics
Cataloging the Visible Universe through Bayesian Inference at Petascale
Deceptive Games
Dual Recurrent Attention Units for Visual Question Answering
Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription
Content based Weighted Consensus Summarization
Tunneling Neural Perception and Logic Reasoning through Abductive Learning
Proposal and implementation of a novel perturb and observe algorithm using embedded software
Deterministic Regular Expressions With Back-References
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation
Partisan: Enabling Cloud-Scale Erlang Applications
Nonspecific biological effects of weak magnetic fields depend on molecular rotations
Evolution of the Science Fiction Writer's Capacity to Imagine the Future
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Attention based Sentence Extraction from Scientific Articles using Pseudo-Labeled data
On the Feasibility of Decentralized Derivatives Markets
Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets
Open Information Extraction on Scientific Text: An Evaluation
Multinomial Adversarial Networks for Multi-Domain Text Classification
Variational Autoencoders for Collaborative Filtering
Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning
Towards a Continuous Knowledge Learning Engine for Chatbots
Combining Textual Content and Structure to Improve Dialog Similarity
Broyden's method for nonlinear eigenproblems
Computing the concurrency threshold of sound free-choice workflow nets
VizWiz Grand Challenge: Answering Visual Questions from Blind People
Inverse Doppler Effects in Pipe Instruments
Evaluating Design Tradeoffs in Numeric Static Analysis for Java
Meta Multi-Task Learning for Sequence Modeling
Decreasing height along continued fractions
Tone Biased MMR Text Summarization
Tool Demonstration: FSolidM for Designing Secure Ethereum Smart Contracts
Analyzing Uncertainty in Neural Machine Translation
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension
Concatenated $p$-mean Word Embeddings as Universal Cross-Lingual Sentence Representations
Calculated attributes of synonym sets
A Genetic Programming Framework for 2D Platform AI
ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks
Self-Attention with Relative Position Representations
Active Particles Bound by Information Flows
An Unsupervised Model with Attention Autoencoders for Question Retrieval
Generalised Operations in Free Harmonic Analysis
Space-Efficient Bimachine Construction Based on the Equalizer Accumulation Principle
FEVER: a large-scale dataset for Fact Extraction and VERification
More Nonlocality with Less Entanglement in CHSH Experiments using Inefficient Detectors
Expeditious Generation of Knowledge Graph Embeddings
On the Invariance of Gödel's Second Theorem with regard to Numberings
The Rapidly Changing Landscape of Conversational Agents
Locally Private Bayesian Inference for Count Models
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
On 2-Group Global Symmetries and Their Anomalies
Comparative Study of Eight Formal Specifications of the Message Authenticator Algorithm
Machine Speech Chain with One-shot Speaker Adaptation
Joint PLDA for Simultaneous Modeling of Two Factors
Attention-based End-to-End Models for Small-Footprint Keyword Spotting
Hyperbolic vortices and Dirac fields in 2+1 dimensions
Automatically augmenting an emotion dataset improves classification using audio
Reusing Neural Speech Representations for Auditory Emotion Recognition
Reactive Supervisory Control of Open Discrete-event Systems
Completely Unsupervised Phoneme Recognition by Adversarially Learning Mapping Relationships from Audio Embeddings
High-quality nonparallel voice conversion based on cycle-consistent adversarial network
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification
CIKM AnalytiCup 2017 Lazada Product Title Quality Challenge An Ensemble of Deep and Shallow Learning to predict the Quality of Product Titles
The Factorization Problem in Jackiw-Teitelboim Gravity
Abstractive Tabular Dataset Summarization via Knowledge Base Semantic Embeddings
Integrating Software Engineering Key Practices into an OOP Massive In-Classroom Course: an Experience Report
Learning a Text-Video Embedding from Incomplete and Heterogeneous Data
Path-integral representation of diluted pedestrian dynamics
Flexible and Scalable Deep Learning with MMLSpark
Learning Abstractions for Program Synthesis
A denotational account of C11-style memory
Singularities of Transition Processes in Dynamical Systems: Qualitative Theory of Critical Delays
Loading a Bose-Einstein Condensate onto an Optical Lattice: an Application of Optimal Control Theory to The Non Linear Schrödinger Equation
Axiomatic Synthesis of Computer Programs and Computability Theorems
Generic Global Constraints based on MDDs
Noise and Fluctuations in Semiclassical Gravity
Two-Time Physics with gravitational and gauge field backgrounds
Open String on Symmetric Product
Integrability of generalized (matrix) Ernst equations in string theory
The Euclidean geometry deformations and capacities of their application to microcosm space-time geometry
From Classical to Quantum Mechanics: "How to translate physical ideas into mathematical language"
Culminating paths
A metageometric enquiry concerning time, space, and quantum physics
Factor-Group-Generated Polar Spaces and (Multi-)Qudits
RPO, Second-order Contexts, and Lambda-calculus
Calculating energy shifts in terms of phase shifts
A measure on the set of compact Friedmann-Lemaitre-Robertson-Walker models
Reflection and Hyper-Programming in Persistent Programming Systems
Structure of Lanczos-Lovelock Lagrangians in Critical Dimensions
Operator Spin Foam Models
Correlations in Hawking radiation and the infall problem
A Sequence of Qubit-Qudit Pauli Groups as a Nested Structure of Doilies
Entropy-driven cutoff phenomena
Some Quantum-Like Features of Mass Politics in Two-Party Systems
Dust driven mass loss from carbon stars as function of stellar parameters - II. Effects of grain size on wind properties
Effect of a relativistic correction to the Coulomb potential on the energy levels of hydrogen atom
Causal graph dynamics
ASR Context-Sensitive Error Correction Based on Microsoft N-Gram Dataset
Asymptotic probabilities of extension properties and random $l$-colourable structures
Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods
Towards Cancer Hybrid Automata
Prolongation of quasi-principal frame bundles and geometry of flag structures on manifolds
Filtergraph: A Flexible Web Application for Instant Data Visualization of Astronomy Datasets
Distributed optimization of deeply nested systems
Generic Strategies for Chemical Space Exploration
Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior
Filtergraph: An Interactive Web Application for Visualization of Astronomy Datasets
Symbolic Abstractions of Networked Control Systems
Inducing chaos by breaking axial symmetry in a black hole magnetosphere
COFFEE: an Optimizing Compiler for Finite Element Local Assembly
Computational Analysis of Perfect-Information Position Auctions
Broadcasting Automata and Patterns on Z^2
Simple, Parallel, High-Performance Virtual Machines for Extreme Computations
A Direct Symbolic Execution of SQL Code for Testing of Data-Oriented Applications
A Symbolic Execution Algorithm for Constraint-Based Testing of Database Programs
DopeLearning: A Computational Approach to Rap Lyrics Generation
Lattice-Theoretic Progress Measures and Coalgebraic Model Checking (with Appendices)
A general framework for Noetherian well ordered polynomial reductions
A Neural Transducer
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
Boundary action of automaton groups without singular points and Wang tilings
Maps of Computer Science
Word Network Topic Model: A Simple but General Solution for Short and Imbalanced Texts
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
Concentration Independent Random Number Generation in Tile Self-Assembly
Generalizing Permissive-Upgrade in Dynamic Information Flow Analysis
On the evolution of word usage of classical Chinese poetry
Unified view of quantum amplification based on quantum transformation
Just Another Gibbs Additive Modeller: Interfacing JAGS and mgcv
Identifying Structures in Social Conversations in NSCLC Patients through the Semi-Automatic extraction of Topical Taxonomies
Tensor networks, $p$-adic fields, and algebraic curves: arithmetic and the AdS$_3$/CFT$_2$ correspondence
The Ryu-Takayanagi Formula from Quantum Error Correction
Multi-document abstractive summarization using ILP based multi-sentence compression
RNN Approaches to Text Normalization: A Challenge
Sampled Image Tagging and Retrieval Methods on User Generated Content
Invariant Representations for Noisy Speech Recognition
The SP Theory of Intelligence as a Foundation for the Development of a General, Human-Level Thinking Machine
DySign: Dynamic Fingerprinting for the Automatic Detection of Android Malware
Surface Charges for Gravity and Electromagnetism in the First Order Formalism
La forma della Terra: una lezione sulla gravità Newtoniana
Clingcon: The Next Generation
On the arithmetic of graphs
An online sequence-to-sequence model for noisy speech recognition
The Intricacies of 3-Valued Extensional Semantics for Higher-Order Logic Programs
Inspecting Maude Variants with GLINTS
Enabling Mutation Testing for Android Apps
Full-Network Embedding in a Multimodal Embedding Pipeline
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
Exploiting Semantic Contextualization for Interpretation of Human Activity in Videos
Neural Collaborative Filtering
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Study on cluster algebras via abstract pattern and two conjectures on d-vectors and g-vector
Video Captioning with Guidance of Multimodal Latent Topics
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
Programming Not Only by Example
Word problems in Elliott monoids
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
EmbedRank: Unsupervised Keyphrase Extraction using Sentence Embeddings
Generalized Points-to Graphs: A New Abstraction of Memory in the Presence of Pointers
Search Based Code Generation for Machine Learning Programs
Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama's voice using GAN, WaveNet and low-quality found data
Reality-check for Econophysics: Likelihood-based fitting of physics-inspired market models to empirical data
Discovering Users Topic of Interest from Tweet
Combinatorial Register Allocation and Instruction Scheduling
Ontology Verbalization using Semantic-Refinement
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
Integrating design synthesis and assembly of structured objects in a visual design language
Cross-lingual keyword assignment
CP-logic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming
Automated languages phylogeny from Levenshtein distance
An Implementation of the Language Lambda Prolog Organized around Higher-Order Pattern Unification
Reactive Imperative Programming with Dataflow Constraints
The origin of Mayan languages from Formosan language group of Austronesian
Q#, a quantum computation package for the .NET platform
Unambiguous Buchi is weak
Array operators using multiple dispatch: a design methodology for array implementations in dynamic languages
The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages
Spoken Language Translation for Polish
Implementation of the Programming Language Dino -- A Case Study in Dynamic Language Performance
$μ$Puppet: A Declarative Subset of the Puppet Configuration Language
A Large-Scale Multilingual Disambiguation of Glosses
How much is said in a microblog? A multilingual inquiry based on Weibo and Twitter
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
On the emergence of syntactic structures: quantifying and modelling duality of patterning
Complex Networks of Words in Fables
Nesting Depth of Operators in Graph Database Queries: Expressiveness Vs. Evaluation Complexity
Nominal Automata with Name Binding
The Social Dynamics of Language Change in Online Networks
The Algebra of Open and Interconnected Systems
Using Natural Language Processing and Qualitative Analysis to Intervene in Gang Violence: A Collaboration Between Social Work Researchers and Data Scientists
A Comparison of Word Embeddings for English and Cross-Lingual Chinese Word Sense Disambiguation
Learning a Natural Language Interface with Neural Programmer
Families of DFAs as Acceptors of $ω$-Regular Languages
Constrained Topological Sorting
Towards a Question Answering System over the Semantic Web
An End-to-end Neural Natural Language Interface for Databases
Rationality, irrationality, and Wilf equivalence in generalized factor order
The Sheaf-Theoretic Structure Of Non-Locality and Contextuality
Randomized Distributed Decision
Abstracting Abstract Control (Extended)
A cognitive neural architecture able to learn and communicate through natural language
Why Nominal-Typing Matters in OOP
Coupled dynamics of node and link states in complex networks: A model for language competition
Damage to white matter bottlenecks contributes to language impairments after left hemispheric stroke
Parallel ICA reveals linked patterns of structural damage and fMRI language task activation in chronic post-stroke aphasia
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Gleam: the GLAST Large Area Telescope Simulation Framework
Dimers on a simple-quartic net with a vacancy
Mixing patterns in networks
A Farewell to Liouvillians
A symmetry principle for Topological Quantum Order
Modelling Contractual Arguments
Nonmonotonic Logics and Semantics
Nonmonotonic Reasoning, Preferential Models and Cumulative Logics
The Sketch of a Polymorphic Symphony
User software for the next generation
Learning Algorithms for Keyphrase Extraction
Coherent Keyphrase Extraction via Web Mining
Distributed WWW Programming using (Ciao-)Prolog and the PiLLoW library
Proving Correctness and Completeness of Normal Programs - a Declarative Approach
A Generic Framework for the Analysis and Specialization of Logic Programs
Combining decision procedures for the reals
The OverRelational Manifesto
A tool set for the quick and efficient exploration of large document collections
Geometrisation of Statistical Mechanics
The One-Way Speed of Light on Rotating Earth and the Definition of the Meter
Non-commutative Unification in Brane World
Seiberg Duality for Quiver Gauge Theories
De Sitter and Schwarzschild-De Sitter According to Schwarzschild and De Sitter
Almost locally free groups and the genus question
The Theory of Ultralogics Part I
Adjusted Viterbi training
The bicategories of corings
On umbral extensions of Stirling numbers and Dobinski-like formulas
New crisis in geometry?
Stability and Paradox in Algorithmic Logic
Aspects of the stochastic Burgers equation and their connection with turbulence
A Multi-Phase Transport Model for Relativistic Heavy Ion Collisions
Renormalization of the ETAS branching model of triggered seismicity from total to observable seismicity
Description of Quantum Entanglement with Nilpotent Polynomials
Web Server Benchmark Application WiiBench using Erlang/OTP R11 and Fedora-Core Linux 5.0
Visibly Tree Automata with Memory and Constraints
On the derived category of a regular toric scheme
NLS1 galaxies and estimation of their central black hole masses from the X-ray excess variance method
Quantum chromodynamics at high energy and statistical physics
Domain Structure of Black Hole Space-Times
Characterizations of Stable Model Semantics for Logic Programs with Arbitrary Constraint Atoms
Programming Realization of Symbolic Computations for Non-linear Commutator Superalgebras over the Heisenberg--Weyl Superalgebra: Data Structures and Processing Methods
A GPU based real-time software correlation system for the Murchison Widefield Array prototype
Algorithms for Glushkov K-graphs
New ideas about multiplication of tensorial distributions
Quantifying the implicit process flow abstraction in SBGN-PD diagrams with Bio-PEPA
PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation
A secured Cryptographic Hashing Algorithm
From Holant To #CSP And Back: Dichotomy For Holant$^c$ Problems
Making Access to Astronomical Software More Efficient
Dynamical properties of profinite actions
Query Routing and Processing in Peer-To-Peer Data Sharing Systems
Imperfect Dark Energy from Kinetic Gravity Braiding
Equational Characterization of Covariant-Contravariant Simulation and Conformance Simulation Semantics
Fourier expansions of GL(2) newforms at various cusps
Complex sequencing rules of birdsong can be explained by simple hidden Markov processes
Schaefer's theorem for graphs
Bifix codes and Sturmian words
Geometry and Energy of Non-abelian Vortices
Searching for simplicity: Approaches to the analysis of neurons and behavior
The Structure of First-Order Causality (extended version)
Generalized Remez Inequality for $(s,p)$-Valent Functions
Improving Image Search based on User Created Communities
DB Category: Denotational Semantics for View-based Database Mappings
Simulating Spiking Neural P systems without delays using GPUs
Three form potential in (special) minimal supergravity superspace and supermembrane supercurrent
Analogy perception applied to seven tests of word comprehension
Scoring Strategies for the Underdog: A general, quantitative method for determining optimal sports strategies
PyCOOL - a Cosmological Object-Oriented Lattice code written in Python
Parallel Spell-Checking Algorithm Based on Yahoo! N-Grams Dataset
Estimating the Prevalence of Deception in Online Review Communities
Software Mutational Robustness
Context-sensitive Spelling Correction Using Google Web 1T 5-Gram Information
Citations, Sequence Alignments, Contagion, and Semantics: On Acyclic Structures and their Randomness
High Accuracy Gravitational Waveforms from Black Hole Binary Inspirals Using OpenCL
Twisted vertex algebras, bicharacter construction and boson-fermion correspondences
Quantum chromodynamics at high energy and noisy traveling waves
Content-based Text Categorization using Wikitology
Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series
Runtime Verification Based on Register Automata
The Effective Field Theory of Dark Energy
Effective hydrodynamics of black D3-branes
An improved semantic similarity measure for document clustering based on topic maps
An Algorithm to Find Optimal Attack Paths in Nondeterministic Scenarios
Survey on Instruction Selection: An Extensive and Modern Literature Review
A biomechanical modeling study of the effects of the orbicularis oris muscle and jaw posture on lip shape
Ontology Based Data Integration Over Document and Column Family Oriented NOSQL
Lie algebroids, non-associative structures and non-geometric fluxes
Webs and Posets
Facebook and the Epistemic Logic of Friendship
Systems Variability Modeling: A Textual Model Mixing Class and Feature Concepts
piBUSS: a parallel BEAST/BEAGLE utility for sequence simulation under complex evolutionary scenarios
Concave Penalized Estimation of Sparse Gaussian Bayesian Networks
An EMG study of the lip muscles during covert auditory verbal hallucinations in schizophrenia
Complex Question Answering: Unsupervised Learning Approaches and Experiments
An $\infty$-categorical approach to $R$-line bundles, $R$-module Thom spectra, and twisted $R$-homology
Adaptive MCMC-Based Inference in Probabilistic Logic Programs
Infinite-State Energy Games
KMCLib: A general framework for lattice kinetic Monte Carlo (KMC) simulations
Entity-Linking via Graph-Distance Minimization
Automatic Completion of Distributed Protocols with Symmetry
A synchronous rendering of hybrid systems for designing Plant-on-a-Chip (PoC)
Wormholes, Emergent Gauge Fields, and the Weak Gravity Conjecture
What's Decidable about Syntax-Guided Synthesis?
Structure theory of flip graphs with applications to Weak Symmetry Breaking
Thermodynamics of quantum feedback cooling
TGSum: Build Tweet Guided Multi-Document Summarization Dataset
Equivariant Structure on Smash Powers
FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs
On the Use of Computer Programs as Money
Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework
A Dictionary-based Approach to Racism Detection in Dutch Social Media
Strange Beta: An Assistance System for Indoor Rock Climbing Route Setting Using Chaotic Variations and Machine Learning
Non-equilibrium statistical mechanics: From a paradigmatic model to biological transport
Semantics and Algorithms for Parametric Monitoring
ClassSTRONG: Classical simulations of Strong Field processes
Sentiment in New York City: A High Resolution Spatial and Temporal View
Testing Noninterference, Quickly
A Parameterized Study of Maximum Generalized Pattern Matching Problems
Modular Description of a Comprehensive Semantics Model for the UML (Version 2.0)
Deep Structured Output Learning for Unconstrained Text Recognition
On the Effects of Low-Quality Training Data on Information Extraction from Clinical Reports
Local Linearizability
Support for Eschenmoser's Glyoxylate Scenario
Hybrid Automata for Formal Modeling and Verification of Cyber-Physical Systems
Cramér's theorem is atypical
A Gibbs Sampler for Multivariate Linear Regression
An Operator for Entity Extraction in MapReduce
Asymptotically hyperbolic connections
Deep Reinforcement Learning in Large Discrete Action Spaces
The Abstract Structure of Quantum Algorithms
Improved Spoken Document Summarization with Coverage Modeling Techniques
Exact Finite-State Machine Identification from Scenarios and Temporal Properties
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents
A Computational Method to Calculate the Exact Solution for Acoustic Scattering by Liquid Spheroids
Online shopping behavior study based on multi-granularity opinion mining: China vs. America
Fixed Parameter Approximations for k-Center Problems in Low Highway Dimension Graphs
Latent Tree Models for Hierarchical Topic Detection
Source-LDA: Enhancing probabilistic topic models using prior knowledge sources
Captioning Images with Diverse Objects
Review Based Rating Prediction
Representing Pattern Matching Algorithms by Polynomial-Size Automata
TensiStrength: Stress and relaxation magnitude detection for social media texts
Master equations and the theory of stochastic path integrals
Creating Causal Embeddings for Question Answering with Minimal Supervision
A DIY Ultrasonic Signal Generator for Sound Experiments
Cellular Automata and Finite Groups
Multi-colony Wright-Fisher with seed-bank
Schwinger-Keldysh formalism II: Thermal equivariant cohomology
Equidistribution, Uniform distribution: a probabilist's perspective
Computing Integrated Information
The first Cheeger constant of a simplex
A pumping lemma for non-cooperative self-assembly
Noncommutative Cantor-Bendixson derivatives and scattered $C^*$-algebras
ModelHub: Towards Unified Data and Lifecycle Management for Deep Learning
Geometric deep learning on graphs and manifolds using mixture model CNNs
Graph or Relational Databases: A Speed Comparison for Process Mining Algorithm
Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling
Polynomial-Time Proactive Synthesis of Tree-to-String Functions from Examples
Independent sets in hypergraphs and Ramsey properties of graphs and the integers
Investigating the Application of Common-Sense Knowledge-Base for Identifying Term Obfuscation in Adversarial Communication
A Load-Buffer Semantics for Total Store Ordering
Context-Bounded Analysis for POWER
Improving the upper bound on the length of the shortest reset words
Polynomial Time Efficient Construction Heuristics for Vertex Separation Minimization Problem
Guided Deep List: Automating the Generation of Epidemiological Line Lists from Open Sources
Emergent Gravity of Fractons: Mach's Principle Revisited
Sequential Monte Carlo Methods in the nimble R Package
Rapid-Rate: A Framework for Semi-supervised Real-time Sentiment Trend Detection in Unstructured Big Data
MultiBUGS: Massively parallel MCMC for Bayesian hierarchical models
A GRU-Gated Attention Model for Neural Machine Translation
Item Recommendation with Evolving User Preferences and Experience
Simplicity condition and boundary-bulk duality
Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale age
Supervising Neural Attention Models for Video Captioning by Human Gaze Data
Video as a By-Product of Digital Prototyping: Capturing the Dynamic Aspect of Interaction
Long range forces in a performance portable Molecular Dynamics framework
Identification of Probabilities
Proving Expected Sensitivity of Probabilistic Programs
Cross-Sentence N-ary Relation Extraction with Graph LSTMs
EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets
Efficient Online Inference for Infinite Evolutionary Cluster models with Applications to Latent Social Event Discovery
One-Shot Concept Learning by Simulating Evolutionary Instinct Development
Interpretable Categorization of Heterogeneous Time Series Data
Learning Invariant Riemannian Geometric Representations Using Deep Nets
Edina: Building an Open Domain Socialbot with Self-dialogues
Distributed and Managed: Research Challenges and Opportunities of the Next Generation Cyber-Physical Systems
A retrieval-based dialogue system utilizing utterance and context embeddings
Scalar-tensor gravity in the Palatini approach
Automated Lemma Synthesis in Symbolic-Heap Separation Logic
Bifurcation of solutions to Hamiltonian boundary value problems
Semantic Code Repair using Neuro-Symbolic Transformation Networks
P4-compatible High-level Synthesis of Low Latency 100 Gb/s Streaming Packet Parsers in FPGAs
Acoustic-To-Word Model Without OOV
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Deep Learning Scaling is Predictable, Empirically
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
Rate-Distributed Spatial Filtering Based Noise Reduction in Wireless Acoustic Sensor Networks
Asynchronous Bidirectional Decoding for Neural Machine Translation
Pilot-Streaming: A Stream Processing Framework for High-Performance Computing
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Multimodal Image Captioning for Marketing Analysis
A unifying framework for the modelling and analysis of STR DNA samples arising in forensic casework
Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines
Clique-Based Lower Bounds for Parsing Tree-Adjoining Grammars
Improved Landauer's principle and generalized second law of thermodynamics with initial correlations and non-equilibrium surrounding environments
Finite-Temperature Scrambling of a Random Hamiltonian
End-to-End Dense Video Captioning with Masked Transformer
Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations
From Regular Expression Matching to Parsing
Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media
Abstract Machine for Typed Feature Structures
On Using Selectional Restriction in Language Models for Speech Recognition
Qualitative and Quantitative Models of Speech Translation
Corpus-based Method for Automatic Identification of Support Verbs for Nominalizations
Semantic Ambiguity and Perceived Ambiguity
Ubiquitous Talker: Spoken Language Interaction with Real World Objects
Hybrid Transfer in an English-French Spoken Language Translator
Robust Processing of Natural Language
The Unsupervised Acquisition of a Lexicon from Continuous Speech
Fishing for Exactness
Nonuniform Markov models
A Robust Text Processing Technique Applied to Lexical Error Recovery
Fast Statistical Parsing of Noun Phrases for Document Indexing
Recycling Lingware in a Multilingual MT System
Automatic Discovery of Non-Compositional Compounds in Parallel Data
Topology of the conceptual network of language
The syntactic processing of particles in Japanese spoken language
Retrieval from Captioned Image Databases Using Natural Language Processing
A Bit of Progress in Language Modeling
Part of Speech Tagging in Thai Language Using Support Vector Machine
Syntax, Parsing and Production of Natural Language in a Framework of Information Compression by Multiple Alignment, Unification and Search
Effective XML Representation for Spoken Language in Organisations
An Application of Rational Trees in a Logic Programming Interpreter for a Procedural Language
CLAIRE: Combining Sets, Search And Rules To Better Express Algorithms
Optimizing compilation of constraint handling rules in HAL
Better than the real thing? Iterative pseudo-query processing using cluster-based language models
A Visual Query Language for Complex-Value Databases
Context-Sensitive Languages, Rational Graphs and Determinism
Use of UML and Model Transformations for Workflow Process Definitions
Automatic annotation of multilingual text collections with a conceptual thesaurus
Geocoding multilingual texts: Recognition, disambiguation and visualisation
Algebraic recognizability of regular tree languages
A Quantum Computer Foundation for the Standard Model and SuperString Theories
Regular Expression Subtyping for XML Query and Update Languages
Methods to integrate a language model with semantic information for a word prediction component
Structure and Interpretation of Computer Programs
Complexity of Hybrid Logics over Transitive Frames
A Bialgebraic Approach to Automata and Formal Language Theory
Commonsense Knowledge, Ontology and Ordinary Language
Soft Uncoupling of Markov Chains for Permeable Language Distinction: A New Algorithm
New parallel programming language design: a bridge between brain models and multi-core/many-core computers?
Highly Undecidable Problems For Infinite Computations
On the Entropy of Written Spanish
Languages recognized by nondeterministic quantum finite automata
Hyperset Approach to Semi-structured Databases and the Experimental Implementation of the Query Language Delta
Nondeterministic one-tape off-line Turing machines and their time complexity
Properties of quasi-alphabetic tree bimorphisms
Employing Wikipedia's Natural Intelligence For Cross Language Information Retrieval
Object-Oriented Intensional Programming: Intensional Classes Using Java and Lucid
Type Safe Extensible Programming
Advanced Technology in Speech Disorder Therapy of Romanian Language
Undecidability Results for Finite Interactive Systems
Type Inference for Deadlock Detection in a Multithreaded Polymorphic Typed Assembly Language
On Decidable Growth-Rate Properties of Imperative Programs
On Omega Context Free Languages which are Borel Sets of Infinite Rank
Importance of interlinguistic similarity and stable bilingualism when two languages compete
Offline Arabic Handwriting Recognition Using Artificial Neural Network
Representing Small Ordinals by Finite Automata
Component Specification in the Cactus Framework: The Cactus Configuration Language
A comprehensive operational semantics of the SCOOP programming model
Universal Higher Order Grammar
Identification of arabic word from bilingual text using character features
Separation of Test-Free Propositional Dynamic Logics over Context-Free Languages
A Knowledge Compilation Map
Proceedings First International Workshop on Process Algebra and Coordination
Implementing Explicit and Finding Implicit Sharing in Embedded DSLs
Proceedings Fifth Workshop on Formal Languages and Analysis of Contract-Oriented Software
Quotient Complexities of Atoms of Regular Languages
Modeling two-language competition dynamics
GUBS, a Behavior-based Language for Open System Dedicated to Synthetic Biology
Upgrading EasyTime: from a textual to a visual language
JooFlux: Hijacking Java 7 InvokeDynamic To Support Live Code Modifications
From Regexes to Parsing Expression Grammars
Some Chances and Challenges in Applying Language Technologies to Historical Studies in Chinese
The automatic creation of concept maps from documents written using morphologically rich languages
Large Scale Language Modeling in Automatic Speech Recognition
A Type System for the Automatic Distribution of Higher-order Synchronous Dataflow Programs
A Principled Approach to Grammars for Controlled Natural Languages and Predictive Editors
(Extended Version) Algebraic Characterization of the Class of Languages recognized by Measure Only Quantum Automata
Towards the Rapid Development of a Natural Language Understanding Module
NLP and CALL: integration is working
Towards Python-based Domain-specific Languages for Self-reconfigurable Modular Robotics Research
Japanese-Spanish Thesaurus Construction Using English as a Pivot
An Overview of Hindi Speech Recognition
S+Net: extending functional coordination with extra-functional semantics
Polyglot: Distributed Word Representations for Multilingual NLP
Clustering Algorithm for Gujarati Language
Text segmentation with character-level text embeddings
Denotational Semantics of A User-Oriented, Domain-Specific Language
Introduction to Functional Grammars
Using Robust PCA to estimate regional characteristics of language use from geo-tagged Twitter messages
Illustrating the Mezzo programming language
Function Overloading Implementation in C++
Entropy analysis of word-length series of natural language texts: Effects of text language and genre
Wikipedia-based Semantic Interpretation for Natural Language Processing
Reasoning about Meaning in Natural Language with Compact Closed Categories and Frobenius Algebras
A Compilation Target for Probabilistic Programming Languages
Inducing Language Networks from Continuous Space Word Representations
Semantic Unification A sheaf theoretic approach to natural language
A hybrid formalism to parse Sign Languages
Towards Active Logic Programming
Kalman filter in quantum language
Customisable Handling of Java References in Prolog Programs
A Survey of Named Entity Recognition in Assamese and other Indian Languages
Process-Oriented Parallel Programming with an Application to Data-Intensive Computing
Principles and Parameters: a coding theory perspective
The Final Solutions of Monty Hall Problem and Three Prisoners Problem
Modeling Basic Aspects of Cyber-Physical Systems, Part II
A Language Support for Exhaustive Fault-Injection in Message-Passing System Models
ROSS User's Guide and Reference Manual (Version 1.0)
KitRobot: A multi-platform graphical programming IDE to program mini-robotic agents
A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees
Applied Metamodelling: A Foundation for Language Driven Development (Third Edition)
Sparse Automatic Differentiation for Large-Scale Computations Using Abstract Elementary Algebra
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
Horn Clauses as an Intermediate Representation for Program Analysis and Transformation
The height of piecewise-testable languages with applications in logical complexity
Overcoming Language Variation in Sentiment Analysis with Social Attention
Enhancements in statistical spoken language translation by de-normalization of ASR results
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss
Dialog-based Language Learning
Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
Syntactic complexity of bifix-free languages
On Implementing Real-time Specification Patterns Using Observers
Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees
Using the Output Embedding to Improve Language Models
Vicious Circle Principle and Formation of Sets in ASP Based Languages
Context-Oriented Programming: A Programming Paradigm for Autonomic Systems
A Scalable Module System
Robust Sign Language Recognition System Using ToF Depth Cameras
Abstracting Abstract Machines: A Systematic Approach to Higher-Order Program Analysis
Graph Reachability and Pebble Automata over Infinite Alphabets
Using Constraint Handling Rules to Provide Static Type Analysis for the Q Functional Language
Logical analysis of natural language semantics to solve the problem of computer understanding
Implementing Constraint Handling Rules as a Domain-Specific Language Embedded in Java
ANOVA (analysis of variance) in the quantum linguistic formulation of statistics
LIQUi|>: A Software Design Architecture and Domain-Specific Language for Quantum Computing
Linguistic Analysis of Requirements of a Space Project and their Conformity with the Recommendations Proposed by a Controlled Natural Language
RuleCNL: A Controlled Natural Language for Business Rule Specifications
A composable language for action models
How Easy is it to Learn a Controlled Natural Language for Building a Knowledge Base?
Delta-oriented Architectural Variability Using MontiCore
Confluence for classical logic through the distinction between values and computations
Alternating Towers and Piecewise Testable Separators
Rediscovering the Alphabet - On the Innate Universal Grammar
A Study of Sindhi Related and Arabic Script Adapted languages Recognition
Incremental Adaptation Strategies for Neural Network Language Models
Complexity and universality in the long-range order of words
Consequences of a Goedel's misjudgment
Lucretia - intersection type polymorphism for scripting languages
Guided Grammar Convergence
Fine-grained Language Composition: A Case Study
A Linear First-Order Functional Intermediate Language for Verified Compilers
Supporting Language Learners with the Meanings Of Closed Class Items
A data-based classification of Slavic languages: Indices of qualitative variation applied to grapheme frequencies
Document Classification by Inversion of Distributed Language Representations
A Nivat Theorem for Weighted Timed Automata and Weighted Relative Distance Logic
Automata networks for memory loss effects in the formation of linguistic conventions
Relating BIP and Reo
A large annotated corpus for learning natural language inference
Computational Sociolinguistics: A Survey
Description of the Odin Event Extraction Framework and Rule Language
Multilingual Language Processing From Bytes
Augmenting Phrase Table by Employing Lexicons for Pivot-based SMT
In the Age of Web: Typed Functional-First Programming Revisited
A Hidden Markov Model Based System for Entity Extraction from Social Media English Text at FIRE 2015
Recurrent Memory Networks for Language Modeling
Exploring the Limits of Language Modeling
Reversible Communicating Processes
Automated Word Prediction in Bangla Language Using Stochastic Language Models
Unsupervised word segmentation and lexicon discovery using acoustic word embeddings
Zipf's law emerges asymptotically during phase transitions in communicative systems
Inter-Paradigm Translation of Process Models using Simulation and Mining
Towards an Automated Requirements-driven Development of Smart Cyber-Physical Systems
Default Rules for Curry
Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification
Gated Word-Character Recurrent Language Model
Language-integrated provenance
User interfaces for computational science: a domain specific language for OOMMF embedded in Python
A Character-level Convolutional Neural Network for Distinguishing Similar Languages and Dialects
Modeling Language Change in Historical Corpora: The Case of Portuguese
Supervisory Control of Fuzzy Discrete Event Systems for Simulation Equivalence
Emergence of linguistic laws in human voice
Compressing Neural Language Models by Sparse Word Representations
Translation Quality Estimation using Recurrent Neural Network
Learning variable length units for SMT between related languages via Byte Pair Encoding
Experiments with POS Tagging Code-mixed Indian Social Media Text
Structure vs. Language: Investigating the Multi-factors of Asymmetric Opinions on Online Social Interrelationship with a Case Study
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies
Differentiable Functional Program Interpreters
Minimal and Reduced Reversible Automata
A Natural Language Query Interface for Searching Personal Information on Smartwatches
How Are Programs Found? Speculating About Language Ergonomics With Curry-Howard
Towards better decoding and language model integration in sequence to sequence models
A Character-Word Compositional Neural Language Model for Finnish
A POS Tagger for Code Mixed Indian Social Media Text - ICON-2016 NLP Tools Contest Entry from Surukam
Local Modules in Imperative Languages
A Higher-Order Logic for Concurrent Termination-Preserving Refinement
Analysing Temporal Evolution of Interlingual Wikipedia Article Pairs
Y-Calculus: A Language for Real Matrices Derived from the ZX-Calculus
Named Entity Evolution Recognition on the Blogosphere
A Structural and Nominal Syntax for Diagrams
Regular Separability of Well Structured Transition Systems
Critical Survey of the Freely Available Arabic Corpora
A survey on difference hierarchies of regular languages
Using Off-the-Shelf Exception Support Components in C++ Verification
Data Noising as Smoothing in Neural Network Language Models
A Quasi-Linear Time Algorithm Deciding Whether Weak Büchi Automata Reading Vectors of Reals Recognize Saturated Languages
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems
Sequence-to-Sequence Models Can Directly Translate Foreign Speech
Topic modeling of public repositories at scale using names in source code
Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary
Word and Phrase Translation with word2vec
Phone-aware Neural Language Identification
Building a Semantic Role Labelling System for Vietnamese
A Co-contextual Type Checker for Featherweight Java (incl. Proofs)
W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis
A Low Dimensionality Representation for Language Variety Identification
Using of heterogeneous corpora for training of an ASR system
Decoding Lua: Formal Semantics for the Developer and the Semanticist
Dataset for a Neural Natural Language Interface for Databases (NNLIDB)
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation
Localizing Moments in Video with Natural Language
On the Learnability of Programming Language Semantics
N-gram and Neural Language Models for Discriminating Similar Languages
An Annotated Corpus of Relational Strategies in Customer Service
Neural Networks Compression for Language Modeling
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse
Abstractions for AI-Based User Interfaces and Systems
Context-Updates Analysis and Refinement in Chisel
Lexical Disambiguation in Natural Language Questions (NLQs)
Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning
Emergent Translation in Multi-Agent Communication
Unsupervised Machine Translation Using Monolingual Corpora Only
Evaluation of Croatian Word Embeddings
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension
Recurrent Neural Networks as Weighted Language Recognizers
One Model for the Learning of Language
WYS*: A Verified Language Extension for Secure Multi-party Computations
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
G-CORE: A Core for Future Graph Query Languages
Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks
Automated rating of recorded classroom presentations using speech analysis in kazakh
Scilla: a Smart Contract Intermediate-Level LAnguage
Hierarchical Memory Management for Mutable State
CutLang: A Particle Physics Analysis Description Language and Runtime Interpreter
Transfer Learning for Improving Speech Emotion Classification Accuracy
Deep Reinforcement Learning for Programming Language Correction
Bayesian Models for Unit Discovery on a Very Low Resource Language
Learning Word Vectors for 157 Languages
LIDIOMS: A Multilingual Linked Idioms Data Set
Towards end-to-end spoken language understanding
The origins of the Malagasy people, some certainties and a few mysteries
Syntax-Aware Language Modeling with Recurrent Neural Networks
Knowledge Aided Consistency for Weakly Supervised Phrase Grounding
Polyglot Semantic Parsing in APIs
Natural Language or Not (NLoN) - A Package for Software Engineering Text Analysis Pipeline
AllenNLP: A Deep Semantic Natural Language Processing Platform
Video Object Segmentation with Language Referring Expressions
Unsupervised Separation of Transliterable and Native Words for Malayalam
An Experiment in Ping-Pong Protocol Verification by Nondeterministic Pushdown Automata
Robust Cross-lingual Hypernymy Detection using Dependency Context
Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language Text
Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts
Positivity and conservation of superenergy tensors
Tracing the evolution of NGC6397 through the chemical composition of its stellar populations
A generalized palindromization map in free monoids
Counting and generating lambda terms
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model
An Alternative Conception of Tree-Adjoining Derivation
Acquiring Receptive Morphology: A Connectionist Model
Structural Tags, Annealing and Automatic Word Classification
An Attributive Logic of Set Descriptions and Set Operations
Aligning a Parallel English-Chinese Corpus Statistically with Lexical Criteria
An Empirical Model of Acknowledgment for Spoken-Language Systems
Tricolor DAGs for Machine Translation
A Sequential Algorithm for Training Text Classifiers
Comparative Discourse Analysis of Parallel Texts
Parsing with Principles and Probabilities
Building a Parser That can Afford to Interact with Semantics
Treating `Free Word Order' in Machine Translation
Aligning Noisy Parallel Corpora Across Language Groups : Word Pair Feature Matching by Dynamic Time Warping
XTAG system - A Wide Coverage Grammar for English
A Freely Available Syntactic Lexicon for English
Reference Resolution Using Semantic Patterns in Japanese Newspaper Articles
Probabilistic Tagging with Feature Structures
Feature-Based TAG in place of multi-component adjunction: Computational Implications
Interlanguage Signs and Lexical Transfer Errors
Reverse Queries in DATR
Manipulating Human-oriented Dictionaries with very simple tools
Analysis of Japanese Compound Nouns using Collocational Information
Integrating "Free" Word Order Syntax and Information Structure
An Algorithm to Co-Ordinate Anaphora Resolution and PPS Disambiguation Process
Implementation and evaluation of a German HMM for POS disambiguation
Creating a tagset, lexicon and guesser for a French tagger
Incremental Interpretation: Applications, Theory, and Relationship to Dynamic Semantics
Incremental Interpretation of Categorial Grammar
Automatic processing proper names in texts
A Uniform Treatment of Pragmatic Inferences in Simple and Complex Utterances and Sequences of Utterances
Encoding Lexicalized Tree Adjoining Grammars with a Nonmonotonic Inheritance Hierarchy
Using Higher-Order Logic Programming for Semantic Interpretation of Coordinate Constructs
Automatic Extraction of Tagset Mappings from Parallel-Annotated Corpora
A Matching Technique in Example-Based Machine Translation
On Constraint-Based Lambek Calculi
How much is enough?: Data requirements for statistical NLP
ParseTalk about Textual Ellipsis
Attempto Controlled English (ACE)
Active Constraints for a Direct Interpretation of HPSG
Functional Centering
Classification in Feature-based Default Inheritance Hierarchies
Research on Architectures for Integrated Speech/Language Systems in Verbmobil
Head Automata and Bilingual Tiling: Translation with Minimal Representations
Unsupervised Discovery of Phonological Categories through Supervised Learning of Morphological Rules
A Corpus Study of Negative Imperatives in Natural Language Instructions
Phonological modeling for continuous speech recognition in Korean
Multiple Discourse Relations on the Sentential Level in Japanese
Connected Text Recognition Using Layered HMMs and Token Passing
Classifiers in Japanese-to-English Machine Translation
Gathering Statistics to Aspectually Classify Sentences with a Genetic Algorithm
A Morphology-System and Part-of-Speech Tagger for German
Information Extraction - A User Guide
A Maximum Entropy Approach to Identifying Sentence Boundaries
Finite State Transducers Approximating Hidden Markov Models
Global Thresholding and Multiple Pass Parsing
Learning Methods for Combining Linguistic Indicators to Classify Verbs
A Hybrid Environment for Syntax-Semantic Tagging
Nymble: a High-Performance Learning Name-finder
Long-range fractal correlations in literary corpora
Expoiting Syntactic Structure for Language Modeling
A Structured Language Model
Learning Transformation Rules to Find Grammatical Relations
HMM Specialization with Selective Lexicalization
Mixed-Level Knowledge Representation and Variable-Depth Inference in Natural Language Processing
Refinement of a Structured Language Model
Programming in Alma-0, or Imperative and Declarative Programming Reconciled
A Compact Architecture for Dialogue Management Based on Scripts and Meta-Outputs
A Tableaux Calculus for Ambiguous Quantification
A Tableau Calculus for Pronoun Resolution
A Resolution Calculus for Dynamic Semantics
Verification of Timed Automata Using Rewrite Rules and Strategies
Richer Syntactic Dependencies for Structured Language Modeling
An Integrated Framework for Treebanks and Multilayer Annotations
Applying a Hybrid Query Translation Method to Japanese/English Cross-Language Patent Retrieval
PRIME: A System for Multi-lingual Patent Retrieval
Language Modeling for Multi-Domain Speech-Driven Text Retrieval
Speech-Driven Text Retrieval: Using Target IR Collections for Statistical Language Model Adaptation in Speech Recognition
Monadic Style Control Constructs for Inference Systems
Issues in Communication Game
A Grid Based Architecture for High-Performance NLP
Building a Test Collection for Speech-Driven Web Retrieval
Application Architecture for Spoken Language Resources in Organisational Settings
Polarity sensitivity and evaluation order in type-logical grammar
Knowledge And The Action Description Language A
Aspects de la Programmation d'Applications Win32 avec un Langage Fonctionnel
The First-Order Theory of Sets with Cardinality Constraints is Decidable
Application of the Double Metaphone Algorithm to Amharic Orthography
Overhead-Free Computation, DCFLs, and CFLs
Robust Dialogue Understanding in HERALD
Proof rules for purely quantum programs
A Formal Foundation for ODRL
Utilisation de la linguistique en reconnaissance de la parole : un état de l'art
Dealing with Metonymic Readings of Named Entities
Residual Finite Tree Automata
XString: XML as a String
Experiments on predictability of word in context and information rate in natural language
De l'oprateur de trace dans les jeux de Conway
Interlinguistic similarity and language death dynamics
Quantum Pushdown Automata
Quantum Property Testing
A Note on Ontology and Ordinary Language
On logical characterization of henselianity
Linearly bounded infinite graphs
Bootstrapping Deep Lexical Resources: Resources for Courses
Network model of human language
Using Description Logics for Recognising Textual Entailment
Automatic Coding Rule Conformance Checking Using Logic Programs
On Infinite Real Trace Rational Languages of Maximum Topological Complexity
Policies of System Level Pipeline Modeling
A Survey of Quantum Programming Languages: History, Methods, and Tools
A classification of invasive patterns in AOP
A language for mathematical knowledge management
Topological Complexity of Context-Free omega-Languages: A Survey
What It Feels Like To Hear Voices: Fond Memories of Julian Jaynes
Combining Semantic Wikis and Controlled Natural Language
The Wadge Hierarchy of Deterministic Tree Languages
Towards a Theory of Requirements Elicitation: Acceptability Condition for the Relative Validity of Requirements
Compilation of extended recursion in call-by-value functional languages
Detecting patterns in finite regular and context-free languages
A Decision Problem for Ultimately Periodic Sets in Non-standard Numeration Systems
BPDMN: A Conservative Extension of BPMN with Enhanced Data Representation Capabilities
Bit Copying - The Ultimate Computational Simplicity
Decision Problems For Turing Machines
Vers la reconnaissance de mini-messages manuscrits
Standards for Language Resources
A computational definition of the notion of vectorial space
Typage fort et typage souple des collections topologiques et des transformations
Public-key cryptography in functional programming context
Complexity of Problems for Commutative Grammars
Positive Supercompilation for a Higher-Order Call-By-Value Language
Purely Functional Structured Programming
A Non-Null Annotation Inferencer for Java Bytecode
Proceedings Ninth International Workshop on the Foundations of Coordination Languages and Software Architectures
The Maximal Subword Complexity of Quasiperiodic Infinite Words
Towards a Property Preserving Transformation from IEC 61131-3 to BIP
Constructions définitoires des tables du Lexique-Grammaire
Realizing evaluation strategies by hierarchical graph rewriting
Closure properties of predicates recognized by deterministic and non-deterministic asynchronous automata
Application of a Quantum Ensemble Model to Linguistic Analysis
Querying Biomedical Ontologies in Natural Language using Answer Set
Syntax and Semantics of Babel-17
ECLiPSe - from LP to CLP
Natural Language Processing (almost) from Scratch
Proceedings Types for Proofs and Programs, Revised Selected Papers
Arc Consistency and Friends
A Simple Multi-Processor Computer Based on Subleq
Algorithmic Programming Language Identification
Factor frequencies in languages invariant under more symmetries
Observational equivalences for linear logic CC languages
Domain-specific Languages in a Finite Domain Constraint Programming System
Conjure Revisited: Towards Automated Constraint Modelling
Object-oriented semantics of English in natural language understanding system
Reaction Automata
A sound and complete axiomatization for Dynamic Topological Logic
Function call overhead benchmarks with MATLAB, Octave, Python, Cython and C
Tree Transducers, Machine Translation, and Cross-Language Divergences
Re-differentiation as collective intelligence: The Ktunaxa language online community
Numeration Systems: a Link between Number Theory and Formal Language Theory
Cell Decomposition for semibounded p-adic sets
Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
A Note on Limited Pushdown Alphabets in Stateless Deterministic Pushdown Automata
Programming Languages for Scientific Computing
A Lightweight Stemmer for Gujarati
A Scale-Space Theory for Text
Quantifier Alternation in Two-Variable First-Order Logic with Successor Is Decidable
Translating NP-SPEC into ASP
Language ASP{f} with Arithmetic Expressions and Consistency-Restoring Rules
Two-Sided Derivatives for Regular Expressions and for Hairpin Expressions
Formal Verification of Hardware Synthesis
Connecting the Dots: Computer Systems Education using a Functional Hardware Description Language
Kleene Algebra with Tests and Coq Tools for While Programs
Unified Modeling Language for Describing Business Value Chain Activities
Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique
Type-theoretical natural language semantics: on the system F for meaning assembly
A weak HOAS approach to the POPLmark Challenge
A fast method for implementation of the property lists in programming languages
Sofic-Dyck shifts
Towards Tree Automata-based Success Types
Genetic approach for arabic part of speech tagging
Experimenting with X10 for Parallel Constraint-Based Local Search
Characterizing traits of coordination
Towards Meta-Reasoning in the Concurrent Logical Framework CLF
Emptiness and Universality Problems in Timed Automata with Positive Frequency
Pretty-big-step-semantics-based Certified Abstract Interpretation (Preliminary version)
Keyboard for inputting Chinese language
Applying quantitative semantics to higher-order quantum computing
Authorship Attribution Using Word Network Features
Proceedings Second International Workshop on Trends in Tree Automata and Tree Transducers
Higher-order semantics for quantum programming languages with classical control
From Lock Freedom to Progress Using Session Types
Session Types Go Dynamic or How to Verify Your Python Conversations
Axioms for Definability and Full Completeness
Upper Bounds on Syntactic Complexity of Left and Two-Sided Ideals
Hybrid Approach to English-Hindi Name Entity Transliteration
Challenges in Persian Electronic Text Analysis
A Technology for BigData Analysis Task Description using Domain-Specific Languages
An Account of Opinion Implicatures
Towards a Benchmark of Natural Language Arguments
On state complexity of unions of binary factor-free languages
ESmodels: An Epistemic Specification Solver
Logic Programming as Scripting Language for Bots in Computer Games -- Research Overview
Language to Specify Syntax-Guided Synthesis Problems
Boolean Circuit Complexity of Regular Languages
Verified Subtyping with Traits and Mixins
Autonomous requirements specification processing using natural language processing
Effective model-completeness for p-adic analytic structures
Controlled Natural Language Processing as Answer Set Programming: an Experiment
Star-free languages and local divisors
Unsupervised Keyword Extraction from Polish Legal Texts
Kleene Algebras, Regular Languages and Substructural Logics
Not All Neural Embeddings are Born Equal
Riesz Logic
Optimizing the For loop: Comparison of For loop and micro For loop
A Survey on the Local Divisor Technique
Parallel Prefix Polymorphism Permits Parallelization, Presentation & Proof
Confusion in the Church-Turing Thesis
A Category Theory of Communication Theory
Talking to the crowd: What do people react to in online discussions?
Abstract Gringo
Sequent Calculus and Equational Programming
Polish to English Statistical Machine Translation
The Essence of JavaScript
On the Problem of Computing the Probability of Regular Sets of Trees
Helping Domain Experts Build Speech Translation Systems
Feedforward Sequential Memory Neural Networks without Recurrent Feedback
BASEL (Buffering Architecture SpEcification Language)
Random graphs and Lindstrom quantifiers for natural graph properties
Data Language Specification via Terminal Attribution
Full abstraction for probabilistic PCF
Information retrieval in folktales using natural language processing
Paxos Made Switch-y
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
SMT Solving for Functional Programming over Infinite Structures
Compositionality and String Diagrams for Game Theory
A Revision of the Mool Language
The IBM 2016 English Conversational Telephone Speech Recognition System
Word Ordering Without Syntax
Towards a native toplevel for the OCaml language
A Type System for Unstructured Locking that Guarantees Deadlock Freedom without Imposing a Lock Ordering
Decorated proofs for computational effects: States
Dynamic Construction of Belief Networks
Dealing with natural language interfaces in a geolocation context
C++11 - określanie typów
A New Statement for Selection and Exception Handling in Imperative Languages
Handwritten Character Recognition In Malayalam Scripts- A Review
A Programming Language Oriented Approach to Computability
The dagger lambda calculus
From XML Schema to JSON Schema: Translation with CHR
Finite Automata With Restricted Two-Way Motion
Executable Modeling with UML. A Vision or a Nightmare?
Critical Systems Development Using Modeling Languages. (CSDUML-04): Current Developments and Future Challenges (Report on the Third International Workshop)
Semi-supervised Classification for Natural Language Processing
Towards a graphical language for quadrotor missions
QPEL: Quantum Program and Effect Language
Semantics for a Quantum Programming Language by Operator Algebras
Geometry of Resource Interaction - A Minimalist Approach
On the Coverability Problem for Pushdown Vector Addition Systems in One Dimension
A Knowledge-poor Pronoun Resolution System for Turkish
Model Driven Reactive Applications
A Publicly Available Cross-Platform Lemmatizer for Bulgarian
Egison: Non-Linear Pattern-Matching against Non-Free Data Types
jUCM: Universal Class Morphing (position paper)
Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs
JuMP: A Modeling Language for Mathematical Optimization
Posterior calibration and exploratory analysis for natural language processing models
A commentary on "The now-or-never bottleneck: a fundamental constraint on language", by Christiansen and Chater (2016)
Proceedings Thirteenth Workshop on Quantitative Aspects of Programming Languages and Systems
Predicting the top and bottom ranks of billboard songs using Machine Learning
Modular implicits
A Context-Oriented Extension of F#
Incorporating Structural Alignment Biases into an Attentional Neural Translation Model
The Essence of Inheritance
On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation
A Probabilistic Dependent Type System based on Non-Deterministic Beta Reduction
Trace semantics for polymorphic references
Towards a DSL for Perception-Based Safety Systems
Model Completeness for Henselian Fields with finite ramification valued in a $Z$-Group
The Diagonal Problem for Higher-Order Recursion Schemes is Decidable
Adobe-MIT submission to the DSTC 4 Spoken Language Understanding pilot task
Automatic TM Cleaning through MT and POS Tagging: Autodesk's Submission to the NLP4TM 2016 Shared Task
As Cool as a Cucumber: Towards a Corpus of Contemporary Similes in Serbian
Perspectives for proof unwinding by programming languages techniques
Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning
Evidences of the mismatch between industry and academy on modelling language quality evaluation
Deciding Equivalence of Linear Tree-to-Word Transducers in Polynomial Time
The Vopěnka principle is inequivalent to but conservative over the Vopěnka scheme
Zero-Resource Translation with Multi-Lingual Neural Machine Translation
Constitutional Precedent of Amicus Briefs
Event-driven Adaptation in COP
From Events to Reactions: A Progress Report
On the Solvability of Inductive Problems: A Study in Epistemic Topology
Sequential Convolutional Neural Networks for Slot Filling in Spoken Language Understanding
Learning Crosslingual Word Embeddings without Bilingual Corpora
Mapping distributional to model-theoretic semantic spaces: a baseline
A XML Based Datagrid Description Language
Fragment Allocation Configuration in Distributed Database Systems
2016 Google Scholar Metrics released: a matter of languages... and something else
Grounded Lexicon Acquisition - Case Studies in Spatial Language
The Number of Atomic Models of Uncountable Theories
Byte-based Language Identification with Deep Convolutional Networks
Psychologically Motivated Text Mining
Survey on the Use of Typological Information in Natural Language Processing
A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts
Personalized Machine Translation: Preserving Original Author Traits
The Intelligent Voice 2016 Speaker Recognition System
1.5 billion words Arabic Corpus
Optimal Test Sets for Context-Free Languages
Fill it up: Exploiting partial dependency annotations in a minimum spanning tree parser
Geometry of Compositionality
Stateology: State-Level Interactive Charting of Language, Feelings, and Values
Cross-Lingual Dependency Parsing with Late Decoding for Truly Low-Resource Languages
A Concurrent Model for Imperative Languages with Improved Atomicity
Constraint Handling Rules - What Else?
Non-Blocking Concurrent Imperative Programming with Session Types
emLam -- a Hungarian Language Modeling baseline
A Comprehensive Survey on Bengali Phoneme Recognition
Multilingual Multi-modal Embeddings for Natural Language Processing
Comparative Study of CNN and RNN for Natural Language Processing
Fast and unsupervised methods for multilingual cognate clustering
Verified type checker for Jolie programming language
A Short Review of Ethical Challenges in Clinical Natural Language Processing
Environment-Independent Task Specifications via GLTL
Beating Atari with Natural Language Guided Reinforcement Learning
An Analysis of Action Recognition Datasets for Language and Vision Tasks
Learning Structured Natural Language Representations for Semantic Parsing
Duluth at SemEval-2017 Task 6: Language Models in Humor Detection
Extending and Improving Wordnet via Unsupervised Word Embeddings
Efficient Natural Language Response Suggestion for Smart Reply
A Systematic Review of Hindi Prosody
Spelling Correction as a Foreign Language
Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages
Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning
Efficient Textual Representation of Structure
Learning Pairwise Disjoint Simple Languages from Positive Examples
Computational Thinking in Patch
German in Flux: Detecting Metaphoric Change via Word Entropy
Ins-Robust Primitive Words
An Embedded Deep Learning based Word Prediction
Open Quantum Assembly Language
Proceedings 15th Workshop on Quantitative Aspects of Programming Languages and Systems
Rotations and Interpretability of Word Embeddings: the Case of the Russian Language
On the State of the Art of Evaluation in Neural Language Models
Improving Language Modeling using Densely Connected Recurrent Neural Networks
A Sub-Character Architecture for Korean Language Processing
Identifying civilians killed by police with distantly supervised entity-event extraction
Improving coreference resolution with automatically predicted prosodic information
Natural Language Processing with Small Feed-Forward Networks
Confluence in Probabilistic Rewriting
Automatic Identification of AltLexes using Monolingual Parallel Corpora
Towards Syntactic Iberian Polarity Classification
CD Grammar Systems with Two Propagating Scattered Context Components Characterize the Family of Context Sensitive Languages
Cryptographically Secure Information Flow Control on Key-Value Stores
Model Checking Regular Language Constraints
Towards Neural Machine Translation with Latent Tree Attention
Data-Driven Dialogue Systems for Social Agents
A Domain-specific Language for High-reliability Software used in the JUICE SWI Instrument - The hO Language Manual
Limitations of Cross-Lingual Learning from Image Search
Two-way Two-tape Automata
A Practical Python API for Querying AFLOWLIB
Transferring Semantic Roles Using Translation and Syntactic Information
Robot-Initiated Specification Repair through Grounded Language Interaction
A New Technique for Reachability of States in Concatenation Automata
Tensor network language model
A Comparison of Feature-Based and Neural Scansion of Poetry
Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task
Towards operational natural language
Event-Clock Nested Automata
Characterizing the hyper-parameter space of LSTM language models for mixed context applications
Creating New Language and Voice Components for the Updated MaryTTS Text-to-Speech Synthesis Platform
Rasa: Open Source Language Understanding and Dialogue Management
word representation or word embedding in Persian text
PWCT: Visual Language for IoT and Cloud Computing Applications and Systems
VnCoreNLP: A Vietnamese Natural Language Processing Toolkit
Theory of higher order interpretations and application to Basic Feasible Functions
Sequential Circuits from Regular Expressions Revisited
Can One Escape Red Chains? Regular Path Queries Determinacy is Undecidable
Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility Report
Logic Programming Applications: What Are the Abstractions and Implementations?
Formal Semantics of the Language Cypher
The Importance of Being Recurrent for Modeling Hierarchical Structure
Vehicle Platooning Simulations with Functional Reactive Programming
Real-Time Prediction of the Duration of Distribution System Outages
A ZX-Calculus with Triangles for Toffoli-Hadamard, Clifford+T, and Beyond
Einstein-Yang-Mills Isolated Horizons: Phase Space, Mechanics, Hair and Conjectures
Webs, Lenard schemes, and the local geometry of bihamiltonian Toda and Lax structures
Measuring and Synthesizing Systems in Probabilistic Environments
Rhythms of Memory and Bits on Edge: Symbol Recognition as a Physical Phenomenon
Learning Content Selection Rules for Generating Object Descriptions in Dialogue
Towards automating the generation of derivative nouns in Sanskrit by simulating Panini
An Extended action for the effective field theory of dark energy: a stability analysis and a complete guide to the mapping at the basis of EFTCAMB
Flow- and Context-Sensitive Points-to Analysis using Generalized Points-to Graphs
Using Multiple Sources of Information for Constraint-Based Morphological Disambiguation
Data-Oriented Language Processing. An Overview
Discovery of Linguistic Relations Using Lexical Attraction
Language evolution and population Dynamics in a system of two interacting species
Similarity-Based Models of Word Cooccurrence Probabilities
Specialization of Functional Logic Programs Based on Needed Narrowing
A machine-independent port of the SR language run-time system to the NetBSD operating system
A Machine-Independent port of the MPD language run time system to NetBSD
Lineal: A linear-algebraic Lambda-calculus
Mykyta the Fox and networks of language
Querying XML Documents in Logic Programming
Computational modelling of evolution: ecosystems and language
Statistical analysis of the Indus script using $n$-grams
Accelerating the Execution of Matrix Languages on the Cell Broadband Engine Architecture
First-order Fragments with Successor over Infinite Words
Phylogeny and geometry of languages from normalized Levenshtein distance
Digraph Complexity Measures and Applications in Formal Language Theory
Complex network analysis of literary and scientific texts
On distributed monitoring of asynchronous systems
A Trichotomy for Regular Simple Path Queries on Graphs
Proceedings Fifth Workshop on Programming Language Approaches to Concurrency- and Communication-cEntric Software
Mining and Exploiting Domain-Specific Corpora in the PANACEA Platform
JRC EuroVoc Indexer JEX - A freely available multi-label categorisation tool
Resolving and Exploiting the $k$-CFA Paradox
Hierarchies
Quantitative methods for Phylogenetic Inference in Historical Linguistics: An experimental case study of South Central Dravidian
Assessing Wikipedia-Based Cross-Language Retrieval Models
A Modality Lexicon and its use in Automatic Tagging
Regularity Preserving but not Reflecting Encodings
Streaming Property Testing of Visibly Pushdown Languages
Freshman or Fresher? Quantifying the Geographic Variation of Internet Language
Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes
What is India speaking: The "Hinglish" invasion
FrameNet Resource Grammar Library for GF
On the Sizes of DPDAs, PDAs, LBAs
On the universal structure of human lexical semantics
Denotational cost semantics for functional languages with inductive types
Fully automatic multi-language translation with a catalogue of phrases - successful employment for the Swiss avalanche bulletin
The scarcity of crossing dependencies: a direct outcome of a specific constraint?
COGENT: Certified Compilation for a Functional Systems Language
Domain Specific Author Attribution Based on Feedforward Neural Network Language Models
Dimension Projection among Languages based on Pseudo-relevant Documents for Query Translation
Multilinear Grammar: Ranks and Interpretations
A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder
Multi-Agent Cooperation and the Emergence of (Natural) Language
Implementing GraphQL as a Query Language for Deductive Databases in SWI-Prolog Using DCGs, Quasi Quotations, and Dicts
Email Babel: Does Language Affect Criminal Activity in Compromised Webmail Accounts?
Robust clustering of languages across Wikipedia growth
A Semantics Comparison Workbench for a Concurrent, Asynchronous, Distributed Programming Language
Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
Basic concepts and tools for the Toki Pona minimalist and constructed language: Wordnet synsets; analysis of the vocabulary; synthesis and syntax highlighting of texts
The Enemy Among Us: Detecting Hate Speech with Threats Based 'Othering' Language Embeddings
Putting in All the Stops: Execution Control for JavaScript
Sequence-based Multi-lingual Low Resource Speech Recognition
Resource Polymorphism
RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language
RUSSE: The First Workshop on Russian Semantic Similarity
P4K: A Formal Semantics of P4 and Applications
Dual-Coding Theory and Connectionist Lexical Selection
Intentions and Information in Discourse
Graded Unification: A Framework for Interactive Processing
Syntactic Analysis by Local Grammars Automata: an Efficient Algorithm
PRINCIPAR---An Efficient, Broad-coverage, Principle-based Parser
LHIP: Extended DCGs for Configurable Robust Parsing
Emergent Linguistic Rules from Inducing Decision Trees: Disambiguating Discourse Clue Words
Statistical versus symbolic parsing for captioned-information retrieval
An Experiment on Learning Appropriate Selectional Restrictions from a Parsed Corpus
Dutch Cross Serial Dependencies in HPSG
A Rule-Based Approach To Prepositional Phrase Attachment Disambiguation
Planning Argumentative Texts
The "Whiteboard" Architecture: a way to integrate heterogeneous components of NLP systems
Comlex Syntax: Building a Computational Lexicon
An HPSG Parser Based on Description Logics
Higher-order Linear Logic Programming of Categorial Deduction
Stochastic HPSG
A Robust Parser Based on Syntactic Information
Lexical Acquisition via Constraint Solving
Phonological Derivation in Optimality Theory
Constraints, Exceptions and Representations
Utilizing Statistical Dialogue Act Processing in Verbmobil
Tagging the Teleman Corpus
Quantifier Scope and Constituency
Features and Agreement
Empirical Discovery in Linguistics
Analysis of the Arabic Broken Plural and Diminutive
Computing Prosodic Morphology
Processing Complex Sentences in the Centering Framework
Efficient Algorithms for Parsing the DOP Model? A Reply to Joshua Goodman
Notes on LR Parser Design
Semantic-based Transfer
Efficient Implementation of a Semantic-based Transfer Approach
Stylistic Variation in an Information Retrieval Experiment
A Czech Morphological Lexicon
Features as Resources in R-LFG
Valence Induction with a Head-Lexicalized PCFG
Using WordNet for Building WordNets
An Empirical Investigation of Proposals in Collaborative Dialogues
Centering in Dynamic Semantics
Spotting Prosodic Boundaries in Continuous Speech in French
Towards an implementable dependency grammar
Autocatalytic Theory of Meaning
PAL: Pertinence Action Language
Historical Dynamics of Lexical System as Random Walk Process
Phonology
Efficient Deep Processing of Japanese
The Rank-Frequency Analysis for the Functional Style Corpora in the Ukrainian Language
Tabular Parsing
On Invariance and Convergence in Time Complexity theory
The One Page Model Checker
The SL synchronous language, revisited
Reactive concurrent programming revisited
D2D: Digital Archive to MPEG-21 DIDL
Numeration-automatic sequences
Algebraic recognizability of languages
The parallel implementation of the Astrée static analyzer
Sociophysics Simulations I: Language Competition
Microscopic Abrams-Strogatz model of language competition
Computer simulation of language competition by physicists
Formation of Languages; Equality, Hierarchy and Teachers
Quantum Domain Theory - Definitions and Applications
Elementary transformation analysis for Array-OL
Simulation of Quantum Algorithms with a Symbolic Programming Language
Special relativity in complex vector algebra
A Universal Kernel for Learning Regular Languages
Weak index versus Borel rank
A Compositional Query Algebra for Second-Order Logic and Uncertain Databases
On NFAs Where All States are Final, Initial, or Both
Subshifts, Languages and Logic
Answers to Questions Formulated in the Paper "On States Observability in Deterministic Finite Automata"
Non-Parametric Bayesian Areal Linguistics
jYang : A YANG parser in java
Empowering OLAC Extension using Anusaaraka and Effective text processing using Double Byte coding
Representing human and machine dictionaries in Markup languages
LoopW Technical Reference v0.3
Causality in the Semantics of Esterel: Revisited
Operator-oriented programming: a new paradigm for implementing window interfaces and parallel algorithms
State complexity of union and intersection combined with star and reversal
Model Theory of the Inaccessibility Scheme
Events! (Reactivity in urbiscript)
Dependently-Typed Formalisation of Typed Term Graphs
Higher-order Rewriting for Executable Compiler Specifications
ALPprolog --- A New Logic Programming Method for Dynamic Domains
The complexity of tangent words
Constructing Premaximal Binary Cube-free Words of Any Level
Billiard complexity in the hypercube
Groups, Graphs, Languages, Automata, Games and Second-order Monadic Logic
Lambda-lifting and CPS conversion in an imperative language
Caterpillar dualities and regular languages
Querying Source Code with Natural Language
Musings on Encodings and Expressiveness
A Method for Selecting Noun Sense using Co-occurrence Relation in English-Korean Translation
Introduction of the weight edition errors in the Levenshtein distance
Equivalence of Deterministic One-Counter Automata is NL-complete
Probing the statistical properties of unknown texts: application to the Voynich Manuscript
Weak morphisms of higher dimensional automata
Bounded Choice Queries for Logic Programming
The DeLiVerMATH project - Text analysis in mathematics
Exploiting Parallelism in Coalgebraic Logic Programming
Does Syntactic Knowledge help English-Hindi SMT?
Experience in using a typed functional language for the development of a security application
Vicious Circle Principle and Logic Programs with Aggregates
Building of Networks of Natural Hierarchies of Terms Based on Analysis of Texts Corpora
Derivational modal logics with the difference modality
Functorial Zeta Integrals
Introduction to ROSS: A New Representational Scheme
Mutually Exclusive Procedures in Imperative Languages
An ACCL which is not a CRCL
What Your Username Says About You
RDF Knowledge Graph Visualization From a Knowledge Extraction System
Quantifier Scope in Categorical Compositional Distributional Semantics
Word Segmentation on Micro-blog Texts with External Lexicon and Heterogeneous Data
Authorship clustering using multi-headed recurrent neural networks
Many-Task Computing Tools for Multiscale Modeling
PDDL 2.1: Representation vs. Computation
Asymptotic Entropy of Random Walks on Regular Languages over a Finite Alphabet
Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
A Brief State of the Art for Ontology Authoring
Multiparty Sessions based on Proof Nets
Using crowdsourcing system for creating site-specific statistical machine translation engine
CLAZY: Lazy Calling for Common Lisp
Recognisable languages over monads
What the F-measure doesn't measure: Features, Flaws, Fallacies and Fixes
Programs as Polypeptides
Evaluation of the Accuracy of the BGLemmatizer
Automata and automata mappings of semigroups
Autocorrelated errors explain the apparent relationship between disapproval of the US Congress and prosocial language
Inference rules for RDF(S) and OWL in N3Logic
Co-Occurrence Patterns in the Voynich Manuscript
A formal language for cyclic operads
On the axiomatizability of $\mathrm{C}^*$-algebras as operator systems
Multi-Level Analysis and Annotation of Arabic Corpora for Text-to-Sign Language MT
Data as processes: introducing measurement data into CARMA models
Using Recurrent Neural Network for Learning Expressive Ontologies
Convexity and Order in Probabilistic Call-by-Name FPC
A New Bengali Readability Score
Modeling selectional restrictions in a relational type system
The canonical semantic network supports residual language function in chronic post-stroke aphasia
First-Order Bayesian Network Specifications Capture the Complexity Class PP
Italy goes to Stanford: a collection of CoreNLP modules for Italian
Lindenbaum method (propositional language)
Structural characterization of Cayley graphs
A Hackathon for Classical Tibetan
Toward a new instances of NELL
Chinese Restaurant Process for cognate clustering: A threshold free approach
Early Evolution of Bird-Type Language without Grammar: Duplication and Mutation
A recurrent neural network without chaos
ASHACL: Alternative Shapes Constraint Language
Deep Learning applied to NLP
Is this word borrowed? An automatic approach to quantify the likeliness of borrowing in social media
Approximation of Weighted Automata with Storage
Medical Text Classification using Convolutional Neural Networks
A Biomedical Information Extraction Primer for NLP Researchers
Canonical Selection of Colimits
Overview of the NLPCC 2017 Shared Task: Chinese News Headline Categorization
Non-locality of the meet levels of the Trotter-Weil Hierarchy
Scoped Extension Methods in Dynamically-Typed Languages
A Mathematical Picture Language Program
When rule-based models need to count
Closure Properties in the Class of Multiple Context Free Groups
Internal Language of Finitely Complete $(\infty, 1)$-categories
Symbol, Conversational, and Societal Grounding with a Toy Robot
Language-depedent I-Vectors for LRE15
Natural Language Inference from Multiple Premises
Homogeneous 3-dimensional permutation structures
Cons-free Programming with Immutable Functions
Quantifier elimination on some pseudo-algebraically closed valued fields
MIZAN: A Large Persian-English Parallel Corpus
Unsupervised Part-of-Speech Induction
Distributed NLP
On the scaling of polynomial features for representation matching
DEMorphy, German Language Morphological Analyzer
OpenMath and SMT-LIB
Machine Learning and Applied Linguistics
The Logical Essentials of Bayesian Reasoning
Scaling in the Universe
A Multi-Threaded Fast Convolver for Dynamically Parallel Image Filtering
Structuring eccentric-narrow planetary rings
Lopsided spiral galaxies: evidence for gas accretion
Simulating the High Energy Gamma-ray sky seen by the GLAST Large Area Telescope
Abundances of Na, Mg and Al in stars with giant planets
The energetics, evolution, and stellar depletion of Li6 in the early Galaxy
The halo-model description of marked statistics
The Luminosity-Weighted or `Marked' Correlation Function
Disordered Flat Phase in a Solid on Solid Model of Fcc(110) Surfaces and Dimer States in Quantum Spin-1/2 Chains
Polymer Winding Numbers and Quantum Mechanics
Conductances in normal and normal-superconductor structures
Kinetical theory beyond conventional approximations and 1/f-noise
First-Principles Based Matrix-Green's Function Approach to Molecular Electronic Devices: General Formalism
Voting and Catalytic Processes with Inhomogeneities
Quantum transport properties of two-dimensional electron gases under high magnetic fields
First-Order Conditional Logic Revisited
The "Fodor"-FODOR fallacy bites back
Some Remarks on the Geometry of Grammar
SLT-Resolution for the Well-Founded Semantics
On Redundancy Elimination Tolerant Scheduling Rules
Meta-Learning for Phonemic Annotation of Corpora
A Knowledge-based Automated Debugger in Learning System
Decomposing Non-Redundant Sharing by Complementation
The Limits of Horn Logic Programs
Computing Preferred Answer Sets by Meta-Interpretation in Answer Set Programming
What does a conditional knowledge base entail?
Visual Environment for Rapid Composition of Parameter-Sweep Applications for Distributed Processing on Global Grids
The new BaBar Data Reconstruction Control System
Minimum Model Semantics for Logic Programs with Negation-as-Failure
The Athena Startup Kit
Human-Level Performance on Word Analogy Questions by Latent Relational Analysis
Stabilization of Cooperative Information Agents in Unpredictable Environment: A Logic Programming Approach
Software Libraries and Their Reuse: Entropy, Kolmogorov Complexity, and Zipf's Law
Naming Games in Spatially-Embedded Random Networks
Static Analysis using Parameterised Boolean Equation Systems
Similarity of Semantic Relations
Automata with Nested Pebbles Capture First-Order Logic with Transitive Closure
Statistical Geometry in Quantum Mechanics
Acoustic black holes: horizons, ergospheres, and Hawking radiation
Gravitons from a loop representation of linearised gravity
Matter with dilaton charge in Weyl-Cartan spacetime and evolution of the Universe
Monodromy-data parameterization of spaces of local solutions of integrable reductions of Einstein's field equations
Quantum Mechanics without spacetime V - Why a quantum theory of gravity should be non-linear-
The Use of Schoonschip and Form in Perturbative Lattice Calculations
Perturbative renormalization of moments of quark momentum, helicity and transversity distributions with overlap and Wilson fermions
TIME EVOLUTION OF $K^0-\bar{K^0}$ SYSTEM IN SPECTRAL FORMULATION
Diffractive J/Psi production in high energy gamma-gamma collisions as a probe of the QCD pomeron
Physical mechanisms generating spontaneous symmetry breaking and a hierarchy of scales
On Nonperturbative Calculations in Quantum Electrodynamics
Geometrical Lattice models for N=2 supersymmetric theories in two dimensions
Central extensions of current groups in two dimensions
The Mechanism behind the Embeddings of String Theories
Integrable Hierarchies and Contact Terms in u-plane Integrals of Topologically Twisted Supersymmetric Gauge Theories
Nonperturbative Formulations of Superstring Theory
DGP Brane as a Gravity Conductor
Gauge Transformations, BRST Cohomology and Wigner's Little Group
Fayet-Iliopoulos Terms in Supergravity and Cosmology
Transgression forms as unifying principle in field theory
Asymmetric Nondegenerate Geometry
The uniqueness of the spectral flow on spaces of unbounded self--adjoint Fredholm operators
Foundations of real analysis and computability theory in non-Aristotelian finitary logic
Krein duality, positive 2-algebras, and dilation of comultiplications (To the centenary of Mark G.Krein)
Landau-Lifshitz hierarchy and infinite dimensional Grassmann variety
Bosonization of the Pairing Hamiltonian
The Wondrous Design and Non-random Character of "Chance" Events
The National Ignition Facility: Status and Plans for Laser Fusion and High-Energy-Density Experimental Studies
The Leeson effect - Phase noise in quasilinear oscillators
Geometry of Financial Markets -- Towards Information Theory Model of Markets
Transcriptional Regulation by the Numbers 1: Models
Carbon--The First Frontier of Information Processing
Resource Limited Theories and their Extensions
Quantum Limitations on the Storage and Transmission of Information
Entanglement as an Observer-Dependent Concept: An Application to Quantum Phase Transitions
Classical randomness in quantum measurements
Statistical Zero Knowledge and quantum one-way functions
Quantum Hardcore Functions by Complexity-Theoretical Quantum List Decoding
Permutation and Its Partial Transpose
Some Quantitative Aspects of Fractional Computability
Supersymmetric Models with Higher Dimensional Operators
Discrete rearranging disordered patterns, part I: Robust statistical tools in two or three dimensions
Sky in Google Earth: The Next Frontier in Astronomical Data Discovery and Visualization
The Scientific Manuscripts left Unpublished by Ettore Majorana (with outlines of his life and work)
Application of Quantum Theory to Super-parametric Density Estimation
A Joint Search for Gravitational Wave Bursts with AURIGA and LIGO
Causality and Association: The Statistical and Legal Approaches
Common Reusable Verification Environment for BCA and RTL Models
Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data Visualization
Bayesian considerations on the multiverse explanation of cosmic fine-tuning
Lexical growth, entropy and the benefits of networking
Voyage by Catamaran: Effecting Semantic Network "Bricolage" via Infinite-Dimensional Zero-Divisor Ensembles
Topos Mediated Gravity: Toward the Categorical Resolution of the Cosmological Constant Problem
Canonical polygon Queries on the plane: a New Approach
The Infrared Luminosity of Galaxy Clusters
Axions and Photons In Terms of "Particles" and "Anti-Particles"
Interactive Proofs For Quantum Computations
On second quantization on noncommutative spaces with twisted symmetries
Coherent state approach to the cross collisional effects in the population dynamics of a two-mode Bose-Einstein condensate
Temporal Support of Regular Expressions in Sequential Pattern Mining
The Safe Lambda Calculus
Spherically symmetric massive scalar fields in GR
Integrable structure of melting crystal model with two q-parameters
Uncovering shared common genetic risk factors for various aspects of complex disorders captured in multiple traits
Robust Regulatory Networks
Heterotic (0,2) Gepner Models and Related Geometries
Software Model Checking via Large-Block Encoding
Quantum Measure Theory: A New Interpretation
Induction of Word and Phrase Alignments for Automatic Document Summarization
Self-Assembling Systems are Distributed Systems
Equivariant cohomology over Lie groupoids and Lie-Rinehart algebras
Precision multi-epoch astrometry with VLT cameras FORS1/2
Reaching the boundary between stellar kinematic groups and very wide binaries. The Washington Double Stars with the widest angular separations
Disc-planet interactions in sub-keplerian discs
First Passage Distributions in a Collective Model of Anomalous Diffusion with Tunable Exponent
Convergence Time Evaluation of Algorithms in MANETs
Properties of the United States Code Citation Network
Likelihood-based semi-supervised model selection with applications to speech processing
Modelling Cell Cycle using Different Levels of Representation
Calibration of star formation rate tracers for short- and long-lived star formation episodes
Improved Constructions for Non-adaptive Threshold Group Testing
Hardware Implementation of TDES Crypto System with On Chip Verification in FPGA
Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron
A Formal Approach to Modeling the Memory of a Living Organism
Assume-Guarantee Synthesis for Digital Contract Signing
Fully Countering Trusting Trust through Diverse Double-Compiling
Undecidability of linear inequalities in graph homomorphism densities
The cooling time of white dwarfs produced from type Ia supernovae
Quantum optical coherence can survive photon losses: a continuous-variable quantum erasure correcting code
Calculation methods of the nuclear characteristics
Fundamental Relativistic Rotator. Hessian singularity and the issue of the minimal interaction with electromagnetic field
A Bayesian View of the Poisson-Dirichlet Process
Swapping Evaluation: A Memory-Scalable Solution for Answer-On-Demand Tabling
Fixpoint & Proof-theoretic Semantics for CLP with Qualification and Proximity
Top-Down Multilevel Simulation of Tumor Response to Treatment in the Context of In Silico Oncology
Modeling and Analyzing Adaptive User-Centric Systems in Real-Time Maude
The e.m. field nside the unidimensional Photonic Crystals - Study at optical frequencies applying "Quasi-Normal-Modes" theory
Open Graphs and Monoidal Theories
Probabilistic Arithmetic Automata and their Applications
Noncommutative Riemannian geometry on graphs
Chiral and angular momentum content of mesons
Prospects for accurate distance measurements of pulsars with the SKA: enabling fundamental physics
Ologs: a categorical framework for knowledge representation
Cosmology at the Crossroads of the Natural and Human Sciences: is demarcation possible?
First-order Logic: Modality and Intensionality
Happiness is assortative in online social networks
Confronting the models of 3:2 quasiperiodic oscillations with the rapid spin of the microquasar GRS 1915+105
Pushing the limits for medical image reconstruction on recent standard multicore processors
GPU-Based Heuristic Solver for Linear Sum Assignment Problems Under Real-time Constraints
A continuum solvent model: the DISOLV program - algorithms, implementation, and validation
Computing Distances between Probabilistic Automata
Efficient Loop Navigation for Symbolic Execution
Three Looks at Instantons in F-theory -- New Insights from Anomaly Inflow, String Junctions and Heterotic Duality
On the Zipf strategy for short-term investments in WIG20 futures
Diagrammatics for SU(2) invariant matrix product states
Hasenohrl and the Equivalence of Mass and Energy
Simulation-based optimal Bayesian experimental design for nonlinear systems
A higher Chern-Weil derivation of AKSZ sigma-models
Variational Minimization of Orbital-dependent Density Functionals
Optical lattice quantum simulator for QED in strong external fields: spontaneous pair creation and the Sauter-Schwinger effect
Adding Logical Operators to Tree Pattern Queries on Graph-Structured Data
Non-Archimedean Whitney stratifications
Stackable groups, tame filling invariants, and algorithmic properties of groups
A Massive Data Parallel Computational Framework for Petascale/Exascale Hybrid Computer Systems
Processor Allocation for Optimistic Parallelization of Irregular Programs
Mixed Mimetic Spectral Element Method for Stokes Flow: A Pointwise Divergence-Free Solution
First-Order Logic on Higher-Order Nested Pushdown Trees
Retrieving the three-dimensional matter power spectrum and galaxy biasing parameters from lensing tomography
Directed Spontaneous Emission from $N$-atom Extended Ensemble
New remarks on the Cosmological Argument
Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm
FragIt: A Tool to Prepare Input Files for Fragment Based Quantum Chemical Calculations
Revisiting Waiting Times in DNA evolution
Binary hidden Markov models and varieties
Fuzzy Knowledge Representation, Learning and Optimization with Bayesian Analysis in Fuzzy Semantic Networks
Optimization of Fuzzy Semantic Networks Based on Galois Lattice and Bayesian Formalism
Bisimilarity on Basic Process Algebra is in 2-ExpTime (an explicit proof)
Beyond crystals: the dialectic of materials and information
WikiSent : Weakly Supervised Sentiment Analysis Through Extractive Summarization With Wikipedia
Factorization from an order-theoretic view 1&2
Learn with SAT to Minimize Büchi Automata
The μ-Calculus Alternation Hierarchy Collapses over Structures with Restricted Connectivity
La fonte algébrique des Méthodes nouvelles de la mécanique céleste d'Henri Poincaré
Opinion Mining for Relating Subjective Expressions and Annual Earnings in US Financial Statements
Project G.N.O.S.I.S.: Geographical Network Of Synoptic Information System
Tensor Rank, Invariants, Inequalities, and Applications
Bijective Projections on Parabolic Quotients of Affine Weyl Groups
Global well-posedness of the spatially homogeneous Hubbard-Boltzmann equation
Learning Joint Query Interpretation and Response Ranking
Proceedings First International Workshop on Formal Techniques for Safety-Critical Systems
Amplitudes for Spacetime Regions and the Quantum Zeno Effect: Pitfalls of Standard Path Integral Constructions
From 9-IM Topological Operators to Qualitative Spatial Relations using 3D Selective Nef Complexes and Logic Rules for bodies
Source Code Analysis to Remove Security Vulnerabilities in Java Socket Programs: A Case Study
The covariant description of electric and magnetic field lines of null fields: application to Hopf-Ranada solutions
Recommending Given Names
Word sense disambiguation via high order of learning in complex networks
Decidable Classes of Tree Automata Mixing Local and Global Constraints Modulo Flat Theories
A Semantic approach for effective document clustering using WordNet
The Velocity of Censorship: High-Fidelity Detection of Microblog Post Deletions
Query Expansion Using Term Distribution and Term Association
Quantum Mechanics in symmetry language
An efficient method for evaluating BEM singular integrals on curved elements with application in acoustic analysis
Probability Distinguishes Different Types of Conditional Statements
Young stellar clusters in the Rosette molecular cloud. Arguments against triggered star formation
CAD-based robot programming: The role of Fuzzy-PI force control in unstructured environments
Enumeration of the adjunctive hierarchy of hereditarily finite sets
On SAT representations of XOR constraints
Indexing by Latent Dirichlet Allocation and Ensemble Model
Naming Game on Networks: Let Everyone be Both Speaker and Hearer
Covering Pairs in Directed Acyclic Graphs
Abstract Acceleration of General Linear Loops
Weak Singular Hybrid Automata
Physics Items and Student's Performance at Enem
AnaDroid: Malware Analysis of Android with User-supplied Predicates
On the definition of a general learning system with user-defined operators
XQuery Streaming by Forest Transducers
Covariant Bardeen Perturbation Formalism
A Survey of Data Mining Techniques for Social Media Analysis
Foundations of biology
Learning Document-Level Semantic Properties from Free-Text Annotations
Constructing Reference Sets from Unstructured, Ungrammatical Text
Centrality-as-Relevance: Support Sets and Similarity as Geometric Proximity
Reduction in mechanical systems with symmetry
Critical points and symmetries of a free energy function for biaxial nematic liquid crystals
Negative index Jacobi forms and quantum modular forms
Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks
Microeconomic Structure determines Macroeconomic Dynamics. Aoki defeats the Representative Agent
SERPent: Automated reduction and RFI-mitigation software for e-MERLIN
Consequences of the Lakshmibai-Sandhya Theorem: the ubiquity of permutation patterns in Schubert calculus and related geometry
Atmospheric parameters and chemical properties of red giants in the CoRoT asteroseismology fields
A really simple approximation of smallest grammar
Heap Abstractions for Static Analysis
Integrated Data Acquisition, Storage, Retrieval and Processing Using the COMPASS DataBase (CDB)
On redundancy of memoryless sources over countable alphabets
Worst-case Throughput Analysis for Parametric Rate and Parametric Actor Execution Time Scenario-Aware Dataflow Graphs
A Family of Descriptive Approaches To Preferred Answer Sets
Be In The Know: Connecting News Articles to Relevant Twitter Conversations
How to Ask for a Favor: A Case Study on the Success of Altruistic Requests
Resource Usage Analysis of Logic Programs via Abstract Interpretation Using Sized Types
Undecidable propositional bimodal logics and one-variable first-order linear temporal logics with counting
Dissimilarity-based Sparse Subset Selection
Double Counting in $2^t$-ary RSA Precomputation Reveals the Secret Exponent
Enhanced usage of keys obtained by physical, unconditionally secure distributions
Visibly Pushdown Modular Games
Polarization Measurement of High Dimensional Social Media Messages With Support Vector Machine Algorithm Using Mapreduce
Comprehensive and Macrospin-Based Magnetic Tunnel Junction Spin Torque Oscillator Model - Part I: Analytical Model of the MTJ STO
Converse Lyapunov theorems for discrete-time linear switching systems with regular switching sequences
Phenotypic Plasticity, the Baldwin Effect, and the Speeding up of Evolution: the Computational Roots of an Illusion
Bulk Locality and Quantum Error Correction in AdS/CFT
Millisecond Pulsars in Close Binaries
Relational semantics of linear logic and higher-order model-checking
Nilpotent integrability, reduction of dynamical systems and a third-order Calogero-Moser system
Representing Objects, Relations, and Sequences
Recurrent Neural Network Training with Dark Knowledge Transfer
Synthesis through Unification
Polynomially Low Error PCPs with polyloglog n Queries via Modular Composition
A model building framework for Answer Set Programming with external computations
Programmatic and Direct Manipulation, Together at Last
What are essential concepts about networks?
Tag-Weighted Topic Model For Large-scale Semi-Structured Documents
Efficient Ranking of Lyndon Words and Decoding Lexicographically Minimal de Bruijn Sequence
Regular Symmetry Patterns (Technical Report)
Generating Text with Deep Reinforcement Learning
Revisiting noninteracting string partition functions in Rindler space
Multi-task Sequence to Sequence Learning
Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition
Conducting sparse feature selection on arbitrarily long phrases in text corpora with a focus on interpretability
Adding Gradient Noise Improves Learning for Very Deep Networks
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources
Optimizing Solution Quality in Synchronization Synthesis
On the étale homotopy type of higher stacks
Learning with Memory Embeddings
Towards Universal Paraphrastic Sentence Embeddings
Approximating Optimal Bounds in Prompt-LTL Realizability in Doubly-exponential Time
Solving Diophantine Equations
The Complexity of Synchronizing Markov Decision Processes
TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild
Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System
Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN
Stability and chaos in Kustaanheimo-Stiefel space induced by the Hopf fibration
Minimizing Expected Cost Under Hard Boolean Constraints, with Applications to Quantitative Synthesis
Computational Higher Type Theory I: Abstract Cubical Realizability
Universal Indexes for Highly Repetitive Document Collections
World Knowledge as Indirect Supervision for Document Clustering
Synchronizing Automata with Extremal Properties
Serendipity and strategy in rapid innovation
A General Framework for Static Profiling of Parametric Resource Usage
Similarity Search on Automata Processors
Monochromatic factorisations of words and periodicity
The XML and Semantic Web Worlds: Technologies, Interoperability and Integration. A Survey of the State of the Art
Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities
Class Invariants: Concepts, Problems, Solutions
Some consequences of a recursive number-theoretic relation that is not the standard interpretation of any of its formal representations
Mark My Words! Linguistic Style Accommodation in Social Media
Tverberg's theorem and graph coloring
Mapping Relational Operations onto Hypergraph Model
An hbar-expansion of the Toda hierarchy: a recursive construction of solutions
Lazy Pointer Analysis
The Non-Archimedean Theory of Discrete Systems
The FuturICT Education Accelerator
Life Before Earth
A Semantics for Approximate Program Transformations
Permutation 2-groups I: structure and splitness
Sublinear Matching With Finite Automata Using Reverse Suffix Scanning
THELI -- Convenient reduction of any optical, near- and mid-infrared imaging data
Linking Rigid Bodies Symmetrically
Analysis of Watson's Strategies for Playing Jeopardy!
Breaking a monad-comonad symmetry between computational effects
Intelligent User Interface in Fuzzy Environment
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics
Quantum Pushdown Automata with a Garbage Tape
Particles, waves and trajectories: 210 years after Young's experiment
Learning Natural Coding Conventions
Regular path queries on graphs with data: A rigid approach
Improving Collaborative Filtering based Recommenders using Topic Modelling
Data Definitions in the ACL2 Sedan
Typed Hilbert Epsilon Operators and the Semantics of Determiner Phrases (Invited Lecture)
Spiral Galaxies - classical description of spiral arms and rotational velocity pattern - toy model
Crowd-Sourcing Fuzzy and Faceted Classification for Concept Search
Building DNN Acoustic Models for Large Vocabulary Speech Recognition
Neural Machine Translation by Jointly Learning to Align and Translate
Bypassing Captcha By Machine A Proof For Passing The Turing Test
Ground state connectivity of local Hamiltonians
Sequence to Sequence Learning with Neural Networks
Sandboxing for Software Transactional Memory with Deferred Updates
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Twisted chiral de Rham complex, generalized geometry, and T-duality
Communication complexity and the reality of the wave-function
Distributed Protocols and Heterogeneous Trust: Technical Report
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
LineCAPTCHA Mobile: A User Friendly Replacement for Unfriendly Reverse Turing Tests for Mobile Devices (ICIAfS14)
Algebraic synchronization criterion and computing reset words
Applying deep learning techniques on medical corpora from the World Wide Web: a prototypical system and evaluation
A $(1 + {\varepsilon})$-Embedding of Low Highway Dimension Graphs into Bounded Treewidth Graphs
Rule-and Dictionary-based Solution for Variations in Written Arabic Names in Social Networks, Big Data, Accounting Systems and Large Databases
When Are Tree Structures Necessary for Deep Learning of Representations?
Decidable Horn Systems with Difference Constraints Arithmetic
Efficiently intertwining widening and narrowing
Nonparametric Relational Topic Models through Dependent Gamma Processes
Quantum theory of measurements as quantum decision theory
Open systems dynamics: Simulating master equations in the computer
Timed Orchestration for Component-based Systems
Optimal Shuffle Code with Permutation Instructions
Nodal Discontinuous Galerkin Simulations for Reverse-Time Migration on GPU Clusters
Spatial Symmetry Driven Pruning Strategies for Efficient Declarative Spatial Reasoning
Comparing and evaluating extended Lambek calculi
LCSTS: A Large Scale Chinese Short Text Summarization Dataset
Solving two-dimensional density classification problem with two probabilistic cellular automata
Response Operators for Markov Processes in a Finite State Space: Radius of Convergence and Link to the Response Theory for Axiom A Systems
WYSIWYE: An Algebra for Expressing Spatial and Textual Rules for Visual Information Extraction
Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
Superspace Unitary Operator in Superfield Approach to Non-Abelian Gauge Theory with Dirac Fields
Selecting Relevant Web Trained Concepts for Automated Event Retrieval
Neural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts
A Restricted Visual Turing Test for Deep Scene and Event Understanding
Inhomogeneous field theory inside the arctic circle
Scalable Package Queries in Relational Database Systems
Edge instabilities of topological superconductors
Quantifying Public Response towards Islam on Twitter after Paris Attacks
Probabilistic Programming with Gaussian Process Memoization
The Impact of Technical Domain Expertise on Search Behavior and Task Outcome
Modeling Variations of First-Order Horn Abduction in Answer Set Programming
Write a Classifier: Predicting Visual Classifiers from Unstructured Text
EvoGrader: an online formative assessment tool for automatically evaluating written evolutionary explanations
TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth
Baby Steps in Quantum Ring Theory: towards a background independent framework for Quantum Gravity
Sentiment Analysis of Twitter Data: A Survey of Techniques
Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)
The X-ray/radio and UV luminosity expected from symbiotic systems as the progenitor of SNe Ia
Multi-Object Reasoning with Constrained Goal Models
Lorentz Constraints on Massive Three-Point Amplitudes
Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences
Shelah's eventual categoricity conjecture in universal classes. Part II
Endless love: On the termination of a playground number game
Control-Flow Integrity: Precision, Security, and Performance
Constructive Patterns of Logical Truth
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Automatic Verification of Iterated Separating Conjunctions using Symbolic Execution
If-Conversion Optimization using Neuro Evolution of Augmenting Topologies
On point processes in multitarget tracking
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge
The Road to Popularity: The Dilution of Growing Audience on Twitter
Diagonal Unloading Beamforming for Source Localization
Extremes and Recurrence in Dynamical Systems
Assessment of Effectiveness of Content Models for Approximating Twitter Social Connection Structures
Advanced geometrical constructs in a Pueblo ceremonial site, c 1200 CE
Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks
Application-Driven Near-Data Processing for Similarity Search
Active Discriminative Text Representation Learning
Using Word Embeddings in Twitter Election Classification
Is a Picture Worth Ten Thousand Words in a Review Dataset?
Exact gradient updates in time independent of output size for the spherical loss family
Finite time blowup for Lagrangian modifications of the three-dimensional Euler equation
Optimising The Input Window Alignment in CD-DNN Based Phoneme Recognition for Low Latency Processing
Incremental Quantitative Analysis on Dynamic Costs
An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling
La representación de la variación contextual mediante definiciones terminológicas flexibles
Automated Prediction of Temporal Relations
TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs
Fusion basis for lattice gauge theory and loop quantum gravity
CRTS: A type system for representing clinical recommendations
Ask the GRU: Multi-Task Learning for Deep Text Recommendations
Detecting Singleton Review Spammers Using Semantic Similarity
Multi-view Dimensionality Reduction for Dialect Identification of Arabic Broadcast Speech
The Complexity of Flat Freeze LTL
A Theory of Interactive Debugging of Knowledge Bases in Monotonic Logics
OCR++: A Robust Framework For Information Extraction from Scholarly Articles
Organized Complexity: is Big History a Big Computation?
Polynomial Time Corresponds to Solutions of Polynomial Ordinary Differential Equations of Polynomial Length (Journal version)
Monaural Multi-Talker Speech Recognition using Factorial Speech Processing Models
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
Joint Resource Bidding and Tipping Strategies in Multi-hop Cognitive Networks
Possible regular phenomena in EXO 2030+375
Acoustic Reflector Localization: Novel Image Source Reversion and Direct Localization Methods
End-to-End Training Approaches for Discriminative Segmental Models
Programming Heterogeneous Systems from an Image Processing DSL
FEAST: An Automated Feature Selection Framework for Compilation Tasks
Learning to superoptimize programs
Contradiction Detection for Rumorous Claims
Fractal Analysis Based on Hierarchical Scaling in Complex Systems
Geometric deep learning: going beyond Euclidean data
A Computational Non-Commutative Geometry Program for Disordered Topological Insulators
Improved Image Captioning via Policy Gradient optimization of SPIDEr
Zipf's law, unbounded complexity and open-ended evolution
EchoWear: Smartwatch Technology for Voice and Speech Treatments of Patients with Parkinson's Disease
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
Abelian-Square-Rich Words
sTools - a data reduction pipeline for the GREGOR Fabry-Pérot Interferometer and the High-resolution Fast Imager at the GREGOR solar telescope
Just an Update on PMING Distance for Web-based Semantic Similarity in Artificial Intelligence and Data Mining
Pair production processes and flavor in gauge-invariant perturbation theory
Attention-Based Multimodal Fusion for Video Description
Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code
Leptoquark mechanism of neutrino masses within the grand unification framework
Deep Learning the Indus Script
Mining User/Movie Preferred Features Based on Reviews for Video Recommendation System
Latent Room-Temperature T$_c$ in Cuprate Superconductors
Sampling strategies for fast updating of Gaussian Markov random fields
Stability of Topic Modeling via Matrix Factorization
From Complex Event Processing to Simple Event Processing
A Monadic Framework for Relational Verification: Applied to Information Security, Program Equivalence, and Optimizations
Query Expansion Based on Crowd Knowledge for Code Search
Cats and Captions vs. Creators and the Clock: Comparing Multimodal Content to Context in Predicting Relative Popularity
Customer Lifetime Value Prediction Using Embeddings
Foraging patterns in online searches
MetaPAD: Meta Pattern Discovery from Massive Text Corpora
Toward a Formal Model of Cognitive Synergy
Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
Subset Synchronization in Monotonic Automata
Flare: Native Compilation for Heterogeneous Workloads in Apache Spark
Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models
Does Outside-In Teaching Improve the Learning of Object-Oriented Programming?
Towards Building Large Scale Multimodal Domain-Aware Conversation Systems
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
Towards Industry 4.0: Gap Analysis between Current Automotive MES and Industry Standards using Model-Based Requirement Engineering
Gang-GC: Locality-aware Parallel Data Placement Optimizations for Key-Value Storages
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning
The Conway Moonshine Module is a Reflected K3 Theory
FML-based Prediction Agent and Its Application to Game of Go
Learning to Reason: End-to-End Module Networks for Visual Question Answering
Recognizability for sequences of morphisms
Sonata: Query-Driven Network Telemetry
Multigraded Hilbert Series of noncommutative modules
Maximizing the information learned from finite data selects a simple model
Pixie: A heterogeneous Virtual Coarse-Grained Reconfigurable Array for high performance image processing applications
Exploring Latent Semantic Factors to Find Useful Product Reviews
On the role of words in the network structure of texts: application to authorship attribution
A Finitary Analogue of the Downward Löwenheim-Skolem Property
Engineering Record And Replay For Deployability: Extended Technical Report
Matching Logic
Learning Semantic Relatedness From Human Feedback Using Metric Learning
Discontinuous Hamiltonian Monte Carlo for models with discrete parameters and discontinuous likelihoods
Accelerating Neural Architecture Search using Performance Prediction
Renormalization and Coarse-graining of Loop Quantum Gravity
Attention Is All You Need
Exploring Code Clones in Programmable Logic Controller Software
RELink: A Research Framework and Test Collection for Entity-Relationship Retrieval
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
Algebras of linear growth and the dynamical Mordell-Lang conjecture
Jointly Learning Word Embeddings and Latent Topics
End-to-end Conversation Modeling Track in DSTC6
Image Processing in Floriculture Using a robotic Mobile Platform
Combinatorics and Topology of Kawai-Lewellen-Tye Relations
Stealthy Deception Attacks Against SCADA Systems
Tableaux for Policy Synthesis for MDPs with PCTL* Constraints
Real-time colouring and filtering with graphics shaders
Hidden-Markov-Model Based Speech Enhancement
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
Look Who's Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches
The Case for Being Average: A Mediocrity Approach to Style Masking and Author Obfuscation
Bridging Static and Dynamic Program Analysis using Fuzzy Logic
Toward Incorporation of Relevant Documents in word2vec
AutOMP: An Automatic OpenMP Parallelization Generator for Variable-Oriented High-Performance Scientific Codes
ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
Opening the black box of energy modelling: Strategies and lessons learned
A network approach to topic models
On-Stack Replacement à la Carte
"Is there anything else I can help you with?": Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent
Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network
Polarizability Extraction for Waveguide-Fed Metasurfaces
Large-Scale Domain Adaptation via Teacher-Student Learning
Abstractions for Verifying Isolation Properties in Stateful Networks
Leveraging Deep Neural Network Activation Entropy to cope with Unseen Data in Speech Recognition
A Semi-Supervised Approach to Detecting Stance in Tweets
Towards Runtime Adaptation of Actor Systems
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
Foundations of Complex Event Processing
Nonnegative HMM for Babble Noise Derived from Speech HMM: Application to Speech Enhancement
Word Vector Enrichment of Low Frequency Words in the Bag-of-Words Model for Short Text Multi-class Classification Problems
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
Analyzing users' sentiment towards popular consumer industries and brands on Twitter
A Symbolic Approach to Safety LTL Synthesis
MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
Enumeration Problems for Regular Path Queries
Infinitesimal deformations of Poisson bi-vectors using the Kontsevich graph calculus
Neural Program Meta-Induction
(3+1)-Dimensional Topologically Massive 2-form Gauge Theory: Geometrical Superfield Approach
Paxos Made EPR: Decidable Reasoning about Distributed Protocols
The Emptiness Problem for Valence Automata over Graph Monoids
Program Synthesis using Abstraction Refinement
Worldlines and worldsheets for non-abelian lattice field theories: Abelian color fluxes and Abelian color cycles
Probabilistic Couplings for Probabilistic Reasoning
Sequence-to-Sequence ASR Optimization via Reinforcement Learning
Learning K-way D-dimensional Discrete Code For Compact Embedding Representations
Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning
The Exoplanet Simple Orbit Fitting Toolbox (ExoSOFT): An Open-Source Tool for Efficient Fitting of Astrometric and Radial Velocity Data
SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring
Dual-Path Convolutional Image-Text Embedding
Game Characterization of Probabilistic Bisimilarity, and Applications to Pushdown Automata
Cache-based Document-level Neural Machine Translation
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
Formal Representation of SysML/KAOS Domain Model (Complete Version)
Automated Refactoring of Nested-IF Formulae in Spreadsheets
Visual Text Correction
Are the different layers of a social network conveying the same information?
A Survey on Compiler Autotuning using Machine Learning
Sequences, yet Functions: The Dual Nature of Data-Stream Processing
Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network
Lensing of Fast Radio Bursts by Binaries to Probe Compact Dark Matter
DKN: Deep Knowledge-Aware Network for News Recommendation
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions
Open Material Property Library With Native Simulation Tool Integrations -- MASTO
On-the-fly Detection of Autogenerated Tweets
Effective Quantization Approaches for Recurrent Neural Networks
ReinforceWalk: Learning to Walk in Graph with Monte Carlo Tree Search
TVM: End-to-End Optimization Stack for Deep Learning
NtMalDetect: A Machine Learning Approach to Malware Detection Using Native API System Calls
HWNet v2: An Efficient Word Image Representation for Handwritten Documents
Borel Kernels and their Approximation, Categorically
Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings
Spatial Graph Convolutions for Drug Discovery
Combining Symbolic Execution and Model Checking to Verify MPI Programs
Learning Eligibility in Cancer Clinical Trials using Deep Neural Networks
Clipping free attacks against artificial neural networks
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data
Real Time Sentiment Change Detection of Twitter Data Streams
An Extended Low Fat Allocator API and Applications
Reactive Control Improvisation
Tree Structures: A Variational Approach to Shannon--Wiener Information
Low-resolution spectroscopy of main sequence stars belonging to 12 Galactic globular clusters. I. CH and CN band strength variations
Kepler's Orbits and Special Relativity in Introductory Classical Mechanics
Expressibility in the Lambda Calculus with Letrec
The homotopy theory of diffeological spaces
The Classification of Two-Dimensional Extended Topological Field Theories
Towards the most general scalar-tensor theories of gravity: a unified approach in the language of differential forms
Massive migration from the steppe is a source for Indo-European languages in Europe
Formation {à} distance et outils num{é}riques pour l'enseignement sup{é}rieur et la recherche en Asie-Pacifique (Cambodge, Laos, Vietnam). Partie 02 : recommandations et feuille de route
The Effective Electroweak Chiral Lagrangian: The Matter Sector
Hecke Algebras, SVD, and Other Computational Examples with {\sc CLIFFORD}
Structural and Dynamical Aspects of the AdS/CFT Correspondence: a Rigorous Approach
Evolution of magnetic fields in galaxies and future observational tests with the Square Kilometre Array
Optics for X-ray telescopes: analytical treatment of the off-axis effective area of mirrors in optical modules
The MeqTrees software system and its use for third-generation calibration of radio interferometers
Resource Bounded Measure
Taming Numbers and Durations in the Model Checking Integrated Planning System
Obligation Blackwell Games and p-Automata
Quantum cohomology and toric minimal model programs
Edge state inner products and real-space entanglement spectrum of trial quantum Hall states
Generalization of the Menger's Theorem to Simplicial Complexes and Certain Invariants of the Underlying Topological Spaces
Orthogonal non-Gaussianity in DBI Galileon: prospect for Planck polarisation and post-Planck experiments
A Constraint Satisfaction Framework for Executing Perceptions and Actions in Diagrammatic Reasoning
Exact and Approximate Determinization of Discounted-Sum Automata
The Mathematical Abstraction Theory, The Fundamentals for Knowledge Representation and Self-Evolving Autonomous Problem Solving Systems
Gaps, Rings, and Non-Axisymmetric Structures in Protoplanetary Disks - From Simulations to ALMA Observations
Enabling High-Level Application Development for the Internet of Things
Scheme theoretic tropicalization
Towards Scalable Synthesis of Stochastic Control Systems
A Novel Framework for Robustness Analysis of Visual QA Models
Dataflow Matrix Machines and V-values: a Bridge between Programs and Neural Nets
Distributions and Euler systems for the general linear group
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
Toric varieties and minimal complexes
Similarity-Based Estimation of Word Cooccurrence Probabilities
Parsing of Spoken Language under Time Constraints
Knowledge Representation for Lexical Semantics: Is Standard First Order Logic Enough?
A syntax-based part-of-speech analyser
Using a Corpus for Teaching Turkish Morphology
Prepositional Phrase Attachment through a Backed-Off Model
Conserving Fuel in Statistical Language Learning: Predicting Data Requirements
Syntactic Analyses for Parallel Grammars: Auxiliaries and Genitive NPs
Noun-Phrase Analysis in Unrestricted Text for Information Retrieval
An Empirical Study of Smoothing Techniques for Language Modeling
TSNLP - Test Suites for Natural Language Processing
Morphological Analysis as Classification: an Inductive-Learning Approach
Algorithms for Speech Recognition and Language Processing
How much has information technology contributed to linguistics?
Intonational Boundaries, Speech Repairs and Discourse Markers: Modeling Spoken Dialog
Text Segmentation Using Exponential Models
Structure and Ostension in the Interpretation of Discourse Deixis
DIA-MOLE: An Unsupervised Learning Approach to Adaptive Dialogue Models for Spoken Dialogue Systems
explanation-based learning of data oriented parsing
Integrating a Lexical Database and a Training Collection for Text Categorization
Using WordNet to Complement Training Information in Text Categorization
Hierarchical Non-Emitting Markov Models
Treatment of Epsilon-Moves in Subset Construction
Word-to-Word Models of Translational Equivalence
Graph Interpolation Grammars as Context-Free Automata
Parallel Strands: A Preliminary Investigation into Mining the Web for Bilingual Text
Deriving the Predicate-Argument Structure for a Free Word Order Language
Prefix Probabilities from Stochastic Tree Adjoining Grammars
Evaluation of the NLP Components of the OVIS2 Spoken Dialogue System
The Alma Project, or How First-Order Logic Can Help Us in Imperative Programming
Verbal Interactions in Virtual Worlds
Sintesi di algoritmi con SKY
An Integrated Development Environment for Declarative Multi-Paradigm Programming
Models and Tools for Collaborative Annotation
Transformations of Logic Programs with Goals as Arguments
Information Revolution
Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems
Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval
A Tribute to Alain Colmerauer
CHR Grammars
Logic Column 10: Specifying Confidentiality
Vers un environnement multi personnalites pour la configuration et le deploiement d'applications a base de composants logiciels
Toward a Human-Centered Uml for Risk Analysis
Combining Independent Modules in Lexical Multiple-Choice Problems
On the Complexity of Nonrecursive XQuery and Functional Query Languages on Complex Values
Checking C++ Programs for Dimensional Consistency
Object-Oriented Modeling of Programming Paradigms
On the tree-transformation power of XSLT
Packrat Parsing: Simple, Powerful, Lazy, Linear Time
An Internet Framework to Bring Coherence between WAP and HTTP Ensuring Better Mobile Internet Security
Design Strategies and Knowledge in Object-Oriented Programming: Effects of Experience
Users' participation to the design process in an Open Source Software online community
Logic Meets Algebra: the Case of Regular Languages
Supersymmetries and Gauge Natural Theories
Notes on 2D Conformal Field Theory and String Theory
Semiinfinite cohomology of Tate Lie algebras
Elementary equivalence versus Isomorphism
Bunches of cones in the divisor class group -- A new combinatorial language for toric varieties
Model theoretic reformulation of the Baum-Connes and Farrell-Jones conjectures
Pattern avoiding permutations are context-sensitive
Uniqueness of quantization of complex contact manifolds
On the equivalence of two quantifier elimination tests
Bounded fitness landscapes and the evolution of the linguistic diversity
A functional quantum programming language
Opinion Dynamics and Sociophysics
On the Hopcroft's minimization algorithm
Abstract numeration systems on bounded languages and multiplication by a constant
Rational subsets of polycyclic monoids and valence automata
An approximation trichotomy for Boolean #CSP
Let's get the student into the driver's seat
Ontology and Formal Semantics - Integration Overdue
Modular Compilation of a Synchronous Language
The topology of syntax relations of a formal language
Music, Complexity, Information
Highly Undecidable Problems about Recognizability by Tiling Systems
Kruskal's theorem
A Lightweight Combination of Semantics for Non-deterministic Functions
Formally Specifying and Proving Operational Aspects of Forensic Lucid in Isabelle
Fragments of first-order logic over infinite words
Function Interface Models for Hardware Compilation: Types, Signatures, Protocols
Bounded Languages Meet Cellular Automata with Sparse Communication
Word Sense Disambiguation Using English-Spanish Aligned Phrases over Comparable Corpora
On Pebble Automata for Data Languages with Decidable Emptiness Problem
Building a Vietnamese Language Query Processing Framework for ELibrary Searching Systems
Lexical evolution rates by automated stability measure
Document Searching System based on Natural Language Query Processing for Vietnam Open Courseware Library
Sentence Simplification Aids Protein-Protein Interaction Extraction
Deriving Relationship Between Semantic Models - An Approach for cCSP
Polychronous Interpretation of Synoptic, a Domain Specific Modeling Language for Embedded Flight-Software
Cloud Process Execution Engine - Evaluation of the Core Concepts
Proceedings Eighth Workshop on Quantitative Aspects of Programming Languages
Automated co-evolution of GMF editor models
Comparative Studies of Programming Languages; Course Lecture Notes
CHR(PRISM)-based Probabilistic Logic Learning
Decidability properties for fragments of CHR
Simplifying Complex Software Assembly: The Component Retrieval Language and Implementation
A Model of Cooperative Threads
Vector bundles on elliptic curves and factors of automorphy
Fuzzy Ontology Representation using OWL 2
Smart Bengali Cell Phone Keypad Layout
Integration of Agile Ontology Mapping towards NLP Search in I-SOAS
Continuation-Passing C: compiling threads to events through continuations
The growth function of S-recognizable sets
Quantum picturalism for topological cluster-state computing
A decompilation of the pi-calculus and its application to termination
The effect of linguistic constraints on the large scale organization of language
The Language Features and Architecture of B-Prolog
Nested Refinements for Dynamic Languages
A Data Mining view on Class Room Teaching Language
The Complexity of Mean-Payoff Automaton Expression
Conditional Elimination through Code Duplication
Answer Set Planning Under Action Costs
Entropy of Telugu
Rascal: From Algebraic Specification to Meta-Programming
Proceedings Ninth Workshop on Quantitative Aspects of Programming Languages
Event-Clock Automata: From Theory to Practice
Reasoning in the OWL 2 Full Ontology Language using First-Order Automated Theorem Proving
Linearization of CIF Through SOS
Some Measurements of Nullable and Non-Nullable Parameter Declarations in Relation to Software Malleability
MELT - a Translated Domain Specific Language Embedded in the GCC Compiler
Rule based Part of speech Tagger for Homoeopathy Clinical realm
Well-typed Islands Parse Faster
Organizing the Aggregate: Languages for Spatial Computing
Modelling Social Structures and Hierarchies in Language Evolution
On Global Types and Multi-Party Session
Observability of Turing Machines: a Refinement of the Theory of Computation
Proceedings 6th Workshop on Logical and Semantic Frameworks with Applications
The Power of Linear Programming for Valued CSPs
Multilingual Topic Models for Unaligned Text
Dynamic Verification for File Safety of Multithreaded Programs
C to O-O Translation: Beyond the Easy Stuff
The Complexity of Learning Principles and Parameters Grammars
Proceedings 10th Workshop on Quantitative Aspects of Programming Languages and Systems
Towards the Formal Specification and Verification of Maple Programs
Distinct word length frequencies: distributions and symbol entropies
The abelian complexity of the paperfolding word
One-Way Reversible and Quantum Finite Automata with Advice
Evaluation of some Information Retrieval models for Gujarati Ad hoc Monolingual Tasks
Ostrowski Numeration and the Local Period of Sturmian Words
Design of English-Hindi Translation Memory for Efficient Translation
A Rule-Based Approach For Aligning Japanese-Spanish Sentences From A Comparable Corpora
JooFlux : modification de code à chaud et injection d'aspects directement dans une JVM 7
The Twitter of Babel: Mapping World Languages through Microblogging Platforms
Building a reordering system using tree-to-string hierarchical model
Integers in number systems with positive and negative quadratic Pisot base
A Classification of Adjectives for Polarity Lexicons Enhancement
The Story of Telebrain: A multi-performer telematic platform for performatization
Determination through Universals: An Application of Category Theory in the Life Sciences
A Case Study in Coordination Programming: Performance Evaluation of S-Net vs Intel's Concurrent Collections
The Quantum Challenge in Concept Theory and Natural Language Processing
From Principles to Practice with Class in the First Year
Competency Tracking for English as a Second or Foreign Language Learners
Artificial Intelligence MArkup Language: A Brief Tutorial
Physics as a Mechanism for Including ELLs in Classroom Discourse
Single-tape and Multi-tape Turing machines through the lens of the Grossone methodology
An Overview of Nominal-Typing versus Structural-Typing in OOP
Preliminary Notes on Termination and Non-Termination Reasoning
Colourful Language: Measuring Word-Colour Associations
Google Scholar Metrics evolution: an analysis according to languages
Individual Biases, Cultural Evolution, and the Statistical Nature of Language Universals: The Case of Colour Naming Systems
Impact of Indentation in Programming
Semantic Types, Lexical Sorts and Classifiers
Factorial Hidden Markov Models for Learning Representations of Natural Language
Multilingual Distributed Representations without Word Alignment
The confidence interval methods in quantum language
Learning Multilingual Word Representations using a Bag-of-Words Autoencoder
Improving Performance Of English-Hindi Cross Language Information Retrieval Using Transliteration Of Query Terms
Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches
Language Heedless of Logic - Philosophy Mindful of What? Failures of Distributive and Absorption Laws
ImNet: An Imperative Network Programming Language
An Intensional Concurrent Faithful Encoding of Turing Machines
Semantics and Validation of Shapes Schemas for RDF
Extracting a bilingual semantic grammar from FrameNet-annotated corpora
An Efficient Solution for Model Checking Abstract State Machine Using Bogor
Overview of Stemming Algorithms for Indian and Non-Indian Languages
On the Role of Canonicity in Bottom-up Knowledge Compilation
FO(C) and Related Modelling Paradigms
FO(C): A Knowledge Representation Language of Causality
Haskell for OCaml programmers
Buffered Simulation Games for Büchi Automata
MotàMot project: conversion of a French-Khmer published dictionary for building a multilingual lexical system
Mathematical Language Processing Project
Symbolic Algorithms for Language Equivalence and Kleene Algebra with Tests
The two envelopes paradox in non-Bayesian and Bayesian statistics
Using Mechanical Turk to Build Machine Translation Evaluation Sets
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling
Rapid Adaptation of POS Tagging for Domain Specific Uses
SCULPT: A Schema Language for Tabular Data on the Web
Imaginaries in bounded pseudo real closed fields
Step-Indexed Logical Relations for Probability (long version)
Communication and games in the online foreign language educational system. User behavior study
Consensus Game Acceptors and Iterated Transductions
Implementation of an Automatic Syllabic Division Algorithm from Speech Files in Portuguese Language
Turn Segmentation into Utterances for Arabic Spontaneous Dialogues and Instance Messages
Recursion in RDF Data Shape Languages
Boosting Named Entity Recognition with Neural Character Embeddings
Embedding rationally independent languages into maximal ones
A New Approach to Probabilistic Programming Inference
Dependency Recurrent Neural Language Models for Sentence Completion
Reversible Watson-Crick Automata
Buzz: An Extensible Programming Language for Self-Organizing Heterogeneous Robot Swarms
The Polylingual Labeled Topic Model
Everything old is new again: Quoted Domain Specific Languages
Towards a Decoupled Context-Oriented Programming Language for the Internet of Things
Choreographies, Computationally
Noisy-parallel and comparable corpora filtering methodology for the extraction of bi-lingual equivalent data at sentence level
Statistical Parsing by Machine Learning from a Classical Arabic Treebank
Bound Your Models! How to Make OWL an ASP Modeling Language
Quantum Alternation: Prospects and Problems
Multi-lingual Geoparsing based on Machine Translation
Growing Wikipedia Across Languages via Recommendation
Visual Storytelling
Eilenberg theorems for many-sorted formations
Clustering Comparable Corpora of Russian and Ukrainian Academic Texts: Word Embeddings and Semantic Fingerprints
A declarative JVM Language for Automated Validation
The Dichotomy for Conservative Constraint Satisfaction is Polynomially Decidable
New word analogy corpus for exploring embeddings of Czech words
Dual Density Operators and Natural Language Meaning
A Formal, Resource Consumption-Preserving Translation of Actors to Haskell
Devito: automated fast finite difference computation
Where are my followers? Understanding the Locality Effect in Twitter
Autonomous Agents Coordination: Action Languages meet CLP(FD) and Linda
Iterated Hairpin Completions of Non-crossing Words
Types for X10 Clocks
The Automorphism Group of a Resplendent Model
The risks of mixing dependency lengths from sequences of different length
A Web-based Multilingual Intelligent Tutor System based on Jackson's Learning Styles Profiler and Expert Systems
Automatic Construction and Natural-Language Description of Nonparametric Regression Models
When Learners Surpass their Sources: Mathematical Modeling of Learning from an Inconsistent Source
An efficient algorithm for computing the edit distance of a regular language via input-altering transducers
Subword complexity and decomposition of the set of factors
A survey on phrase structure learning methods for text classification
Modal Object Diagrams
Delta Modeling for Software Architectures
Statistical Patterns in Written Language
Diverse Embedding Neural Network Language Models
Scaling Recurrent Neural Network Language Models
A Falsification View of Success Typing
Automata and rational expressions
Varieties
Encoding Source Language with Convolutional Neural Network for Machine Translation
IMP with exceptions over decorated logic
Phrase database Approach to structural and semantic disambiguation in English-Korean Machine Translation
Implementing an intelligent version of the classical sliding-puzzle game for unix terminals using Golang's concurrency primitives
Proceedings Tenth International Workshop on Developments in Computational Models
A Definability Dichotomy for Finite Valued CSPs
Symbolic Manipulation of Code Properties
Trust, but Verify: Two-Phase Typing for Dynamic Languages
Texts in, meaning out: neural language models in semantic similarity task for Russian
Do Multi-Sense Embeddings Improve Natural Language Understanding?
Author Identification using Multi-headed Recurrent Neural Networks
Pragmatic Side Effects
Noncommutative Valiant's Classes: Structure and Complete Problems
Automata networks model for alignment and least effort on vocabulary formation
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
Syntax Evolution: Problems and Recursion
The influence of Chunking on Dependency Crossing and Distance
The USFD Spoken Language Translation System for IWSLT 2014
Iteration Algebras for UnQL Graphs and Completeness for Bisimulation
Efficient Algorithms for Morphisms over Omega-Regular Languages
Towards a Direct, By-Need Evaluator for Dependently Typed Languages
A Sound and Complete Hoare Logic for Dynamically-Typed, Object-Oriented Programs -- Extended Version --
Polish - English Speech Statistical Machine Translation Systems for the IWSLT 2014
Real-Time Statistical Speech Translation
A Sentence Meaning Based Alignment Method for Parallel Text Corpora Preparation
Polish - English Speech Statistical Machine Translation Systems for the IWSLT 2013
Proceedings ML Family/OCaml Users and Developers workshops
Semantic Boolean Arabic Information Retrieval
Strategies for Training Large Vocabulary Neural Language Models
Technical Report: a tool for measuring Prosodic Accommodation
Part-of-Speech Tagging for Code-mixed Indian Social Media Text at ICON 2015
Elaborate lexicon extended language with a lot of conceptual information
Empirical Gaussian priors for cross-lingual transfer learning
Unrestricted State Complexity of Binary Operations on Regular Languages
Fantastic 4 system for NIST 2015 Language Recognition Evaluation
Contextual Media Retrieval Using Natural Language Queries
Eilenberg Theorems for Free
Bioinformatics and Classical Literary Study
Embedding by Normalisation
Segmentation from Natural Language Expressions
Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
Prepositional Attachment Disambiguation Using Bilingual Parsing and Alignments
Semantic Spaces
Universal Dependencies for Learner English
Word2Vec is a special case of Kernel Correspondence Analysis and Kernels for Natural Language Processing
iStar 2.0 Language Guide
SS4MCT: A Statistical Stemmer for Morphologically Complex Texts
Using stories to bridge the chasm between perspectives: How metaphors and genres are used to share meaning
Semi-Supervised Learning for Neural Machine Translation
A Distributional Semantics Approach to Implicit Language Learning
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
A Note on Nested String Replacements
Syntactic Phylogenetic Trees
Neural Semantic Encoders
Enriching Word Vectors with Subword Information
Is spoken language all-or-nothing? Implications for future speech-based human-machine interaction
Mining Arguments from Cancer Documents Using Natural Language Processing and Ontologies
Characterizations and Effective Computation of Supremal Relatively Observable Sublanguages
A Large Scale Corpus of Gulf Arabic
Bounded-oscillation Pushdown Automata
Grammatical Templates: Improving Text Difficulty Evaluation for Language Learners
Taming Context-Sensitive Languages with Principled Stateful Parsing
Enhanced LSTM for Natural Language Inference
Pointer Sentinel Mixture Models
emoji2vec: Learning Emoji Representations from their Description
Syntactic Structures and Code Parameters
Refinement Reflection (or, how to turn your favorite language into a proof assistant using SMT)
Path discovery by Querying the federation of Relational Database and RDF Graph
Multiactive objects and their applications
CBAS: context based arabic stemmer
Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
Cruciform: Solving Crosswords with Natural Language Processing
Multi-Language Identification Using Convolutional Recurrent Neural Network
Mixing Metaphors: Actors as Channels and Channels as Actors (Extended Version)
On the quantized dynamics of factorial languages
Formal Languages, Formally and Coinductively
End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager
Coupling Distributed and Symbolic Execution for Natural Language Queries
Machine Reading with Background Knowledge
A CRF Based POS Tagger for Code-mixed Indian Social Media Text
Language Modeling with Gated Convolutional Networks
What the Language You Tweet Says About Your Occupation
Systems of natural-language-facilitated human-robot cooperation: A review
Using English as Pivot to Extract Persian-Italian Parallel Sentences from Non-Parallel Corpora
Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey
An introduction to singular SPDEs
Universal Semantic Parsing
Bilateral Multi-Perspective Matching for Natural Language Sentences
Handwritten Arabic Numeral Recognition using Deep Learning Neural Networks
Person Search with Natural Language Description
Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages
Jolie Static Type Checker: a prototype
Dynamic Word Embeddings for Evolving Semantic Discovery
A Comment on "Asking photons where they have been in plain language"
Language Use Matters: Analysis of the Linguistic Structure of Question Texts Can Characterize Answerability in Quora
Morphological Analysis for the Maltese Language: The Challenges of a Hybrid System
Learning Simpler Language Models with the Differential State Framework
I CAN HAS SUPERCOMPUTER? A Novel Approach to Teaching Parallel and Distributed Computing Concepts Using a Meme-Based Programming Language
Character-Word LSTM Language Models
Spatio-temporal Person Retrieval via Natural Language Queries
Mapping Objects to Persistent Predicates
Towards Practical, Precise and Parametric Energy Analysis of IT Controlled Systems
TALL: Temporal Activity Localization via Language Query
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
On the Height of Towers of Subsequences and Prefixes
Formalising opencypher Graph Queries in Relational Algebra
Sequential Dialogue Context Modeling for Spoken Language Understanding
Logical and Algebraic Characterizations of Rational Transductions
Decoding Sentiment from Distributed Representations of Sentences
A Lightweight Regression Method to Infer Psycholinguistic Properties for Brazilian Portuguese
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
On Multilingual Training of Neural Dependency Parsers
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Acquisition of Translation Lexicons for Historically Unwritten Languages via Bridging Loanwords
Verb Physics: Relative Physical Knowledge of Actions and Objects
Cross-lingual Speaker Verification with Deep Feature Learning
Document Spanners for Extracting Incomplete Information: Expressiveness and Complexity
N-GrAM: New Groningen Author-profiling Model
Quon language: surface algebras and Fourier duality
A Web-Based Tool for Analysing Normative Documents in English
Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies
Computing LPMLN Using ASP and MLN Solvers
Exploring Neural Transducers for End-to-End Speech Recognition
Image Pivoting for Learning Multilingual Multimodal Representations
Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks
A mathematically derived definitional/semantical theory of truth
Revisiting Activation Regularization for Language RNNs
Recursive Whitening Transformation for Speaker Recognition on Language Mismatched Condition
Regularizing and Optimizing LSTM Language Models
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
A Semiotics-inspired Domain-Specific Modeling Language for Complex Event Processing Rules
Future Word Contexts in Neural Network Language Models
Patterns versus Characters in Subword-aware Neural Language Modeling
Getting Reliable Annotations for Sarcasm in Online Dialogues
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment
MK-fuzzy Automata and MSO Logics
Cross-lingual Word Segmentation and Morpheme Segmentation as Sequence Labelling
On Decidability of the Ordered Structures of Numbers
Language Modeling with Highway LSTM
Using objective words in the reviews to improve the colloquial arabic sentiment analysis
Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languages
Towards a Minimal Stabilizer ZX-calculus
Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition
Mathematical foundations of matrix syntax
AutoMode: Relational Learning With Less Black Magic
Visual and Textual Programming Languages: A Systematic Review of the Literature
Experimental Biological Protocols with Formal Semantics
Linear Haskell: practical linearity in a higher-order polymorphic language
Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning
Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models
Unbounded cache model for online language modeling with open vocabulary
Multilingual Adaptation of RNN Based ASR Systems
A Language for Probabilistically Oblivious Computation
A critical analysis of string APIs: The case of Pharo
Curriculum Q-Learning for Visual Vocabulary Acquisition
ARbis Pictus: A Study of Language Learning with Augmented Reality
Neural Cross-Lingual Entity Linking
Learning Interpretable Spatial Operations in a Rich 3D Blocks World
A High-Level Rule-based Language for Software Defined Network Programming based on OpenFlow
Quaternionic Wavefunction
Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings
Object-Orientation in Graph-Based Design Grammars
Building a Sentiment Corpus of Tweets in Brazilian Portuguese
Higher physical fitness levels are associated with less language decline in healthy ageing
Applying Vector Space Model (VSM) Techniques in Information Retrieval for Arabic Language
Enhancing Translation Language Models with Word Embedding for Information Retrieval
Common factors in automatic and Sturmian sequences
Web-Based Implementation of Travelling Salesperson Problem Using Genetic Algorithm
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
Space Improvements and Equivalences in a Functional Core Language
Randomized sliding window algorithms for regular languages
Improving Sentiment Analysis in Arabic Using Word Representation
Dolphin: a task orchestration language for autonomous vehicle networks
On Modular Training of Neural Acoustics-to-Word Model for LVCSR
Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017
English-Catalan Neural Machine Translation in the Biomedical Domain through the cascade approach
MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification
Jumps in speeds of hereditary properties in finite relational languages
An LP-based hyperparameter optimization model for language modeling
Vision as an Interlingua: Learning Multilingual Semantic Embeddings of Untranscribed Speech
Coverability: Realizability Lower Bounds
Sentiment Transfer using Seq2Seq Adversarial Autoencoders
Predicting Twitter User Socioeconomic Attributes with Network and Language Information
Language Recognition using Time Delay Deep Neural Network
Exponentially more concise quantum recognition of non-RMM regular languages
An overview of Ciao and its design philosophy
An Expressive Language and Efficient Execution System for Software Agents
A new keyphrases extraction method based on suffix tree data structure for arabic documents clustering
Semantics derived automatically from language corpora contain human-like biases
Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner
El Lenguaje Natural como Lenguaje Formal
Scaling Reliably: Improving the Scalability of the Erlang Distributed Actor Platform
Language Design and Renormalization
Natural Language Parsing as Statistical Pattern Recognition
Inducing Features of Random Fields
Combining Hand-crafted Rules and Unsupervised Learning in Constraint-based Morphological Disambiguation
An Abstract Machine for Unification Grammars
Speech Repairs, Intonational Boundaries and Discourse Markers: Modeling Speakers' Utterances in Spoken Dialog
Recognizing Syntactic Errors in the Writing of Second Language Learners
A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity
Japanese/English Cross-Language Information Retrieval: Exploration of Query Translation and Transliteration
A Chart-Parsing Algorithm for Efficient Semantic Analysis
Analyzing language development from a network approach
Characterizations of 1-Way Quantum Finite Automata
Computational approach to the emergence and evolution of language - evolutionary naming game model
On Recognizable Languages of Infinite Pictures
Du corpus au dictionnaire
Probabilistic Weighted Automata
Automated words stability and languages phylogeny
A Topological derivative based image segmentation for sign language recognition system using isotropic filter
Minimisation of Deterministic Parity and Buchi Automata and Relative Minimisation of Deterministic Finite Automata
Towards Design and Implementation of a Language Technology based Information Processor for PDM Systems
Recovering Grammar Relationships for the Java Language Specification
Proposing LT based Search in PDM Systems for Better Information Retrieval
The Design and Implementation of Typed Scheme: From Scripts to Programs
A Regularity Measure for Context Free Grammars
Left Recursion in Parsing Expression Grammars
The challenges of statistical patterns of language: the case of Menzerath's law in genomes
MJ no more: Using Concurrent Wikipedia Edit Spikes with Social Network Plausibility Checks for Breaking News Detection
Modeling the emergence of a new language: Naming Game with hybridization
Small Depth Proof Systems
Proceedings Combined 20th International Workshop on Expressiveness in Concurrency and 10th Workshop on Structural Operational Semantics
TCC, with History
Intensional Cyberforensics
Deterministic Logics for UL
Evaluating the fully automatic multi-language translation of the Swiss avalanche bulletin
Proceedings Combined 21st International Workshop on Expressiveness in Concurrency and 11th Workshop on Structural Operational Semantics
A complete graphical calculus for Spekkens' toy bit theory
Description and Optimization of Abstract Machines in a Dialect of Prolog
A Note on Monitors and Büchi automata
Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation
A Productivity Checker for Logic Programming
Channels as Objects in Concurrent Object-Oriented Programming
Visualization of Object Oriented Modeling from the Perspective of Set theory
Improving Accessibility of Archived Raster Dictionaries of Complex Script Languages
Text mixing shapes the anatomy of rank-frequency distributions: A modern Zipfian mechanics for natural language
Exemplar Dynamics and Sound Merger in Language
Open System Categorical Quantum Semantics in Natural Language Processing
Personalizing Universal Recurrent Neural Network Language Model with User Characteristic Features by Social Network Crowdsouring
Block-Level Parallelism in Parsing Block Structured Languages
Words are not Equal: Graded Weighting Model for building Composite Document Vectors
Topical differences between Chinese language Twitter and Sina Weibo
Translingual Obfuscation
Sound and Complete Runtime Security Monitor for Application Software
A Graph-Based Semantics Workbench for Concurrent Asynchronous Programs
Spatial logic of modal mu-calculus and tangled closure operators
Multi-domain machine translation enhancements by parallel data extraction from comparable corpora
Ask Your Neurons: A Deep Learning Approach to Visual Question Answering
Complexity Bounds of Constant-Space Quantum Computation
Complexity of Universality and Related Problems for Partially Ordered NFAs
Mapping Between fMRI Responses to Movies and their Natural Language Annotations
Understanding and maintaining tactics graphically OR how we are learning that a diagram can be worth more than 10K LoC
Type oriented parallel programming for Exascale
Recurrent Neural Network Language Model Adaptation Derived Document Vector
Language Support for Reliable Memory Regions
Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence
Wikiwhere: An interactive tool for studying the geographical provenance of Wikipedia references
Building Code with Dynamic Staging
Arabic Language Sentiment Analysis on Health Services
Toward Semantic Foundations for Program Editors
Modeling the life and death of competing languages from a physical and mathematical perspective
Structural Stability of Lexical Semantic Spaces: Nouns in Chinese and French
A Pattern Language for High-Performance Computing Resilience
Cross-language Framework for Word Recognition and Spotting of Indic Scripts
Detecting Cross-Lingual Plagiarism Using Simulated Word Embeddings
Shielding Google's language toxicity model against adversarial attacks
Is the coexistence of Catalan and Spanish possible in Catalonia?
Efficient Gradual Typing
Stability of items: an experimental test
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation
Testing the complexity of a valued CSP language
A Comparison of the XTAG and CLE Grammars for English
Complexity of Self-Assembled Shapes
The propagation of a cultural or biological trait by neutral genetic drift in a subdivided population
On the security of AlphaEta: Response to `Some attacks on quantum-based cryptographic protocols'
Parts-of-Speech Tagger Errors Do Not Necessarily Degrade Accuracy in Extracting Information from Biomedical Text
From formulas to cirquents in computability logic
Polytropic neutron star - black hole merger simulations with a Paczynski-Wiita potential
Jacobi Equations and Comparison Theorems for Corank 1 sub-Riemannian Structures with Symmetries
On the Sets of Real Numbers Recognized by Finite Automata in Multiple Bases
The second and third parameters of the Horizontal Branch in Globular Clusters
Online Verification of Control Parameter Calculations in Communication Based Train Control System
The evolving slope of the stellar mass function at 0.6 <= z < 4.5 from deep WFC3 data
Static and dynamic variational principles for strongly correlated electron systems
Pitfalls of Path Integrals: Amplitudes for Spacetime Regions and the Quantum Zeno Effect
The Hilbert space of conditional clauses
Structural connections between a forcing class and its modal logic
Embracing divergence: a formalism for when your semiring is simply not complete, with applications in quantum simulation
PyCosmic: a robust method to detect cosmics in CALIFA and other fiber-fed integral-field spectroscopy datasets
On the Nature of Reality
Efficient deconvolution methods for astronomical imaging: algorithms and IDL-GPU codes
Unifying Büchi Complementation Constructions
EURETILE 2010-2012 summary: first three years of activity of the European Reference Tiled Experiment
Harmonic maps of finite uniton type into non-compact inner symmetric spaces
Chemical compositions of six metal-poor stars in the ultra-faint dwarf spheroidal galaxy Boötes I
Theoretical Foundations of Equitability and the Maximal Information Coefficient
A Framework for Lattice QCD Calculations on GPUs
Noise Robustness of the Incompatibility of Quantum Measurements
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
Topological dynamics and the complexity of strong types
What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)
An automaton approach for waiting times in DNA evolution
Joint Video and Text Parsing for Understanding Events and Answering Queries
Performance comparison between Java and JNI for optimal implementation of computational micro-kernels
Gaia FGK Benchmark Stars: Effective temperatures and surface gravities
Horava Gravity in the Effective Field Theory formalism: from cosmology to observational constraints
(Almost) C*-algebras as sheaves with self-action
Geometric aspects of the symmetric inverse M-matrix problem
Towards Evaluation of Cultural-scale Claims in Light of Topic Model Sampling Effects
Recovering Structured Probability Matrices
Universality of Black Hole Quantum Computing
Belief-Invariant and Quantum Equilibria in Games of Incomplete Information
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
4D Scattering Amplitudes and Asymptotic Symmetries from 2D CFT
Causal structures and the classification of higher order quantum computations
Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library
The Parameterized Complexity of Positional Games
The Covering Problem
Go with the Flow: Compositional Abstractions for Concurrent Data Structures (Extended Version)
In-RDBMS Hardware Acceleration of Advanced Analytics
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
Abelian networks IV. Dynamics of nonhalting networks
BigSR: an empirical study of real-time expressive RDF stream reasoning on modern Big Data platforms
Event Data Definition in LHCb
Some Problems in Automata Theory Which Depend on the Models of Set Theory
HOPE: A Python Just-In-Time compiler for astrophysical computations
Time-space tradeoffs for two-way finite automata
Yin and Yang: Balancing and Answering Binary Visual Questions
Low-dimensional Query Projection based on Divergence Minimization Feedback Model for Ad-hoc Retrieval
Unrestricted State Complexity of Binary Operations on Regular and Ideal Languages
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks
Which NP-Hard SAT and CSP Problems Admit Exponentially Improved Algorithms?
PIE: A Domain-Specific Language for Interactive Software Development Pipelines
The Shear TEsting Programme 2: Factors affecting high precision weak lensing analyses
Pyrochlore Photons: The U(1) Spin Liquid in a S=1/2 Three-Dimensional Frustrated Magnet
An Efficient and Flexible Engine for Computing Fixed Points
Universal Similarity
DMTCP: Transparent Checkpointing for Cluster Computations and the Desktop
Warped Geometry of Brane Worlds
Quantum Hyperbolic State Sum Invariants of 3-Manifolds
Homological perturbations, equivariant cohomology, and Koszul duality
An XML Driven Graphical User Interface and Application Management Toolkit
Relativistic Quantum Dynamics: A non-traditional perspective on space, time, particles, fields, and action-at-a-distance
Process, System, Causality, and Quantum Mechanics, A Psychoanalysis of Animal Faith
On the Power of Random Bases in Fourier Sampling: Hidden Subgroup Problem in the Heisenberg Group
"Gravitational mass" of information?
Infinitesimal or cocommutative dipterous bialgebras and good triples of operads
Hydrodynamics of spacetime and vacuum viscosity
Kinematics of the old stellar population at the Galactic Center
Numerical method for Darcy flow derived using Discrete Exterior Calculus
Signatures of intrinsic Li depletion and Li-Na anti-correlation in the metal-poor globular cluster NGC6397
The Second INTEGRAL AGN Catalogue
The dilution peak, metallicity evolution, and dating of galaxy interactions and mergers
Self force on a scalar charge in Kerr spacetime: circular equatorial orbits
Does resolving PvNP require a paradigm shift?
Passively Mobile Communicating Logarithmic Space Machines
Star formation in Cometary globule 1: the second generation
On the non-Abelian monopoles on the background of spaces with constant curvature
Least squares deconvolution of the stellar intensity and polarization spectra
Photospheric and coronal abundances in solar-type stars: the peculiar case of Tau Bootis
Dust formation in the ejecta of the Type II-P supernova 2004dj
Good Friends, Bad News - Affect and Virality in Twitter
The flare model for X-ray variability of NGC 4258
Actions des groupes topologiques sur les objets universels
Reducing Interpolation on Multi-Grid to Quantizing Grid's Data-Base as a Recursion
Exact computation of joint spectral characteristics of linear operators
Estimating the overlap between dependent computations for automatic parallelization
The Control Theory of Motion-Based Communication: Problems in Teaching Robots to Dance
Four Degrees of Separation
Harmony Explained: Progress Towards A Scientific Theory of Music
A way to deal with the fringe-like pattern in VIMOS-IFU data
Recompression: a simple and powerful technique for word equations
The Quantum Frontier
Self-similarity of temperature profiles in distant galaxy clusters: the quest for a Universal law
CDAS: A Crowdsourcing Data Analytics System
Invariant Generation through Strategy Iteration in Succinctly Represented Control Flow Graphs
Probabilities on Sentences in an Expressive Logic
Inelastic electron and Raman scattering from the collective excitations in quantum wires: Zero magnetic field
A Rule-based Model of a Hypothetical Zombie Outbreak: Insights on the role of emotional factors during behavioral adaptation of an artificial population
The development of the Heliometer of the Observatorio Nacional of Rio de Janeiro and application to the study of the Sun-Earth system
Calibration of quasi-static aberrations in exoplanet direct-imaging instruments with a Zernike phase-mask sensor
Network Sparsification for Steiner Problems on Planar and Bounded-Genus Graphs
Dynamics of the dust rings and satellites of Uranus and of the Saturn's F-ring
Intrinsic structure of liquid surface and capillary waves on the Density Functional Theory
Circum-stellar medium around rotating massive stars at solar metallicity
Analysis of chemical abundances in planetary nebulae with [WC] central stars. II. Chemical abundances and the abundance discrepancy factor
High-level programming and control for industrial robotics: using a hand-held accelerometer-based input device for gesture and posture recognition
Verification of Imperative Programs by Constraint Logic Program Transformation
The SPARQL2XQuery Interoperability Framework. Utilizing Schema Mapping, Schema Transformation and Query Translation to Integrate XML and the Semantic Web
Why does the effective field theory of inflation work?
PsrPopPy: An open-source package for pulsar population simulations
An Effective End-User Development Approach Through Domain-Specific Mashups for Research Impact Evaluation
Bayesian regression discontinuity designs: Incorporating clinical knowledge in the causal analysis of primary care data
Chemical composition and constraints on mass loss for globular clusters in dwarf galaxies: WLM and IKN
Concise comparative summaries (CCS) of large text corpora with a human experiment
Kernels in tropical geometry and a Jordan-Hölder Theorem
Global disease monitoring and forecasting with Wikipedia
Constructing small tree grammars and small circuits for formulas
Towards Refactoring DMARF and GIPSY OSS
Presence-absence reasoning for evolutionary phenotypes
Large Vocabulary Arabic Online Handwriting Recognition System
Faster graphical model identification of tandem mass spectra using peptide word lattices
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolution
SunPy - Python for Solar Physics
Concrete resource analysis of the quantum linear system algorithm used to compute the electromagnetic scattering cross section of a 2D target
Multi-GPU Distributed Parallel Bayesian Differential Topic Modelling
General relativistic magnetohydrodynamical simulations of the jet in M87
Regularity theory and extension problem for fractional nonlocal parabolic equations and the master equation
Context-Content Systems of Random Variables: The Contextuality-by-Default Theory
Fast and Lean Immutable Multi-Maps on the JVM based on Heterogeneous Hash-Array Mapped Tries
OCR of historical printings with an application to building diachronic corpora: A case study using the RIDGES herbal corpus
Quantum mechanics in an evolving Hilbert space
Relating Weight Constraint and Aggregate Programs: Semantics and Representation
The Estimation of Subjective Probabilities via Categorical Judgments of Uncertainty
The VLT-FLAMES Tarantula Survey XVI. The optical+NIR extinction laws in 30 Doradus and the photometric determination of the effective temperatures of OB stars
About Adaptive Coding on Countable Alphabets: Max-Stable Envelope Classes
Acyclicity Notions for Existential Rules and Their Application to Query Answering in Ontologies
Low delta-V near-Earth asteroids: A survey of suitable targets for space missions
Efficient Gluing of Numerical Continuation and a Multiple Solution Method for Elliptic PDEs
Musical elements in the discrete-time representation of sound
ADS: The Next Generation Search Platform
q-randomized Robinson-Schensted-Knuth correspondences and random polymers
If the Current Clique Algorithms are Optimal, so is Valiant's Parser
Typologies of the Popular Science Web Video
Maximum-Entropy Inference with a Programmable Annealer
Rotational Virtual Knots and Quantum Link Invariants
Testing particle trapping in transition disks with ALMA
Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks
Implementation of the Tangent Sphere and Cutting Plane Methods in the Quantitative Determination of Ligand Binding Site Burial Depths in Proteins Using FORTRAN 77/90 Language
Two applications of the spectrum of numbers
Resurgent transseries $\&$ Dyson-Schwinger equations
Deep Learning on FPGAs: Past, Present, and Future
Multi-frequency studies of galaxies and groups: I. Environmental effect on galaxy stellar mass and morphology
Kitaev honeycomb tensor networks: exact unitary circuits and applications
Document Retrieval on Repetitive String Collections
Ensemble X-ray variability of Active Galactic Nuclei. II. Excess Variance and updated Structure Function
LP-branching algorithms based on biased graphs
Semantic Information Encoding in One Dimensional Time Domain Signals
Computational Interpretations of Markov's principle
GaDei: On Scale-up Training As A Service For Deep Learning
How to measure the topological quality of protein grammars?
Evolution of the real-space correlation function from next generation cluster surveys
Comparing MapReduce and Pipeline Implementations for Counting Triangles
Extracting and Analyzing Hidden Graphs from Relational Databases
Quantum Break-Time of de Sitter
Social media mining for identification and exploration of health-related information from pregnant women
Spin-projected matrix product states (SP-MPS): a versatile tool for strongly correlated systems
Soundness in negotiations
DGSAT: Dwarf Galaxy Survey with Amateur Telescopes II. A catalogue of isolated nearby edge-on disk galaxies and the discovery of new low surface brightness systems
Graphical Models: An Extension to Random Graphs, Trees, and Other Objects
Learning to Associate Words and Images Using a Large-scale Graph
Exoplanet Biosignatures: Future Directions
Simulated Galactic methanol maser distribution to constrain Milky Way parameters
Skin Temperature Measurement
Production of vector resonances at the LHC via WZ-scattering: a unitarized EChL analysis
The Complexity Landscape of Fixed-Parameter Directed Steiner Network Problems
Raum, Zeit und Wechselwirkung in der Quantentheorie der Ur-Alternativen
Efficient Algorithms for Checking Fast Termination in VASS
Model Checking for Fragments of Halpern and Shoham's Interval Temporal Logic Based on Track Representatives
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks
How intelligent are convolutional neural networks?
Characterizing Diabetes, Diet, Exercise, and Obesity Comments on Twitter
Weak Memory Models with Matching Axiomatic and Operational Definitions
Indirect Supervision for Relation Extraction using Question-Answer Pairs
SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRank
Optical-NIR dust extinction towards Galactic O stars
Pebble isolation mass --- scaling law and implications for the formation of super-Earths and gas giants
Galactic cold cores IX. Column density structures and radiative transfer modelling
Detecting Zones and Threat on 3D Body for Security in Airports using Deep Machine Learning
Mechanical Stresses Estimation in Silicon and Glass Bonded at Elevated Temperature
Comment on "Linguistic Features of Noncoding DNA Sequences"
Lessons from a Restricted Turing Test
An Empirically Motivated Reinterpretation of Dependency Grammar
Adjuncts and the Processing of Lexical Rules
Temporal Relations: Reference or Discourse Coherence?
Efficiency, Robustness, and Accuracy in Picky Chart Parsing
A Stochastic Finite-State Word-Segmentation Algorithm for Chinese
Collaboration on reference to objects that are not mutually known
Precise n-gram Probabilities from Stochastic Context-free Grammars
An Extended Theory of Head-Driven Parsing
Modularity in a Connectionist Model of Morphology Acquisition
Relating Complexity to Practical Performance in Parsing with Wide-Coverage Unification Grammars
Statistical Augmentation of a Chinese Machine-Readable Dictionary
An Automatic Method of Finding Topic Boundaries
A Spanish Tagset for the CRATER Project
Resolution of Syntactic Ambiguity: the Case of New Subjects
The complexity of normal form rewrite sequences for Associativity
Anytime Algorithms for Speech Parsing?
Multi-Paragraph Segmentation of Expository Text
Learning unification-based grammars using the Spoken English Corpus
Interleaving Syntax and Semantics in an Efficient Bottom-Up Parser
Discourse Obligations in Dialogue Processing
Parsing as Tree Traversal
Computing FIRST and FOLLOW Functions for Feature-Theoretic Grammars
The Correct and Efficient Implementation of Appropriateness Specifications for Typed Feature Structures
Typed Feature Structures as Descriptions
Tagging accurately -- Don't guess if you know
Distributional Clustering of English Words
Experimentally Evaluating Communicative Strategies: The Effect of the Task
A Formal Look at Dependency Grammars and Phrase-Structure Grammars, with Special Consideration of Word-Order Phenomena
Recognizing Text Genres with Simple Metrics Using Discriminant Analysis
Automatic Error Detection in Part of Speech Tagging
Part-of-Speech Tagging with Neural Networks
Concurrent Lexicalized Dependency Parsing: A Behavioral View on ParseTalk Events
Sublanguage Terms: Dictionaries, Usage, and Automatic Classification
Extending DRT with a Focusing Mechanism for Pronominal Anaphora and Ellipsis Resolution
Extraction in Dutch with Lexical Rules
Lexical Knowledge Representation in an Intelligent Dictionary Help System
Interlingual Lexical Organisation for Multilingual Lexical Databases in NADIA
Bottom-Up Earley Deduction
Utilization of a Lexicon for Spelling Correction in Modern Greek
A Robust and Efficient Three-Layered Dialogue Component for a Speech-to-Speech Translation System
Ambiguity resolution in a reductionistic parser
Ellipsis and Quantification: a substitutional approach
Deterministic Consistency Checking of LP Constraints
A Tractable Extension of Linear Indexed Grammars
On Reasoning with Ambiguities
Towards an Account of Extraposition in HPSG
Computational dialectology in Irish Gaelic
ParseTalk about Sentence- and Text-Level Anaphora
The Semantics of Motion
Non-Constituent Coordination: Theory and Practice
Discourse and Deliberation: Testing a Collaborative Strategy
SATZ - An Adaptive Sentence Segmentation System
From compositional to systematic semantics
Co-occurrence Vectors from Corpora vs. Distance Vectors from Dictionaries
Cues and control in Expert-Client Dialogues
An Implemented Formalism for Computing Linguistic Presuppositions and Existential Commitments
A Morphographemic Model for Error Correction in Nonconcatenative Strings
Corpus Statistics Meet the Noun Compound: Some Empirical Results
Compiling HPSG type constraints into definite clause programs
Treating Coordination with Datalog Grammars
Compilation of HPSG to TAG
Tagset Reduction Without Information Loss
Evaluation of Semantic Clusters
Exploring the role of Punctuation in Parsing Natural Text
Combining Multiple Knowledge Sources for Discourse Segmentation
D-Tree Grammars
Syllable parsing in English and French
Filling Knowledge Gaps in a Broad-Coverage Machine Translation System
The Effect of Pitch Accenting on Pronoun Referent Resolution
An Approach to Proper Name Tagging for German
Constraint Categorial Grammars
A Natural Law of Succession
The Development and Migration of Concepts from Donor to Borrower Disciplines: Sublanguage Term Use in Hard & Soft Sciences
POS Tagging Using Relaxation Labelling
A Proposal for Word Sense Disambiguation using Conceptual Distance
Developing and Evaluating a Probabilistic LR Parser of Part-of-Speech and Punctuation Labels
An investigation into the correlation of cue phrases, unfilled pauses and the structuring of spoken discourse
Using Information Content to Evaluate Semantic Similarity in a Taxonomy
Limited Attention and Discourse Structure
Similarity between Words Computed by Spreading Activation on an English Dictionary
Text Segmentation Based on Similarity between Words
Situations and Computation: An Overview of Recent Research
Assessing agreement on classification tasks: the kappa statistic
A Constraint-based Case Frame Lexicon
Off-line Constraint Propagation for Efficient HPSG Processing
SemHe: A Generalised Two-Level System
Magic for Filter Optimization in Dynamic Bottom-up Processing
Unsupervised Learning of Word-Category Guessing Rules
Learning Part-of-Speech Guessing Rules from Lexicon: Extension to Non-Concatenative Operations
Yet Another Paper about Partial Verb Phrase Fronting in German
Resolving Anaphors in Embedded Sentences
Counting Coordination Categorially
A Conceptual Reasoning Approach to Textual Ellipsis
Incremental Centering and Center Ambiguity
Parsing Algorithms and Metrics
Part-of-Speech-Tagging using morphological information
Coordination in Tree Adjoining Grammars: Formalization and Implementation
Word Sense Disambiguation using Conceptual Density
Modularizing Contexted Constraints
Relating Turing's Formula and Zipf's Law
Stabilizing the Richardson Algorithm by Controlling Chaos
Compilation of Weighted Finite-State Transducers from Decision Trees
Computational Complexity of Probabilistic Disambiguation by means of Tree-Grammars
Inducing Constraint Grammars
Integrating Syntactic and Prosodic Information for the Efficient Detection of Empty Categories
Pattern-Based Context-Free Grammars for Machine Translation
A Machine Learning Approach to the Classification of Dialogue Utterances
Automatic Construction of Clean Broad-Coverage Translation Lexicons
CLEARS - An Education and Research Tool for Computational Semantics
The discourse functions of Italian subjects: a centering approach
Corrections and Higher-Order Unification
Automatic Detection of Omissions in Translations
Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning
Automatic Extraction of Subcategorization from Corpora
Concept Clustering and Knowledge Integration from a Children's Dictionary
Insights into the Dialogue Processing of VERBMOBIL
Centering in-the-large: Computing referential discourse segments
Sloppy Identity
Grammatical analysis in the OVIS spoken-dialogue system
Computing Parallelism in Discourse
A Lexicon for Underspecified Semantic Tagging
Comparing a Linguistic and a Stochastic Tagger
Probabilistic Coreference in Information Extraction
Name Searching and Information Retrieval
Efficient Construction of Underspecified Semantics under Massive Ambiguity
Automatic Detection of Text Genre
Recognizing Referential Links: An Information Extraction Perspective
Similarity-Based Methods For Word Sense Disambiguation
Encoding Frequency Information in Lexicalized Grammars
The Complexity of Recognition of Linguistically Adequate Dependency Grammars
Segmentation of Expository Texts by Hierarchical Agglomerative Clustering
Use of Weighted Finite State Transducers in Part of Speech Tagging
Disambiguating with Controlled Disjunctions
Look-Back and Look-Ahead in the Conversion of Hidden Markov Models into Finite State Transducers
On the existence of certain total recursive functions in nontrivial axiom systems, I
Parsing Inside-Out
Automatic summarising: factors and directions
Rationality, Cooperation and Conversational Implicature
Can Subcategorisation Probabilities Help a Statistical Parser?
Word Sense Disambiguation using Optimised Combinations of Knowledge Sources
Never Look Back: An Alternative to Centering
Evaluating a Focus-Based Approach to Anaphora Resolution
A Maximum-Entropy Partial Parser for Unrestricted Text
Chunk Tagger - Statistical Recognition of Noun Phrases
A Linguistically Interpreted Corpus of German Newspaper Text
Automatically Creating Bilingual Lexicons for Machine Translation from Bilingual Text
Statistical Models for Unsupervised Prepositional Phrase Attachment
Combining Expression and Content in Domains for Dialog Managers
How to define a context-free backbone for DGs: Implementing a DG in the LFG formalism
Separating Surface Order and Syntactic Relations in a Dependency Grammar
A Comparison of WordNet and Roget's Taxonomy for Measuring Semantic Similarity
Entropic analysis of the role of words in literary texts
Extended Comment on Language Trees and Zipping
Separating Dependency from Constituency in a Tree Rewriting System
Improving Tagging Performance by Using Voting Taggers
Object Oriented and Functional Programming for Symbolic Manipulation
Memory-Based Shallow Parsing
Formal Modeling in a Commercial Setting: A Case Study
Self-Specifying Machines
Deduction over Mixed-Level Logic Representations for Text Passage Retrieval
A database and lexicon of scripts for ThoughtTreasure
DLV - A System for Declarative Problem Solving
Variable Word Rate N-grams
Improving Testsuites via Instrumentation
Bagging and Boosting a Treebank Parser
Comparing two trainable grammatical relations finders
More accurate tests for the statistical significance of result differences
Metonymy Interpretation Using X NO Y Examples
Bunsetsu Identification Using Category-Exclusive Rules
Temporal Expressions in Japanese-to-English Machine Translation
Anaphora Resolution in Japanese Sentences Using Surface Expressions and Examples
Computing Presuppositions by Contextual Reasoning
Using existing systems to supplement small amounts of annotated grammatical relations training data
Reduction of Intermediate Alphabets in Finite-State Transducer Cascades
Utilizing the World Wide Web as an Encyclopedia: Extracting Term Descriptions from Semi-Structured Texts
A Novelty-based Evaluation Method for Information Retrieval
The Use of Instrumentation in Grammar Engineering
Mathematical Model of Word Length on the Basis of the Cebanov-Fucks Distribution with Uniform Parameter Distribution
Correction of Errors in a Modality Corpus Used for Machine Translation by Using Machine-learning Method
Reverse Engineering from Assembler to Formal Specifications via Program Transformations
Component Programming and Interoperability in Constraint Solver Design
Joint and conditional estimation of tagging and parsing models
Computational properties of environment-based disambiguation
Using the Distribution of Performance for Studying Statistical NLP Systems and Corpora
Integrating Multiple Knowledge Sources for Robust Semantic Parsing
Learning class-to-class selectional preferences
Using a Support-Vector Machine for Japanese-to-English Translation of Tense, Aspect, and Modality
Towards practical meta-querying
Annotation Graphs and Servers and Multi-Modal Resources: Infrastructure for Interdisciplinary Education, Research and Development
Semantic Properties for Lightweight Specification in Knowledgeable Development Environments
Unsupervised Discovery of Morphemes
Defining Rough Sets by Extended Logic Programs
Exploiting Sublanguage and Domain Characteristics in a Bootstrapping Approach to Lexicon and Ontology Creation
An Alternative to RDF-Based Languages for the Representation and Processing of Ontologies in the Semantic Web
DUCT: An Interactive Define-Use Chain Navigation Tool for Relative Debugging
Memory As A Monadic Control Construct In Problem-Solving
The Design of a COM-Oriented Module System
Secure Prolog-Based Mobile Code
O(1) Reversible Tree Navigation Without Cycles
Well-Definedness and Semantic Type-Checking in the Nested Relational Calculus and XQuery
Annotating Predicate-Argument Structure for a Parallel Treebank
Proofing Tools Technology at Neurosoft S.A.
CrocoPat 2.1 Introduction and Reference Manual
Optimal Union-Find in Constraint Handling Rules
An Audit Logic for Accountability
In the beginning was game semantics
Planning with Preferences using Logic Programming
Integration of Declarative and Constraint Programming
Statistical Parameters of the Novel "Perekhresni stezhky" ("The Cross-Paths") by Ivan Franko
Prolog Server Pages
Remote-control and clustering of physical computations using the XML-RPC protocol and the open-Mosix system
Continuations, proofs and tests
Semantics of Separation-Logic Typing and Higher-order Frame Rules for
Algol-like Languages
Complexity of Data Flow Analysis for Non-Separable Frameworks
Scaling Construction Grammar up to Production Systems: the SCIM
Fingerprinting Logic Programs
The pitfalls of verifying floating-point computations
An Abstract Monte-Carlo Method for the Analysis of Probabilistic Programs
A Formal Model for Programming Wireless Sensor Networks
Dependency Parsing with Dynamic Bayesian Network
Universal Nonperturbative Effects in Event Shapes from Soft-Collinear Effective Theory
Extremal Transitions in Heterotic String Theory
On the harmonic superspace language adapted to the Gelfand-Dickey algebra of differential operators
On the Hopf Structure of W(2) Algebra and N=1 Superconformal Algebra in the Ope Language
Two-Loop Superstrings in Hyperelliptic Language II: the Vanishing of the Cosmological Constant and the Non-Renormalization Theorem
An application of Shoenfield's absoluteness theorem to the theory of uniform distribution
Using Automata to obtain Regular Expressions for Induced Actions
Model Companions of T_σfor stable T
A language for multiplicative-additive linear logic
Divisibility Theory and Complexity of Algorithms in Free Partially Commutative Groups
Formal languages and groups as memory
Some thoughts upon axiomatized languages with estension tools, a focus on probability theory and error calculus with Dirichlet forms
Life in Silico - Simulation of Complex Systems by Enzymatic Computation
Problem Solving and the Use of Math in Physics Courses
An Evolutionary Picture for Quantum Physics
Exact results for accepting probabilities of quantum automata
Linear optics implementation of weak values in Hardy's paradox
An Algebra of Pure Quantum Programming
Quantum Interpretations
Arabic Speech Recognition System using CMU-Sphinx4
Learning Phonotactics Using ILP
Undecidability in function fields of positive characteristic
Quantized Detector Networks: A review of recent developments
Approximately Independent Features of Languages
Zipf's Law and Avoidance of Excessive Synonymy
Remarks on Jurdzinski and Lorys' proof that palindromes are not a Church-Rosser language
The predictability of letters in written english
Evaluation of a Grammar of French Determiners
Freeware solutions for spectropolarimetric data reduction
A logical analysis of entanglement and separability in quantum higher-order functions
Hypergames and full completeness for system F (rough draft)
Groups that do and do not have context-sensitive word problem
Concerning Olga, the Beautiful Little Street Dancer (Adjectives as Higher-Order Polymorphic Functions)
Platform-Independent Firewall Policy Representation
A chain dictionary method for Word Sense Disambiguation and applications
Automata and cells in affine Weyl groups
AceWiki: Collaborative Ontology Management in Controlled Natural Language
Initial Results on the F-logic to OWL Bi-directional Translation on a Tabled Prolog Engine
Peek Arc Consistency
Software dependability modeling using an industry-standard architecture description language
Non procedural language for parallel programs
A formally verified compiler back-end
Ambiguity and Communication
Syntactic variation of support verb constructions
On the Morse-Hedlund complexity gap
A Type System for Parallel Components
A Particular Universal Cellular Automaton
Search-based Structured Prediction
Continuum multi-physics modeling with scripting languages: the Nsim simulation compiler prototype for classical field theory
Bayesian Query-Focused Summarization
Modular Verification of Recursive Programs
Serializing the Parallelism in Parallel Communicating Pushdown Automata Systems
Marking-up multiple views of a Text: Discourse and Reference
A Note On Higher Order Grammar
Ludics and its Applications to natural Language Semantics
Decision problems for inverse monoids presented by a single sparse relator
A Type System for a Stochastic CLS
A non-interleaving process calculus for multi-party synchronisation
Graph-Links
Integer Reset Timed Automata: Clock Reduction and Determinizability
Speech Recognition of the letter 'zha' in Tamil Language using HMM
Morphological study of Albanian words, and processing with NooJ
Complete Context Calculus Design and Implementation in GIPSY
Les Entités Nommées : usage et degrés de précision et de désambiguïsation
Graph Creation, Visualisation and Transformation
The Semantics of Graph Programs
Algèbre OLAP et langage graphique
Views, Program Transformations, and the Evolutivity Problem in a Functional Language
Quantitative parametrization of texts written by Ivan Franko: An attempt of the project
Quantifier elimination and minimality conditions in algebraically closed valued fields
Mirrored Language Structure and Innate Logic of the Human Brain as a Computable Model of the Oracle Turing Machine
Mapping Business Process Modeling constructs to Behavior Driven Development Ubiquitous Language
Static and Dynamic Quality Assurance by Aspect Oriented Techniques
A tool stack for implementing Behaviour-Driven Development in Python Language
Transition Complexity of Incomplete DFAs
Transformations Between Different Types of Unranked Bottom-Up Tree Automata
Learning Residual Finite-State Automata Using Observation Tables
Proceedings Seventh Workshop on Structural Operational Semantics
Exact Bivariate Polynomial Factorization in Q by Approximation of Roots
AI 3D Cybug Gaming
Proceedings Fourth International Workshop on Testing, Analysis and Verification of Web Software
An algorithmic approximation of the infimum reachability probability for Probabilistic Finite Automata
Introduction to the iDian
A Decidable Characterization of a Graphical Pi-calculus with Iterators
A Path Algebra for Multi-Relational Graphs
A PDTB-Styled End-to-End Discourse Parser
An Introduction to Software Engineering and Fault Tolerance
Emoticonsciousness
SICStus Prolog -- the first 25 years
SPARQL Assist Language-Neutral Query Composer
Severe Language Effect in University Rankings: Particularly Germany and France are wronged in citation-based rankings
Matrix Insertion-Deletion Systems
NP has log-space verifiers with fixed-size public quantum registers
The Geometry of T-Varieties
Transforming ASN.1 Specifications into CafeOBJ to assist with Property Checking
Mixing, Ergodic, and Nonergodic Processes with Rapidly Growing Information between Blocks
Representing First-Order Causal Theories by Logic Programs
A Universal Part-of-Speech Tagset
Handwritten Character Recognition of South Indian Scripts: A Review
Cognitive Binary Logic - The Natural Unified Formal Theory of Propositional Binary Logic
Clasificarea distribuita a mesajelor de e-mail
Development and performance analysis of a UPC Particle-in-Cell code
On the system F as a glue language for natural-language compositional-semantics
Tutorial on Online Partial Evaluation
Modular Abstractions of Reactive Nodes using Disjunctive Invariants
PDDL2.1 - The Art of the Possible? Commentary on Fox and Long
Specification of photonic circuits using Quantum Hardware Description Language
Compiler Optimization: A Case for the Transformation Tool Contest
HelloWorld! An Instructive Case for the Transformation Tool Contest
Saying Hello World with MOLA - A Solution to the TTC 2011 Instructive Case
Saying HelloWorld with QVTR-XSLT - A Solution to the TTC 2011 Instructive Case
Dynamic Logics of Imperfect Information: from Teams and Games to Transitions
On Some Entertaining Applications of the Concept of Set in Computer Science Course
A Description Logic Primer
Segmenting DNA sequence into `words'
Realisation d'un systeme de reconnaissance automatique de la parole arabe base sur CMU Sphinx
Programming with Algebraic Effects and Handlers
Using Signals to Improve Automatic Classification of Temporal Relations
Semi-Automatically Extracting FAQs to Improve Accessibility of Software Development Knowledge
Reasoning on Schemata of Formulae
The logic of quantum mechanics - Take II
The Design of GP 2
Lazy AC-Pattern Matching for Rewriting
Degree two approximate Boolean #CSPs with variable weights
Catroid: A Mobile Visual Programming System for Children
Some Combinatorial Operators in Language Theory
On Formal Specification of Maple Programs
Clustering based approach extracting collocations
Frame Interpretation and Validation in a Open Domain Dialogue System
Ockham's Razor, Probability and Quantum Physics as Logic
Numerical methods with Sage
Design and Implementation A different Architectures of mixcolumn in FPGA
Forests and the W Construction
A Myhill-Nerode theorem for automata with advice
A call-by-value lambda-calculus with lists and control
The Jasper Framework: Towards a Platform Independent, Formal Treatment of Web Programming
Adding Sessions to BPEL
Text Classification with Compression Algorithms
An Experiment on the Connection between the DLs' Family DL and the Real World
Two Algorithms for Finding $k$ Shortest Paths of a Weighted Pushdown Automaton
The Grammar Hammer of 2012
Three-Element Min-Sol and Conservative Min-Cost-Hom
Adaptation of fictional and online conversations to communication media
Realizability Categories
Cutting Recursive Autoencoder Trees
Logarithmic Space and Permutations
Enabling Operator Reordering in Data Flow Programs Through Static Code Analysis
Forty hours of declarative programming: Teaching Prolog at the Junior College Utrecht
Merging Uncertain Knowledge Bases in a Possibilistic Logic Framework
Arguing for Decisions: A Qualitative Model of Decision Making
Variant-Frequency Semantics for Green Futures
Modularizing and Specifying Protocols among Threads
Bisimulation and p-morphism for branching-time logics with indistinguishability relations
Automatic Detection of Non-deverbal Event Nouns for Quick Lexicon Production
Modeling Basic Aspects of Cyber-Physical Systems
A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure
A representation of context-free grammars with the help of finite digraphs
Eventual Linear Ranking Functions
A framework for (under)specifying dependency syntax without overloading annotators
Accomplishable Tasks in Knowledge Representation
Hilbert's Tenth Problem over Function Fields of Positive Characteristic Not Containing the Algebraic Closure of a Finite Field
Suggest an Aspect-Oriented Design Approach for UML Communication Diagram
Proceedings Fourth International Symposium on Games, Automata, Logics and Formal Verification
An Algorithm Enumerating All Infinite Repetitions in a D0L System
Reasoning for Moving Blocks Problem: Formal Representation and Implementation
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction
Introducing Access Control in Webdamlog
Proceedings First Workshop on Control Operators and their Semantics
C++11 -- idea r-wartości i przenoszenia
Environmental Bisimulations for Delimited-Control Operators
Online partial evaluation of sheet-defined functions
A Simple Semantics and Static Analysis for Stack Inspection
LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual
The Joseph Greenberg problem: combinatorics and comparative linguistics
Development and Transcription of Assamese Speech Corpus
Treating clitics with minimalist grammars
Some Remarks on Lower Bounds for Queue Machines (Preliminary Report)
Fighting network space: it is time for an SQL-type language to filter phylogenetic networks
What Mathematical Theories of Truth Should be Like (and Can be)
Problems in Systematic Application of Software Metrics and Possible Solution
HEVAL: Yet Another Human Evaluation Metric
Programming with Permissions in Mezzo
Remarks on Privileged Words
Hop and HipHop : Multitier Web Orchestration
The bitwise operations related to a fast sorting algorithm
The Petri-Nets to Statecharts Transformation Case
Analyzing Flowgraphs with ATL
Bidirectional Recursive Neural Networks for Token-Level Labeling with Structure
Finite automata with advice tapes
Inferring Algebraic Effects
Coinductive Big-Step Semantics for Concurrency
Deep Learning Embeddings for Discontinuous Linguistic Units
Suffix Stripping Problem as an Optimization Problem
Plurals: individuals and sets in a richly typed semantics
Undecidable properties of self-affine sets and multi-tape automata
On the Potential of Twitter for Understanding the Tunisia of the Post-Arab Spring
Büchi Types for Infinite Traces and Liveness
Finite difference numerical method for the superlattice Boltzmann transport equation and case comparison of CPU(C) and GPU(CUDA) implementations
Category theory, logic and formal linguistics: some connections, old and new
A Simple Method to Reduce Thermodynamic Derivatives by Computer
Design a Persian Automated Plagiarism Detector (AMZPPD)
Encapsulating Formal Methods within Domain Specific Languages: A Solution for Verifying Railway Scheme Plans
Enforcing Operational Properties including Blockfreeness for Deterministic Pushdown Automata
Languages of lossless seeds
Information Retrieval (IR) through Semantic Web (SW): An Overview
Songlines and Navigation in Wardaman and other Australian Aboriginal Cultures
Going higher in the First-order Quantifier Alternation Hierarchy on Words
On the Relative Expressiveness of Argumentation Frameworks, Normal Logic Programs and Abstract Dialectical Frameworks
Three Semantics for Modular Systems
Measuring Communication in Parallel Communicating Finite Automata
Cooperating Distributed Grammar Systems of Finite Index Working in Hybrid Modes
A Logical Formalization of a Secure XML Database
Inter-Rater Agreement Study on Readability Assessment in Bengali
Substitute Based SCODE Word Embeddings in Supervised NLP Tasks
Uniformly defining $p$-henselian valuations
Towards a Domain Specific Language for a Scene Graph based Robotic World Model
Making FPGAs Accessible to Scientists and Engineers as Domain Expert Software Programmers with LabVIEW
Proceedings Fifth International Symposium on Games, Automata, Logics and Formal Verification
Tree games with regular objectives
On the compactness property of extensions of first-order Gödel logic
Refinement Checking for Multirate Hybrid ZIA
Arabic Language Text Classification Using Dependency Syntax-Based Feature Selection
Modeling Word Relatedness in Latent Dirichlet Allocation
Weighted automata on infinite words in the context of Attacker-Defender games
Root-Weighted Tree Automata and their Applications to Tree Kernels
Tree-based language complexity of Thompson's group F
Semantics for Locking Specifications
Proving Looping and Non-Looping Non-Termination by Finite Automata
A Formalisation of Finite Automata using Hereditarily Finite Sets
Palindromic complexity of trees
Arabic Inquiry-Answer Dialogue Acts Annotation Schema
Tracking Causal Dependencies in Web Services Orchestrations Defined in ORC
On Web-based Domain-Specific Language for Internet of Things
An Implementation Model for Interaction Nets
Abstract Interpretation of Supermodular Games
Logic and Branching Automata
Classifier-Based Text Simplification for Improved Machine Translation
Why teach an introductory course in Mathematical Logic in the Philosophy curriculum?
Using interrogative logic to teach classical logic
The tree machine
Deterministic parallel communicating Watson-Crick automata systems
P-trac Procedure: The Dispersion and Neutralization of Contrasts in Lexicon
Type-Based Analysis for Session Inference
On the Number of Many-to-Many Alignments of Multiple Sequences
Identifying Actionable Messages on Social Media
Learning about Spanish dialects through Twitter
A Roadmap towards Machine Intelligence
Cross-lingual Models of Word Embeddings: An Empirical Comparison
An Extremal Series of Eulerian Synchronizing Automata
Using Sentence-Level LSTM Language Models for Script Inference
Shallow Parsing Pipeline for Hindi-English Code-Mixed Social Media Text
Synthesizing Program Input Grammars
Winograd Schemas and Machine Translation
Canonical Correlation Inference for Mapping Abstract Scenes to Text
Syntactically Informed Text Compression with Recurrent Neural Networks
A Practical Quantum Instruction Set Architecture
Extracting Biological Pathway Models From NLP Event Representations
Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway
Julia Implementation of the Dynamic Distributed Dimensional Data Model
Homological combinatorics and extensions of the cd-index
Static Trace-Based Deadlock Analysis for Synchronous Mini-Go
If more than Analytical Modeling is Needed to Predict Real Agents' Strategic Interaction
Ideogram Based Chinese Sentiment Word Orientation Computation
An Implementation of Bubbling
Dependent Types for JavaScript
Quantified Data Automata on Skinny Trees: an Abstract Domain for Lists
MCE Reasoning in Recursive Causal Networks
Objective Probability
Global Life Patterns: A Methodology for Designing a Personal Global Life
Comparing the usage of global and local Wikipedias with focus on Swedish Wikipedia
Authorship Analysis based on Data Compression
IVOA Recommendation: TAPRegExt: a VOResource Schema Extension for Describing TAP Services
McCammond's normal forms for free aperiodic semigroups revisited
The Best Templates Match Technique For Example Based Machine Translation
Coherence for Skew-Monoidal Categories
Constraint Handling Rules with Multiset Comprehension Patterns
Sessions as Propositions
Question Answering with Subgraph Embeddings
Mapping the Economic Crisis: Some Preliminary Investigations
A CNL for Contract-Oriented Diagrams
Subshifts, MSO Logic, and Collapsing Hierarchies
An NLP Assistant for Clide
Towards Architectural Programming of Embedded Systems
Innocent Strategies are Sheaves over Plays---Deterministic, Non-deterministic and Probabilistic Innocence
Static Enforcement of Role-Based Access Control
UML-F: A Modeling Language for Object-Oriented Frameworks
Quipper: Concrete Resource Estimation in Quantum Algorithms
Integer-Programming Ensemble of Temporal-Relations Classifiers
Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework
Biips: Software for Bayesian Inference with Interacting Particle Systems
The Expressive Power of DL-Lite
Ensaio sobre o Auto-Aproveitamento: um relato de investidas naturais na participação social
Reply to the commentary "Be careful when assuming the obvious", by P. Alday
From Logical to Distributional Models
Towards a Systems Engineering Essence
Unary probabilistic and quantum automata on promise problems
WIKI THANKS: Cultural Differences in Thanks Network of Different-Language Wikipedias
Learning to Search for Dependencies
Syntagma Lexical Database
Unsupervised POS Induction with Word Embeddings
Open Transactions on Shared Memory
A Robust Approximation to a Lambert-Type Function
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation
Homology and closure properties of autostackable groups
Content Translation: Computer-assisted translation tool for Wikipedia articles
Applicative Bisimulation and Quantum $λ$-Calculi (Long Version)
Class Vectors: Embedding representation of Document Classes
A versatile DAQ, monitoring and data processing system for nuclear experiments in CAMAC and VME standards
Unlocking Blocked Communicating Processes
Learning Meta-Embeddings by Using Ensembles of Embedding Sets
Echoes of Persuasion: The Effect of Euphony in Persuasive Communication
Weight Assignment Logic
Alignment-based compositional semantics for instruction following
Better Document-level Sentiment Analysis from RST Discourse Parsing
Exploiting Out-of-Domain Data Sources for Dialectal Arabic Statistical Machine Translation
A Parallel Corpus of Translationese
Frequency Distribution of Error Messages
Characterization and Complexity Results on Jumping Finite Automata
Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base
Hankel Matrices for Weighted Visibly Pushdown Automata
Minimum Risk Training for Neural Machine Translation
The Improvement of Negative Sentences Translation in English-to-Korean Machine Translation
Natural Language Inference by Tree-Based Convolution and Heuristic Matching
Learning to Compose Neural Networks for Question Answering
Visual Script and Language Identification
On probability and logic
The Utility of Hedged Assertions in the Emergence of Shared Categorical Labels
Massively Multilingual Word Embeddings
The "Sprekend Nederland" project and its application to accent location
Relations on words
Time Window Temporal Logic
Learning to SMILE(S)
Refinement types in Jolie
Note on the construction of globular weak omega-groupoids from types, topological spaces etc
Proving completeness of logic programs with the cut
CVXPY: A Python-Embedded Modeling Language for Convex Optimization
Optimized Polynomial Evaluation with Semantic Annotations
Elementary equivalences and accessible functors
Training with Exploration Improves a Greedy Stack-LSTM Parser
Operations on Weakly Recognizing Morphisms
Bayesian Neural Word Embedding
A Classical Realizability Model for a Semantical Value Restriction
A Comparison of NOOP to Structural Domain-Theoretic Models of OOP
Unprovability of circuit upper bounds in Cook's theory PV
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
Neural Recovery Machine for Chinese Dropped Pronoun
Calculational Design of Information Flow Monitors (extended version)
Syntactically Guided Neural Machine Translation
Recurrent Neural Network for Text Classification with Multi-Task Learning
Query Expansion with Locally-Trained Word Embeddings
Improving Recurrent Neural Networks For Sequence Labelling
Neural Word Segmentation Learning for Chinese
Improving Testability and Reuse by Transitioning to Functional Programming
On the decidability of the $Σ_2$ theories of the arithmetic and hyperarithmetic degrees as uppersemilattices
A Dynamic Epistemic Framework for Conformant Planning
Efficient Parallel Learning of Word2Vec
Intrinsic Subspace Evaluation of Word Embedding Representations
Hilbert series of symmetric ideals in infinite polynomial rings via formal languages
Cayley Automatic Groups and Numerical Characteristics of Turing Transducers
"Show me the cup": Reference with Continuous Representations
Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection
Target-Side Context for Discriminative Models in Statistical Machine Translation
Extracting Formal Models from Normative Texts
Towards Trustworthy Refactoring in Erlang
Implicit Negative Feedback in Clinical Information Retrieval
Learning Nominal Automata
Monadic second-order properties of very sparse random graphs
Sentiment Classification of Food Reviews
Reasoning about Graph Programs
Self-Sustaining Iterated Learning
Liveness for Verification
miniAdapton: A Minimal Implementation of Incremental Computation in Scheme
Advances in All-Neural Speech Recognition
Learning Robust Representations of Text
Large-Scale Machine Translation between Arabic and Hebrew: Available Corpora and Initial Results
Learning to Translate for Multilingual Question Answering
Jolie Community on the Rise
ECAT: Event Capture Annotation Tool
A tentative model for dimensionless phoneme distance from binary distinctive features
Causally consistent dynamic slicing
A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech
Combining Treewidth and Backdoors for CSP
Keystroke dynamics as signal for shallow syntactic parsing
A Comprehensive Comparative Study of Word and Sentence Similarity Measures
Vietnamese Named Entity Recognition using Token Regular Expressions and Bidirectional Inference
An Alternating Automaton for First-Order Linear Temporal Logic--Tech Report
Improving historical spelling normalization with bi-directional LSTMs and multi-task learning
Parameterized Dataflow (Extended Abstract)
An empirical study for Vietnamese dependency parsing
An Automated System for Essay Scoring of Online Exams in Arabic based on Stemming Techniques and Levenshtein Edit Operations
When silver glitters more than gold: Bootstrapping an Italian part-of-speech tagger for Twitter
Character-level Convolutional Network for Text Classification Applied to Chinese Corpus
Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
Data Minimisation: a Language-Based Approach (Long Version)
Learning to Compose Words into Sentences with Reinforcement Learning
CoALP-Ty'16
Information Extraction with Character-level Neural Networks and Free Noisy Supervision
Grammatical Constraints on Intra-sentential Code-Switching: From Theories to Working Models
Neural Networks Classifier for Data Selection in Statistical Machine Translation
On incomplete and synchronizing finite sets
Shamela: A Large-Scale Historical Arabic Corpus
Proceedings Third International Workshop on Rewriting Techniques for Program Transformations and Evaluation
An Environment for Analyzing Space Optimizations in Call-by-Need Functional Languages
A Modularity Bug in Java 8
JSON: data model, query languages and schema specification
Lie groupoid, deformation of unstable curve, and construction of equivariant Kuranishi charts
De-identification In practice
Parsing Universal Dependencies without training
An Introduction to Liquid Haskell
A Data-Oriented Model of Literary Language
SMARTies: Sentiment Models for Arabic Target Entities
Towards a Decidable LogicWeb via Length-Bounded Derivations
A Tutorial on Using Dafny to Construct Verified Software
Irreducible compositions of degree two polynomials over finite fields have regular structure
GADTs and Exhaustiveness: Looking for the Impossible
Learning to Parse and Translate Improves Neural Machine Translation
JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision
Systèmes du LIA à DEFT'13
Are Emojis Predictable?
Practical Magick with C, PDL, and PDL::PP -- a guide to compiled add-ons for PDL
Detecting Sockpuppets in Deceptive Opinion Spam
Effects of Limiting Memory Capacity on the Behaviour of Exemplar Dynamics
Why we have switched from building full-fledged taxonomies to simply detecting hypernymy relations
Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks
InScript: Narrative texts annotated with script information
From visual words to a visual grammar: using language modelling for image classification
Foundations for a Probabilistic Event Calculus
Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention
CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity
MRA - Proof of Concept of a Multilingual Report Annotator Web Application
Conversation Modeling on Reddit using a Graph-Structured LSTM
Lean and Full Congruence Formats for Recursion
Distributional Modeling on a Diet: One-shot Word Learning from Text Only
Towards String-to-Tree Neural Machine Translation
Deep Joint Entity Disambiguation with Local Neural Attention
Automated Sized-Type Inference and Complexity Analysis
Redefining Context Windows for Word Embedding Models: An Experimental Study
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads
Attention Strategies for Multi-Source Sequence-to-Sequence Learning
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks
Ontology-Aware Token Embeddings for Prepositional Phrase Attachment
Proof Mining with Dependent Types
CodeCity for (and by) JavaScript
The careless use of language in quantum information
The language of Stratified Sets is confluent and strongly normalising
Deep Learning for Hate Speech Detection in Tweets
Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages
Deep learning evaluation using deep linguistic processing
Marmara Turkish Coreference Corpus and Coreference Resolution Baseline
Translating Event-B machines to Eiffel programs
An Automatic Approach for Document-level Topic Model Evaluation
An exploration to visualize finite element data with a DSL
THUMT: An Open Source Toolkit for Neural Machine Translation
Extract with Order for Coherent Multi-Document Summarization
A Deep Network with Visual Text Composition Behavior
LIUM-CVC Submissions for WMT17 Multimodal Translation Task
LIUM Machine Translation Systems for WMT17 News Translation Task
Detecting Off-topic Responses to Visual Prompts
Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking
Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records
Analogs of Linguistic Structure in Deep Representations
Men Are from Mars, Women Are from Venus: Evaluation and Modelling of Verbal Associations
Analysis of Italian Word Embeddings
Skill2vec: Machine Learning Approach for Determining the Relevant Skills from Job Description
Towards Semantic Modeling of Contradictions and Disagreements: A Case Study of Medical Guidelines
A Comparison of Neural Models for Word Ordering
Rookie: A unique approach for exploring news archives
Neural and Statistical Methods for Leveraging Meta-information in Machine Translation
Emotion Intensities in Tweets
Leveraging Sparse and Dense Feature Combinations for Sentiment Classification
Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts
The CLaC Discourse Parser at CoNLL-2016
ClaC: Semantic Relatedness of Words and Phrases
The CLaC Discourse Parser at CoNLL-2015
Neural Machine Translation with Extended Context
Variational Inference for Logical Inference
Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection
Hypothesis Testing based Intrinsic Evaluation of Word Embeddings
Optimizing for Measure of Performance in Max-Margin Parsing
Leveraging Discourse Information Effectively for Authorship Attribution
Human Associations Help to Detect Conventionalized Multiword Expressions
Deadlock detection of Java Bytecode
Rbox: an integrated R package for ATOM Editor
Graph Convolutional Networks for Named Entity Recognition
Towards Universal Semantic Tagging
Improving Lexical Choice in Neural Machine Translation
Learning Word Embeddings for Hyponymy with Entailment-Based Distributional Semantics
Interactive Learning of State Representation through Natural Language Instruction and Explanation
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description
Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach
Impact of Coreference Resolution on Slot Filling
Fine-tuning Tree-LSTM for phrase-level sentiment classification on a Polish dependency treebank. Submission to PolEval task 2
On the incorporation of interval-valued fuzzy sets into the Bousi-Prolog system: declarative semantics, implementation and applications
h: A Plank for Higher-order Attribute Contraction Schemes
Fast Reading Comprehension with ConvNets
Singular value automata and approximate minimization
Fast BTG-Forest-Based Hierarchical Sub-sentential Alignment
Effective Strategies in Zero-Shot Neural Machine Translation
Declarativeness: the work done by something else
Enabling Embodied Analogies in Intelligent Music Systems
Improving Visually Grounded Sentence Representations with Self-Attention
A Quantitative Study of Java Software Buildability
Contextualized Word Representations for Reading Comprehension
Social Media Writing Style Fingerprint
Learning when to skim and when to read
Subword and Crossword Units for CTC Acoustic Models
Advances in Pre-Training Distributed Word Representations
Letter-Based Speech Recognition with Gated ConvNets
Disentangled Representations for Manipulation of Sentiment in Text
Image Captioning using Deep Neural Architectures
Ongoing Events in Wikipedia: A Cross-lingual Case Study
A Survey of Word Embeddings Evaluation Methods
Twists and Twistability
Zero-Cost Coercions for Program and Proof Reuse
Network Features Based Co-hyponymy Detection
QWIRE Practice: Formal Verification of Quantum Circuits in Coq
Representing Verbs as Argument Concepts
Automatic Transferring between Ancient Chinese and Contemporary Chinese
Linking ImageNet WordNet Synsets with Wikidata
Automatic Detection of Online Jihadist Hate Speech
SentEval: An Evaluation Toolkit for Universal Sentence Representations
Advancing Connectionist Temporal Classification With Attention Modeling
Dear Sir or Madam, May I introduce the YAFC Corpus: Corpus, Benchmarks and Metrics for Formality Style Transfer
Attention on Attention: Architectures for Visual Question Answering (VQA)
Reversibility of Extreme Relational Structures
Domain Adaptation for Statistical Machine Translation
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction
A Survey on Neural Network-Based Summarization Methods
A Processing Model for Free Word Order Languages
Weak subsumption Constraints for Type Diagnosis: An Incremental Algorithm
A Constraint-based Case Frame Lexicon Architecture
Algebraic Approach to Interacting Quantum Systems
Comment on Reply of Benedetto et al
Resolution of Indirect Anaphora in Japanese Sentences Using Examples 'X no Y (Y of X)'
An Usage Measure Based on Psychophysical Relations
Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech?
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
Recursively Undecidable Properties of NP
A theory of experiment
Logic programming in the context of multiparadigm programming: the Oz experience
Small Spans in Scaled Dimension
Toward the Implementation of Functions in the DLV System (Preliminary Technical Report)
Symmetry and interactivity in Programming
Deductive Object Programming
Dependency Treebanks: Methods, Annotation Schemes and Tools
$W_{1+\infty}$ as a Discretization of Virasoro Algebra
Self Avoiding Walks, the Language of Science, and Fibonacci Numbers
On groups whose word problem is solved by a nested stack automaton
Semiinfinite cohomology of Lie-* algebras
The Automorphism Groups of the Groups of Order 8p^2
On genus-change in algebraic curves over nonperfect fields
Modeling the Co-occurrence Principles of the Consonant Inventories: A Complex Network Approach
Quantum Communication Complexity
Topological decomposition of composite quantum state spaces
The sum-over-histories formulation of quantum computing
Quantum Mechanics in Phase Space
Social applications of two-dimensional Ising models
How to realize "a sense of humour" in computers ?
Calculating Colimits Compositionally
Le terme et le concept : fondements d'une ontoterminologie
Program Promises
Some properties of the Ukrainian writing system
Geometry of the Standard Model
Offloading Cognition onto Cognitive Technology
Logics for XML
Cubefree words with many squares
The Depth of a Hypersubstitution
Dejean's conjecture holds for n>=27
A decidable policy language for history-based transaction monitoring
A proof of Dejean's conjecture
Slowly synchronizing automata with zero and incomplete sets
Missing data in a stochastic Dollo model for cognate data, and its application to the dating of Proto-Indo-European
Approximating the minimum length of synchronizing words is hard
A Framework for Specifying, Prototyping, and Reasoning about Computational Systems
Weak Kleene Algebra is Sound and (Possibly) Complete for Simulation
Ludique : une logique sans axiome d'identité
Standardization of the formal representation of lexical information for NLP
Frequency of Occurrence and Information Entropy of American Sign Language
Syllable Analysis to Build a Dictation System in Telugu language
Enumeration Order Reducibility
Diversity, competition, extinction: the ecophysics of language change
The Cerny conjecture for one-cluster automata with prime length cycle
The Lambek-Grishin calculus is NP-complete
Hopf-Galois objects and cogroupoids
Neutrino Mean Free Path in Neutron Star
Free iterative and iteration K-semialgebras
Proceedings of CICLOPS-WLPE 2010
Geometric Properties of Boundary Orbit Accumulation Points
Decomposition Complexity
Bacteria inspired patterns grown with hyperbolic cellular automata
Proceedings 24th International Workshop on Unification
Status of GDL - GNU Data Language
A variant of Hofstadter's sequence and finite automata
On primary and secondary repetitions in words
Sound and complete axiomatizations of coalgebraic language equivalence
The settlement of Madagascar: what dialects and languages can tell
Attacker Control and Impact for Confidentiality and Integrity
Absoluteness of subword inequality is undecidable
Biologically Inspired Process Calculi, Petri Nets and Membrane Computing
Saying Hello World with GReTL - A Solution to the TTC 2011 Instructive Case
Autonomous push-down automaton built on DNA
Harbingers of Artin's Reciprocity Law. III. Gauss's Lemma and Artin's Transfer
Singular and Plural Functions for Functional Logic Programming
A Survey of Multi-Tape Automata
Cell decomposition and definable functions for weak p-adic structures
Modular session types for objects
A Fast and Simple Algorithm for Training Neural Probabilistic Language Models
An axiomatic look at a windmill
Extensions of the Minimum Cost Homomorphism Problem
Typed Answer Set Programming and Inverse Lambda Algorithms
cphVB: A System for Automated Runtime Optimization and Parallelization of Vectorized Applications
The Model of Semantic Concepts Lattice For Data Mining Of Microblogs
Visual Recognition of Isolated Swedish Sign Language Signs
Simplification and integration in computing and cognition: the SP theory and the multiple alignment concept
Knowledge Base Approach for 3D Objects Detection in Point Clouds Using 3D Processing and Specialists Knowledge
Termhood-based Comparability Metrics of Comparable Corpus in Special Domain
The most controversial topics in Wikipedia: A multilingual and geographical analysis
Reachability in Higher-Order-Counters
Was ist Unendlichkeit - und wenn ja, wie viele?
Jensen-type inequality for non-convex functions
Proceedings Second Workshop on Trends in Functional Programming In Education
Counting words with vector spaces
An exercise on streams: convergence acceleration
Some criteria to check if a projective hypersurfaces is smooth or singular
Yoneda lemma for complete Segal spaces
Towards Focus on Time
Transformation of UML Behavioral Diagrams to Support Software Model Checking
Clingo = ASP + Control: Preliminary Report
Model Checking Markov Chains Against Unambiguous Buchi Automata
Non-termination using Regular Languages
Py-oopsi: the python implementation of the fast-oopsi algorithm
Weighted finite automata with output
Distributed Representations for Compositional Semantics
Model theory of special subvarieties and Schanuel-type conjectures
Equation $x^iy^jx^k=u^iv^ju^k$ in words
Modular Action Language ALM
Classifying informative and imaginative prose using complex networks
Most Complex Regular Ideal Languages
Learning Articulated Motion Models from Visual and Lingual Signals
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies
Liveness-Based Garbage Collection for Lazy Languages
Close Encounters of the Higher Kind Emulating Constructor Classes in Standard ML
The Frobenius problem for the shuffle operation
A Simulation and Modeling of Access Points with Definition Language
Detecting Data Races on OpenCL Kernels with Symbolic Execution
Proceedings 7th Workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software
Reconciliation of RDF* and Property Graphs
Derived $(\infty,1)$-categories of two kinds
Logic programming beyond Prolog
Functional Automata - Formal Languages for Computer Science Students
Embedding Word Similarity with Neural Machine Translation
On Subword Complexity of Morphic Sequences
Context-free Algorithms
YesWorkflow: A User-Oriented, Language-Independent Tool for Recovering Workflow Information from Scripts
Kickstarting Choreographic Programming
Fully bordered words
Analysis of Stopping Active Learning based on Stabilizing Predictions
On the "Naturalness" of Buggy Code
The ultimate tactics of self-referential systems
Avoidability index for binary patterns with reversal
Genomic study of the Ket: a Paleo-Eskimo-related ethnic group with significant ancient North Eurasian ancestry
On McKay's propagation theorem for the Foulkes conjecture
Rehearsal: A Configuration Verification Tool for Puppet
CARMA: Collective Adaptive Resource-sharing Markovian Agents
Neural Enquirer: Learning to Query Tables with Natural Language
Henselianity in the language of rings
Practical State Machines for Computer Software and Engineering
The logic of the reverse mathematics zoo
Computational Soundness Results for Stateful Applied pi Calculus
Towards Turkish ASR: Anatomy of a rule-based Turkish g2p
Completeness and the ZX-calculus
Lexical bundles in computational linguistics academic literature
The trace monoids in the queue monoid and in the direct product of two free monoids
Model Theory of Adeles I
CAIR: Using Formal Languages to Study Routing, Leaking, and Interception in BGP
Valued modules over skew polynomial rings 1
Going Deeper for Multilingual Visual Sentiment Detection
The Meaning of Null in Databases and Programming Languages
Shortest Trajectories and Reversibility in Boolean Automata Networks
The LAMBADA dataset: Word prediction requiring a broad discourse context
Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content
Lowering IrGL to CUDA
Novel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization
The weakest nontrivial idempotent equations
On the Existence of Weak One-Way Functions
Multiplex lexical networks reveal patterns in early word acquisition in children
Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep Recurrent models
Fully Character-Level Neural Machine Translation without Explicit Segmentation
Visually pleasing knot projections in R
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
Foundations of Modern Query Languages for Graph Databases
Topological aspects of the multi-language phases of the Naming Game on community-based networks
Application of Case-Based Teaching and Learning in Compiler Design Course
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
Moore: Interval Arithmetic in Modern C++
Multilingual Multiword Expressions
Improving the Performance of Neural Machine Translation Involving Morphologically Rich Languages
Few more Comments on Benford's Law
Ambiguity and Incomplete Information in Categorical Models of Language
Primitivity, Uniform Minimality and State Complexity of Boolean Operations
Proceedings ML Family / OCaml Users and Developers workshops
Étude sur les portails et agrégateurs des ressources pédagogiques universitaires francophones en accès libre
Efficient Analytical Queries on Semantic Web Data Cubes
Analysis of the Ratio $D(n)/n$
Profinite semigroups
On the Implementation of a Scalable Simulator for Multiscale Hybrid-Mixed Methods
Reply to L.Vaidman's comment on "Asking photons where they have been in plain language"
Pay Attention to Those Sets! Learning Quantification from Images
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities
Learning Convolutional Text Representations for Visual Question Answering
Definable sets up to definable bijections in Presburger groups
Six Challenges for Neural Machine Translation
Statistical Inferences for Polarity Identification in Natural Language
The Complex Negotiation Dialogue Game
A characterization of strongly dependent ordered Abelian groups
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
SKOS Concepts and Natural Language Concepts: an Analysis of Latent Relationships in KOSs
Identifying Phrasemes via Interlingual Association Measures -- A Data-driven Approach on Dependency-parsed and Word-aligned Parallel Corpora
Extracting Ontological Knowledge from Textual Descriptions
Expressing and verifying embedded software requirements
A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences
Aicyber's System for NLPCC 2017 Shared Task 2: Voting of Baselines
Proof Complexity Meets Algebra
Language Bootstrapping: Learning Word Meanings From Perception-Action Association
Efficient reduction of nondeterministic automata with application to language inclusion testing
The Maximal MAM, a Reasonable Implementation of the Maximal Strategy
A Practical Approach for Detecting Logical Error in Object Oriented Environment
Coarse homology theories and finite decomposition complexity
Dual Long Short-Term Memory Networks for Sub-Character Representation Learning
Rooted Divergence-Preserving Branching Bisimilarity is a Congruence
A Stitch in Time Saves Nine -- SPARQL querying of Property Graphs using Gremlin Traversals
On B. Mossé's unilateral recognizability theorem
Forest Categories
Classifying medical notes into standard disease codes using Machine Learning
When Good Components Go Bad: Formally Secure Compilation Despite Dynamic Compromise
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
Dynamic Natural Language Processing with Recurrence Quantification Analysis
StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow
Socioeconomic Dependencies of Linguistic Patterns in Twitter: A Multivariate Analysis
The Computational Complexity of Symbolic Dynamics at the Onset of Chaos
Pearl: A Probabilistic Chart Parser
Determination of referential property and number of nouns in Japanese sentences for machine translation into English
Detecting and Correcting Speech Repairs
Exploring the Statistical Derivation of Transformational Rule Sequences for Part-of-Speech Tagging
DISCO---An HPSG-based NLP System and its Application for Appointment Scheduling (Project Note)
Building a Large-Scale Knowledge Base for Machine Translation
A Probabilistic Model of Compound Nouns
Situated Modeling of Epistemic Puzzles
Towards an Automatic Dictation System for Translators: the TransTalk Project
A Centering Approach to Pronouns
Dilemma - An Instant Lexicographer
A Freely Available Wide Coverage Morphological Analyzer for English
Acquiring Knowledge from Encyclopedic Texts
Adaptive Sentence Boundary Disambiguation
Dependency Grammar and the Parsing of Chinese Sentences
Coupling Phonology and Phonetics in a Constraint-Based Gestural Model
Using default inheritance to describe LTAG
ProFIT: Prolog with Features, Inheritance and Templates
Specifying a shallow grammatical representation for parsing purposes
Bi-directional memory-based dialog translation: The KEMDT approach
An NLP Approach to a Specific Type of Texts: Car Accident Reports
Tagging French -- comparing a statistical and a constraint-based method
Linear Logic for Meaning Assembly
Robust Parsing Based on Discourse Information: Completing partial parses of ill-formed sentences on the basis of discourse information
Automatic Evaluation and Uniform Filter Cascades for Inducing N-Best Translation Lexicons
A Study of the Context(s) in a Specific Type of Texts: Car Accident Reports
Disambiguating bilingual nominal entries against WordNet
Incorporating Discourse Aspects in English -- Polish MT: Towards Robust Implementation
Automatic Identification of Support Verbs: A Step Towards a Definition of Semantic Weight
Disambiguating Noun Groupings with Respect to WordNet Senses
Automatic Inference of DATR Theories
Report of the Study Group on Assessment and Evaluation
The Measure of a Model
Towards a Workbench for Acquisition of Domain Knowledge from Natural Language
Towards Understanding Spontaneous Speech: Word Accuracy vs. Concept Accuracy
Directed Replacement
Minimizing Manual Annotation Cost In Supervised Training From Corpora
Beyond Word N-Grams
Applying Winnow to Context-Sensitive Spelling Correction
A Sign-Based Phrase Structure Grammar for Turkish
Inferring Acceptance and Rejection in Dialogue by Default Rules of Inference
A Faster Structured-Tag Word-Classification Method
Dialogos: a Robust System for Human-Machine Spoken Dialogue on the Telephone
Sequential Model Selection for Word Sense Disambiguation
Quantitative Constraint Logic Programming for Weighted Grammar Applications
The TreeBanker: a Tool for Supervised Training of Parsed Corpora
Exemplar-Based Word Sense Disambiguation: Some Recent Improvements
Applying Reliability Metrics to Co-Reference Annotation
A Linear Observed Time Statistical Parser Based on Maximum Entropy Models
A Model of Lexical Attraction and Repulsion
An Empirical Approach to Temporal Reference Resolution
A Lexicalist Approach to the Translation of Colloquial Text
A Word-to-Word Model of Translational Equivalence
Discourse Preferences in Dynamic Logic
Stressed and Unstressed Pronouns: Complementary Preferences
Experiences with the GTU grammar development environment
Similarity-Based Approaches to Natural Language Processing
Probabilistic Event Categorization
Approximating Context-Free Grammars with a Finite-State Calculus
Probabilistic Parsing Using Left Corner Language Models
Multi-document Summarization by Graph Search and Matching
Identifying Discourse Markers in Spoken Dialog
Automating Coreference: The Role of Annotated Training Data
The Proper Treatment of Optimality in Computational Phonology
Models of Co-occurrence
Computing Dialogue Acts from Features with Transformation-Based Learning
An Investigation of Transformation-Based Learning in Discourse
Partial Evaluation for Efficient Access to Inheritance Lexicons
Error-Driven Pruning of Treebank Grammars for Base Noun Phrase Identification
Some Ontological Principles for Designing Upper Level Lexical Resources
What's in a name?
Spoken Language Dialogue Systems and Components: Best practice in development and evaluation (DISC 24823) - Periodic Progress Report 1: Basic Details of the Action
The Boolean Hierarchy over Level 1/2 of the Straubing-Therien Hierarchy
Resources for Evaluation of Summarization Techniques
Semi-Automatic Indexing of Multilingual Documents
Two-way finite automata with quantum and classical states
An Estimate of Referent of Noun Phrases in Japanese Sentences
cc-Golog: Towards More Realistic Logic-Based Robot Controllers
Multimethods and separate static typechecking in a language with C++-like object model
Exploiting Diversity for Natural Language Parsing
Turning Speech Into Scripts
Entropy-based Pruning of Backoff Language Models
Japanese Probabilistic Information Retrieval Using Location and Category Information
Finding consensus in speech recognition: word error minimization and other applications of confusion networks
EquiX---A Search and Query Language for XML
Quantum Multi-Prover Interactive Proof Systems with Limited Prior Entanglement
Multi-Syllable Phonotactic Modelling
Robust Probabilistic Predictive Syntactic Processing
Transformation-Based Learning in the Fast Lane
Multidimensional Transformation-Based Learning
Classes for Fast Maximum Entropy Training
Convergent Approximate Solving of First-Order Constraints by Approximate Quantifiers
Portability of Syntactic Structure for Language Modeling
Information Extraction Using the Structured Language Model
Probabilistic asynchronous pi-calculus
EquiX--A Search and Query Language for XML
Proliferation of SDDS Support for Various Platforms and Languages
TableTrans, MultiTrans, InterTrans and TreeTrans: Diverse Tools Built on the Annotation Graph Toolkit
Soft Concurrent Constraint Programming
Rerendering Semantic Ontologies: Automatic Extensions to UMLS through Corpus Analytics
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
Approximate Grammar for Information Extraction
Quanta: a Language for Modeling and Manipulating Information Structures
Collaborative Creation of Digital Content in Indian Languages
CSIEC (Computer Simulator in Educational Communication): An Intelligent Web-Based Teaching System for Foreign Language Learning
Greedy Algorithms in Datalog
A system for reflection in C++
Finite-Tree Analysis for Constraint Logic-Based Languages: The Complete Unabridged Version
Schema-based Scheduling of Event Processors and Buffer Minimization for Queries on Structured Data Streams
A CHR-based Implementation of Known Arc-Consistency
A new architecture for making highly scalable applications
An Introduction to the Summarization of Evolving Events: Linear and Non-linear Evolution
Programming Finite-Domain Constraint Propagators in Action Rules
ATNoSFERES revisited
OpenVanilla - A Non-Intrusive Plug-In Framework of Text Services
Summarizing Reports on Evolving Events; Part I: Linear Evolution
Haskell's overlooked object system
Nonmonotonic Trust Management for P2P Applications
Forward slicing of functional logic programs by partial evaluation
Combining Relational Algebra, SQL, Constraint Modelling, and Local Search
Constraint Functional Logic Programming over Finite Domains
Compositional Semantics for the Procedural Interpretation of Logic
Language Support for Optional Functionality
On the Complexity of Limit Sets of Cellular Automata Associated with Probability Measures
Multilingual person name recognition and transliteration
Verification, Validation and Integrity of Distributed and Interchanged Rule Based Policies and Contracts in the Semantic Web
Memory and compiler optimizations for low-power and -energy
Language, logic and ontology: uncovering the structure of commonsense knowledge
Liveness of Heap Data for Functional Programs
APEmille: a parallel processor in the teraflop range
The Wholeness Axioms and V=HOD
Wikipedias: Collaborative web-based encyclopedias as complex networks
Network properties of written human language
Self-organization of the Sound Inventories: Analysis and Synthesis of the Occurrence and Co-occurrence Networks of Consonants
How Difficult is it to Develop a Perfect Spell-checker? A Cross-linguistic Analysis through Complex Network Approach
Classical Concepts in Quantum Programming
The language of Einstein spoken by optical instruments
Physical propositions and quantum languages
Towards Understanding the Origin of Genetic Languages
Separation Logic for Small-step Cminor
Emergence of Scale-Free Syntax Networks
The structure of verbal sequences analyzed with unsupervised learning techniques
Automated Synthesis of Assertion Monitors using Visual Specifications
Amélioration des Performances des Systèmes Automatiques de Reconnaissance de la Parole pour la Parole Non Native
Very strict selectional restrictions
Acoustic Features and Perceptive Cues of Songs and Dialogues in Whistled Speech: Convergences with Sung Speech
Valence extraction using EM selection and co-occurrence matrices
Borel Ranks and Wadge Degrees of Context Free Omega Languages
Framework and Resources for Natural Language Parser Evaluation
An omega-power of a context-free language which is Borel above Delta^0_omega
Programming an interpreter using molecular dynamics
A Comparison of natural (english) and artificial (esperanto) languages. A Multifractal method based analysis
Robustness Evaluation of Two CCG, a PCFG and a Link Grammar Parsers
Complexity of Combinatorial Market Makers
Efficient Algorithms for Membership in Boolean Hierarchies of Regular Languages
The Abella Interactive Theorem Prover (System Description)
Towards a stable definition of Kolmogorov-Chaitin complexity
Respect My Authority! HITS Without Hyperlinks, Utilizing Cluster-Based Language Models
Graph Algorithms for Improving Type-Logical Proof Search
Modeling the Structure and Dynamics of the Consonant Inventories: A Complex Network Approach
An overview of QML with a concrete implementation in Haskell
A Layered Grammar Model: Using Tree-Adjoining Grammars to Build a Common Syntactic Kernel for Related Dialects
A sound spatio-temporal Hoare logic for the verification of structured interactive programs with registers and voices
The Application of Fuzzy Logic to Collocation Extraction
Approaching the linguistic complexity
New Confidence Measures for Statistical Machine Translation
Hilbert's epsilon as an Operator of Indefinite Committed Choice
Network of two-Chinese-character compound words in Japanese language
Decompositions of Grammar Constraints
Deterministic pushdown automata and unary languages
A Theory of Explicit Substitutions with Safe and Full Composition
Towards Automated Deduction in Blackmail Case Analysis with Forensic Lucid
NLP-SIR: A Natural Language Approach for Spreadsheet Information Retrieval
From Declarative Languages to Declarative Processing in Computer Games
A Random Matrix Approach to Language Acquisition
The Complexity of Infinite Computations In Models of Set Theory
Classification with Tarskian system executions (Bakery Algorithms as an example)
A Graph Model for Imperative Computation
Expressing the Behavior of Three Very Different Concurrent Systems by Using Natural Extensions of Separation Logic
A New Look at the Classical Entropy of Written English
Cove: A Practical Quantum Computer Programming Framework
Distributed Quantum Programming
Linear Recursion
Towards a Unified Framework for Declarative Structured Communications
Proceedings Sixth Workshop on Structural Operational Semantics
Syntactic Topic Models
A Mathematical Approach to the Study of the United States Code
Relating Nominal and Higher-order Abstract Syntax Specifications
Spoken Language Identification Using Hybrid Feature Extraction Methods
Verification of Object-Oriented Programs: a Transformational Approach
An Overview: Extensible Markup Language Technology
Inflection system of a language as a complex network
Space and the Synchronic A-Ram
Don't 'have a clue'? Unsupervised co-learning of downward-entailing operators
Functorial Data Migration
Tableaux for the Lambek-Grishin calculus
Portability of Prolog programs: theory and case-studies
A Short Decidability Proof for DPDA Language Equivalence via First-Order Grammars
History-sensitive versus future-sensitive approaches to security in distributed systems
Expressiveness modulo Bisimilarity of Regular Expressions with Parallel Composition (Extended Abstract)
Lamplighter Random Walks and Entropy-Sensitivity of Languages
MiniAgda: Integrating Sized and Dependent Types
A Symbolic Transformation Language and its Application to a Multiscale Method
Synthese des Controleurs Optimaux pour les Systemes a Evenements Discrets
Finite state verifiers with constant randomness
A system of relational syllogistic incorporating full Boolean reasoning
Higher-Order Symbolic Execution via Contracts
Self reference in word definitions
Languages invariant under more symmetries: overlapping factors versus palindromic richness
Complexity Results for Modal Dependence Logic
Seeking Meaning in a Space Made out of Strokes, Radicals, Characters and Compounds
Systematic Abstraction of Abstract Machines
The Magic of Logical Inference in Probabilistic Programming
Modelling and Simulation of Asynchronous Real-Time Systems using Timed Rebeca
Unit Testing in ASPIDE
Parsing Combinatory Categorial Grammar with Answer Set Programming: Preliminary Report
A Framework for Devanagari Script-based Captcha
Devnagari document segmentation using histogram approach
SLALOM: a Language for SLA specification and monitoring
3D Model Retrieval Based on Semantic and Shape Indexes
Managing Communication Latency-Hiding at Runtime for Parallel Programming Languages and Libraries
Irrelevance, Heterogeneous Equality, and Call-by-value Dependent Type Systems
Proceedings 8th Workshop on Fixed Points in Computer Science
Lattices of Logical Fragments over Words
Queries with Guarded Negation (full version)
A Programmer-Centric Approach to Program Verification in ATS
The FO^2 alternation hierarchy is decidable
Information Retrieval Systems Adapted to the Biomedical Domain
Communication Language Specifications For Digital Ecosystems
Management Language Specifications For Digital Ecosystems
An Efficient Finite Tree Automata Library
The non-algorithmic side of the mind
OTS/CafeOBJ2JML: An attempt to combine Design By Contract with Behavioral Specifications
FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language Model
A Machine Learning Approach For Opinion Holder Extraction In Arabic Language
Multilingual Medical Documents Classification Based on MesH Domain Ontology
What is Statistics?; The Answer by Quantum Language
Flexible Dynamic Information Flow Control in the Presence of Exceptions
Arabic CALL system based on pedagogically indexed text
Isomorphisms of types in the presence of higher-order references (extended version)
Programing Using High Level Design With Python and FORTRAN: A Study Case in Astrophysics
A prototype for projecting HPSG syntactic lexica towards LMF
Recent Technological Advances in Natural Language Processing and Artificial Intelligence
A Procedure for Splitting Processes and its Application to Coordination
Proceedings Sixth Workshop on Formal Languages and Analysis of Contract-Oriented Software
Higher-Order Pushdown Systems with Data
A decidable quantified fragment of set theory with ordered pairs and some undecidable extensions
Lightweight compilation of (C)LP to JavaScript
The complexity of finite-valued CSPs
Recognizing Static Signs from the Brazilian Sign Language: Comparing Large-Margin Decision Directed Acyclic Graphs, Voting Support Vector Machines and Artificial Neural Networks
A Hindi Speech Actuated Computer Interface for Web Search
Parallel BioScape: A Stochastic and Parallel Language for Mobile and Spatial Interactions
Mahotas: Open source software for scriptable computer vision
Measuring Time in Sporting Competitions with the Domain-Specific Language EasyTime
Model completeness of o-minimal fields with convex valuations
Semantics and Security Issues in JavaScript
Diachronic Variation in Grammatical Relations
Classifier Fusion Method to Recognize Handwritten Kannada Numerals
Identifying trends in word frequency dynamics
Typing Context-Dependent Behavioural Variation
Static and dynamic semantics of NoSQL languages
Automatic lexical semantic classification of nouns
Using Mathematica & Matlab for CAGD/CAD research and education
A Dataflow Language for Decentralised Orchestration of Web Service Workflows
Where the "it from bit" come from?
Recognition of Indian Sign Language in Live Video
Kolmogorov Complexity of Categories
On a compact encoding of the swap automaton
On the state complexity of semi-quantum finite automata
Conversion of Braille to Text in English, Hindi and Tamil Languages
Rule Based Transliteration Scheme for English to Punjabi
Functional framework for representing and transforming quantum channels
Meta SOS - A Maude Based SOS Meta-Theory Framework
What is Decidable about Partially Observable Markov Decision Processes with ω-Regular Objectives
Simulation of Two-Way Pushdown Automata Revisited
Alternating Turing machines for inductive languages
On the Semantics of ReFLect as a Basis for a Reflective Theorem Prover
JRC-Names: A freely available, highly multilingual named entity resource
Step-Indexed Relational Reasoning for Countable Nondeterminism
To parallelize or not to parallelize, bugs issue
Checking Race Freedom of Clocked X10 Programs
Flow analysis, linearity, and PTIME
On the Expressiveness of TPTL and MTL over ω-Data Words
Empowering Evolving Social Network Users with Privacy Rights
Monotonic References for Gradual Typing
The Glasgow Parallel Reduction Machine: Programming Shared-memory Many-core Systems using Parallel Task Composition
Minimising virtual machine support for concurrency
Dynamics in atomic signaling games
On Verifying Resource Contracts using Code Contracts
Design & Development of the Graphical User Interface for Sindhi Language
From Safety To Termination And Back: SMT-Based Verification For Lazy Languages
Timed Soft Concurrent Constraint Programs: An Interleaved and a Parallel Approach
On the penetration distance in Garside monoids
HPS: a hierarchical Persian stemming method
Verifying Web Applications: From Business Level Specifications to Automated Model-Based Testing
Application of Ontologies in Identifying Requirements Patterns in Use Cases
A Convolutional Neural Network for Modelling Sentences
Computer Simulation Codes for the Quine-McCluskey Method of Logic Minimization
On Backdoors To Tractable Constraint Languages
Multilingual Models for Compositional Distributed Semantics
Multiplicative Bidding in Online Advertising
Representation of a Sentence using a Polar Fuzzy Neutrosophic Semantic Net
Data-flow Analysis of Programs with Associative Arrays
Simulating dynamic systems using Linear Time Calculus theories
A Study of Entanglement in a Categorical Framework of Natural Language
Les mathématiques de la langue : l'approche formelle de Montague
Formal Consistency Checking over Specifications in Natural Languages
Learning to Exploit Different Translation Resources for Cross Language Information Retrieval
Error Reporting in Parsing Expression Grammars
AIOCJ: A Choreographic Framework for Safe Adaptive Distributed Applications
Merlin: A Language for Provisioning Network Resources
Joint Energy-based Detection and Classificationon of Multilingual Text Lines
Crowdsourcing Dialect Characterization through Twitter
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs
Language-based Examples in the Statistics Classroom
Hybrid approaches for automatic vowelization of Arabic texts
Analysis of Named Entity Recognition and Linking for Tweets
Experiments to Improve Named Entity Recognition on Turkish Tweets
Program certification with computational effects
Coarse-grained Cross-lingual Alignment of Comparable Texts with Topic Models and Encyclopedic Knowledge
Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums
Proceedings XIV Jornadas sobre Programación y Lenguajes
XQOWL: An Extension of XQuery for OWL Querying and Reasoning
Phrase Based Language Model for Statistical Machine Translation: Empirical Study
The effect of using facebook markup language (fbml) for designing an e-learning model in higher education
A unified framework for modeling and implementation of hybrid systems with synchronous controllers
!-Graphs with Trivial Overlap are Context-Free
A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models
A First Class Boolean Sort in First-Order Theorem Proving and TPTP
Web ontology representation and reasoning via fragments of set theory
A Frobenius Model of Information Structure in Categorical Compositional Distributional Semantics
SQL for SRL: Structure Learning Inside a Database System
LDQL: A Query Language for the Web of Linked Data (Extended Version)
Proceedings Tenth International Workshop on Logical Frameworks and Meta Languages: Theory and Practice
Resolving References to Objects in Photographs using the Words-As-Classifiers Model
Markovian language model of the DNA and its information content
A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia
Prevalence and recoverability of syntactic parameters in sparse distributed memories
A Formal Model for Direct-style Asynchronous Observables
Lowering the learning curve for declarative programming: a Python API for the IDP system
Data-driven Workflows for Microservices
Deep Reinforcement Learning with a Natural Language Action Space
A framework for deadlock detection in core ABS
sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings
The Journal Coverage of Web of Science and Scopus: a Comparative Analysis
Proceedings 6th Workshop on Mathematically Structured Functional Programming
Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project
Distributed Entity Disambiguation with Per-Mention Learning
SweLL on the rise: Swedish Learner Language corpus for European Reference Level studies
A Hybrid Approach to Query Answering under Expressive Datalog+/-
Quantum Algorithms for Compositional Natural Language Processing
Precise Complexity Guarantees for Pointer Analysis via Datalog with Extensions
Bi-directional Attention with Agreement for Dependency Parsing
Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understanding
An Erlang Implementation of Multiparty Session Actors
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
MiniZinc with Strings
Redefining part-of-speech classes with distributional semantic models
Undecidability of the Lambek calculus with subexponential and bracket modalities
Neural versus Phrase-Based Machine Translation Quality: a Case Study
Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts
Type Inference for Static Compilation of JavaScript (Extended Version)
Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions
American Sign Language fingerspelling recognition from video: Methods for unrestricted recognition and signer-independence
Multilingual lexicon design tool and database management system for MT
Excess entropy in natural language: present state and perspectives
Approximating Petri Net Reachability Along Context-free Traces
On the Limitations of Provenance for Queries With Difference
Garbage Collection for Multicore NUMA Machines
Semantic Vector Machines
SNEG - Mathematica package for symbolic calculations with second-quantization-operator expressions
Towards cross-lingual alerting for bursty epidemic events
Identifying Reference Objects by Hierarchical Clustering in Java Environment
Proceedings Third Workshop on Programming Language Approaches to Concurrency and communication-cEntric Software
Topological Logics with Connectedness over Euclidean Spaces
Creating a Live, Public Short Message Service Corpus: The NUS SMS Corpus
Isomorphisms of types in the presence of higher-order references
A cookbook of translating English to Xapi
Interaction Nets in Russian
The Rational and Computational Scope of Probabilistic Rule-Based Expert Systems
On the Relation between Context-Free Grammars and Parsing Expression Grammars
Spaces, Trees and Colors: The Algorithmic Landscape of Document Retrieval on Sequences
Blind-date Conversation Joining
Local Type Checking for Linked Data Consumers
Bounded-Choice Statements for User Interaction in Imperative and Object-Oriented Programming
Phase transition and fast agreement in Naming Game with preference for multi-word agents
Proceedings Workshop on Fixed Points in Computer Science
Natural Language Inference for Arabic Using Extended Tree Edit Distance with Subtrees
Event Structure of Transitive Verb: A MARVS perspective
A new model for Context-Oriented Programs
Bridging the gap between Legal Practitioners and Knowledge Engineers using semi-formal KR
Proceedings Twelfth International Workshop on Quantitative Aspects of Programming Languages and Systems
Stochastically timed predicate-based communication primitives for autonomic computing
POS Tagging and its Applications for Mathematics
Relating the Time Complexity of Optimization Problems in Light of the Exponential-Time Hypothesis
Zipf's law holds for phrases, not words
First-order definable string transformations
Semantically Configurable Consistency Analysis for Class and Object Diagrams
To found or not to found: that is the question
An Approach to Reducing Annotation Costs for BioNLP
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping
Model Driven Testing of Time Sensitive Distributed Systems
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach
Towards a Formalization of the Unified Modeling Language
Arabic Spelling Correction using Supervised Learning
Skip-gram Language Modeling Using Sparse Non-negative Matrix Probability Estimation
Different Similarities
Learn Physics by Programming in Haskell
New results on classical and quantum counter automata
Grammar as a Foreign Language
A Fuzzy Based Model to Identify Printed Sinhala Characters (ICIAfS14)
Jif: Language-based Information-flow Security in Java
Authorship recognition via fluctuation analysis of network topology and word intermittency
Necessary conditions for tractability of valued CSPs
Locally-Oriented Programming: A Simple Programming Model for Stencil-Based Computations on Multi-Level Distributed Memory Architectures
Type Classes for Lightweight Substructural Types
Transducer Descriptions of DNA Code Properties and Undecidability of Antimorphic Problems
Context-Dependent Translation Selection Using Convolutional Neural Network
On Using Monolingual Corpora in Neural Machine Translation
Non-normal modalities in variants of Linear Logic
Long Short-Term Memory Over Tree Structures
Sign Language Fingerspelling Classification from Depth and Color Images using a Deep Belief Network
Factorization in Formal Languages
Equivalence of Deterministic Top-Down Tree-to-String Transducers is Decidable
A Query Language for Multi-version Data Web Archives
Equational reasoning with context-free families of string diagrams
Design Issues of JPQ: a Pattern-based Query Language for Document Databases
Lexical Translation Model Using a Deep Neural Network Architecture
Recurrent Neural Networks with External Memory for Language Understanding
Diversity in Spectral Learning for Natural Language Parsing
Traversing Knowledge Graphs in Vector Space
Modeling Order in Neural Word Embeddings at Scale
Teaching Machines to Read and Comprehend
Algebraic Characterization of Forest Logics
Gradual Certified Programming in Coq
Towards a unified query language for provenance and versioning
Editorial for the First Workshop on Mining Scientific Papers: Computational Linguistics and Bibliometrics
A model of language inflection graphs
Language Understanding for Text-based Games Using Deep Reinforcement Learning
Equations for Hereditary Substitution in Leivant's Predicative System F: A Case Study
Listen, Attend and Spell
Learning Structural Kernels for Natural Language Processing
Depth-Gated LSTM
Towards Enabling Overture as a Platform for Formal Notation IDEs
End-to-End Attention-based Large Vocabulary Speech Recognition
Euskahaldun: Euskararen Aldeko Martxa Baten Sare Sozialetako Islaren Bilketa eta Analisia
Crossings as a side effect of dependency lengths
On Compensation Primitives as Adaptable Processes
On TimeML-Compliant Temporal Expression Extraction in Turkish
Splitting Compounds by Semantic Analogy
Telugu OCR Framework using Deep Learning
Level Two of the Quantifier Alternation Hierarchy over Infinite Words
An IMS DSL Developed at Ericsson
Deep Multimodal Embedding: Manipulating Novel Objects with Point-clouds, Language and Trajectories
What Makes it Difficult to Understand a Scientific Literature?
Improving Type Error Messages in OCaml
Visibly Linear Dynamic Logic
Proceedings XV Jornadas sobre Programación y Lenguajes
Service Choreography, SBVR, and Time
NodIO, a JavaScript framework for volunteer-based evolutionary algorithms : first results
Dynamic Games and Strategies
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
A Kernel Independence Test for Geographical Language Variation
Long Short-Term Memory-Networks for Machine Reading
WASSUP? LOL : Characterizing Out-of-Vocabulary Words in Twitter
The IMP game: Learnability, approximability and adversarial learning beyond $Σ^0_1$
Simple Search Algorithms on Semantic Networks Learned from Language Use
Complexity of regular abstractions of one-counter languages
Precise subtyping for synchronous multiparty sessions
A Language for the Declarative Composition of Concurrent Protocols
A Simplified Stabilizer ZX-calculus
One-Counter Automata with Counter Observability
PCA Method for Automated Detection of Mispronounced Words
Adaptive Frequency Cepstral Coefficients for Word Mispronunciation Detection
Identification of Parallel Passages Across a Large Hebrew/Aramaic Corpus
Character-based Neural Machine Translation
Neural Architectures for Named Entity Recognition
Part-of-Speech Tagging for Historical English
Personalized Speech recognition on mobile devices
DSCMC: Distributed Stateless Code Model Checker
A Signaling Game Approach to Databases Querying and Interaction
Multi-Task Cross-Lingual Sequence Tagging from Scratch
Static and Dynamic Feature Selection in Morphosyntactic Analyzers
Semi-supervised Word Sense Disambiguation with Neural Models
The Anatomy of a Search and Mining System for Digital Archives
Model Interpolation with Trans-dimensional Random Field Language Models for Speech Recognition
Differentially Private Bayesian Programming
Block Shelves for Visual Programming Languages
Compression and the origins of Zipf's law for word frequencies
Inference-based semantics in Data Exchange
Grammatical Case Based IS-A Relation Extraction with Boosting for Polish
Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health
Overcoming the language barrier in mobile user interface design: A case study on a mobile health app
Learning Deep Representations of Fine-grained Visual Descriptions
Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
On the Complexity and Decidability of Some Problems Involving Shuffle
A Theoretical Approach to initiate Mobile Assisted Language Learning among school leavers and University Students of Sri Lanka
Coordination in Categorical Compositional Distributional Semantics
Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features
Natural Language Comprehension with the EpiReader
Optimizing Spectral Learning for Parsing
First Result on Arabic Neural Machine Translation
MuFuRU: The Multi-Function Recurrent Unit
PSDVec: a Toolbox for Incremental and Scalable Word Embedding
Decidable Characterization of FO2(<,+1) and locality of DA
External Lexical Information for Multilingual Part-of-Speech Tagging
Matching Networks for One Shot Learning
DiSquawk: 512 cores, 512 memories, 1 JVM
Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis
Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015
A Nonparametric Bayesian Approach for Spoken Term detection by Example Query
Introducing a Calculus of Effects and Handlers for Natural Language Semantics
A Data-Driven Approach for Semantic Role Labeling from Induced Grammar Structures in Language
Neighborhood Mixture Model for Knowledge Base Completion
Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions
Neural Morphological Tagging from Characters for Morphologically Rich Languages
Evaluation method of word embedding by roots and affixes
Towards Self-explanatory Ontology Visualization with Contextual Verbalization
Neural Tree Indexers for Text Understanding
An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks
Reasoning about Body-Parts Relations for Sign Language Recognition
Reductionism and the Universal Calculus
Forward-Mode Automatic Differentiation in Julia
The DLVHEX System for Knowledge Representation: Recent Advances (System Description)
Distributed agent-based automated theorem proving in order-sorted first-order logic
INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis
The Microsoft 2016 Conversational Speech Recognition System
In-place Graph Rewriting with Interaction Nets
Multi-Buffer Simulations for Trace Language Inclusion
Maximal Repetition and Zero Entropy Rate
Multiparty Session Actors
Automatic Quality Assessment for Speech Translation Using Joint ASR and MT Features
Weakly supervised spoken term discovery using cross-lingual side information
Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
Aligning Coordinated Text Streams through Burst Information Network Construction and Decipherment
Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks
Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification
L-Convex Polyominoes are Recognizable in Real Time by 2D Cellular Automata
Gaps between equations and experiments in quantum cryptography
Tutorial on Answering Questions about Images with Deep Learning
Visual Question Answering: Datasets, Algorithms, and Future Challenges
Clinical Text Prediction with Numerically Grounded Conditional Language Models
A Novel Learning Algorithm for Büchi Automata based on Family of DFAs and Classification Trees
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
The Geometry of Parallelism. Classical, Probabilistic, and Quantum Effects
Inference Compilation and Universal Probabilistic Programming
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Counterexamples and Proof Loophole for the C/C++ to POWER and ARMv7 Trailing-Sync Compiler Mappings
LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems
Latent Attention For If-Then Program Synthesis
Sums of Uncertainty: Refinements Go Gradual
End-to-End Subtitle Detection and Recognition for Videos in East Asian Languages via CNN Ensemble with Near-Human-Level Performance
Visualizing Linguistic Shift
Leveraging Parallel Data Processing Frameworks with Verified Lifting
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Towards Accurate Word Segmentation for Chinese Patents
Transaction-based Sandboxing for JavaScript
Automated assessment of non-native learner essays: Investigating the role of linguistic features
Self-composable Programming
Ontohub: A semantic repository for heterogeneous ontologies
Runtime enforcement of reactive systems using synchronous enforcers
A Two-Phase Approach Towards Identifying Argument Structure in Natural Language
Knowledge Engineering for Hybrid Deductive Databases
A Simulation Tool for tccp Programs
A Practical Study of Control in Objected-Oriented--Functional--Logic Programming with Paisley
World Literature According to Wikipedia: Introduction to a DBpedia-Based Framework
Neural Machine Translation on Scarce-Resource Condition: A case-study on Persian-English
DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning
A Crevice on the Crane Beach: Finite-Degree Predicates
A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions
Cross-lingual RST Discourse Parsing
Proceedings XVI Jornadas sobre Programación y Lenguajes
QCRI Machine Translation Systems for IWSLT 16
Up-To Techniques for Weighted Systems (Extended Version)
Assessing User Expertise in Spoken Dialog System Interactions
Minimization of Visibly Pushdown Automata Using Partial Max-SAT
Multilingual and Cross-lingual Timeline Extraction
Word equations in linear space
Neural Semantic Parsing over Multiple Knowledge-bases
ZX-Calculus: Cyclotomic Supplementarity and Incompleteness for Clifford+T quantum mechanics
Representations of language in a model of visually grounded speech signal
Unveiling Eilenberg-type Correspondences: Birkhoff's Theorem for (finite) Algebras + Duality
An efficient algorithm to decide periodicity of b-recognisable sets using MSDF convention
The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations
Reinforcement Learning Based Argument Component Detection
Spatial evolution of human dialects
Optimal Non-blocking Decentralized Supervisory Control Using G-Control Consistency
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial
Refactoring Legacy JavaScript Code to Use Classes: The Good, The Bad and The Ugly
Establishing Role-based Access Control in Viewpoint-oriented Variability Management
Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small Datasets
A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection
Sequential Recurrent Neural Networks for Language Modeling
Visually grounded learning of keyword prediction from untranscribed speech
Opinion Mining on Non-English Short Text
Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality for Increased Model Capacity and Performance With No Computational Overhead
Rhetorical relations for information retrieval
What do Neural Machine Translation Models Learn about Morphology?
Proceedings 8th Workshop on Developments in Implicit Computational Complexity and 5th Workshop on Foundational and Practical Aspects of Resource Analysis
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Stability and Fluctuations in a Simple Model of Phonetic Category Change
Translating Neuralese
Russellian Propositional Logic and the BHK Interpretation
From Characters to Words to in Between: Do We Capture Morphology?
A Reasoning System for a First-Order Logic of Limited Belief
Crowdsourcing Argumentation Structures in Chinese Hotel Reviews
Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion
Phonetic Temporal Neural Model for Language Identification
Learning Semantic Correspondences in Technical Documentation
Annotating and Modeling Empathy in Spoken Conversations
Agent-based model for the origins of scaling in human language
Formalized Lambek Calculus in Higher Order Logic (HOL4)
The Meaning of Memory Safety
Transformation of Python Applications into Function-as-a-Service Deployments
Analysing Timelines of National Histories across Wikipedia Editions: A Comparative Computational Approach
ASR error management for improving spoken language understanding
Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines
The complexity of recognizing minimally tough graphs
A Complete Axiomatisation of the ZX-Calculus for Clifford+T Quantum Mechanics
Recognizing Handwritten Source Code
Teaching Machines to Describe Images via Natural Language Feedback
Learning to Compute Word Embeddings On the Fly
Transfer Learning for Speech Recognition on a Budget
Function Assistant: A Tool for NL Querying of APIs
Concept Transfer Learning for Adaptive Language Understanding
Dynamic Integration of Background Knowledge in Neural NLU Systems
Constraint Satisfaction Problem Dichotomy for Finite Templates: a Proof Via Consistency Checks
Exploring the Syntactic Abilities of RNNs with Multi-task Learning
Attention-based Vocabulary Selection for NMT Decoding
Transfer Learning for Neural Semantic Parsing
A Survey Of Cross-lingual Word Embedding Models
Number game
JaTeCS an open-source JAva TExt Categorization System
Stance Detection in Turkish Tweets
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
AP17-OLR Challenge: Data, Plan, and Baseline
Cross-Lingual Sentiment Analysis Without (Good) Translation
Development and Maintenance of XML-Based Versus HTML-Based Websites: A Case Study
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling
Refinable Function : An Object-oriented Approach to Procedure Modularity
Probabilistic Program Equivalence for NetKAT
Learning to Compose Task-Specific Tree Structures
Tabula: A Language to Model Spreadsheet Tables
Source-Target Inference Models for Spatial Instruction Understanding
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model
Memoisation: Purely, Left-recursively, and with (Continuation Passing) Style
Iris: A Conversational Agent for Complex Tasks
The Digital Flynn Effect: Complexity of Posts on Social Media Increases over Time
Fast and Accurate OOV Decoder on High-Level Features
LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction
An Executable Specification of Typing Rules for Extensible Records based on Row Polymorphism
The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
Shortcut-Stacked Sentence Encoders for Multi-Domain Inference
Learning how to Active Learn: A Deep Reinforcement Learning Approach
Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean?
Recent Trends in Deep Learning Based Natural Language Processing
Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese
Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks
Exploring Directional Path-Consistency for Solving Constraint Networks
Neural machine translation for low-resource languages
CLaC @ QATS: Quality Assessment for Text Simplification
Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks
A Batch Noise Contrastive Estimation Approach for Training Large Vocabulary Language Models
Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks
The Microsoft 2017 Conversational Speech Recognition System
Verifying Quantum Programs: From Quipper to QPMC
Trustworthy Refactoring via Decomposition and Schemes: A Complex Case Study
NNVLP: A Neural Network-Based Vietnamese Language Processing Toolkit
A Computational Interpretation of Context-Free Expressions
SPARQL as a Foreign Language
Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline
Understanding the Logical and Semantic Structure of Large Documents
The Voynich Manuscript is Written in Natural Language: The Pahlavi Hypothesis
Monadic Second-Order Logic with Arbitrary Monadic Predicates
Combining Static and Dynamic Contract Checking for Curry
Structural Resolution for Abstract Compilation of Object-Oriented Languages
Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog
MERF: Morphology-based Entity and Relational Entity Extraction Framework for Arabic
Self-Similar Algebras with connections to Run-length Encoding and Rational Languages
Language Independent Acquisition of Abbreviations
Prosodic Features from Large Corpora of Child-Directed Speech as Predictors of the Age of Acquisition of Words
Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets
A Deep Neural Network Approach To Parallel Sentence Extraction
On the Effective Use of Pretraining for Natural Language Inference
OSU Multimodal Machine Translation System Report
Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis
Multitask training with unlabeled data for end-to-end sign language fingerspelling recognition
The Refinement Calculus of Reactive Systems
End-to-end Network for Twitter Geolocation Prediction and Hashing
EffectiveSan: Type and Memory Error Detection using Dynamically Typed C/C++
System Description: Russell - A Logical Framework for Deductive Systems
Learning Differentially Private Recurrent Language Models
Safe Pointers in SPARK 2014
Spoken Language Biomarkers for Detecting Cognitive Impairment
Transparent Replication Using Metaprogramming in Cyan
Linking Tweets with Monolingual and Cross-Lingual News using Transformed Word Embeddings
Logical relations for coherence of effect subtyping
Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models
Capturing the Future by Replaying the Past
Learning neural trans-dimensional random field language models with noise-contrastive estimation
Whodunnit? Crime Drama as a Case for Natural Language Understanding
Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
Compressing Word Embeddings via Deep Compositional Code Learning
Comparison of Parallelisation Approaches, Languages, and Compilers for Unstructured Mesh Algorithms on GPUs
Real-time Stream-based Monitoring
Zero-Shot Style Transfer in Text Using Recurrent Neural Networks
Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition
Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian
SCTP in Go
Self-Supervised Vision-Based Detection of the Active Speaker as a Prerequisite for Socially-Aware Language Acquisition
Refinement Types for Ruby
Vietnamese Semantic Role Labelling
Embodied Question Answering
Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing
On a question of Krajewski's
A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits
CoDraw: Visual Dialog for Collaborative Drawing
Morphology dictates a robot's ability to ground crowd-proposed language
A simple script language for choreography of multiple, synchronizing non-anthropomorphic robots
Presburger-Definable Parameterized Typestates
A Compare-Propagate Architecture with Alignment Factorization for Natural Language Inference
Social Media Analysis based on Semanticity of Streaming and Batch Data
A diagrammatic axiomatisation of fermionic quantum circuits
Object Referring in Videos with Language and Human Gaze
Indian Regional Movie Dataset for Recommender Systems
OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding
Building an Ellipsis-aware Chinese Dependency Treebank for Web Text
Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks
Choreographies for Reactive Programming
MAttNet: Modular Attention Network for Referring Expression Comprehension
A Sheaf Model of Contradictions and Disagreements. Preliminary Report and Discussion
The recovery of George Berkeley's objective science of 1710 and its implications for traditional science
Accelerating recurrent neural network language model based online speech recognition system
A State-of-the-Art of Semantic Change Computation
Pilot study for the COST Action "Reassembling the Republic of Letters": language-driven network analysis of letters from the Hartlib's Papers
Disunited Nations? A Multiplex Network Approach to Detecting Preference Affinity Blocs using Texts and Votes
Proceedings First Workshop on Architectures, Languages and Paradigms for IoT
DisMo: A Morphosyntactic, Disfluency and Multi-Word Unit Annotator. An Evaluation on a Corpus of French Spontaneous and Read Speech
Zero-Resource Neural Machine Translation with Multi-Agent Communication Game
Understanding Recurrent Neural State Using Memory Signatures
Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline
End-to-End Automatic Speech Translation of Audiobooks
Extending the DEVS Formalism with Initialization Information
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
Structured-based Curriculum Learning for End-to-end English-Japanese Speech Translation
Deep Multimodal Learning for Emotion Recognition in Spoken Language
Stateful Behavioral Types for ABS
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System
One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data
Collective Entity Disambiguation with Structured Gradient Tree Boosting
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources
Continuity and Rational Functions
Cross-lingual and Multilingual Speech Emotion Recognition on English and French
Explain Yourself: A Natural Language Interface for Scrutable Autonomous Robots
Extracting Action Sequences from Texts Based on Deep Reinforcement Learning
Umbral Calculus, a Different Mathematical Language
Degrees of Infinite Words, Polynomials, and Atoms (Extended Version)
Decision support with text-based emotion recognition: Deep learning for affective computing
Low-Resource Speech-to-Text Translation
Ten Diverse Formal Models for a CBTC Automatic Train Supervision System
Operator algebras for higher rank analysis and their application to factorial languages
Automatic Normalization of Word Variations in Code-Mixed Social Media Text
Visual augmentation of source code editors: A systematic review
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning
Reasoning with Higher-Order Abstract Syntax in a Logical Framework
A structured alternative to Prolog with simple compositional semantics
Proceedings 11th International Workshop on Foundations of Coordination Languages and Self Adaptation
AND and/or OR: Uniform Polynomial-Size Circuits
Oracle Pushdown Automata, Nondeterministic Reducibilities, and the CFL Hierarchy over the Family of Context-Free Languages
Predicate Logic as a Modelling Language: The IDP System
Oracle performance for visual captioning
Is language evolution grinding to a halt? The scaling of lexical turbulence in English fiction suggests it is not
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Harmonizing Signals and Events with a Lightweight Extension to Java
A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems
The Kepler characterization of the variability among A- and F-type stars. I. General overview
A Deductive Account of Quantification in LFG
Intensional Verbs Without Type-Raising or Lexical Ambiguity
Tracking Point of View in Narrative
Uniform Representations for Syntax-Semantics Arbitration
A Formalism and an Algorithm for Computing Pragmatic Inferences and Detecting Infelicities
TAKTAG: Two-phase learning method for hybrid statistical/rule-based part-of-speech disambiguation
A Robust Parsing Algorithm For Link Grammars
The Effect of Resource Limits and Task Complexity on Collaborative Planning in Dialogue
Extraction of V-N-Collocations from Text Corpora: A Feasibility Study for German
Discourse Coherence and Shifting Centers in Japanese Texts
Library of Practical Abstractions, Release 1.2
A lexical database tool for quantitative phonological research
Type-driven semantic interpretation and feature dependencies in R-LFG
Corpus-Based Word Sense Disambiguation
Complexity of Two-Dimensional Patterns
Scoping Constructs in Logic Programming: Implementation Problems and their Solution
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Learning to Resolve Natural Language Ambiguities: A Unified Approach
Compositionality, Synonymy, and the Systematic Representation of Meaning
Specifying and Implementing Security Policies Using LaSCO, the Language for Security Constraints on Objects
Applying Constraint Handling Rules to HPSG
Probabilistic Constraint Logic Programming. Formal Foundations of Quantitative and Statistical Inference in Constraint-Based Natural Language Processing
Bootstrapping Structure into Language: Alignment-Based Learning
Modeling Complex Domains of Actions and Change
Introducing Dynamic Behavior in Amalgamated Knowledge Bases
The partition semantics of questions, syntactically
Automated Pattern Detection--An Algorithm for Constructing Optimally Synchronizing Multi-Regular Language Filters
Parallel Evaluation of Mathematica Programs in Remote Computers Available in Network
A Recipe for Symbolic Geometric Computing: Long Geometric Product, BREEFS and Clifford Factorization
Sharp transition towards shared vocabularies in multi-agent systems
Quantum Certificate Verification: Single versus Multiple Quantum Certificates
Towards a Coherent Theory of Physics and Mathematics
Equilibrium (Zipf) and Dynamic (Grasseberg-Procaccia) method based analyses of human texts. A comparison of natural (english) and artificial (esperanto) languages
Mining Meaning from Wikipedia
The transmission sense of information
Modeling Discrete Combinatorial Systems as Alphabetic Bipartite Networks: Theory and Applications
Prospective Study for Semantic Inter-Media Fusion in Content-Based Medical Image Retrieval
OntoELAN: An Ontology-based Linguistic Multimedia Annotator
Fuzzy Linguistic Logic Programming and its Applications
ANN-based Innovative Segmentation Method for Handwritten text in Assamese
Coding Guidelines for Prolog
Proceedings Second International Workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software
Coinductive subtyping for abstract compilation of object-oriented languages into Horn formulas
The Need to Support of Data Flow Graph Visualization of Forensic Lucid Programs, Forensic Evidence, and their Evaluation by GIPSY
Partition Refinement of Component Interaction Automata: Why Structure Matters More Than Size
A Logical Foundation for Environment Classifiers
Local Distributed Decision
Using Java for distributed computing in the Gaia satellite data processing
Correlating Formal Semantic Models of Reo Connectors: Connector Coloring and Constraint Automata
Multilingual ontology matching based on Wiktionary data accessible via SPARQL endpoint
A Domain-Specific Language for Incremental and Modular Design of Large-Scale Verifiably-Safe Flow Networks (Preliminary Report)
Classification of Flames in Computer Mediated Communications
Rapid Development of Interferometric Software Using MIRIAD and Python
On Quotients of Formal Power Series
Discrimination of English to other Indian languages (Kannada and Hindi) for OCR system
kLog: A Language for Logical and Relational Learning with Kernels
Broccoli: Semantic Full-Text Search at your Fingertips
Translation of Bengali Terms in Mobile Phones: a Simplified Approach Based on the Prescriptions of Conventional Accent Understand Ability
Inferring SQL Queries Using Program Synthesis
Software Security analysis, static and dynamic testing in java and C environment, a comparative study
Value production in a collaborative environment
Diffusion of Lexical Change in Social Media
Adaptable processes
Modeling in OWL 2 without Restrictions
Regular Cost Functions, Part I: Logic and Algebra over Words
Extending FO(ID) with Knowledge Producing Definitions: Preliminary Results
Large Scale Distributed Acoustic Modeling With Back-off N-grams
Object-Oriented Bayesian Networks
Ethics of using language editing services in an era of digital communication and heavily multiauthored papers
Soft Contract Verification
Algebraic Structure of Combined Traces
Language change in a multiple group society
Saying What You're Looking For: Linguistics Meets Video Search
Recognizing Speech in a Novel Accent: The Motor Theory of Speech Perception Reframed
Sentiment Analysis in the News
QEMU/CPC: Static Analysis and CPS Conversion for Safe, Portable, and Efficient Coroutines
A language independent web data extraction using vision based page segmentation algorithm
The Complexity of Flow Analysis in Higher-Order Languages
Learning to Win by Reading Manuals in a Monte-Carlo Framework
A Proof Theoretic Study of Soft Concurrent Constraint Programming
Interactions of cultures and top people of Wikipedia from ranking of 24 language editions
Zipf's law for word frequencies: word forms versus lemmas in long texts
Non-Standard Words as Features for Text Categorization
Omitting types in logic of metric structures
Galois Transformers and Modular Abstract Interpreters
Sifting Robotic from Organic Text: A Natural Language Approach for Detecting Automation on Twitter
Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph
Combining Models of Approximation with Partial Learning
Markov Logic Networks for Natural Language Question Answering
Pushdown Control-Flow Analysis for Free
Bias and population structure in the actuation of sound change
Mining Local Gazetteers of Literary Chinese with CRF and Pattern based Methods for Biographical Information in Chinese History
Population size predicts lexical diversity, but so does the mean sea level - why it is important to correctly account for the structure of temporal data
Joint Word Representation Learning using a Corpus and a Semantic Lexicon
Mental Lexicon Growth Modelling Reveals the Multiplexity of the English Language
Desiree - a Refinement Calculus for Requirements Engineering
What we write about when we write about causality: Features of causal statements across large-scale social discourse
Semantic Code Browsing
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content
Model Checking of Boolean Process Models
Domain Specific Language for Geometric Relations between Rigid Bodies targeted to robotic applications
Measure Transformer Semantics for Bayesian Machine Learning
Clash of the Lambdas
Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method
Structural characterizations of the navigational expressiveness of relation algebras on a tree
All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement
Really Natural Linear Indexed Type Checking
Automated Analysis and Prediction of Job Interview Performance
GraphVista: Interactive Exploration Of Large Graphs
Idioms-Proverbs Lexicon for Modern Standard Arabic and Colloquial Sentiment Analysis
Deductive Verification of Parallel Programs Using Why3
Unsupervised Discovery of Linguistic Structure Including Two-level Acoustic Patterns Using Three Cascaded Stages of Iterative Optimization
Semiring-based Specification Approaches for Quantitative Security
Symbol Emergence in Robotics: A Survey
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Sculpting Quantum Speedups
Termination of canonical context-sensitive rewriting and productivity of rewrite systems
Transforming Javascript Event-Loop Into a Pipeline
The White Matter Query Language: A Novel Approach for Describing Human White Matter Anatomy
A Lambda-Calculus Foundation for Universal Probabilistic Programming
A Constraint Satisfaction Method for Configuring Non-Local Service Interfaces
Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions
Observing Trends in Automated Multilingual Media Analysis
Polymorphic Type Inference for Machine Code
Enabling Cognitive Intelligence Queries in Relational Databases using Low-dimensional Word Embeddings
Validating an Approach to Formalize Use Cases with Ontologies
Rolex: Resilience-Oriented Language Extensions for Extreme-Scale Systems
Build It, Break It, Fix It: Contesting Secure Development
Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning
STransE: a novel embedding model of entities and relationships in knowledge bases
Predicting and Understanding Law-Making with Word Vectors and an Ensemble Model
Honey: A dataflow programming language for the processing, featurization and analysis of multivariate, asynchronous and non-uniformly sampled scalar symbolic time sequences
Factored Neural Machine Translation
Prioritized Garbage Collection: Explicit GC Support for Software Caches
Gradual Typing in an Open World
Benchmarking Web-testing - Selenium versus Watir and the Choice of Programming Language and Browser
A Fault-tolerance Linguistic Structure for Distributed Applications
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
Automatic recognition of child speech for robotic applications in noisy environments
Ranking medical jargon in electronic health record notes by adapted distant supervision
Zero-resource Machine Translation by Multimodal Encoder-decoder Network with Multimedia Pivot
Web-based Argumentation
Attentive Explanations: Justifying Decisions and Pointing to the Evidence
Quantitative Regular Expressions for Arrhythmia Detection Algorithms
Source Code Verification for Embedded Systems using Prolog
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Extracting Bilingual Persian Italian Lexicon from Comparable Corpora Using Different Types of Seed Dictionaries
Constraint Answer Set Solver EZCSP and Why Integration Schemas Matter
The Universal Fragment of Presburger Arithmetic with Unary Uninterpreted Predicates is Undecidable
Normalisation de la langue et de lecriture arabe : enjeux culturels regionaux et mondiaux
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
Modelling System of Systems Interface Contract Behaviour
Bag-of-Words Method Applied to Accelerometer Measurements for the Purpose of Classification and Energy Estimation
Prosody: The Rhythms and Melodies of Speech
Neural Machine Translation Model with a Large Vocabulary Selected by Branching Entropy
Monoidal computer III: A coalgebraic view of computability and complexity
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification
Brzozowski Goes Concurrent - A Kleene Theorem for Pomset Languages
Data-Driven Program Completion
Towards a Knowledge Graph based Speech Interface
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis
Where is my forearm? Clustering of body parts from simultaneous tactile and linguistic input using sequential mapping
Is Natural Language a Perigraphic Process? The Theorem about Facts and Words Revisited
Probabilistic Model Checking of Incomplete Models
Improving Scalability of Inductive Logic Programming via Pruning and Best-Effort Optimisation
Unsupervised Iterative Deep Learning of Speech Features and Acoustic Tokens with Applications to Spoken Term Detection
Comparing Classical and Relativistic Kinematics in First-Order Logic
Language modeling with Neural trans-dimensional random fields
Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks
A framework for quantitative modeling and analysis of highly (re)configurable systems
Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Representations and evaluation strategies for feasibly approximable functions
Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF
Formally Secure Compilation of Unsafe Low-Level Components (Extended Abstract)
A First Step in Combining Cognitive Event Features and Natural Language Representations to Predict Emotions
Personalized word representations Carrying Personalized Semantics Learned from Social Network Posts
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation
Learning to Represent Programs with Graphs
Programming Bots by Synthesizing Natural Language Expressions into API Invocations
Depression Severity Estimation from Multiple Modalities
Speech recognition for medical conversations
Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition
Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection
An innovative solution for breast cancer textual big data analysis
Building competitive direct acoustics-to-word models for English conversational speech recognition
Emo, Love, and God: Making Sense of Urban Dictionary, a Crowd-Sourced Online Dictionary
Unifying Theories of Reactive Design Contracts
First Draft on the xInf Model for Universal Physical Computation and Reverse Engineering of Natural Intelligence
Exploring Architectures, Data and Units For Streaming End-to-End Speech Recognition with RNN-Transducer
Quantitative Analysis of Smart Contracts
SWRL2SPIN: A tool for transforming SWRL rule bases in OWL ontologies to object-oriented SPIN rules
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling
Tree-to-tree Neural Networks for Program Translation
Tornado: A Practical And Efficient Heterogeneous Programming Framework For Managed Languages
Interval-based Resource Usage Verification by Translation into Horn Clauses and an Application to Energy Consumption
Safe Non-blocking Synchronization in Ada 202x
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
A Computational Model of Syntactic Processing: Ambiguity Resolution from Interpretation
Topological relaxation of entangled flux lattices: Single vs collective line dynamics
The physical Meaning of Replica Symmetry Breaking
A Hydrodynamic Approach to Superconductivity
Logic Programming, Functional Programming, and Inductive Definitions
Mini-indexes for literate programs
Is Word Sense Disambiguation just one more NLP task?
Cascaded Markov Models
Combining Inclusion Polymorphism and Parametric Polymorphism
Human-Computer Conversation
A Unified Example-Based and Lexicalist Approach to Machine Translation
MAP Lexicon is useful for segmentation and word discovery in child-directed speech
A statistical model for word discovery in child directed speech
Measures of Distributional Similarity
Advances in domain independent linear text segmentation
The Light Lexicographic path Ordering
Do All Fragments Count?
The alldifferent Constraint: A Survey
Object-oriented solutions
CLP Approaches to 2D Angle Placements
A procedure for unsupervised lexicon learning
A Statistical Model for Word Discovery in Transcribed Speech
Evaluating the Effectiveness of Ensembles of Decision Trees in Disambiguating Senseval Lexical Samples
Assessing System Agreement and Instance Difficulty in the Lexical Sample Tasks of Senseval-2
Unsupervised Learning in a Framework of Information Compression by Multiple Alignment, Unification and Search
Empirical Methods for Compound Splitting
gTybalt - a free computer algebra system
Pure Prolog Execution in 21 Rules
WSAT(cc) - a fast local-search ASP solver
Computing Convex Hulls with a Linear Solver
Building a linguistic corpus from bee dance data
On Modal Logics of Partial Recursive Functions
A Bimachine Compiler for Ranked Tagging Rules
On computing the fixpoint of a set of boolean equations
The state complexity of L^2 and L^k
Widening Operators for Weakly-Relational Numeric Abstractions (Extended Abstract)
Transforming Business Rules Into Natural Language Text
Metalinguistic Information Extraction for Terminology
ReacProc: A Tool to Process Reactions Describing Particle Interactions
Word sense disambiguation criteria: a systematic study
Approximability of Bounded Occurrence Max Ones
A Predicative Harmonization of the Time and Provable Hierarchies
Recurrence with affine level mappings is P-time decidable for CLP(R)
Algorithm of Segment-Syllabic Synthesis in Speech Recognition Problem
Untersuchungen zur Jet-Parton-Korrelation in der tief-inelastischen Streuung
Three-Loop Results on the Lattice
Lattice Perturbation Theory by Computer Algebra: A Three-Loop Result for the Topological Susceptibility
Perturbative renormalization for overlap fermions
Correlations, correlation integrals and application to Bose-Einstein interferometry
Gluon-Meson Duality in the Mean Field Approximation
Introductory lecture notes on the Karabali-Nair theory
Hard collisions of photons: plea for a common language
A Feynman graph selection tool in GRACE system
QCD and Hadron Dynamics
A verification of the Optimal Jet Finder
Leading rescattering effects cannot improve the description of $B \to K π$ data
GiNaC - Symbolic computation with C++
Heavy quarkonium decays and transitions in the language of effective field theories
Duality Principle and Braided Geometry
Integrability and Seiberg-Witten Theory: Curves and Periods
Correlation functions for the Z-invariant Ising model
Branes and Six Dimensional Fixed Points
String theory as a universal language
Projection on higher Landau levels and non-commutative geometry
Noncommutative gerbes and deformation quantization
Two-Loop Computation in Superstring Theory
Operads and Quantum Gravity
On Concept of Parity for a Fermion
Quantum Field Theory in the Language of Light-cone String
On a Classification of Irreducible Almost Commutative Geometries
AKSZ-BV Formalism and Courant Algebroid-induced Topological Field Theories
Many simple cardinal invariants
Convergence in homogeneous random graphs
The Knowlton-Graham partition problem
Existence of Orbifolds IV: Examples
A Note on Superamorphous Sets and Dual Dedekind-Infinity
New results on binary linear codes
Lectures on Special Lagrangian Submanifolds
Three-Dimensional 2-Framed TQFTs and Surgery
One-Dimensional Peg Solitaire
Weak Hopf algebra symmetries of C^*-algebra inclusions
Universal numerical algorithms and their software implementation
3-Manifolds from Platonic Solids
Functions on groups and computational complexity
Word Hyperbolic Semigroups
A Universal Approach to Self-Referential Paradoxes, Incompleteness and Fixed Points
Der rechnende Dichter (The calculating poet)
Internal bialgebroids, entwining structures and corings
Grothendieck rings of \mathbb{Z}-valued fields
Realizations of Bialgebras (II) : Duality Theorem
Extending the Language of Set Theory
Quantum random walks and their convergence
Continuity of the Mixing Operator
Moduli stacks of vector bundles on curves and the King-Schofield rationality proof
The Stasheff model of a simply-connected manifold and the string bracket
A recursive method for computing zeta functions of varieties
The word problem distinguishes counter languages
A fansy divisor on M_{0,n}
English Russian Scientific Dictionary
On the context-freeness of the set of words containing overlaps
The Feynman Legacy
Polynômes de Hua, noyau de Bergman des domaines de Cartan-Hartogs et problème de Lu Qikeng
Derived Algebraic Geometry III: Commutative Algebra
Jets, frames, and their Cartan geometry
CARPS: An integrated proposal and data collection system
Boltzmann's H-theorem and time irreversibility
Brownian Motion for the School-Going Child
Tensor operators in R-matrix approach
Simulation in Biology
Search for bottleneck effects in Penna ageing and Schulze language model
A small 1-way quantum finite automaton
Easy Control over Fermionic Computations
The Complexity of Probabilistic versus Quantum Finite Automata
Quantum Implementation of Parrondo's Paradox
Quantum measurement act as a "speech act"
Another Look at Quantum Teleportation
Adiabatic Quantum Computing with Phase Modulated Laser Pulses
Scopes and Limits of Modality in Quantum Mechanics
Distributions of Roots of Reduced Cubic Equations with Random Coefficients
The Mathematics
Applying the Z-transform for the static analysis of floating-point numerical filters
Elementary gates for cartoon computation
Nagata's embedding theorem
Assisted Problem Solving and Decompositions of Finite Automata
Projection semantics for rigid loops
Guerra's interpolation using Derrida-Ruelle cascades
CD(4) has bounded width
Simulation of ratio of old to young people in countries like Poland
Differential Complexes and Stratified Pro-Modules
Software (Re-)Engineering with PSF
Clones and Genoids in Lambda Calculus and First Order Logic
A Unified Approach to Local Cohomology Modules Using Serre Classes
Finite Automata Based on Quantum Logic and Their Determinization
Sheaves on local Calabi-Yau varieties
Infinite words containing squares at every position
Symbolic computations in differential geometry
Embeddings of four-valent framed graphs into two-surfaces
Marginal Likelihood Integrals for Mixtures of Independence Models
Relaxed optimality conditions for mu-differentiable functions
Open architecture for multilingual parallel texts
Linear algebra meets Lie algebra: the Kostant-Wallach theory
A computer verified, monadic, functional implementation of the integral
Distribution of complexities in the Vai script
Integrability and Chaos - algebraic and geometric approach
Text as Statistical Mechanics Object
Practical language based on systems of definitions
Two Forms of One Useful Logic: Existential Fixed Point Logic and Liberal Datalog
On the stability of the overconvergence under the direct image by a proper smooth morphism
A Note on Self-Dual Yang-Mills Theory
Some peculiarities in response on filling up the Fermi sphere by quarks
There are k-uniform cubefree binary morphisms for all k >= 0
A Simple, Linear-Time Algorithm for x86 Jump Encoding
An Array Algebra
State Space Realization Theorems For Data Mining
A Note on Symmetries in the Rauzy Graph and Factor Frequencies
Yet Another Deep Embedding of B:Extending de Bruijn Notations
A new universal cellular automaton on the ternary heptagrid
On polynomial growth functions of D0L-systems
Classical Combinatory Logic
Axiomatizing mathematical conceptualism in third order arithmetic
Iterative Methods for Systems' Solving - a C# approach
Lexicographically least words in the orbit closure of the Rudin-Shapiro word
Triplet-like correlation symmetry of continuous variable entangled states
Some Considerations on Universality
Intrinsically Universal Cellular Automata
Small Turing universal signal machines
On the boundaries of solvability and unsolvability in tag systems. Theoretical and Experimental Results
Fairness as a QoS Measure for Web Services
Cross-Task Knowledge-Constrained Self Training
Iterative pushdown automata and hyperbolic contour words
State Complexity Approximation
Programming with Quantum Communication
Proceedings Eleventh International Workshop on Descriptional Complexity of Formal Systems
Periodicity in tilings
The averaging trick and the Cerny conjecture
How Do Interactive Virtual Operas Shift Relationships between Music, Text and Image?
Outer billiard outside regular polygons
On Event Structure in the Torn Dress
A weakly universal cellular automaton in the hyperbolic 3D space with three states
Noncommutative rational functions, their difference-differential calculus and realizations
Recent Development of QCD Factorization for B-> M1 M2
Deformations of algebraic subvarieties
Local positivity, multiplier ideals, and syzygies of abelian varieties
Proceedings Tenth International Workshop on Rule-Based Programming
On the palindromic decomposition of binary words
A new measure of asymmetry of binary words
About the embedding of one dimensional cellular automata into hyperbolic cellular automata
The Socceral Force
Morphonette: a morphological network of French
A new weakly universal cellular automaton in the 3D hyperbolic space with two states
An Effective Extension of the Wagner Hierarchy to Blind Counter Automata
An upper bound on the number of states for a strongly universal hyperbolic cellular automaton on the pentagrid
A Saturation Method for the Modal Mu-Calculus with Backwards Modalities over Pushdown Systems
Empowering Collections with Swarm Behavior
A measure of state transition of collective of stateless automata in discrete environment
A Weakly Intuitionistic Quantum Logic
Variations on a theme of Beurling
Steering Fragments of Instruction Sequences
Diffieties and Liouvillian Systems
Static vs Dynamic SAGAs
Network motifs in music sequences
Proceedings 12th International Workshop on Verification of Infinite-State Systems
Topological Modal Logics with Difference Modality
Slopes of Tilings
A Simple Correctness Proof for Magic Transformation
Across Browsers SVG Implementation
Coordinates for a new triangular tiling of the hyperbolic plane
Visualizing quantum mechanics in phase space
Jancar's formal system for deciding bisimulation of first-order grammars and its non-soundness
Proceedings 5th International Workshop on Higher-Order Rewriting
Fife's Theorem Revisited
Remarks on separating words
Hypermaps and multiply quasiplatonic Riemann surfaces
ReveR: Software Simulator of Reversible Processor with Stack
Geometry of warped products
Esparsidade, Estrutura, Escalamento e Estabilidade em Algebra Linear Computacional
Invariant number triangles, eigentriangles and Somos-4 sequences
Reversibility in Massive Concurrent Systems
Petri Nets and Bio-Modelling - and how to benefit from their synergy
On the Delone property of (-β)-integers
Une analyse basée sur la S-DRT pour la modélisation de dialogues pathologiques
Super-Poincare' algebras, space-times and supergravities (II)
Introducing LoCo, a Logic for Configuration Problems
Pure spinor superfields and Born-Infeld theory
Inclusion of Unambiguous RE#s is NP-Hard
The Krohn-Rhodes Theorem and Local Divisors
Solving the TTC 2011 Compiler Optimization Case with QVTR-XSLT
Solving the TTC 2011 Compiler Optimization Case with GReTL
Solving the TTC 2011 Reengineering Case with GReTL
Saying Hello World with Henshin - A Solution to the TTC 2011 Instructive Case
Saying Hello World with GrGen.NET - A Solution to the TTC 2011 Instructive Case
Independent sets of words and the synchronization problem
Adinkras for Mathematicians
Unique decodability of bigram counts by finite automata
Exploring Twitter Hashtags
Bispecial factors in circular non-pushy D0L languages
Formalization of semantic network of image constructions in electronic content
Enumerating Trees
The GF Mathematics Library
On equivalence and emptiness problems of multi-letter (measure many) quantum finite automata
Ahlfors-Beurling operator on radial functions
Automatic Theorem-Proving in Combinatorics on Words
Roget's Thesaurus as a Lexical Resource for Natural Language Processing
Implementation of Kalman Filter with Python Language
Synthesising Choreographies from Local Session Types (extended version)
Isomorphisms of scattered automatic linear orders
Indices to Quantify the Ranking of Arabic Journals and Research Output
Structured Grammars are Effective
Of priors and prejudice
Juppix: a Linux Live-CD for Undergraduate Students
Visibly pushdown automata on trees: universality and u-universality
Visualization of features of a series of measurements with one-dimensional cellular structure
Emotion Detection from Text
An n log n Alogrithm for Deterministic Kripke Structure Minimization
Cellular automata on regular rooted trees
BPA Bisimilarity is EXPTIME-hard
The hardest logic puzzle ever becomes even tougher
The Goedel's legacy: revisiting the Logic
Locally compact groups and continuous logic
Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing
Challenges for Distributional Compositional Semantics
Infinite ternary square-free words concatenated from permutations of a single word
Quantifier Elimination For Tame Fields
Complexity of testing morphic primitivity
On the Existence of Universal Finite or Pushdown Automata
Long time existence of Minimizing Movement solutions of Calabi flow
Identification of Probabilities of Languages
Robopinion: Opinion Mining Framework Inspired by Autonomous Robot Navigation
Characterizing Successful Formulas: the Multi-agent Case
Rewriting and narrowing for constructor systems with call-time choice semantics
A Linguistic Model for Terminology Extraction based Conditional Random Fields
Formally Checking Large Data Sets in the Railways
Bisimilarity of Pushdown Systems is Nonelementary
Proceedings 6th Workshop on Membrane Computing and Biologically Inspired Process Calculi
Balance properties of Arnoux-Rauzy words
On an algorithm for multiperiodic words
The Černý conjecture for small automata: experimental report
A New Proof of P-time Completeness of Linear Lambda Calculus
Global Mackey functors with operations and n-special lambda rings
Towards Interactive Object-Oriented Programming
Weak Concurrent Kleene Algebra with Application to Algebraic Verification
Exploiting Uncertain and Temporal Information in Correlation
Reachability in Two-Clock Timed Automata is PSPACE-complete
Double cosets in free groups
Two Variable vs. Linear Temporal Logic in Model Checking and Games
The untyped stack calculus and Bohm's theorem
On p-form vortex-lines equations on extended phase space
An Inventory of Preposition Relations
Algebraic geometry over Boolean algebras in the language with constants
Tweets Miner for Stock Market Analysis
Compact Notation for Finite Transformations
The User Feedback on SentiWordNet
The Holonomy Decomposition of Circular Semi-Flower Automata
Words with unbounded periodicity complexity
On disjunction of equations in inverse semigroups
A counterexample to a question of Hof, Knill and Simon
On Negotiation as Concurrency Primitive
A Haskell Library for Term Rewriting
Numerical computations in cobordism categories
Contextuality: Wheeler's universal regulating principle
Compilation for QCSP
Cartesian closed 2-categories and permutation equivalence in higher-order rewriting
Finite State Machine Synthesis for Evolutionary Hardware
The homology graph of a higher dimensional automaton
Disquisitiones 235
Chemical concrete machine
Abstract interpretation as anti-refinement
Implementing Computations in Automaton (Semi)groups
Epistemic Logic for Communication Chains
Paracontrolled Distributions and the 3-dimensional Stochastic Quantization Equation
Variedades de álgebras topologicas
The decomposability problem for torsion-free abelian groups is analytic complete
Counting the Palstars
Finite-type-Dyck shift spaces
A Proof of the Barsotti-Chevalley Theorem on Algebraic Groups
Solving the Flowgraphs Case with Eclectic
Periods and global invariants of automorphic representations
The hidden symmetry and Mr. Higgs!
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
A Microkernel Architecture for Constraint Programming
SAP Speaks PDDL: Exploiting a Software-Engineering Model for Planning in Business Process Management
Algorithmic Verification of Continuous and Hybrid Systems
A Polynomial Time Solution to the Clique Problem
Clinical TempEval
Towards a GPU-based implementation of interaction nets
Open Verlinde line operators
Intégration des données d'un lexique syntaxique dans un analyseur syntaxique probabiliste
Why must we work in the phase space?
Analysis of first order systems for the solution of Laplace's equation
Object-Oriented Parallel Programming
Piecewise Boolean algebras and their domains
Synchronizing automata with random inputs
A graph-based mathematical morphology reader
Learning Bilingual Word Representations by Marginalizing Alignments
Separably closed fields and contractive Ore modules
The Correctness of Launchbury's Natural Semantics for Lazy Evaluation
Optimality Theory as a Framework for Lexical Acquisition
Text Classification Using Association Rules, Dependency Pruning and Hyperonymization
On multiply-exponential write-once Turing machines
Modeling Cassava Yield: A Response Surface Approach
Negational Fragment of Intuitionistic Control Logic
Transition regime of the one-dimensional two-stream instability
SAT for pedestrians
A Morphological Analyzer for Japanese Nouns, Verbs and Adjectives
Dependent Types for Pragmatics
The Visualization of Change in Word Meaning over Time using Temporal Word Embeddings
A note on two notions of compliance
An Intuitive Procedure for Converting PDA to CFG, by Construction of Single State PDA
Yesquel: scalable SQL storage for Web applications
Approaches for Synthesis Conjectures in an SMT Solver
FREC 14: FRontiers of RECognizability
On the quantifier complexity of definable canonical henselian valuations
A Meta-Logic of Inference Rules: Syntax
Uniform Definability in Propositional Dependence Logic
Static Analysis for Biological Systems (BioAmbients)
Tokuyama's Identity for Factorial Schur Functions
A One-Dimensional Physically Universal Cellular Automaton
Regroupement sémantique de définitions en espagnol
Finitely Balanced Sequences and Plasticity of 1-Dimensional Tilings
Fibered Multiderivators and (co)homological descent
Featherweight PINQ
Pattern avoidance is not P-recursive
Representation Theorems for Strong Predicate Exchangeability in Pure Inductive Logic
Simple, Fast Semantic Parsing with a Tensor Kernel
On a Unified Analysis in the language of preordered sets
Complexity of Substitutive Sequences - Calculation of the Complexities of Substitutive Sequences Over a Binary Alphabet
Finite-Degree Predicates and Two-Variable First-Order Logic
The challenges of SVM optimization using Adaboost on a phoneme recognition problem
YARBUS : Yet Another Rule Based belief Update System
Property irrelevant predicates
Normal forms for linear displacement context-free grammars
Using Ontology-Based Context in the Portuguese-English Translation of Homographs in Textual Dialogues
DEMONIC programming: a computational language for single-particle equilibrium thermodynamics, and its formal semantics
Multinomial Loss on Held-out Data for the Sparse Non-negative Matrix Language Model
Comparing Writing Styles using Word Embedding and Dynamic Time Warping
Good, Better, Best: Choosing Word Embedding Context
Regular sequences and the joint spectral radius
On Basic Properties of Jumping Finite Automata
Beyond OWL 2 QL in OBDA: Rewritings and Approximations (Extended Version)
Hopf-Galois objects of Calabi-Yau Hopf algebras
Online Updating of Word Representations for Part-of-Speech Tagging
The Line Graph of the Universal Homogeneous Triangle-free Graph
Refinement Types for TypeScript
Improving sentence compression by learning to predict gaze
From Incremental Meaning to Semantic Unit (phrase by phrase)
Restricted trichotomy in higher dimensions
Efficient Calculation of Bigram Frequencies in a Corpus of Short Texts
Is 1+1=2 an empirical proposition?
Detecting state of aggression in sentences using CNN
Desiderata for Vector-Space Word Representations
Holophrasm: a neural Automated Theorem Prover for higher-order logic
On the Regular Emptiness Problem of Subzero Automata
TerpreT: A Probabilistic Programming Language for Program Induction
Quicksort with median of medians is considered practical
A special case of quasiminimality
A Simple Guide to S3 Methods
Computational Aspects of Asynchronous CA
Putting Instruction Sequences into Effect
The Syllogistic with Unity
Certifying and reasoning about cost annotations of functional programs
Logical Fuzzy Optimization
Shortest Repetition-Free Words Accepted by Automata
A polytime complexity analyser for Probabilistic Polynomial Time over imperative stack programs
Expressando Atributos Não-Funcionais em Workflows Científicos
Declarative Ajax Web Applications through SQL++ on a Unified Application State
A proposal for a Chinese keyboard for cellphones, smartphones, ipads and tablets
Linear models and linear mixed effects models in R with linguistic applications
How Does Latent Semantic Analysis Work? A Visualisation Approach
A note on groups of a family of hyperbolic tessellations
Two-dimensional Sentiment Analysis of text
(k,l)-Unambiguity and Quasi-Deterministic Structures
A Clustering Analysis of Tweet Length and its Relation to Sentiment
Deformed one-loop amplitudes in N = 4 super-Yang-Mills theory
Quantum finite automata: A modern introduction
Vector Clocks in Coq: An Experience Report
The word problem in Hanoi Towers groups
Abelian networks II. Halting on all inputs
Object-Oriented Programming, Functional Programming and R
Event Handling in ET++ - A Case Study in the Algebraic Specification of Object-Oriented Application Frameworks
Coffman deadlocks in SCOOP
The Emptiness Problem for Tree Automata with at Least One Disequality Constraint is NP-hard
A Note on a Recent Attempt to Improve the Pin-Frankl Bound
Zipf's Law and the Frequency of Characters or Words of Oracles
Non-termination of Dalvik bytecode via compilation to CLP
The boundness of distance between two sets of fixed volume inside the multidimensional ball or cube
An in-between "implicit" and "explicit" complexity: Automata
Expressiveness of the modal mu-calculus on monotone neighborhood structures
Practical Realization of the Self-Balancing Robot Using Infrared Sensors
Diverse Palindromic Factorization is NP-Complete
A Finite Model Property for Intersection Types
Building the distributed WPS-services execution environment
On the Combinatorics of Palindromes and Antipalindromes
A definable henselian valuation with high quantifier complexity
Classification of some Global Integrals related to groups of type $A_n$
A Simple Parallel Implementation of Interaction Nets in Haskell
Temporal ordering of clinical events
Controlled Query Evaluation for Datalog and OWL 2 Profile Ontologies
Logic Blog 2014
Medical Synonym Extraction with Concept Space Models
Syntactic semigroup problem for the semigroup reducts of Affine Near-semirings over Brandt Semigroups
Words with the Maximum Number of Abelian Squares
An aperiodic set of 11 Wang tiles
Incorporating Inductions and Game Semantics into Logic Programming
Logic Programming with Macro Connectives
Encoding TLA+ set theory into many-sorted first-order logic
Exploring Metaphorical Senses and Word Representations for Identifying Metonyms
Factorizations of the Fibonacci Infinite Word
Analysis of Communication Pattern with Scammers in Enron Corpus
Implementing a teleo-reactive programming system
Tree Automata
About the review in Mathematical Reviews of my paper: The two-cardinal problem for languages of arbitrary cardinality The Journal of Symbolic Logic 75, Number 3, Sept., 2010, pp. 785-801
Robustly Solvable Constraint Satisfaction Problems
A token-passing net implementation of optimal reduction with embedded read-back
Deformations of holomorphic Poisson maps
Multi-Source Neural Translation
Some Landau--Ginzburg models viewed as rational maps
Hierarchical Latent Word Clustering
Probabilistic Models for Computerized Adaptive Testing: Experiments
Repetition-Free Derivability from a Regular Grammar is NP-Hard
On automatic subsets of the Gaussian integers
Easy-First Dependency Parsing with Hierarchical Tree LSTMs
Electromagnetic waves, gravitational waves and the prophets who predicted them
Derived noncommutative Zariski immersion and an equivalent reformulation of Friedlander-Milnor conjecture
Multichannel Variable-Size Convolution for Sentence Classification
Differential K-characters and D-branes
The Yahoo Query Treebank, V. 1.0
The bitwise operations in relation to obtaining Latin squares
Prefix frequency of lost positions
Proof nets for the Displacement calculus
The masterpieces of John Forbes Nash Jr
Calculi for Intuitionistic Normal Modal Logic
Shallow Discourse Parsing Using Distributed Argument Representations and Bayesian Optimization
Evaluating Informal-Domain Word Representations With UrbanDictionary
Asking photons where they have been in plain language
State complexity of multiple catenation
Shift registers fool finite automata
Modeling, refining and analyzing Incomplete Büchi Automata
LAYERS: Yet another Neural Network toolkit
Fairness as a Program Property
Sequence-to-sequence neural network models for transliteration
Representing regular pseudocomplemented Kleene algebras by tolerance-based rough sets
Defects and boundary RG flows in $\mathbb{C}/\mathbb{Z}_d$
More on Compression and Ranking
Improving Reliability of Word Similarity Evaluation by Redesigning Annotation Task and Performance Measure
Expertise revisited I: Interactional Expertise
Some conjectures on codes
Gowers norms for the Thue-Morse and Rudin-Shapiro sequences
Discovering Conversational Dependencies between Messages in Dialogs
On the synchronization of planar automata
Matrix Dirichlet processes
Arcs, hypercubes, and graphs as quotients of projective Fraïssé limits
Faà di Bruno's note on eponymous formula, trilingual version
Getting Started with PATSTAT Register
RSSL: Semi-supervised Learning in R
Stance detection in online discussions
Towards Smart Proof Search for Isabelle
Job Detection in Twitter
Semantic classifier approach to document classification
A new lower bound for reset threshold of synchronizing automata with sink state
Undecidability and Finite Automata
Proceedings Eighth Workshop on Intersection Types and Related Systems
A short proof of correctness of the quasi-polynomial time algorithm for parity games
Cohomology with values in a sheaf of crossed groups over a site
Intelligent User Interfaces - A Tutorial
On the Comparison of Context-Free Grammars
Variations on a Visserian Theme
English Conversational Telephone Speech Recognition by Humans and Machines
On the $k$-abelian complexity of the Cantor sequence
Complexity of Verifying Nonblockingness in Modular Supervisory Control
Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model
The NLTK FrameNet API: Designing for Discoverability with a Rich Linguistic Resource
Labeled homology of higher-dimensional automata
Integral points on curves, the unit equation, and motivic periods
The Interplay of Semantics and Morphology in Word Embeddings
The Boolean SATisfiability Problem in Clifford algebra
The $Σ_2$ theory of $\mathscr{D}_h(\leq_h \mathcal{O})$ as an uppersemilattice with least and greatest element is decidable
Duluth at Semeval-2017 Task 7 : Puns upon a midnight dreary, Lexical Semantics for the weak and weary
Learning Product Automata
A Survey of Deep Learning Methods for Relation Extraction
Ten Conferences WORDS: Open Problems and Conjectures
StegIbiza: Steganography in Club Music Implemented in Python
On the modularity of endomorphism algebras
Properties of Normalization for a math based intermediate representation
Algebraic theories in monoidal categories
Choreographies for Automatic Recovery
word2vec Skip-Gram with Negative Sampling is a Weighted Logistic PCA
Persistent topology for natural data analysis - A survey
Myhill-Nerode Relation for Sequentiable Structures
Alignment Elimination from Adams' Grammars
Turing Completeness of Finite, Epistemic Programs
Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles
A universal completion of the ZX-calculus
Grammatical Error Correction with Neural Reinforcement Learning
Complete Call-by-Value Calculi of Control Operators II: Strong Termination
Complete Call-by-Value Calculi of Control Operators, I
External Evaluation of Event Extraction Classifiers for Automatic Pathway Curation: An extended study of the mTOR pathway
State complexity of catenation combined with boolean operations
A Critique of a Critique of Word Similarity Datasets: Sanity Check or Unnecessary Confusion?
A Short Survey of Biomedical Relation Extraction Techniques
Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification
Can string kernels pass the test of time in Native Language Identification?
Star Height via Games
Improved Abusive Comment Moderation with User Embeddings
Derivations of Group Algebras
Constructing an olfactory perceptual space and predicting percepts from molecular structure
A rule based algorithm for detecting negative words in Persian
IVOA Recommendation: SSO - Single-Sign-On Profile: Authentication Mechanisms Version 2.0
Arc-Standard Spinal Parsing with Stack-LSTMs
Complexity of term representations of finitary functions
OpenNMT: Open-source Toolkit for Neural Machine Translation
LoIDE: a web-based IDE for Logic Programming - Preliminary Technical Report
Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering
Neural Networks for Text Correction and Completion in Keyboard Decoding
Neural Machine Translation
A new indexed approach to render the attractors of Kleinian groups
Communicating Finite-State Machines and Two-Variable Logic
On Vague Computers
Compiling and Processing Historical and Contemporary Portuguese Corpora
A geometer's view of the the Cramér-Rao bound on estimator variance
Wembedder: Wikidata entity embedding web service
Lagrange's Theorem for Binary Squares
Convolutional Attention-based Seq2Seq Neural Network for End-to-End ASR
A note on stability of Hardy inequalities
Typesafe Abstractions for Tensor Operations
When is an automatic set an additive basis?
Permutation complexity of images of Sturmian words by marked morphisms
A bound for the shortest reset words for semisimple synchronizing automata via the packing number
A Refutation of Guinea's "Understanding SAT is in P"
Automata in the Category of Glued Vector Spaces
Verification of PCP-Related Computational Reductions in Coq
Predicting readmission risk from doctors' notes
A Taxonomy of Morphic Sequences
Notes on bounded induction for the compositional truth predicate
Scott Ranks of Classifications of the Admissibility Equivalence Relation
Outer billiards outside regular octagon: set of full measure and an aperiodic point
On co-counter-fragments of automata
Sentiment Predictability for Stocks
Trading Zones Revisited
On 'categories' of quantum field theories
On strong alt-induced codes
Using Sat solvers for synchronization issues in non-deterministic automata
Behavior Trees as a Representation for Medical Procedures
Multi-optional Many-sorted Past Present Future structures and its description
Finitary-based Domain Theory in Coq: An Early Report
Higher-dimensional automata modeling shared-variable systems
Some Issues on the Theory of the Mimic-Computing-Oriented Automata
Hardy's paradox according to non-classical semantics
Call-by-need, neededness and all that
diagnoseIT: Expertengestützte automatische Diagnose von Performance-Probleme in Enterprise-Anwendungen (Abschlussbericht)
A Scheme-Driven Approach to Learning Programs from Input/Output Equations
Layered structure and leveled function of a human brain
Alternating Nonzero Automata
Proceedings Fourth International Workshop on Rewriting Techniques for Program Transformations and Evaluation
A Method to Translate Order-Sorted Algebras to Many-Sorted Algebras
Automatic supermartingales acting on sequences
Sequentializing cellular automata
Principles of design and software development models of ontological-driven computer systems
Improved Upper Bounds on all Maximal $α$-gapped Repeats and Palindromes
A Study of Recent Contributions on Information Extraction
Unsupervised Keyphrase Extraction with Multipartite Graphs
Chart Parsing Multimodal Grammars
Neural models of factuality
On \emptyset-definable elements in a field
AGN host galaxies at redshift z~0.7: peculiar or not?
Pure Differential Modules and a Result of Macaulay on Unmixed Polynomial Ideals
The GAPS programme with HARPS-N at TNG XI. Pr~0211 in M~44: the first multi-planet system in an open cluster
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Functional Dynamics I : Articulation Process
Sherpa: a Mission-Independent Data Analysis Application
An Etymological Dictionary of Astronomy and Astrophysics: English-French-Persian
Truncated horseshoes and formal languages in chaotic scattering
Common Topics and Coherent Situations: Interpreting Ellipsis in the Context of Discourse Inference
An Optimal Tabular Parsing Algorithm
Semantics of Complex Sentences in Japanese
Extracting Noun Phrases from Large-Scale Texts: A Hybrid Approach and Its Automatic Evaluation
Speech Dialogue with Facial Displays: Multimodal Human-Computer Conversation
Towards a Principled Representation of Discourse Plans
Word-Sense Disambiguation Using Decomposable Models
Parsing Turkish with the Lexical Functional Grammar Formalism
Some Advances in Transformation-Based Part of Speech Tagging
A Psycholinguistically Motivated Parser for CCG
Verb Semantics and Lexical Selection
Morphology with a Null-Interface
Multi-Tape Two-Level Morphology: A Case Study in Semitic Non-linear Morphology
Computational Analyses of Arabic Morphology
A Modular and Flexible Architecture for an Integrated Corpus Query System
Training and Scaling Preference Functions for Disambiguation
On Implementing an HPSG theory -- Aspects of the logical architecture, the formalization, and the implementation of head-driven phrase structure grammars
Reaping the Benefits of Interactive Syntax and Semantics
Integrating Knowledge Bases and Statistics in MT
Conceptual Association for Compound Noun Analysis
Korean to English Translation Using Synchronous TAGs
Disambiguation of Super Parts of Speech (or Supertags): Almost Parsing
Towards a More User-friendly Correction
A Comparison of Two Smoothing Methods for Word Bigram Models
Status of the XTAG System
The Linguistic Relevance of Quasi-Trees
Bootstrapping A Wide-Coverage CCG from FB-LTAG
Automatically Identifying Morphological Relations in = Machine-Readable Dictionaries
Segmenting speech without a lexicon: The roles of phonotactics and speech source
The Semantics of Resource Sharing in Lexical-Functional Grammar
Cooperative Error Handling and Shallow Processing
Redundancy in Collaborative Dialogue
Assessing Complexity Results in Feature Theories
Mixed Initiative in Dialogue: An Investigation into Discourse Segmentation
Estimating Lexical Priors for Low-Frequency Syncretic Forms
Discourse Processing of Dialogues with Multiple Threads
Efficient Analysis of Complex Diagrams using Constraint-Based Parsing
Robust Parsing of Spoken Dialogue Using Contextual Knowledge and Recognition Probabilities
The Compactness of Construction Grammars
Context and ontology in understanding of dialogs
Development of a Spanish Version of the Xerox Tagger
Text Chunking using Transformation-Based Learning
Using Decision Trees for Coreference Resolution
Ambiguity in the Acquisition of Lexical Information
A Categorial Framework for Composition in Multiple Linguistic Domains
A Computational Approach to Aspectual Composition
A Labelled Analytic Theorem Proving Environment for Categorial Grammar
Heuristics and Parse Ranking
Toward an MT System without Pre-Editing --- Effects of New Methods in ALT-J/E ---
Term Encoding of Typed Feature Structures
Context-Sensitive Measurement of Word Distance by Adaptive Scaling of a Semantic Space
Another Facet of LIG Parsing
The importance of being lazy -- using lazy evaluation to process queries to HPSG grammars
Extended Dependency Structures and their Formal Interpretation
Compiling a Partition-Based Two-Level Formalism
Learning similarity-based word sense disambiguation from sparse data
A New Statistical Parser Based on Bigram Lexical Dependencies
Learning Dependencies between Case Frame Slots
Clustering Words with the MDL Principle
Combining Trigram-based and Feature-based Methods for Context-Sensitive Spelling Correction
A Bayesian hybrid method for context-sensitive spelling correction
An Efficient Inductive Unsupervised Semantic Tagger
A Probabilistic Disambiguation Method Based on Psycholinguistic Principles
A Robust System for Natural Spoken Dialogue
Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach
From Submit to Submitted via Submission: On Lexical Rules in Large-Scale Lexicon Acquisition
A Divide-and-Conquer Strategy for Parsing
The Grammar of Sense: Is word-sense tagging much more than part-of-speech tagging?
A Lexical Semantic Database for Verbmobil
Using textual clues to improve metaphor processing
Controlling Functional Uncertainty
Centering in Italian
Punctuation in Quoted Speech
A Word Grammar of Turkish with Morphophonemic Rules
Morphological Productivity in the Lexicon
Automatic Alignment of English-Chinese Bilingual Texts of CNS News
Using sentence connectors for evaluating MT output
A Geometric Approach to Mapping Bitext Correspondence
Learning string edit distance
Selective Sampling of Effective Example Sentence Sets for Word Sense Disambiguation
Improvising Linguistic Style: Social and Affective Bases for Agent Personality
Representing Constraints with Automata
Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation
Morphological Disambiguation by Voting Constraints
Distinguishing Word Senses in Untagged Text
Evaluating Competing Agent Strategies for a Voice Email Agent
An Efficient Distribution of Labor in a Two Stage Robust Interpretation Process
Reluctant Paraphrase: Textual Restructuring under an Optimisation Model
Intrasentential Centering: A Case Study
Towards a PURE Spoken Dialogue System for Information Access
Epistemic NP Modifiers
Combining Multiple Methods for the Automatic Construction of Multilingual WordNets
Semantic Processing of Out-Of-Vocabulary Words in a Spoken Dialogue System
Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy
The effect of alternative tree representations on tree bank grammars
Do not forget: Full memory in memory-based learning of word pronunciation
Modularity in inductively-learned word pronunciation systems
Manual Annotation of Translational Equivalence: The Blinker Project
Lazy Transformation-Based Learning
Eliminating deceptions and mistaken belief to infer conversational implicature
Dialogue Act Tagging with Transformation-Based Learning
Unlimited Vocabulary Grapheme to Phoneme Conversion for Korean TTS
Bayesian Stratified Sampling to Assess Corpus Utility
Word Clustering and Disambiguation Based on Co-occurrence Data
A Projection Architecture for Dependency Grammar and How it Compares to LFG
Indexing with WordNet synsets can improve Text Retrieval
Word Length Frequency and Distribution in English: Observations, Theory, and Implications for the Construction of Verse Lines
Bosonization Rules for Electron-Hole Systems - II
Euclidean and Riemannian Geometrical Approaches to Non-Extensive Thermo-Statistical Mechanics
Core Lexicon and Contagious Words
Utterance Selection Model of Language Change
Flexibly Instructable Agents
Set-Theoretic Completeness for Epistemic and Conditional Logic
Comparing the expressive power of the Synchronous and the Asynchronous pi-calculus
Producing NLP-based On-line Contentware
A Formal Framework for Linguistic Annotation
An ascription-based approach to speech acts
Pronoun Resolution in Japanese Sentences Using Surface Expressions and Examples
An Example-Based Approach to Japanese-to-English Translation of Tense, Aspect, and Modality
Minimum Description Length and Compositionality
Why C++ is not very fit for GUI programming
A Polyvariant Binding-Time Analysis for Off-line Partial Deduction
A Denotational Semantics for First-Order Logic
On the Scalability of the Answer Extraction System "ExtrAns"
Accuracy, Coverage, and Speed: What Do They Mean to Users?
Type Classes and Constraint Handling Rules
Selectional Restrictions in HPSG
Combining Linguistic and Spatial Information for Document Analysis
Contextual Inference in Computational Semantics
On Exponential-Time Completeness of the Circularity Problem for Attribute Grammars
One Sense per Collocation and Genre/Topic Variations
A Formal Framework for Linguistic Annotation (revised version)
A Lambda-Calculus with letrec, case, constructors and non-determinism
Slicing of Constraint Logic Programs
Semantics and Termination of Simply-Moded Logic Programs with Dynamic Scheduling
An Effective Fixpoint Semantics for Linear Logic Programs
Magical Number Seven Plus or Minus Two: Syntactic Structure Recognition in Japanese and English Sentences
Meaning Sort - Three examples: dictionary construction, tagged corpus construction, and information presentation system
CRL at Ntcir2
Chain Programs for Writing Deterministic Metainterpreters
Bootstrapping Syntax and Recursion using Alignment-Based Learning
Software Toolkit for Building Embedded and Distributed Knowledge-based Systems
Transformations of CCP programs
Combining a self-organising map with memory-based learning
Two-way Quantum One-counter Automata
On Equivalence and Canonical Forms in the LF Type Theory
Practical Aspects for a Working Compile Time Garbage Collection System for Mercury
An Environment for the Exploration of Non Monotonic Logic Programs
A Straightforward Approach to Morphological Analysis and Synthesis
Incremental Construction of Compact Acyclic NFAs
BSML: A Binding Schema Markup Language for Data Interchange in Problem Solving Environments (PSEs)
Querying Databases of Annotated Speech
Decision Lists for English and Basque
A variable-free dynamic semantics
Simple Strategies for Large Zero-Sum Games with Applications to Complexity Theory
Using the Annotated Bibliography as a Resource for Indicative Summarization
Agent Programming with Declarative Goals
Question Answering over Unstructured Data without Domain Restrictions
A continuation semantics of interrogatives that accounts for Baker's ambiguity
ExploitingWeb Service Semantics: Taxonomies vs. Ontologies
An Approach for Resource Sharing in Multilingual NLP
On Decidability of Expressive Description Logics with Composition of Roles in Number Restrictions
Parametric Connectives in Disjunctive Logic Programming
Derivation of Efficient Logic Programs by Specialization and Reduction of Nondeterminism
On Structuring Proof Search for First Order Linear Logic
Weight Constraints as Nested Expressions
A correct, precise and efficient integration of set-sharing, freeness and linearity for the analysis of finite and rational tree languages
Constraint Logic Programming with Hereditary Harrop Formula
A lambda calculus for quantum computation with classical control
Autogenic Training With Natural Language Processing Modules: A Recent Tool For Certain Neuro Cognitive Studies
Model Checking of Statechart Models: Survey and Research Directions
On Role Logic
An electronic dictionary as a basis for NLP tools: The Greek case
A Model for Fine-Grained Alignment of Multilingual Texts
Demo or Practice: Critical Analysis of the Language/Action Perspective
Detecting User Engagement in Everyday Conversations
Intuitionistic computability logic
TulaFale: A Security Tool for Web Services
Playful, streamlike computation
Fine-Grained Word Sense Disambiguation Based on Parallel Corpora, Word Alignment, Word Clustering and Aligned Wordnets
Contextual equivalence for higher-order pi-calculus revisited
An Operational Foundation for Delimited Continuations in
the
CPS
Hierarchy
Complexity of Networks
The Nature of Novelty Detection
Book review "The Haskell Road to Logic, Maths and Programming"
Digital Libraries: From Process Modelling to Grid-based Service Oriented Architecture
An Explicit Solution to Post's Problem over the Reals
A compositional Semantics for CHR
Enhanced Prolog Remote Predicate Call Protocol
Uniform Random Sampling of Traces in Very Large Models
Representing Knowledge about Norms
Deriving Escape Analysis by Abstract Interpretation: Proofs of results
Resource Usage Analysis for the Pi-Calculus
Nominal Logic Programming
Building and displaying name relations using automatic unsupervised analysis of newspaper articles
Event-based Information Extraction for the biomedical domain: the Caderige project
ECA-RuleML: An Approach combining ECA Rules with temporal interval-based KR Event/Action Logics and Transactional Update Logics
Consistent Streaming Through Time: A Vision for Event Stream Processing
Statistical keyword detection in literary corpora
A decision procedure for linear "big O" equations
Bistable Biorders: A Sequential Domain Theory
FIPA agent based network distributed control system
Simplifications in Lagrangian BV quantization exemplified by the anomalies of chiral $W_3$ gravity
Lorentz Group derivable from Polarization Optics
Branes at Orbifolds versus Hanany Witten in Six Dimensions
Comments on Duality in MQCD
Clifford algebra as quantum language
Hagedorn transition, vortices and D0 branes: Lessons from 2+1 confining strings
Open Superstring on Symmetric Product
Moyal Formulation of Witten's Star Product in the Fermionic Ghost Sector
Two-Loop Superstrings in Hyperelliptic Language I: the Main Results
Two-Loop Superstrings in Hyperelliptic Language III: the Four-Particle Amplitude
Fields in the Language of String: Divergences and Renormalization
Some compact logics --- results in ZFC
Can you feel the double jump?
Very weak zero one law for random graphs with order and random binary functions
Numerical Calculations Using Maple: Why & How?
Nonstandard Consequence Operators
One-Dimensional Peg Solitaire, and Duotaire
The theorem of the complement for nested subpfaffian sets
Wigner's new physics frontier: Physics of two-by-two matrices, including the Lorentz group and optical instruments
Dynamics and computation in functional shifts
Control System for the LEDA 6.7-MeV Proton Beam Halo Experiment
Theoretical model for the evolution of the linguistic diversity
Statistical Dynamics of Religions and Adherents
Cultural route to the emergence of linguistic categories
The theory of physical superselection sectors in terms of vertex operator algebra language
Operational quantum logic: An overview
Quantum machine language and quantum computation with Josephson junctions
Schwinger, Pegg and Barnett approaches and a relationship between angular and Cartesian quantum descriptions II: Phase Spaces
Quantum-mechanical motion and the stillness of experimental records
Toward a Quantum Process Algebra
Lorentz Group in Ray Optics
A Process Algebraic Approach to Concurrent and Distributed Quantum Computation: Operational Semantics
Communicating Quantum Processes
Exponential Separation of Quantum and Classical Online Space Complexity
Some observations on two-way finite automata with quantum and classical states
Worst-Case Background Knowledge for Privacy-Preserving Data Publishing
A discussion on particle number and quantum indistinguishability
Curry-style type Isomorphisms and Game Semantics
Recursive n-gram hashing is pairwise independent, at best
How to be correct, lazy and efficient ?
Provenance as Dependency Analysis
A proof of strong normalisation using domain theory
Bio-linguistic transition and Baldwin effect in an evolutionary naming-game model
Implementation, Compilation, Optimization of Object-Oriented Languages, Programs and Systems - Report on the Workshop ICOOOLPS'2006 at ECOOP'06
A quick search method for audio signals based on a piecewise linear representation of feature trajectories
Some Reflections on the Task of Content Determination in the Context of Multi-Document Summarization of Evolving Events
Declarative Diagnosis of Floundering
Outilex, plate-forme logicielle de traitement de textes écrits
Implementation, Compilation, Optimization of Object-Oriented Languages, Programs and Systems - Report on the Workshop ICOOOLPS'2007 at ECOOP'07
Concepts and their Use for Modelling Objects and References in Programming Languages
Corpus sp{é}cialis{é} et ressource de sp{é}cialit{é}
Quantum entanglement analysis based on abstract interpretation
Independence and concurrent separation logic
Textual Fingerprinting with Texts from Parkin, Bassewitz, and Leander
Sign Language Tutoring Tool
A Semi-Automatic Framework to Discover Epistemic Modalities in Scientific Articles
Alternating Automata on Data Trees and XPath Satisfiability
Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods
AceWiki: A Natural and Expressive Semantic Wiki
Coinductive big-step operational semantics
Providing Virtual Execution Environments: A Twofold Illustration
On the Vocabulary of Grammar-Based Codes and the Logical Consistency of Texts
A TLA+ Proof System
Grapham: Graphical Models with Adaptive Random Walk Metropolis Algorithms
Logical Algorithms meets CHR: A meta-complexity result for Constraint Handling Rules with rule priorities
Consensus and ordering in language dynamics
Palindromes in infinite ternary words
What's in a Message?
Succinctness of two-way probabilistic and quantum finite automata
Reformulating Global Grammar Constraints
Stochastic Constraint Programming: A Scenario-Based Approach
Thermodynamics of Information Retrieval
Comment on "Language Trees and Zipping" arXiv:cond-mat/0108530
Complexity of Fractran and Productivity
A Note on the Complexity of the Satisfiability Problem for Graded Modal Logics
Scenario-based Stochastic Constraint Programming
The cost of being co-Buchi is nonlinear
Knowledge-Based Synthesis of Distributed Systems Using Event Structures
Relational Parametricity for Computational Effects
The Complexity of Approximating Bounded-Degree Boolean \sharp CSP
Verification of Timed Automata Using Rewrite Rules and Strategies
Why a splitting in the final state cannot explain the GSI-Oscillations
Translation from Classical Two-Way Automata to Pebble Two-Way Automata
Using the Deutsch-Jozsa algorithm to determine parts of an array and apply a specified function to each independent part
Polynomial-Space Approximation of No-Signaling Provers
Consideration Points Detecting Cross-Site Scripting
FastFlow: Efficient Parallel Streaming Applications on Multi-core
A Common XML-based Framework for Syntactic Annotations
Grouping Synonyms by Definitions
The meta book and size-dependent properties of written language
Evaluation of Hindi to Punjabi Machine Translation System
Proceedings 7th International Workshop on Security Issues in Concurrency
Correctness Kernels of Abstract Interpretations
Algorithm as Defining Dynamic Systems
A New Computational Schema for Euphonic Conjunctions in Sanskrit Processing
Quantum Reality and Measurement: A Quantum Logical Approach
Proceedings Fifth Workshop on Developments in Computational Models--Computational Models From Nature
Flare: Architecture for rapid and easy development of Internet-based Applications
Deciding Regularity of the Set of Instances of a Set of Terms with Regular Constraints is EXPTIME-Complete
Integrable systems and holomorphic curves
Gouverner la standardisation par les changements d'arene. Le cas du XML
Computable de Finetti measures
Emotions in Pervasive Computing Environments
Collapsing and Separating Completeness Notions under Average-Case and Worst-Case Hypotheses
Formalizing cCSP Synchronous Semantics in PVS
Towards Effective Sentence Simplification for Automatic Processing of Biomedical Text
The Complexity of Approximating Bounded-Degree Boolean #CSP (Extended Abstract)
Recognition and translation Arabic-French of Named Entities: case of the Sport places
Session-Based Programming for Parallel Algorithms: Expressiveness and Performance
Thai Rhetorical Structure Analysis
A proof Procedure for Testing Membership in Regular Expressions
From Frequency to Meaning: Vector Space Models of Semantics
Formalization and Validation of Safety-Critical Requirements
La représentation formelle des concepts spatiaux dans la langue
Lazy Evaluation and Delimited Control
QMIP = MIP*
Liberalizing Dependency
Information Cost Tradeoffs for Augmented Index and Streaming Language Recognition
Punctuation effects in English and Esperanto texts
Proceedings International Workshop on Developments in Implicit Computational complExity
On the Module of Internet Banking System
A Meta-Programming Approach to Realizing Dependently Typed Logic Programming
The Problem of the Observer in Physics
Toward a language theoretic proof of the four color theorem
Object-oriented modelling with unified modelling language 2.0 for simple software application based on agile methodology
The duality of computation under focus
Orthogonal Persistence Revisited
On the Implementation of the Probabilistic Logic Programming Language ProbLog
Constructing Active Architectures in the ArchWare ADL
Proof-theoretic Analysis of Rationality for Strategic Games with Arbitrary Strategy Sets
Linguistic complexity: English vs. Polish, text vs. corpus
Verification of Java Bytecode using Analysis and Transformation of Logic Programs
Sawja: Static Analysis Workshop for Java
Applying Prolog to Develop Distributed Systems
A simple model for the evolution of molecular codes driven by the interplay of accuracy, diversity and cost
Abstracting Abstract Machines
Catching the Ouroboros: On Debugging Non-ground Answer-Set Programs
Handling Data-Based Concurrency in Context-Aware Service Protocols
Resumptions, Weak Bisimilarity and Big-Step Semantics for While with Interactive I/O: An Exercise in Mixed Induction-Coinduction
Modelling the Dynamics of an Aedes albopictus Population
Minimization Strategies for Maximally Parallel Multiset Rewriting Systems
Extending the Real-Time Maude Semantics of Ptolemy to Hierarchical DE Models
Certifying cost annotations in compilers
A Graphical Approach to Progress for Structured Communication in Web Services
Introducing Business Language Driven Development
Logical Foundations and Complexity of 4QL, a Query Language with Unrestricted Negation
Robust Simulations and Significant Separations
On Probabilistic Parallel Programs with Process Creation and Synchronisation
Secure Information Flow by Model Checking Pushdown System
Physics of the mind: Concepts, emotions, language, cognition, consciousness, beauty, music, and symbolic culture
On the expressiveness of Parikh automata and related models
A Review of Research on Devnagari Character Recognition
Geometric representations for minimalist grammars
Automata and Differentiable Words
CFA2: a Context-Free Approach to Control-Flow Analysis
Drive for Creativity
Linear Dependent Types and Relative Completeness
SLDs for Visualizing Multicolor Elevation Contour Lines in Geo-Spatial Web Applications
Phase Transitions in Knowledge Compilation: an Experimental Study
Expression Templates Revisited: A Performance Analysis of the Current ET Methodology
Streaming Tree Transducers
Towards OWL-based Knowledge Representation in Petrology
Constraint solving in non-permutative nominal abstract syntax
Grounded Semantic Composition for Visual Scenes
Prototyping the Semantics of a DSL using ASF+SDF: Link to Formal Verification of DSL Models
Optimal Divide and Query (extended version)
A Semantic Relatedness Measure Based on Combined Encyclopedic, Ontological and Collocational Knowledge
A Verified Algebra for Linked Data
Decoupled execution of synchronous coordination models via behavioural automata
Contracts in distributed systems
Product Lines for Service Oriented Applications - PL for SOA
Formal Component-Based Semantics
A Spatial Calculus of Wrapped Compartments
Modelling of Genetic Regulatory Mechanisms with GReg
Stepping Lazy Programs
Specific "scientific" data structures, and their processing
Approximate Policy Iteration with a Policy Language Bias: Solving Relational Markov Decision Processes
The SeaLion has Landed: An IDE for Answer-Set Programming---Preliminary Report
Actor Continuation Passing: Efficient and Extensible Request Routing for Event-Driven Architectures
Domain Adaptation for Statistical Classifiers
Reasoning with Very Expressive Fuzzy Description Logics
An Improved Implementation and Abstract Interface for Hybrid
A fusion algorithm for joins based on collections in Odra (Object Database for Rapid Application development)
Extending the adverbial coverage of a NLP oriented resource for French
Semantic Navigation on the Web of Data: Specification of Routes, Web Fragments and Actions
Effects for Funargs
Proceedings Second Workshop on Developments in Implicit Computational Complexity
Information Hiding in CSS : A Secure Scheme Text-Steganography using Public Key Cryptosystem
A Well-typed Lightweight Situation Calculus
A dependent nominal type theory
An Authoring System for Editing Lessons in Phonetic English in SMIL3.0
Inference and Plausible Reasoning in a Natural Language Understanding System Based on Object-Oriented Semantics
A non-local method for robustness analysis of floating point programs
QRB-Domains and the Probabilistic Powerdomain
Scaling Laws in Human Language
Theorem proving for prenex Gödel logic with Delta: checking validity and unsatisfiability
Establishing linguistic conventions in task-oriented primeval dialogue
Manual and Fast C Code Optimization
An MLP based Approach for Recognition of Handwritten `Bangla' Numerals
Handwritten Bangla Alphabet Recognition using an MLP Based Classifier
Distributional Measures of Semantic Distance: A Survey
A Unifying Framework to Characterize the Power of a Language to Express Relations
Patterns in rational base number systems
Massively Increasing TIMEX3 Resources: A Transduction Approach
A Data Driven Approach to Query Expansion in Question Answering
The magnetospheric chaos hypothesis: a new point of view of the magnetospheric dynamics
Roget's Thesaurus and Semantic Similarity
Not As Easy As It Seems: Automating the Construction of Lexical Chains Using Roget's Thesaurus
Segmentation Similarity and Agreement
Lower Complexity Bounds for Lifted Inference
Biographical Social Networks on Wikipedia - A cross-cultural study of links that made history
The Distributed Ontology Language (DOL): Ontology Integration and Interoperability Applied to Mathematical Formalization
Learning Semantic String Transformations from Examples
Cologne: A Declarative Distributed Constraint Optimization Platform
Specification and Verification of Uplink Framework for Application of Software Engineering using RM-ODP
Constraint LTL Satisfiability Checking without Automata
Characterizing Ranked Chinese Syllable-to-Character Mapping Spectrum: A Bridge Between the Spoken and Written Chinese Language
A Common Evaluation Setting for Just.Ask, Open Ephyra and Aranea QA systems
Issues of Architectural Description Languages for Handling Dynamic Reconfiguration
Language-Constraint Reachability Learning in Probabilistic Graphs
Behavioural Types for Actor Systems
Temporal expression normalisation in natural language texts
Invariant measures concentrated on countable structures
Learning to Identify Regular Expressions that Describe Email Campaigns
Detection of Configuration Vulnerabilities in Distributed (Web) Environments
Intellectual Management of Enterprise
A multi-prover interactive proof for NEXP sound against entangled provers
An Exploratory Study of Forces and Frictions affecting Large-Scale Model-Driven Development
MDM: A Mode Diagram Modeling Framework for Periodic Control Systems
The law of brevity in macaque vocal communication is not an artifact of analyzing mean call durations
A Logic Programming Framework for Possibilistic Argumentation with Vague Knowledge
Completeness of algebraic CPS simulations
Efficient Indexing and Querying over Syntactically Annotated Trees
The Distributed Ontology Language (DOL): Use Cases, Syntax, and Extensibility
Guidelines for a Dynamic Ontology - Integrating Tools of Evolution and Versioning in Ontology
Proceedings Combined 19th International Workshop on Expressiveness in Concurrency and 9th Workshop on Structured Operational Semantics
Proceedings 18th international workshop on Cellular Automata and Discrete Complex Systems and 3rd international symposium Journées Automates Cellulaires
Two-Way Finite Automata: Old and Recent Results
Ordered {AND, OR}-Decomposition and Binary-Decision Diagram
Concept driven framework for Latent Table Discovery
Parameterized Concurrent Multi-Party Session Types
Relaxed Operational Semantics of Concurrent Programming Languages
Authorship Identification in Bengali Literature: a Comparative Analysis
History-Register Automata
Blackboard Rules for Coordinating Context-aware Applications in Mobile Ad Hoc Networks
Knots, Braids and First Order Logic
A Note on Program Specialization. What Can Syntactical Properties of Residual Programs Reveal?
Automatic Unbounded Verification of Alloy Specifications with Prover9
On the Automation of Encoding Processes in the Quantum IO Monad
Superdense Coding with GHZ and Quantum Key Distribution with W in the ZX-calculus
A Semantic Approach for Automatic Structuring and Analysis of Software Process Patterns
Improved Quantum Query Algorithms for Triangle Finding and Associativity Testing
Efficient Tabling of Structured Data with Enhanced Hash-Consing
Inference of Fine-grained Attributes of Bengali Corpus for Stylometry Detection
Advanced Automata Minimization
The Hangulphabet: A Descriptive Alphabet
Optimal size, freshness and time-frame for voice search vocabulary
Dating Texts without Explicit Temporal Cues
Fault Localization Using Textual Similarities
Combining Insertion and Deletion in RNA-editing Preserves Regularity
Tracking and Quantifying Censorship on a Chinese Microblogging Site
Letter counting: a stem cell for Cryptology, Quantitative Linguistics, and Statistics
Using external sources of bilingual information for on-the-fly word alignment
Evolution of the most common English words and phrases over the centuries
Coherent Minimisation: Towards efficient tamper-proof compilation
Operational semantics for product-form solution
libcppa - Designing an Actor Semantic for C++11
Distinguishing Models by Formulas and the Number of Countable Models
SPARC - Sorted ASP with Consistency Restoring Rules
Planning and Scheduling in Hybrid Domains Using Answer Set Programming
A modal perspective on the transverse Anderson localization of light in disordered optical lattices
TEI and LMF crosswalks
The Expressive Power of Word Embeddings
Proceedings First International Workshop on Trends in Functional Programming in Education
From 3D Point Clouds To Semantic Objects An Ontology-Based Detection Approach
From Two-Way to One-Way Finite State Transducers
Real-Time Specification Patterns and Tools
A Survey on Array Storage, Query Languages, and Systems
Tag-based Semantic Website Recommendation for Turkish Language
Arabic text summarization based on latent semantic analysis to enhance arabic documents clustering
Learning Universally Quantified Invariants of Linear Data Structures
An Algebraic Semantics for Possibilistic Logic
Stochastic dynamics of lexicon learning in an uncertain and nonuniform world
Role of temporal inference in the recognition of textual inference
Development of Yes/No Arabic Question Answering System
Non-simplifying Graph Rewriting Termination
KSU KDD: Word Sense Induction by Clustering in Topic Space
Object-oriented programming: some history, and challenges for the next fifty years
Efficient learning strategy of Chinese characters based on network approach
Automatic Verification of Erlang-Style Concurrency
Semantics for Probabilistic Inference
Effective Characterizations of Simple Fragments of Temporal Logic Using Carton--Michel Automata
Semantic Matching of Security Policies to Support Security Experts
The operad of wiring diagrams: formalizing a graphical language for databases, recursion, and plug-and-play circuits
Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic
Binary Tree based Chinese Word Segmentation
Random crossings in dependency trees
On the Concept of Variable Roles and its Use in Software Analysis
Towards an Ontology based integrated Framework for Semantic Web
Proceedings 11th International Workshop on Quantitative Aspects of Programming Languages and Systems
A Universal Machine for Biform Theory Graphs
Extended to Multi-Tilde-Bar Regular Expressions and Efficient Finite Automata Constructions
Semantics and pragmatics in actual software applications and in web search engines: exploring innovations
Improving Pointwise Mutual Information (PMI) by Incorporating Significant Co-occurrence
Intelligent Hybrid Man-Machine Translation Quality Estimation
Alias and Change Calculi, Applied to Frame Inference
Says who? Automatic Text-Based Content Analysis of Television News
The operad of temporal wiring diagrams: formalizing a graphical language for discrete-time processes
A Novel Architecture For Question Classification Based Indexing Scheme For Efficient Question Answering
Annotations for Intersection Typechecking
Tagging Scientific Publications using Wikipedia and Natural Language Processing Tools. Comparison on the ArXiv Dataset
Combining and Relating Control Effects and their Semantics
Structure Learning of Probabilistic Logic Programs by Searching the Clause Space
A Machine-Checked Proof for a Translation of Event-B Machines to JML
SafeJS: Hermetic Sandboxing for JavaScript
Even the Abstract have Colour: Consensus in Word-Colour Associations
From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales
An Inter-lingual Reference Approach For Multi-Lingual Ontology Matching
$μ$-Limit Sets of Cellular Automata from a Computational Complexity Perspective
Retargeting GCC: Do We Reinvent the Wheel Every Time?
Pathwise Taylor Expansions for Random Fields on Multiple Dimensional Paths
Subjective and Objective Evaluation of English to Urdu Machine Translation
Formal verification in Coq of program properties involving the global state effect
Introducing Enriched Concrete Syntax Trees
A Scratch-like visual programming system for Microsoft Windows Phone 8
Evolution of the Modern Phase of Written Bangla: A Statistical Study
Named entity recognition using conditional random fields with non-local relational constraints
Speculative Staging for Interpreter Optimization
Certified proofs in programs involving exceptions
Spatio-temporal variation of conversational utterances on Twitter
Keyboards for inputting Japanese language -A study based on US patents
Ehrenfeucht-Fraisse Games on Omega-Terms
Can Twitter Predict Royal Baby's Name ?
Description and Evaluation of Semantic Similarity Measures Approaches
Identifying Purpose Behind Electoral Tweets
SBML for optimizing decision support's tools
Planning by case-based reasoning based on fuzzy logic
On the Structure of Bispecial Sturmian Words
Clustering and Relational Ambiguity: from Text Data to Natural Data
Flexible Invariants Through Semantic Collaboration
Efficient XML Keyword Search based on DAG-Compression
Applying AOSE Concepts to Model Crosscutting Variability in Variant-Rich Processes
A Theory of Changes for Higher-Order Languages - Incrementalizing λ-Calculi by Static Differentiation
Formal Model of Web Service Composition: An Actor-Based Approach to Unifying Orchestration and Choreography
Session Types with Runtime Adaptation: Overview and Examples
Towards deductive verification of MPI programs against session types
Domain adaptation for sequence labeling using hidden Markov models
Effective Slot Filling Based on Shallow Distant Supervision Methods
Linear Temporal Logic for Regular Cost Functions
Dictionary-Based Concept Mining: An Application for Turkish
ONTS: "Optima" News Translation System
Logical Foundations of RDF(S) with Datatypes
The Opposite of Smoothing: A Language Model Approach to Ranking Query-Specific Document Clusters
Defeasible Inclusions in Low-Complexity DLs
Functorial Semantics of Second-Order Algebraic Theories
Formalization and Verification of Hierarchical Use of Interaction Overview Diagrams Using Timing Diagrams
Identification of Pleonastic It Using the Web
Inferring Shallow-Transfer Machine Translation Rules from Small Parallel Corpora
Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection
The Readability of Tweets and their Geographic Correlation with Education
Controlling Complexity in Part-of-Speech Induction
Safety verification of asynchronous pushdown systems with shaped stacks
A Machine Learning Approach for the Identification of Bengali Noun-Noun Compound Multiword Expressions
Keyword and Keyphrase Extraction Using Centrality Measures on Collocation Networks
Behavior recognition and analysis in smart environments for context-aware applications
Free Applicative Functors
Finding Eyewitness Tweets During Crises
Using Entropy Estimates for DAG-Based Ontologies
Transaction Repair: Full Serializability Without Locks
K-Position, Follow, Equation and K-C-Continuation Tree Automata Constructions
Updating RDFS ABoxes and TBoxes in SPARQL
Axiomatization of Finite Algebras
Proceedings 9th International Workshop on Developments in Computational Models
Towards Formal Interaction-Based Models of Grid Computing Infrastructures
MTL-Model Checking of One-Clock Parametric Timed Automata is Undecidable
Mining Idioms from Source Code
A Note on Relative Observability in Coordination Control
Meta-evaluation of comparability metrics using parallel corpora
An Empirical Comparison of Parsing Methods for Stanford Dependencies
Inference in the FO(C) Modelling Language
The Dafny Integrated Development Environment
FoCaLiZe: Inside an F-IDE
Reducing Clocks in Timed Automata while Preserving Bisimulation
A tlm-based platform to specify and verify component-based real-time systems
Exemplar Dynamics Models of the Stability of Phonological Categories
A Concurrent Pattern Calculus
Using Tabled Logic Programming to Solve the Petrobras Planning Problem
Comparison of the language networks from literature and blogs
Méthodes pour la représentation informatisée de données lexicales / Methoden der Speicherung lexikalischer Daten
New Perspectives in Sinographic Language Processing Through the Use of Character Structure
Decision Problems for Deterministic Pushdown Automata on Infinite Words
Machine Translation Model based on Non-parallel Corpus and Semi-supervised Transductive Learning
Efficient and Reasonable Object-Oriented Concurrency
Lockdown: Dynamic Control-Flow Integrity
The double-slit quantum eraser experiments and Hardy's paradox in the quantum linguistic interpretation
A Fast Hierarchical Method for Multi-script and Arbitrary Oriented Scene Text Extraction
Strategic Port Graph Rewriting: An Interactive Modelling and Analysis Framework
Specifying and Executing Optimizations for Parallel Programs
A Dual-Engine for Early Analysis of Critical Systems
Fixed-point Characterization of Compositionality Properties of Probabilistic Processes Combinators
Database Queries that Explain their Work
Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient
Distributed Graph Automata and Verification of Distributed Algorithms
Opinion mining of movie reviews at document level
Verifiable UML Artifact-Centric Business Process Models (Extended Version)
When is a container a comonad?
Hourglass Automata
Evaluating Neural Word Representations in Tensor-Based Compositional Settings
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input
Supervised learning Methods for Bangla Web Document Categorization
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
Space-Efficient Manifest Contracts
Towards Static Analysis of Functional Programs using Tree Automata Completion
Relational Linear Programs
Process-aware web programming with Jolie
Learning Distributed Word Representations for Natural Logic Reasoning
Convex Optimization in Julia
On the Relation of Interaction Semantics to Continuations and Defunctionalization
Learning Vague Concepts for the Semantic Web
Proving Safety with Trace Automata and Bounded Model Checking
A type assignment for lambda-calculus complete both for FPTIME and strong normalization
A classical approach to smooth supermanifolds
A random forest system combination approach for error detection in digital dictionaries
Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users
Retrofitting Word Vectors to Semantic Lexicons
A Pooling Approach to Modelling Spatial Relations for Image Retrieval and Annotation
Type-Driven Incremental Semantic Parsing with Polymorphism
A Robust Class of Data Languages and an Application to Learning
LABR: A Large Scale Arabic Sentiment Analysis Benchmark
The influence of infant-directed speech on 12-month-olds' intersensory perception of fluent speech
Roman Urdu Opinion Mining System (RUOMiS)
Combining Language and Vision with a Multimodal Skip-gram Model
Closing the Gap -- Formally Verifying Dynamically Typed Programs like Statically Typed Ones Using Hoare Logic -- Extended Version --
Real Time Collaborative Platform for Learning and Teaching Foreign Languages
Mining Scientific Papers for Bibliometrics: a (very) Brief Survey of Methods and Tools
A comparative study of approaches in user-centered health information retrieval
Counting Branches in Trees Using Games
From Non-preemptive to Preemptive Scheduling using Synchronization Synthesis
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
Translation Memory Retrieval Methods
The IBM 2015 English Conversational Telephone Speech Recognition System
Needed Computations Shortcutting Needed Steps
Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis
Overview of the NLPCC 2015 Shared Task: Chinese Word Segmentation and POS Tagging for Micro-blog Texts
Supervised Fine Tuning for Word Embedding with Integrated Knowledge
The canonical measure on a reductive p-adic group is motivic
FAQ-based Question Answering via Word Alignment
Multi-Lingual Ontology Server (MOS) for discovering Web services
Synthesis of Recursive ADT Transformations from Reusable Templates
Putting Logic-Based Distributed Systems on Stable Grounds
Event-Driven Network Programming
Determination of the Internet Anonymity Influence on the Level of Aggression and Usage of Obscene Lexis
The ICSTM+TUM+UP Approach to the 3rd CHIME Challenge: Single-Channel LSTM Speech Enhancement with Multi-Channel Correlation Shaping Dereverberation and LSTM Language Models
A counterexample to the reconstruction of $ω$-categorical structures from their endomorphism monoids
Automatic Taxonomy Extraction from Query Logs with no Additional Sources of Information
Calculating entropy at different scales among diverse communication systems
Formulae in noncommutative Hodge theory
Multi-head Watson-Crick automata
SAFE: A Declarative Trust Management System with Linked Credentials
Shape Expressions Schemas
Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network
Architectural Consistency Checking in Plugin-Based Software Systems
A web-based IDE for IDP
Detecting Interrogative Utterances with Recurrent Neural Networks
Color Aesthetics and Social Networks in Complete Tang Poems: Explorations and Discoveries
An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding
Profinite Monads, Profinite Equations, and Reiterman's Theorem
Stacked Attention Networks for Image Question Answering
Learning Linguistic Biomarkers for Predicting Mild Cognitive Impairment using Compound Skip-grams
Proceedings First International Workshop on Focusing
Investigating the stylistic relevance of adjective and verb simile markers
From Images to Sentences through Scene Description Graphs using Commonsense Reasoning and Knowledge
Learning to Represent Words in Context with Multilingual Supervision
A System for Extracting Sentiment from Large-Scale Arabic Social Data
Harvesting comparable corpora and mining them for equivalent bilingual sentences using statistical classification and analogy- based heuristics
Alternative structures for character-level RNNs
Skip-Thought Memory Networks
A C-LSTM Neural Network for Text Classification
Semi-supervised and Unsupervised Methods for Categorizing Posts in Web Discussion Forums
Nonparametric Spherical Topic Modeling with Word Embeddings
Automatic Annotation of Structured Facts in Images
Variations on Noetherianness
A Software Methodology for Compiling Quantum Programs
Applying Ontological Modeling on Quranic Nature Domain
Speed-Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization
Understanding Rating Behaviour and Predicting Ratings by Identifying Representative Users
M$^2$S-Net: Multi-Modal Similarity Metric Learning based Deep Convolutional Network for Answer Selection
Advances in Property-Based Testing for $α$Prolog
Visual Relationship Detection with Language Priors
Structured prediction models for RNN based sequence labeling in clinical text
Two-Buffer Simulation Games
Efficient Algebraic Effect Handlers for Prolog
Semantic Representations of Word Senses and Concepts
Lock-free atom garbage collection for multithreaded Prolog
To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation
Language free character recognition using character sketch and center of gravity shifting
Words, Concepts, and the Geometry of Analogy
Entailment Relations on Distributions
AngularJS in the Wild: A Survey with 460 Developers
ASP for Minimal Entailment in a Rational Extension of SROEL
Determining Health Utilities through Data Mining of Social Media
Analysis of Morphology in Topic Modeling
Learning Latent Local Conversation Modes for Predicting Community Endorsement in Online Discussions
Slicing Concurrent Constraint Programs
Modeling Human Reading with Neural Attention
CurryCheck: Checking Properties of Curry Programs
Learning Word Embeddings from Intrinsic and Extrinsic Views
An Incremental Parser for Abstract Meaning Representation
Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine
Tracking Amendments to Legislation and Other Political Texts with a Novel Minimum-Edit-Distance Algorithm: DocuToads
Using Semantic Similarity for Input Topic Identification in Crawling-based Web Application Testing
Semantic descriptions of 24 evaluational adjectives, for application in sentiment analysis
A Parallel Memory-efficient Epistemic Logic Program Solver: Harder, Better, Faster
Machine Comprehension Using Match-LSTM and Answer Pointer
Visual Question: Predicting If a Crowd Will Agree on the Answer
NoFAQ: Synthesizing Command Repairs from Examples
Hash2Vec, Feature Hashing for Word Embeddings
Accelerating QDP++ using GPUs
Workflows for the Management of Change in Science, Technologies, Engineering and Mathematics
TRX: A Formally Verified Parser Interpreter
Perception of Personality and Naturalness through Dialogues by Native Speakers of American English and Arabic
ISICSoo: a class for the calculation of ionization cross sections from ECPSSR and PWBA theory
CinemaGazer: a System for Watching Video at Very High Speed
Modelling Mixed Discrete-Continuous Domains for Planning
Symmetric Encapsulated Multi-Methods
Proceedings Sixth International Workshop on Logical Frameworks and Meta-languages: Theory and Practice
Grammatical Relations of Myanmar Sentences Augmented by Transformation-Based Learning of Function Tagging
The Language of Two-by-two Matrices spoken by Optical Devices
Meaning-focused and Quantum-inspired Information Retrieval
Parameterized Verification of Asynchronous Shared-Memory Systems
Object-Oriented Translation for Programmable Relational System (DRAFT)
Probability Distributions Over Possible Worlds
FooPar: A Functional Object Oriented Parallel Framework in Scala
Power of the interactive proof systems with verifiers modeled by semi-quantum two-way finite automata
Abstract machines for game semantics, revisited
On the Complexity of Verifying Regular Properties on Flat Counter Systems
Constant conditional entropy and related hypotheses
A formalisation of XMAS
First-Class Functions for First-Order Database Engines
A Comparison of Named Entity Recognition Tools Applied to Biographical Texts
Science Fiction as a Worldwide Phenomenon: A Study of International Creation, Consumption and Dissemination
Exploratory Analysis of Highly Heterogeneous Document Collections
Hidden Structure and Function in the Lexicon
Linearizability with Ownership Transfer
B(eo)W(u)LF: Facilitating recurrence analysis on multi-level language
Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech Recognition
Towards an Effective Decision Procedure for LTL formulas with Constraints
PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts
Second-Order Algebraic Theories
Heisenberg uncertainty principle and quantum Zeno effects in the linguistic interpretation of quantum mechanics
Bots vs. Wikipedians, Anons vs. Logged-Ins
Topic Segmentation and Labeling in Asynchronous Conversations
Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition
Towards High Performance Computing (Hpc) Through Parallel Programming Paradigms and Their Principles
Predicting Crowd Behavior with Big Public Data
An evaluation of keyword extraction from online communication for the characterisation of social relations
Rigorous Description Of Design Components Functionality: An Approach Based Contract
An evaluative baseline for geo-semantic relatedness and similarity
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
Extracting Networks of Characters and Places from Written Works with CHAPLIN
Zappa-Szép products of Garside monoids
Nested Regular Path Queries in Description Logics
Koka: Programming with Row Polymorphic Effect Types
Foundations of Total Functional Data-Flow Programming
Multiparty Session Actors
Representing Network Trust and Using It to Improve Anonymous Communication
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network
The Frobenius anatomy of word meanings II: possessive relative pronouns
Predicting Motivations of Actions by Leveraging Text
SurveyMan: Programming and Automatically Debugging Surveys
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
Central compact objects, superslow X-ray pulsars, gamma-ray bursts: do they have anything to do with magnetars?
Visual Speech Recognition
Exercises for Children with Dyslalia-Software Infrastructure
Model Evolution and Management
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis
A Study of Association Measures and their Combination for Arabic MWT Extraction
Designing and Deploying Online Field Experiments
An Algorithm Based on Empirical Methods, for Text-to-Tuneful-Speech Synthesis of Sanskrit Verse
Decidability Problems for Actor Systems
The UML as a Formal Modeling Notation
Expression-based aliasing for OO-languages
A stencil-based implementation of Parareal in the C++ domain specific embedded language STELLA
RoboBrain: Large-Scale Knowledge Engine for Robots
Tiered Clustering to Improve Lexical Entailment
Horn Clauses for Communicating Timed Systems
High Performance Computing Evaluation A methodology based on Scientific Application Requirements
Deep Learning for Answer Sentence Selection
Practice in Synonym Extraction at Large Scale
Declaratively solving tricky Google Code Jam problems with Prolog-based ECLiPSe CLP system
Optimization models of natural communication
Word learning under infinite uncertainty
A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging
Towards Live Programming in ROS with PhaROS and LRP
Report on a User Test and Extension of a Type Debugger for Novice Programmers
Inducing Semantic Representation from Text by Jointly Predicting and Factorizing Relations
Learning Longer Memory in Recurrent Neural Networks
A key to the projective model of homogeneous metric spaces
Tensors, !-graphs, and non-commutative quantum structures
On products of elementarily indivisible structures
Text Understanding from Scratch
Stakeholders, Viewpoints and Languages of a Modelling Framework for the Design and Development of Data-Intensive Mobile Apps
A Linear Dynamical System Model for Text
Observationally Cooperative Multithreading
Sherali-Adams relaxations for valued CSPs
Elements of style of BPMN language
Pantheon 1.0, a manually verified dataset of globally famous biographies
Task-Oriented Learning of Word Embeddings for Semantic Relation Classification
The lambda mechanism in lambda calculus and in other calculi
Natural Notation for the Domestic Internet of Things
Proceedings Seventh Workshop on Intersection Types and Related Systems
A Context-Based Semantics for SPARQL Property Paths over the Web (Extended Version)
A Denotational Semantics for Communicating Unstructured Code
Prediction Using Note Text: Synthetic Feature Creation with word2vec
Yara Parser: A Fast and Accurate Dependency Parser
Normalization of Non-Standard Words in Croatian Texts
Conditioning in Probabilistic Programming
Evaluation Evaluation a Monte Carlo study
Mining and discovering biographical information in Difangzhi with a language-model-based approach
Neostability in countable homogeneous metric spaces
Blade: A Data Center Garbage Collector
Robobarista: Object Part based Transfer of Manipulation Trajectories from Crowd-sourcing in 3D Pointclouds
Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking
Handshaking Protocol for Distributed Implementation of Reo
Some New Directions for ACP Research
Towards a relation extraction framework for cyber-security concepts
Self-Adaptive Hierarchical Sentence Model
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space
Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods
Stochastic And-Or Grammars: A Unified Framework and Logic Perspective
What value do explicit high level concepts have in vision to language problems?
Visualizing and Understanding Recurrent Networks
Confounds and Consequences in Geotagged Twitter Data
Connotation Frames: A Data-Driven Investigation
Formalization of closure properties for context-free grammars
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
Distilling Word Embeddings: An Encoding Approach
Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game
"The Sum of Its Parts": Joint Learning of Word and Phrase Representations with Autoencoders
Weighted Automata and Logics for Infinite Nested Words
Topic2Vec: Learning Distributed Representations of Topics
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
Mimicry Is Presidential: Linguistic Style Matching in Presidential Debates and Improved Polling Numbers
Call Graph Profiling for Multi Agent Systems
Continuous model theories for von Neumann algebras
Information-theoretical analysis of the statistical dependencies among three variables: Applications to written language
Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path
Formal Reasoning Using an Iterative Approach with an Integrated Web IDE
Andriod Based Punjabi TTS System
Better Summarization Evaluation with Word Embeddings for ROUGE
Regular Hilberg Processes: An Example of Processes with a Vanishing Entropy Rate
A fully data-driven method to identify (correlated) changes in diachronic corpora
DSL-based Design Space Exploration for Temporal and Spatial Parallelism of Custom Stream Computing
A Strong Distillery
Analyse lexicale outill{é}e de la parole transcrite de patients schizophr{è}nes
Integrate Document Ranking Information into Confidence Measure Calculation for Spoken Term Detection
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map
Towards Understanding Egyptian Arabic Dialogues
A Formal C Memory Model for Separation Logic
A Higher-Order Abstract Syntax Approach to Verified Transformations on Functional Programs
Inkdots as advice for finite automata
Kannada named entity recognition and classification (nerc) based on multinomial naïve bayes (mnb) classifier
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge
Noise-Robust ASR for the third 'CHiME' Challenge Exploiting Time-Frequency Masking based Multi-Channel Speech Enhancement and Recurrent Neural Network
Bilingual Distributed Word Representations from Document-Aligned Comparable Data
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
Semantics, Representations and Grammars for Deep Learning
Tuned and GPU-accelerated parallel data mining from comparable corpora
Polish -English Statistical Machine Translation of Medical Texts
LSTM Neural Reordering Feature for Statistical Machine Translation
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Unsupervised comparable corpora preparation and exploration for bi-lingual translation equivalents
Using Functional Programming for Development of Distributed, Cloud and Web Applications in F#
Making an Embedded DBMS JIT-friendly
And the math will set you free
Verifying Temporal Properties of Reactive Systems by Transformation
Control Flow Analysis for SF Combinator Calculus
Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation
BayesDB: A probabilistic programming system for querying the probable implications of data
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Dynamic Graph Queries
A Survey of Available Corpora for Building Data-Driven Dialogue Systems
Path computation in multi-layer multi-domain networks: A language theoretic approach
Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency
Statistical methods for linguistic research: Foundational Ideas - Part I
From Word Embeddings to Item Recommendation
Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling
Reflections on Monadic Lenses
JikesRVM: Internal Mechanisms Study and Garbage Collection with MMTk
Adding Real-time Capabilities to a SML Compiler
Predicting the Effectiveness of Self-Training: Application to Sentiment Classification
A Taxonomy for Tools, Processes and Languages in Automotive Software Engineering
Multimodal Pivots for Image Caption Translation
Probabilistic Inference of Twitter Users' Age based on What They Follow
Efficient Quantile Computation in Markov Chains via Counting Problems for Parikh Images
Joint Source-Channel Decoding of Polar Codes for Language-Based Source
Character-Level Incremental Speech Recognition with Recurrent Neural Networks
Statistical methods for linguistic research: Foundational Ideas - Part II
From $μ$-Calculus to Alternating Tree Automata using Parity Games
Results and Analysis of SyGuS-Comp'15
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
Using session types as an effect system
Behavioural types for non-uniform memory accesses
Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation
Attention-Based Convolutional Neural Network for Machine Comprehension
Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation
Overview of Annotation Creation: Processes & Tools
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
Text Matching as Image Recognition
Multilingual Twitter Sentiment Classification: The Role of Human Annotators
Choreographies in Practice
Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition
Segmental Recurrent Neural Networks for End-to-end Speech Recognition
Event Search and Analytics: Detecting Events in Semantically Annotated Corpora for Search and Analytics
Porting Code Across Simple Mobile Robots
From manuscript catalogues to a handbook of Syriac literature: Modeling an infrastructure for Syriaca.org
Dynamic Memory Networks for Visual and Textual Question Answering
Sentiment Analysis in Scholarly Book Reviews
Solutions of Word Equations over Partially Commutative Structures
Fuzzy alternating $\mathrm{B\ddot{u}chi}$ automata over distributive lattices
Two-variable Logic with a Between Predicate
Bank distress in the news: Describing events through deep learning
Stack-propagation: Improved Representation Learning for Syntax
Array Folds Logic
Summaries for Context-Free Games
Semantic Regularities in Document Representations
Recursive Neural Language Architecture for Tag Prediction
Part-of-Speech Relevance Weights for Learning Word Embeddings
Pointing the Unknown Words
A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data
LifeJacket: Verifying precise floating-point optimizations in LLVM
Expressivity and Complexity of MongoDB (Extended Version)
Differentiable Pooling for Unsupervised Acoustic Model Adaptation
MWStat: A Modulated Web-Based Statistical System
Stance and Sentiment in Tweets
Detecting Context Dependence in Exercise Item Candidates Selected from Corpora
Robust Dialog State Tracking for Large Ontologies
Types from data: Making structured data first-class citizens in F#
Vocabulary Manipulation for Neural Machine Translation
Tweet2Vec: Character-Based Distributed Representations for Social Media
Relation Schema Induction using Tensor Factorization with Side Information
A Relatively Small Turing Machine Whose Behavior Is Independent of Set Theory
Joint Learning of Sentence Embeddings for Relevance and Entailment
Automatic Detection and Categorization of Election-Related Tweets
Montre: A Tool for Monitoring Timed Regular Expressions
Programming with a Differentiable Forth Interpreter
Automatic Construction of Discourse Corpora for Dialogue Translation
Textual Paralanguage and its Implications for Marketing Communications
Classifying discourse in a CSCL platform to evaluate correlations with Teacher Participation and Progress
Design and development a children's speech database
CrowdSource: Automated Inference of High Level Malware Functionality from Low-Level Symbols Using a Crowd Trained Machine Learning Model
Logic of Local Inference for Contextuality in Quantum Physics and Beyond
The Mathematical Foundations for Mapping Policies to Network Devices (Technical Report)
Preliminary Results Towards Contract Monitorability
Stochastic Structured Prediction under Bandit Feedback
The Role of Translated Information Quality in a Global e-Retailing Context
Correlation and Substitution in SPARQL
CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
A Formal Semantic for UML 2.0 Activity Diagram based on Institution Theory
On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets
DefExt: A Semi Supervised Definition Extraction Tool
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
Edinburgh Neural Machine Translation Systems for WMT 16
Word Sense Disambiguation using a Bidirectional LSTM
cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Spectral decomposition method of dialog state tracking via collective matrix factorization
Multiparty Compatibility for Concurrent Objects
From Push/Enter to Eval/Apply by Program Transformation
Adaptive Just-in-time Value Class Optimization for Lowering Memory Consumption and Improving Execution Time Performance
Explaining Predictions of Non-Linear Classifiers in NLP
The emotional arcs of stories are dominated by six basic shapes
P2P-PL: A Pattern Language to Design Efficient and Robust Peer-to-Peer Systems
Corpus-level Fine-grained Entity Typing Using Contextual Information
This before That: Causal Precedence in the Biomedical Domain
HUME: Human UCCA-Based Evaluation of Machine Translation
Throwing fuel on the embers: Probability or Dichotomy, Cognitive or Linguistic?
Biabduction (and Related Problems) in Array Separation Logic
Hybrid Information Flow Analysis for Programs with Arrays
Translating Bayesian Networks into Entity Relationship Models, Extended Version
Actionable and Political Text Classification using Word Embeddings and LSTM
Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach
Recurrent Highway Networks
Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis
Modeling Software Development Methodologies: A Logic Based Approach
LightDP: Towards Automating Differential Privacy Proofs
All Fingers are not Equal: Intensity of References in Scientific Articles
Citation Classification for Behavioral Analysis of a Scientific Field
Improving Correlation with Human Judgments by Integrating Semantic Similarity with Second--Order Vectors
Skipping Word: A Character-Sequential Representation based Framework for Question Answering
Lexical-Morphological Modeling for Legal Text Analysis
Using Natural Language Processing to Screen Patients with Active Heart Failure: An Exploration for Hospital-wide Surveillance
Divide and...conquer? On the limits of algorithmic approaches to syntactic semantic structure
Spheres as Frobenius objects
Efficient softmax approximation for GPUs
An Iterative Transfer Learning Based Ensemble Technique for Automatic Short Answer Grading
Stereotypes in Search Engine Results: Understanding The Role of Local and Global Factors
Graph-Structured Representations for Visual Question Answering
The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition
Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention
The distribution of information content in English sentences
Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation
Effective Combination of Language and Vision Through Model Composition and the R-CCA Method
Learning to Translate in Real-time with Neural Machine Translation
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks
An Arabic-Hebrew parallel corpus of TED talks
Are Word Embedding-based Features Useful for Sarcasm Detection?
Embracing data abundance: BookTest Dataset for Reading Comprehension
Toward Automatic Understanding of the Function of Affective Language in Support Groups
There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction
Mining the Web for Pharmacovigilance: the Case Study of Duloxetine and Venlafaxine
Interpreting Neural Networks to Improve Politeness Comprehension
Open-Ended Visual Question-Answering
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation
Real meromorphic differentials: a language for the meron configurations in planar nanomagnets
Achieving Human Parity in Conversational Speech Recognition
Weighted Positive Binary Decision Diagrams for Exact Probabilistic Inference
Synthesis from Assume-Guarantee Contracts using Skolemized Proofs of Realizability
Automating Induction for Solving Horn Clauses
LambdaDL: Syntax and Semantics (Preliminary Report)
Bridging Neural Machine Translation and Bilingual Dictionaries
Reordering rules for English-Hindi SMT
UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation
EmojiNet: Building a Machine Readable Sense Inventory for Emoji
Broad Context Language Modeling as Reading Comprehension
On the Expressive Power of User-Defined Effects: Effect Handlers, Monadic Reflection, Delimited Control
Push vs. Pull-Based Loop Fusion in Query Engines
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition
A Performance Survey on Stack-based and Register-based Virtual Machines
Dual Attention Networks for Multimodal Reasoning and Matching
A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
All or nothing: toward a promise problem dichotomy for constraint problems
Learning Recurrent Span Representations for Extractive Question Answering
Quasi-Recurrent Neural Networks
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
Boosting Image Captioning with Attributes
Deep Biaffine Attention for Neural Dependency Parsing
Self-Wiring Question Answering Systems
Increasing the throughput of machine translation systems using clouds
Getting Started with Neural Models for Semantic Matching in Web Search
UTCNN: a Deep Learning Model of Stance Classificationon on Social Media Text
Semi-automatic Simultaneous Interpreting Quality Evaluation
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
Variable Computation in Recurrent Neural Networks
Visualizing and Understanding Curriculum Learning for Long Short-Term Memory Networks
Tracking Words in Chinese Poetry of Tang and Song Dynasties with the China Biographical Database
Recurrent Memory Addressing for describing videos
The intuitionistic temporal logic of dynamical systems
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition
The Bricklayer Ecosystem - Art, Math, and Code
Deep encoding of etymological information in TEI
Unit Dependency Graph and its Application to Arithmetic Word Problem Solving
Studying Academic Indicators within Virtual Learning Environment Using Educational Data Mining
The Complexity of Bayesian Networks Specified by Propositional and Relational Languages
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)
Privacy Patterns
Quons: A 3D Language for Quantum Information
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training
Grammar rules for the isiZulu complex verb
Inferring the location of authors from words in their texts
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
NVST data archiving system based on fastbit nosql database
ProjectQ: An Open Source Software Framework for Quantum Computing
Intelligent information extraction based on artificial neural network
PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese
Proceedings 29th and 30th Workshops on (Constraint) Logic Programming and 24th International Workshop on Functional and (Constraint) Logic Programming
Abstracting Event-Driven Systems with Lifestate Rules
Proving Non-Deterministic Computations in Agda
Neural Probabilistic Model for Non-projective MST Parsing
Sign Language Recognition Using Temporal Classification
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities
Crowdsourcing Ground Truth for Medical Relation Extraction
Decoding with Finite-State Transducers on GPUs
Towards a Semantics-Aware Code Transformation Toolchain for Heterogeneous Systems
Neural Models for Sequence Chunking
Static Detection of DoS Vulnerabilities in Programs that use Regular Expressions (Extended Version)
An attempt to physical science basis of climate changes in early Seventeenth century and the influence the Little Ice Age in south Italy
Proceedings Fourth International Workshop on Linearity
Decidability, Complexity, and Expressiveness of First-Order Logic Over the Subword Ordering
Learning Word-Like Units from Joint Audio-Visual Analysis
Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis
Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
Living a discrete life in a continuous world: Reference with distributed representations
The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task
Neural Machine Translation with Source-Side Latent Graph Parsing
Intersections and Unions of Session Types
Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction
Local Lexing
Universal Dependencies to Logical Forms with Negation Scope
Parallel Long Short-Term Memory for Multi-stream Classification
Vector Embedding of Wikipedia Concepts and Entities
Offline bilingual word vectors, orthogonal transformations and the inverted softmax
Existential length universality
A Dependency-Based Neural Reordering Model for Statistical Machine Translation
Luandri: a Clean Lua Interface to the Indri Search Engine
Filtering Tweets for Social Unrest
Unsupervised Learning of Morphological Forests
Discriminating Traces with Time
Curie: Policy-based Secure Data Exchange
Political Homophily in Independence Movements: Analysing and Classifying Social Media Users by National Identity
A Knowledge-Based Approach to Word Sense Disambiguation by distributional selection and semantic features
Approches d'analyse distributionnelle pour améliorer la désambiguïsation sémantique
Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ
End-to-End Task-Completion Neural Dialogue Systems
Deterministic Temporal Logics and Interval Constraints
End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks
Information Extraction in Illicit Domains
Extending Automatic Discourse Segmentation for Texts in Spanish to Catalan
Multichannel End-to-end Speech Recognition
A Motif-based Approach for Identifying Controversy
Identifying Partisan Slant in News Articles and Twitter during Political Crises
Paper2vec: Citation-Context Based Document Distributed Representation for Scholar Recommendation
Process algebra with strategic interleaving
An overview of embedding models of entities and relationships for knowledge base completion
Data-Mining Textual Responses to Uncover Misconception Patterns
Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding
Linguistic Matrix Theory
Simplified End-to-End MMI Training and Voting for ASR
Neutral evolution and turnover over centuries of English word popularity
The pragmatics of clone detection and elimination
An Outline of Separation Logic
Moderately Complex Paxos Made Simple: High-Level Specification of Distributed Algorithm
$α$Check: A mechanized metatheory model-checker
Deriving Probability Density Functions from Probabilistic Functional Programs
Linear Ensembles of Word Embedding Models
Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution
Adposition and Case Supersenses v2: Guidelines for English
On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models
Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments
Bayesian Recurrent Neural Networks
ROSA: R Optimizations with Static Analysis
Automatic semantic role labeling on non-revised syntactic trees of journalistic texts
An entity-driven recursive neural network model for chinese discourse coherence modeling
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
MUSE: Modularizing Unsupervised Sense Embeddings
Learning Character-level Compositionality with Visual Features
Mining Worse and Better Opinions. Unsupervised and Agnostic Aggregation of Online Reviews
Predicting Role Relevance with Minimal Domain Expertise in a Financial Domain
An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Learning to Skim Text
Using Global Constraints and Reranking to Improve Cognates Detection
Robust Incremental Neural Semantic Graph Parsing
Recognizing Descriptive Wikipedia Categories for Historical Figures
A Challenge Set Approach to Evaluating Machine Translation
Taxonomy Induction using Hypernym Subsequences
Undecidability of the first order theories of free non-commutative Lie algebras
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
Learning a Neural Semantic Parser from User Feedback
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
Modulo quantifiers over functional vocabularies extending addition
Dependency Parsing with Dilated Iterated Graph CNNs
The Promise of Premise: Harnessing Question Premises in Visual Question Answering
A polynomial time algorithm for the Lambek calculus with brackets of bounded order
A Hybrid Architecture for Multi-Party Conversational Systems
Going Wider: Recurrent Neural Network With Parallel Cells
Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting
Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers
The Pragmatics of Indirect Commands in Collaborative Discourse
Survey of Visual Question Answering: Datasets and Techniques
Evaluating vector-space models of analogy
Operational Semantics of Process Monitors
Handwritten Urdu Character Recognition using 1-Dimensional BLSTM Classifier
Text-based Adventures of the Golovin AI Agent
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
Information Density as a Factor for Variation in the Embedding of Relative Clauses
Universal Dependencies Parsing for Colloquial Singaporean English
Introducing Geometric Algebra to Geometric Computing Software Developers: A Computational Thinking Approach
Verifying Programs via Intermediate Interpretation
Search Engine Guided Non-Parametric Neural Machine Translation
Mixed Membership Word Embeddings for Computational Social Science
Recurrent Additive Networks
SmartPaste: Learning to Adapt Source Code
Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech Recognition
Second-Order Word Embeddings from Nearest Neighbor Topological Features
Parsing with CYK over Distributed Representations: "Classical" Syntactic Parsing in the Novel Era of Neural Networks
Deriving Neural Architectures from Sequence and Graph Kernels
Max-Cosine Matching Based Neural Models for Recognizing Textual Entailment
Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs
Semi-Supervised Model Training for Unbounded Conversational Speech Recognition
Multiplex model of mental lexicon reveals explosive learning in humans
Extending programs with debug-related features, with application to hardware development
From Temporal Models to Property-Based Testing
Neural Embeddings of Graphs in Hyperbolic Space
On the "Calligraphy" of Books
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition
Machine Assisted Analysis of Vowel Length Contrasts in Wolof
Content-Based Table Retrieval for Web Queries
Context encoders as a simple but powerful extension of word2vec
Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017)
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM
A Full Non-Monotonic Transition System for Unrestricted Non-Projective Parsing
Acoustic data-driven lexicon learning based on a greedy pronunciation selection framework
An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
Towards Neural Phrase-based Machine Translation
pyRecLab: A Software Library for Quick Prototyping of Recommender Systems
ParVecMF: A Paragraph Vector-based Matrix Factorization Recommender System
Modalities in homotopy type theory
Named Entity Recognition with stack residual LSTM and trainable bias decoding
Automated text summarisation and evidence-based medicine: A survey of two domains
Parikh Image of Pushdown Automata
The Bernays-Schönfinkel-Ramsey Fragment with Bounded Difference Constraints over the Reals is Decidable
Constrained Type Families
Stronger Baselines for Trustable Results in Neural Machine Translation
Church-Rosser Systems, Codes with Bounded Synchronization Delay and Local Rees Extensions
The Fall of the Empire: The Americanization of English
Development and Verification of a Flight Stack for a High-Altitude Glider in Ada/SPARK 2014
Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models
Sentiment Identification in Code-Mixed Social Media Text
Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
Cross-linguistic differences and similarities in image descriptions
Cooperative Kernels: GPU Multitasking for Blocking Algorithms (Extended Version)
What Works Better? A Study of Classifying Requirements
A non-projective greedy dependency parser with bidirectional LSTMs
Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data Analysis
Modeling the dynamics of domain specific terminology in diachronic corpora
Geospatial Semantics
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
Design and Optimisation of the FlyFast Front-end for Attribute-based Coordination
Linguistic Markers of Influence in Informal Interactions
Lyrics-Based Music Genre Classification Using a Hierarchical Attention Network
Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation
A Comparative Analysis of Social Network Pages by Interests of Their Followers
Deep Active Learning for Named Entity Recognition
Improving Discourse Relation Projection to Build Discourse Annotated Corpora
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
Ultraslow diffusion in language: Dynamics of appearance of already popular adjectives on Japanese blogs
Attention-Based End-to-End Speech Recognition on Voice Search
Seminar Users in the Arabic Twitter Sphere
Developing a Molecular Theory of Electromechanical Responses
Extracting Core Claims from Scientific Articles
Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question Answering
Question Dependent Recurrent Entity Network for Question Answering
Determining Semantic Textual Similarity using Natural Deduction Proofs
Deep Residual Learning for Weakly-Supervised Relation Extraction
ASDA : Analyseur Syntaxique du Dialecte Alg{é}rien dans un but d'analyse s{é}mantique
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation
A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously
Zero-Shot Activity Recognition with Verb Attribute Induction
Method and apparatus for automatic text input insertion in digital devices with a restricted number of keys
Learned in Translation: Contextualized Word Vectors
Improving Part-of-Speech Tagging for NLP Pipelines
Exploiting Linguistic Resources for Neural Machine Translation Using Multi-task Learning
Automatic Question-Answering Using A Deep Similarity Neural Network
A Syllable-based Technique for Word Embeddings of Korean Words
D4M 3.0: Extended Database and Language Capabilities
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search
Simple and Effective Dimensionality Reduction for Word Embeddings
WASSA-2017 Shared Task on Emotion Intensity
Sentiment Analysis by Joint Learning of Word Embeddings and Classifier
Comparison of Decoding Strategies for CTC Acoustic Models
Identifying Harm Events in Clinical Care through Medical Narratives
Deconvolutional Paragraph Representation Learning
Category Theory for Genetics
Cultural Structures of Knowledge from Wikipedia Networks of First Links
The Natural Stories Corpus
Measuring the Effect of Discourse Relations on Blog Summarization
Descriptional Complexity of Non-Unary Self-Verifying Symmetric Difference Automata
Handling Homographs in Neural Machine Translation
Software engineering and the SP theory of intelligence
Divide-and-Conquer Checkpointing for Arbitrary Programs with No User Annotation
Transforming Coroutining Logic Programs into Equivalent CHR Programs
From Concurrent Programs to Simulating Sequential Programs: Correctness of a Transformation
KEGGexpressionMapper allows for analysis of pathways over multiple conditions by integrating transcriptomics and proteomics measurements
The Unfolding Semantics of Functional Programs
Verification of Programs via Intermediate Interpretation
Modelling Protagonist Goals and Desires in First-Person Narrative
PersonaBank: A Corpus of Personal Narratives and Their Story Intention Graphs
Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion
Type Safe Redis Queries: A Case Study of Type-Level Programming in Haskell
TANKER: Distributed Architecture for Named Entity Recognition and Disambiguation
Fighting with the Sparsity of Synonymy Dictionaries
Inference of Fine-Grained Event Causality from Blogs and Films
Unsupervised Induction of Contingent Event Pairs from Film Scenes
Glyph-aware Embedding of Chinese Characters
Disentangling ASR and MT Errors in Speech Translation
Formalising Type-Logical Grammars in Agda
Compositional Approaches for Representing Relations Between Words: A Comparative Study
Using $k$-way Co-occurrences for Learning Word Embeddings
Quantum machines with classical control
Training RNNs as Fast as CNNs
CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification
Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings
A certified reference validation mechanism for the permission model of Android
Neural Network Based Nonlinear Weighted Finite Automata
A Rewriting System for Convex Optimization Problems
Trace and Stable Failures Semantics for CSP-Agda
And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue
Acquiring Background Knowledge to Improve Moral Value Prediction
Towards Building a Knowledge Base of Monetary Transactions from a News Collection
A Fast and Accurate Vietnamese Word Segmenter
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words
Updating the silent speech challenge benchmark with deep learning
De-identification of medical records using conditional random fields and long short-term memory networks
Predicting interviewee attitude and body language from speech descriptors
EZLearn: Exploiting Organic Supervision in Large-Scale Data Annotation
Integration of Japanese Papers Into the DBLP Data Set
A Preliminary Study for Building an Arabic Corpus of Pair Questions-Texts from the Web: AQA-Webcorp
A Permission-Dependent Type System for Secure Information Flow Analysis
Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks
Structured Embedding Models for Grouped Data
Synonym Discovery with Etymology-based Word Embeddings
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction
The Dependence of Frequency Distributions on Multiple Meanings of Words, Codes and Signs
Annotation and Detection of Emotion in Text-based Dialogue Systems with CNN
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl
Model-Theoretic Characterizations of Boolean and Arithmetic Circuit Classes of Small Depth
Czech Text Document Corpus v 2.0
Bilingual Words and Phrase Mappings for Marathi and Hindi SMT
On the Challenges of Sentiment Analysis for Dynamic Events
Rybu: Imperative-style Preprocessor for Verification of Distributed Systems in the Dedan Environment
Smarnet: Teaching Machines to Read and Comprehend Like Human
The IIT Bombay English-Hindi Parallel Corpus
Geo-referencing Place from Everyday Natural Language Descriptions
Decision support from financial disclosures with deep neural networks and transfer learning
NoReC: The Norwegian Review Corpus
Paying Attention to Multi-Word Expressions in Neural Machine Translation
Approximate Reduction of Finite Automata for High-Speed Network Intrusion Detection (Technical Report)
Scaling Text with the Class Affinity Model
$Q|SI\rangle$: A Quantum Programming Environment
One-shot and few-shot learning of word embeddings
Understanding Hidden Memories of Recurrent Neural Networks
Named Entity Recognition in Twitter using Images and Text
Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German
Unsupervised Neural Machine Translation
Proving Soundness of Extensional Normal-Form Bisimilarities
Improving Neural Machine Translation through Phrase-based Forced Decoding
Semantic Structure and Interpretability of Word Embeddings
Text Annotation Graphs: Annotating Complex Natural Language Phenomena
Extracting an English-Persian Parallel Corpus from Comparable Corpora
SPARK: Static Program Analysis Reasoning and Retrieving Knowledge
Neural Speed Reading via Skim-RNN
Improved training for online end-to-end speech recognition systems
Structure Regularized Bidirectional Recurrent Convolutional Neural Network for Relation Classification
Improving Hypernymy Extraction with Distributional Semantic Classes
Open-World Knowledge Graph Completion
Learning Multi-Modal Word Representation Grounded in Visual Context
Towards the Use of Deep Reinforcement Learning with Global Policy For Query-based Extractive Summarisation
Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT
From Word Segmentation to POS Tagging for Vietnamese
On Extending Neural Networks with Loss Ensembles for Text Classification
A Deep Learning Approach for Expert Identification in Question Answering Communities
Can clone detection support quality assessments of requirements specifications?
Investigating Inner Properties of Multimodal Representation and Semantic Compositionality with Brain-based Componential Semantics
Programming the Interactions of Collective Adaptive Systems by Relying on Attribute-based Communication
Intelligent Word Embeddings of Free-Text Radiology Reports
Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Model
Facets, Tiers and Gems: Ontology Patterns for Hypernormalisation
Automated Analysis of Topic-Actor Networks on Twitter: New approach to the analysis of socio-semantic networks
Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN
Hilbert's tenth problem for complex meromorphic functions in several variables
Machine Translation Using Semantic Web Technologies: A Survey
Aprendizagem significativa através da modelagem computacional de sistemas físicos (Meaningful learning through computational modeling of physics systems)
Unsupervised Discovery of Structured Acoustic Tokens with Applications to Spoken Term Detection
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
Speaker-Sensitive Dual Memory Networks for Multi-Turn Slot Tagging
Predicting and Explaining Human Semantic Search in a Cognitive Model
On Asynchrony and Choreographies
SyGuS-Comp 2017: Results and Analysis
Language and Proofs for Higher-Order SMT (Work in Progress)
Deep Semantic Role Labeling with Self-Attention
Improving the Performance of Online Neural Transducer Models
Distance-based Self-Attention Network for Natural Language Inference
StrassenNets: Deep learning with a multiplication budget
Interactive graph query language for multidimensional data in Collaboration Spotting visual analytics framework
Differentiable lower bound for expected BLEU score
Sockeye: A Toolkit for Neural Machine Translation
Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic
The NarrativeQA Reading Comprehension Challenge
Ethical Questions in NLP Research: The (Mis)-Use of Forensic Linguistics
A Compositional Coalgebraic Semantics of Strategic Games
Are words easier to learn from infant- than adult-directed speech? A quantitative corpus-based investigation
More on the dynamics of the symbolic square root map
Slugbot: An Application of a Novel and Scalable Open Domain Socialbot Framework
Towards Understanding and Answering Multi-Sentence Recommendation Questions on Tourism
Explorations in an English Poetry Corpus: A Neurocognitive Poetics Perspective
Improved English to Russian Translation by Neural Suffix Prediction
EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs
Did William Shakespeare and Thomas Kyd Write Edward III?
Detecting Offensive Language in Tweets Using Deep Learning
NELS - Never-Ending Learner of Sounds
Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations
Internal Universes in Models of Homotopy Type Theory
Logic characterisation of p/q-recognisable sets
A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction
Object-based reasoning in VQA
A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts
Paraphrase-Supervised Models of Compositionality
Submodularity-inspired Data Selection for Goal-oriented Chatbot Training based on Sentence Embeddings
Order matters: Distributional properties of speech to young children bootstraps learning of semantic representations
Preserved Structure Across Vector Space Representations
Multi-attention Recurrent Network for Human Communication Comprehension
Parametric Presburger Arithmetic: Complexity of Counting and Quantifier Elimination
Praaline: Integrating Tools for Speech Corpus Research
TextZoo, a New Benchmark for Reconsidering Text Classification
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Deep contextualized word representations
Deep Learning for Lip Reading using Audio-Visual Information for Urdu Language
Calculating the similarity between words and sentences using a lexical database and corpus statistics
Compositional Verification of Compiler Optimisations on Relaxed Memory
Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?
Entropy Guided Spectrum Based Bug Localization Using Statistical Language Model
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
Evaluating Scoped Meaning Representations
A Hybrid Word-Character Model for Abstractive Summarization
Memory-based Parameter Adaptation
Comparing Downward Fragments of the Relational Calculus with Transitive Closure on Trees
On Extended Long Short-term Memory and Dependent Bidirectional Recurrent Neural Network
From SysML/KAOS Domain Models to B System Specifications
Code Review Comments: Language Matters
Towards the Creation of a Large Corpus of Synthetically-Identified Clinical Notes
IcoRating: A Deep-Learning System for Scam ICO Identification
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge
Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
Enriching Frame Representations with Distributionally Induced Senses
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model
Ready, Set, Verify! Applying hs-to-coq to real-world Haskell code
Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation
Stance Detection on Tweets: An SVM-based Approach
A Resourceful Reframing of Behavior Trees
Pay More Attention - Neural Architectures for Question-Answering
English verb regularization in books and tweets
Fast Parametric Learning with Activation Memorization
The Worm Calculus
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only
Deep Cascade Multi-task Learning for Slot Filling in Chinese E-commerce Shopping Guide Assistant
Fine-Grained Attention Mechanism for Neural Machine Translation
Synthesis of Differentiable Functional Programs for Lifelong Learning
Entrenamiento de una red neuronal para el reconocimiento de imagenes de lengua de senas capturadas con sensores de profundidad
The Training of Neuromodels for Machine Comprehension of Text. Brain2Text Algorithm
Specification-Driven Multi-Perspective Predictive Business Process Monitoring (Extended Version)
In-depth Question classification using Convolutional Neural Networks
Clinical Concept Embeddings Learned from Massive Sources of Medical Data
A Large-Scale Study of Language Models for Chord Prediction
Enrichment of OntoSenseNet: Adding a sense-annotated Telugu lexicon
Automatic symbolic computation for discontinuous Galerkin finite element methods
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection
Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings
Mining Social Media for Newsgathering
Deep Learning for Digital Text Analytics: Sentiment Analysis
Emergent Communication through Negotiation
The Arabic discourse about support for ISIS on Twitter and what we can learn from that
Word2Vec applied to Recommendation: Hyperparameters Matter
Debugging Program Verification Proof Scripts (Tool Paper)
Learning Multilingual Embeddings for Cross-Lingual Information Retrieval in the Presence of Topically Aligned Corpora
Stream Runtime Monitoring on UAS
Per-Corpus Configuration of Topic Modelling for GitHub and Stack Overflow Collections
Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition
Formalizing common sense for scalable inconsistency-robust information integration using Direct Logic(TM) reasoning and the Actor Model
Complexity of Networks (reprise)
Imitation learning of motor primitives and language bootstrapping in robots
La réduction de termes complexes dans les langues de spécialité
Pictures of Processes: Automated Graph Rewriting for Monoidal Categories and Applications to Quantum Computing
Region-based memory management for Mercury programs
A probabilistic framework for analysing the compositionality of conceptual combinations
Competitive dynamics of lexical innovations in multi-layer networks
A model of grassroots changes in linguistic systems
Fault Detection in C Programs using Monitoring of Range Values: Preliminary Results
Higher-order symbolic execution for contract verification and refutation
Sheaf-Theoretic Methods in Quantum Mechanics and Quantum Information Theory
An Action Language for Multi-Agent Domains: Foundations
In narrative texts punctuation marks obey the same statistics as words
A Principled Approach to Bridging the Gap between Graph Data and their Schemas
Maximum Entropy, Word-Frequency, Chinese Characters, and Multiple Meanings
Étude cognitive des processus de construction d'une requête dans un système de gestion de connaissances médicales
The exp-log normal form of types
Automated Synthesis of Distributed Controllers
Hybrid VCSPs with crisp and conservative valued templates
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection
Interactive Tools and Tasks for the Hebrew Bible
Penambahan emosi menggunakan metode manipulasi prosodi untuk sistem text to speech bahasa Indonesia
Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings
Application Embedding: A Language Approach to Declarative Web Programming
N-gram Language Modeling using Recurrent Neural Network Estimation
Does Python Smell Like Java? Tool Support for Design Defect Discovery in Python
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning
Active learning in annotating micro-blogs dealing with e-reputation
Information Theory and the Length Distribution of all Discrete Systems
Deadlock-Free Typestate-Oriented Programming
Computing the Cohomological Brauer Group of a Toric Variety
Pfaffian Subschemes
Equivariant intersection theory
Analysis of isoplanatic high resolution stellar fields by Starfinder code
CMBEASY:: an Object Oriented Code for the Cosmic Microwave Background
Restart Strategies and Internet Congestion
Noncommutative Martin-Lof randomness : on the concept of a random sequence of qubits
Landau level bosonization of a 2D electron gas
Lorentz Group in Condensed Matter Physics
Topics in Quantum Computers
The 2D J_1-J_2 XY and XY-Ising Models
Universality Classes for Extreme Value Statistics
The roughening transition of interfaces in disordered media
Lattice Models for Magnetic Fluids: Correlations Between Order Parameters
Geometrical description of vortices in Ginzburg-Landau billiards
Fermionic field theory for directed percolation in (1+1) dimensions
Time reparametrization group and the long time behaviour in quantum glassy systems
Spinor Bosonic Atoms in Optical Lattices: Symmetry Breaking and Fractionalization
The noncommutative replica approach
Entropy estimation of symbol sequences
A form factor approach to finite temperature correlation functions in $c=1$ CFT
Spin Waves in Random Spin Chains
A New face on old code
Computing at Hasylab: Perl/PerlTk is the new scripting language for Spectra
User office proposal handling and analysis software
Combinatorics of Hard Particles on Planar Graphs
Quenched Disorder From Sea-Bosons
Periodic diffraction patterns for 1D quasicrystals
Nuclear Magnetic Relaxation in the Haldane-Gap Antiferromagnet Ni(C_2_H_8_N_2_)_2_NO_2_(ClO_4_)
Quantum Hydrodynamics of Fermi Fluids
Towards a Hydrodynamic Theory of Infinite Neutral Nonrelativistic Matter
Dictionary between scattering matrix and Keldysh formalisms for quantum transport driven by time-periodic fields
Absorbing states and elastic interfaces in random media: two equivalent descriptions of self-organized criticality
Designing a Theorem Prover
Well-Founded Semantics for Extended Logic Programs with Dynamic Preferences
Linear Segmentation and Segment Significance
A Freely Available Morphological Analyzer, Disambiguator and Context Sensitive Lemmatizer for German
On the Evaluation and Comparison of Taggers: The Effect of Noise in Testing Corpora
A Natural Deduction style proof system for propositional $μ$-calculus and its formalization in inductive type theories
On Dart-Zobel Algorithm for Testing Regular Type Inclusion
A Proof Theoretic View of Constraint Programming
A Polymorphic Groundness Analysis of Logic Programs
Choosing the Word Most Typical in Context Using a Lexical Co-occurrence Network
An Emptiness Algorithm for Regular Types with Set Operators
Automatic Hardware Synthesis for a Hybrid Reconfigurable CPU Featuring Philips CPLDs
Optimal Multi-Paragraph Text Segmentation by Dynamic Programming
PSPACE has 2-round quantum interactive proof systems
Mixing Metaphors
A Computational Memory and Processing Model for Processing
Inside-Outside Estimation of a Lexicalized PCFG for German
Cascaded Grammatical Relation Assignment
Mapping Multilingual Hierarchies Using Relaxation Labeling
Robust Grammatical Analysis for Spoken Dialogue Systems
Events in Property Patterns
Representing Text Chunks
Explanation-based Learning for Machine Translation
Selective Magic HPSG Parsing
Corpus Annotation for Parser Evaluation
Prospects for in-depth story understanding by computer
TSIA: A Dataflow Model
Hypothetical revision and matter-of-fact supposition
Problem solving in ID-logic with aggregates: some experiments
Declarative Representation of Revision Strategies
TnT - A Statistical Part-of-Speech Tagger
Message Classification in the Call Center
A Finite State and Data-Oriented Method for Grapheme to Phoneme Conversion
Task Frames
A Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation
Finite-State Reduplication in One-Level Prosodic Morphology
Semantic Parsing based on Verbal Subcategorization
Using compression to identify acronyms in text
Parameter-free Model of Rank Polysemantic Distribution
Mapping WordNets Using Structural Information
Applying System Combination to Base Noun Phrase Identification
Efficient probabilistic top-down and left-corner parsing
Compact non-left-recursive grammars using the selective left-corner transform and factoring
A Learning Approach to Shallow Parsing
Estimators for Stochastic ``Unification-Based'' Grammars
Lexicalized Stochastic Modeling of Constraint-Based Grammars using Log-Linear Measures and EM Training
Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution
Automatic Extraction of Subcategorization Frames for Czech
Parsing with the Shortest Derivation
An improved parser for data-oriented lexical-functional analysis
An Approach to the Implementation of Overlapping Rules in Standard ML
On a cepstrum-based speech detector robust to white noise
Tree-gram Parsing: Lexical Dependencies and Structural Relations
Apache web server execution tracing using Third Eye
Properties of Input-Consuming Derivations
Quantitative Neural Network Model of the Tip-of-the-Tongue Phenomenon Based on Synthesized Memory-Psycholinguistic-Metacognitive Approach
A Decision Tree of Bigrams is an Accurate Predictor of Word Sense
Man [and Woman] vs. Machine: A Case Study in Base Noun Phrase Learning
Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking
A Complete WordNet1.5 to WordNet1.6 Mapping
Solving Composed First-Order Constraints from Discrete-Time Robust Control
Constraint Propagation in Presence of Arrays
Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation
The Set of Equations to Evaluate Objects
Objects and their computational framework
Lower Bounds for Zero-knowledge on the Internet
Coupled Clustering: a Method for Detecting Structural Correspondence
Conceptual Analysis of Lexical Taxonomies: The Case of WordNet Top-Level
Enriching WordNet concepts with topic signatures
Testing for Mathematical Lineation in Jim Crace's "Quarantine" and T. S. Eliot's "Four Quartets"
Variable and Value Ordering When Solving Balanced Academic Curriculum Problems
What is the minimal set of fragments that achieves maximal parse accuracy?
Teaching Parallel Programming Using Both High-Level and Low-Level Languages
User-friendly explanations for constraint programming
Towards a characterization of the star-free sets of integers
The Deductive Database System LDL++
Three Optimisations for Sharing
A Framework for Datatype Transformation
Computational Phonology
Fast Hands-free Writing by Gaze Direction
Memory-Based Shallow Parsing
Three-Tiered Specification of Micro-Architectures
Machine Learning with Lexical Features: The Duluth Approach to Senseval-2
Thumbs up? Sentiment Classification using Machine Learning Techniques
Characterization of Strongly Equivalent Logic Programs in Intermediate Logics
Interleaved semantic interpretation in environment-based parsing
Evaluation of Coreference Rules on Complex Narrative Texts
Three New Methods for Evaluating Reference Resolution
Reference Resolution Beyond Coreference: a Conceptual Frame and its Application
Proving correctness of Timed Concurrent Constraint Programs
Probabilistic Reversible Automata and Quantum Automata
Efficient Solving of Quantified Inequality Constraints over the Real Numbers
Algorithms using Java for Spreadsheet Dependent Cell Recomputation
Recursive function templates as a solution of linear algebra expressions in C++
A Development Calculus for Specifications
An XML based Document Suite
Techniques for effective vocabulary selection
The FRED Event Display: an Extensible HepRep Client for GLAST
Design, implementation and deployment of the Saclay muon reconstruction algorithms (Muonbox/y) in the Athena software framework of the ATLAS experiment
Information Compression by Multiple Alignment, Unification and Search as a Unifying Principle in Computing and Cognition
Proposed Specification of a Distributed XML-Query Network
A Dynamic Programming Algorithm for the Segmentation of Greek Texts
Fine-Grained Authorization for Job Execution in the Grid: Design and Implementation
Designing of a Community-based Translation Center
Soft lambda-calculus: a language for polynomial time computation
Inferring Termination Conditions for Logic Programs using Backwards Analysis
An Open Ended Tree
Diagnostic reasoning with A-Prolog
Acquiring Lexical Paraphrases from a Single Corpus
Design of a Community-based Translation Center
Unifying Computing and Cognition: The SP Theory and its Applications
Transformation Rules for Locally Stratified Constraint Logic Programs
A Comparative Study of Arithmetic Constraints on Integer Intervals
A Flexible Rule Compiler for Speech Synthesis
Multi-Threading And Message Communication In Qu-Prolog
On the Expressive Power of First-Order Boolean Functions in PCF
A Proof Theoretic Approach to Failure in Functional Logic Programming
Summarizing Encyclopedic Term Descriptions on the Web
Exploiting Semidefinite Relaxations in Constraint Programming
A Hyper-Arc Consistency Algorithm for the Soft Alldifferent Constraint
On Global Warming (Softening Global Constraints)
Incremental Construction of Minimal Acyclic Sequential Transducers from Unsorted Data
Verbal chunk extraction in French using limited resources
FORM Matters: Fast Symbolic Computation under UNIX
A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
Inside-Outside Estimation Meets Dynamic EM
Formal Languages and Algorithms for Similarity based Retrieval from Sequence Databases
Corpus based Enrichment of GermaNet Verb Frames
Context Related Derivation of Word Senses
Transforming and Enriching Documents for the Semantic Web
Pushdown dimension
A Scalable Stream-Oriented Framework for Cluster Applications
Minimal Eulerian trail in a labeled digraph
Text Compression and Superfast Searching
Polynomial Synthesis of Asynchronous Automata
Automatic extraction of paraphrastic phrases from medium size corpora
Practical Datatype Specializations with Phantom Types and Recursion Schemes
Logic Column 14: Nominal Logic and Abstract Syntax
An elitist approach for extracting automatically well-realized speech sounds with high confidence
Building Scenarios for Environmental Management and Planning: An IT-Based Approach
Unification of multi-lingual scientific terminological resources using the ISO 16642 standard. The TermSciences initiative
Applied MVC Patterns. A pattern language
Semantics and Complexity of SPARQL
Logics for Unranked Trees: An Overview
An Analysis of Arithmetic Constraints on Integer Intervals
Towards "Propagation = Logic + Control"
Modules over Monads and Linearity
FOSS-Based Grid Computing
The role of time in considering collections
Rapport technique du projet OGRE
Norm Based Causal Reasoning in Textual Corpus
Reuse of Specification Patterns with the B Method
Quantifier elimination for the reals with a predicate for the powers of two
Interactive Problem Solving in Prolog
Developing efficient parsers in Prolog: the CLF manual (v1.0)
Demaq: A Foundation for Declarative XML Message Processing
On vocabulary size of grammar-based codes
A Note on Local Ultrametricity in Text
Menzerath-Altmann Law for Syntactic Structures in Ukrainian
A Virtual Logo Keyboard for People with Motor Disabilities
The Suspension Calculus and its Relationship to Other Explicit Treatments of Substitution in Lambda Calculi
Efficient First-Order Temporal Logic for Infinite-State Systems
Logic Programming with Satisfiability
Relational Abstract Domains for the Detection of Floating-Point Run-Time Errors
Umbral Calculus and Cancellative Semigroup Algebras
Analysis in $R^{1,1}$ or the Principal Function Theory
The LCDROOT Analysis Package
DIS Structure Functions in Lattice QCD
Quadratically optimized polynomials for fermion simulations
Three-State Complex Valued Spins Coupled to Binary Branched Polymers in Two-Dimensional Quantum Gravity
Higher-twist contributions to the Structure Functions coming from 4-fermion operators
The Berry Phase and Monopoles in Gluodynamics
Glueball and gluelump spectrum in abelian projected QCD
Applied lattice gauge calculations: diquark content of the nucleon
Experiences with the multi-level algorithm
Geometry of percolating monopole clusters
Representations of classical groups on the lattice and its application to the field theory on discrete space-time
Vector-Boson versus Gluon Fusion at Hadron Colliders
Remarks on the Quark-diagram Description of Two-body Nonleptonic B-meson Decays
Classical Kinetics of Hard Thermal Phenomena in High Temperature QCD
Renormalons and 1/Q^2 Corrections
New Physics from HERA?
The Confinement
Collisional Energy Loss of Fast Charged Particles in Relativistic Plasmas
Quark level linear σmodel (LσM) via loop graphs
Twin Peaks
Nonfactorizable contributions to the decay mode D^0 -> K^0 \bar{K^0}
A closed analytical formula for two-loop massive tadpoles with arbitrary tensor numerators
Monte-Carlo Simulation of Exclusive Channels in e+e- Annihilation at Low Energy
Leptonic Unitarity Triangles in Matter
Chiral symmetry and pentaquarks
PHOTOS as a pocket parton shower: flexibility tests for the algorithm
Meson Mixing in Pion Superfluid
Some classical properties of the non-abelian Yang-Mills theories
Gluon Condensate, Wilson Loops and Gauge/String Duality
WBase: a C package to reduce tensor products of Lie algebra representations
Quantum Groups on Fibre Bundles
Chiral Rings Do Not Suffice: N=(2,2) Theories with Nonzero Fundamental Group
Higher algebras and mesonic spectrum in two-dimensional QCD
Free Variables and the Two Matrix Model
Sp(2)-Symmetric Lagrangian BRST Quantization
The Unreasonable Effectiveness of Quantum Field Theory
A D=4 N=1 Orbifold of Type I Strings
On the strongly coupled heterotic string
Remarks on T-duality for open strings
On the Equivalence of Affine sl(2) and N=2 Superconformal Representation Theories
The Ring Division Self Duality
More on Mixed Boundary Conditions and D-branes Bound States
Lectures on D-branes, Gauge Theory and M(atrices)
On N=8 Supergravity on $AdS_5$ and N=4 Superconformal Yang-Mills theory
Large N limit of orbifold field theories
Momentum Lattice for CHL String
The Principle of Equivalence as a Guide towards Matrix Theory Compactifications
Bag Model for a Link in a Closed Gluonic Chain
Projective resolutions of coherent sheaves and descent relations between branes
Irreducible Decomposition of Products of 10D Chiral Sigma Matrices
Chern-Simons terms and the Three Notions of Charge
Explicit derivation of a Central extended Hyper-Kahler Metric
Holographic Renormalisation and Anomalies
Fuzzy Non-Trivial Gauge Configurations
Supergravity Solution of Intersecting Branes and AdS/CFT with Flavor
Lectures on D-branes, tachyon condensation, and string field theory
Kahler Potentials on Toric Varieties
Renormalizability of N=1/2 Wess-Zumino model in superspace
A quantum BRST anti-BRST approach to classical integrable systems
Ultraviolet finiteness of Chiral Perturbation Theory for two-dimensional Quantum Electrodynamics
Quantum Corrections to the Universal Hypermultiplet and Superspace
The Circular, Elliptic Three Spin String from the SU(3) Spin Chain
Holography for fermions
Minimal Superstrings and Loop Gas Models
Supersymmetric AdS(4) compactifications of IIA supergravity
Mixed-symmetry massless gauge fields in AdS(5)
ADHM is Tachyon Condensation
M-Theory Brane as Giant Graviton and the Fractional Quantum Hall Effect
Quantization of Flag Manifolds and their Supersymmetric Extensions
D4-branes on Complete Intersection in Toric Variety
Some applications of the ultrapower theorem to the theory of compacta
Flattening and subanalytic sets in rigid analytic geometry
The Complexity of Fuzzy Logic
On the Cappell-Lee-Miller glueing theorem
Resonance Relations for Solutions of the Elliptic QKZB Equations, Fusion Rules, and Eigenvectors of Transfer Matrices of Restricted Interaction-round-a-face Models
Using Rewriting Systems to Compute Kan Extensions and Induced Actions of Categories
A Revision Theoretic Model for NF
Cohomology of Lie Superalgebras of Hamiltonian Vector Fields: Computer Analysis
The Maximality of Cartesian Categories
The Projective Theory of Ruled Surfaces
Box-ball systems and Robinson-Schensted-Knuth correspondence
Feynman Diagrams via Graphical Calculus
Rational homology of spaces of complex monic polynomials with multiple roots
Noether's variational theorem II and the BV formalism
Presburger sets and p-minimal fields
Hypothesis of the Functional Semantic Constructions and Mathematics in the Functional Semantic Aspect
Motion planning and control problems for underactuated robots
Quantum normal families: normal families of holomorphic functions and mappings on a Banach space
Quantization of non-unitary geometric classical r-matrices
Gerbes, Clifford modules and the index theorem
On braid monodromy factorizations
Presentations of Noneffective Orbifolds
Automorphisms and strongly invariant relations
================================================
FILE: data/arxiv_dataset.py
================================================
import os, random, json, pickle, re
import numpy as np
import torch.utils.data
class ArxivDataset(torch.utils.data.Dataset):
"""
A dataset for Arxiv
"""
def __init__(self, texts, preprocess=lambda x: x, sort=False):
super().__init__()
self.texts = texts
self.preprocess = preprocess
self.sort=sort
# if self.sort:
# self.data = []
# for i in range(len(self.texts)):
# type, title, story = self.texts[i]
#
# title = type + ' ' + title.strip()
# story = story.strip()
# text_raw_dict = {'title': title, 'story': story}
#
# text = self.preprocess(text_raw_dict)
# self.data.append(text)
# self.data.sort(key=lambda x: len(x[0]), reverse=True)
def __len__(self):
return len(self.texts)
def __getitem__(self, i):
if self.sort:
return self.data[i]
else:
type, title, story = self.texts[i]
title = type + ' ' + title.strip()
story = story.strip()
text_raw_dict = {'title': title, 'story': story}
text = self.preprocess(text_raw_dict)
return text
================================================
FILE: data/plot_dataset.py
================================================
import os, random, json, pickle, re
import numpy as np
import torch.utils.data
class PlotDataset(torch.utils.data.Dataset):
"""
A dataset for WikiPlots
"""
def __init__(self, texts, preprocess=lambda x: x, sort=False):
super().__init__()
self.texts = texts
self.preprocess = preprocess
self.sort=sort
# if self.sort:
# self.data = []
# for i in range(len(self.texts)):
# title, story = self.texts[i]
#
# title = title.strip()
# story = story.strip()
# text_raw_dict = {'title': title, 'story': story}
#
# text = self.preprocess(text_raw_dict)
# self.data.append(text)
# self.data.sort(key=lambda x: len(x[0]), reverse=True)
def __len__(self):
return len(self.texts)
def __getitem__(self, i):
if self.sort:
return self.data[i]
else:
title, story = self.texts[i]
title = title.strip()
story = story.strip()
text_raw_dict = {'title': title, 'story': story}
text = self.preprocess(text_raw_dict)
return text
================================================
FILE: data/prompt_dataset.py
================================================
import os, random, json, pickle, re
import numpy as np
import torch.utils.data
class PromptDataset(torch.utils.data.Dataset):
"""
A dataset for Writing Prompts
"""
def __init__(self, source, target, preprocess=lambda x: x, sort=False):
super().__init__()
self.preprocess = preprocess
self.sort=sort
print('Loading writing prompts...')
with open(source, errors='ignore') as fs:
with open(target, errors='ignore') as ft:
self.prompts = list(zip(fs.readlines(), ft.readlines()))
print('Done.')
# if self.sort:
# self.data = []
# for i in range(len(self.prompts)):
# prompt, story = self.prompts[i]
#
# # Remove extra annotation on prompt from WP dataset
# prompt = re.sub('\[ (.*) \]', '', prompt)
# prompt = prompt.strip()
# story = story.strip()
# text_raw_dict = {'prompt': prompt, 'story': story}
#
# text = self.preprocess(text_raw_dict)
# self.data.append(text)
# self.data.sort(key=lambda x: len(x[0]), reverse=True)
def __len__(self):
return len(self.prompts)
def __getitem__(self, i):
if self.sort:
return self.data[i]
else:
prompt, story = self.prompts[i]
# Remove extra annotation on prompt from WP dataset
#prompt = re.sub('\[ (.*) \]', '', prompt)
prompt = prompt.strip()
story = story.strip()
text_raw_dict = {'prompt': prompt, 'story': story}
text = self.preprocess(text_raw_dict)
return text
================================================
FILE: data/util.py
================================================
import random, re, os
from data.prompt_dataset import *
from data.plot_dataset import *
from data.arxiv_dataset import *
from data.yelp_dataset import *
import torch
import torch.utils.data as data
from torch.utils.data.distributed import DistributedSampler
from unidecode import unidecode
import functools
from rake_nltk import Rake
import urllib, sys
import urllib.request
import json, re
import numpy as np
from scipy.spatial.distance import cdist
from bert_serving.client import BertClient
from tqdm import trange
from random import shuffle
def compose(*functions):
""" Executes a list of functions in order """
return functools.reduce(lambda f, g: lambda x: g(f(x)), functions, lambda x: x)
def prefix_truncate(window):
""" truncates text to the prefix window size """
def f(text):
if len(text) > window:
text = text[:window]
return text
return f
class Preprocessor_base():
def __init__(self):
self.fn = None
def make_fn(self):
raise NotImplementedError()
def __call__(self, x):
try:
if self.fn is None:
self.fn = self.make_fn()
x = self.fn(x)
return x
except Exception as e:
print('Error in preprocessing', repr(e))
raise e
def encode_tuple(tokenizer, t):
return tokenizer.encode(t[0]), tokenizer.encode(t[1]), tokenizer.encode(t[2])
def truncate_tuple(truncator, t):
return truncator(t[0]), truncator(t[1]), truncator(t[2])
class Preprocessor(Preprocessor_base):
def __init__(self, tokenizer, seq_len, data_type):
super().__init__()
self.tokenizer = tokenizer
self.seq_len = seq_len
self.data_type = data_type
def make_fn(self):
return compose(
insert_keywords(self.tokenizer, self.data_type),
lambda input: encode_tuple(self.tokenizer, input) if isinstance(input, tuple) else [encode_tuple(self.tokenizer, inp) for inp in input],
lambda input: truncate_tuple(prefix_truncate(self.seq_len), input) if isinstance(input, tuple) else [truncate_tuple(prefix_truncate(self.seq_len), inp) for inp in input]
)
################# for WP dataset start
def wp_preprocess(text):
# Standardize some symbols
text = text.replace('', '\n')
text = text.replace('``', '"')
text = text.replace("''", '"')
# Detokenize
text = re.sub(' +', ' ', text) # replace multiple ' ' as one
text = re.sub(' (\'|\.|\,|\:|\?|\!|;)', '\g<1>', text) # remove ' ' before ,
text = re.sub('" ([^"]*) "', '"\g<1>"', text) # remove ' ' before and after ", " a " -> "a"
text = text.replace(" n't", "n't")
return text
def detect_dialog(t):
if t.startswith('"') or t.startswith("'") or t.startswith("``") or t.startswith("`") or t.startswith(
"''") or t.startswith("'") or t.startswith('“') or t.startswith('’') or t.startswith("‘") or t.startswith(
'”'):
return True
else:
return False
def get_paragraph(story):
# split as paragraphs
# re.split("( ){2,}", story) will keep ' ' delimeter
p = [x.strip() for x in re.split("( ){2,}", story) if x != ' ']
# add dialog to preceding paragraph
pp = [p[0]]
for ii in range(1, len(p)):
if detect_dialog(p[ii]) or len(p[ii]) < 114:
pp[-1] = pp[-1] + ' ' + p[ii]
else:
pp.append(p[ii])
pp = [wp_preprocess(pt) for pt in pp]
return pp
################# for WP dataset end
def extract_keywords(text, r):
r.extract_keywords_from_text(text)
# 114 2, +1 per 228, add one key per 2 sentences, which is 114 in length
num = min(5, max(2, int(len(text) / 228.0 + 1.5)))
key = [re.sub(' (\'|\.|\,|\:|\?|\!|;)', '\g<1>', k.strip('\'.,:?!;" ')) for k in r.get_ranked_phrases()[:num]]
return key
# def insert_keywords(tokenizer, data_type):
# def f(text_raw_dict):
# # 'prompt' in text_raw_dict --> wp dataset; 'title' in text_raw_dict --> wi dataset and other well preprocessed dataset
# summary = text_raw_dict['prompt'] if 'prompt' in text_raw_dict else text_raw_dict['title']
# story = text_raw_dict['story']
#
# if data_type == 't0': # x, y, y
# if 'prompt' in text_raw_dict:
# pp = get_paragraph(story)
# story = '\n\n'.join(pp)
# else:
# pp = story.split('')
# story = '\n\n'.join(pp)
#
# return summary, story + tokenizer.eos_token, tokenizer.eos_token + story + tokenizer.eos_token
# elif data_type == 't1': # x, x + y, y
# if 'prompt' in text_raw_dict:
# pp = get_paragraph(story)
# story = '\n\n'.join(pp)
# else:
# pp = story.split('')
# story = '\n\n'.join(pp)
#
# return summary, summary + tokenizer.eos_token + story + tokenizer.eos_token, tokenizer.eos_token + story + tokenizer.eos_token
# elif data_type == 't2': # x, x + o + y, y, append
# if 'title' in text_raw_dict:
# pp = story.split('')
# else:
# pp = get_paragraph(story)
#
# story = '\n\n'.join(pp)
#
# # extract keywords
# r = Rake(min_length=1, max_length=4)
# keys = [extract_keywords(text, r) for text in pp]
# keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
# story_appended = summary + ''.join(keys_str) + tokenizer.eos_token + '\n\n'.join(pp)
# return summary, story_appended + tokenizer.eos_token, tokenizer.eos_token + story + tokenizer.eos_token
# elif data_type == 't3': # x, x + o + y, y, insert
# if 'title' in text_raw_dict:
# pp = story.split('')
# else:
# pp = get_paragraph(story)
#
# story = '\n\n'.join(pp)
#
# # extract keywords
# r = Rake(min_length=1, max_length=4)
# keys = [extract_keywords(text, r) for text in pp]
# keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
# keys_str[0] += tokenizer.eos_token
# story_inserted = summary + ''.join([k + pt for k, pt in zip(keys_str, pp)])
# return summary, story_inserted + tokenizer.eos_token, tokenizer.eos_token + story + tokenizer.eos_token
# elif data_type == 't4': # x + o, y, y
# if 'title' in text_raw_dict:
# pp = story.split('')
# else:
# pp = get_paragraph(story)
#
# story = '\n\n'.join(pp)
#
# # extract keywords
# r = Rake(min_length=1, max_length=4)
# keys = [extract_keywords(text, r) for text in pp]
# keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
# return summary + ''.join(keys_str), story + tokenizer.eos_token, tokenizer.eos_token + story + tokenizer.eos_token
# elif data_type == 't5': # x + o, x + o + y, y, append
# if 'title' in text_raw_dict:
# pp = story.split('')
# else:
# pp = get_paragraph(story)
#
# story = '\n\n'.join(pp)
#
# # extract keywords
# r = Rake(min_length=1, max_length=4)
# keys = [extract_keywords(text, r) for text in pp]
# keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
# story_appended = summary + ''.join(keys_str) + tokenizer.eos_token + '\n\n'.join(pp)
# return summary + ''.join(keys_str), story_appended + tokenizer.eos_token, tokenizer.eos_token + story + tokenizer.eos_token
# elif data_type == 't6': # x + o, x + o + y, y, insert
# if 'title' in text_raw_dict:
# pp = story.split('')
# else:
# pp = get_paragraph(story)
#
# story = '\n\n'.join(pp)
#
# # extract keywords
# r = Rake(min_length=1, max_length=4)
# keys = [extract_keywords(text, r) for text in pp]
# keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
# keys_str[0] += tokenizer.eos_token
# story_inserted = summary + ''.join([k + pt for k, pt in zip(keys_str, pp)])
# return summary + ''.join(keys_str), story_inserted + tokenizer.eos_token, tokenizer.eos_token + story + tokenizer.eos_token
# elif data_type == 't7': # x + o, x + o + y, y, append, extend
# if 'title' in text_raw_dict:
# pp = story.split('')
# else:
# pp = get_paragraph(story)
#
# story = '\n\n'.join(pp)
#
# # extract keywords
# r = Rake(min_length=1, max_length=4)
# keys = [extract_keywords(text, r) for text in pp]
# keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
# keys_str[0] += tokenizer.eos_token
#
# extended_res = []
# for i in range(len(pp)):
# k_i, p_i = keys_str[:i], pp[:i]
# out_i = summary + ''.join(k_i)
# story_appended_i = summary + ''.join(k_i) + tokenizer.eos_token + '\n\n'.join(p_i) + tokenizer.eos_token
# story_i = tokenizer.eos_token + '\n\n'.join(p_i) + tokenizer.eos_token
# extended_res.append((out_i, story_appended_i, story_i))
# return extended_res
# elif data_type == 't8': # x + o, x + o + y, y, insert, extend
# if 'title' in text_raw_dict:
# pp = story.split('')
# else:
# pp = get_paragraph(story)
#
# story = '\n\n'.join(pp)
#
# # extract keywords
# r = Rake(min_length=1, max_length=4)
# keys = [extract_keywords(text, r) for text in pp]
# keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
# keys_str[0] += tokenizer.eos_token
#
# extended_res = []
# for i in range(len(pp)):
# k_i, p_i = keys_str[:i], pp[:i]
# out_i = summary + ''.join(k_i)
# story_inserted_i = summary + ''.join([k + pt for k, pt in zip(k_i, p_i)]) + tokenizer.eos_token
# story_i = tokenizer.eos_token + '\n\n'.join(p_i) + tokenizer.eos_token
# extended_res.append((out_i, story_inserted_i, story_i))
# return extended_res
# else:
# raise Exception('Data type not implemented.')
#
# return f
def insert_keywords(tokenizer, data_type):
def f(text_raw_dict):
# 'prompt' in text_raw_dict --> wp dataset; 'title' in text_raw_dict --> wi dataset and other well preprocessed dataset
summary = text_raw_dict['prompt'] if 'prompt' in text_raw_dict else text_raw_dict['title']
story = text_raw_dict['story']
if data_type == 't0': # x, y, y
if 'prompt' in text_raw_dict:
pp = get_paragraph(story)
story = '\n\n'.join(pp)
else:
pp = story.split('')
story = '\n\n'.join(pp)
return summary + tokenizer.eos_token, story + tokenizer.eos_token, tokenizer.eos_token + story + tokenizer.eos_token
elif data_type == 't1': # x, x + y, x + y
if 'prompt' in text_raw_dict:
pp = get_paragraph(story)
story = '\n\n'.join(pp)
else:
pp = story.split('')
story = '\n\n'.join(pp)
summary_story = summary + tokenizer.eos_token + story + tokenizer.eos_token
return summary + tokenizer.eos_token, summary_story, tokenizer.eos_token + summary_story
elif data_type == 't2': # x, x + o + y, x + o + y, append
if 'title' in text_raw_dict:
pp = story.split('')
else:
pp = get_paragraph(story)
story = '\n\n'.join(pp)
# extract keywords
r = Rake(min_length=1, max_length=4)
keys = [extract_keywords(text, r) for text in pp]
keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
story_appended = summary + ''.join(keys_str) + tokenizer.eos_token + '\n\n'.join(pp)
return summary + tokenizer.eos_token, story_appended + tokenizer.eos_token, tokenizer.eos_token + story_appended + tokenizer.eos_token
elif data_type == 't3': # x, x + o + y, x + o + y, insert
if 'title' in text_raw_dict:
pp = story.split('')
else:
pp = get_paragraph(story)
story = '\n\n'.join(pp)
# extract keywords
r = Rake(min_length=1, max_length=4)
keys = [extract_keywords(text, r) for text in pp]
keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
keys_str[0] += tokenizer.eos_token
story_inserted = summary + ''.join([k + pt for k, pt in zip(keys_str, pp)])
return summary + tokenizer.eos_token, story_inserted + tokenizer.eos_token, tokenizer.eos_token + story_inserted + tokenizer.eos_token
elif data_type == 't4': # x + o, y, x + o + y
if 'title' in text_raw_dict:
pp = story.split('')
else:
pp = get_paragraph(story)
story = '\n\n'.join(pp)
# extract keywords
r = Rake(min_length=1, max_length=4)
keys = [extract_keywords(text, r) for text in pp]
keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
summary_story = tokenizer.eos_token + summary + ''.join(keys_str) + tokenizer.eos_token + story + tokenizer.eos_token
return summary + ''.join(keys_str) + tokenizer.eos_token, story + tokenizer.eos_token, summary_story
elif data_type == 't5': # x + o, x + o + y, x + o + y, append
if 'title' in text_raw_dict:
pp = story.split('')
else:
pp = get_paragraph(story)
story = '\n\n'.join(pp)
# extract keywords
r = Rake(min_length=1, max_length=4)
keys = [extract_keywords(text, r) for text in pp]
keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
story_appended = summary + ''.join(keys_str) + tokenizer.eos_token + '\n\n'.join(pp)
return summary + ''.join(keys_str) + tokenizer.eos_token, story_appended + tokenizer.eos_token, tokenizer.eos_token + story_appended + tokenizer.eos_token
elif data_type == 't6': # x + o, x + o + y, x + o + y, insert
if 'title' in text_raw_dict:
pp = story.split('')
else:
pp = get_paragraph(story)
story = '\n\n'.join(pp)
# extract keywords
r = Rake(min_length=1, max_length=4)
keys = [extract_keywords(text, r) for text in pp]
keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
keys_str[0] += tokenizer.eos_token
story_inserted = summary + ''.join([k + pt for k, pt in zip(keys_str, pp)])
return summary + ''.join(keys_str) + tokenizer.eos_token, story_inserted + tokenizer.eos_token, tokenizer.eos_token + story_inserted + tokenizer.eos_token
elif data_type == 't7': # x + o, x + o + y, x + o + y, append, extend
if 'title' in text_raw_dict:
pp = story.split('')
else:
pp = get_paragraph(story)
story = '\n\n'.join(pp)
# extract keywords
r = Rake(min_length=1, max_length=4)
keys = [extract_keywords(text, r) for text in pp]
keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
extended_res = []
for i in range(len(pp)):
k_i, p_i = keys_str[:i], pp[:i]
out_i = summary + ''.join(k_i) + tokenizer.eos_token
story_appended_i = summary + ''.join(k_i) + tokenizer.eos_token + '\n\n'.join(p_i) + tokenizer.eos_token
story_i = tokenizer.eos_token + summary + ''.join(k_i) + tokenizer.eos_token + '\n\n'.join(p_i) + tokenizer.eos_token
extended_res.append((out_i, story_appended_i, story_i))
return extended_res
elif data_type == 't8': # x + o, x + o + y, x + o + y, insert, extend
if 'title' in text_raw_dict:
pp = story.split('')
else:
pp = get_paragraph(story)
story = '\n\n'.join(pp)
# extract keywords
r = Rake(min_length=1, max_length=4)
keys = [extract_keywords(text, r) for text in pp]
keys_str = [tokenizer.cls_token + tokenizer.sep_token.join(key) + tokenizer.mask_token for key in keys]
keys_str[0] += tokenizer.eos_token
extended_res = []
for i in range(len(pp)):
k_i, p_i = keys_str[:i], pp[:i]
out_i = summary + ''.join(k_i).replace(tokenizer.eos_token, '') + tokenizer.eos_token
story_inserted_i = summary + ''.join([k + pt for k, pt in zip(k_i, p_i)]) + tokenizer.eos_token
story_i = tokenizer.eos_token + summary + ''.join([k + pt for k, pt in zip(k_i, p_i)]) + tokenizer.eos_token
extended_res.append((out_i, story_inserted_i, story_i))
return extended_res
else:
raise Exception('Data type not implemented.')
return f
def collate_fn(samples):
""" Creates a batch out of samples """
x_max_len = max(map(lambda s: len(s[0]), samples))
# Zero pad mask
x_mask = torch.ByteTensor([[1] * len(ss[0]) + [0] * (x_max_len - len(ss[0])) for ss in samples])
# tokenizer.convert_tokens_to_ids('<|startoftext|>') = 50257, endoftext 50256, use 50257 here causes errors!!
x = torch.LongTensor([ss[0] + [50256] * (x_max_len - len(ss[0])) for ss in samples])
max_len = max(map(lambda s: len(s[1]), samples))
# Zero pad mask
y_mask = torch.ByteTensor([[1] * len(ss[1]) + [0] * (max_len - len(ss[1])) for ss in samples])
# tokenizer.convert_tokens_to_ids('<|startoftext|>') = 50257
y = torch.LongTensor([ss[1] + [50256] * (max_len - len(ss[1])) for ss in samples])
max_len = max(map(lambda s: len(s[2]), samples))
# Zero pad mask
input_mask = torch.ByteTensor([[1] * len(ip[2]) + [0] * (max_len - len(ip[2])) for ip in samples])
# tokenizer.convert_tokens_to_ids('<|startoftext|>') = 50257
input = torch.LongTensor([ip[2] + [50256] * (max_len - len(ip[2])) for ip in samples])
return x_mask, x, y_mask, y, input[:, :-1], input[:, 1:].contiguous(), input_mask[:, 1:]
def prepare_dataset(data_dir, dataset_name, tokenizer, train_bsz, train_seq_len, val_bsz, val_seq_len, test_bsz=1,
test_seq_len=1024, data_type='t0', num_workers=1, make_train=True, make_val=True, make_test=False):
# data_dir, dataset_name, tokenizer, train_bsz, train_seq_len, val_bsz, val_seq_len, num_workers = args.data_dir, args.dataset, tokenizer, batch_schedule[cur_b_schedule][0], batch_schedule[cur_b_schedule][1], batch_schedule[-1][0], batch_schedule[-1][1], args.workers
loaders = []
if dataset_name == 'wp':
train_collate_fn = collate_fn
val_collate_fn = collate_fn
test_collate_fn = collate_fn
if make_train:
train_preproc = Preprocessor(tokenizer, train_seq_len, data_type)
d_train = PromptDataset(
os.path.join(data_dir, 'writingPrompts/train.wp_source'),
os.path.join(data_dir, 'writingPrompts/train.wp_target'),
train_preproc)
if data_type == 't7' or data_type == 't8':
d_train = [t for lt in d_train for t in lt]
print('Train dataset size', len(d_train))
loaders.append(data.DataLoader(d_train,
# sampler=DistributedSampler(d_train) if distributed else None,
batch_size=train_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=train_collate_fn) if d_train else None)
if make_val:
val_preproc = Preprocessor(tokenizer, val_seq_len, data_type)
d_val = PromptDataset(
os.path.join(data_dir, 'writingPrompts/valid.wp_source'),
os.path.join(data_dir, 'writingPrompts/valid.wp_target'),
val_preproc)
if data_type == 't7' or data_type == 't8':
d_val = [t for lt in d_val for t in lt]
print('Val dataset size', len(d_val))
loaders.append(data.DataLoader(d_val,
# sampler=DistributedSampler(d_val),
batch_size=val_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=val_collate_fn) if d_val else None)
if make_test:
test_preproc = Preprocessor(tokenizer, test_seq_len, data_type)
d_test = PromptDataset(
os.path.join(data_dir, 'writingPrompts/test.wp_source'),
os.path.join(data_dir, 'writingPrompts/test.wp_target'),
test_preproc)
if data_type == 't7' or data_type == 't8':
d_test = [t for lt in d_test for t in lt]
print('Test dataset size', len(d_test))
loaders.append(data.DataLoader(d_test,
# sampler=DistributedSampler(d_val),
batch_size=test_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=test_collate_fn) if d_test else None)
elif dataset_name == 'wi':
train_collate_fn = collate_fn
val_collate_fn = collate_fn
test_collate_fn = collate_fn
print('Loading wikiplot dataset...')
data_plots = os.path.join(data_dir, 'wikiPlots/plots_paragraph')
data_titles = os.path.join(data_dir, 'wikiPlots/titles')
with open(data_plots, errors='ignore') as fp:
plots = fp.readlines()
with open(data_titles, errors='ignore') as ft:
titles = ft.readlines()
texts = [(t, p) for t, p in zip(titles, plots) if t.strip() != '' and p.strip() != '']
print('Done.')
train_text = texts[:int(len(texts) * 0.9)]
val_text = texts[int(len(texts) * 0.9):int(len(texts) * 0.95)]
test_text = texts[int(len(texts) * 0.95):]
if make_train:
train_preproc = Preprocessor(tokenizer, train_seq_len, data_type)
d_train = PlotDataset(train_text, train_preproc)
if data_type == 't7' or data_type == 't8':
d_train = [t for lt in d_train for t in lt]
print('Train dataset size', len(d_train))
loaders.append(data.DataLoader(d_train,
# sampler=DistributedSampler(d_train) if distributed else None,
batch_size=train_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=train_collate_fn) if d_train else None)
if make_val:
val_preproc = Preprocessor(tokenizer, val_seq_len, data_type)
d_val = PlotDataset(val_text, val_preproc)
if data_type == 't7' or data_type == 't8':
d_val = [t for lt in d_val for t in lt]
print('Val dataset size', len(d_val))
loaders.append(data.DataLoader(d_val,
# sampler=DistributedSampler(d_val),
batch_size=val_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=val_collate_fn) if d_val else None)
if make_test:
test_preproc = Preprocessor(tokenizer, test_seq_len, data_type)
d_test = PlotDataset(test_text, test_preproc)
if data_type == 't7' or data_type == 't8':
d_test = [t for lt in d_test for t in lt]
print('Test dataset size', len(d_test))
loaders.append(data.DataLoader(d_test,
# sampler=DistributedSampler(d_val),
batch_size=test_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=test_collate_fn) if d_test else None)
elif dataset_name == 'ax':
train_collate_fn = collate_fn
val_collate_fn = collate_fn
test_collate_fn = collate_fn
print('Loading arxiv dataset...')
data_abs = os.path.join(data_dir, 'arxiv/artificial intelligence_10047_15000_15_abs.txt')
data_titles = os.path.join(data_dir, 'arxiv/artificial intelligence_10047_15000_15_title.txt')
with open(data_abs, errors='ignore') as fp:
abs = fp.readlines()
with open(data_titles, errors='ignore') as ft:
titles = ft.readlines()
assert len(titles) == len(abs)
ai_data = [('ai', t.strip(), p.strip()) for t, p in zip(titles, abs) if t.strip() != '' and p.strip() != '']
data_abs = os.path.join(data_dir, 'arxiv/computer vision_14582_15000_15_abs.txt')
data_titles = os.path.join(data_dir, 'arxiv/computer vision_14582_15000_15_title.txt')
with open(data_abs, errors='ignore') as fp:
abs = fp.readlines()
with open(data_titles, errors='ignore') as ft:
titles = ft.readlines()
assert len(titles) == len(abs)
cv_data = [('cv', t.strip(), p.strip()) for t, p in zip(titles, abs) if t.strip() != '' and p.strip() != '']
data_abs = os.path.join(data_dir, 'arxiv/language generation_14514_15000_15_abs.txt')
data_titles = os.path.join(data_dir, 'arxiv/language generation_14514_15000_15_title.txt')
with open(data_abs, errors='ignore') as fp:
abs = fp.readlines()
with open(data_titles, errors='ignore') as ft:
titles = ft.readlines()
assert len(titles) == len(abs)
lg_data = [('lg', t.strip(), p.strip()) for t, p in zip(titles, abs) if t.strip() != '' and p.strip() != '']
texts = ai_data + cv_data + lg_data
shuffle(texts)
print('Done.')
train_text = texts[:int(len(texts) * 0.9)]
val_text = texts[int(len(texts) * 0.9):int(len(texts) * 0.95)]
test_text = texts[int(len(texts) * 0.95):]
if make_train:
train_preproc = Preprocessor(tokenizer, train_seq_len, data_type)
d_train = ArxivDataset(train_text, train_preproc)
print('Train dataset size', len(d_train))
loaders.append(data.DataLoader(d_train,
# sampler=DistributedSampler(d_train) if distributed else None,
batch_size=train_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=train_collate_fn) if d_train else None)
if make_val:
val_preproc = Preprocessor(tokenizer, val_seq_len, data_type)
d_val = ArxivDataset(val_text, val_preproc)
print('Val dataset size', len(d_val))
loaders.append(data.DataLoader(d_val,
# sampler=DistributedSampler(d_val),
batch_size=val_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=val_collate_fn) if d_val else None)
if make_test:
test_preproc = Preprocessor(tokenizer, test_seq_len, data_type)
d_test = ArxivDataset(test_text, test_preproc)
print('Test dataset size', len(d_test))
loaders.append(data.DataLoader(d_test,
# sampler=DistributedSampler(d_val),
batch_size=test_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=test_collate_fn) if d_test else None)
elif dataset_name == 'yp':
train_collate_fn = collate_fn
val_collate_fn = collate_fn
test_collate_fn = collate_fn
if make_train:
train_preproc = Preprocessor(tokenizer, train_seq_len, data_type)
d_train = YelpDataset(os.path.join(data_dir, 'yelp/yelp.train.txt'), train_preproc)
print('Train dataset size', len(d_train))
loaders.append(data.DataLoader(d_train,
# sampler=DistributedSampler(d_train) if distributed else None,
batch_size=train_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=train_collate_fn) if d_train else None)
if make_val:
val_preproc = Preprocessor(tokenizer, val_seq_len, data_type)
d_val = YelpDataset(os.path.join(data_dir, 'yelp/yelp.valid.txt'), val_preproc)
print('Val dataset size', len(d_val))
loaders.append(data.DataLoader(d_val,
# sampler=DistributedSampler(d_val),
batch_size=val_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=val_collate_fn) if d_val else None)
if make_test:
test_preproc = Preprocessor(tokenizer, test_seq_len, data_type)
d_test = YelpDataset(os.path.join(data_dir, 'yelp/yelp.test.txt'), test_preproc)
print('Test dataset size', len(d_test))
loaders.append(data.DataLoader(d_test,
# sampler=DistributedSampler(d_val),
batch_size=test_bsz,
pin_memory=True,
drop_last=True,
num_workers=num_workers,
collate_fn=test_collate_fn) if d_test else None)
else:
raise Exception('Invalid dataset')
return loaders
================================================
FILE: data/yelp_dataset.py
================================================
import os, random, json, pickle, re
import numpy as np
import torch.utils.data
class YelpDataset(torch.utils.data.Dataset):
"""
A dataset for Yelp
"""
def __init__(self, source, preprocess=lambda x: x, sort=False):
super().__init__()
self.preprocess = preprocess
self.sort=sort
print('Loading Yelp...')
with open(source, errors='ignore') as fs:
self.source = fs.readlines()
print('Done.')
def __len__(self):
return len(self.source)
def __getitem__(self, i):
raw = self.source[i]
title, story = raw[:1], raw[2:].strip()
text_raw_dict = {'title': title, 'story': story}
text = self.preprocess(text_raw_dict)
return text
================================================
FILE: dist_utils.py
================================================
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
"""
A modified version of the legacy DistributedDataParallel module that uses c10d
communication primitives. This is necessary for models that have conditional
computation (e.g., AdaptiveSoftmax) and which therefore do not work with the
c10d version of DDP.
This version also supports the *accumulate_grads* feature, which allows faster
training with `--update-freq`.
"""
import copy
import torch
from torch import nn
from torch.autograd import Variable
import torch.distributed as dist
class SimpleDistributedDataParallel(nn.Module):
"""Implements distributed data parallelism at the module level.
A simplified version of :class:`torch.nn.parallel.DistributedDataParallel`.
This version uses a c10d process group for communication and does not
broadcast buffers.
Args:
module (~torch.nn.Module): module to be parallelized
world_size (int): number of parallel workers
process_group (optional): the c10d process group to be used for
distributed data all-reduction. If None, the default process group
will be used.
buffer_size (int, optional): number of elements to buffer before
performing all-reduce (default: 256M).
"""
def __init__(self, module, world_size, process_group=None, buffer_size=2 ** 28):
super().__init__()
self.module = module
self.world_size = world_size
self.process_group = dist.group.WORLD if process_group is None else process_group
# Never use a bigger buffer than the number of model params
self.buffer_size = min(buffer_size, sum(p.numel() for p in module.parameters()))
self.buffer = None
# Flag used by the NCCL backend to make sure we only reduce gradients
# one time in the execution engine
self.need_reduction = False
# We can also forcibly accumulate grads locally and only do the
# all-reduce at some later time
self.accumulate_grads = False
# For NCCL backend, since every single NCCL call is asynchoronous, we
# therefore directly enqueue all the NCCL reduction calls to the
# default CUDA stream without spawning up other reduction threads.
# This achieves the best performance.
self._register_grad_hook()
def __getstate__(self):
attrs = copy.copy(self.__dict__)
return attrs
def __setstate__(self, state):
super().__setstate__(state)
self._register_grad_hook()
def forward(self, *inputs, **kwargs):
return self.module(*inputs, **kwargs)
def _register_grad_hook(self):
"""
This function registers the callback all-reduction function for the
NCCL backend. All gradients will be all reduced in one single step.
The NCCL reduction will directly be enqueued into the default CUDA
stream. Therefore, no synchronization is needed.
"""
def all_reduce(params):
buffer = self.buffer
nonzero_buffer = False
if len(params) > 1:
offset = 0
for p in params:
sz = p.numel()
if p.grad is not None:
buffer[offset:offset + sz].copy_(p.grad.data.view(-1))
nonzero_buffer = True
else:
buffer[offset:offset + sz].zero_()
offset += sz
else:
# we only have a single grad to all-reduce
p = params[0]
if p.grad is not None:
buffer = p.grad.data
nonzero_buffer = True
elif p.numel() <= self.buffer.numel():
buffer = buffer[:p.numel()]
buffer.zero_()
else:
buffer = torch.zeros_like(p)
if nonzero_buffer:
buffer.div_(self.world_size)
dist.all_reduce(buffer, group=self.process_group)
# copy all-reduced grads back into their original place
offset = 0
for p in params:
sz = p.numel()
if p.grad is not None:
p.grad.data.copy_(buffer[offset:offset + sz].view_as(p))
else:
p.grad = buffer[offset:offset + sz].view_as(p).clone()
offset += sz
def reduction_fn():
# This function only needs to be called once
if not self.need_reduction or self.accumulate_grads:
return
self.need_reduction = False
if self.buffer is None:
self.buffer = next(self.module.parameters()).new(self.buffer_size)
# All-reduce the gradients in buckets
offset = 0
buffered_params = []
for param in self.module.parameters():
if not param.requires_grad:
continue
if param.grad is None:
param.grad = torch.zeros_like(param)
if param.grad.requires_grad:
raise RuntimeError("DistributedDataParallel only works "
"with gradients that don't require "
"grad")
sz = param.numel()
if sz > self.buffer.numel():
# all-reduce big params directly
all_reduce([param])
else:
if offset + sz > self.buffer.numel():
all_reduce(buffered_params)
offset = 0
buffered_params.clear()
buffered_params.append(param)
offset += sz
if len(buffered_params) > 0:
all_reduce(buffered_params)
# Now register the reduction hook on the parameters
for p in self.module.parameters():
def allreduce_hook(*unused):
self.need_reduction = True
Variable._execution_engine.queue_callback(reduction_fn)
if p.requires_grad:
p.register_hook(allreduce_hook)
================================================
FILE: eval_ppl.py
================================================
import pickle
import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import argparse
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
from tqdm import tqdm
from tqdm import trange
import importlib
import logging
import copy
from data.util import *
from util import *
from model import *
def compute_loss(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta):
input_tokens = input_tokens.to(device)
target_tokens = target_tokens.to(device)
mask = mask.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
outputs = model(input_ids=input_tokens, attention_mask=mask, x_mask=x_mask, x_tokens=x_tokens, y_mask=y_mask,
y_tokens=y_tokens, from_prior=True)
logits = outputs[0]
kl_loss = outputs[-1]
num_logits = logits.size(-1)
# Perform masking
if mask is not None:
mask = mask.type(torch.bool)
mask = mask.to(device)
logits = logits.masked_select(mask.unsqueeze(-1))
target_tokens = target_tokens.masked_select(mask)
ce_loss = loss_fn(logits.view(-1, num_logits), target_tokens.view(-1)).mean()
kl_loss = kl_loss.mean()
loss = ce_loss + beta * kl_loss
return loss, ce_loss, kl_loss
def compute_loss_ae(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta):
input_tokens = input_tokens.to(device)
target_tokens = target_tokens.to(device)
mask = mask.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
outputs = model(input_ids=input_tokens, attention_mask=mask, y_mask=x_mask, y_tokens=x_tokens, from_mean=True, from_prior=False)
logits = outputs[0]
kl_loss = outputs[-1]
num_logits = logits.size(-1)
# Perform masking
if mask is not None:
mask = mask.type(torch.bool)
mask = mask.to(device)
logits = logits.masked_select(mask.unsqueeze(-1))
target_tokens = target_tokens.masked_select(mask)
ce_loss = loss_fn(logits.view(-1, num_logits), target_tokens.view(-1)).mean()
kl_loss = kl_loss.mean()
loss = ce_loss
return loss, ce_loss, kl_loss
def run_model():
parser = argparse.ArgumentParser()
parser.add_argument('--model-path', type=str, help='pretrained model path to local checkpoint')
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--batch-size", type=int, default=1)
parser.add_argument('--data-dir', type=str, default='data')
parser.add_argument('--out-dir', type=str, default='out')
parser.add_argument('--data_type', type=str, default='t1', choices=['t' + str(i) for i in range(9)], help="t: type")
parser.add_argument('--model_type', type=str, default='ae_vae_fusion', choices=['cvae', 'ae_vae_fusion'])
parser.add_argument('--dataset', type=str, default='wi', choices=['wp', 'wi'], help="Dataset to use for training")
parser.add_argument('--workers', default=1, type=int, metavar='N', help='number of data loading workers')
# use GPU
parser.add_argument('--gpu', default=3, type=int)
parser.add_argument('--no_gpu', action="store_true")
parser.add_argument('--fp16', action='store_true', help="Train using FP16?")
parser.add_argument('--add_input', action="store_true")
parser.add_argument('--add_attn', action="store_true")
parser.add_argument('--add_softmax', action="store_true")
parser.add_argument('--attn_proj_vary', action="store_true")
parser.add_argument('--learn_prior', action="store_true")
args = parser.parse_args('--model-path out/wi.2.proj_beta_half_ae/model_0150000.pt '
'--add_attn --learn_prior --fp16'.split())
print(args)
if args.model_type == 'cvae':
args.learn_prior = True
else:
args.learn_prior = False
# GPU
if not torch.cuda.is_available(): args.no_gpu = True
gpu = not args.no_gpu
if gpu: torch.cuda.set_device(args.gpu)
device = torch.device(args.gpu if gpu else "cpu")
# randomness
np.random.seed(args.seed)
prng = np.random.RandomState()
torch.random.manual_seed(args.seed)
if gpu: torch.cuda.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed)
if args.batch_size == -1:
args.batch_size = 1
# logging
save_folder = args.model_path + '.eval/'
os.makedirs(save_folder, exist_ok=True)
importlib.reload(logging)
logging.basicConfig(filename=os.path.join(save_folder, 'eval_ppl.log'),
level=logging.INFO, format='%(asctime)s--- %(message)s')
logging.info('\n----------------------------------------------------------------------')
print('Loading models...')
cache_dir = os.path.join(args.out_dir, 'model_cache')
os.makedirs(cache_dir, exist_ok=True)
# Load pre-trained teacher tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2', cache_dir=cache_dir)
tokenizer.max_len = int(1e12)
gpt2_model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir=cache_dir)
print('gpt2_params:', num_params(gpt2_model)) # gpt2: 124439808
config = GPT2Config()
# add special tokens
special_tokens_dict = {
'pad_token': '<|startoftext|>',
'cls_token': '<|startofcond|>',
'sep_token': '<|sepofcond|>',
'mask_token': '<|endofcond|>'
}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('We have added', num_added_toks, 'special tokens')
# Notice: resize_token_embeddings expect to receive the full size of the new vocab
gpt2_model.resize_token_embeddings(len(tokenizer))
assert tokenizer.pad_token == '<|startoftext|>'
VAE = VAEModel(config, add_input=args.add_input, add_attn=args.add_attn, add_softmax=args.add_softmax,
attn_proj_vary=args.attn_proj_vary, learn_prior=args.learn_prior)
init_para_frompretrained(VAE.transformer, gpt2_model.transformer, share_para=True)
init_para_frompretrained(VAE.encoder, gpt2_model.transformer, share_para=False)
if args.learn_prior:
init_para_frompretrained(VAE.encoder_prior, VAE.encoder, share_para=True)
VAE.encoder_prior.averageSelfAttention.attention_weights = VAE.encoder.averageSelfAttention.attention_weights
VAE.lm_head.weight = gpt2_model.lm_head.weight
if VAE.add_softmax:
VAE.lm_head_rep = Conv1D(*gpt2_model.lm_head.weight.size())
# VAE.lm_head_rep = LM_head_rep(*gpt2_model.lm_head.weight.size()[::-1])
print('VAE_params:', num_params(VAE)) # 286694400
args.load = args.model_path
if args.load:
print('Loading model weights...')
state = torch.load(os.path.join(args.load), map_location='cpu')
if 'module' in list(state.keys())[0]: # model_path is data parallel model with attr 'module'
state_copy = copy.copy(state)
keys = state_copy.keys()
for k in keys:
state[k.replace('module.', '')] = state.pop(k)
VAE.load_state_dict(state)
gc.collect()
print('Model loaded.')
print('Setup data...')
seq_len = VAE.config.n_ctx
train_loader, val_loader, test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
1, seq_len, 1, seq_len, args.batch_size, seq_len,
make_test=True,
num_workers=args.workers, data_type=args.data_type
)
print('Done.')
if args.fp16:
VAE = VAE.half()
VAE.eval() # be careful about VAE.eval() vs VAE.train()
VAE.to(device)
loss_fn = nn.CrossEntropyLoss(reduction='none')
logging.info('\n----------------------------------------------------------------------')
logging.info("Testing loop. batches: %d" % len(test_loader))
endoftext = tokenizer.convert_tokens_to_ids("<|endoftext|>")
startofcond = tokenizer.convert_tokens_to_ids("<|startofcond|>")
endofcond = tokenizer.convert_tokens_to_ids("<|endofcond|>")
n_words_bpe = 0
n_words = 0
logp_sum = 0.0
n_words_bpe_l = []
n_words_l = []
logp_sum_l = []
stats = []
# test_iter = iter(test_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(test_iter)
with tqdm(total=len(test_loader)) as pbar:
for i_test, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(test_loader):
with torch.no_grad():
if args.model_type == 'cvae':
loss, ce_loss, kl_loss = compute_loss(device, VAE, x_mask, x_tokens, y_mask, y_tokens, input_tokens,
target_tokens, mask, loss_fn, 1.0)
else:
loss, ce_loss, kl_loss = compute_loss_ae(device, VAE, x_mask, x_tokens, y_mask, y_tokens, input_tokens,
target_tokens, mask, loss_fn, 1.0)
stats.append([ce_loss.item(), math.exp(min(ce_loss.item(), 100)), kl_loss.item()])
if len(target_tokens.size()) == 1:
target_tokens = target_tokens.unsqueeze(0)
n, l = target_tokens.size()
tokens = target_tokens.tolist()
tokens = [t[:t.index(endoftext) + 1] if endoftext in t else t for t in tokens]
words_bpe = sum([len(t) for t in tokens])
n_words_bpe += words_bpe
n_words_bpe_l.append(words_bpe)
story = [tokenizer.decode(target_tokens[i, :]) for i in range(n)]
story = [s[:s.find("<|endoftext|>") + len("<|endoftext|>")] if "<|endoftext|>" in s else s for s in story]
words = sum([len([t for t in re.split('("|\'|!|\?|\.|,|:| |\n|’|“|”|;|\(|\)|`)', s) if t != ' ' and t != '']) for s in story])
n_words += words
n_words_l.append(words)
logp_sum += ce_loss.item() * words_bpe
logp_sum_l.append(ce_loss.item() * words_bpe)
#logging.info('test sample %05d finished.', i_test)
pbar.update(1)
print('Test complete with %05d samples.' % len(test_loader))
logging.info("Test complete with %05d samples.", len(test_loader))
print(' loss_bpe :', logp_sum / n_words_bpe)
logging.info('loss_bpe: %f', logp_sum / n_words_bpe)
ppl_bpe = round(math.exp(logp_sum / n_words_bpe), 3)
ppl_word = round(math.exp(logp_sum / n_words), 3)
print(' ppl_word:', ppl_word)
print(' ppl_bpe :', ppl_bpe)
logging.info('logp_sum: %f', logp_sum)
logging.info('n_words_bpe: %d', n_words_bpe)
logging.info('n_words : %d', n_words)
logging.info(' ppl_bpe : %f', ppl_bpe)
logging.info(' ppl_word: %f', ppl_word)
stats = np.mean(stats, axis=0)
print(stats)
logging.info(' stats: %s', str(stats))
if __name__ == '__main__':
run_model()
================================================
FILE: eval_ppl_prefix.py
================================================
import pickle
import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import argparse
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
from tqdm import tqdm
from tqdm import trange
import importlib
import logging
import copy
from data.util import *
from util import *
from model import *
def compute_loss(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta):
input_tokens = input_tokens.to(device)
target_tokens = target_tokens.to(device)
mask = mask.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
outputs = model(input_ids=input_tokens, attention_mask=mask, x_mask=x_mask, x_tokens=x_tokens, y_mask=y_mask,
y_tokens=y_tokens, from_prior=True)
logits = outputs[0]
kl_loss = outputs[-1]
num_logits = logits.size(-1)
# Perform masking
if mask is not None:
mask = mask.type(torch.bool)
mask = mask.to(device)
logits = logits.masked_select(mask.unsqueeze(-1))
target_tokens = target_tokens.masked_select(mask)
ce_loss = loss_fn(logits.view(-1, num_logits), target_tokens.view(-1)).mean()
kl_loss = kl_loss.mean()
loss = ce_loss + beta * kl_loss
return loss, ce_loss, kl_loss
def compute_loss_ae(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta):
input_tokens = input_tokens.to(device)
target_tokens = target_tokens.to(device)
mask = mask.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
outputs = model(input_ids=input_tokens, attention_mask=mask, y_mask=x_mask, y_tokens=x_tokens, from_mean=True, from_prior=False)
logits = outputs[0]
kl_loss = outputs[-1]
num_logits = logits.size(-1)
# Perform masking
if mask is not None:
mask = mask.type(torch.bool)
mask = mask.to(device)
logits = logits.masked_select(mask.unsqueeze(-1))
target_tokens = target_tokens.masked_select(mask)
ce_loss = loss_fn(logits.view(-1, num_logits), target_tokens.view(-1))
kl_loss = kl_loss.mean()
loss = ce_loss
return loss, ce_loss, kl_loss
def run_model():
parser = argparse.ArgumentParser()
parser.add_argument('--model-path', type=str, help='pretrained model path to local checkpoint')
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--batch-size", type=int, default=1)
parser.add_argument('--data-dir', type=str, default='data')
parser.add_argument('--out-dir', type=str, default='out')
parser.add_argument('--data_type', type=str, default='t1', choices=['t' + str(i) for i in range(9)], help="t: type")
parser.add_argument('--model_type', type=str, default='ae_vae_fusion', choices=['cvae', 'ae_vae_fusion'])
parser.add_argument('--dataset', type=str, default='wi', choices=['wp', 'wi'], help="Dataset to use for training")
parser.add_argument('--workers', default=1, type=int, metavar='N', help='number of data loading workers')
# use GPU
parser.add_argument('--gpu', default=3, type=int)
parser.add_argument('--no_gpu', action="store_true")
parser.add_argument('--fp16', action='store_true', help="Train using FP16?")
parser.add_argument('--add_input', action="store_true")
parser.add_argument('--add_attn', action="store_true")
parser.add_argument('--add_softmax', action="store_true")
parser.add_argument('--attn_proj_vary', action="store_true")
parser.add_argument('--learn_prior', action="store_true")
args = parser.parse_args('--model-path out/wi.12.proj_beta_half_ae/model_0000000.pt '
'--add_input --add_attn --attn_proj_vary --learn_prior --fp16'.split())
print(args)
if args.model_type == 'cvae':
args.learn_prior = True
else:
args.learn_prior = False
# GPU
if not torch.cuda.is_available(): args.no_gpu = True
gpu = not args.no_gpu
if gpu: torch.cuda.set_device(args.gpu)
device = torch.device(args.gpu if gpu else "cpu")
# randomness
np.random.seed(args.seed)
prng = np.random.RandomState()
torch.random.manual_seed(args.seed)
if gpu: torch.cuda.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed)
if args.batch_size == -1:
args.batch_size = 1
# logging
save_folder = args.model_path + '.eval/'
os.makedirs(save_folder, exist_ok=True)
importlib.reload(logging)
logging.basicConfig(filename=os.path.join(save_folder, 'eval_ppl.log'),
level=logging.INFO, format='%(asctime)s--- %(message)s')
logging.info('\n----------------------------------------------------------------------')
print('Loading models...')
cache_dir = os.path.join(args.out_dir, 'model_cache')
os.makedirs(cache_dir, exist_ok=True)
# Load pre-trained teacher tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2', cache_dir=cache_dir)
tokenizer.max_len = int(1e12)
gpt2_model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir=cache_dir)
print('gpt2_params:', num_params(gpt2_model)) # gpt2: 124439808
config = GPT2Config()
# add special tokens
special_tokens_dict = {
'pad_token': '<|startoftext|>',
'cls_token': '<|startofcond|>',
'sep_token': '<|sepofcond|>',
'mask_token': '<|endofcond|>'
}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('We have added', num_added_toks, 'special tokens')
# Notice: resize_token_embeddings expect to receive the full size of the new vocab
gpt2_model.resize_token_embeddings(len(tokenizer))
assert tokenizer.pad_token == '<|startoftext|>'
VAE = VAEModel(config, add_input=args.add_input, add_attn=args.add_attn, add_softmax=args.add_softmax,
attn_proj_vary=args.attn_proj_vary, learn_prior=args.learn_prior)
init_para_frompretrained(VAE.transformer, gpt2_model.transformer, share_para=True)
init_para_frompretrained(VAE.encoder, gpt2_model.transformer, share_para=False)
if args.learn_prior:
init_para_frompretrained(VAE.encoder_prior, VAE.encoder, share_para=True)
VAE.encoder_prior.averageSelfAttention.attention_weights = VAE.encoder.averageSelfAttention.attention_weights
VAE.lm_head.weight = gpt2_model.lm_head.weight
if VAE.add_softmax:
VAE.lm_head_rep = Conv1D(*gpt2_model.lm_head.weight.size())
# VAE.lm_head_rep = LM_head_rep(*gpt2_model.lm_head.weight.size()[::-1])
print('VAE_params:', num_params(VAE)) # 286694400
args.load = args.model_path
if args.load:
print('Loading model weights...')
state = torch.load(os.path.join(args.load), map_location='cpu')
if 'module' in list(state.keys())[0]: # model_path is data parallel model with attr 'module'
state_copy = copy.copy(state)
keys = state_copy.keys()
for k in keys:
state[k.replace('module.', '')] = state.pop(k)
VAE.load_state_dict(state)
gc.collect()
print('Model loaded.')
print('Setup data...')
seq_len = VAE.config.n_ctx
train_loader, val_loader, test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
1, seq_len, 1, seq_len, args.batch_size, seq_len,
make_test=True,
num_workers=args.workers, data_type=args.data_type
)
print('Done.')
if args.fp16:
VAE = VAE.half()
VAE.eval() # be careful about VAE.eval() vs VAE.train()
VAE.to(device)
loss_fn = nn.CrossEntropyLoss(reduction='none')
logging.info('\n----------------------------------------------------------------------')
logging.info("Testing loop. batches: %d" % len(test_loader))
endoftext = tokenizer.convert_tokens_to_ids("<|endoftext|>")
startofcond = tokenizer.convert_tokens_to_ids("<|startofcond|>")
endofcond = tokenizer.convert_tokens_to_ids("<|endofcond|>")
n_words_bpe = 0
n_words = 0
logp_sum = 0.0
n_words_bpe_l = []
n_words_l = []
logp_sum_l = []
# test_iter = iter(test_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(test_iter)
with tqdm(total=len(test_loader)) as pbar:
for i_test, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(test_loader):
with torch.no_grad():
if args.model_type == 'cvae':
loss, ce_loss, kl_loss = compute_loss(device, VAE, x_mask, x_tokens, y_mask, y_tokens, input_tokens,
target_tokens, mask, loss_fn, 1.0)
else:
loss, ce_loss, kl_loss = compute_loss_ae(device, VAE, x_mask, x_tokens, y_mask, y_tokens, input_tokens,
target_tokens, mask, loss_fn, 1.0)
if len(target_tokens.size()) == 1:
target_tokens = target_tokens.unsqueeze(0)
n, l = target_tokens.size()
text = target_tokens[0, :].tolist()
logprob = ce_loss.tolist()
assert len(text) == len(logprob)
# only for story
idx = text.index(endoftext)
text = text[idx + 1:]
logprob = logprob[idx + 1:]
if endoftext in text:
idx = text.index(endoftext)
text = text[:idx]
logprob = logprob[:idx]
logp_sum += sum(logprob)
logp_sum_l.append(sum(logprob))
n_words_bpe += len(text)
n_words_bpe_l.append(len(text))
story = [tokenizer.decode(target_tokens[i, :]) for i in range(n)]
story = [s[s.find("<|endoftext|>") + len("<|endoftext|>"):] for s in story]
story = [s[:s.find("<|endoftext|>") + len("<|endoftext|>")] if "<|endoftext|>" in s else s for s in story]
words = sum([len([t for t in re.split('("|\'|!|\?|\.|,|:| |\n|’|“|”|;|\(|\)|`)', s) if t != ' ' and t != '']) for s in story])
n_words += words
n_words_l.append(words)
#logging.info('test sample %05d finished.', i_test)
pbar.update(1)
print('Test complete with %05d samples.' % len(test_loader))
logging.info("Test complete with %05d samples.", len(test_loader))
print(' loss_bpe :', logp_sum / n_words_bpe)
logging.info('loss_bpe: %f', logp_sum / n_words_bpe)
ppl_bpe = round(math.exp(min(logp_sum / n_words_bpe, 100)), 3)
ppl_word = round(math.exp(min(logp_sum / n_words, 100)), 3)
print(' ppl_word:', ppl_word)
print(' ppl_bpe :', ppl_bpe)
logging.info('logp_sum: %f', logp_sum)
logging.info('n_words_bpe: %d', n_words_bpe)
logging.info('n_words : %d', n_words)
logging.info(' ppl_bpe : %f', ppl_bpe)
logging.info(' ppl_word: %f', ppl_word)
if __name__ == '__main__':
run_model()
================================================
FILE: generate.py
================================================
import pickle
import os
import math
import torch
import torch.nn.functional as F
from torch.nn import DataParallel
import numpy as np
import argparse
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
from tqdm import tqdm
from tqdm import trange
import importlib
import logging
import copy
from data.util import *
from collections import Counter
from nltk.translate.bleu_score import sentence_bleu
from nltk.translate.bleu_score import SmoothingFunction
from rouge import Rouge
from util import *
from model import *
def top_k_top_p_filtering(logits, top_k=100, top_p=0.95, filter_value=-float('Inf')):
""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (vocabulary size)
top_k > 0: keep only top k tokens with highest probability (top-k filtering).
top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
top_k = min(top_k, logits.size(-1)) # Safety check
if top_k > 0:
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove)
logits[indices_to_remove] = filter_value
return logits
def repeat_score(text, ngram=[3, 4, 5, 6]):
ngram_list = []
for ng in ngram:
ngram_list.append([text[idx:idx + ng] for idx in range(len(text) - ng - 1)])
max_occurs = []
for ngrams in ngram_list:
count_result = Counter([' '.join(n) for n in ngrams])
try:
max_occurs.append(
max(count_result.values())
)
except:
pass
scores = [max_oc / ((len(text) / ngram[idx]) + ngram[idx]) for idx, max_oc in enumerate(max_occurs)]
return max(scores) if len(scores) >= 1 else 1.0
def sample_sequence(model, tokenizer, length, batch_size=None, x_mask=None, x_tokens=None, y_mask=None, y_tokens=None,
temperature=1, top_k=100, top_p=0.95, device='cuda', sample=True, eos_token=None, model_type='cvae'):
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
mem = None
prev = torch.tensor([[eos_token]] * batch_size, dtype=torch.long, device=device)
output = prev
probability = torch.tensor([], dtype=torch.float, device=device)
if_end = torch.tensor([False] * batch_size, dtype=torch.bool, device=device)
with torch.no_grad():
if model_type == 'cvae':
try:
prior_mean, prior_logvar = model.encoder_prior(input_ids=x_tokens, attention_mask=x_mask)[:2]
except:
prior_mean = prior_logvar = torch.zeros([batch_size, model.config.n_embd], device=device)
latent_mean, latent_logvar = prior_mean, prior_logvar
z = model.reparameterize(latent_mean, latent_logvar)
assert not torch.isnan(z).any(), 'training get nan z'
else:
posterior_mean, posterior_logvar = model.encoder(input_ids=x_tokens, attention_mask=x_mask)[:2]
latent_mean, latent_logvar = posterior_mean, posterior_logvar
z = latent_mean
assert not torch.isnan(z).any(), 'training get nan z'
for i in range(length): #trange
logits, mem = model.transformer(input_ids=prev, past=mem, representations=z)
logits = model.lm_head(logits)
if model.add_softmax:
logits_rep = model.lm_head_rep(z)
logits = logits + logits_rep.unsqueeze(dim=1)
logits = logits[:, -1, :] / temperature
logits = top_k_top_p_filtering(logits, top_k, top_p)
probs = F.softmax(logits, dim=-1)
if sample:
next_token = torch.multinomial(probs, num_samples=1)
else:
_, next_token = torch.topk(probs, k=1, dim=-1)
probability = torch.cat((probability, probs.gather(1, next_token)), dim=1)
output = torch.cat((output, next_token), dim=1)
prev = next_token
# early stopping if all sents have ended once
if_end[next_token.view(-1).eq(eos_token)] = True
if if_end.all(): break
return output, probability
def run_model():
parser = argparse.ArgumentParser()
parser.add_argument('--model-path', type=str, help='pretrained model path to local checkpoint')
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--nsamples", type=int, default=1)
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--length", type=int, default=-1)
parser.add_argument("--temperature", type=int, default=0.95)
parser.add_argument('--top_p', type=float, default=0.95)
parser.add_argument('--top_k', type=int, default=100)
parser.add_argument('--data-dir', type=str, default='data')
parser.add_argument('--out-dir', type=str, default='out')
parser.add_argument('--data_type', type=str, default='t1', choices=['t' + str(i) for i in range(9)], help="t: type")
parser.add_argument('--model_type', type=str, default='cvae', choices=['cvae', 'ae_vae_fusion'])
parser.add_argument('--dataset', type=str, default='wi', choices=['wp', 'wi'], help="Dataset to use for training")
# use GPU
parser.add_argument('--gpu', default=2, type=int)
parser.add_argument('--no_gpu', action="store_true")
parser.add_argument('--add_input', action="store_true")
parser.add_argument('--add_attn', action="store_true")
parser.add_argument('--add_softmax', action="store_true")
parser.add_argument('--attn_proj_vary', action="store_true")
parser.add_argument('--learn_prior', action="store_true")
args = parser.parse_args('--model-path out/wi.1.proj_vary_cyc_cvae/model_0030000.pt '
'--add_input --learn_prior '.split())
print(args)
if args.model_type == 'cvae':
args.learn_prior = True
else:
args.learn_prior = False
# GPU
if not torch.cuda.is_available(): args.no_gpu = True
gpu = not args.no_gpu
if gpu: torch.cuda.set_device(args.gpu)
device = torch.device(args.gpu if gpu else "cpu")
# randomness
np.random.seed(args.seed)
prng = np.random.RandomState()
torch.random.manual_seed(args.seed)
if gpu: torch.cuda.manual_seed(args.seed)
if args.batch_size == -1:
args.batch_size = 1
assert args.nsamples % args.batch_size == 0
# logging
save_folder = args.model_path + '.eval/'
os.makedirs(save_folder, exist_ok=True)
importlib.reload(logging)
logging.basicConfig(filename=os.path.join(save_folder, 'eval.log'),
level=logging.INFO, format='%(asctime)s--- %(message)s')
logging.info('\n----------------------------------------------------------------------')
print('Loading models...')
cache_dir = os.path.join(args.out_dir, 'model_cache')
os.makedirs(cache_dir, exist_ok=True)
# Load pre-trained teacher tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2', cache_dir=cache_dir)
tokenizer.max_len = int(1e12)
gpt2_model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir=cache_dir)
print('gpt2_params:', num_params(gpt2_model)) # gpt2: 124439808
config = GPT2Config()
# # add special tokens
# special_tokens_dict = {
# 'pad_token': '<|startoftext|>',
# 'cls_token': '<|startofcond|>',
# 'sep_token': '<|sepofcond|>',
# 'mask_token': '<|endofcond|>'
# }
# num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
# print('We have added', num_added_toks, 'special tokens')
# # Notice: resize_token_embeddings expect to receive the full size of the new vocab
# gpt2_model.resize_token_embeddings(len(tokenizer))
# assert tokenizer.pad_token == '<|startoftext|>'
VAE = VAEModel(config, add_input=args.add_input, add_attn=args.add_attn, add_softmax=args.add_softmax,
attn_proj_vary=args.attn_proj_vary, learn_prior=args.learn_prior)
init_para_frompretrained(VAE.transformer, gpt2_model.transformer, share_para=True)
init_para_frompretrained(VAE.encoder, gpt2_model.transformer, share_para=False)
if args.learn_prior:
init_para_frompretrained(VAE.encoder_prior, VAE.encoder, share_para=True)
VAE.encoder_prior.averageSelfAttention.attention_weights = VAE.encoder.averageSelfAttention.attention_weights
VAE.lm_head.weight = gpt2_model.lm_head.weight
if VAE.add_softmax:
VAE.lm_head_rep = Conv1D(*gpt2_model.lm_head.weight.size())
# VAE.lm_head_rep = LM_head_rep(*gpt2_model.lm_head.weight.size()[::-1])
print('VAE_params:', num_params(VAE)) # 286694400
args.load = args.model_path
if args.load:
print('Loading model weights...')
state = torch.load(os.path.join(args.load), map_location='cpu')
if 'module' in list(state.keys())[0]: # model_path is data parallel model with attr 'module'
state_copy = copy.copy(state)
keys = state_copy.keys()
for k in keys:
state[k.replace('module.', '')] = state.pop(k)
VAE.load_state_dict(state)
gc.collect()
print('Model loaded.')
print('Setup data...')
seq_len = VAE.config.n_ctx
test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
1, seq_len, 1, seq_len, args.batch_size, seq_len,
make_train=False, make_val=False, make_test=True, data_type=args.data_type
)[0]
print('Done.')
VAE.eval() # be careful about VAE.eval() vs VAE.train()
VAE.to(device)
loss_fn = nn.CrossEntropyLoss(reduction='none')
logging.info('\n----------------------------------------------------------------------')
logging.info("Testing loop. batches: %d" % len(test_loader))
endoftext = tokenizer.convert_tokens_to_ids("<|endoftext|>")
startofcond = tokenizer.convert_tokens_to_ids("<|startofcond|>")
endofcond = tokenizer.convert_tokens_to_ids("<|endofcond|>")
n_samples = 0
bleu4_sum = 0.0
rouge_scores_values_sum = [0.0] * 9
model_type = args.model_type
# test_iter = iter(test_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(test_iter)
with tqdm(total=len(test_loader)) as pbar:
for i_test, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(test_loader):
length = args.length
if length == -1:
length = VAE.config.n_ctx - 1
elif length > VAE.config.n_ctx - 1:
raise ValueError("Can't get samples longer than window size: %s" % VAE.config.n_ctx)
eff_samples = []
n, l = target_tokens.size()
storys = [tokenizer.decode(target_tokens[i, :]) for i in range(n)]
storys_str = [s[:s.find("<|endoftext|>") + len("<|endoftext|>")] if "<|endoftext|>" in s else s for s in storys]
for _ in range(args.nsamples // args.batch_size):
# model, batch_size, temperature, top_k, top_p, eos_token, sample = VAE, args.batch_size, args.temperature, args.top_k, args.top_p, tokenizer.encoder['<|endoftext|>'], True
out, _ = sample_sequence(
model=VAE,
tokenizer=tokenizer,
length=length,
batch_size=args.batch_size,
x_mask=x_mask,
x_tokens=x_tokens,
y_mask=y_mask,
y_tokens=y_tokens,
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
device = device,
eos_token=tokenizer.encoder['<|endoftext|>'],
model_type=model_type
)
out = out.tolist()
# extract story, check metrics
for i in range(len(out)):
text = out[i]
text = text[text.index(endoftext) + 1:]
if endoftext in text:
idx = text.index(endoftext)
text = text[:idx]
text = tokenizer.decode(text).strip()
# score for one long text, higher than 0.075 usually means repetition
# rep_score = repeat_score(text.split(), ngram=[3, 4, 5, 6, 7, 8])
# if rep_score > 0.075:
# # print(rep_score)
# continue
try:
# check bleu
bleu4 = sentence_bleu([storys_str[i].split()], text, smoothing_function=SmoothingFunction().method7)
# check rouge
rouge = Rouge()
rouge_scores = rouge.get_scores(text, storys_str[i])
rouge_scores_values = [v for k in rouge_scores[0].keys() for v in rouge_scores[0][k].values()]
bleu4_sum += bleu4
rouge_scores_values_sum = [v1 + v2 for v1, v2 in zip(rouge_scores_values_sum, rouge_scores_values)]
n_samples += 1
except:
bleu4 = 0.0
rouge_scores = [{'rouge-1': {'f': 0.0, 'p': 0.0, 'r': 0.0},
'rouge-2': {'f': 0.0, 'p': 0.0, 'r': 0.0},
'rouge-l': {'f': 0.0, 'p': 0.0, 'r': 0.0}}]
eff_samples.append((text, bleu4, rouge_scores))
# write samples to file
samples_file = open(save_folder + 'batch-' + '%04d' % i_test + '.txt', 'w', encoding='utf8')
for i in range(len(eff_samples)):
samples_file.write("=" * 50 + " SAMPLE " + str(i) + " " + "=" * 50)
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Outlines " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(tokenizer.decode(x_tokens[i, :][x_mask[i, :] == 1].tolist()))
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Story " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(storys_str[i])
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Generated " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(eff_samples[i][0])
samples_file.write('\n' * 4)
samples_file.flush()
logging.info('batch %04d finished.', i_test)
pbar.update(1)
print('Test complete with %05d samples.' % n_samples)
logging.info("Test complete with %05d samples.", n_samples)
bleu4 = round(bleu4_sum / n_samples, 3)
rouge_scores_values = [round(r / n_samples, 3) for r in rouge_scores_values_sum]
print(' bleu-4:', bleu4)
print(' rouge :', rouge_scores_values)
logging.info(' bleu-4: %f', bleu4)
logging.info(' rouge : %s', str(rouge_scores_values))
if __name__ == '__main__':
run_model()
================================================
FILE: generate_prefix.py
================================================
import pickle
import os
import math
import torch
import torch.nn.functional as F
from torch.nn import DataParallel
import numpy as np
import argparse
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
from tqdm import tqdm
from tqdm import trange
import importlib
import logging
import copy
from data.util import *
from collections import Counter
from nltk.translate.bleu_score import sentence_bleu
from nltk.translate.bleu_score import SmoothingFunction
from rouge import Rouge
from util import *
from model import *
def top_k_top_p_filtering(logits, top_k=100, top_p=0.95, filter_value=-float('Inf')):
""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (vocabulary size)
top_k > 0: keep only top k tokens with highest probability (top-k filtering).
top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
top_k = min(top_k, logits.size(-1)) # Safety check
if top_k > 0:
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove)
logits[indices_to_remove] = filter_value
return logits
def repeat_score(text, ngram=[3, 4, 5, 6]):
ngram_list = []
for ng in ngram:
ngram_list.append([text[idx:idx + ng] for idx in range(len(text) - ng - 1)])
max_occurs = []
for ngrams in ngram_list:
count_result = Counter([' '.join(n) for n in ngrams])
try:
max_occurs.append(
max(count_result.values())
)
except:
pass
scores = [max_oc / ((len(text) / ngram[idx]) + ngram[idx]) for idx, max_oc in enumerate(max_occurs)]
return max(scores) if len(scores) >= 1 else 1.0
def sample_sequence(model, tokenizer, length, batch_size=None, x_mask=None, x_tokens=None, y_mask=None, y_tokens=None,
temperature=1, top_k=100, top_p=0.95, device='cuda', sample=True, eos_token=None, model_type='cvae'):
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
with torch.no_grad():
if model_type == 'cvae':
try:
prior_mean, prior_logvar = model.encoder_prior(input_ids=x_tokens, attention_mask=x_mask)[:2]
except:
prior_mean = prior_logvar = torch.zeros([batch_size, model.config.n_embd], device=device)
latent_mean, latent_logvar = prior_mean, prior_logvar
z = model.reparameterize(latent_mean, latent_logvar)
assert not torch.isnan(z).any(), 'training get nan z'
else:
posterior_mean, posterior_logvar = model.encoder(input_ids=x_tokens, attention_mask=x_mask)[:2]
latent_mean, latent_logvar = posterior_mean, posterior_logvar
z = latent_mean
assert not torch.isnan(z).any(), 'training get nan z'
_, mem = model.transformer(input_ids=x_tokens[:, :-1], past=None, attention_mask=x_mask[:, :-1], representations=z)
prev = x_tokens[:, -1].view(batch_size, -1)
output = prev
probability = torch.tensor([], dtype=torch.float, device=device)
if_end = torch.tensor([False] * batch_size, dtype=torch.bool, device=device)
for i in range(length): #trange
logits, mem = model.transformer(input_ids=prev, past=mem, representations=z)
logits = model.lm_head(logits)
if model.add_softmax:
logits_rep = model.lm_head_rep(z)
logits = logits + logits_rep.unsqueeze(dim=1)
logits = logits[:, -1, :] / temperature
logits = top_k_top_p_filtering(logits, top_k, top_p)
probs = F.softmax(logits, dim=-1)
if sample:
next_token = torch.multinomial(probs, num_samples=1)
else:
_, next_token = torch.topk(probs, k=1, dim=-1)
probability = torch.cat((probability, probs.gather(1, next_token)), dim=1)
output = torch.cat((output, next_token), dim=1)
prev = next_token
# early stopping if all sents have ended once
if_end[next_token.view(-1).eq(eos_token)] = True
if if_end.all(): break
return output, probability
def run_model():
parser = argparse.ArgumentParser()
parser.add_argument('--model-path', type=str, help='pretrained model path to local checkpoint')
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--nsamples", type=int, default=1)
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--length", type=int, default=-1)
parser.add_argument("--temperature", type=int, default=0.95)
parser.add_argument('--top_p', type=float, default=0.95)
parser.add_argument('--top_k', type=int, default=100)
parser.add_argument('--data-dir', type=str, default='data')
parser.add_argument('--out-dir', type=str, default='out')
parser.add_argument('--data_type', type=str, default='t1', choices=['t' + str(i) for i in range(9)], help="t: type")
parser.add_argument('--model_type', type=str, default='cvae', choices=['cvae', 'ae_vae_fusion'])
parser.add_argument('--dataset', type=str, default='wi', choices=['wp', 'wi'], help="Dataset to use for training")
# use GPU
parser.add_argument('--gpu', default=2, type=int)
parser.add_argument('--no_gpu', action="store_true")
parser.add_argument('--add_input', action="store_true")
parser.add_argument('--add_attn', action="store_true")
parser.add_argument('--add_softmax', action="store_true")
parser.add_argument('--attn_proj_vary', action="store_true")
parser.add_argument('--learn_prior', action="store_true")
args = parser.parse_args('--model-path out/wi.1.proj_vary_cyc_cvae/model_0030000.pt '
'--add_input --learn_prior '.split())
print(args)
if args.model_type == 'cvae':
args.learn_prior = True
else:
args.learn_prior = False
# GPU
if not torch.cuda.is_available(): args.no_gpu = True
gpu = not args.no_gpu
if gpu: torch.cuda.set_device(args.gpu)
device = torch.device(args.gpu if gpu else "cpu")
# randomness
np.random.seed(args.seed)
prng = np.random.RandomState()
torch.random.manual_seed(args.seed)
if gpu: torch.cuda.manual_seed(args.seed)
if args.batch_size == -1:
args.batch_size = 1
assert args.nsamples % args.batch_size == 0
# logging
save_folder = args.model_path + '.eval/'
os.makedirs(save_folder, exist_ok=True)
importlib.reload(logging)
logging.basicConfig(filename=os.path.join(save_folder, 'eval.log'),
level=logging.INFO, format='%(asctime)s--- %(message)s')
logging.info('\n----------------------------------------------------------------------')
print('Loading models...')
cache_dir = os.path.join(args.out_dir, 'model_cache')
os.makedirs(cache_dir, exist_ok=True)
# Load pre-trained teacher tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2', cache_dir=cache_dir)
tokenizer.max_len = int(1e12)
gpt2_model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir=cache_dir)
print('gpt2_params:', num_params(gpt2_model)) # gpt2: 124439808
config = GPT2Config()
# # add special tokens
# special_tokens_dict = {
# 'pad_token': '<|startoftext|>',
# 'cls_token': '<|startofcond|>',
# 'sep_token': '<|sepofcond|>',
# 'mask_token': '<|endofcond|>'
# }
# num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
# print('We have added', num_added_toks, 'special tokens')
# # Notice: resize_token_embeddings expect to receive the full size of the new vocab
# gpt2_model.resize_token_embeddings(len(tokenizer))
# assert tokenizer.pad_token == '<|startoftext|>'
VAE = VAEModel(config, add_input=args.add_input, add_attn=args.add_attn, add_softmax=args.add_softmax,
attn_proj_vary=args.attn_proj_vary, learn_prior=args.learn_prior)
init_para_frompretrained(VAE.transformer, gpt2_model.transformer, share_para=True)
init_para_frompretrained(VAE.encoder, gpt2_model.transformer, share_para=False)
if args.learn_prior:
init_para_frompretrained(VAE.encoder_prior, VAE.encoder, share_para=True)
VAE.encoder_prior.averageSelfAttention.attention_weights = VAE.encoder.averageSelfAttention.attention_weights
VAE.lm_head.weight = gpt2_model.lm_head.weight
if VAE.add_softmax:
VAE.lm_head_rep = Conv1D(*gpt2_model.lm_head.weight.size())
# VAE.lm_head_rep = LM_head_rep(*gpt2_model.lm_head.weight.size()[::-1])
print('VAE_params:', num_params(VAE)) # 286694400
args.load = args.model_path
if args.load:
print('Loading model weights...')
state = torch.load(os.path.join(args.load), map_location='cpu')
if 'module' in list(state.keys())[0]: # model_path is data parallel model with attr 'module'
state_copy = copy.copy(state)
keys = state_copy.keys()
for k in keys:
state[k.replace('module.', '')] = state.pop(k)
VAE.load_state_dict(state)
gc.collect()
print('Model loaded.')
print('Setup data...')
seq_len = VAE.config.n_ctx
test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
1, seq_len, 1, seq_len, args.batch_size, seq_len,
make_train=False, make_val=False, make_test=True, data_type=args.data_type
)[0]
print('Done.')
VAE.eval() # be careful about VAE.eval() vs VAE.train()
VAE.to(device)
loss_fn = nn.CrossEntropyLoss(reduction='none')
logging.info('\n----------------------------------------------------------------------')
logging.info("Testing loop. batches: %d" % len(test_loader))
endoftext = tokenizer.convert_tokens_to_ids("<|endoftext|>")
startofcond = tokenizer.convert_tokens_to_ids("<|startofcond|>")
endofcond = tokenizer.convert_tokens_to_ids("<|endofcond|>")
n_samples = 0
bleu4_sum = 0.0
rouge_scores_values_sum = [0.0] * 9
model_type = args.model_type
# test_iter = iter(test_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(test_iter)
with tqdm(total=len(test_loader)) as pbar:
for i_test, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(test_loader):
length = args.length
if length == -1:
length = VAE.config.n_ctx - x_tokens.size(1) - 1
elif length > VAE.config.n_ctx - x_tokens.size(1) - 1:
raise ValueError("Can't get samples longer than window size: %s" % VAE.config.n_ctx)
eff_samples = []
n, l = target_tokens.size()
storys = [tokenizer.decode(target_tokens[i, :]) for i in range(n)]
storys = [s[s.find("<|endoftext|>") + len("<|endoftext|>"):] for s in storys]
storys_str = [s[:s.find("<|endoftext|>") + len("<|endoftext|>")] if "<|endoftext|>" in s else s for s in storys]
for _ in range(args.nsamples // args.batch_size):
# model, batch_size, temperature, top_k, top_p, eos_token, sample = VAE, args.batch_size, args.temperature, args.top_k, args.top_p, tokenizer.encoder['<|endoftext|>'], True
out, _ = sample_sequence(
model=VAE,
tokenizer=tokenizer,
length=length,
batch_size=args.batch_size,
x_mask=x_mask,
x_tokens=x_tokens,
y_mask=y_mask,
y_tokens=y_tokens,
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
device = device,
eos_token=tokenizer.encoder['<|endoftext|>'],
model_type=model_type
)
out = out.tolist()
# extract story, check metrics
for i in range(len(out)):
text = out[i]
text = text[text.index(endoftext) + 1:]
if endoftext in text:
idx = text.index(endoftext)
text = text[:idx]
text = tokenizer.decode(text).strip()
# score for one long text, higher than 0.075 usually means repetition
# rep_score = repeat_score(text.split(), ngram=[3, 4, 5, 6, 7, 8])
# if rep_score > 0.075:
# # print(rep_score)
# continue
try:
# check bleu
bleu4 = sentence_bleu([storys_str[i].split()], text, smoothing_function=SmoothingFunction().method7)
# check rouge
rouge = Rouge()
rouge_scores = rouge.get_scores(text, storys_str[i])
rouge_scores_values = [v for k in rouge_scores[0].keys() for v in rouge_scores[0][k].values()]
bleu4_sum += bleu4
rouge_scores_values_sum = [v1 + v2 for v1, v2 in zip(rouge_scores_values_sum, rouge_scores_values)]
n_samples += 1
except:
bleu4 = 0.0
rouge_scores = [{'rouge-1': {'f': 0.0, 'p': 0.0, 'r': 0.0},
'rouge-2': {'f': 0.0, 'p': 0.0, 'r': 0.0},
'rouge-l': {'f': 0.0, 'p': 0.0, 'r': 0.0}}]
eff_samples.append((text, bleu4, rouge_scores))
# write samples to file
samples_file = open(save_folder + 'batch-' + '%04d' % i_test + '.txt', 'w', encoding='utf8')
for i in range(len(eff_samples)):
samples_file.write("=" * 50 + " SAMPLE " + str(i) + " " + "=" * 50)
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Outlines " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(tokenizer.decode(x_tokens[i, :][x_mask[i, :] == 1].tolist()))
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Story " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(storys_str[i])
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Generated " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(eff_samples[i][0])
samples_file.write('\n' * 4)
samples_file.flush()
logging.info('batch %04d finished.', i_test)
pbar.update(1)
print('Test complete with %05d samples.' % n_samples)
logging.info("Test complete with %05d samples.", n_samples)
bleu4 = round(bleu4_sum / n_samples, 3)
rouge_scores_values = [round(r / n_samples, 3) for r in rouge_scores_values_sum]
print(' bleu-4:', bleu4)
print(' rouge :', rouge_scores_values)
logging.info(' bleu-4: %f', bleu4)
logging.info(' rouge : %s', str(rouge_scores_values))
if __name__ == '__main__':
run_model()
================================================
FILE: model.py
================================================
from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import math
import os
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import copy
from transformers.modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
from transformers.modeling_gpt2 import *
from transformers.modeling_bert import gelu
from transformers.configuration_gpt2 import GPT2Config
from transformers.file_utils import add_start_docstrings
####################### auxiliary attention blocks #######################
class Unmasked_Attention(Attention):
def _attn(self, q, k, v, attention_mask=None, head_mask=None):
w = torch.matmul(q, k)
if self.scale:
w = w / math.sqrt(v.size(-1))
if attention_mask is not None:
# Apply the attention mask
w = w + attention_mask
w = nn.Softmax(dim=-1)(w)
w = self.attn_dropout(w)
# Mask heads if we want to
if head_mask is not None:
w = w * head_mask
outputs = [torch.matmul(w, v)]
if self.output_attentions:
outputs.append(w)
return outputs
class Unmasked_Block(Block):
def __init__(self, n_ctx, config, scale=False):
super(Block, self).__init__()
nx = config.n_embd
self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
self.attn = Unmasked_Attention(nx, n_ctx, config, scale)
self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
self.mlp = MLP(4 * nx, config)
class AverageSelfAttention(nn.Module):
def __init__(self, attention_size):
super(AverageSelfAttention, self).__init__()
w = torch.empty(attention_size)
nn.init.normal_(w, std=0.02)
self.attention_weights = nn.Parameter(w)
self.softmax = nn.Softmax(dim=-1)
self.non_linearity = gelu
def forward(self, inputs, attention_mask=None):
##################################################################
# STEP 1 - perform dot product
# of the attention vector and each hidden state
##################################################################
# inputs is a 3D Tensor: batch, len, hidden_size
# scores is a 2D Tensor: batch, len
scores = self.non_linearity(inputs.matmul(self.attention_weights))
##################################################################
# Step 2 - Masking
##################################################################
if attention_mask is not None:
scores = scores + attention_mask
##################################################################
# Step 3 - Weighted sum of hidden states, by the attention scores
##################################################################
scores = self.softmax(scores)
# multiply each hidden state with the attention weights
weighted = torch.mul(inputs, scores.unsqueeze(-1).expand_as(inputs))
# sum the hidden states
representations = weighted.sum(1).squeeze(1)
return representations, scores
# Pseudo self-attention
class Cond_Attention(Attention):
def __init__(self, nx, n_ctx, config, scale=False):
super(Attention, self).__init__()
self.output_attentions = config.output_attentions
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implem]
assert n_state % config.n_head == 0
self.register_buffer("bias", torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
self.n_head = config.n_head
self.split_size = n_state
self.scale = scale
self.c_attn = Conv1D(n_state * 3, nx)
self.c_proj = Conv1D(n_state, nx)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.pruned_heads = set()
# add code here
self.c_z = Conv1D(n_state * 2, nx)
def _attn(self, q, k, v, attention_mask=None, head_mask=None):
w = torch.matmul(q, k)
if self.scale:
w = w / math.sqrt(v.size(-1))
nd, ns = w.size(-2), w.size(-1)
b = self.bias[:, :, ns - nd : ns, :ns]
w = w * b - 1e4 * (1 - b)
if attention_mask is not None:
# add code here: w size has been bsz * n_heads * L * (L+1), mask bsz * 1 * 1 * L
assert attention_mask.size()[-1] == w.size()[-1] - 1
zeros = torch.zeros(attention_mask.size()[:-1], device=attention_mask.device, dtype=attention_mask.dtype).unsqueeze(-1)
attention_mask = torch.cat((zeros, attention_mask), dim=-1)
# Apply the attention mask
w = w + attention_mask
w = nn.Softmax(dim=-1)(w)
w = self.attn_dropout(w)
# Mask heads if we want to
if head_mask is not None:
w = w * head_mask
outputs = [torch.matmul(w, v)]
if self.output_attentions:
outputs.append(w)
return outputs
def forward(self, x, z, layer_past=None, attention_mask=None, head_mask=None):
x = self.c_attn(x)
query, key, value = x.split(self.split_size, dim=2)
query = self.split_heads(query)
key = self.split_heads(key, k=True)
value = self.split_heads(value)
if layer_past is not None:
past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1] # transpose back cf below
key = torch.cat((past_key, key), dim=-1)
value = torch.cat((past_value, value), dim=-2)
present = torch.stack((key.transpose(-2, -1), value)) # transpose to have same shapes for stacking
z_conv = self.c_z(z)
key_z, value_z = z_conv.split(self.split_size, dim=2)
key_z = self.split_heads(key_z, k=True)
value_z = self.split_heads(value_z)
key = torch.cat((key_z, key), dim=-1)
value = torch.cat((value_z, value), dim=-2)
attn_outputs = self._attn(query, key, value, attention_mask, head_mask)
a = attn_outputs[0]
a = self.merge_heads(a)
a = self.c_proj(a)
a = self.resid_dropout(a)
outputs = [a, present] + attn_outputs[1:]
return outputs # a, present, (attentions)
class Cond_Block(Block):
def __init__(self, n_ctx, config, scale=False):
super(Block, self).__init__()
nx = config.n_embd
self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
self.attn = Cond_Attention(nx, n_ctx, config, scale)
self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
self.mlp = MLP(4 * nx, config)
def forward(self, x, z, layer_past=None, attention_mask=None, head_mask=None):
output_attn = self.attn(
self.ln_1(x), z, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask
)
a = output_attn[0] # output_attn: a, present, (attentions)
x = x + a
m = self.mlp(self.ln_2(x))
x = x + m
outputs = [x] + output_attn[1:]
return outputs # x, present, (attentions)
####################### transformer-based vae #######################
class Encoder(GPT2Model):
def __init__(self, config):
super(GPT2Model, self).__init__(config)
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.output_past = config.output_past
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
self.wpe = nn.Embedding(config.n_positions, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
# manually modify number of layers in encoder to accommodate GPU memory
n = 6 # config.n_layer
self.h = nn.ModuleList([Unmasked_Block(config.n_ctx, config, scale=True) for _ in range(n)])
self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.init_weights()
# added code here
self.averageSelfAttention = AverageSelfAttention(config.n_embd)
nx = config.n_embd
nz = config.n_embd
self.mean = Conv1D(nz, nx)
self.logvar = Conv1D(nz, nx)
def forward(
self,
input_ids=None,
past=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
):
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if past is None:
past_length = 0
past = [None] * len(self.h)
else:
past_length = past[0][0].size(-2)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
attention_mask = attention_mask.view(-1, input_shape[-1])
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# head_mask has shape n_layer x batch x n_heads x N x N
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(self.config.n_layer, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = (
head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
) # We can specify head_mask for each layer
head_mask = head_mask.to(
dtype=next(self.parameters()).dtype
) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.config.n_layer
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
else:
token_type_embeds = 0
hidden_states = inputs_embeds + position_embeds + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
presents = ()
all_attentions = []
all_hidden_states = ()
for i, (block, layer_past) in enumerate(zip(self.h, past)):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
outputs = block(
hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i]
)
hidden_states, present = outputs[:2]
if self.output_past:
presents = presents + (present,)
if self.output_attentions:
all_attentions.append(outputs[2])
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(*output_shape)
# Add last hidden state
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# added code here
representations, _ = self.averageSelfAttention(hidden_states, attention_mask.squeeze(1).squeeze(1))
mean = self.mean(representations)
logvar = self.logvar(representations)
outputs = (mean, logvar, hidden_states,)
if self.output_past:
outputs = outputs + (presents,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
# let the number of heads free (-1) so we can extract attention even after head pruning
attention_output_shape = input_shape[:-1] + (-1,) + all_attentions[0].shape[-2:]
all_attentions = tuple(t.view(*attention_output_shape) for t in all_attentions)
outputs = outputs + (all_attentions,)
return outputs # mean, logvar, last hidden state, (presents), (all hidden_states), (attentions)
class Decoder(GPT2Model):
def __init__(self, config, add_input=False, add_attn=False, attn_proj_vary=False):
super(GPT2Model, self).__init__(config)
# added code here
self.add_input = add_input
self.add_attn = add_attn
self.attn_proj_vary = attn_proj_vary
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.output_past = config.output_past
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
self.wpe = nn.Embedding(config.n_positions, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
if self.add_input:
nz = config.n_embd
nx = config.n_embd
self.input_proj = nn.Linear(nz, nx, bias=False)
if self.add_attn:
nz = config.n_embd
nx = config.n_embd
n = config.n_layer
if self.attn_proj_vary:
self.attn_proj = nn.Linear(nz, nx * n, bias=False)
else:
self.attn_proj = nn.Linear(nz, nx, bias=False)
self.h = nn.ModuleList([Cond_Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
else:
self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.init_weights()
def forward(
self,
input_ids=None,
past=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
representations=None
):
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if past is None:
past_length = 0
past = [None] * len(self.h)
else:
past_length = past[0][0].size(-2)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
attention_mask = attention_mask.view(-1, input_shape[-1])
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# head_mask has shape n_layer x batch x n_heads x N x N
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(self.config.n_layer, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = (
head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
) # We can specify head_mask for each layer
head_mask = head_mask.to(
dtype=next(self.parameters()).dtype
) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.config.n_layer
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
else:
token_type_embeds = 0
hidden_states = inputs_embeds + position_embeds + token_type_embeds
# add code here
if self.add_input:
assert (representations is not None)
input_proj = self.input_proj(representations).unsqueeze(1)
hidden_states = hidden_states + input_proj
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
# add code here
if self.add_attn:
assert (representations is not None)
attn_proj = self.attn_proj(representations).unsqueeze(1)
if self.attn_proj_vary:
attn_proj = attn_proj.split(hidden_states.size(-1), dim=-1)
assert len(attn_proj) == len(self.h)
presents = ()
all_attentions = []
all_hidden_states = ()
for i, (block, layer_past) in enumerate(zip(self.h, past)):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
if self.add_attn:
if self.attn_proj_vary:
z = attn_proj[i]
else:
z = attn_proj
outputs = block(
hidden_states, z, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i]
)
else:
outputs = block(
hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i]
)
hidden_states, present = outputs[:2]
if self.output_past:
presents = presents + (present,)
if self.output_attentions:
all_attentions.append(outputs[2])
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(*output_shape)
# Add last hidden state
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_past:
outputs = outputs + (presents,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
# let the number of heads free (-1) so we can extract attention even after head pruning
attention_output_shape = input_shape[:-1] + (-1,) + all_attentions[0].shape[-2:]
all_attentions = tuple(t.view(*attention_output_shape) for t in all_attentions)
outputs = outputs + (all_attentions,)
return outputs # last hidden state, (presents), (all hidden_states), (attentions)
class LM_head_rep(nn.Module):
def __init__(self, in_dim=768, out_dim=50257):
super().__init__()
self.Nu_fc1 = nn.Linear(in_dim, 1024)
self.Nu_fc2 = nn.Linear(1024, out_dim)
def forward(self, z):
z = F.leaky_relu(self.Nu_fc1(z))
z = self.Nu_fc2(z)
return z
class VAEModel(GPT2LMHeadModel):
def __init__(self, config, add_input=False, add_attn=False, add_softmax=False, attn_proj_vary=False, learn_prior=False):
super(GPT2LMHeadModel, self).__init__(config)
# add code here
self.add_input = add_input
self.add_attn = add_attn
self.add_softmax = add_softmax
self.attn_proj_vary = attn_proj_vary
self.learn_prior = learn_prior
self.transformer = Decoder(config, add_input, add_attn, attn_proj_vary)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.encoder = Encoder(config)
if self.learn_prior:
self.encoder_prior = Encoder(config)
if self.add_softmax:
nz = config.n_embd
self.lm_head_rep = Conv1D(config.vocab_size, nz)
# self.lm_head_rep = LM_head_rep(nz, config.vocab_size)
def reparameterize(self, mean, logvar, z=None):
std = logvar.mul(0.5).exp()
if z is None:
z = torch.randn(std.size(), device=mean.device, dtype=mean.dtype)
return z.mul(std) + mean
def kl_loss(self, mean1, logvar1, mean2, logvar2):
exponential = logvar1 - logvar2 - torch.pow(mean1 - mean2, 2) / logvar2.exp() - torch.exp(logvar1 - logvar2) + 1
result = -0.5 * torch.sum(exponential, tuple(range(1, len(exponential.shape))))
return result.mean()
def forward(
self,
input_ids=None,
past=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
x_mask=None,
x_tokens=None,
y_mask=None,
y_tokens=None,
from_prior=False,
from_mean=False
):
# latent representation
posterior_mean, posterior_logvar = self.encoder(input_ids=y_tokens, attention_mask=y_mask)[:2]
if self.learn_prior:
prior_mean, prior_logvar = self.encoder_prior(input_ids=x_tokens, attention_mask=x_mask)[:2]
else:
prior_mean = prior_logvar = torch.zeros([input_ids.size(0), self.config.n_embd], device=input_ids.device)
prior_mean, prior_logvar = prior_mean.to(posterior_mean.dtype), prior_logvar.to(posterior_logvar.dtype)
if from_prior:
latent_mean, latent_logvar = prior_mean, prior_logvar
else:
latent_mean, latent_logvar = posterior_mean, posterior_logvar
if from_mean:
z = latent_mean
else:
z = self.reparameterize(latent_mean, latent_logvar)
assert not torch.isnan(z).any(), 'training get nan z'
transformer_outputs = self.transformer(input_ids,
past=past,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
representations=z)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
if self.add_softmax:
lm_logits_rep = self.lm_head_rep(z)
lm_logits = lm_logits + lm_logits_rep.unsqueeze(dim=1)
outputs = (lm_logits,) + transformer_outputs[1:]
# kl_loss
kl_loss = self.kl_loss(posterior_mean, posterior_logvar, prior_mean, prior_logvar).unsqueeze(0)
outputs = outputs + (kl_loss,)
return outputs # lm_logits, presents, (all hidden_states), (attentions), (kl_loss)
================================================
FILE: train.py
================================================
import os, time, gc, json, pickle, argparse, math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
from torch.nn import DataParallel
import torch.distributed as dist
import torch.multiprocessing as mp
import numpy as np
import transformers
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config, AdamW, get_linear_schedule_with_warmup, Conv1D
from tensorboardX import SummaryWriter
from tqdm import tqdm
import importlib
import logging
import copy
from apex.optimizers import FusedAdam
from apex import amp
from apex.fp16_utils import FP16_Optimizer
from data.util import *
from util import *
from model import *
from collections import Counter
from nltk.translate.bleu_score import sentence_bleu
from nltk.translate.bleu_score import SmoothingFunction
from rouge import Rouge
from sklearn.manifold import TSNE
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
devices = '0'
os.environ["CUDA_VISIBLE_DEVICES"] = devices
def compute_loss(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta):
input_tokens = input_tokens.to(device)
target_tokens = target_tokens.to(device)
mask = mask.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
outputs = model(input_ids=input_tokens, attention_mask=mask, x_mask=x_mask, x_tokens=x_tokens, y_mask=y_mask,
y_tokens=y_tokens)
logits = outputs[0]
kl_loss = outputs[-1]
num_logits = logits.size(-1)
# Perform masking
if mask is not None:
mask = mask.type(torch.bool)
mask = mask.to(device)
logits = logits.masked_select(mask.unsqueeze(-1))
target_tokens = target_tokens.masked_select(mask)
ce_loss = loss_fn(logits.view(-1, num_logits), target_tokens.view(-1))
kl_loss = kl_loss.mean()
loss = ce_loss.mean() + beta * kl_loss
return loss, ce_loss, kl_loss
def compute_loss_ae(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta):
input_tokens = input_tokens.to(device)
target_tokens = target_tokens.to(device)
mask = mask.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
outputs = model(input_ids=input_tokens, attention_mask=mask, y_mask=x_mask, y_tokens=x_tokens, from_mean=True, from_prior=False)
logits = outputs[0]
kl_loss = outputs[-1]
num_logits = logits.size(-1)
# Perform masking
if mask is not None:
mask = mask.type(torch.bool)
mask = mask.to(device)
logits = logits.masked_select(mask.unsqueeze(-1))
target_tokens = target_tokens.masked_select(mask)
ce_loss = loss_fn(logits.view(-1, num_logits), target_tokens.view(-1))
kl_loss = kl_loss.mean()
loss = ce_loss.mean()
return loss, ce_loss, kl_loss
def train_step(device, model, optimizer, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta, model_type):
output = []
if model_type == 'ae_vae_fusion':
optimizer.zero_grad()
loss, ce_loss, kl_loss = compute_loss_ae(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens,
target_tokens, mask, loss_fn, beta)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), 5.0) # max_grad_norm=1.0
# loss.backward()
# torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # max_grad_norm=1.0
optimizer.step()
output.append((loss.item(), ce_loss.mean().item(), kl_loss.item()))
optimizer.zero_grad()
loss, ce_loss, kl_loss = compute_loss(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens,
target_tokens, mask, loss_fn, beta)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), 5.0) # max_grad_norm=1.0
# loss.backward()
# torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # max_grad_norm=1.0
optimizer.step()
output.append((loss.item(), ce_loss.mean().item(), kl_loss.item()))
return output
def top_k_top_p_filtering(logits, top_k=100, top_p=0.95, filter_value=-float('Inf')):
""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (vocabulary size)
top_k > 0: keep only top k tokens with highest probability (top-k filtering).
top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
top_k = min(top_k, logits.size(-1)) # Safety check
if top_k > 0:
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove)
logits[indices_to_remove] = filter_value
return logits
def repeat_score(text, ngram=[3, 4, 5, 6]):
ngram_list = []
for ng in ngram:
ngram_list.append([text[idx:idx + ng] for idx in range(len(text) - ng - 1)])
max_occurs = []
for ngrams in ngram_list:
count_result = Counter([' '.join(n) for n in ngrams])
try:
max_occurs.append(
max(count_result.values())
)
except:
pass
scores = [max_oc / ((len(text) / ngram[idx]) + ngram[idx]) for idx, max_oc in enumerate(max_occurs)]
return max(scores) if len(scores) >= 1 else 1.0
def sample_sequence(model, tokenizer, length, batch_size=None, x_mask=None, x_tokens=None, y_mask=None, y_tokens=None,
temperature=1, top_k=100, top_p=0.95, device='cuda', sample=True, eos_token=None, model_type='cvae'):
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
with torch.no_grad():
if model_type == 'cvae':
try:
prior_mean, prior_logvar = model.encoder_prior(input_ids=x_tokens, attention_mask=x_mask)[:2]
except:
prior_mean = prior_logvar = torch.zeros([batch_size, model.config.n_embd], device=device)
latent_mean, latent_logvar = prior_mean, prior_logvar
z = model.reparameterize(latent_mean, latent_logvar)
assert not torch.isnan(z).any(), 'training get nan z'
else:
posterior_mean, posterior_logvar = model.encoder(input_ids=x_tokens, attention_mask=x_mask)[:2]
latent_mean, latent_logvar = posterior_mean, posterior_logvar
z = latent_mean
assert not torch.isnan(z).any(), 'training get nan z'
_, mem = model.transformer(input_ids=x_tokens[:, :-1], past=None, attention_mask=x_mask[:, :-1], representations=z)
prev = x_tokens[:, -1].view(batch_size, -1)
output = prev
probability = torch.tensor([], dtype=z.dtype, device=device)
if_end = torch.tensor([False] * batch_size, dtype=torch.bool, device=device)
for i in range(length): #trange
logits, mem = model.transformer(input_ids=prev, past=mem, representations=z)
logits = model.lm_head(logits)
if model.add_softmax:
logits_rep = model.lm_head_rep(z)
logits = logits + logits_rep.unsqueeze(dim=1)
logits = logits[:, -1, :] / temperature
logits = top_k_top_p_filtering(logits, top_k, top_p)
probs = F.softmax(logits, dim=-1)
if sample:
next_token = torch.multinomial(probs, num_samples=1)
else:
_, next_token = torch.topk(probs, k=1, dim=-1)
probability = torch.cat((probability, probs.gather(1, next_token)), dim=1)
output = torch.cat((output, next_token), dim=1)
prev = next_token
# early stopping if all sents have ended once
if_end[next_token.view(-1).eq(eos_token)] = True
if if_end.all(): break
return output, probability
def main():
parser = argparse.ArgumentParser()
parser.add_argument('experiment', type=str)
# Default parameters are set based on single GPU training
parser.add_argument('--lr', type=float, default=5e-5)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument('--data_type', type=str, default='t1', choices=['t' + str(i) for i in range(9)], help="t: type")
parser.add_argument('--model_type', type=str, default='cvae', choices=['cvae', 'ae_vae_fusion'])
parser.add_argument('--iterations', type=int, default=101640 * 4) # wp 850001 wi 300001 ax 300001 yp 800001
parser.add_argument('--dataset', type=str, default='wi', choices=['ax', 'yp', 'wp', 'wi'], help="Dataset to use for training")
parser.add_argument('--warmup', type=int, default=10000,
help="Amount of iterations to warmup, then decay. (-1 for no warmup and decay)")
parser.add_argument('--batch-sizes', nargs='+', type=int, default=[1],
help='batch size per GPU. Lists the schedule.')
parser.add_argument('--seq-lens', nargs='+', type=int, default=[1024],
help='seq length per sample. Lists the schedule.')
parser.add_argument('--switch-time', type=float, default=0,
help="Percentage of iterations to spend on short sequence training.")
parser.add_argument('--data-dir', type=str, default='data')
parser.add_argument('--out-dir', type=str, default='out')
parser.add_argument('--load', type=str, help='path to load model from') # , default='out/test/'
parser.add_argument('--workers', default=1, type=int, metavar='N',
help='number of data loading workers')
# use GPU
parser.add_argument('--gpu', default=0, type=int)
parser.add_argument('--no_gpu', action="store_true")
parser.add_argument('--fp16', action='store_true', help="Train using FP16?")
parser.add_argument('--fp16_opt_level', default='O0', type=str, required=False)
# KL cost annealing, increase beta from beta_0 to 1 in beta_warmup steps
parser.add_argument('--beta_0', default=1.00, type=float)
parser.add_argument('--beta_warmup', type=int, default=50000)
# cyc_vae parameters
parser.add_argument('--cycle', type=int, default=101640)
parser.add_argument('--add_input', action="store_true")
parser.add_argument('--add_attn', action="store_true")
parser.add_argument('--add_softmax', action="store_true")
parser.add_argument('--attn_proj_vary', action="store_true")
parser.add_argument('--learn_prior', action="store_true")
args = parser.parse_args('test --batch-sizes 1 --seq-lens 1024 '
'--add_input --learn_prior --fp16'.split()) # wi.12.proj_vary_beta_cvae
if args.model_type == 'cvae':
args.learn_prior = True
else:
args.learn_prior = False
# GPU
if not torch.cuda.is_available(): args.no_gpu = True
gpu = not args.no_gpu
if gpu:
print("There are ", torch.cuda.device_count(), " available GPUs!")
# print('Setting GPUs {}'.format(args.device))
print('Using GPU devices {}'.format(devices))
torch.cuda.set_device(args.gpu)
print('Current single GPU: {}'.format(torch.cuda.current_device()))
device = torch.device(args.gpu if gpu else "cpu")
# randomness
np.random.seed(args.seed)
prng = np.random.RandomState()
torch.random.manual_seed(args.seed)
if gpu: torch.cuda.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed)
# logging
save_folder = os.path.join(args.out_dir, args.experiment)
os.makedirs(save_folder, exist_ok=True)
t_writer = SummaryWriter(os.path.join(save_folder, 'train'), flush_secs=5)
v_writer = SummaryWriter(os.path.join(save_folder, 'val'), flush_secs=5)
importlib.reload(logging)
logging.basicConfig(filename=os.path.join(save_folder, 'train.log'),
level=logging.INFO, format='%(asctime)s--- %(message)s')
logging.info('\n*******************************************************************************\n')
logging.info("the configuration:")
logging.info(str(args).replace(',', '\n'))
print('Loading models...')
cache_dir = os.path.join(args.out_dir, 'model_cache')
os.makedirs(cache_dir, exist_ok=True)
# Load pre-trained teacher tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2', cache_dir=cache_dir)
# Hack to allow tokenizing longer sequences.
tokenizer.max_len = int(1e12)
gpt2_model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir=cache_dir)
print('gpt2_params:', num_params(gpt2_model)) # gpt2: 124439808
config = GPT2Config()
# add special tokens
# special_tokens_dict = {
# 'pad_token': '<|startoftext|>',
# 'cls_token': '<|startofcond|>',
# 'sep_token': '<|sepofcond|>',
# 'mask_token': '<|endofcond|>'
# }
# num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
# print('We have added', num_added_toks, 'special tokens')
# # Notice: resize_token_embeddings expect to receive the full size of the new vocab
# gpt2_model.resize_token_embeddings(len(tokenizer))
# assert tokenizer.pad_token == '<|startoftext|>'
VAE = VAEModel(config, add_input=args.add_input, add_attn=args.add_attn, add_softmax=args.add_softmax,
attn_proj_vary=args.attn_proj_vary, learn_prior=args.learn_prior)
init_para_frompretrained(VAE.transformer, gpt2_model.transformer, share_para=True)
init_para_frompretrained(VAE.encoder, gpt2_model.transformer, share_para=False)
if args.learn_prior:
init_para_frompretrained(VAE.encoder_prior, VAE.encoder, share_para=True)
VAE.encoder_prior.averageSelfAttention.attention_weights = VAE.encoder.averageSelfAttention.attention_weights
VAE.lm_head.weight = gpt2_model.lm_head.weight
if VAE.add_softmax:
VAE.lm_head_rep = Conv1D(*gpt2_model.lm_head.weight.size())
# VAE.lm_head_rep = LM_head_rep(*gpt2_model.lm_head.weight.size()[::-1])
print('VAE_params:', num_params(VAE)) # 286694400
if args.load:
print('Loading model weights...')
state = torch.load(os.path.join(args.load, 'model_latest.pt')) # , map_location='cpu' model_latest.pt
if 'module' in list(state.keys())[0]: # model_path is data parallel model with attr 'module'
state_copy = copy.copy(state)
keys = state_copy.keys()
for k in keys:
state[k.replace('module.', '')] = state.pop(k)
VAE.load_state_dict(state)
gc.collect()
print('Done.')
# fix pre-trained parameters before certain iterations
tuning_all_after_iters = 40000
tuning_all = False
for name, parameter in VAE.named_parameters():
# print((name, parameter.requires_grad))
new_pars = ['c_z', 'attention_weights', 'mean', 'logvar', 'input_proj', 'attn_proj', 'Nu_fc1', 'Nu_fc2', 'lm_head_rep']
if not any([True if n in name else False for n in new_pars]):
parameter.requires_grad = False
print('Setup data...')
# Batch and sequence length schedule
assert len(args.batch_sizes) == len(args.seq_lens)
batch_schedule = list(zip(map(int, args.batch_sizes), map(int, args.seq_lens)))
assert len(batch_schedule) <= 2, 'Currently not supporting multiple schedule'
cur_b_schedule = len(batch_schedule) - 1 if args.switch_time == 0 else 0
print('Batch schedule', batch_schedule)
train_loader, val_loader, test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
batch_schedule[cur_b_schedule][0], batch_schedule[cur_b_schedule][1],
batch_schedule[-1][0], batch_schedule[-1][1],
batch_schedule[-1][0], batch_schedule[-1][1],
make_test=True,
num_workers=args.workers, data_type=args.data_type
)
print('Done.')
###
val_loader = test_loader
###
print('Wrapping models and optimizers...')
# Apply linear scaling rule to increase batch size for short sequence training.
lr_schedule = switch_schedule(linear_schedule(args), batch_schedule[cur_b_schedule][0] / batch_schedule[-1][0],
int(args.iterations * args.switch_time))
VAE = VAE.to(device)
VAE.train()
optimizer = AdamW(VAE.parameters(), lr=args.lr, correct_bias=True)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_schedule)
VAE, optimizer = amp.initialize(VAE, optimizer, opt_level=args.fp16_opt_level)
loss_fn = nn.CrossEntropyLoss(reduction='none')
print('Done.')
print('Begin training iterations')
logging.info("Begin training iterations")
max_val_batches = 20000 # max num. of val batches
logging.info("Total iteration: %d" % args.iterations)
e = 0 # number of epoch
num_iters = 0
optimizer.zero_grad()
beta = args.beta_0
endoftext = tokenizer.convert_tokens_to_ids("<|endoftext|>")
def val_step(val_loader):
VAE.eval()
n_words_bpe = 0
n_words = 0
logp_sum = 0.0
kl_loss_sum = 0.0
logging.info("Validation loop. Batches: %d" % len(val_loader))
logging.info("Validation loop. max_val_batches: %d" % max_val_batches)
# val_iter = iter(val_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(val_iter)
with tqdm(total=min(len(val_loader), max_val_batches)) as pbar:
for i, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(val_loader):
with torch.no_grad():
if args.model_type == 'cvae':
loss, ce_loss, kl_loss = compute_loss(device, VAE, x_mask, x_tokens, y_mask, y_tokens,
input_tokens, target_tokens, mask, loss_fn, 1.0)
else:
loss, ce_loss, kl_loss = compute_loss_ae(device, VAE, x_mask, x_tokens, y_mask, y_tokens,
input_tokens, target_tokens, mask, loss_fn, 1.0)
if len(target_tokens.size()) == 1:
target_tokens = target_tokens.unsqueeze(0)
n, l = target_tokens.size()
text = target_tokens[0, :].tolist()
logprob = ce_loss.tolist()
assert len(text) == len(logprob)
# only for story
idx = text.index(endoftext)
text = text[idx + 1:]
logprob = logprob[idx + 1:]
if endoftext in text:
idx = text.index(endoftext)
text = text[:idx]
logprob = logprob[:idx]
logp_sum += sum(logprob)
n_words_bpe += len(text)
story = [tokenizer.decode(target_tokens[i, :]) for i in range(n)]
story = [s[s.find("<|endoftext|>") + len("<|endoftext|>"):] for s in story]
story = [s[:s.find("<|endoftext|>") + len("<|endoftext|>")] if "<|endoftext|>" in s else s for s in
story]
words = sum([len(
[t for t in re.split('("|\'|!|\?|\.|,|:| |\n|’|“|”|;|\(|\)|`)', s) if t != ' ' and t != '']) for
s in story])
n_words += words
kl_loss_sum += kl_loss.item()
if i > max_val_batches:
break
pbar.update(1)
loss_bpe = logp_sum / n_words_bpe
ppl_bpe = round(math.exp(min(logp_sum / n_words_bpe, 100)), 3)
ppl_word = round(math.exp(min(logp_sum / n_words, 100)), 3)
kl = kl_loss_sum / len(val_loader)
v_writer.add_scalar('loss', loss_bpe, num_iters)
v_writer.add_scalar('ppl_bpe', ppl_bpe, num_iters)
v_writer.add_scalar('ppl_word', ppl_word, num_iters)
v_writer.add_scalar('kl', kl, num_iters)
logging.info('val loss : %.4f' % loss_bpe)
logging.info('val ppl_bpe : %.4f' % ppl_bpe)
logging.info('val ppl_word: %.4f' % ppl_word)
logging.info('val kl : %.4f' % kl)
VAE.train()
def test_plot(test_loader, num_iters):
VAE.eval()
# get embedding
X_emb = None
y = None
# test_iter = iter(test_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(test_iter)
with tqdm(total=len(test_loader)) as pbar:
for i, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(
test_loader):
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
with torch.no_grad():
if args.model_type == 'cvae':
latent_mean, latent_logvar = VAE.encoder_prior(input_ids=x_tokens, attention_mask=x_mask)[:2]
else:
latent_mean, latent_logvar = VAE.encoder(input_ids=x_tokens, attention_mask=x_mask)[:2]
if args.dataset == 'ax' or args.dataset == 'yp':
label = [tokenizer.decode(l)[:2] for l in x_tokens.tolist()]
elif args.dataset == 'wp':
label = []
prompts = [tokenizer.decode(l)[:6].lower() for l in x_tokens.tolist()]
for prom in prompts:
if prom[0] in ['[', '('] and prom[5] in [']', ')']:
label.append(prom[2:4])
else:
label.append(None)
elif args.dataset == 'wi':
# 0. TV, play, miniseries, telenovela; 1.film; 2. music; 3. manga, comic, 4. book, novel, story 5. game
label = []
prompts = [tokenizer.decode(l) for l in x_tokens.tolist()]
for prom in prompts:
if 'TV' in prom or 'play' in prom or 'miniseries' in prom or 'telenovela' in prom:
label.append(0)
elif 'film' in prom:
label.append(1)
elif 'music' in prom:
label.append(2)
elif 'manga' in prom or 'comic' in prom:
label.append(3)
elif 'book' in prom or 'novel' in prom or 'story' in prom:
label.append(4)
elif 'game' in prom:
label.append(5)
else:
label.append(None)
else:
raise Exception
if i == 0:
X_emb = latent_mean.data
y = label
else:
X_emb = torch.cat((X_emb, latent_mean.data), dim=0)
y.extend(label)
pbar.update(1)
X_emb = X_emb.cpu().numpy()
try:
if args.dataset == 'yp':
y = ['0' if l in ['0', '1'] else l for l in y]
y = ['4' if l in ['3', '4'] else l for l in y]
X_emb = X_emb[[l != '2' for l in y], :]
y = [l for l in y if l != '2']
if args.dataset == 'wp':
topics = [['wp', 'sp', 'tt'], ['eu'], ['cw'], ['pm'], ['mp', 'ip'], ['pi', 'cc'], ['ot'], ['rf']]
match = [[True if l in t else False for t in topics] for l in y]
y = [m.index(True) if True in m else None for m in match]
X_emb = X_emb[[l is not None for l in y], :]
y = [l for l in y if l is not None]
if args.dataset == 'wi':
X_emb = X_emb[[l is not None for l in y], :]
y = [l for l in y if l is not None]
# to 2D
# X_emb_2d = TSNE(n_components=2, init='pca', verbose=1).fit_transform(X_emb)
X_emb_2d = TSNE(n_components=2, verbose=1, perplexity=40).fit_transform(X_emb)
def remove_outliers(data, r=2.0):
outliers_data = abs(data - np.mean(data, axis=0)) >= r * np.std(data, axis=0)
outliers = np.any(outliers_data, axis=1)
keep = np.logical_not(outliers)
return outliers, keep
outliers, keep = remove_outliers(X_emb_2d)
X_emb_2d = X_emb_2d[keep, :]
y = [l for l, k in zip(y, keep.tolist()) if k]
# plot
fig = plt.figure(figsize=(4, 4))
ax = fig.add_axes([0, 0, 1, 1])
cc = ['r', 'b', 'g', 'y', 'k', 'c', 'm', 'tab:blue']
for i, l in enumerate(sorted(set(y))):
idx = [yl == l for yl in y]
plt.scatter(X_emb_2d[idx, 0], X_emb_2d[idx, 1], c=cc[i], s=10, edgecolor='none', alpha=0.5)
ax.axis('off') # adding it will get no axis
plt.savefig(os.path.join(save_folder, 'tSNE_' + '{:07d}'.format(num_iters) + '.png'))
plt.close(fig)
except:
pass
VAE.train()
def generate(test_loader, num_iters):
VAE.eval()
n_samples = 0
bleu4_sum = 0.0
rouge_scores_values_sum = [0.0] * 9
args.nsamples = 1
args.batch_size = 1
args.temperature = 0.95
args.top_k = 100
args.top_p = 0.95
model_type = args.model_type
# write samples to file
samples_file = open(os.path.join(save_folder, 'generate-' + '%07d' % num_iters + '.txt'), 'w', encoding='utf8')
# test_iter = iter(test_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(test_iter)
with tqdm(total=len(test_loader)) as pbar:
for i_test, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(
test_loader):
if i_test >= 10: break
length = -1
if length == -1:
length = VAE.config.n_ctx - x_tokens.size(1) - 1
elif length > VAE.config.n_ctx - x_tokens.size(1) - 1:
raise ValueError("Can't get samples longer than window size: %s" % VAE.config.n_ctx)
eff_samples = []
n, l = target_tokens.size()
storys = [tokenizer.decode(target_tokens[i, :]) for i in range(n)]
storys = [s[s.find("<|endoftext|>") + len("<|endoftext|>"):] for s in storys]
storys_str = [s[:s.find("<|endoftext|>") + len("<|endoftext|>")] if "<|endoftext|>" in s else s for s in
storys]
for _ in range(args.nsamples // args.batch_size):
# model, batch_size, temperature, top_k, top_p, eos_token, sample = VAE, args.batch_size, args.temperature, args.top_k, args.top_p, tokenizer.encoder['<|endoftext|>'], True
out, _ = sample_sequence(
model=VAE,
tokenizer=tokenizer,
length=length,
batch_size=args.batch_size,
x_mask=x_mask,
x_tokens=x_tokens,
y_mask=y_mask,
y_tokens=y_tokens,
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
device=device,
eos_token=tokenizer.encoder['<|endoftext|>'],
model_type=model_type
)
out = out.tolist()
# extract story, check metrics
for i in range(len(out)):
text = out[i]
text = text[text.index(endoftext) + 1:]
if endoftext in text:
idx = text.index(endoftext)
text = text[:idx]
text = tokenizer.decode(text).strip()
# score for one long text, higher than 0.075 usually means repetition
# rep_score = repeat_score(text.split(), ngram=[3, 4, 5, 6, 7, 8])
# if rep_score > 0.075:
# # print(rep_score)
# continue
try:
# check bleu
bleu4 = sentence_bleu([storys_str[i].split()], text,
smoothing_function=SmoothingFunction().method7)
# check rouge
rouge = Rouge()
rouge_scores = rouge.get_scores(text, storys_str[i])
rouge_scores_values = [v for k in rouge_scores[0].keys() for v in
rouge_scores[0][k].values()]
bleu4_sum += bleu4
rouge_scores_values_sum = [v1 + v2 for v1, v2 in
zip(rouge_scores_values_sum, rouge_scores_values)]
n_samples += 1
except:
bleu4 = 0.0
rouge_scores = [{'rouge-1': {'f': 0.0, 'p': 0.0, 'r': 0.0},
'rouge-2': {'f': 0.0, 'p': 0.0, 'r': 0.0},
'rouge-l': {'f': 0.0, 'p': 0.0, 'r': 0.0}}]
eff_samples.append((text, bleu4, rouge_scores))
pbar.update(1)
for i in range(len(eff_samples)):
samples_file.write("=" * 50 + " SAMPLE " + str(i_test) + " " + "=" * 50)
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Outlines " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(tokenizer.decode(x_tokens[i, :][x_mask[i, :] == 1].tolist()))
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Story " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(storys_str[i])
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Generated " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(eff_samples[i][0])
samples_file.write('\n' * 4)
samples_file.flush()
print('Test complete with %05d samples.' % n_samples)
logging.info("Test complete with %05d samples.", n_samples)
logging.info("Iteration completed: %d" % num_iters)
bleu4 = round(bleu4_sum / n_samples, 3)
rouge_scores_values = [round(r / n_samples, 3) for r in rouge_scores_values_sum]
print(' bleu-4:', bleu4)
print(' rouge :', rouge_scores_values)
logging.info(' bleu-4: %f', bleu4)
logging.info(' rouge : %s', str(rouge_scores_values))
VAE.train()
test_plot(test_loader, num_iters)
val_step(val_loader)
generate(test_loader, num_iters)
torch.save(VAE.state_dict(), os.path.join(save_folder, 'model_' + '{:07d}'.format(num_iters) + '.pt'))
while num_iters < args.iterations:
# Run epoch
st = time.time()
# Training
print('Training loop. Batches:', len(train_loader))
logging.info('\n----------------------------------------------------------------------')
logging.info("Training loop. Batches: %d" % len(train_loader))
# train_iter = iter(train_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(train_iter)
with tqdm(total=len(train_loader)) as pbar:
for i, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(train_loader):
# if num_iters % args.cycle >= args.cycle - args.beta_warmup:
# beta = min(1.0, beta + (1. - args.beta_0) / args.beta_warmup)
if not tuning_all and num_iters >= tuning_all_after_iters:
for name, parameter in VAE.named_parameters():
# print((name, parameter.requires_grad))
parameter.requires_grad = True
tuning_all = True
output = train_step(device, VAE, optimizer, x_mask, x_tokens, y_mask, y_tokens,
input_tokens, target_tokens, mask, loss_fn, beta, args.model_type)
loss, ce_loss, kl_loss = output[-1]
lr = scheduler.get_last_lr()[0]
# Log to Tensorboard
t_writer.add_scalar('loss', loss, num_iters)
t_writer.add_scalar('ppl', math.exp(min(ce_loss, 10)), num_iters)
t_writer.add_scalar('lr', lr, num_iters)
t_writer.add_scalar('iter_time', time.time() - st, num_iters)
t_writer.add_scalar('kl', kl_loss, num_iters)
t_writer.add_scalar('beta', beta, num_iters)
if args.model_type == 'ae_vae_fusion':
loss, ce_loss, kl_loss = output[0]
# Log to Tensorboard
t_writer.add_scalar('ae_loss', loss, num_iters)
t_writer.add_scalar('ae_kl', kl_loss, num_iters)
st = time.time()
end = num_iters >= args.iterations
if args.warmup != -1:
scheduler.step()
if end: break
num_iters += 1
pbar.update(1)
if num_iters % args.cycle == 0:
beta = args.beta_0
logging.info('KL annealing restart')
if num_iters % 10000 == 0:
test_plot(test_loader, num_iters)
val_step(val_loader)
generate(test_loader, num_iters)
if num_iters % 50000 == 0:
print('Saving model...')
logging.info("Iteration completed: %d, remained %d" % (num_iters, args.iterations - num_iters))
logging.info("Saving model...")
logging.info('\n------------------------------------------------------')
torch.save(VAE.state_dict(), os.path.join(save_folder, 'model_' + '{:07d}'.format(num_iters) + '.pt'))
if args.switch_time > 0 and num_iters == int(args.iterations * args.switch_time):
print('Switch to long sequence training')
logging.info("Switch to long sequence training")
cur_b_schedule += 1
train_loader, val_loader, test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
batch_schedule[cur_b_schedule][0], batch_schedule[cur_b_schedule][1],
batch_schedule[-1][0], batch_schedule[-1][1],
batch_schedule[-1][0], batch_schedule[-1][1],
make_test=True,
num_workers=args.workers, data_type=args.data_type
)
if not end:
e += 1
logging.info("Training loop. The ith epoch completed: %d" % e)
torch.save(VAE.state_dict(), os.path.join(save_folder, 'model_latest.pt'))
print('Training complete.')
logging.info("Training complete.")
if __name__ == "__main__":
main()
================================================
FILE: train_dist.py
================================================
import os, time, gc, json, pickle, argparse, math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
from torch.nn import DataParallel
import torch.distributed as dist
import torch.multiprocessing as mp
import numpy as np
import transformers
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config, AdamW, get_linear_schedule_with_warmup, Conv1D
from tensorboardX import SummaryWriter
from tqdm import tqdm
import importlib
import logging
import copy
from apex.optimizers import FusedAdam
from apex import amp
from apex.fp16_utils import FP16_Optimizer
from apex.parallel import DistributedDataParallel as DDP
from torch.nn.parallel import DistributedDataParallel
from data.util import *
from util import *
from model import VAEModel
from collections import Counter
from nltk.translate.bleu_score import sentence_bleu
from nltk.translate.bleu_score import SmoothingFunction
from rouge import Rouge
from sklearn.manifold import TSNE
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
devices = '2,1,0'
os.environ["CUDA_VISIBLE_DEVICES"] = devices
def compute_loss(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta):
input_tokens = input_tokens.to(device)
target_tokens = target_tokens.to(device)
mask = mask.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
outputs = model(input_ids=input_tokens, attention_mask=mask, x_mask=x_mask, x_tokens=x_tokens, y_mask=y_mask,
y_tokens=y_tokens)
logits = outputs[0]
kl_loss = outputs[-1]
num_logits = logits.size(-1)
# Perform masking
if mask is not None:
mask = mask.type(torch.bool)
mask = mask.to(device)
logits = logits.masked_select(mask.unsqueeze(-1))
target_tokens = target_tokens.masked_select(mask)
ce_loss = loss_fn(logits.view(-1, num_logits), target_tokens.view(-1))
kl_loss = kl_loss.mean()
loss = ce_loss.mean() + beta * kl_loss
return loss, ce_loss, kl_loss
def compute_loss_ae(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta):
input_tokens = input_tokens.to(device)
target_tokens = target_tokens.to(device)
mask = mask.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
outputs = model(input_ids=input_tokens, attention_mask=mask, y_mask=x_mask, y_tokens=x_tokens, from_mean=True, from_prior=False)
logits = outputs[0]
kl_loss = outputs[-1]
num_logits = logits.size(-1)
# Perform masking
if mask is not None:
mask = mask.type(torch.bool)
mask = mask.to(device)
logits = logits.masked_select(mask.unsqueeze(-1))
target_tokens = target_tokens.masked_select(mask)
ce_loss = loss_fn(logits.view(-1, num_logits), target_tokens.view(-1))
kl_loss = kl_loss.mean()
loss = ce_loss.mean()
return loss, ce_loss, kl_loss
def train_step(device, model, optimizer, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta, model_type):
output = []
if model_type == 'ae_vae_fusion':
optimizer.zero_grad()
loss, ce_loss, kl_loss = compute_loss_ae(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens,
target_tokens, mask, loss_fn, beta)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), 5.0) # max_grad_norm=1.0
optimizer.step()
output.append((loss.item(), ce_loss.mean().item(), kl_loss.item()))
optimizer.zero_grad()
loss, ce_loss, kl_loss = compute_loss(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens,
target_tokens, mask, loss_fn, beta)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), 5.0) # max_grad_norm=1.0
optimizer.step()
output.append((loss.item(), ce_loss.mean().item(), kl_loss.item()))
return output
def top_k_top_p_filtering(logits, top_k=100, top_p=0.95, filter_value=-float('Inf')):
""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (vocabulary size)
top_k > 0: keep only top k tokens with highest probability (top-k filtering).
top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
top_k = min(top_k, logits.size(-1)) # Safety check
if top_k > 0:
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove)
logits[indices_to_remove] = filter_value
return logits
def repeat_score(text, ngram=[3, 4, 5, 6]):
ngram_list = []
for ng in ngram:
ngram_list.append([text[idx:idx + ng] for idx in range(len(text) - ng - 1)])
max_occurs = []
for ngrams in ngram_list:
count_result = Counter([' '.join(n) for n in ngrams])
try:
max_occurs.append(
max(count_result.values())
)
except:
pass
scores = [max_oc / ((len(text) / ngram[idx]) + ngram[idx]) for idx, max_oc in enumerate(max_occurs)]
return max(scores) if len(scores) >= 1 else 1.0
def sample_sequence(model, tokenizer, length, batch_size=None, x_mask=None, x_tokens=None, y_mask=None, y_tokens=None,
temperature=1, top_k=100, top_p=0.95, device='cuda', sample=True, eos_token=None, model_type='cvae'):
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
with torch.no_grad():
if model_type == 'cvae':
try:
prior_mean, prior_logvar = model.encoder_prior(input_ids=x_tokens, attention_mask=x_mask)[:2]
except:
prior_mean = prior_logvar = torch.zeros([batch_size, model.config.n_embd], device=device)
latent_mean, latent_logvar = prior_mean, prior_logvar
z = model.reparameterize(latent_mean, latent_logvar)
assert not torch.isnan(z).any(), 'training get nan z'
else:
posterior_mean, posterior_logvar = model.encoder(input_ids=x_tokens, attention_mask=x_mask)[:2]
latent_mean, latent_logvar = posterior_mean, posterior_logvar
z = latent_mean
assert not torch.isnan(z).any(), 'training get nan z'
_, mem = model.transformer(input_ids=x_tokens[:, :-1], past=None, attention_mask=x_mask[:, :-1], representations=z)
prev = x_tokens[:, -1].view(batch_size, -1)
output = prev
probability = torch.tensor([], dtype=z.dtype, device=device)
if_end = torch.tensor([False] * batch_size, dtype=torch.bool, device=device)
for i in range(length): #trange
logits, mem = model.transformer(input_ids=prev, past=mem, representations=z)
logits = model.lm_head(logits)
if model.add_softmax:
logits_rep = model.lm_head_rep(z)
logits = logits + logits_rep.unsqueeze(dim=1)
logits = logits[:, -1, :] / temperature
logits = top_k_top_p_filtering(logits, top_k, top_p)
probs = F.softmax(logits, dim=-1)
if sample:
next_token = torch.multinomial(probs, num_samples=1)
else:
_, next_token = torch.topk(probs, k=1, dim=-1)
probability = torch.cat((probability, probs.gather(1, next_token)), dim=1)
output = torch.cat((output, next_token), dim=1)
prev = next_token
# early stopping if all sents have ended once
if_end[next_token.view(-1).eq(eos_token)] = True
if if_end.all(): break
return output, probability
def main_worker(gpu, ngpus_per_node, args):
if args.model_type == 'cvae':
args.learn_prior = True
else:
args.learn_prior = False
# GPU
args.gpu = gpu
print("There are ", torch.cuda.device_count(), " available GPUs!")
# print('Setting GPUs {}'.format(args.device))
print('Using GPU devices {}'.format(devices))
device = torch.device('cuda', args.gpu)
torch.cuda.set_device(device)
print('Current single GPU: {}'.format(torch.cuda.current_device()))
# randomness
np.random.seed(args.seed)
prng = np.random.RandomState()
torch.random.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# For multiprocessing distributed training, rank needs to be the global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
print('Setting rank', args.rank)
recon_attempt = 1
connected = False
if args.rank != 0:
# Stall to have rank 0 node go first
time.sleep(3)
while not connected:
try:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
connected = True
print('Established connection. Rank:', args.rank)
except Exception as e:
# Sometimes the head node launches after the worker, which would cause an issue
print('Failed to init process group. Retrying...', recon_attempt, e)
recon_attempt += 1
time.sleep(10)
# logging
if args.rank == 0:
save_folder = os.path.join(args.out_dir, args.experiment)
os.makedirs(save_folder, exist_ok=True)
t_writer = SummaryWriter(os.path.join(save_folder, 'train'), flush_secs=5)
v_writer = SummaryWriter(os.path.join(save_folder, 'val'), flush_secs=5)
importlib.reload(logging)
logging.basicConfig(filename=os.path.join(save_folder, 'train.log'),
level=logging.INFO, format='%(asctime)s--- %(message)s')
logging.info('\n*******************************************************************************\n')
logging.info("the configuration:")
logging.info(str(args).replace(',', '\n'))
print('Loading models...')
cache_dir = os.path.join(args.out_dir, 'model_cache')
os.makedirs(cache_dir, exist_ok=True)
# Load pre-trained teacher tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2', cache_dir=cache_dir)
# Hack to allow tokenizing longer sequences.
tokenizer.max_len = int(1e12)
gpt2_model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir=cache_dir)
print('gpt2_params:', num_params(gpt2_model)) # gpt2: 124439808
config = GPT2Config()
# add special tokens
# special_tokens_dict = {
# 'pad_token': '<|startoftext|>',
# 'cls_token': '<|startofcond|>',
# 'sep_token': '<|sepofcond|>',
# 'mask_token': '<|endofcond|>'
# }
# num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
# print('We have added', num_added_toks, 'special tokens')
# # Notice: resize_token_embeddings expect to receive the full size of the new vocab
# gpt2_model.resize_token_embeddings(len(tokenizer))
# assert tokenizer.pad_token == '<|startoftext|>'
VAE = VAEModel(config, add_input=args.add_input, add_attn=args.add_attn, add_softmax=args.add_softmax,
attn_proj_vary=args.attn_proj_vary, learn_prior=args.learn_prior)
init_para_frompretrained(VAE.transformer, gpt2_model.transformer, share_para=True)
init_para_frompretrained(VAE.encoder, gpt2_model.transformer, share_para=False)
if args.learn_prior:
init_para_frompretrained(VAE.encoder_prior, VAE.encoder, share_para=True)
VAE.encoder_prior.averageSelfAttention.attention_weights = VAE.encoder.averageSelfAttention.attention_weights
VAE.lm_head.weight = gpt2_model.lm_head.weight
if VAE.add_softmax:
VAE.lm_head_rep = Conv1D(*gpt2_model.lm_head.weight.size())
# VAE.lm_head_rep = LM_head_rep(*gpt2_model.lm_head.weight.size()[::-1])
print('VAE_params:', num_params(VAE)) # 286694400
if args.load:
print('Loading model weights...')
state = torch.load(os.path.join(args.load, 'model_latest.pt')) # , map_location='cpu'
if 'module' in list(state.keys())[0]: # model_path is data parallel model with attr 'module'
state_copy = copy.copy(state)
keys = state_copy.keys()
for k in keys:
state[k.replace('module.', '')] = state.pop(k)
VAE.load_state_dict(state)
gc.collect()
print('Done.')
# fix pre-trained parameters before certain iterations
tuning_all_after_iters = 10000
tuning_all = False
for name, parameter in VAE.named_parameters():
# print((name, parameter.requires_grad))
new_pars = ['c_z', 'attention_weights', 'mean', 'logvar', 'input_proj', 'attn_proj', 'Nu_fc1', 'Nu_fc2', 'lm_head_rep']
if not any([True if n in name else False for n in new_pars]):
parameter.requires_grad = False
print('Setup data...')
# Batch and sequence length schedule
assert len(args.batch_sizes) == len(args.seq_lens)
batch_schedule = list(zip(map(int, args.batch_sizes), map(int, args.seq_lens)))
assert len(batch_schedule) <= 2, 'Currently not supporting multiple schedule'
cur_b_schedule = len(batch_schedule) - 1 if args.switch_time == 0 else 0
print('Batch schedule', batch_schedule)
train_loader, val_loader, test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
batch_schedule[cur_b_schedule][0], batch_schedule[cur_b_schedule][1],
batch_schedule[-1][0], batch_schedule[-1][1],
batch_schedule[-1][0], batch_schedule[-1][1],
make_test=True,
num_workers=args.workers, data_type=args.data_type
)
print('Done.')
###
val_loader = test_loader
###
print('Wrapping models and optimizers...')
# Apply linear scaling rule to increase batch size for short sequence training.
lr_schedule = switch_schedule(linear_schedule(args), batch_schedule[cur_b_schedule][0] / batch_schedule[-1][0],
int(args.iterations * args.switch_time))
VAE = VAE.to(device)
VAE = VAE.train()
optimizer = AdamW(VAE.parameters(), lr=args.lr, correct_bias=True)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_schedule)
VAE, optimizer = amp.initialize(VAE, optimizer, opt_level=args.fp16_opt_level)
loss_model = DDP(VAE) # , delay_allreduce=True
loss_fn = nn.CrossEntropyLoss(reduction='none')
print('Done.')
print('Begin training iterations')
logging.info("Begin training iterations")
max_val_batches = 20000 # max num. of val batches
logging.info("Total iteration: %d" % args.iterations)
e = 0 # number of epoch
num_iters = 0
optimizer.zero_grad()
beta = args.beta_0
endoftext = tokenizer.convert_tokens_to_ids("<|endoftext|>")
def val_step(val_loader):
VAE.eval()
n_words_bpe = 0
n_words = 0
logp_sum = 0.0
kl_loss_sum = 0.0
logging.info("Validation loop. Batches: %d" % len(val_loader))
logging.info("Validation loop. max_val_batches: %d" % max_val_batches)
# val_iter = iter(val_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(val_iter)
with tqdm(total=min(len(val_loader), max_val_batches)) as pbar:
for i, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(val_loader):
with torch.no_grad():
if args.model_type == 'cvae':
loss, ce_loss, kl_loss = compute_loss(device, VAE, x_mask, x_tokens, y_mask, y_tokens,
input_tokens, target_tokens, mask, loss_fn, 1.0)
else:
loss, ce_loss, kl_loss = compute_loss_ae(device, VAE, x_mask, x_tokens, y_mask, y_tokens,
input_tokens, target_tokens, mask, loss_fn, 1.0)
if len(target_tokens.size()) == 1:
target_tokens = target_tokens.unsqueeze(0)
n, l = target_tokens.size()
text = target_tokens[0, :].tolist()
logprob = ce_loss.tolist()
assert len(text) == len(logprob)
# only for story
idx = text.index(endoftext)
text = text[idx + 1:]
logprob = logprob[idx + 1:]
if endoftext in text:
idx = text.index(endoftext)
text = text[:idx]
logprob = logprob[:idx]
logp_sum += sum(logprob)
n_words_bpe += len(text)
story = [tokenizer.decode(target_tokens[i, :]) for i in range(n)]
story = [s[s.find("<|endoftext|>") + len("<|endoftext|>"):] for s in story]
story = [s[:s.find("<|endoftext|>") + len("<|endoftext|>")] if "<|endoftext|>" in s else s for s in
story]
words = sum([len(
[t for t in re.split('("|\'|!|\?|\.|,|:| |\n|’|“|”|;|\(|\)|`)', s) if t != ' ' and t != '']) for
s in story])
n_words += words
kl_loss_sum += kl_loss.item()
if i > max_val_batches:
break
pbar.update(1)
loss_bpe = logp_sum / n_words_bpe
ppl_bpe = round(math.exp(min(logp_sum / n_words_bpe, 100)), 3)
ppl_word = round(math.exp(min(logp_sum / n_words, 100)), 3)
kl = kl_loss_sum / len(val_loader)
v_writer.add_scalar('loss', loss_bpe, num_iters)
v_writer.add_scalar('ppl_bpe', ppl_bpe, num_iters)
v_writer.add_scalar('ppl_word', ppl_word, num_iters)
v_writer.add_scalar('kl', kl, num_iters)
logging.info('val loss : %.4f' % loss_bpe)
logging.info('val ppl_bpe : %.4f' % ppl_bpe)
logging.info('val ppl_word: %.4f' % ppl_word)
logging.info('val kl : %.4f' % kl)
VAE.train()
def test_plot(test_loader, num_iters):
VAE.eval()
# get embedding
X_emb = None
y = None
# test_iter = iter(test_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(test_iter)
with tqdm(total=len(test_loader)) as pbar:
for i, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(
test_loader):
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
with torch.no_grad():
if args.model_type == 'cvae':
latent_mean, _ = VAE.encoder_prior(input_ids=x_tokens, attention_mask=x_mask)[:2]
else:
latent_mean, _ = VAE.encoder(input_ids=x_tokens, attention_mask=x_mask)[:2]
if args.dataset == 'ax' or args.dataset == 'yp':
label = [tokenizer.decode(l)[:2] for l in x_tokens.tolist()]
elif args.dataset == 'wp':
label = []
prompts = [tokenizer.decode(l)[:6].lower() for l in x_tokens.tolist()]
for prom in prompts:
if prom[0] in ['[', '('] and prom[5] in [']', ')']:
label.append(prom[2:4])
else:
label.append(None)
elif args.dataset == 'wi':
# 0. TV, play, miniseries, telenovela; 1.film; 2. music; 3. manga, comic, 4. book, novel, story 5. game
label = []
prompts = [tokenizer.decode(l) for l in x_tokens.tolist()]
for prom in prompts:
if 'TV' in prom or 'play' in prom or 'miniseries' in prom or 'telenovela' in prom:
label.append(0)
elif 'film' in prom:
label.append(1)
elif 'music' in prom:
label.append(2)
elif 'manga' in prom or 'comic' in prom:
label.append(3)
elif 'book' in prom or 'novel' in prom or 'story' in prom:
label.append(4)
elif 'game' in prom:
label.append(5)
else:
label.append(None)
else:
raise Exception
if i == 0:
X_emb = latent_mean.data
y = label
else:
X_emb = torch.cat((X_emb, latent_mean.data), dim=0)
y.extend(label)
pbar.update(1)
X_emb = X_emb.cpu().numpy()
try:
if args.dataset == 'yp':
y = ['0' if l in ['0', '1'] else l for l in y]
y = ['4' if l in ['3', '4'] else l for l in y]
X_emb = X_emb[[l != '2' for l in y], :]
y = [l for l in y if l != '2']
if args.dataset == 'wp':
topics = [['wp', 'sp', 'tt'], ['eu'], ['cw'], ['pm'], ['mp', 'ip'], ['pi', 'cc'], ['ot'], ['rf']]
match = [[True if l in t else False for t in topics] for l in y]
y = [m.index(True) if True in m else None for m in match]
X_emb = X_emb[[l is not None for l in y], :]
y = [l for l in y if l is not None]
if args.dataset == 'wi':
X_emb = X_emb[[l is not None for l in y], :]
y = [l for l in y if l is not None]
# to 2D
# X_emb_2d = TSNE(n_components=2, init='pca', verbose=1).fit_transform(X_emb)
X_emb_2d = TSNE(n_components=2, verbose=1, perplexity=40).fit_transform(X_emb)
def remove_outliers(data, r=2.0):
outliers_data = abs(data - np.mean(data, axis=0)) >= r * np.std(data, axis=0)
outliers = np.any(outliers_data, axis=1)
keep = np.logical_not(outliers)
return outliers, keep
outliers, keep = remove_outliers(X_emb_2d)
X_emb_2d = X_emb_2d[keep, :]
y = [l for l, k in zip(y, keep.tolist()) if k]
# plot
fig = plt.figure(figsize=(4, 4))
ax = fig.add_axes([0, 0, 1, 1])
cc = ['r', 'b', 'g', 'y', 'k', 'c', 'm', 'tab:blue']
for i, l in enumerate(sorted(set(y))):
idx = [yl == l for yl in y]
plt.scatter(X_emb_2d[idx, 0], X_emb_2d[idx, 1], c=cc[i], s=10, edgecolor='none', alpha=0.5)
ax.axis('off') # adding it will get no axis
plt.savefig(os.path.join(save_folder, 'tSNE_' + '{:07d}'.format(num_iters) + '.png'))
plt.close(fig)
except:
pass
VAE.train()
def generate(test_loader, num_iters):
VAE.eval()
n_samples = 0
bleu4_sum = 0.0
rouge_scores_values_sum = [0.0] * 9
args.nsamples = 1
args.batch_size = 1
args.temperature = 0.95
args.top_k = 100
args.top_p = 0.95
model_type = args.model_type
# write samples to file
samples_file = open(os.path.join(save_folder, 'generate-' + '%07d' % num_iters + '.txt'), 'w', encoding='utf8')
# test_iter = iter(test_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(test_iter)
with tqdm(total=len(test_loader)) as pbar:
for i_test, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(
test_loader):
if i_test >= 10: break
length = -1
if length == -1:
length = VAE.config.n_ctx - x_tokens.size(1) - 1
elif length > VAE.config.n_ctx - x_tokens.size(1) - 1:
raise ValueError("Can't get samples longer than window size: %s" % VAE.config.n_ctx)
eff_samples = []
n, l = target_tokens.size()
storys = [tokenizer.decode(target_tokens[i, :]) for i in range(n)]
storys = [s[s.find("<|endoftext|>") + len("<|endoftext|>"):] for s in storys]
storys_str = [s[:s.find("<|endoftext|>") + len("<|endoftext|>")] if "<|endoftext|>" in s else s for s in
storys]
for _ in range(args.nsamples // args.batch_size):
# model, batch_size, temperature, top_k, top_p, eos_token, sample = VAE, args.batch_size, args.temperature, args.top_k, args.top_p, tokenizer.encoder['<|endoftext|>'], True
out, _ = sample_sequence(
model=VAE,
tokenizer=tokenizer,
length=length,
batch_size=args.batch_size,
x_mask=x_mask,
x_tokens=x_tokens,
y_mask=y_mask,
y_tokens=y_tokens,
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
device=device,
eos_token=tokenizer.encoder['<|endoftext|>'],
model_type=model_type
)
out = out.tolist()
# extract story, check metrics
for i in range(len(out)):
text = out[i]
text = text[text.index(endoftext) + 1:]
if endoftext in text:
idx = text.index(endoftext)
text = text[:idx]
text = tokenizer.decode(text).strip()
# score for one long text, higher than 0.075 usually means repetition
# rep_score = repeat_score(text.split(), ngram=[3, 4, 5, 6, 7, 8])
# if rep_score > 0.075:
# # print(rep_score)
# continue
try:
# check bleu
bleu4 = sentence_bleu([storys_str[i].split()], text,
smoothing_function=SmoothingFunction().method7)
# check rouge
rouge = Rouge()
rouge_scores = rouge.get_scores(text, storys_str[i])
rouge_scores_values = [v for k in rouge_scores[0].keys() for v in
rouge_scores[0][k].values()]
bleu4_sum += bleu4
rouge_scores_values_sum = [v1 + v2 for v1, v2 in
zip(rouge_scores_values_sum, rouge_scores_values)]
n_samples += 1
except:
bleu4 = 0.0
rouge_scores = [{'rouge-1': {'f': 0.0, 'p': 0.0, 'r': 0.0},
'rouge-2': {'f': 0.0, 'p': 0.0, 'r': 0.0},
'rouge-l': {'f': 0.0, 'p': 0.0, 'r': 0.0}}]
eff_samples.append((text, bleu4, rouge_scores))
pbar.update(1)
for i in range(len(eff_samples)):
samples_file.write("=" * 50 + " SAMPLE " + str(i_test) + " " + "=" * 50)
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Outlines " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(tokenizer.decode(x_tokens[i, :][x_mask[i, :] == 1].tolist()))
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Story " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(storys_str[i])
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Generated " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(eff_samples[i][0])
samples_file.write('\n' * 4)
samples_file.flush()
print('Test complete with %05d samples.' % n_samples)
logging.info("Test complete with %05d samples.", n_samples)
logging.info("Iteration completed: %d" % num_iters)
bleu4 = round(bleu4_sum / n_samples, 3)
rouge_scores_values = [round(r / n_samples, 3) for r in rouge_scores_values_sum]
print(' bleu-4:', bleu4)
print(' rouge :', rouge_scores_values)
logging.info(' bleu-4: %f', bleu4)
logging.info(' rouge : %s', str(rouge_scores_values))
VAE.train()
if args.rank == 0:
test_plot(test_loader, num_iters)
val_step(val_loader)
generate(test_loader, num_iters)
torch.save(VAE.state_dict(), os.path.join(save_folder, 'model_' + '{:07d}'.format(num_iters) + '.pt'))
while num_iters < args.iterations:
# Run epoch
st = time.time()
# Training
print('Training loop. Batches:', len(train_loader))
logging.info('\n----------------------------------------------------------------------')
logging.info("Training loop. Batches: %d" % len(train_loader))
# train_iter = iter(train_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(train_iter)
with tqdm(total=len(train_loader)) as pbar:
for i, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(train_loader):
if num_iters % args.cycle >= args.cycle - args.beta_warmup - 25000:
beta = min(1.0, beta + (1. - args.beta_0) / args.beta_warmup)
if not tuning_all and num_iters >= tuning_all_after_iters:
for name, parameter in VAE.named_parameters():
# print((name, parameter.requires_grad))
parameter.requires_grad = True
tuning_all = True
output = train_step(device, loss_model, optimizer, x_mask, x_tokens, y_mask, y_tokens,
input_tokens, target_tokens, mask, loss_fn, beta, args.model_type)
if args.rank == 0:
loss, ce_loss, kl_loss = output[-1]
lr = scheduler.get_last_lr()[0]
# Log to Tensorboard
t_writer.add_scalar('loss', loss, num_iters)
t_writer.add_scalar('ppl', math.exp(min(ce_loss, 10)), num_iters)
t_writer.add_scalar('lr', lr, num_iters)
t_writer.add_scalar('iter_time', time.time() - st, num_iters)
t_writer.add_scalar('kl', kl_loss, num_iters)
t_writer.add_scalar('beta', beta, num_iters)
if args.model_type == 'ae_vae_fusion':
loss, ce_loss, kl_loss = output[0]
# Log to Tensorboard
t_writer.add_scalar('ae_loss', loss, num_iters)
t_writer.add_scalar('ae_kl', kl_loss, num_iters)
st = time.time()
end = num_iters >= args.iterations
if args.warmup != -1:
scheduler.step()
if end: break
num_iters += 1
pbar.update(1)
if num_iters % args.cycle == 0:
beta = args.beta_0
logging.info('KL annealing restart')
if args.rank == 0 and num_iters % 10000 == 0:
test_plot(test_loader, num_iters)
val_step(val_loader)
generate(test_loader, num_iters)
if args.rank == 0 and num_iters % 10000 == 0:
print('Saving model...')
logging.info("Iteration completed: %d, remained %d" % (num_iters, args.iterations - num_iters))
logging.info("Saving model...")
logging.info('\n------------------------------------------------------')
torch.save(VAE.state_dict(), os.path.join(save_folder, 'model_' + '{:07d}'.format(num_iters) + '.pt'))
if args.switch_time > 0 and num_iters == int(args.iterations * args.switch_time):
print('Switch to long sequence training')
logging.info("Switch to long sequence training")
cur_b_schedule += 1
train_loader, val_loader, test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
batch_schedule[cur_b_schedule][0], batch_schedule[cur_b_schedule][1],
batch_schedule[-1][0], batch_schedule[-1][1],
batch_schedule[-1][0], batch_schedule[-1][1],
make_test=True,
num_workers=args.workers, data_type=args.data_type
)
if not end:
e += 1
logging.info("Training loop. The ith epoch completed: %d" % e)
if args.rank == 0:
torch.save(VAE.state_dict(), os.path.join(save_folder, 'model_latest.pt'))
print('Training complete.')
logging.info("Training complete.")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('experiment', type=str)
# Default parameters are set based on single GPU training
parser.add_argument('--lr', type=float, default=5e-5)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument('--data_type', type=str, default='t1', choices=['t' + str(i) for i in range(9)], help="t: type")
parser.add_argument('--model_type', type=str, default='cvae', choices=['cvae', 'ae_vae_fusion'])
parser.add_argument('--iterations', type=int, default=101640 * 2) # wp 850001 wi 300001 ax 300001 yp 800001
parser.add_argument('--dataset', type=str, default='wi', choices=['ax', 'yp', 'wp', 'wi'], help="Dataset to use for training")
parser.add_argument('--warmup', type=int, default=10000,
help="Amount of iterations to warmup, then decay. (-1 for no warmup and decay)")
parser.add_argument('--batch-sizes', nargs='+', type=int, default=[1],
help='batch size per GPU. Lists the schedule.')
parser.add_argument('--seq-lens', nargs='+', type=int, default=[1024],
help='seq length per sample. Lists the schedule.')
parser.add_argument('--switch-time', type=float, default=0,
help="Percentage of iterations to spend on short sequence training.")
parser.add_argument('--data-dir', type=str, default='data')
parser.add_argument('--out-dir', type=str, default='out')
parser.add_argument('--load', type=str, help='path to load model from') # , default='out/test/'
parser.add_argument('--world-size', default=1, type=int,
help='number of nodes for distributed training')
parser.add_argument('--rank', default=0, type=int,
help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
help='distributed backend')
parser.add_argument('--workers', default=1, type=int, metavar='N',
help='number of data loading workers')
parser.add_argument('--fp16', action='store_true', help="Train using FP16?")
parser.add_argument('--fp16_opt_level', default='O0', type=str, required=False)
# KL cost annealing, increase beta from beta_0 to 1 in beta_warmup steps
parser.add_argument('--beta_0', default=0.01, type=float)
parser.add_argument('--beta_warmup', type=int, default=25000)
# cyc_vae parameters
parser.add_argument('--cycle', type=int, default=101640)
parser.add_argument('--add_input', action="store_true")
parser.add_argument('--add_attn', action="store_true")
parser.add_argument('--add_softmax', action="store_true")
parser.add_argument('--attn_proj_vary', action="store_true")
parser.add_argument('--learn_prior', action="store_true")
args = parser.parse_args('wi.1.proj_vary_cyc_cvae --batch-sizes 1 --seq-lens 1024 '
' --add_input --learn_prior --fp16'.split())
# Each node is expected to have same number of GPUs
ngpus_per_node = torch.cuda.device_count()
# Since we have ngpus_per_node processes per node, the total world_size
# needs to be adjusted accordingly
args.world_size = ngpus_per_node * args.world_size
# Use torch.multiprocessing.spawn to launch distributed processes: the
# main_worker process function
mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
================================================
FILE: train_dist_half.py
================================================
import os, time, gc, json, pickle, argparse, math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
from torch.nn import DataParallel
import torch.distributed as dist
import torch.multiprocessing as mp
import numpy as np
import transformers
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config, AdamW, get_linear_schedule_with_warmup, Conv1D
from tensorboardX import SummaryWriter
from tqdm import tqdm
import importlib
import logging
import copy
from apex.optimizers import FusedAdam
from apex import amp
from apex.fp16_utils import FP16_Optimizer
from apex.parallel import DistributedDataParallel as DDP
from torch.nn.parallel import DistributedDataParallel
from data.util import *
from util import *
from dist_utils import *
from model import VAEModel
from collections import Counter
from nltk.translate.bleu_score import sentence_bleu
from nltk.translate.bleu_score import SmoothingFunction
from rouge import Rouge
from sklearn.manifold import TSNE
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
devices = '3,2,1,0'
os.environ["CUDA_VISIBLE_DEVICES"] = devices
def compute_loss(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta):
input_tokens = input_tokens.to(device)
target_tokens = target_tokens.to(device)
mask = mask.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
outputs = model(input_ids=input_tokens, attention_mask=mask, x_mask=x_mask, x_tokens=x_tokens, y_mask=y_mask,
y_tokens=y_tokens)
logits = outputs[0]
kl_loss = outputs[-1]
num_logits = logits.size(-1)
# Perform masking
if mask is not None:
mask = mask.type(torch.bool)
mask = mask.to(device)
logits = logits.masked_select(mask.unsqueeze(-1))
target_tokens = target_tokens.masked_select(mask)
ce_loss = loss_fn(logits.view(-1, num_logits), target_tokens.view(-1))
kl_loss = kl_loss.mean()
loss = ce_loss.mean() + beta * kl_loss
return loss, ce_loss, kl_loss
def compute_loss_ae(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta):
input_tokens = input_tokens.to(device)
target_tokens = target_tokens.to(device)
mask = mask.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
outputs = model(input_ids=input_tokens, attention_mask=mask, y_mask=x_mask, y_tokens=x_tokens, from_mean=True, from_prior=False)
logits = outputs[0]
kl_loss = outputs[-1]
num_logits = logits.size(-1)
# Perform masking
if mask is not None:
mask = mask.type(torch.bool)
mask = mask.to(device)
logits = logits.masked_select(mask.unsqueeze(-1))
target_tokens = target_tokens.masked_select(mask)
ce_loss = loss_fn(logits.view(-1, num_logits), target_tokens.view(-1))
kl_loss = kl_loss.mean()
loss = ce_loss.mean()
return loss, ce_loss, kl_loss
def train_step(device, model, optimizer, x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask, loss_fn, beta, model_type):
output = []
if model_type == 'ae_vae_fusion':
optimizer.zero_grad()
loss, ce_loss, kl_loss = compute_loss_ae(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens,
target_tokens, mask, loss_fn, beta)
optimizer.backward(loss)
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), 5.0) # max_grad_norm=1.0
optimizer.step()
output.append((loss.item(), ce_loss.mean().item(), kl_loss.item()))
optimizer.zero_grad()
loss, ce_loss, kl_loss = compute_loss(device, model, x_mask, x_tokens, y_mask, y_tokens, input_tokens,
target_tokens, mask, loss_fn, beta)
optimizer.backward(loss)
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), 5.0) # max_grad_norm=1.0
optimizer.step()
output.append((loss.item(), ce_loss.mean().item(), kl_loss.item()))
return output
def top_k_top_p_filtering(logits, top_k=100, top_p=0.95, filter_value=-float('Inf')):
""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (vocabulary size)
top_k > 0: keep only top k tokens with highest probability (top-k filtering).
top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
top_k = min(top_k, logits.size(-1)) # Safety check
if top_k > 0:
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove)
logits[indices_to_remove] = filter_value
return logits
def repeat_score(text, ngram=[3, 4, 5, 6]):
ngram_list = []
for ng in ngram:
ngram_list.append([text[idx:idx + ng] for idx in range(len(text) - ng - 1)])
max_occurs = []
for ngrams in ngram_list:
count_result = Counter([' '.join(n) for n in ngrams])
try:
max_occurs.append(
max(count_result.values())
)
except:
pass
scores = [max_oc / ((len(text) / ngram[idx]) + ngram[idx]) for idx, max_oc in enumerate(max_occurs)]
return max(scores) if len(scores) >= 1 else 1.0
def sample_sequence(model, tokenizer, length, batch_size=None, x_mask=None, x_tokens=None, y_mask=None, y_tokens=None,
temperature=1, top_k=100, top_p=0.95, device='cuda', sample=True, eos_token=None, model_type='cvae'):
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
with torch.no_grad():
if model_type == 'cvae':
try:
prior_mean, prior_logvar = model.encoder_prior(input_ids=x_tokens, attention_mask=x_mask)[:2]
except:
prior_mean = prior_logvar = torch.zeros([batch_size, model.config.n_embd], device=device)
latent_mean, latent_logvar = prior_mean, prior_logvar
z = model.reparameterize(latent_mean, latent_logvar)
assert not torch.isnan(z).any(), 'training get nan z'
else:
posterior_mean, posterior_logvar = model.encoder(input_ids=x_tokens, attention_mask=x_mask)[:2]
latent_mean, latent_logvar = posterior_mean, posterior_logvar
z = latent_mean
assert not torch.isnan(z).any(), 'training get nan z'
_, mem = model.transformer(input_ids=x_tokens[:, :-1], past=None, attention_mask=x_mask[:, :-1], representations=z)
prev = x_tokens[:, -1].view(batch_size, -1)
output = prev
probability = torch.tensor([], dtype=z.dtype, device=device)
if_end = torch.tensor([False] * batch_size, dtype=torch.bool, device=device)
for i in range(length): #trange
logits, mem = model.transformer(input_ids=prev, past=mem, representations=z)
logits = model.lm_head(logits)
if model.add_softmax:
logits_rep = model.lm_head_rep(z)
logits = logits + logits_rep.unsqueeze(dim=1)
logits = logits[:, -1, :] / temperature
logits = top_k_top_p_filtering(logits, top_k, top_p)
probs = F.softmax(logits, dim=-1)
if sample:
next_token = torch.multinomial(probs, num_samples=1)
else:
_, next_token = torch.topk(probs, k=1, dim=-1)
probability = torch.cat((probability, probs.gather(1, next_token)), dim=1)
output = torch.cat((output, next_token), dim=1)
prev = next_token
# early stopping if all sents have ended once
if_end[next_token.view(-1).eq(eos_token)] = True
if if_end.all(): break
return output, probability
def main_worker(gpu, ngpus_per_node, args):
if args.model_type == 'cvae':
args.learn_prior = True
else:
args.learn_prior = False
# GPU
args.gpu = gpu
print("There are ", torch.cuda.device_count(), " available GPUs!")
# print('Setting GPUs {}'.format(args.device))
print('Using GPU devices {}'.format(devices))
device = torch.device('cuda', args.gpu)
torch.cuda.set_device(device)
print('Current single GPU: {}'.format(torch.cuda.current_device()))
# randomness
np.random.seed(args.seed)
prng = np.random.RandomState()
torch.random.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# For multiprocessing distributed training, rank needs to be the global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
print('Setting rank', args.rank)
recon_attempt = 1
connected = False
if args.rank != 0:
# Stall to have rank 0 node go first
time.sleep(3)
while not connected:
try:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
connected = True
print('Established connection. Rank:', args.rank)
except Exception as e:
# Sometimes the head node launches after the worker, which would cause an issue
print('Failed to init process group. Retrying...', recon_attempt, e)
recon_attempt += 1
time.sleep(10)
# logging
if args.rank == 0:
save_folder = os.path.join(args.out_dir, args.experiment)
os.makedirs(save_folder, exist_ok=True)
t_writer = SummaryWriter(os.path.join(save_folder, 'train'), flush_secs=5)
v_writer = SummaryWriter(os.path.join(save_folder, 'val'), flush_secs=5)
importlib.reload(logging)
logging.basicConfig(filename=os.path.join(save_folder, 'train.log'),
level=logging.INFO, format='%(asctime)s--- %(message)s')
logging.info('\n*******************************************************************************\n')
logging.info("the configuration:")
logging.info(str(args).replace(',', '\n'))
print('Loading models...')
cache_dir = os.path.join(args.out_dir, 'model_cache')
os.makedirs(cache_dir, exist_ok=True)
# Load pre-trained teacher tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2', cache_dir=cache_dir)
# Hack to allow tokenizing longer sequences.
tokenizer.max_len = int(1e12)
gpt2_model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir=cache_dir)
print('gpt2_params:', num_params(gpt2_model)) # gpt2: 124439808
config = GPT2Config()
# # add special tokens
# special_tokens_dict = {
# 'pad_token': '<|startoftext|>',
# 'cls_token': '<|startofcond|>',
# 'sep_token': '<|sepofcond|>',
# 'mask_token': '<|endofcond|>'
# }
# num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
# print('We have added', num_added_toks, 'special tokens')
# # Notice: resize_token_embeddings expect to receive the full size of the new vocab
# gpt2_model.resize_token_embeddings(len(tokenizer))
# assert tokenizer.pad_token == '<|startoftext|>'
VAE = VAEModel(config, add_input=args.add_input, add_attn=args.add_attn, add_softmax=args.add_softmax,
attn_proj_vary=args.attn_proj_vary, learn_prior=args.learn_prior)
init_para_frompretrained(VAE.transformer, gpt2_model.transformer, share_para=True)
init_para_frompretrained(VAE.encoder, gpt2_model.transformer, share_para=False)
if args.learn_prior:
init_para_frompretrained(VAE.encoder_prior, VAE.encoder, share_para=True)
VAE.encoder_prior.averageSelfAttention.attention_weights = VAE.encoder.averageSelfAttention.attention_weights
VAE.lm_head.weight = gpt2_model.lm_head.weight
if VAE.add_softmax:
VAE.lm_head_rep = Conv1D(*gpt2_model.lm_head.weight.size())
# VAE.lm_head_rep = LM_head_rep(*gpt2_model.lm_head.weight.size()[::-1])
print('VAE_params:', num_params(VAE)) # 286694400
if args.load:
print('Loading model weights...')
state = torch.load(os.path.join(args.load, 'model_latest.pt')) # , map_location='cpu'
if 'module' in list(state.keys())[0]: # model_path is data parallel model with attr 'module'
state_copy = copy.copy(state)
keys = state_copy.keys()
for k in keys:
state[k.replace('module.', '')] = state.pop(k)
VAE.load_state_dict(state)
gc.collect()
print('Done.')
# fix pre-trained parameters before certain iterations
tuning_all_after_iters = 10000
tuning_all = False
for name, parameter in VAE.named_parameters():
# print((name, parameter.requires_grad))
new_pars = ['c_z', 'attention_weights', 'mean', 'logvar', 'input_proj', 'attn_proj', 'Nu_fc1', 'Nu_fc2', 'lm_head_rep']
if not any([True if n in name else False for n in new_pars]):
parameter.requires_grad = False
print('Setup data...')
# Batch and sequence length schedule
assert len(args.batch_sizes) == len(args.seq_lens)
batch_schedule = list(zip(map(int, args.batch_sizes), map(int, args.seq_lens)))
assert len(batch_schedule) <= 2, 'Currently not supporting multiple schedule'
cur_b_schedule = len(batch_schedule) - 1 if args.switch_time == 0 else 0
print('Batch schedule', batch_schedule)
train_loader, val_loader, test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
batch_schedule[cur_b_schedule][0], batch_schedule[cur_b_schedule][1],
batch_schedule[-1][0], batch_schedule[-1][1],
batch_schedule[-1][0], batch_schedule[-1][1],
make_test=True,
num_workers=args.workers, data_type=args.data_type
)
print('Done.')
###
val_loader = test_loader
###
print('Wrapping models and optimizers...')
# Apply linear scaling rule to increase batch size for short sequence training.
lr_schedule = switch_schedule(linear_schedule(args), batch_schedule[cur_b_schedule][0] / batch_schedule[-1][0],
int(args.iterations * args.switch_time))
if args.fp16:
VAE = VAE.half()
VAE = VAE.to(device)
VAE = VAE.train()
params = [p for p in VAE.parameters() if p.requires_grad]
optimizer = FusedAdam(params, lr=args.lr)
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True, verbose=False)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer.optimizer, lr_schedule)
loss_model = SimpleDistributedDataParallel(VAE, args.world_size)
loss_fn = nn.CrossEntropyLoss(reduction='none')
print('Done.')
print('Begin training iterations')
logging.info("Begin training iterations")
max_val_batches = 20000 # max num. of val batches
logging.info("Total iteration: %d" % args.iterations)
e = 0 # number of epoch
num_iters = 0
optimizer.zero_grad()
beta = args.beta_0
endoftext = tokenizer.convert_tokens_to_ids("<|endoftext|>")
def val_step(val_loader):
VAE.eval()
n_words_bpe = 0
n_words = 0
logp_sum = 0.0
kl_loss_sum = 0.0
logging.info("Validation loop. Batches: %d" % len(val_loader))
logging.info("Validation loop. max_val_batches: %d" % max_val_batches)
# val_iter = iter(val_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(val_iter)
with tqdm(total=min(len(val_loader), max_val_batches)) as pbar:
for i, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(val_loader):
with torch.no_grad():
if args.model_type == 'cvae':
loss, ce_loss, kl_loss = compute_loss(device, VAE, x_mask, x_tokens, y_mask, y_tokens,
input_tokens, target_tokens, mask, loss_fn, 1.0)
else:
loss, ce_loss, kl_loss = compute_loss_ae(device, VAE, x_mask, x_tokens, y_mask, y_tokens,
input_tokens, target_tokens, mask, loss_fn, 1.0)
if len(target_tokens.size()) == 1:
target_tokens = target_tokens.unsqueeze(0)
n, l = target_tokens.size()
text = target_tokens[0, :].tolist()
logprob = ce_loss.tolist()
assert len(text) == len(logprob)
# only for story
idx = text.index(endoftext)
text = text[idx + 1:]
logprob = logprob[idx + 1:]
if endoftext in text:
idx = text.index(endoftext)
text = text[:idx]
logprob = logprob[:idx]
logp_sum += sum(logprob)
n_words_bpe += len(text)
story = [tokenizer.decode(target_tokens[i, :]) for i in range(n)]
story = [s[s.find("<|endoftext|>") + len("<|endoftext|>"):] for s in story]
story = [s[:s.find("<|endoftext|>") + len("<|endoftext|>")] if "<|endoftext|>" in s else s for s in
story]
words = sum([len(
[t for t in re.split('("|\'|!|\?|\.|,|:| |\n|’|“|”|;|\(|\)|`)', s) if t != ' ' and t != '']) for
s in story])
n_words += words
kl_loss_sum += kl_loss.item()
if i > max_val_batches:
break
pbar.update(1)
loss_bpe = logp_sum / n_words_bpe
ppl_bpe = round(math.exp(min(logp_sum / n_words_bpe, 100)), 3)
ppl_word = round(math.exp(min(logp_sum / n_words, 100)), 3)
kl = kl_loss_sum / len(val_loader)
v_writer.add_scalar('loss', loss_bpe, num_iters)
v_writer.add_scalar('ppl_bpe', ppl_bpe, num_iters)
v_writer.add_scalar('ppl_word', ppl_word, num_iters)
v_writer.add_scalar('kl', kl, num_iters)
logging.info('val loss : %.4f' % loss_bpe)
logging.info('val ppl_bpe : %.4f' % ppl_bpe)
logging.info('val ppl_word: %.4f' % ppl_word)
logging.info('val kl : %.4f' % kl)
VAE.train()
def test_plot(test_loader, num_iters):
VAE.eval()
# get embedding
X_emb = None
y = None
# test_iter = iter(test_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(test_iter)
with tqdm(total=len(test_loader)) as pbar:
for i, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(
test_loader):
y_mask = y_mask.to(device)
y_tokens = y_tokens.to(device)
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
with torch.no_grad():
if args.model_type == 'cvae':
latent_mean, _ = VAE.encoder_prior(input_ids=x_tokens, attention_mask=x_mask)[:2]
else:
latent_mean, _ = VAE.encoder(input_ids=x_tokens, attention_mask=x_mask)[:2]
if args.dataset == 'ax' or args.dataset == 'yp':
label = [tokenizer.decode(l)[:2] for l in x_tokens.tolist()]
elif args.dataset == 'wp':
label = []
prompts = [tokenizer.decode(l)[:6].lower() for l in x_tokens.tolist()]
for prom in prompts:
if prom[0] in ['[', '('] and prom[5] in [']', ')']:
label.append(prom[2:4])
else:
label.append(None)
elif args.dataset == 'wi':
# 0. TV, play, miniseries, telenovela; 1.film; 2. music; 3. manga, comic, 4. book, novel, story 5. game
label = []
prompts = [tokenizer.decode(l) for l in x_tokens.tolist()]
for prom in prompts:
if 'TV' in prom or 'play' in prom or 'miniseries' in prom or 'telenovela' in prom:
label.append(0)
elif 'film' in prom:
label.append(1)
elif 'music' in prom:
label.append(2)
elif 'manga' in prom or 'comic' in prom:
label.append(3)
elif 'book' in prom or 'novel' in prom or 'story' in prom:
label.append(4)
elif 'game' in prom:
label.append(5)
else:
label.append(None)
else:
raise Exception
if i == 0:
X_emb = latent_mean.data
y = label
else:
X_emb = torch.cat((X_emb, latent_mean.data), dim=0)
y.extend(label)
pbar.update(1)
X_emb = X_emb.cpu().numpy()
try:
if args.dataset == 'yp':
y = ['0' if l in ['0', '1'] else l for l in y]
y = ['4' if l in ['3', '4'] else l for l in y]
X_emb = X_emb[[l != '2' for l in y], :]
y = [l for l in y if l != '2']
if args.dataset == 'wp':
topics = [['wp', 'sp', 'tt'], ['eu'], ['cw'], ['pm'], ['mp', 'ip'], ['pi', 'cc'], ['ot'], ['rf']]
match = [[True if l in t else False for t in topics] for l in y]
y = [m.index(True) if True in m else None for m in match]
X_emb = X_emb[[l is not None for l in y], :]
y = [l for l in y if l is not None]
if args.dataset == 'wi':
X_emb = X_emb[[l is not None for l in y], :]
y = [l for l in y if l is not None]
# to 2D
# X_emb_2d = TSNE(n_components=2, init='pca', verbose=1).fit_transform(X_emb)
X_emb_2d = TSNE(n_components=2, verbose=1, perplexity=40).fit_transform(X_emb)
def remove_outliers(data, r=2.0):
outliers_data = abs(data - np.mean(data, axis=0)) >= r * np.std(data, axis=0)
outliers = np.any(outliers_data, axis=1)
keep = np.logical_not(outliers)
return outliers, keep
outliers, keep = remove_outliers(X_emb_2d)
X_emb_2d = X_emb_2d[keep, :]
y = [l for l, k in zip(y, keep.tolist()) if k]
# plot
fig = plt.figure(figsize=(4, 4))
ax = fig.add_axes([0, 0, 1, 1])
cc = ['r', 'b', 'g', 'y', 'k', 'c', 'm', 'tab:blue']
for i, l in enumerate(sorted(set(y))):
idx = [yl == l for yl in y]
plt.scatter(X_emb_2d[idx, 0], X_emb_2d[idx, 1], c=cc[i], s=10, edgecolor='none', alpha=0.5)
ax.axis('off') # adding it will get no axis
plt.savefig(os.path.join(save_folder, 'tSNE_' + '{:07d}'.format(num_iters) + '.png'))
plt.close(fig)
except:
pass
VAE.train()
def generate(test_loader, num_iters):
VAE.eval()
n_samples = 0
bleu4_sum = 0.0
rouge_scores_values_sum = [0.0] * 9
args.nsamples = 1
args.batch_size = 1
args.temperature = 0.95
args.top_k = 100
args.top_p = 0.95
model_type = args.model_type
# write samples to file
samples_file = open(os.path.join(save_folder, 'generate-' + '%07d' % num_iters + '.txt'), 'w', encoding='utf8')
# test_iter = iter(test_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(test_iter)
with tqdm(total=len(test_loader)) as pbar:
for i_test, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(
test_loader):
if i_test >= 10: break
length = -1
if length == -1:
length = VAE.config.n_ctx - x_tokens.size(1) - 1
elif length > VAE.config.n_ctx - x_tokens.size(1) - 1:
raise ValueError("Can't get samples longer than window size: %s" % VAE.config.n_ctx)
eff_samples = []
n, l = target_tokens.size()
storys = [tokenizer.decode(target_tokens[i, :]) for i in range(n)]
storys = [s[s.find("<|endoftext|>") + len("<|endoftext|>"):] for s in storys]
storys_str = [s[:s.find("<|endoftext|>") + len("<|endoftext|>")] if "<|endoftext|>" in s else s for s in
storys]
for _ in range(args.nsamples // args.batch_size):
# model, batch_size, temperature, top_k, top_p, eos_token, sample = VAE, args.batch_size, args.temperature, args.top_k, args.top_p, tokenizer.encoder['<|endoftext|>'], True
out, _ = sample_sequence(
model=VAE,
tokenizer=tokenizer,
length=length,
batch_size=args.batch_size,
x_mask=x_mask,
x_tokens=x_tokens,
y_mask=y_mask,
y_tokens=y_tokens,
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
device=device,
eos_token=tokenizer.encoder['<|endoftext|>'],
model_type=model_type
)
out = out.tolist()
# extract story, check metrics
for i in range(len(out)):
text = out[i]
text = text[text.index(endoftext) + 1:]
if endoftext in text:
idx = text.index(endoftext)
text = text[:idx]
text = tokenizer.decode(text).strip()
# score for one long text, higher than 0.075 usually means repetition
# rep_score = repeat_score(text.split(), ngram=[3, 4, 5, 6, 7, 8])
# if rep_score > 0.075:
# # print(rep_score)
# continue
try:
# check bleu
bleu4 = sentence_bleu([storys_str[i].split()], text,
smoothing_function=SmoothingFunction().method7)
# check rouge
rouge = Rouge()
rouge_scores = rouge.get_scores(text, storys_str[i])
rouge_scores_values = [v for k in rouge_scores[0].keys() for v in
rouge_scores[0][k].values()]
bleu4_sum += bleu4
rouge_scores_values_sum = [v1 + v2 for v1, v2 in
zip(rouge_scores_values_sum, rouge_scores_values)]
n_samples += 1
except:
bleu4 = 0.0
rouge_scores = [{'rouge-1': {'f': 0.0, 'p': 0.0, 'r': 0.0},
'rouge-2': {'f': 0.0, 'p': 0.0, 'r': 0.0},
'rouge-l': {'f': 0.0, 'p': 0.0, 'r': 0.0}}]
eff_samples.append((text, bleu4, rouge_scores))
pbar.update(1)
for i in range(len(eff_samples)):
samples_file.write("=" * 50 + " SAMPLE " + str(i_test) + " " + "=" * 50)
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Outlines " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(tokenizer.decode(x_tokens[i, :][x_mask[i, :] == 1].tolist()))
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Story " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(storys_str[i])
samples_file.write('\n' * 2)
samples_file.write("=" * 40 + " Generated " + "=" * 40)
samples_file.write('\n' * 2)
samples_file.write(eff_samples[i][0])
samples_file.write('\n' * 4)
samples_file.flush()
print('Test complete with %05d samples.' % n_samples)
logging.info("Test complete with %05d samples.", n_samples)
logging.info("Iteration completed: %d" % num_iters)
bleu4 = round(bleu4_sum / n_samples, 3)
rouge_scores_values = [round(r / n_samples, 3) for r in rouge_scores_values_sum]
print(' bleu-4:', bleu4)
print(' rouge :', rouge_scores_values)
logging.info(' bleu-4: %f', bleu4)
logging.info(' rouge : %s', str(rouge_scores_values))
VAE.train()
if args.rank == 0:
test_plot(test_loader, num_iters)
val_step(val_loader)
generate(test_loader, num_iters)
torch.save(loss_model.state_dict(), os.path.join(save_folder, 'model_' + '{:07d}'.format(num_iters) + '.pt'))
while num_iters < args.iterations:
# Run epoch
st = time.time()
# Training
print('Training loop. Batches:', len(train_loader))
logging.info('\n----------------------------------------------------------------------')
logging.info("Training loop. Batches: %d" % len(train_loader))
# train_iter = iter(train_loader); x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask = next(train_iter)
with tqdm(total=len(train_loader)) as pbar:
for i, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(train_loader):
# if num_iters % args.cycle >= args.cycle - args.beta_warmup:
# beta = min(1.0, beta + (1. - args.beta_0) / args.beta_warmup)
if not tuning_all and num_iters >= tuning_all_after_iters:
for name, parameter in VAE.named_parameters():
# print((name, parameter.requires_grad))
parameter.requires_grad = True
tuning_all = True
output = train_step(device, loss_model, optimizer, x_mask, x_tokens, y_mask, y_tokens,
input_tokens, target_tokens, mask, loss_fn, beta, args.model_type)
if args.rank == 0:
loss, ce_loss, kl_loss = output[-1]
lr = scheduler.get_last_lr()[0]
# Log to Tensorboard
t_writer.add_scalar('loss', loss, num_iters)
t_writer.add_scalar('ppl', math.exp(min(ce_loss, 10)), num_iters)
t_writer.add_scalar('lr', lr, num_iters)
t_writer.add_scalar('iter_time', time.time() - st, num_iters)
t_writer.add_scalar('kl', kl_loss, num_iters)
t_writer.add_scalar('beta', beta, num_iters)
if args.model_type == 'ae_vae_fusion':
loss, ce_loss, kl_loss = output[0]
# Log to Tensorboard
t_writer.add_scalar('ae_loss', loss, num_iters)
t_writer.add_scalar('ae_kl', kl_loss, num_iters)
st = time.time()
end = num_iters >= args.iterations
if args.warmup != -1:
scheduler.step()
if end: break
num_iters += 1
pbar.update(1)
if num_iters % args.cycle == 0:
beta = args.beta_0
logging.info('KL annealing restart')
if args.rank == 0 and num_iters % 10000 == 0:
test_plot(test_loader, num_iters)
val_step(val_loader)
generate(test_loader, num_iters)
if args.rank == 0 and num_iters % 10000 == 0:
print('Saving model...')
logging.info("Iteration completed: %d, remained %d" % (num_iters, args.iterations - num_iters))
logging.info("Saving model...")
logging.info('\n------------------------------------------------------')
torch.save(loss_model.state_dict(), os.path.join(save_folder, 'model_' + '{:07d}'.format(num_iters) + '.pt'))
if args.switch_time > 0 and num_iters == int(args.iterations * args.switch_time):
print('Switch to long sequence training')
logging.info("Switch to long sequence training")
cur_b_schedule += 1
train_loader, val_loader, test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
batch_schedule[cur_b_schedule][0], batch_schedule[cur_b_schedule][1],
batch_schedule[-1][0], batch_schedule[-1][1],
batch_schedule[-1][0], batch_schedule[-1][1],
make_test=True,
num_workers=args.workers, data_type=args.data_type
)
if not end:
e += 1
logging.info("Training loop. The ith epoch completed: %d" % e)
if args.rank == 0:
torch.save(loss_model.state_dict(), os.path.join(save_folder, 'model_latest.pt'))
print('Training complete.')
logging.info("Training complete.")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('experiment', type=str)
# Default parameters are set based on single GPU training
parser.add_argument('--lr', type=float, default=5e-5)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument('--data_type', type=str, default='t5', choices=['t' + str(i) for i in range(9)], help="t: type")
parser.add_argument('--model_type', type=str, default='cvae', choices=['cvae', 'ae_vae_fusion'])
parser.add_argument('--iterations', type=int, default=101640) # wp 850001 wi 300001 ax 300001 yp 800001
parser.add_argument('--dataset', type=str, default='wi', choices=['ax', 'yp', 'wp', 'wi'], help="Dataset to use for training")
parser.add_argument('--warmup', type=int, default=10000,
help="Amount of iterations to warmup, then decay. (-1 for no warmup and decay)")
parser.add_argument('--batch-sizes', nargs='+', type=int, default=[1],
help='batch size per GPU. Lists the schedule.')
parser.add_argument('--seq-lens', nargs='+', type=int, default=[1024],
help='seq length per sample. Lists the schedule.')
parser.add_argument('--switch-time', type=float, default=0,
help="Percentage of iterations to spend on short sequence training.")
parser.add_argument('--data-dir', type=str, default='data')
parser.add_argument('--out-dir', type=str, default='out')
parser.add_argument('--load', type=str, help='path to load model from') # , default='out/test/'
parser.add_argument('--world-size', default=1, type=int,
help='number of nodes for distributed training')
parser.add_argument('--rank', default=0, type=int,
help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
help='distributed backend')
parser.add_argument('--workers', default=1, type=int, metavar='N',
help='number of data loading workers')
parser.add_argument('--fp16', action='store_true', help="Train using FP16?")
parser.add_argument('--fp16_opt_level', default='O0', type=str, required=False)
# KL cost annealing, increase beta from beta_0 to 1 in beta_warmup steps
parser.add_argument('--beta_0', default=1.0, type=float)
parser.add_argument('--beta_warmup', type=int, default=50000)
# cyc_vae parameters
parser.add_argument('--cycle', type=int, default=101640)
parser.add_argument('--add_input', action="store_true")
parser.add_argument('--add_attn', action="store_true")
parser.add_argument('--add_softmax', action="store_true")
parser.add_argument('--attn_proj_vary', action="store_true")
parser.add_argument('--learn_prior', action="store_true")
args = parser.parse_args('wi.o2s.12.proj_vary_beta_half_cvae --batch-sizes 1 --seq-lens 1024 '
'--add_input --add_attn --attn_proj_vary --learn_prior --fp16'.split())
# Each node is expected to have same number of GPUs
ngpus_per_node = torch.cuda.device_count()
# Since we have ngpus_per_node processes per node, the total world_size
# needs to be adjusted accordingly
args.world_size = ngpus_per_node * args.world_size
# Use torch.multiprocessing.spawn to launch distributed processes: the
# main_worker process function
mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
================================================
FILE: tsne_plot.py
================================================
import os, time, gc, json, pickle, argparse, math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
from torch.nn import DataParallel
import torch.distributed as dist
import torch.multiprocessing as mp
import numpy as np
import transformers
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config, AdamW, get_linear_schedule_with_warmup, Conv1D
from tensorboardX import SummaryWriter
from tqdm import tqdm
import importlib
import logging
import copy
from data.util import *
from util import *
from model import VAEModel
from sklearn.manifold import TSNE
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
devices = '3'
os.environ["CUDA_VISIBLE_DEVICES"] = devices
# specify for the trained VAE model
add_input = True
add_softmax = False
add_attn = False
parser = argparse.ArgumentParser()
# global parameters
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--batch_size', default=16, type=int)
# use GPU
parser.add_argument('--gpu', default=0, type=int)
parser.add_argument('--no_gpu', action="store_true")
parser.add_argument('--model_type', type=str, default='t0', choices=['t0', 't1'], help="t: type")
parser.add_argument('--dataset', type=str, default='yp', choices=['ax', 'yp', 'wp', 'wi'],
help="Dataset to use for training")
parser.add_argument('--load', type=str, default='out/yelp.2/', help='path to load model from')
parser.add_argument('--data-dir', type=str, default='data')
parser.add_argument('--out-dir', type=str, default='out')
if sys.argv[1:] == ['--mode=server']:
args = parser.parse_args([]) # run in pycharm console
else:
args = parser.parse_args() # run in cmd
# gpu
if not torch.cuda.is_available(): args.no_gpu = True
gpu = not args.no_gpu
if gpu: torch.cuda.set_device(args.gpu)
device = torch.device(args.gpu if gpu else "cpu")
np.random.seed(args.seed)
random.seed(args.seed)
torch.manual_seed(args.seed)
if gpu: torch.cuda.manual_seed(args.seed)
# logging
save_folder = os.path.join(args.load)
os.makedirs(save_folder, exist_ok=True)
logging.basicConfig(filename=os.path.join(save_folder, 'tSNE.log'),
level=logging.INFO, format='%(asctime)s--- %(message)s')
print('Loading models...')
cache_dir = os.path.join(args.out_dir, 'model_cache')
os.makedirs(cache_dir, exist_ok=True)
# Load pre-trained teacher tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2', cache_dir=cache_dir)
# Hack to allow tokenizing longer sequences.
tokenizer.max_len = int(1e12)
gpt2_model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir=cache_dir)
print('gpt2_params:', num_params(gpt2_model)) # gpt2: 124439808
config = GPT2Config()
# add special tokens
special_tokens_dict = {
'pad_token': '<|startoftext|>',
'cls_token': '<|startofcond|>',
'sep_token': '<|sepofcond|>',
'mask_token': '<|endofcond|>'
}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('We have added', num_added_toks, 'special tokens')
# Notice: resize_token_embeddings expect to receive the full size of the new vocab
gpt2_model.resize_token_embeddings(len(tokenizer))
assert tokenizer.pad_token == '<|startoftext|>'
VAE = VAEModel(config, add_input=add_input, add_attn=add_attn, add_softmax=add_softmax)
init_para_frompretrained(VAE.transformer, gpt2_model.transformer, share_para=True)
init_para_frompretrained(VAE.encoder, gpt2_model.transformer, share_para=False)
VAE.lm_head.weight = gpt2_model.lm_head.weight
if VAE.add_softmax:
VAE.lm_head_rep = Conv1D(*gpt2_model.lm_head.weight.size())
print('VAE_params:', num_params(VAE)) # 286694400
print('Done.')
print('Loading model weights...')
state = torch.load(os.path.join(args.load, 'model_latest.pt'), map_location='cpu')
if 'module' in list(state.keys())[0]: # model_path is data parallel model with attr 'module'
state_copy = copy.copy(state)
keys = state_copy.keys()
for k in keys:
state[k.replace('module.', '')] = state.pop(k)
VAE.load_state_dict(state)
VAE.eval()
VAE = VAE.to(device)
print('Done.')
print('Setup data...')
test_loader = prepare_dataset(
args.data_dir, args.dataset, tokenizer,
1, 1024, 1, 1024, args.batch_size, 1024,
make_train=False, make_val=False, make_test=True, model_type=args.model_type
)[0]
print('Done.')
# get embedding
X_emb = None
y = None
# test_iter = iter(test_loader); c_mask, c_tokens, x_mask, x_tokens, input_tokens, target_tokens, mask = next(test_iter)
with torch.no_grad():
with tqdm(total=len(test_loader)) as pbar:
for i, (c_mask, c_tokens, x_mask, x_tokens, input_tokens, target_tokens, mask) in enumerate(test_loader):
x_mask = x_mask.to(device)
x_tokens = x_tokens.to(device)
latent_mean, _ = VAE.encoder(input_ids=x_tokens, attention_mask=x_mask)[:2]
if i == 0:
X_emb = latent_mean.data
y = [tokenizer.decode(l)[:2] for l in c_tokens.tolist()]
else:
X_emb = torch.cat((X_emb, latent_mean.data), dim=0)
y.extend([tokenizer.decode(l)[:2] for l in c_tokens.tolist()])
pbar.update(1)
X_emb = X_emb.cpu().numpy()
try:
if args.dataset == 'yp':
y = ['0' if l in ['0', '1'] else l for l in y]
y = ['4' if l in ['3', '4'] else l for l in y]
X_emb = X_emb[[l != '2' for l in y], :]
y = [l for l in y if l != '2']
if args.dataset == 'wp':
topics = [['wp', 'sp', 'tt'], ['eu'], ['cw'], ['pm'], ['mp', 'ip'], ['pi', 'cc'], ['ot'], ['rf']]
match = [[True if l in t else False for t in topics] for l in y]
y = [m.index(True) if True in m else None for m in match]
X_emb = X_emb[[l is not None for l in y], :]
y = [l for l in y if l is not None]
if args.dataset == 'wi':
X_emb = X_emb[[l is not None for l in y], :]
y = [l for l in y if l is not None]
# to 2D
# X_emb_2d = TSNE(n_components=2, init='pca', verbose=1).fit_transform(X_emb)
X_emb_2d = TSNE(n_components=2, verbose=1, perplexity=40).fit_transform(X_emb)
def remove_outliers(data, r=2.0):
outliers_data = abs(data - np.mean(data, axis=0)) >= r * np.std(data, axis=0)
outliers = np.any(outliers_data, axis=1)
keep = np.logical_not(outliers)
return outliers, keep
outliers, keep = remove_outliers(X_emb_2d)
X_emb_2d = X_emb_2d[keep, :]
y = [l for l, k in zip(y, keep.tolist()) if k]
# plot
fig = plt.figure(figsize=(4, 4))
ax = fig.add_axes([0, 0, 1, 1])
cc = ['r', 'b', 'g', 'y', 'k', 'c', 'm', 'tab:blue']
for i, l in enumerate(sorted(set(y))):
idx = [yl == l for yl in y]
plt.scatter(X_emb_2d[idx, 0], X_emb_2d[idx, 1], c=cc[i], s=10, edgecolor='none', alpha=0.5)
ax.axis('off') # adding it will get no axis
plt.savefig(os.path.join(save_folder, 'tSNE.png'))
plt.close(fig)
except:
pass
================================================
FILE: util.py
================================================
import os, time, gc, json, pickle, argparse, math
import torch
import torch.nn as nn
import torch.utils.data as data
import torch.distributed as dist
import torch.multiprocessing as mp
import numpy as np
from data.util import *
import copy
def num_params(model):
return sum([np.prod(p.size()) for p in model.parameters() if p.requires_grad])
def init_para_frompretrained(m, pm, share_para=False):
m.wte.weight = pm.wte.weight
m.wpe.weight = pm.wpe.weight
for i in range(min(len(m.h), len(pm.h))):
m.h[i].ln_1.weight = pm.h[i].ln_1.weight if share_para else copy.copy(pm.h[i].ln_1.weight)
m.h[i].ln_1.bias = pm.h[i].ln_1.bias if share_para else copy.copy(pm.h[i].ln_1.bias)
m.h[i].attn.c_attn.weight = pm.h[i].attn.c_attn.weight if share_para else copy.copy(pm.h[i].attn.c_attn.weight)
m.h[i].attn.c_attn.bias = pm.h[i].attn.c_attn.bias if share_para else copy.copy(pm.h[i].attn.c_attn.bias)
m.h[i].attn.c_proj.weight = pm.h[i].attn.c_proj.weight if share_para else copy.copy(pm.h[i].attn.c_proj.weight)
m.h[i].attn.c_proj.bias = pm.h[i].attn.c_proj.bias if share_para else copy.copy(pm.h[i].attn.c_proj.bias)
m.h[i].ln_2.weight = pm.h[i].ln_2.weight if share_para else copy.copy(pm.h[i].ln_2.weight)
m.h[i].ln_2.bias = pm.h[i].ln_2.bias if share_para else copy.copy(pm.h[i].ln_2.bias)
m.h[i].mlp.c_fc.weight = pm.h[i].mlp.c_fc.weight if share_para else copy.copy(pm.h[i].mlp.c_fc.weight)
m.h[i].mlp.c_fc.bias = pm.h[i].mlp.c_fc.bias if share_para else copy.copy(pm.h[i].mlp.c_fc.bias)
m.h[i].mlp.c_proj.weight = pm.h[i].mlp.c_proj.weight if share_para else copy.copy(pm.h[i].mlp.c_proj.weight)
m.h[i].mlp.c_proj.bias = pm.h[i].mlp.c_proj.bias if share_para else copy.copy(pm.h[i].mlp.c_proj.bias)
m.ln_f.weight = pm.ln_f.weight if share_para else copy.copy(pm.ln_f.weight)
m.ln_f.bias = pm.ln_f.bias if share_para else copy.copy(pm.ln_f.bias)
def switch_schedule(schedule, mult, switch):
""" Apply LR multiplier before iteration "switch" """
def f(e):
s = schedule(e)
if e < switch:
return s * mult
return s
return f
def linear_schedule(args):
def f(e):
if e <= args.warmup:
return e / args.warmup
return max((e - args.iterations) / (args.warmup - args.iterations), 0)
return f